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Objectives

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Objectives

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  1. DETERMINATION OF UNSTEADY CONTAINER TEMPERATURES DURING FREEZING OF THREE-DIMENSIONAL ORGANS WITH CONSTRAINED THERMAL STRESSESBrian DennisAerospace EngineeringPenn State UniversityG.S. DULIKRAVICHMultidisciplinary Analysis, Inverse Design, and Optimization (MAIDO)Department of Mechanical and Aerospace EngineeringThe University of Texas at Arlington

  2. Objectives • Use finite element method(FEM) model of transient heat conduction and thermal stress analysis together with a Genetic Algorithm(GA) to determine the time varying temperature distribution that will cool the organ at the maximum cooling rate allowed without exceeding allowed stresses

  3. Mathematical Model Thermoelasticity

  4. Mathematical Model Heat Conduction

  5. Comparison with Analytic Solution

  6. Geometry and Material Properties • Actual Geometry from MRI or other sources was not available so a kidney like object was constructed from analytical functions • Some material properties are available in various publications but some had to be estimated for this test case • The procedure can be easily repeated when accurate geometry and material data are available

  7. Algorithm

  8. Outer Boundary

  9. Kidney Cortex

  10. Kidney Medula

  11. Kidney Cortex and Medula

  12. Parameterization • Outer sphere (cooling container) divided into six equally spaced patches. • Each patch is covered by biquadratic Lagrange polynomials with nine nodes • 26 design variables describing container surface temperature • Allowed to vary from +20 C to -30 C

  13. Objective Function Minimize average cooling rate with max. stress as a constraint If( max stress > yield stress) P = 100 else P = 0

  14. Numerical Results • Time interval for outer loop was 5 minutes • Time step for analysis was 15 seconds • Was run for 30 minutes (6 surface temperature distribution optimizations) • Grid with 5184 parabolic tetrahedral elements was used for heat conduction • Grid with 5184 linear elements was used for stress analysis

  15. GA Parameters Used • Each design variable represented with 4-bit binary string • Uniform crossover operator was used • 50% probability of crossover, 2% probability of mutation • 15 generations were run for each optimization • Used a population size of 31 • Was run on a 32 processor parallel computer composed of commodity components • One optimization takes ~45 minutes

  16. Numerical Results

  17. Numerical Results

  18. Numerical Results

  19. Numerical Results

  20. Conclusion • An algorithm was developed for the inverse determination of surface temperature distributions in the freezing of a kidney organ • Current results show that after 30 minutes the kidney is not entirely frozen and more time intervals are needed • This simulation process was made practical through the use of parallel optimization algorithms(GA) and cheap parallel computer composed of PC components

  21. Results(movie) Animation of temperature contours along slice at z=0 for every 5 minute interval

  22. Results( movie) Animation of container surface temperature contours for every 5 minute interval

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