# DDR Presentation

P13621: CONDUCTIVE HEAT TRANSFER LAB EQUIPMENT
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MSD 1: Detailed Design Review
2 November, 2012
RIT KGCOE
Project Participants
Project 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 &amp; 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
instructions
Transfer of heat
Linear profile
Size of cold plate
Constant pressure application
Thermal stickers for visual
Losses
Specimen Dimension Analysis
Specimen Dimension Analysis
Specimen Dimension Analysis
Cooling Capacity
Insulation Dimension Analysis
𝑋=
𝑘𝐴 𝑇1 − 𝑇2
𝑞
X = 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)
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.
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
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?