CAE 해석을 통한 클린 룸 설비 내 - cadgraphics.co.kr · XFlow 특징 및 기능 6....

CAE 해석을 통한 클린 룸 설비 내 기류 및 파티클 관리 기술

2013. 11.

원 영 수

(주) Delta ES www.deltaes.co.kr

[email protected]

http://www.deltaes.co.kr/

목차 1. Cleanroom 개요

1) 크린룸의 정의

2) 크린룸의 분류

3) 크린룸의 규격

4) 크린룸 계획

5) 청정도 유지를 위한 관리요소

2. 클린룸 내 기류 시뮬레이션 기술 소개

3. FloVENT 특징 및 기능

4. Application Examples

5. XFlow 특징 및 기능

6. Application Examples

7. 결론

Cleanroom 개요

Chapter I. CLEANROOM 개요

Cleanroom의 정의 Cleanroom의 4대 원칙 Cleanroom의 분류 FFU System 그 외 설비 Cleanroom System의 변화

크린룸의 정의

Structure of Cleanroom System

OAC (Outdoor Air Conditioning) System

크린룸의 정의

1) 공기중의 부유 미립자가 규정된 청정도 이하로 관리되고

2) 입자의 유입, 생성 및 체류를 최소화 할 수 있는 공간을 의미

3) 온/습도 등 환경에 대해서도 관리할 수 있도록 만들어진 구역이나 공간

크린룸의 4대 원칙

(1) Preventing: 이물 침입을 방지 (2) Prohibiting: 이물 발생을 방지 (3) Protecting: 이물 누적을 방지 (4) Purging: 발생된 이물 제거

많은 비용 동반, 비용측면 비경제적.

발생된 오염원 주위 교차오염 발생 전 즉시 효과적 처리 시스템 구현 필요.

필요 부분 고청정 구역 유지, 국소청정 시스템 도입

크린룸의 분류 - 용도

1) ICR (Industrial Clean Room)

2) BCR (Biological Clean Room)

• 입자/가스상 오염물질 농도 및 온,습도 제어 • 양압 유지 청정 환경 • 반도체, FPDs, 하드웨어 등

크린룸 설비 요구 산업분야 · 반도체 · 전자 공학 · 정밀 공학 · 광학 계측

• 공기 청정화뿐 아니라 세균이나 미생물 제어 • 양압/음압 유지 청정 환경 • 항생물질, 유해균 실험, 종양바이러스 등

생체입자 제거용 크린 룸 · 제약 · 식품 · 병원 · 동물 사육실

크린룸 구성 시설

건축내장 (Interior) Partition, Raised Floor, Paint (Base Floor)

공조설비 (HVAC) Mechanical, Duct, Piping

전기설비 (Electrical) Power, Lighting System, Fire Fighting, Communication System,

Receptacle, Public Address System

자동제어 (Control) FMCS (Facility Management Control System), HVAC control system,

Particle Monitoring System

유틸리티 (Utility) DI, PCW, CDA, PV, CV, WWT, Exhaust System, Bulk Gas System,

CCSS, CGSS, …

크린룸 구성 시설

FFU(Fab)/ EFU(Equipment) ACF series

Supply Plenum

OAC

VOC & Gas Pollution Reduction Facility

Return Plenum

Sub FAB

Dry Cooling Fan Unit

Dry Coil

Cooling EFU

Mini-Environment Air Shower, P/Box, R/Damper,

크린룸 분류 - 기류방식

난류형 C/R (Non Unidirectional, Mixed Flow)

층류형 C/R (Unidirectional Flow)

기류 혼합으로 오염입자 제거 기류 이동으로 오염입자 제거

크린룸 분류 – System 기능

클린룸 시스템의 변화 트랜드

The Future Trend for Cleanroom Technology

Ballroom Type Cleanroom

Isolation 기술 증가

전체 환기량 감소

[천정의 FFU 수량 감소]

국소환경 고청정 유지

에너지 절약형 클린룸 시스템

Mini-environment Type Cleanroom

Mini-environment Type Cleanroom


ME(Mini-environment) System의 특징


The Future Trend for Cleanroom Technology

1. High Performance System • 생산방식 변화 [낱장이송, Flowshop] 2. Reduction of Initial Investment & Running Cost • 건물 층고 감소 < 초기 투자비 감소 • Change to Mini-environment Type from Ballroom Type

[Process위주의 국소 청정 환경 개념 도입] • Air Stagnation Reduction in Cleanroom System [환기횟수 최소화] 3. 인간 및 환경친화적 시스템 • Clean Toilet

Reduction of Contagious Chemicals

청정도를 위한 관리요소

이물(Particle, Dust) 란 ?

공기 혹은 가스나 액체에 존재하는 고체성 물질을 말한다. - 크기에 따른 분류: 1 ㎛ 보다 큰 것은 미립자 (Dust), 1 ㎛ 보다 작은 것은 먼지 (Particle) 라고 한다. 먼지 크기의 결정 불규칙한 형태의 먼지의 각 치수의 큰 규격을 먼지의 크기로 정의 한다. 먼지 크기의 단위 ( ㎛ : 마이크론) 1 ㎛ = 1백만분의 1미터 (1/1,000,000m) * 머리카락의 지름 : 100 ㎛

1) 기본용어의 정의

청정도를 위한 관리요소

일정 공간내에 일정 크기 이상의 입자가 일정 개수 이내로 포함되어 있는 정도. 미연방규격 기준 (U.S.Fed.Std)

0.5 ㎛ 이상 크기의 입자가 1 ft³ 중에 몇 개 포함되어 있는가에 따라 CLASS로 표시하며 가장 널리 사용하는 규격이다. (예 : CLASS100,000) ISO규격 기준 0.1 ㎛ 이상 크기의 입자가 1 m³ 중에 몇 개 포함되어 있는가에 따라 CLASS로 표시한다. ( 예 : ISO CLASS 5일 경우 1 m³ 내에 0.1 ㎛ 이상의 입자가 100,000개 이내로 포함되어 있는 정도의 청정도를 나타냄.) BCR(Bio Clean Room)에서 규격 기준 1 m³ 공기중의 미생물 수(CFU/m³)를 CLASS로 표시한다. * CFU: Colony Forming Unit

2) 청정도의 기준

크린룸 규격

청정도 기준 – FED-STD-209E (미연방 규격)

크린룸 규격

청정도 기준 – ISO

크린룸 규격

산업군별 청정도 관리 기준

26

Outline

• Aerosol Technology • Introduction to theory • Examples of applications

• Cyclone chamber • HVAC office • Free surface test • Soiling

• Particle Treatment for Numerical Simulation • Emission scheme improvements • Particles as rigid bodies • Collisions between particles • Adhesion model • Two-way coupling

Aerosol Technology

Fab 내 파티클 영향

Fab 내 파티클 영향

Killing Particle Size 원하지 않은 particle이 device의 동작영역에 앉는다면 defect(결함, 불량)을 유발시킬수 있다. Particle size는 회로 선폭에 비례해서 defect을 일으킬 수 있는 particle인가 아닌가를 결정하는 요소이다. 1/10 rule이란 회로 최소 선폭의 1/10 size particle이 불량을 유발시키는 치명적임을 일컫는 말이다.(일명 Killer defect이라고 함) 예를 들어 반도체의 회로 최소 치수가 1μm이라면 1/10 rule에 의해 0.1μm보다 큰 size의particle은 집적 회로의 불량 및 고장을 일으킬 수 있음을 의미한다. Particle 오염에 의한 defect는 chip size가 증가하고 회로 size가 감소할수록 증가한다. (현재는 1/10 rule에서 1/5, 1/2 rule까지 낮추어 관리하고 있는 실정임)

오염 입자 발생 원

Test Method of Cleanroom

오염입자 발생 원

Aerosol Technology

Introduction to theory

43

Application: Simulation of spherical particles dispersed in the continuous phase. Requirement: Main phase flow Physics:

One-way coupling: • Fluid flow influences particles via drag and turbulence • Particles have no influence on the fluid flow

No particle-particle collisions

Trajectory: particle advances in time along with the flow (transient)

Dispersion of particles due to turbulent fluctuations

Continuous phase

flow field

Particle trajectory


44

Particle properties:

•Density (ρp)

•Diameter (Mean & Standard deviation)

•Restitution coefficient (collision with surfaces)

Restitution coefficient = 0 Restitution coefficient = 1


45

Particle dynamics:

•aD(f-p) : acceleration of the particle due to the fluid drag

•aExt(f,p) : external acceleration affecting both phases (f,p), e.g. gravity

•aExt(p) : external acceleration affecting only the dispersed phase (p)

Particles as rigid bodies

• Particles as rigid body instead of point mass • Translation and rotation

• Magnus force due to particle rotation

• This model allows particles collision implemen

tation between them, because the particles has volume

46

Collisions between particles

• Support for new behaviors • Stacking

• Sliding

• Soiling

• Sedimentation

• …

• More complex equations

47

Adhesion model

48

• Electrostatic forces

• Electric charge of particles

• Precipitation

• Powder painting

• Soiling

Particle Treatment for Numerical Simulation

49

Fluid Particles

Momentum (fluid exerts forces to particles)

Boundary conditions (particles considered moving geometry)

The traditional mesh-based approach presents several problems:

• Most time spent preparing the mesh

• Convergence issues

• Reliability highly dependent on the quality of the mesh

• Topology of the domain remains fixed

The Challenge of Traditional CFD

52

The traditional mesh-based approach

• Reliability depends on the ability to choose the right combination of models

The Challenge of Traditional CFD

53

Numerical scheme

Mesh

Turbulence

Structured

Unstructured

Hybrid

Elements type

Wake prediction

RANS

DNS

LES*

Compliance with

Mesh

Eulerian/ALE

Convection sche

me

Stabilization

The motivation for XFlow is to overcome the previous problems

XFlow CFD

54

Numerical scheme

Discretization scheme

Turbulence

Particle based approach

Adaptive Wake

refinement

State of the art WMLES

Generalized law

of the Wall

Lagrangian formulation

Low numerical

viscosity

Minimize algorithm parameters

Numerical approach

55

Discretization method

Mesh-based

DSMC LBM SPH

VPM

FVM

FEM

FDM

Particle-based

Molecular Mesoscopic Macroscopic

Navier-Stokes Boltzmann Navier-Stokes

Numerical methodology

56

• Solves the Boltzmann transport equation

• Fluid behavior modeled at mesoscopic scale

• Inspired by the kinetic theory of gases

The kinetic theory of gases is a physical theory that explains the macroscopic behavior and properties of gases from a statistical description of the microscopic molecular processes (L. Boltzmann, J.C. Maxwell, s. XIX)

Boltzmann Navier Stokes

Euler

Lattice-Boltzmann Method

57

Technology Overview

• Macroscopic variables are moments of the distribution function (f)


Density Pressure Linear momentum Velocity

f = f (x,v,t)


Boltzmann transport equation

Probability Distribution Function (PDF)

Streaming: Collision:

58

• fi : particle distribution function in

the direction i, • ei the discrete velocity in the direct

ion i, • Ωi the collision operator.



Redistributes the particles that arrive simultaneously at the same position

Lagrangian advection that exactly satisfies conservation laws.

The collision operator in XFlow is based on a Multiple Relaxation Time scheme:

59

Lattice-Boltzmann Equation

Unique LBM approach

MRT

SRT

(Relaxation Characteristic time, Ƭ, is related to Macroscopic Viscosity)

Collision operator


The scattering operator is implemented in central moment space.

Raw moments Central moments

(More symmetries, better stability and Galilean invariance)

Unique LBM approach

Factorized Central Moment Lattice Boltzmann

u

S

o

u S’

o’


The scattering operator is implemented in central moment space.

Raw moments Central moments

(More symmetries, better stability and Galilean invariance)

Unique LBM approach

Factorized Central Moment Lattice Boltzmann

• 4th order spatial discretization

• Higher accuracy

• Lower numerical dissipation

• Positive effective viscosity

• A-stable scheme

• Stability issues at vanishing visco

sity

• Lower Mach number limit

• Galilean invariance issues


62

• Wall-Modeled Large Eddie Simulation (WMLES)

ld

Resolved

Modeled Resolved

Modeled Resolved

Modeled

L

DNS

LES

VLES

RANS

Eddy size


Turbulence modeling:

63

Wall-Adapting Local Eddy (WALE)

ld

Resolved

Resolved

Modeled Resolved

Modeled

L

DNS

LES

VLES

RANS

Eddy size

Modeled



• Wall-Modeled Large Eddie Simulation (WMLES)


64

Technology Overview


• Wall-Modeled Large Eddie Simulation (WMLES) → Turbulent viscosity: Wall- Adapting Local Eddy (WALE) model


65

Technology Overview

• Continuous blending between viscous sub-layer and logarithmic layer • Adverse and favorable pressure gradients • Surface curvature

• Generalized law of the wall

Wall modeling:


66

Technology Overview

Lattice structure

• A lattice structure is used for spatial discretization: PDF are defined at every lattice node • Refinement can be applied: each level solves spatial and temporal scales two times smaller than the previous level • Data stored in Octree structure


67

Technology Overview

Resolved scale = h Resolved scale = h/2 Resolved scale = h/4

Resolved scale = h and target scale near walls =h/4

Lattice structure

68

1st High Lift Prediction Workshop

Technology Overview

Preparation time

5 minutes

Computation time

Longest run ~36 hours on two processors (8

cores) and 25Million elements

69

Technology Overview

Polar Sweep

Preparation time

15 minutes

Computation time

~100 hours on two processors (8 cores) and 20

Million elements

1st High Lift Prediction Workshop

DJET - Diamond Aircraft – Dutch Roll

70

Technology Overview

Rigid body – 3 DoF: •Pitch rotation •Yaw rotation •Roll rotation Initial conditions: •Start time: 7.6 s •Centered rudder

Yaw

Roll

Initial angles

Back-and-forth rolling and yawing motion

VIRTUAL WIND TUNNEL

71

Software environment

CAD geometry

Simulation setup

Initial Lattice generation

Simulation

Post-processing

5 min

2 min 25M elements

30h on 8 cores (workstation)

1h

Engin

eering tim

e

Com

pute

r tim

e

Engin

eering tim

e

Workflow illustration: 1st High Lift Prediction Workshop

Connectivity

72


Geometry

STEP, IGES and STL

User Defined Input

Functions and tabular data

FSI

One way coupling to MSC.ADAMS

Two way coupling to MSC.NASTRAN under

development

Post-Processing Paraview and Ensight Gold

73

Technology Overview

Flexible floating license with the ability to run local or remote computations

• All computations can be run directly through the GUI

• Unassisted workflow through command line is also supported

• The job scheduler allows queuing jobs locally or through a job-scheduler

• The DMP solver makes use of MPI, and HPC with shared file system or group

of workstations with no shared file system are supported


Performance

74

Technology Overview

Number of cores Times faster Seconds per time step 1 1 9200

2 1.98 4646

4 3.78 2434 8 7.58 1214

16 14.02 656

32 23.04 399

External aerodynamics around commercial vehicle

Range of resolved scales: (0.005m - 0.125m)

Number of elements: 111,8 millions

Peak memory usage: 25Gb

Hardware:

x3950M2 - Intel® Xeon® Processor X7350 (2.93GHz, 2x4MB L2 cache)

8 processors/32 cores

128GB of memory

2 * (4 * 73GB SAS Internal disks)

Serial scalability

Performance

75

Technology Overview

MPI scalability

Lid-Driven Cavity Reynolds 50000

16 million elements

MILLIONS OF ELEMENTS PER SECOND

TIMES FASTER EFFICIENCY

246.1538462 607.2307692 84.34% 211.9205298 522.781457 87.13% 168.4210526 415.4736842 86.56%

128 315.76 87.71% 86.48648649 213.3513514 88.90% 43.12668464 106.3881402 88.66% 23.63367799 58.30132939 91.10% 11.92250373 29.41132638 91.91% 6.003752345 14.81050657 92.57% 3.036053131 7.489563567 93.62% 1.548886738 3.820909971 95.52% 0.806858296 1.990418558 99.52% 0.405371168 1 100.00%

Application examples: Cyclone chamber

76

Passive


77

DPM – Diameter = 2e-05 m


78

DPM – Diameter = 0.01 m

Application examples: HVAC office

79

DPM – Density = 5000 kg/m3

Application examples: HVAC office

80

DPM – Density = 1 kg/m3 – Similar to dust

© 2012 Next Limit Technologies

Application examples: Free surface test

DPM Capabilities & Roadmap 81

82

전기/전자 분야 응용 사례

LCD 제조 공정 내 기류 및 파티클 시뮬레이션

Application examples: Display FAB

83 83


FAB 내 공정 설비 파티클 시뮬레이션 (설비 내 동적 운동 고려) - 반도체 8대 공정에 대한 기류 및 파티클 시뮬레이션 - 클린 룸 및 설비 운전 조건 개선 - 설비 내 기류 관리 조건 파악

84 84


FAB 내 공정 설비 파티클 시뮬레이션

Geometry Construction

Syste

m

Config

uratio

n

Scanning System


해석 결과의 정확성 검토(validation)

기류는 설비 기류 측정과 시뮬레이션 결과에서 유사하게 나타났다.

Fog generator 기류분석) 시뮬레이션 결과 (기류분포)


4.1 해석 결과의 정확성 검토(validation)

- 스토커 내 기류 및 파티클 시뮬레이션

- 랙마운터 동적 운동에 따른 기류 및 파티클 시뮬레이션 - 카세트 동적 운동에 따른 파티클 유입방지를 위한 카세트 형상 설계

- 기류 시뮬레이션을 통한 스토커 내 기류관리 항목 결정 및 관리수준 규정.


89 89

0.97 1.1

1.13 0.95

1.06 0.9

0.36 0.4

1.18 1.1

1.23 1.3

1.45 1.5

1.9 2.15 0.44

0.65

—— 해석값

—— 측정값


90 90

Ball Screw에서 Transient DPM사용법

오염원 (직경 1.0µm)

오염원 (직경 10µm)

오염원 궤적: 오염원 직경이 클수록 자중으로

궤적이 중력방향으로 이동됨

오염원 분포: 초기의 오염원은 넓게 분포시킴

오염원 분포: Stage는 우측으로 이동하며,

오염원은 ball screw회전운동에 의하여 발생된 기류로 회전축을 기준으로 회전하면서 원심력과 자중에 의하여

회전반경이 점점 커지면서 하향으로 낙하됨.

Time=1.2 s Time=2.2 s Time=3.2 s Time=0.0 s Time=4.2 s


목차 1. 개 요

2. 수치해석 방법 및 경계조건

3. 클린룸 내 기류분포 해석 결과

4. 측정 및 해석

5. 오염물질 유출 역추적

FloVENT를 이용한 클린룸 내 기류 해석

FloVENT Models

데이터센터와 냉각 최적화

FloVENT 응용 사례

외부 유동 및 확산 Modelling

클린룸 및 오염물질 Modelling

사무실 환경 및 쾌적성 Modelling

Software의 시험

Simulation vs. Smoke Test

FloVENT를 이용한 공기 유동 모델링이 예측하게 해주는 것은 …

공기 속도

온도

압력

오염물질 이산 (dispersion)

외부적으로 내부적으로


1. 개 요

- 세부형상 및 명칭

실제 장비와 동일 조건.

i) FFU

ii) Cleanroom 조건

iii) Bottom (Grating wall)


1. 개 요


FAN이 적용된 장비


1. 개 요


Bottom(wall)



- 경계조건 설정

격자 크기 배치 및 계산 영역 설정

i) System Grid

Drawing Board view (Grid)

Max Size = 100mm

System


FloVENT Drawing Board view

Solution Domain

Gravity

Bottom (Open)

Ambient Temp = 25℃ Gauge Pressure = 0 pa

Air


- 경계조건 설정

격자 크기 배치 및 계산 영역 설정


3. 클린룸 내 기류분포 해석

- Section view

Velocity

Vector Pressure

재순환 발생


3. 클린룸 내 기류분포 해석

- 동영상



- 연구 대상

– Clean Room(Include_FFU, Perforated panel, Opening area).

Cleanroom Geometry Perforated panel



- 연구 대상

9 8 7 6 5 4 3 2 1


5. 오염물질 위치 역추적

- Where is the source of Leakage?

i) 대부분의 CFD tool

→ 오염발생위치 지정 후 이동 궤적 추적.

→ 일부 오염원 발생위치 예측은 가능하지만, 100% 확신 불가

ii) FloVENT 역추적

→ 기류해석 결과에 근거하여 오염물질 궤적을

역추적하여 발생위치 확인 가능.


5. 오염물질 위치 역추적

- Where is the source of Leakage?

i) 측정

→ 3~5개 포인트에서 실제 오염 물질의 농도값을 측정

→ 측정값의 정확도에 따라 결과 차이

ii) FloVENT 역추적

→ 입력받은 농도값을 가지고 역추적 실행

→ 측정 위치 및 정확도에 따라 역추적 위치의

정확도 및 영역이 달라짐.

→ 결과는 영역으로 표현 가능.

107 www.deltaes.co.kr

FloVENT의 특별한 Focus

Unique Industrial Experience meaning:

— 소프트웨어가 원래 Building Services Research & Information

Association (BSRIA)와 공동으로 개발됨

— 컨설턴트업 및 HVAC산업의 실제적인 측면 모두에서 나온 자체 경험

In-house experience from both consultancy and practical aspects of

the HVAC industry

— 산업계의 주요 기관/업체들과 긴밀한 관계 Close relationship with major

players in the industry

FloVENT Tech Advantage 1: Our Unique Localized Meshing Approach

FloVENT use the most robust cartesian “cut cell” structured meshing approach available to CFD solvers today

Massive disparities in geometric length scales are resolved using our unique ‘localized-grid’ technique, which allows for integrally matched, nested, non-conformal grid interfaces between different parts of the solution domain. This also makes our solvers very stable and some of the fastest in the CFD Industry and has also paved the way for both codes being almost real-time design tools

Our pragmatic, unique and accurate solution termination criteria produces useful results in engineering - not academic - time scales

In addition, over the years we have tuned our solvers for the industry applications they are targeted at in electronics cooling , datacenters & HVAC.

FloVENT Tech Advantage 2: Our MCAD Bridge Implementation & Knowhow

Unlike other thermal analysis software, FloTHERM automatically prepares the geometry for efficient and accurate analysis.

Our development group has world-leading MCAD to CFD knowhow and expertise

Our MCAD Bridge software connects directly to all the main 3d MCAD packages and also allows for import of geometrical files in a wide range of formats: — ACIS — STEP — IGES — STL — DXF

FloVENT Tech Advantage 3: New XML interface

Automated pre- and postprocessing workflows in Version 9 can now include the following steps: 1. Equipment audit of existing or proposed data center 2. Data entry of equipment and data center layout (via Excel) 3 FloVENT model creation via a PDML macro 4a. Option 1: Import of FloVENT model for further refinement and simulation 4b. Option2: CFD simulation via Excel interface and subsequent results export

into pre-formatted Excel spreadsheet.

FloVENT Tech Advantage 4: HVAC Tool “SmartParts” Libraries

Standard FloVENT HVAC Tools and Libraries: – Drag & Drop Perforated Tiles

– Diffusers, Coolers, and Floor Tiles Available from Library

– Furnitures, Doors & Windows Available from Library

– Materials, resistances & fluids

– Fans & Blowers, CRAC’s

Diffuser Library

Cooler Library

FloVENT SmartParts Panel

Clean Room & Isolation Room Modelling Application Examples

113

CFD provides design information: — Visualization of flow fields — Air flow design of ventilation

systems — Transient analysis for moisture

condensation — Analysis of cross contamination

Clean Rooms & Isolation Rooms

114

Merck Optimizes Cleanroom Design & Prevents Condensation Using CFD

CFD simulation of cleanroom airflow indicating the path of air

and water vapor - red areas indicate regions of saturated air &

possible condensation

CFD simulation of cleanroom airflow after the addition of high-level exhausts behind the cylinders to fix condensation problems

Merck is one of the world’s leading pharmaceutical manufacturers

They needed to troubleshoot and solve a cleanroom problem being experienced in one of their manufacturing facilities

A precise CFD model of the cleanroom incorporating features was built. Engineers were then able to create a series of animated three-dimensional views that accurately simulated the actual airflow and moisture content conditions in the room

FloVENT simulations were then used to optimize the position of high level exhausts

Based on these results Merck made the appropriate cleanroom modifications and economically solved the condensation problem while at the same time continuing normal cleanroom operations.

115

Vial Filling Production Room Airflow Optimized thanks to CFD

Chiron, a biotechnology company in the US had a vial filling production room in which they wanted to replace the filling machine

While taking the room out of commission to carry out this task they wanted to see if they could improve the airflow design in the room the better protect the filling process

Key factors under consideration were maintaining a undirectional flow down over the product & limiting recirculation regions in which contamination could get caught

Also issues of protecting the product and the personnel as well as maintaining the classification of the room needed to be taken into account

With CFD the layout of the filters on the ceiling of the side wall extracts were optimized for particle scavenging

The most important thing about this design is that they are not putting anymore air into the room and so the running costs remain unchanged

http://en.wikipedia.org/wiki/File:Chironlogo.png

116

HNC Develops Innovative Cleanroom Designs with FloVENT

HNC provides cleanroom and mini-environment systems that minimize the inflow, generation, and retention of airborne particles while meeting temperature, humidity and pressure requirements

These systems are used in facilities for manufacturing displays, semiconductors, and electronic equipment; operating theatres and patient rooms; pharmaceutical and food processing; and biotechnology laboratories

With CFD the approach is to first design for the overall system requirements and then develop the isolated mini-environments required to meet local cleanliness requirements.

“Many contamination problems can only be solved by totally new design concepts and the only way to evaluate and optimize these concepts, short of building test facilities, is with simulation... FloVENT has demonstrated the ability to simulate a wide range of airflow patterns with a high level of precision, even at the concept stage."

J Park, Mechanical Engineer, HNC

117

Isolation Room Ventilation and UV System Designed Using CFD

Patient Isolation Rooms are used to keep infected patients away from other people

The purpose of this CFD study

was to evaluate a typical

tuberculosis isolation room,

and in particular, a ventilation

system which would provide the

most effective removal of

airborne bacteria particles by: — Ventilation air flows

— Use of upper room Ultraviolet

Germicidal Irradiation (UVGI) - to

have the bacteria receive

sufficient UVGI dosage to kill it

— Optimal cost of UV system

recommended by the study

door

Vestibule

Isolation room

Bathroom

External window

doors

UV Fixture

Thank you

• TEL : 070-8255-6001~5

• FAX : 032-322-6636

• http://www.DeltaES.co.kr

• Email : [email protected]

• 경기도 부천시 원미구 중동 1149

리슈빌엔클래스 2007호

http://www.deltaes.co.kr/

mailto:[email protected]

CAE 해석을 통한 클린 룸 설비 내 - cadgraphics.co.kr · XFlow 특징 및 기능 6....

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Transcript of CAE 해석을 통한 클린 룸 설비 내 - cadgraphics.co.kr · XFlow 특징 및 기능 6....