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 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 & 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 CAD Drawings CAD Drawings CAD Drawings 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?