Ijitce Nov

IJITCE PUBLICATION

INTERNATIONAL JOURNAL OF INNOVATIVE

TECHNOLOGY & CREATIVE ENGINEERING

Vol.1 No.11

November 2011

UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: [email protected] Phone: +44-773-043-0249

USA: Editor International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA. Phone: 001-706-206-0812 Fax:001-706-542-2626

India: Editor International Journal of Innovative Technology & Creative Engineering Dr. Arthanariee. A. M Finance Tracking Center India 17/14 Ganapathy Nagar 2nd Street Ekkattuthangal Chennai -600032Chennai -600032. Mobile: 91-7598208700

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From Editor's Desk Dear Researcher, Greetings! This monthly journal contains new research topics in the area of Influence of slip conditions heat transfer, Aprori algorithm, reducing the intensity of headlight of the vehicle. Let us review world research focus for this month. We can win the AIDS war with drugs and vaccines. HIV doesn't play by the rules: instead of dodging the immune system it attacks it head on. Now it seems our best hope for a vaccine against the killer virus might also involve tearing up the rule book - by fighting an infection without help from the immune system. Using this approach, mice can keep HIV at bay even when given 100 times the virus that would be needed to cause a lethal infection. Conventional vaccines work by exposing the body to safe versions of a pathogen or parts of it, which primes the immune system to fight off future infection. Throw a typical camera in the air and you're unlikely to capture anything stunning. But now a new ball-shaped camera, created by Jonas Pfeil from the Technical University of Berlin and colleagues, is designed to be tossed upwards to snap panoramas in mid-air. Combat obesity might be to provide more open space for such things as walking and biking trails in dense cities. Another might be to provide healthy alternatives to junk food snacks and sodas at schools. But, how do such proposals reach policymakers and elected officials? And, how do you successfully convince them to adopt obesity-fighting measures, especially in low-income, overcrowded areas where residents are poor and underserved? Talk about egging your siblings on. Baby turtles communicate before hatching to coordinate their arrival into the world. Australian river turtles (Emydura macquarii) lay eggs in a hole in a sandy riverbank. Eggs at the cooler base of the nest develop more slowly and should hatch later than their warmer brethren at the top, says Ricky-John Spencer at the University of Western Sydney, Australia – but all eggs apparently hatch together. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technology-related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue. Thanks, Editorial Team IJITCE

Editorial Members

Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering, Universiti Putra Malaysia,UPM Serdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at Shangai Jiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin, Faculty of Agriculture and Horticulture, Asternplatz 2a, D-12203 Berlin, Germany Dr. Marco L. Bianchini Ph.D Italian National Research Council; IBAF-CNR, Via Salaria km 29.300, 00015 Monterotondo Scalo (RM), Italy Dr. Nijad Kabbara Ph.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh, Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University, No. 303, University Road, Puli Town, Nantou County 54561, Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources, Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Mr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP., ICP Project Manager - Software, Applied Materials, 1a park lane, cranford, UK Dr. Bulent Acma Ph.D Anadolu University, Department of Economics, Unit of Southeastern Anatolia Project(GAP), 26470 Eskisehir, TURKEY Dr. Selvanathan Arumugam Ph.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602, USA.

Review Board Members

Dr. T. Christopher, Ph.D., Assistant Professor & Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India. Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India. Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,Rua Itapeva, 474 (8° andar) ,01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. Benal Yurtlu Assist. Prof. Ondokuz Mayis University Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic & Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India

Chutima Boonthum-Denecke, Ph.D Department of Computer Science,Science & Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRua Itapeva, 474 (8° andar), 01332-000, São Paulo (SP), Brazil Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Javad Robati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran Vinesh Sukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. doc. Ing. Rostislav Choteborský, Ph.D. Katedra materiálu a strojírenské technologie Technická fakulta,Ceská zemedelská univerzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Binod Kumar M.sc,M.C.A.,M.Phil.,ph.d, HOD & Associate Professor, Lakshmi Narayan College of Tech.(LNCT), Kolua, Bhopal (MP) , India. Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic & Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.Chutima Boonthum-Denecke, Ph.D Department of Computer Science,Science & Technology Bldg.,Hampton University,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A., M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India). doc. Ing. Rostislav Chot ěborský,ph.d, Katedra materiálu a strojírenské technologie, Technická fakulta,Česká zemědělská univerzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Amala VijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE Naik Nitin Ashokrao B.sc,M.Sc Lecturer in Yeshwant Mahavidyalaya Nanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MEN GG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed . Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-Banglore Westernly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech & PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road - Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India.

Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India. Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India. Mr. M. Rajasenathipathi M.C.A., M.Phil Assistant professor, Department of Computer Science, Nallamuthu Gounder Mahalingam College, India. Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India Prema Selvaraj Bsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. V. Prabakaran M.C.A., M.Phil Head of the Department, Department of Computer Science, Adharsh Vidhyalaya Arts And Science College For Women, India. Mrs. S. Niraimathi. M.C.A., M.Phil Lecturer, Department of Computer Science, Nallamuthu Gounder Mahalingam College, Pollachi, India. Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., P GDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Mr. R. Vijayamadheswaran, M.C.A.,M.Phil Lecturer, K.S.R College of Ars & Science, India. Ms.S.Sasikala,M.Sc.,M.Phil.,M.C.A.,PGDPM & IR., Assistant Professor,Department of Computer Science,KSR College of Arts & Science,Tiruchengode - 637215 Mr. V. Pradeep B.E., M.Tech Asst. Professor, Department of Computer Science and Engineering, Tejaa Shakthi Institute of Technology for Women, Coimbatore, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Mr. K. Saravanakumar M.C.A.,M.Phil., M.B.A, M.Tech, PGDBA, PGDPM & IR Asst. Professor, PG Department of Computer Applications, Alliance Business Academy, Bangalore, India. Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Research Fellow,Department of Mathematics,National Institute of Technology (REC),Tiruchirappli-620 015, Tamilnadu, India.

Contents 1. Transformerless Inverter for Smart Grid Based On Photovoltaic Systems by B.Nagaraju,

S.Tarakalyani, A.Srinivasula Reddy ……….[1]

2. Influence of Slip Conditions, Wall Properties and Heat Transfer on MHD Peristaltic Transport of a Jeffrey Fluid in a Non-Uniform Porous Channel by R. Saravana,S. Sreenadh, S. Venkataramana,R. Hemadri Reddy,A. Kavitha…..[10]

3. DataAprori algorithm : Implementation of scalable Data Mining by using Aprori algorithm by M Afshar Alam, Sapna Jain,Ranjit Biswas……[25]

4. A Software and a Hardware Interface for Reducing the Intensity Uncertainties Emitted by Vehicular Headlight on Highways by Mrs. Niraimathi.S, Dr.Arthanairee A. M, Mr. M. Sivakumar…….[35]

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING (ISSN:2045-8711) VOL.1 NO.11 NOVEMBER 2011

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Transformerless Inverter for Smart Grid Based On Photovoltaic Systems

B.Nagaraju , S.Tarakalyani , A.Srinivasula Reddy Assistant Professor,Vaagdevi College of Engineering

1 [email protected] Associate professor, JNT University College of Engineering , Hyderabad-India

2 [email protected] Professor, samskruthi College of Engineering and Technology, Ghatkesar-India

3 [email protected]

Abstract- In this paper we have done the Transformerless Inverter Modeling which is used for smart Grid Technology with Phasor measurements units & two way communications systems. We are taken the stand alon e Transformer less Inverter for home appliances. That Inverter is modeling by using Matlab/Simulink. Keywords: Photovoltaic inverter, Transformer less inverter, modeling, MATLAB/SIMULINK

I. INTRODUCTION

Growing demand and advancement in semiconductor technology and magnetic material had significant impact on PV inverter topology. In the past few years the market share for transformerless inverters has steadily increased. Topologies without transformer generally have higher efficiencies and may be cheaper than comparable inverters with transformers.

In this paper a DC-DC boost converter which is used to obtain the stable high input voltage from unstable low input voltage from photovoltaic system and a line transformer in the power-conversion stage, which guarantees galvanic isolation between the grid and the PV system, thus providing personal protection. Due to elimination of transformer there will be a dangerous leakage currents can appear through stray capacitance. In order to avoid these currents and to achieve higher efficiency the circuit is modified. The conversion stages are more advantageous power conversion stage for transformerless grid connected PV systems. I am initially selected a bipolar PWM full-bridge inverter with six switches and two diodes out. The advantage of full

bridge bipolar PWM inverter avoids the varying common-mode voltage and achieves a high efficiency and avoids the losses across stray capacitance i.e, stray losses and there will be low ripple currents

The literature survey is done to collect material, which would focus on the basic theory of PV’s with different inverter. Grid connected photovoltaic (PV) systems, in particular low power, mostly single-phase PV systems and their contribution to clean power generation is recognized more and more worldwide. Grid connected PV systems are generally privately owned, single-phase systems in a power range of up to 10 kW. The main aim of a private operator who owns such a system is to maximize its energy yield i.e. it issues long life time (20 years and longer), high efficiency and good environmental conditions (availability of solar radiation) are hence of importance to the private operator. Other important requirements for these PV systems are the fulfillment of standards concerning power quality, electromagnetic compatibility, acoustic noise limitations as well as safety and protection requirements topology.

Figure (1.1) shows issues regarding grid connected PV systems for

the low power range.


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First commercially available grid connected PV inverters were line commutated inverters, followed by self commutated, Pulse Width Modulation inverters including either line or high frequency transformers. Newest trends in this field are string based units with a power rating around 1 kW and transformerless concepts. For larger systems the overall efficiency can be increased through application of several, small, string inverters replacing a single unit which avoids losses through module mismatch and decreases the DC wiring effort. Transformerless concepts (in particular inverters with high input voltages) are advantageous regarding their high efficiencies. Their peak efficiencies of up to 97% are equivalent to efficiencies reached in drives applications.

Avoiding the transformer has the additional benefits of reducing cost, size, weight and complexity of the inverter. However, the removal of the transformer and hence its isolation capability has to be considered carefully. Multilevel converter topologies are especially suitable for PV applications since due to the modular structure of PV arrays different DC voltage levels can easily be provided. Multilevel voltage source inverters offer several advantages compared to their conventional counterparts. By synthesizing the AC output terminal voltage from several levels of voltages, staircase waveforms can be produced, which approach the sinusoidal waveform with low harmonic distortion, thus reducing filter requirements. The need of several sources on the DC side of the converter makes multilevel technology attractive for photovoltaic applications. This paper provides an overview on different multilevel topologies and investigates their suitability for single-phase grid connected photovoltaic systems. Several transformerless photovoltaic systems incorporating multilevel converters are compared regarding issues such as component count and stress, system power rating and the influence of the photovoltaic array earth capacitance.

Photovoltaic (PV) power supplied to the utility grid is gaining more and more visibility, while the world’s power demand is increasing. Not many PV systems have so far been placed into the grid due to the relatively high cost, compared with more traditional energy sources such as oil, gas, coal, nuclear, hydro, and wind. Solid-state inverters have been shown to be the enabling technology for putting PV systems into the

grid. The price of the PV modules were in the past the major contribution to the cost of these systems.

The photovoltaic system has been growing the demand for improving the system efficiency and reducing the size, weight and cost have been becoming significant. The high-frequency transformer utilized system is an attractive one to obtain isolation between the solar-cells side and the utility side.

Figure (1.2) shows PV inverters with self commutated full bridge and high frequency Transformer

However, the transformerless type is much more attractive from the viewpoint of improving the efficiency, size, weight and cost. Thus, the transformerless type has been becoming the dominant one. In this transformerless system, a boost-type dc-dc converter and an inverter scheme is chosen usually. The boost dc-dc converter is for obtaining a stable and higher dc-input voltage of the inverter from an unstable and lower voltage fed from the solar-cells. The high DC input is fed to the inverter which converts the DC high input voltage to the AC voltage. This dc-dc converter is a two-quadrant type, and it feeds the power obtained from the solar-cells to the inverter with boost-mode operation. On the other hand, it feeds the energy back from the utility to the dc capacitor C, with buck-mode operation. The energy feedback with the buck-mode operation is rarely utilized to control the power factor of the utility to reduce the utility voltage. Under a condition with a higher original voltage of the utility and a high feeding power to the utility, the utility voltage can exceed a limitation. The function is applied in such a condition to reduce the utility voltage. Since this case is occurred very rarely and is not focus here, the detail is omitted in this paper. Since the voltage produced by the solar-cells is not high enough to obtain certain level of ac-voltage through the inverter, a boost-mode dc-dc converter is necessary to connect between the solar-cells and the inverter in the transformer-less system. In this project, however, the solar-cells unit is designed to produce the output voltage of approximately 200 [V] in the maximum output power


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condition (i.e., 4.5kW). A higher voltage can be obtained depending on solar-cells arrangement but it affects reasonable system designing. On the other hand, the inverter needs approximately 400 [V] or more in the conventional system since the utility voltage is up to 280 [VRMS] and the maximum value reaches almost 400 [V].Figure(1.3) shows the transformerless inverter with Dc-DC boost converter and single phase full bridge inverter.

Figure (1.3) shows transformerless inverter with DC-DC boost converter

In such single-phase system, a large capacitance capacitor is connected in the input of the inverter to trap the ripple energy fed from the utility so that the input voltage is kept constant or stable. Further, both the dc-dc converter and the inverter operate with high-frequency switching at all the time and a high amount of switching losses is produced. To overcome the problems, a theory of novel and smart solution was developed. In the proposed utility interactive inverter system, the waveform of the input current of the dc-dc converter (i.e., the waveform of the dc-inductor current in the input) is wave shaped by bang-bang control so that the dc-inductor traps the ripple-power fed back from the utility. This control is available in the period where the solar-cells output-voltage is lower than the absolute-value of the utility voltage.

II. PHOTOVOLTAIC SYSTEMS

2.1 Introduction:

Photovoltaic is the field of technology and research related to the devices which directly convert sunlight into electricity. The solar cell is the elementary building block of the photovoltaic technology. Solar cells are made of semiconductor materials, such as silicon.

One of the properties of semiconductors that makes them most useful is that their conductivity may easily be modified by introducing impurities into their crystal lattice. For instance, in the fabrication of a photovoltaic solar cell, silicon, which has four valence

electrons, is treated to increase its conductivity. Demands Defined by the Photovoltaic Module(s):

A model of a PV cell is sketched in Fig.2.1 (a), and its electrical characteristic is illustrated in Fig2.1 (b).

Fig (2.1) Model and characteristics of a PV cell. (a) Electrical model with current and voltages defined. (b) Electrical characteristic of the PV cell, exposed to a given amount of (sun) light at a given temperature.

As indicated, ripple at the PV module’s terminals results in a somewhat lower power generation, compared with the case where no ripple is present at the terminals.

Where PMPP, V MPP and iMPP are the power, voltage and current at MPP.

I PV and P PV are the photovoltaic current and power.

Fig. (2.2). Photovoltaic installations

2.2 Maximum Power Point Tracking (MPPT):

Power electronic circuits are key elements for renewable energy power generation. The power electronics for solar power conversion shall have the ability of automatically tracking the maximum power point in order


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to achieve the maximum efficiency of the solar cells and inject sinusoidal and in-phase current to the grid so that power quality complies with the power system requirements. Conventionally, these two functions are realized by two-stages, one is a dc/dc converter with maximum power point tracking (MPPT) and the other dc/ac converter for sinusoidal current injection. This kind of two-stage approach typically requires real-time power, voltage or current measurement, and logic procedures to judge the MPP in the dc/dc stage, then a separate dc/ac inverter output the sinusoidal current to the grid. A simple low-cost one-stage inverter with MPPT accuracy is proposed. It has two functions: automatically adjusting output power according to the sunlight level and outputting a sinusoidal current to the grid.

It has the following features.

1) Constant switching frequency.

2) Low output current harmonics and high power factor, i.e.,

3) Simple main circuit with one stage power conversion.

4) A simple controller that only needs some linear components,

i.e., no DSP and no multipliers are necessary. If

DSP is desirable, a low cost one can be used.

5) Maximum power point tracking accuracy.

6) Low cost and high efficiency

Maximum power point trackers utilize some type of control circuit or logic to search for this point and thus to allow the converter circuit to extract the maximum power available from a cell. Traditional solar inverters perform MPPT for an entire array as a whole. In such systems the same current, dictated by the inverter, flows though all panels in the string. But because different panels have different IV curves, i.e. different MPPs (due to manufacturing tolerance, partial shading, etc.) this architecture means some panels will be performing below their MPP, resulting in the loss of energy

At night, an off-grid PV power system uses batteries to supply its loads. Although the battery pack voltage when fully charged may be close to the PV array's peak power point, this is unlikely to be true at

sunrise when the battery is partially discharged. Charging may begin at a voltage considerably below the array peak power point, and a MPPT can resolve this mismatch. When the batteries in an off-grid system are full and PV production exceeds local loads, a MPPT can no longer operate the array at its peak power point as the excess power has nowhere to go. The main disadvantage, however, is the direct connection of the PV array to the grid without galvanic isolation. Depending on the inverter topology this may cause fluctuations of the potential between the PV array and ground.

Tests relating to leakage currents:

Avoiding the transformer in PV grid connected inverter topologies results in the galvanic connection of the grid and the PV array.The potential differences imposed on the capacitance between the PV array and earth, through switching actions of the inverter can inject a leakage or capacitive earth current, leakage as shown in Figure (5.2). The PV array earth capacitance, C earth, is then part of a resonant circuit consisting of the PV array; DC and AC filter elements and the grid impedance. Due to efficiency optimization of PV systems the damping of this circuit can be very small. The leakage currents are driven by topology and control dependent voltages present between the PV array’s active conductors and earth (v+ and v-). The magnitude of the leakage currents depends not only on Cearth, but also on the magnitude, waveform and frequency of v+ and v-. Note that for an earthed array frame, Cearth consists of CFRAME (the capacitance between cell area and array frame) in parallel with Cstray (the stray capacitance between cell area and earth).

Figure (2.3) Grid connected PV system without transformer (using non-earthed array

conductors) including the PV array earth capacitance


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The capacitance depends on the type of PV module technology as well as environmental conditions, with humidity significantly increasing the value of Cearth. The highest maximum efficiencies are achieved with line commutated and self-commutated transformerless inverter is increasing day by day. From 1994 to 2003 their average maximum efficiency increased from 93.5% to 96.5% and from 93.4% to 95.8%, respectively. However, a PV inverter rarely operates at maximum efficiency due to the varying intensity of solar radiation.Only a truly transformerless design allows direct facility connection without any additional transformer equipment, customization. Although the price of solar PV power is becoming more and more competitive, it is vitally important for the industry to continue to find ways to enhance performance, improve efficiency, and drive down costs. Evaluating the quality and performance of large capital equipment is one way to continue to make gains, and just as significant as PV modules and arrays is the performance and efficiency of inverters.

III. PULSE WIDTH MODULATION

Introduction: The energy that a switching power

converter delivers to a motor is controlled by Pulse Width Modulated (PWM) signals, applied to the gates of the power transistors. PWM signals are pulse trains with fixed frequency and magnitude and variable pulse width. There is one pulse of fixed magnitude in every PWM period. However, the width of the pulses changes from period to period according to a modulating signal. When a PWM signal is applied to the gate of a power transistor, it causes the turn on and turns off intervals of the transistor to change from one PWM period to another PWM period according to the same modulating signal. The frequency of a PWM signal must be much higher than that of the modulating signal, the fundamental frequency, such that the energy delivered to the motor and its load depends mostly on the modulating signal. . The pulses of an asymmetric edge-aligned PWM signal always have the same side aligned with one end of each PWM period. Both types of PWM signals are used in this application. It has been shown that symmetric PWM signals generate fewer harmonic in the output current and voltage. Different PWM techniques, or ways of determining the modulating signal and the switch-on/switch-off instants from the modulating signal, exist. The Technique that we use is Natural PWM technique. This technique is commonly used with three phase

Voltage Source power inverters for the control of three-phase AC induction motors.

Figure 3.1 : PWM illustration by the sine-triangle comparison method

(a)Sine-triangle comparison (b) switching pulses.

As is explained the output voltage from the inverter is not smooth but is a discrete waveform and so it is more likely than the output wave consists of harmonics, which are not usually desirable since they deteriorate the performance of the load, to which these voltages are applied. The modulation signals are thus selected so meet some specifications, like harmonic elimination, higher fundamental component and so on.

IV. PHOTOVOLTAIC INVERTER

4.1 Introduction:

In photovoltaic applications the grid interface between source (solar array) and load (utility grid) consists of the inverter. To maximize the system efficiency the inverter must be optimized in design and control. For a 5Kw photovoltaic power system a single phase full bridge inverter is developed which requires only a minimum number of components. Most commercial inverters for photovoltaic applications include a transformer and several sections of power conversion. To reduce the degree of complexity it is proposed to omit the transformer and to use only one section of power conversion. Thereby system losses, size and costs decrease.

Figure (4.1) Main structure of the PV-system


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Figure (4.2) shows proposed topology with Full bridge inverter.

The proposed topology with the modulation technique described below can operate with power factors other than unity. In these cases, the operation analysis would similar.

4.2 MODES OF OPERATION:

To generate the positive half cycle S1 and S4 are on. In order to modulate the input voltage, S5 and S6 commutate at the switching frequency with the same commutation orders. S2 and S3 commutate at the switching frequency together and complementarily to S5

and S6.

. In this situation, when S5 and S6 are on VAB = VPV and the inductor current, which flows through S5, S1, S4 and S6 increases (i.e., From source to S5-S1_load-S4-S6) as shown in figure(4.2) .

Figure (4.3) shows Full bridge inverter in proposed topology with

S5, S1, S4 and S6 on.

In this case the common mode voltage is:

Vcm = VAO+ VBO / 2

Since VPV = VAO+ VBO

Therefore VPV = VAO+ 0

i.e. VPV = VAO

The common mode voltage is Vcm = VAO+0 / 2

Vcm = VAO / 2

Vcm = VPV / 2 ----------------------------- (1)

When S5 and S6 are turned off and S2 and S3 are turned on turned on, the current splits into two paths as shown in figure (4.3).

1. S1 and the freewheeling diode of S3 (S1-load-at point B the current splits into two paths.

2. S4 and the freewheeling diode of S2 (From point B to S4 and D2 and at point A and repeats so on).

Thus S2 and S3 are turned on without no current. Therefore there will be no switching losses appear. In this situation voltages VAB and VCD tend to zero and diode D7 and D8 fix the voltages VAO and VBO to VPV / 2.since VAB is clamped to zero the current decreases. Now the common mode voltage is:

VAO = VBO = Vcm = VPV / 2 --- (2)

To generate negative half cycle S2 and S3 are turned on as shown in Figure (4.3). In this situation again S5 and S6 commutate at the switching frequency in order to modulate the input voltage. S1 and S4 commute at the switching frequency together and complementarily to S5 and S6. In this situation when S5 and S6 are on VAB = - VPV and the inductor current which now flows through S5, S2, S3 and S6 decreases.

Figure (4.4) shows the proposed topology full bridge inverter with

switch S2 and S3 on.

In this situation the common mode voltage is

Vcm = VAO+ VBO / 2


7

Since VPV = VAO+ VBO

Therefore VPV =0+ VBO

VPV = VBO / 2

Vcm = VPV/ 2------------------------------------(3)

When and are turned off S5 and S6 and S1 ,S4 are turned on the current splits into two paths as shown in figure(4.4). The first path consists of S3 and the freewheeling diode of S1, and the second of S2 and the freewheeling diode of S4. Consequently, S1 and S4 are turned on with no current, so no switching losses appear. In this situation, voltages VAO and VBO tend to zero and diodes D7 and D8 fix the voltages VAB and VCD to VPV/ 2. The current decreases because VAB is clamped to zero.

Figure(4.5) shows the proposed topology with S5 ,S6 turned off and

S1 and S4 Turned on.

.

Now, the common-mode voltage

VAO = VBO = Vcm = VPV / 2-------------------(4)

From equations (1) –(4), it is clear that the common-mode voltage remains constant during the four commutation states of the converter. Therefore, no varying common-mode voltage is generated by the proposed topology and, hence, no leakage currents appear. The common-mode voltage remains constant during all commutation states. Additionally, voltage and therefore the inductor current, have the same waveforms as those obtained in the unipolar PWM full bridge. Assuming unity power factor, S5 and S6 commutate at the switching frequency with half of the input voltage Vpv, and the corresponding two freewheeling diodes of the full bridge commutate with Vpv but with half of the current. Therefore, switching

losses will be lower than those of the bipolar PWM full bridge and can be expected to be similar to those of the unipolar PWM full bridge. Since the blocking voltage of S5 and S6 is only half of the input voltage, switches with lower rated blocking voltage can be used and thus will exhibit lower switching losses for the same operating conditions.The IGBT switching losses of the full bridge are neglected, since they switch at the grid frequency. When the power factor decreases, the losses of the proposed topology increase because the switching losses of the full bridge increase. Conduction losses are expected to be greater in the proposed topology, because when S5 and S6 are on current flows through four switches instead of two, as in the full bridge (regardless of the PWM technique used). However, this increment is limited by the fact that and have lower saturation voltages because they have lower rated voltages.

From this we concluded that the switching losses are neglected in the proposed topology.

V. MATLAB SIMULINK DIAGRAMS & RESULTS OF FULL BRIDGE INVERTER

Simulation model of Full bridge inverter:

Fig 5.1 Simulation model of full bridge inverter

Output voltage and current waveforms:

Figure 5.2 output voltage and current waveforms


8

Matlab/simulink diagrams & results of proposed topo logy: The performance of the proposed Full bridge inverter topology is simulated with the Matlab/Simulink software. In the simulation, the utility supply is rated at 400 V and 50 Hz with a load inductance of Lo = 3 mH. The inverter is rated at 5 kw and is driving a R load of R =100Ω. The two dc capacitor(photovoltaic capacitor) Cpv is 1000 µF. SPWM method is used to modulate the inverter with unity power factor. The inverter output voltage is not detected, and therefore,is not tightly controlled. The switching frequency of inverter is 25Hz.

Single phase dc input with frequency 50Hz

Figure(5.3) shows single phase dc input with frequency 50Hz

Output waveforms of proposed topology:

Figure (5.4) output waveforms of proposed topology:

FFTAnalysis:

Figure (5.5) FFT analysis for output voltage without EMC filter of XY phase at 50 Hz frequency

FFTAnalysis:

Figure (5.6) FFT analysis for output current of XY phase at 50 Hz Frequency FFTAnalysis:

Figure (5.7) FFT analysis for output voltage of XY phase at 50 Hz frequency

VI. CONCLUSIONS & FUTURE SCOPE:

A Single-phase PV Full bridge inverter proposes a new transformerless, with six switches and two diodes inverter topology was proposed in this paper. The topology uses only six IGBT devices for dc to ac conversion. The proposed inverter Compared with the conventional Full bridge inverter with bipolar PWM using 6 switches and 2 diodes. The proposed inverter features sinusoidal inputs and outputs, unity input power factor, and low manufacturing cost.The proposed topology generates no common-mode voltage, exhibits a high efficiency, and can operate with any power factor. It has been compared to other topologies and vaIn this paper comparison between Full bridge inverter with four


9

switches and Full bridge inverter with six switches topology using sinusoidal Pulse Width Modulation technique. Furtherly this project can be helpful in eliminating the selected harmonics of 3rd order produced in this method by using selective harmonic elimination method .If in case, there are harmonics even after using this method then any other advanced PWM techniques can be followed to overcome the problem.

REFERENCES [1] M. Calais and V. G. Agelidis, “Multilevel converters for single-phase grid connected photovoltaic systems—An overview,” in Proc. IEEE Int. Symp. Ind. Electron. 1998, vol. 1, pp. 224–229. [2] M. Calais, J. M. A. Myrzik, and V. G. Agelidis, “Inverters for single-phase grid connected photovoltaic systems—Overview and prospects,” in Proc. 17th Eur. Photovoltaic Solar Energy Conf., Munich, Germany, Oct. 22–26, 2001, pp. 437–440. [3] B. Epp, “Big crowds,” Sun & Wind Energy: Photovoltaics, pp. 69–77, Feb. 2005. [4] J. M. A. Myrzik and M. Calais, “String and module integrated inverters for single-phase grid connected photovoltaic systems—A review,” in Proc. IEEE Power Tech. Conf., Bologna, Italy, Jun. 23–26, 2003, vol. 2, pp. 1–8. [5] W. N. Mohan, T. Undeland, and W. P. Robbins, Power Electronics: Converters, Applications, and Design. New York: Wiley, 2003. [6] V Verband der Elektrotechnik, Elektronik und Informationstechnik (VDE), Std. V 0126-1-1, Deutsches Institut für Normung, Feb. 2006. [7] IEEE Standard for Interconnecting Distributed Resources with Electric Power Systems, IEEE Std. 1547, 2003. [8] S. B. Kjaer, J. K. Pedersen, and F. Blaabjerg, “A review of single-phase grid-connected inverters for photovoltaic modules,” IEEE Trans. Ind. Appl., vol. 41, no. 5, pp. 1292–1306, Sep./Oct. 2005. [9] M. F. Arman and L. Zhong, “A new, transformerless, photovoltaic array to utility grid interconnection,” in Proc. Int. Conf. Power Electron. Drive Syst., May 26–29, 1997, vol. 1, pp. 139–143. [10] Y. Nishida, S. Nakamura, N. Aikawa, S. Sumiyoshi, H. Yamashita, and H. Omori, “A novel type of utility-interactive inverter for photovoltaic System,” in Proc. 29th Annu. IEEE Ind. Electron. Soc. Conf., Nov. 2–6, 2003, vol. 3, pp. 2338–2343. [11] Y. Chen and K. M. Smedley, “A cost-effective single-stage inverter With maximum power point tracking,” IEEE Trans. Power Electron. vol. 19, no. 5, pp. 1289–1294, Sep. 2004. [12] Martina Calais1, Andrew Ruscoe2, Michael Dymond “Transformerless PV Inverter Issues Revisited –Are Australian Standards Adequate?”Research Institute for Sustainable Energy (RISE), Murdoch University Solar09, the 47th ANZSES Annual Conference 29 September-2 October 2009, Townsville, Queensland, Australia. [13] Martina Calais’ Johanna Myrzik2 Ted Spoone? Vassilios G. Agelidis” Inverters for Single-phase Grid Connected Photovoltaic Systems - An Overview” 0-7803-7262-X/02/$10.Q00 2 002 LEB. [14] Hinz, H.; Mutschler, P. Darmstadt University of Technology, Institute for Power Electronics and Drives “Voltage Source Inverters for Grid Connected Photovoltaic Systems”. [15] Fritz Schimpf & Lars E. Norum, Norwegian University of Science and Technology, NTNU, Department of Electrical Power Engineering”

Grid connected Converters for Photovoltaic”. NORPIE/2008, Nordic Workshop on Power and Industrial Electronics, June 9-11, 2008.


10

Influence of Slip Conditions, Wall Properties and Heat Transfer on MHD Peristaltic

Transport of a Jeffrey Fluid in a Non-Uniform Porous Channel

R. Saravana* S. Sreenadh* S. Venkataramana* R. Hemadri Reddy** A. Kavitha** *Department of Mathematics, Sri Venkateswara University, Tirupati 517 502, India.

**School of Advanced Sciences, VIT University, Vellore 632 014, India. ABSTRACT: In this paper, we study the Peristaltic transport of magnetohydrodynamic (MHD) Jeffrey flui d in a non-uniform porous channel with the influence of slip, wall properties and heat transfer under the assumpt ions of long wavelength and low Reynolds number. The analytical expressions for the stream function, vel ocity and temperature are obtained. The results for veloc ity, stream function and temperature obtained in the ana lysis are discussed through graphs. It is noticed that th e velocity and temperature decrease with increasing J effrey

parameter 1λ . Further it is observed that the size of the

trapped bolus decreases with increasing 1λ .

Keywords: Peristaltic pumping - Jeffrey fluid - Non-uniform porous medium - MHD - Slip flow - Heat transfer

INTRODUCTION Peristalsis is an important mechanism for mixing

and transporting fluids, which is generated by a progressive wave of contraction or expansion moving on the wall of the tube. This mechanism is found in the swallowing of food through esophagus, chyme motion in the gastro-intestinal tracts, movement of ovum in the fallopian tube and many other glandular ducts in a living body. The mechanism of peristaltic transport has been exploited for industrial applications like sanitary fluid transport, roller and finger pumps, blood pumps in heart lung machine and transport of sensitive or corrosive fluids where the contact of the fluid with the machinery parts is prohibited.

The inertia free peristaltic flow with long wavelength analysis was given by Shapiro et al., [1]. The early development on mathematical

modeling and experimental fluid mechanics of peristaltic flow was given by Jaffrin and Shapiro [2]. A theoretical study of peristaltic transport of two-layered power-law fluids is made by Usha and Rao [3]. Kavitha et al., [4] studied the peristaltic flow of a Williamson fluid in an asymmetric channel through porous medium. Many researchers have contributed to the study of peristaltic transport under the effect of magnetic field and porous channel [5–10].

Peristaltic transport in non-uniform ducts is

considerable interest as many channels in engineering and physiological problems are known to be of non-uniform cross-section. Srivastava et al., [11] and Srivastava and Srivastava [12] studied peristaltic transport of Newtonian and non-Newtonian fluids in non-uniform geometries. Radhakrishnamacharya and Radhakrishna Murthy [13] studied the interaction between peristalsis and heat transfer for the motion of a viscous incompressible fluid in a two-dimensional non-uniform channel. Mekheimer [14] studied the peristaltic flow of blood (obeying couple stress model) under the effect of magnetic field in non-uniform channels. He observed that the pressure rise for a couple stress fluid is greater than that for a Newtonian fluid. Also the pressure rise for uniform geometry is much smaller than that for non-uniform geometry. Hariharana [15] investigates the peristaltic transport of non-Newtonian fluid, modeled as power law and Bingham fluid, in a diverging tube with different wall wave forms.

Mittra and Prasad [16] analyzed the peristaltic motion of Newtonian fluid by considering the influence of the


11

viscoelastic behaviour of walls. They assumed that the driving mechanism is in the form of a sinusoidal wave of moderate amplitude imposed on the flexible walls of the channel. Dynamic boundary conditions were proposed for the fluid motion due to the symmetric motion of the flexible walls which were assumed to be either thin elastic plates or membranes. Radhakrishnamacharya and Srinivasulu [17] studied the influence of wall property on peristaltic transport with heat transfer. Sobh [18] introduced slip effects on couple stress fluid. Ramana Kumari and Radhakrishnamacharya [19] investigated the effect of slip on peristaltic transport in an inclined channel with wall effects. The influence of slip, wall properties on MHD peristaltic transport of a Newtonian fluid with heat transfer and porous medium have been investigated by Srinivas and Kothandapani [20].

Among several non-Newtonian models proposed for physiological fluids, Jeffrey model is significant because Newtonian fluid model can be deduced from this as a special case by taking 1λ = 0. Further

it is speculated that the physiological fluids such as blood exhibit Newtonian and non-Newtonian behaviors during circulation in a living body. The Jeffrey model is relatively simpler linear model using time derivatives instead of convective derivatives for example the Oldroyd-B model [21]. Kothandapani and Srinivas [22] studied the peristaltic transport of a Jeffery fluid under the effect of magnetic field in an asymmetric channel. More recently, Vajravelu et al., [23] studied the influence of heat transfer on peristaltic transport of a Jeffrey fluid in a vertical porous stratum.

Motivated by these studies, in the present investigation we study the Peristaltic transport of magnetohydrodynamic Jeffrey fluid in a non uniform porous channel with the influence of slip, wall properties and heat transfer under the assumptions of long wavelength and low Reynolds number. The closed form of solution for the velocity, stream function and temperature are obtained. The effects of different physical parameters on the velocity, stream function and temperature obtained in the analysis are discussed through graphs.

BASIC EQUATIONS The constitutive equations for an incompressible

Jeffrey fluid are

T p I s= − + (1)

( )211

sµ γ λ γ

λ= +

+& &&

(2)

where T and s are Cauchy stress tensor and extra stress tensor respectively, p is the pressure,

I is the identity tensor, 1λ is the ratio of relaxation

to retardation times, 2λ is the retardation time, γ& is

shear rate and dots over the quantities indicate differentiation with respect to time. MATHEMATICAL FORMULATION

We consider the motion of an incompressible electrically conducting Jeffrey fluid in a two-dimensional non-uniform porous medium channel induced by sinusoidal waves propagating with constant speed ‘c’. The walls of the channel are assumed to be flexible and are taken as a stretched membrane. The wall deformation ( , )h x t

due to the infinite train of peristaltic waves is represented by

( ) ( ) ( )2, siny h x t d x a x ct

πλ

= = + − (3)

where ( ) , 1d x d m x m′ ′= + << , a is the

amplitude, λ is the wavelength, d is the mean half

width of the channel, m′ is the dimensional non-uniformity of the channel.

Fig. 1 Physical Model


12

The equations governing the flow of an

incompressible Jeffrey fluid in a porous medium

under the influence of a magnetic field are

0u v

x y

∂ ∂+ =∂ ∂

(4)

20

xyxxSSu u u p

u vt x y x x y

B u uk

ρ

µσ

∂ ∂∂ ∂ ∂ ∂+ + = − + + ∂ ∂ ∂ ∂ ∂ ∂

− − (5)

xy yyS Sv v v pu v v

t x y y x y k

µρ∂ ∂ ∂ ∂ ∂ ∂+ + = − + + − ∂ ∂ ∂ ∂ ∂ ∂

(6)

and the energy equation [24] is

2 2

2 2

xx yy xy

T T T T Tu v

t x y x y

u v u uS S S

x y x y

ζρ κ ∂ ∂ ∂ ∂ ∂+ + = + ∂ ∂ ∂ ∂ ∂

∂ ∂ ∂ ∂+ + + + ∂ ∂ ∂ ∂ (7)

where

21

21

1xx

uS u v

x y x

µ λλ ∂ ∂ ∂= + + + ∂ ∂ ∂

21

11xy

u vS u v

x y y x

µ λλ ∂ ∂ ∂ ∂= + + + + ∂ ∂ ∂ ∂

21

21

1yy

vS u v

x y y

µ λλ ∂ ∂ ∂= + + + ∂ ∂ ∂

Here ,u vare the velocity components along x

and y directions respectively, ρ is the density, µ is

the coefficient of viscosity of the fluid, p is the

pressure, σ is the electrical conductivity of the

fluid, 0B is the intensity of the magnetic field acting

along the y-axis and the induced magnetic field is assumed to be negligible, k is the permeability of the porous medium, ζ is the specific heat at

constant volume, υ is the kinematic viscosity of

the fluid, κ is the thermal conductivity of the fluid and T is the temperature of the fluid.

The governing equations of motion of the flexible wall may be expressed as

( ) 0L h p p∗ = − (8)

where L∗ is an operator, which is used to represent the motion of stretched membrane with viscosity damping forces such that

2 2

12 2L m C

x t tτ∗ ∂ ∂ ∂= − + +

∂ ∂ ∂ (9)

Continuity of stress at y h= ± and using x

momentum equation yields

( )

20

xyxxSSp u u u

L h u vx x x y t x y

B u uk

ρ

µσ

∗ ∂ ∂∂ ∂ ∂ ∂ ∂= = + − + + ∂ ∂ ∂ ∂ ∂ ∂ ∂

− −

(10)

( )1

2at y sin

uu h h d m x a x ct

y

πλ

∂ ′= = ± = ± + + − ∂ m

(11)

0T T= on y h= −

1T T= on y h= (12)

Hereτ is the elastic tension in the membrane,

m is the mass per unit area, *C is the coefficient of

viscous damping, 0p is the pressure on the outside

surface of the wall due to the tension in the

muscles and 1h is the dimensional slip parameter.

It is assumed that 0 0p = We introduce the stream function ψ such that

,u vy x

ψ ψ∂ ∂= = −∂ ∂

.

and the following non-dimensional quantities are

20

1 0

, , , ,

, , ,

x y ctx y t

d cd

T Td h dS S h p p

c T T d c

ψψλ λ

θµ λµ

′ ′ ′ ′= = = =

−′ ′ ′= = = =−

(13)


13

The non-dimensional governing equations after dropping the primes, we get

2 2 2

2

2 1

xx

xy

SpR

t y y x y x y x x

SM

y y Da y

ψ ψ ψ ψ ψδ δ

ψ ψ

∂∂ ∂ ∂ ∂ ∂ ∂+ − = − + ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂

∂ ∂ ∂+ − −∂ ∂ ∂

(14) 2 2 2

3 22

2

xy

yy

SpR

t x y x x x y y x

S

y Da x

ψ ψ ψ ψ ψδ δ

δ ψδ

∂ ∂ ∂ ∂ ∂ ∂ ∂+ − = − + ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂

∂ ∂+ −∂ ∂

(15) 2 2

22 2

2 2 2 22

2 2

1

Pr

xx xy yy

Rt y x x y x y

E S S Sx y y x y x

θ ψ θ ψ θ θ θδ δ

ψ ψ ψ ψδ δ δ

∂ ∂ ∂ ∂ ∂ ∂ ∂+ − = + + ∂ ∂ ∂ ∂ ∂ ∂ ∂

∂ ∂ ∂ ∂ + − − ∂ ∂ ∂ ∂ ∂ ∂

(16)

where 2

2

1

21

1xx

cS

d y x x y x y

δ δ λ ψ ψ ψλ ∂ ∂ ∂ ∂ ∂= + − + ∂ ∂ ∂ ∂ ∂ ∂

2

1

22

2 2

11

1xy

cS

d y x x y

y x

δ λ ψ ψλ

ψ ψδ

∂ ∂ ∂ ∂= + − + ∂ ∂ ∂ ∂

∂ ∂× − ∂ ∂

22

1

21

1yy

cS

d y x x y x y

δ δ λ ψ ψ ψλ − ∂ ∂ ∂ ∂ ∂= + − + ∂ ∂ ∂ ∂ ∂ ∂

( )2

2at y 1 sin 2h mx x t

y y

ψ ψβ ε π∂ ∂= = ± = ± + + − ∂ ∂m

(17)

( )

2 2 2

2

3 3 22

1 2 33 2

1

xyxxSS

Rx y t y y x y x y

M E E E hy Da y x x t x t

ψ ψ ψ ψ ψδ δ

ψ ψ

∂ ∂ ∂ ∂ ∂ ∂ ∂+ − + − ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂

∂ ∂ ∂ ∂ ∂− − = + + ∂ ∂ ∂ ∂ ∂ ∂ ∂ (18)

Further, it is assumed that the streamline value is

zero at 0y = . i.e. ( )0 0ψ =

(19) 0θ = on y h= − and 1θ = on y h= (20)

where

,a d

dε δ

λ= = are geometric parameters,

cdR

ρµ

= is the Reynolds number, 0M B dσµ

= is

the Hartmann number, 3 3 3

11 2 33 3 2

, ,d m cd Cd

E E Ec

τλ µ λ µ λ µ− −= = =

are the non-dimensional elasticity parameters,

Prρνζ

κ= is the Prandtl number,

( )2

1 0

cEc

T Tζ=

−

is the Eckert number, 2

kDa

d= is the Darcy

number, m

md

λ ′= is the non-uniform parameter

and β is the Knudsen number (Slip parameter). EXACT ANALYTICAL SOLUTION

Under the assumptions of long wavelength (1δ << ) and low Reynolds number, from equations

(14)-(18), we get

( )3

23

1

1 10

1

pM

x y y Da y

ψ ψ ψλ

∂ ∂ ∂ ∂= − + − −∂ + ∂ ∂ ∂

(21)

0p

y

∂= −∂

(22)

Equation (22) implies ( )p p y≠

22 2

2 21

10

Pr 1

Ec

y y

θ ψλ ∂ ∂= + ∂ + ∂

(23)

Elimination of pressure from equations (21) and (22), yields

4 22

4 20N

y y

ψ ψ∂ ∂− =∂ ∂

(24)


14

where ( ) 21

11N M

Daλ = + +

Equation (18) gives

( )3 3 3 2

21 2 33 3 2

N E E E hy y x x t x t

ψ ψ ∂ ∂ ∂ ∂ ∂− = + + ∂ ∂ ∂ ∂ ∂ ∂ ∂ (25)

The closed form solution for stream function and velocity from equation (24) using the boundary conditions (17), (19) and (25) are given by

( )

( )

33

1 22

8cos2 ( ) sin 2 ( )

2

sinh

cosh sinh

EE E x t x t

N

Nyy

N Nh N Nh

επψ π ππ

β

= − + − − −

× − +

(26)

( )

( )

33

1 22

8cos2 ( ) sin 2 ( )

2

cosh1

cosh sinh

Eu E E x t x t

N

Ny

Nh N Nh

επ π ππ

β

= − + − − −

× − +

(27) Substituting equation (26) into equation (23) subject to the boundary condition (20), we get the temperature as

( )( )

( )2 2 2 2 21

2

1

2 cosh 2 cosh 2 2

28(1 ) cosh sinh

BrL N y Ny Nh N h y h

hNh N Nhθ

λ β

− + − += +

+ + (28)

where

( )3

31 1 22

8sin 2 ( ) cos 2 ( )

2

EL x t E E x t

N

επ π ππ

= − − + −

and Br = PrEc⋅ is the Brinkman number.

The coefficient of heat transfer at the wall is given

by x yZ h θ= ⋅

( )( )( )

( ) ( )22 212

1

2 cos 24 2 sinh 2 4 cosh sinh

(1 )8 cosh sinh

m x t BrZ h L N y N Ny Nh N Nh

h Nh N Nh

πε πβ

λβ+ −

= − + + ++ (29)

RESULTS AND DISCUSSION The equation (27) gives the expression for the

velocity in terms of y. Velocity profiles are plotted in figures from (2) to (8) to study the effects of different parameters such as the Jeffrey parameter

1λ , the Darcy number Da, the slip parameter β ,

the amplitude ratio ε , the non-uniform parameter

m , the Hartmann number M, the wall tension 1E ,

the mass characterizing parameter 2E and the

damping nature of the wall 3E . Fig. 2 is drawn to

study the effect of 1λ on the velocity distribution u.

We observe that the increase in 1λ decreases the

velocity.

Fig. 3 is plotted to study the effect of Da on the velocity. We observe that the velocity increases with increasing Da. Further, the permeability parameter causes to strengthen the fluid slip at the wall. Fig. 4 and Fig. 5 shows that an increase in β

and ε results in the increase of velocity distribution. In Fig. 6 we find that the velocity for a divergent channel ( 0m > ) is higher compared to

its value for a uniform channel ( 0m= ), whereas it is lower for a convergent channel ( 0m < ). From Fig. 7 we see that the velocity decreases with increase of M. From Fig. 8 we notice that the velocity increases with increasing E1 and E2 and it decreases with increasing E3.

The equation (28) gives the expression for the temperature in terms of y. Temperature profiles are plotted in figures from (9) to (16) to study the


15

effects of the physical parameters of the problem.

Fig. 9 is drawn to study the effect of 1λ on the

temperature distributionθ . It is observed that the

temperature decreases with increasing 1λ . Figures

10 and 11 are plotted to study the effect of Da and M on the temperature. We observe that the temperature increases with increasing Da and decreases with increasing M. Figures 12 and 13 shows that the temperature decreases by increasing β and it increases with increasingε .

Fig. 14 illustrates the effects of mon the temperature distribution. We notice that the amplitude of the temperature is large in case of divergent channel compared with uniform and convergent channels. Fig.15 is plotted to study the effect of Brinkman number Br on the temperature distribution. We notice that the temperature increases with an increase in Br . Fig. 16 shows that the temperature increases with increasing E1

and E2 and it decreases with increasing E3. The equation (29) gives the expression for the

coefficient of heat transfer at the wall. Figures from (17) to (23) are plotted to observe the variation of heat transfer coefficient at the walls for different values of the physical parameters of interest. We observe that nature of the heat transfer is in oscillatory behavior, which may be due to

peristalsis. From figures 19, 22 and 23, the magnitude of heat transfer coefficient increases by increasing Da, Br, E1, E2 and E3 while from Figures 17, 18, 20, 21 and 22, it decreases by increasing

1, ,λ β M and m.

TRAPPING PHENOMENA

The effect of slip parameter on the streamline pattern is shown in Fig. 24. We observe that the size of the trapping bolus increases with increasing slip parameter. The streamlines for Jeffrey

parameter 1λ are shown in Fig. 25. It is observed

that the size of trapping bolus decreases with

increasing 1λ . The streamlines for uniform and non-

uniform channels are shown in Fig. 26. It is found that the size of the trapped bolus is large in the left hand of the convergent channel while it has opposite behavior for divergent channel. Further, the size of bolus is symmetric for uniform channel. From Fig. 27 it can be seen that the volume of the trapped bolus decreases with increase of M. From Fig. 28 it is clear that the trapped bolus increases in size as Da increases.


16

Fig.2. The variation of u with y for different values of 1λ for fixed

E1=1, E2=0.5, E3=0.5, ε =0.1, β =0.0, M=2, Da=1, m=0.

Fig.3. The variation of u with y for different values of Da for fixed

E1=0.5, E2=0.5, E3=0.1, ε =0.1, β =0.2, M=2, m=0.1, 1λ =1.

Fig.4. The variation of u with y for different values of β for fixed

E1=1, E2=1, E3=0.5, ε =0.1, M=2, Da=2, m=0, 1λ =1.

Fig. 5. The variation of u with y for different values of ε for fixed

E1=2, E2=0.7, E3=0.1, M=3, β =0.2, Da=2, m=0.1, 1λ =1.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

u

y

λ1 = 0.0

λ1 = 0.5

λ1 = 1.0

λ1 = 1.5

λ1 = 2.0

Results of Srinivas et al.[20]

2 2.5 3 3.5 4 4.5 5-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

u

y

Da = 0.5Da = 2.0

Da → ∞

0.5 1 1.5 2 2.5 3 3.5 4 4.5-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

u

y

β = 0.0

β = 0.1

β = 0.2

β = 0.3

1 2 3 4 5 6 7 8-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

u

y

ε = 0.10

ε = 0.15

ε = 0.20

ε = 0.25


17

Fig. 6. The variation of u with y for different values of m for fixed

E1=0.8, E2=0.5, E3=0.5, ε=0.1, β =0.2, M=3, Da=2, 1λ =0.5.

Fig.7. The variation of u with y for different values of M for fixed

E1=2, E2=0.7, E3=0.1, ε=0.2, β =0.2, Da=2, m=0.1, 1λ =1.

Fig.8. The variation of u with y for different values of E1 , E2 and E3

for fixed ε =0.1, β =0.2, M=2, Da=2, m=0, 1 1λ = .

Fig.9. The variation of θ with y for different values of 1λ for fixed

Br=3, E1=0.8, E2=0.5, E3=0.2, ε =0.1, β =0.1, M=2, Da=1, m=0.2.

1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

u

y

m = -0.3

m = 0.0

m = 0.3

2 3 4 5 6 7 8 9 10 11 12-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

u

y

M = 2

M = 3M = 4

0.5 1 1.5 2 2.5 3 3.5-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

u

y

E1=0.5, E2=0.5, E3=0.5

E1=0.8, E2=0.5, E3=0.5

E1=0.8, E2=0.8, E3=0.5

E1=0.8, E2=0.8, E3=0.8

0 2 4 6 8 10 12-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

θ

y

λ1 = 0.0

λ1 = 0.5

λ1 = 1.0

λ1 = 1.5

Results of Srinivas et al.[20]


18

Fig.10. The variation of θ with y for different values of Da for fixed

Br=4, E1=1, E2=0.5, E3=0.2, ε =0.1, M=2, m=0.2, β =0.1, 1λ =1.

Fig.11 The variation of θ with y for different values of M for fixed

Br=2, E1=1, E2=0.8, E3=0.2, ε =0.1,Da=0.5, m=0.2, β =0.1, 1λ =0.5.

Fig.12. The variation of θ with y for different values of β for fixed

Br=3, E1=0.7, E2=0.5, E3=0.2, ε =0.2, M=3, Da=2, m=0.1, 1λ =0.5.

Fig.13. The variation of θ with y for different values of ε for fixed

Br=3, E1=0.8, E2=0.5, E3=0.3, 1λ =1, β =0.1, M=2, Da=1, m=0.2

0 1 2 3 4 5 6 7 8 9

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

θ

y

Da = 0.5Da = 2.0

Da → ∞

0 0.5 1 1.5 2 2.5 3 3.5 4-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

θ

y

M = 2

M = 3

M = 4

0 1 2 3 4 5 6 7-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

θ

y

β = 0.0

β = 0.1

β = 0.2

β = 0.3

0 5 10 15 20 25-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

θ

y

ε = 0.10

ε = 0.15

ε = 0.20

ε = 0.25


19

Fig.14. The variation of θ with y for different values of m for fixed

Br=4, E1=0.8, E2=0.5, E3=0.2, ε =0.1, M=2, Da=1, β =0.1, 1λ =0.5.

Fig.15.The variation of θ with y for different values of Br for fixed

E1=0.8, E2=0.6,E3=0.2, 1λ =1, β =0.1, M=3, Da=1, m=0.1, ε =0.1

Fig.16. The variation of θ with y for different values of E1, E2 and

E3 for fixed Br=3, ε =0.1, β =0.1, M=2, Da=2, m=0.1, 1 1λ = .

Fig. 17. The Coefficient of heat transfer Z with x for different values

of 1λ for fixed E1=0.5, E2=0.4, E3=0.1, ε =0.1, M=3, Br=2, Da=0.2,

m=0.1, β =0.1.

-1 0 1 2 3 4 5 6-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

θ

y

m = -0.3

m = 0.0

m = 0.3

0 0.2 0.4 0.6 0.8 1 1.2 1.4-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

θ

y

Br = 2

Br = 3Br = 4

Br = 5

0 1 2 3 4 5 6-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

θ

y

E1=0.5,E2=0.5,E3=0.5

E1=0.8,E2=0.5,E3=0.5

E1=0.8,E2=0.8,E3=0.5

E1=0.8,E2=0.8,E3=0.8


20

Fig. 18. The variation of Z with x for different values of β for fixed

E1=0.5, E2=0.4, E3=0.1, ε=0.1, M=3, Br=2, Da=0.2, m=0.1, 1λ

=0.4

Fig.19. The variation of Z with x for different values of Da for fixed

E1=1.2, E2=0.1, E3=0.1, ε=0.1, M=5, Br=2, m=0.1, β =0.1, 1λ =0.2.

Fig. 20. The variation of Z with x for different values of M for fixed

E1=0.5, E2=0.4, E3=0.1, ε =0.1, Br=2, Da=0.2, m=0.1, β =0.1, 1λ=0.2.

Fig.21. The variation of Z with x for different values of m for fixed

E1=0.5,E2=0.4,E3=0.1, ε =0.1,Br=3,Da=0.2,M=0.1, β =0.1, 1λ =0.4.

Fig. 22. The variation of Z with x for different values of Br for fixed

E1=0.8,E2=0.4,E3=0.2, ε=0.1,M=3,Da=0.2,m=0.2, β =0.1, 1λ =0.4.

Fig. 23. The variation of Z with x for different values of E1, E2, E3

for fixed M=3, Br=3, Da=0.2, m=0.2, β =0.1, 1λ =0.4.


21

(a)

(b)

(c)

Fig. 24. Streamlines for (a) β =0, (b) β =0.1, (c) β =0.2, with

E1=0.6, E2=0.4, E3=0.1, ε =0.1, m=0.1, M=4, Da=0.1, 1λ =1, t=0.1.

(a)

(b)

(c)

Fig. 25. Streamlines for (a) 1λ =0, (Results of Srinivas et al.[20])

(b) 1λ =0.2, (c) 1λ =0.4, with E1=0.6, E2=0.4, E3=0.1, ε =0.2,

m=0.1, M=4, Da=0.05, β =0.1, t=0.1.

0.0 0.2 0.4 0.6 0.8

1.5

1.0

0.5

0.0

0.5

1.0

1.5

0.0 0.2 0.4 0.6 0.8

1.5

1.0

0.5

0.0

0.5

1.0

1.5

0.0 0.2 0.4 0.6 0.8

1.5

1.0

0.5

0.0

0.5

1.0

1.5

0.0 0.2 0.4 0.6 0.8

1.5

1.0

0.5

0.0

0.5

1.0

1.5

0.0 0.2 0.4 0.6 0.8

1.5

1.0

0.5

0.0

0.5

1.0

1.5

0.0 0.2 0.4 0.6 0.8

1.5

1.0

0.5

0.0

0.5

1.0

1.5


22

(a)

(b)

(c)

Fig. 26. Streamlines for (a) m=-0.3, (b) m=0.0, (c) m=0.3 with

E1=0.5, E2=0.1, E3=0.2, ε =0.2, β =0, M=4, Da=0.1, 1λ =1, t=0.1.

(a)

(b)

(c)

Fig. 27. Streamlines for (a) M=0, (b) M=1, (c) M=2 with E1=0.8,

E2=0.7, E3=0.2, ε =0.1, β =0.1, m=0.2, Da=0.1, 1λ =1, t=0.1.

0.0 0.2 0.4 0.6 0.8

1.5

1.0

0.5

0.0

0.5

1.0

1.5

0.0 0.2 0.4 0.6 0.8

1.5

1.0

0.5

0.0

0.5

1.0

1.5

0.0 0.2 0.4 0.6 0.8

1.5

1.0

0.5

0.0

0.5

1.0

1.5

0.0 0.2 0.4 0.6 0.8

1.5

1.0

0.5

0.0

0.5

1.0

1.5

0.0 0.2 0.4 0.6 0.8

1.5

1.0

0.5

0.0

0.5

1.0

1.5

0.0 0.2 0.4 0.6 0.8

1.5

1.0

0.5

0.0

0.5

1.0

1.5


23

(a)

(b)

(c)

Fig. 28. Streamlines for (a) Da=0.01, (b) Da=0.1, (c) Da= ∞ with

E1=0.6, E2=0.4, E3=0.1, ε =0.1, β =0.1, m=0.1, M=4, 1λ =1, t=0.1.

CONCLUSION In this paper, we investigated the Influence of slip conditions, wall properties and heat transfer on MHD Peristaltic transport of a Jeffrey fluid in a non-uniform porous channel under the assumptions of long wavelength and low Reynolds number. The analytical expressions are obtained for the velocity, stream function and temperature. The main observations of this study are as follows:

• The velocity profile decreases with an increase in Jeffrey parameter 1λ .

• The temperature decreases with an increase in 1λ , β , M and E3 while it increases with increase in Da, ε , m, E1 and E2.

• As expected, the coefficient of heat transfer is oscillatory in nature.

• The size of trapped bolus is smaller in Jeffrey fluid when compared with that of Newtonian fluid ( )1 0λ = .

• As the Jeffrey parameter 1 0λ → , the results deduced are found to be in agreement with the corresponding ones of Srinivas et al. [20].

REFERENCES

1)Shapiro, A.H., Jaffrin, M.Y. and Weinberg, S.L. “Peristaltic pumping with long wavelengths at low Reynolds number”, J. Fluid Mech., vol. 37, pp. 799-825, 1969. 2)Jaffrin, M.Y. and Shapiro, A.H. “Peristaltic pumping”, Ann. Rev. Fluid Mech., vol.3, pp.13-36, 1971. 3)Usha, S., Rao, A.R. “Peristaltic transport of two layered power-law fluid”. J Biomech Eng. Vol. 119, pp. 183–188, 1997. 4)Kavitha, A., Hemadri Reddy, R., Sreenadh, S. and Saravana, R. “Peristaltic flow of a Williamson fluid in an asymmetric channel through porous medium”, International Journal of Innovative Technology and Creative Engineering, vol. 1(1), pp. 48-53, 2011. 5)Mekheimer, Kh. S. and Al-Arabi, T. H. “Nonlinear peristaltic transport of MHD flow through a porous medium” International Journal of Mathematics and Mathematical Sciences, vol. 26, pp. 1663–1682, 2003. 6)Hayat, T. Ali, N. and Asghar, S. “Hall effects on peristaltic flow of a Maxwell fluid in a porous medium,” Physics Letters A, vol. 363(6), pp. 397–403, 2007. Elshehawey, E.F., Elsayed

0.0 0.2 0.4 0.6 0.8

1.5

1.0

0.5

0.0

0.5

1.0

1.5

0.0 0.2 0.4 0.6 0.8

1.5

1.0

0.5

0.0

0.5

1.0

1.5

0.0 0.2 0.4 0.6 0.8

1.5

1.0

0.5

0.0

0.5

1.0

1.5


24

M.E. Elbarbary, Nasser S. Elgazery, “Effect of inclined magnetic field on magnetofluid flow through a porous medium between two inclined wavy porous plates (numerical study)”, Applied Mathematics and Computation, vol. 135 pp. 85–103, 2003. 7)Srinivas, S., Kothandapani, M. “The influence of heat and mass transfer on MHD peristaltic flow through a porous space with compliant walls”, Appl Math Comput, vol. 213, pp. 197–208 2009. 8)Hayat, T., Quereshi, MU, Hussain, Q. “Effect of heat transfer on the peristaltic flow of an electrically conducting fluid in a porous space”. Appl Math Model, vol.33, pp.1862–1873, 2009. 9)Kothandapani, M., Srinivas, S., on the influence of wall properties in the MHD peristaltic transport with heat transfer and porous medium, Phys. Lett. A vol.372, pp. 4586–459, 2008. 10)Srivastava, L. M., Srivastava, V. P. and Sinha, S. N. “Peristaltic Transport of a Physiological Fluid: Part I. Flow in Non–Uniform Geometry”, Biorheol. Vol. 20 pp.153–166, 1983. 11)Srivastava, L. M. and Srivastava, V. P. “Peristaltic Transport of a Power-Law Fluid: Application to the Ductus Efferentes of the Reproductive Tract”, Rheol. Acta, vol.27, pp. 428–433, 1988. 12)Radhakrishnamacharya, G. and Radhakrishna Murthy, V. “Heat transfer to peristaltic transport in a non-uniform channel”, Defence Science Journal. Vol. 43(3), pp. 275-280, 1993. 13)Mekheimer, Kh S. “Peristaltic flow of blood under the effect of magnetic field in a non-uniform channels”. Appl. Math. Comput. Vol.153, pp 763–77, 2004. 14)Prasanna Hariharan, Seshadri, V., Rupak K., Banerjee, “Peristaltic transport of non-Newtonian fluid in a diverging tube with different wave forms”, Mathematical and Computer Modelling vol. 48, pp. 998–1017, 2008. 15)Mittra, T. K. and Prasad, S. N. “On the influence of wall properties and Poiseuille flow in the peristalsis”, J. Biomechanics vol.6, pp. 681–693, 1973. 16)Radhakrishnamacharya, G. and Srinivasulu, C. H. “Influence of wall properties on peristaltic transport with heat transfer,” Mecanique, vol. 335(7), pp. 369–373, 2007. 17)Sobh, A.M., “Interaction of couple stresses and slip flow on peristaltic transport in uniform and non-uniform channels”, Turkish J. Eng. Environ. Sci. vol. 32, pp.117-123, 2008. 18)Ramana Kumari, A.V. and Radhakrishnamacharya, G. “Effect of slip on peristaltic transport in an inclined channel with wall effects”, Int. J. of Appl. Math and Mech. Vol. 7 (1), pp. 1-14, 2011. 19)Srinivas. S., Gayathri, R., Kothandapani, M., “The influence of slip conditions, wall properties and heat transfer on MHD peristaltic transport”. Computer Physics Communication, vol.180, pp. 2115–22, 2009. 20)Oldroyd, J. G. “On the formulation of rheological equations of state”, Proc. Roy. Soc. London. Ser. A. vol. 200, pp. 523–541, 1950. 21)Kothandapani, M., Srinivas, S. Peristaltic transport of a Jeffery fluid under the effect of magnetic field in an asymmetric channel, Int. J. Non-Linear Mech. Vol.43, pp. 915–924, 2008.

22)Vajravelu, K., Sreenadh, S. and Lakshminarayana, P. “The influence of heat transfer on peristaltic transport of a Jeffrey fluid in a vertical porous stratum”, Commun Nonlinear Sci. Numer. Simulat. Vol.16, pp. 3107–3125, 2011. 23)Hayat, T., Hina, S., and Awatif A. Hendi. “Peristaltic Motion of Power-Law Fluid with Heat and Mass Transfer”, CHIN. PHYS. LETT. Vol. 28(8), pp. 084707(1-4). 2011.


25

DataAprori algorithm : Implementation of scalable Data Mining by using Aprori

algorithm M Afshar Alam1 , Sapna Jain2 ,Ranjit Biswas3

Department of Computer Science ,Jamia Hamdard [email protected]

2 [email protected] [email protected]

NewDelhi,India-110062

Abstract - Data Mining is concerned with the development and applications of algorithms for discovery of a priori unknown relationships associations, groupings, classifiers from data. Association rule mining (ARM) is a knowledge discovery technique used in various data mining applications. The task of discovering scalable rule s from the multidimensional database with reduced support is an area for exploration for research . Pruning is a technique for simplifying and hence generalising a decision tree. Error-Based Pruning replace sub-trees with leaves .It uses decision cla ss is the majority. In this paper we have proposed an algorithm DataAprori to generate scaled rules using the alarm technique. Network problems manifest themselves as an alarm sequence. Since network problems repeat more or less frequently, processing of alarm sequences from alarm history can be good base for creation of correlation rules that will be used in the future, when the same problem will appear. In this paper we have proposed DataAprori that induces a set of rules of the potential usage of the mathematical Apriori algorithm in fault management introducing logical inventory data in typical alarm by introducing the sequence detection processes. Experimental on real world datasets show that the proposed approach improves performance over existing approach in the form of High level-correlations (alarm sequences) which are detected in a telecommunication network.

Keywords: Data mining , ABCDE architecture ,pruning, Aprori technique.

I Introduction:

Data mining is an increasingly important branch of computer science that examines data in order to find and describe patterns. Because we live in a world where we can be overwhelmed with information, it is imperative that we find ways to classify this input, to find the information we need, to illuminate structures, and to be able to draw conclusions. Data mining is a very practical discipline with many applications in business, science, and government, such as targeted marketing, web analysis, disease diagnosis and outcome prediction, weather forecasting, credit risk and loan approval, customer relationship modeling, fraud detection, and terrorism threat detection. It is based on methods several fields, but mainly machine learning, statistics, databases, and information visualization[4].

Alarms generated by telecommunication network are processed by network personnel who are required to respond within a reasonable time interval[13,45l. When a global network problem occurs, it is represented as a sequence of alarms coming from one or more different network elements. That sequence is typically not recognized as a global problem, or the presence of global problem is detected, but not its real nature[49]. The reason for that is the huge number of alarms generated, “bombing” the operator. Automatic recognition of network problems is very useful for network monitoring processes. Automatic recognition and detection can be done by simple IF-THEN correlation rules performed on incoming alarm stream. The problem is in recognizing potential correlation rules candidates. In our previous works, we have marked


26

mathematical Apriori algorithm implementation as a potential improvement of correlation rules detection.

This research work is the extension of the previous work where we have proposed Aprori-UB which uses multidimensional access method UB-tree to generate better association rules with high support and confidence[19][20]. The Aprori-Ub approach reduces not only the number of item sets generated but also the overall execution time of the algorithm. In this paper we have used the abcde architecture for high-level correlations discovery as well as typical patterns that can be used for low-level correlations and filtrations[48][49].

The rest of the paper is organized as follows. Section 2 gives the overview of the previous work done in the same field. Section 3 explains the concepts used in this paper. Section 4 gives the proposed work. Section 5 gives the experimentation details. Section 6 and Section 7 discusses the conclusion and future scope.

II .Related Work

We define ,Ck as a candidate itemset of size k ,Zk as a frequent itemset of size k, An AIREP algorithm is

1) Find frequent set Lk-1 2) Join step: Ck is generated by joining Lk-1

with itself (cartesian product Lk-1 x Lk-1) 3) Prune step : Use theIncremental Reduced

Error pruning to generate scalable single rule.

4) Frequent set Lk has been achieved.

The AIREP (Aprori Incremental Reduced Error Pruning) pseudo code :

AIREP (T, ụ)

Z1 large multidimensional itemsets that appear in more than

Of large item set ụ transactions

K 2

While ( Zk-1 ≠ 0 )

Ck Generate (Zk-1 ) // join and prune step

// using IREP

procedure I-REP (Examples, SplitRatio)

Theory = ;

While Positive (Examples) ≠ ;

Clause = ;

Split Examples (Split Ratio, Examples, Growing Set, Pruning Set)

Cover = Growing Set

While Negative (Cover) ≠ ;

Clause = Clause Find Literal (Clause; Cover)

Cover = Cover (Clause, Cover)

loop

NewClause = BestSimplification (Clause, PruningSet)

if Accuracy(NewClause,PruningSet) < Accuracy(Clause,PruningSet)

exit loop

Clause = NewClause

if Accuracy(Clause,PruningSet)<=Accuracy(fail,PruningSet)

exit while

Theory = Theory Clause

Examples = Examples -Cover

return (Theory)

// end of IREP

//frequent set generation

for transaction t € Z

Ck Subset(Ck,t)

for candidates c € Ct


27

count[c] =count[c + 1]

Zk c € Ck| count[c] >= e

k k+1

return Zk

Figure 1: Pseudocode of AIREP algorithm

The basic idea of Incremental Reduced Error Pruning (IREP) is that instead of first growing a complete concept description and pruning it thereafter, each individual clause will be pruned right after it has been generated. This ensures that the algorithm can remove the training examples that are covered by the pruned clause before subsequent clauses are learned thereby preventing these examples from influencing the learning of subsequent clauses.

Figure 1 shows pseudo-code for this algorithm. As usual, the current set of training examples is split into a growing (usually 2/3) and a pruning set (usually 1/3). However, not an entire theory, but only one clause is learned from the growing set. Then, literals are deleted from this clause in a greedy fashion until any further deletion would decrease the accuracy of this clause on the pruning set. Single pruning steps can be performed by submitting a one-clause theory to the same BestSimplification subroutine used in REP or, as in our implementation, one can use a more complex pruning operator that considers every literal in a clause for pruning. The best rule found by repeatedly pruning the original clause is added to the concept description and all covered positive and negative examples are removed from the training growing and pruning set. The remaining training instances are then redistributed into a new growing and a new pruning set to ensure that each of the two sets contains the predefined percentage of the remaining examples. From these sets the next clause is learned. When the predictive accuracy of the pruned clause is below the predictive accuracy of the empty clause (i.e., the clause with the body fail), the clause is not added to the concept description and I-REP returns the learned clauses. Thus,

the accuracy of the pruned clauses on the pruning set also serves as a stopping criterion. Post-pruning methods are used as pre-pruning heuristics.

In figure 2 the attributes of the dataset are divided into instances and converted into divided attributes. In order to build a rule, IREP uses the following strategy. First the uncovered examples are randomly partitioned into two subsets, a growing set and a pruning set. Next, a rule is grown. It begins with an empty conjunction of conditions, and considers adding to this any condition of the form Zn = Ui , Zn <= or Z >= where Zn is a nominal attribute and u is a legal value for Zn , or Zc is a continuous variable and is some value for Zc that occurs in the training data. After growing a rule, the rule is immediately pruned .

After growing a rule, the rule is immediately prunedTo prune a rule, our implementation considers deleting any final sequence of conditions from the rule and chooses the deletion that maximizes the function

u(Rule,PrunePos,PruneNeg) = X + (N –n)

X + N

where X (respectively N), is the total number of examples in PrunePos ,PruneNeg and p ,n, is the number of examples in PrunePos ,PruneNeg covered by Rule.This process is repeated until no deletion improves the value of u.

Figure 2. Partitioning of original data set of labelled instances

III. Concept Used


28

ALARM BASIC CORRELATIONS DISCOVERY ENVIRONMENT ARCHITECTURE (ABCDE)

A. ABCDE architecture overview

Correlation and filtration rules database contains data about correlations and filtrations to be performed in realtime manner by alarm processing engine. Rules from this database are proposed by Correlation discovery and analysis module. This module can be used for discovery of new potential rules performing data mining algorithm on historical alarm data. It can be used for analysis and

evaluation of potential rule candidates also, performing rule execution on sample of historical alarm data.Filtration part of Correlation discovery and analysis module discovers and evaluates potential filter patterns.

Alarm data warehouse is a database containing all raw alarm history data as well as correlated alarm history data for a certain time period, predefined by the operator (e.g. 2 years). Alarm data warehouse is starting point for discovery and analysis of typical correlations from alarm historical data, in order to include it in the Correlation and filtration rules database.

Figure 3 :Basic ABCDE architecture

Incoming network alarms are generated by the telecommunication network. Alarms are consumed and processed by alarm processing engine that performs alarm filtration as well as low and high-level correlation. Processed alarms are presented to the network operator

through alarm surveillance GUI. Alarm processing engine uses correlation and filtration rules stored in database, while incoming alarms are stored into alarm data warehouse. Logical inventory database containing data about network interconnections can be use for more efficient alarm correlation. Logical inventory data can be used for enhancement of incoming alarm data also, tying relevant inventory information with alarm data (for instance,“friendly” alarm location name). Alarm processing engine is not the focus of this paper since number of commercial tools is able to perform alarms processing functions. Not all incoming alarms are relevant for further processing.Alarm classification and filtration are described in details in [11], and will not be discussed here more detailed. Filtering is also not always statically related to predefined, concrete network element; it can be rather dynamically changed, based on certain circumstances in network, such as scheduled maintenance procedure on some network elements. After filtration is done on historical alarm data, low-level correlation discovery and evaluation can be performed. This is primarily related to discovery of general patterns, such as alarm overlapping or alarm jittering. High-level correlation will cope with concrete alarm patterns, coming from specific network elements. At this stage, raw alarm clusters are detected first. Alarm cluster is set of alarms received from the network within certain time interval fenced with cluster borders. Namely, we have detected “long enough” time periods without alarms. Those periods are considered as cluster borders. All alarms suited between two cluster borders belong to the same cluster [2]. Cluster is input for the mathematical Apriori algorithm, but in order to improve algorithm performance, we have proposed usage of logical network inventory data to split raw clusters in smaller parts containing alarms from interconnected alarm locations only. In that case, all interconnections will be taken under consideration while creating alarm clusters: total number of clusters will increase, while average number of alarms in one cluster will decrease. It will drastically improve performance of data mining algorithm execution.

Logical inventory data should be obtained from network operator. However, if it is not obtainable, there is proposed technique how to extract logical inventory data from alarm history. It was described in [7], and it is not


29

primary focus of this paper. However, it was denoted on figure 2 through Logical inventory block.

When clusters are generated, the Apriori algorithm is performed. The final result is the number of alarm sequences that occurred frequently in the past. Those sequences are potential high-level correlation rules candidates for future alarm processing. Criteria for acceptation of those candidates can be rule frequency, but also rule can be accepted based on network expert’s opinion.

B. Low-level correlations

After alarm filtration is performed, low-level correlations are to be performed. Low-level correlations are not related to concrete network elements or alarm types; rather we are going to discover general alarm behavior patterns.Typical behavior is alarm jittering; for some reasons, certain network element may jitter between alarming and non-alarming state. It is represented to network operator in terms of number of (short) alarms with short periods between end of first alarm and start of the second alarm.

We will refer to sequence of jittering alarms as “chained”alarms.Another such behavior is related to alarm overlapping.Generally two alarms can be overlapped completely,partially, or not overlapped. Even in the last case, great role plays time interval between two alarms:

Figure 4 . Alarm overlapping patterns

At low-level correlation, completely and partially overlapped alarms (fig. 3 a, b, c, d, e) coming from the same network element (and, optionally, with the same probable cause) can be considered as one alarm with valueadded information “sticked” to it: number of alarms laying beyond it.

Alarms that are not overlapped have important parameter related to them: time between end of first alarm and start of the second alarm. If that time is short enough, two alarms can be treated as only one alarm, ignoring end of first and start of second alarm.

Combining those two typical patterns, and reducing all hidden alarms from operator’s GUI, number of reduced alarms can increase. On figure 4, there are 8 alarms coming from the same network element within certain time period.Some of those alarms are overlapped, while some are not.

In the case of non-overlapped alarms, timer interval between them is short enough. “Alarm storm” can be hence replaced by only one alarm with value-added information,reducing even 7 alarms from operator’s graphical interface.

AIREP learns the clauses in the order in which they will be used by a PROLOG interpreter. Before subsequent rules are learned, each clause is completed (learned and pruned) and all covered examples are removed. Therefore, the AIREP approach eliminates the problem of incompatibility between the separate-and conquer learning strategy and the reduced-error pruning strategy.

Figure 5. Reduction of alarms by low-level correlations


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C.High-level correlations: raw-cluster detection

After filtration and low-level correlation processing, the incoming alarm stream will be “clustered”: alarm clusters containing alarms potentially belonging to the same network problem will be detected. Alarm cluster detection is described in [2]. The important thing is that the alarm clusters are divided by time intervals without alarms. D. High-level correlations: cluster splitting Typically, a network problem is represented by the number of alarms coming from one or more network elements. If the alarms are coming from more than one network element, it is reasonable to expect that the network elements are interconnected. If we have a logical inventory database at our disposal (i.e., database where information about network element interconnections is stored), we can try to include it in the discovery environment. How? We can consider only the clusters containing alarms from interconnected network elements.

Since a logical inventory database is not always available, there is a possibility to “generate” it, based on the alarm historical data. In that case, we will first analyze alarms by their location only. After that analysis we will have information about the most frequent points of interconnection. This data can be stored in a logical inventory database (using a predefined threshold) and can be used in the cluster splitting process in the future. This concept is described in [7].

E. High-level correlations: Apriori algorithm

The mining of association rules is potentially very interesting for detection of specific alarm “clusters” that can represent a global network problem. What was the original motivation for researching association rules? Let us imagine a supermarket serving a huge number of customers every day. The supermarket manager is responsible for all business aspects, including special offers and promotions.

For instance, the manager can decide to launch chips discount for every customer buying 6 beers. The previously mentioned special offer seems to be very logical, based on our daily experience. However, there are numbers of such association rules that cannot be perceived by casual observation. Hence, the manager is forced to analyze the supermarket’s transaction data

(i.e., customer receipt archive or database) – to examine customer behavior while purchasing products. The result of such analysis is a set of typical association rules describing how often items are purchased together. For instance, rule “Beer _ Chips (80%)” states that four of five customers buying beer are also buying chips [3]. That result can be useful for business decisions related to marketing, pricing and product promotion.We have considered our alarms as products purchased in a supermarket, and alarm clusters as baskets from a specific customer. Hence we have decided to use the Apriori algorithm in order to find and recognize specific alarm sequences – potential correlation rules for the future [2].

Apriori algorithm itself is described in number of papers such as [3]. The final result of high-level correlations is the creation of a correlation rules database. Rules are structured in an IF-THEN manner. It means that the alarm processing engine will receive incoming alarm stream matching incoming patterns with existing patterns in the correlation rules database. When a pattern is matched, a new alarm is generated containing information about the real network root-cause problem.

IV. Proposed work

The Proposed Algorithm : Pseudocode • Join Step: Ck is generated by joining Lk-1 with itself • Prune Step: Any (k-1)-itemset that is not frequent cannot be a subset of a frequent k-itemset • Pseudo-code: Ck : Candidate itemset of size k Lk : frequent itemset of size kL Input: alarm queue (Sij , Wk) Output: t frequent alarm sequence set: F_ ALARMm 1. compute C1:= α | α∈F_ALARM1; 2. m:=1; 3. while Cm≠Φ do 4. begin 5. For all α∈Cm , Search alarm queue Sij to find support(α, Wk); /*Algorithm 2 */ 6. Obtain F_ALARMm= α∈Cm| support(α, Wk)≥ min_support; 7. Generate Candidate Cm+1 from F_ALARMm; /* Algorithm 3 */ 8. m=m+1; 9. end.


31

10. for all m , output F_ALARMm; frequent items; for (k = 1; Lk ;!=∅; k++) do begin Ck+1 candidates generated from Lk; for each transaction t in database do increment the count of all candidates in Ck+1 that are contained in t Lk+1 // candidates in Ck+1 with min_support end return ∪k Lk ; Figure 6 :DataAprori algorithm

Alarm correlation algorithm (Algorithm 1) is composed of two main steps. In the first step, according to the minimum support(Min_support), it searches the frequent alarm type sequence from alarm queues and the discovered frequent alarm type sequences constitute the set of frequent alarm type sequences, denoted by F_ALARMm. In the second step, according to the confidence of correlation rule .It generates the alarm correlation rules from F_ALARMm. The association rules algorithm and its measure of association rule ST, which is defined as confidence(ST) ,=Support(ST/ Support(S), where S and T correspond to a set of attributes and S and T are disjoint.

The support and confidence of an association rule S->Y are defined asSupport=P[ST] and Confidence=P[ST]/S[T]. The confidence is the conditional probability of Tgiven S. If S and T are independent, then Confidence =P[ST]/P[S]=P[T]. Therefore, if P[T] is high, then the confidence of the rules is high, which will make association rule meaningless. In order to solve the problem.The interestingness measure I=P(ST)/(P(S)×P(T)). The interestingness measure is symmetrical, because the confidence of S->Y is equal to the one of T->S.. A rule holds if and only if the confidence of rule is greater than min_conf.

Input: Frequent alarm sequence set F_ALARMm Output: output the correlation rules β→(α-β) and confidence |P(α)/P(β)-P(α-β)|

1. for all αF_ALARMm do /* generate correlation rules */

2. for all βα do

3. if|P(α)/P(β)-P(α-β)|≥min_conf then

4. begin

5. generate the rule β→ (α-β) with

6. confidence |P(α)/P(β)-P(α-β)| ;

7. end

V IMPLEMENTATION ASPECTS AND EXPERIMENTAL RESULTS

DataAprori components are developed using C and C++ programming languages, as a parts of complex application.Central application component is executable file thatinvolves different dynamic-linked libraries (dll) in architecture. Every part is implemented as separated dll. It allows upgrade of separated components without disturbing general application structure. For database access we have used Open Database Connection (ODBC) with all data stored in MS SQL server. For database access we have used standard MFC classes, but all other techniques could be used. The data in experiment 1 are the alarms in GSM Networks, which contain 181 alarm types and 91311 alarm events. The time of alarm events ranges from 1201-03-15-00 to3001-03-79-52. In figure 5 the broken line graph is denoted by win_xy, where x represents the size of additional alarm window i.e.Win_add and y represents the size of frequent alarm window i.e. Win_freq. In figure 6 the Y axis is the number of alarm type sequences and the X axis is Mini_support (using the minimum occurring times ).

Figure 7: The number of frequent sequences changes in

DataAprori.


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Figure 8 : Comparison between dataaprori and aprori method.

From Figure 8, we can find that the reduction rate of our method is a little better than aprori method.However aprori method is not able to filter dataset in real time. It can distinguish true alerts and false ones onOur method has low time consumption as compared to the aprori method . Moreover,this method needs a lot of labeled data to build its modeland can not filter alerts in training phrase, while our method does not have these limits. So using our method,security managers can response to attacks more quickly. From above comparison, we believe that our system has better performance than current methods.

VI. Conclusion

Since the DataAprori algorithm can analyze alarm correlation from alarm database containing noise data, it will generate more alarm sequences, then the number of correlation rules increases. Although the correlation measure can reduce the rules, it still needs people to select the most useful ones from a large number of the rules. Therefore, it is necessary to study how to extract rules more correlated from alarm database containing noise in the future. This number can be reduced if we discover some frequently repeated alarm sequences, and replace it by one alarm. For that purpose, we have used Apriori algorithm, as we discussed in our previous work. However, after sequences are detected, it is necessary to “judge” which sequence is relevant for future and which is not. One of criteria can be frequency of alarm sequence appearing.

Also, some sequences can be very relevant, event if those are not repeated very frequently. DataAprori can be used fordiscovery and statistical processing of alarm sequences,while final decision should be made by human operator.According to our previous and other related works [12],reduction rate at high-level correlations can be rather high,up to 80%. Using test data sample and finding several alarm sequences

confirmed by network experts, reduction rate was 15.41 %.

VII.Future work

Further research efforts should be invested into the full implementation of proposed architecture, improving and introducing new data mining techniques for high-level correlations discovery as well as typical patterns that can be used for low-level correlations and filtrations. Fuzzy technique can also be improvised in the proposed DataAprori in future.

ACKNOWLEDGMENT

The authors wish to thank Jamia Hamdard University

Library , Labortory for allowing experimentation and research . The author wish to acknowledge ,Prof M Afshar Alam ,Prof Ranjit Biswas and others contributors for developing and contribution to this paper.

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Completeness Time

consumption Support

rate Reduction

rate

DataAprori 80-%91% low 100% 78%

Aprori method

79% Middle 93% 80%


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A Software and a Hardware Interface for Reducing the Intensity Uncertainties Emitted

by Vehicular Headlight on Highways Mrs. Niraimathi.S, Dr.Arthanairee A. M, Mr.M. Sivakumar P.G.Department of Computer applications, N.G.M.College

Pollachi-642001, TamilNadu, India [email protected], [email protected],[email protected]

Abstract- This paper proposes a hardware and a software interface for reducing the effects of Headlight glare. It proposes a Fuzzy Sensor and a Fuzzy Controller that uses fuzzy rule based design approach to reduce the Headlight glare emitted by the oncoming vehicles during night on the Highways. This in fact reduces accidents and puts the driver of the oncoming vehicle in a safety zon e which might jeopardize the oncoming driver’s visibility. In the conventional vehicles the illumination is adjusted manually by the driver. Th e proposed approach has the hardware circuit fit on t o the Windshield, which provides ambient light source to oncoming vehicle, there by not producing blinding effect on the vision of the driver . Thi s Hardware has to be fitted on to all the vehicles, s o that it reduces the occurrence of accidents. Microcontrollers are usually designed to interface to and interact with electrical/electronic devices, sensors and high-tech gadgets to automate systems. Microcontrollers are used for automated decision making.

The PIC microcontroller has been used which reduces the intensity of light if it goes beyond tolerable limits, affecting the driver’s vision. T he concept of Fuzziness has been applied to the sensor and the Controller. The light intensity of the oncoming vehicle is received by the Fuzzy sensor. This input light intensity is fuzzified and checke d for the tolerance limit. If this does not lie withi n the tolerance limit, the sensor passes it to the Fuzzy controller which converts it to an ambient light source and then on defuzzifying the output. The software has been developed using MATLAB.

Keywords: Fuzzy rules; fuzzy sensors; fuzzy controllers; fuzzification; defuzzification; Headlight glare; MATLAB

I. INTRODUCTION

Driving at night-time poses a severe challenge, as drivers have to watch the traffic control devices, oncoming vehicles, lane lines, pedestrians, animals, and other dangers. Incandescent light sources can illuminate the highways, but bright light sources or improper lighting may result in glare, thereby posing an unsafe environment to the drivers.

This paper proposes a Fuzzy based approach to reduce the headlight glare. The fuzzy sensor and the fuzzy controllers are fit onto the windshield, gives a solution to the headlight glare. The sensor includes the operation of checking the light source, if it is of over tolerance/under tolerance. There by the controller converting it in to low intensity if it is of high intensity and vice versa, providing ambient light source.

The light intensity(I) measured in Volts and the distance(D) in metres are received by the fuzzy sensor. The input parameters received by the fuzzy sensor are crisp input values (Numerical value). These crisp sets are converted in to fuzzy sets using the process of fuzzification and are evaluated using the fuzzy rules. The output light intensity(OI) calculated using the fuzzy rules is checked for the tolerance limit by the fuzzy sensor. If beyond the tolerance limit, the fuzzy sensor defuzzifies using centroid of maximum and then sends it to the fuzzy controller which converts it to ambient light source. The process of fuzzification and defuzzification is also carried out for the fuzzy controller.


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The literature survey report [1,2,3,4,8] gives basic understanding of Crisp sets, Fuzzy sets, concepts of fuzzy controller, and of the Fuzzy Expert systems. In [5] the application of fuzzy logic controller to improve the energy efficiency of a dimmer light balance implemented in passive optical fiber day lighting system has been demonstrated. The Literature report in [6] proposes an automatic fuzzy controller which controls the switching of headlight intensity of automobiles. In [7] the fuzzy controller for the heart disease has been elucidated which gives the basic understanding of the components in Fuzzy systems.

II. METHODOLOGY

A Fuzzy system consists of four components: Fuzzifier, Inference Engine, Rule base and Defuzzifier. The components and the schematic representation of a Fuzzy system is elucidated in Fig. 1.

Crisp Output

Fig.1. Schematic representation of a Fuzzy System

A. Hardware Environment

The hardware environment consists of a Light Sensor, amplifier, PIC microcontroller, MOSFET driver, MOSFET, Battery and a Headlight as shown in Fig. 2. The prototype of the proposed hardware is shown in Fig. 3.

LDR (Light Dependent Resistor) is used to measure the intensity of light rays. This element endures a change in resistance when it is subjected to light rays. This is used to sense the high beam of the approaching vehicle.

The amplifier is interfaced between LDR and Microcontroller (PIC 16F877A). Amplifier increases the amplitude of the signal sent by LDR to the microcontroller compatible level.

The ultrasonic sensor consists of a crystal oscillator which generates a high frequency signal .This signal is received by the receiver when it is reflected back by the approaching vehicle . The time between transmission and reception of the signal is used to measure the distance between the two vehicles.

The input for the Microcontroller is the Light source and the distance of the light source. PIC Microcontroller is programmed to reduce the intensity of light if it exceeds the tolerance limit. The microcontroller is a device that interfaces to sensors and performs computing. Peripheral Interface Controllers (PICs), are inexpensive microcontroller units that include a central processing unit and peripherals such as memory, timers, and input/output (I/O) functions on an integrated circuit (IC). They are called microcontrollers because they are used to perform control functions. PIC16F877A controller has various peripherals with 4k of flash memory which is very flexible. This IC is used to control the entire process

Headlight is the ultimate output device which is controlled by the IC. This control is enabled by switching the lamp on and off using the MOSFET which acts as a transistor switch. This is connected between the 12v power source and the headlamp.

Fuzzifier

Inference

Engine Rule base

DeFuzzifier

Fuzzified inputs

Crisp inputs


37

Fig.2. Block Diagram of proposed controller

Fig.3. Protype of the proposed hardware

B. Software environment

The Fuzzy sensor(Fig. 4) proposed has two input parameters. The first parameter being the Distance(D), which is the distance between the two approaching vehicles. The second parameter being the Input intensity(I), which is a measure of the light emitted by the oncoming vehicle. The output parameter is the Sensor Output(OI), which would be passed to the Fuzzy controller, if it lies beyond the tolerance limit. Using MATLAB-FuzzyLogicToolbox, the demonstrations of the system are shown in the figures below

Fig. 4. The structure of the fuzzy sensor

1) Fuzzy inference process The Fuzzy Inference Process follows the steps as shown below

Fuzzification of the input variables. Defining Membership functions. Fuzzy Inference. Defuzzification. Fuzzification of input variables.

A crisp set of input data are mapped to fuzzy sets using fuzzy linguistic variables, fuzzy linguistic terms and membership functions. Linguistic variable means the input or output variables of the fuzzy systems whose values are words or sentences, instead of numerical values. A linguistic variable can be decomposed into a set of linguistic terms.The crisp values got for the input parameters D and I are converted in to fuzzy sets. To fuzzify the parameters, linguistic variables are used (Table I, II, III). The Distance(D) has 10 fuzzy sets, input Intensity(I) consists of 6 fuzzy sets, and the output parameter ouput Intensity(OI) consists of 6 fuzzysets.


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TABLE I. FUZZY SETS, LINGUISTIC VARIABLES FOR INPUT

INTENSITY I(V) AND ITS RANGE

Input Parameter

Linguistic value Fuzzy sets

Range

Input Intensity(I)

JustNoticeable JN 0-3.50

Noticeable N 3.00-6.50

Satisfactory S 5.00-8.50

JustAcceptable JA 7.00-10.50

Disturbing D 9.00-12.50

UnBearable UB 11.00-14.50

TABLE II. FUZZY SETS, LINGUISTIC VARIABLES FOR

DISTANCE D(MTS) AND ITS RANGE

Input Parameter

Linguistic value

Fuzzy sets

Range

Distance(D)

VeryClose VC 0-25

Close CL 12-50

VeryNear VN 37-75

Near N 62-100

ModeratelyNear MN 87-125

ModeratelyFar MF 110-150

Far F 135-175

VeryFar VF 160-200

PrettyVeryFar PVF 185-225

BoundaryZone BZ 210-250

TABLE III. FUZZY SETS, LINGUISTIC VARIABLES

FOR SENSOR OUTPUT LIGHT SOURCE OI(V) AND

ITS RANGE

Output Parameter

Linguistic value

Fuzzy sets

Range

Output Intensity(OI)

JustNoticeable JN 0-3.50

Noticeable N 3.00-6.50

Satisfactory S 5.00-8.50

JustAcceptable JA 7.00-10.50

Disturbing D 9.00-12.50

UnBearable UB 11.00-14.50

Defining Membership functions

Membership functions are used to map the crisp input values to fuzzy linguistic terms and vice versa. A membership function is used to quantify a linguistic term. After fuzzification is carried out, the next process is to define the membership functions in the fuzzy sets for the input and output parameters. The Triangular membership function is used for constructing the fuzzy sets. The membership function for the input parameters is shown by the figures (5-6). The membership function of the output parameter is shown in figure.7.


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Fig. 5. The membership function of D(Distance)

Fig.6. The membership function of I(inputIntensity)

Fig. 7. The membership function of OI(Output Intensity of sensor )

The triangular membership function is a function of vector x and it depends on the three scalar parameters a, b, and c and it is represented as in eq. 1

; , ,

0,

0, (1)

For example the input parameter of the fuzzy sensor, Input intensity (I) which takes on 6 fuzzy sets JN, N, S, JA, D, UB and their respective ranges are (0-3.50), (3.00-6.50), (5.00-8.50), (7.00-10.50),(9.00-12.50), (11.00-14.50). The triangular membership function for the fuzzy set JN is given by µJN(x)(eq 2)

µJN

0; 0

. ; 0 1.75

.

. ; 1.75 3.50 0 ; 3.50

(2)

Similarly the triangular membership function is calculated for all the fuzzy sets N, S, JA, D and UB. This as well goes for the other input parameter D.

Fuzzy Inference

A rule base is constructed to control the output variable. A fuzzy rule is a simple IF-THEN rule with a condition and a conclusion. The fuzzy input values are processed using the set of rules. Each rule processes the information using different input parameters; the output of each rule is different. In order to construct the fuzzy rules we construct rule matrix (Table IV) and rule bases. Row captions in the matrix contain the values


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that Distance can take, column captions contain the values for Input Intensity, and each cell is the resulting command when the input variables take the values in that row and column. For example, the cell (4, 3) in the matrix can be read as follows: If Distance is F(far) and the Input Intensity is S(satisfactory) then the Output Intensity is S(satisfactory).

TABLE IV. RULE MATRIX REPRESENTATION

D/I JN N S JA D UB

BZ JN JN JN S JA D

PVF JN JN N S D D

VF JN JN N S D D

F JN N S S D D

MF JN N S JA D D

MN N N S JA D D

N S S S JA D UB

VN S S S D D UB

CL JA JA D D UB UB

VC JA D D UB UB UB

The Rule matrix is a simple graphical tool for mapping the Fuzzy system rules. It accommodates two input variables namely Distance(D), Input Intensity and expresses their logical product (AND) as one output response variable OI(output intensity).

The rule matrix is used to formulate the rule bases. For instance we formulate 10*6 rules for the fuzzy sensor. Linguistic rules describing the control system consist of two parts; an antecedent block (between the IF and THEN) and a consequent block (following THEN). Antecedent block consists of input linguistic variables that may be combined using AND operators. Consequent part contains the output of the fuzzy rule. The figure below (Fig. 8) and (Fig. 9) shows the snapshot of the rule base for the sensor. In the figure

below, the value of D=189, I=7.25 and OI=5.2. This implies that the output light intensity is moderate; the sensor judges it to be of the acceptable limit and it need not send it to the controller. The surface viewer of the fuzzysensor is given(Fig. 10)

Fig. 8. Snapshot of the rulebase for the Fuzzysensor

Fig. 9. Computing the value of OI for I=13.9 and D=250

If the output light intensity I is higher(9.00 and above), the sensor sends it to the controller and the fuzzy


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controller (Fig. 11) converts it into an ambient light source.

Fig. 10. Surface viewer of the Fuzzysensor

Fig.11. The structure of the Fuzzy Controller

The Fuzzy controller accepts the OutputIntensity(OI), if it is of either Disturbing(D),or UNBEARABLE(UB) it converts it to an ambient light source. The Fuzzy controller’s ouput is the ControllerOutputIntensity(COI) (Table V)

TABLE V. THE LINGUISTIC VARIABLES FOR COI AND

ITS NUMERICAL RANGE

Linguistic value Notation Numerical range

ReduceLightSource RLS 9.00-14.50

AmbientLightSource ALS 0-9.00

Fig. 12. The membership function for the ControllerOutputIntensity(COI)

The membership function for the output intensity of the fuzzy controller is shown (Fig. 12). The rule bases for the fuzzy controller(Table VI) is as shown below. The snapshot of the rule base for the fuzzy controller is shown(Fig.13).

TABLE VI. THE RULE BASES FOR THE FUZZY

CONTROLLER

Rule OI COI

1 JN ALS

2 N ALS

3 S ALS

4 JA ALS

5 D RLS

6 UB RLS


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Fig. 13. Snapshot of the rule base for the Fuzzy controller

The figure below (Fig. 14) shows the rule viewer of the Controller. In the figure below, the value of OI=13.2 and that of COI=7.45 . This implies that the output light intensity is Unbearable; the sensor judges it not to be of the acceptable limit and it sends to the controller for turning it in to an ambient light source. The Surface viewer of the controller is shown(Fig. 15).

Fig. 14. Rule viewer of the Fuzzy controller

Fig. 15. Surface viewer of the Fuzzy controller

Defuzzification

The fuzzy sets are converted to crisp values. Here the fuzzy sets represented by s-op(Sensors output) and

ols(fuzzy controllers output) are converted to crisp sets(Numerical values). Centre of area method has been used. General formula for COA is (Eq:3)

z*=∫µc(z)zdz/∫µc(z)dz (3)

III. IMPLEMENTATION

The hardware implementation has been done using the PIC micro controller. The software implementation has been carried out using the Fuzzy Logic Toolbox. MATLAB Fuzzy Logic Toolbox has been used to encode fuzzy sets, fuzzy rules and to perform inference process for both the fuzzy sensor and the fuzzy controller. The hardware and the software has been tested.

IV. CONCLUSION

This paper has proposed a software and a hardware Interface for Reducing the Intensity Uncertainties Emitted by Vehicular Headlight on Highways. Hardware and software interface applies fuzzy design to reduce the headlight glare which in places the Drivers in a comfortable zone of visibility which in turn minimizes the Accidents. The fuzzy sensor and the controller uses the fuzzy logic to control the intensity of light. The conventional controllers would not be very efficient in controlling the headlight glare as there would be discrete values either high/low beam but the fuzzy controller has the continuous light intensities rather than high/low beam. The fuzzy sensor and the controller has to be fitted onto the windshield of the two oncoming cars. This fuzzy system comprising the sensor and the controller reduces the headlight glare and therefore reduces the accidents on the highways during transportation at night. PIC Microcontroller has been used to reduce the intensity of light. This system would necessarily prove to be beneficial for the drivers as the driving becomes secure without ruining the vision of the driver at both the end of the vehicles.


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[14] Niraimathi.S, Dr. Arthanariee.A.M, Sivakumar.M, ”A Fuzzy Approach to

Prevent Headlight Glare”, IJCSIS Vol. 9 No. 2, February 2011.


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