P13621: CONDUCTIVE HEAT TRANSFER LAB EQUIPMENT

HTTPS://EDGE.RIT.EDU/EDGE/P13621/PUBLIC/HOME

MSD 1: Detailed Design Review

2 November, 2012

RIT KGCOE

Project ParticipantsProject Sponsor : RIT KGCOE, Chemical Engineering Dept.

Dr. Karuna S. Koppula

Mr. Paul Gregorius

MSD 1 Team Guides : Neal Eckhaus

Steve Possanza

Chinmay Patil

(field expert)

Team P13621:

Shannon McCormick - (ChemE) PM

Tatiana Stein - (ChemE) Team Facilitator

Shayne Barry - (ME) Procurement

Jordan Hill - (EE)

Piotr Radziszowski - (ME)

Meka Iheme - (ChemE) Risk Manager

Rushil Rane - (ISE) Lead Engineer

Agenda• Project Overview • Customer Needs and Engineering Metrics• Assembly Drawing & CAD Drawings • Feasibility Analysis

• Specimen dimension analysis • Cooling Capacity• Insulation Analysis • Experimental Basis• Safety Analysis

• Bill of Materials • Spec Sheets • Project Plan • Risk Assessment • Test Plan

Project Overview

Mission Statement: To provide students with the ability to observe conductive heat transfer and the ability to measure the thermal conductivity of a material.

Background:

• A material’s ability to transfer heat is a measurable quantity

• RIT ChemE department would like to procure lab equipment that would demonstrate heat transfer such that students may be able to calculate thermal conductivity

• Experimental results would be comparable to published data

Customer Needs

Engineering Metrics

Engineering Metrics

Assembly Drawing

Assembly/ disassembly instructionsTransfer of heatLinear profile

Size of cold plateConstant pressure applicationThermal stickers for visualLosses

CAD Drawings

CAD Drawings

CAD Drawings

Specimen Dimension Analysis

Specimen Dimension Analysis

Specimen Dimension Analysis

Cooling Capacity

Insulation Dimension Analysis

𝑋=𝑘𝐴 (𝑇1−𝑇 2 )

𝑞X = Ideal Insulation Thickness (m)K = Thermal Conductivity (W/mK)A = Area of Sample (m2)T2 = Outside Temperature (K or C)T1 = Sample Temperature (K or C)Q = Power in (W)

Monte Carlo Analysis

K – Held Constant (0.2 W/mK)A – Held Constant (0.0079 m2)T2 – Held Constant (20 C)

-Q and T1 are varied simultaneously-Generate large data set and use stochastic methods to determine best insulation thickness

It is infeasible to use deterministic methods due to the many non-converging values of X resulting from combinations of Q and T1 . T2 values also change along the length of the sample, adding to the complexity of a deterministic model.

Error Minimized using Excel Solver Function

Insulation Dimension Analysis

Insulation Dimension Analysis

Current Lab Set-Up

Experimental Basis

Conclusions from Lab•Aluminum graph was more linear than the copper graph

• Aluminum sample was longer than the copper sample the longer the sample size, the better the accuracy that was achieved

ANSYS – Thermal Model

ANSYS – Heat Generation Model

ANSYS – Temperature Boundary Model

ANSYS – Heat Flux Model

Safety Analysis

Safety Analysis

Bill of Materials

Bill of Materials

Spec Sheets – cartridge heater

Spec Sheets – cold plate

Spec Sheets – cooling unit

NI 9211 DAQ vs. NI USB-TC01

DAQ comparison

Power Supply

• 0 to 48 voltage range• 0-1 A current range• P=I*V• Provides exact method of calculating energy into the system

Project Plan

Project Plan

Risk Assessment

Risk Assessment

Test Plan

Test Procedures

Test Procedures

Test Template

Questions?