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Simulation of Thermal Images

This presentation by Vijaya Priya Govindasamy discusses the simulation of thermal images using the MuSES software, as well as the segmentation of thermal images.

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Simulation of Thermal Images

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  1. Simulation of Thermal Images Presented by Vijaya Priya Govindasamy Masters Student November 11th 2003

  2. Outline • Personal facts • Simulation of Thermal Images • Objective • Introduction to MuSES • Models used for simulations and simulation results • Segmentation of Thermal Images • Objective • Idea of Watershed Segmentation • Preliminary results • Conclusion

  3. Personal Facts • Bachelor of Engineering – Electrical and Electronics Engineering • Communication Between Intelligent Systems Through Controller Area Network (CAN)

  4. Applications of CAN • Passenger cars • CAN is used as the in-vehicle network for the engine management, the body electronic like door and roof control etc • Factory Automation • CAN is used to interconnect machines,process control units and production sub-systems • Industrial Machine control • CAN finds its application as an embedded network for industrial machine control, which links single devices as well as sub-systems http://www.can-cia.de/applications/passengercars/tirepressure.html

  5. Outline • Personal facts • Simulation of Thermal Images • Objective • Introduction to MuSES • Models used for simulations and simulation results • Segmentation of Thermal Images • Objective • Idea of segmentation • Some preliminary results from Spring 2003 • Conclusion

  6. Motivation • Simulate the infrared prediction of the under vehicle parts • Investigate the presence of unusual parts in under vehicle scene, like the presence of cold objects near the muffler • The thermal images are simulated using the software MuSES • Develop under vehicle automotive parts using the software Rhino3d • Simulate thermal images for the models created, downloaded CAD models, and scanned and reconstructed models

  7. Need for Simulation • Reduces the time and cost of developing prototypes for test purposes • Gives more flexibility in controlling the parameters • Infrared prediction measurements are accurate and faster

  8. MuSES • MuSES – Multi-service Electro-optic Signature • MuSES is a thermal modeling tool, used to model the steady state and transient distribution of heat over complex surface descriptions of component systems. • MuSES models 3-D conduction, convection, and radiation. • The output from MuSES is the temperature map of the component system which can be viewed using the integrated post-processor

  9. MuSES Interface Source: MuSES Manual

  10. Poor Mesh • MuSES treats overlapping and adjacent vertices as unconnected parts • The polygons should be uniform with aspect ratio of unity • Overlapping elements should be embedded on the host surface Overlapping elements Unconnected parts Source: MuSES Manual

  11. Meshing Requirements • The characteristics of a good quality mesh for import into MuSES are: • All adjacent polygons share common vertices (equivalenced mesh) • All polygons are 3 or 4-sided (triangles or quads) • All polygons are convex • All polygons have an aspect ratio near unity(e.g. no long and skinny polygons) • Polygons are spread uniformly across the surface (e.g. avoid fans of polygons) • No overlapping or repeated facets • Surface mesh only (e.g. thin plates represented by their exterior surface only)

  12. Part Types • Assigned temperature parts • Temperature curves are assigned for such parts • Surface condition and Bounding box condition are assigned • Calculated Temperature parts • Only initial temperature value is given • Convective heat coefficient value is assigned • Temperature values are calculated as a result of numerical solution • Surface condition, Material properties and Bounding box condition are assigned

  13. Properties • Material Type • Given for calculated parts • Based on the type of material the numerical solution is carried out • Thickness • Does not mean anything with the geometry • Used only for calculation • Surface condition • Defines the emmisivity property • Paint codes are also used • Convective coefficient • The value of ‘H’ is given for calculated parts • Used in numerical solution

  14. Property Definition • Sample Property Definition • Engine Interior • The temperature is very high due to combustion • Engine Exterior • In real time the coolant fluid reduces the outside temperature Engine Temperature curve

  15. Property Definition • Sample property definition • Muffler Interior • Exhaust gas temperature • Muffler Exterior • Outside heat loss to the environment – convection & radiation Muffler Temperature curve

  16. Solution Analysis • Start time and End time • Step size • Tolerance slope and Tolerance • View factor rays • Number of iterations • Accuracy of the solution • Convergence parameter

  17. Models Simulated • Simple rod model to understand the working of MuSES • Exhaust system model • Car underbody model • IRIS lab scanned Water neck and Muffler Models • Toyota Model

  18. Rod Model Model of a simple rod • The model is designed and studied to get familiar with the working of the software • Thermal properties are assigned to one end of the rod and the radiation and conduction effects are studied • The left end temperature is set as 1000 degree Celsius Maximum temperature Minimum temperature

  19. Results Rod simulation result • The result is found to be exact • The bottom of the temperature is less than the temperature at the left end Maximum temperature Minimum temperature

  20. Exhaust System Model Exhaust System • Experimental setup • The model consists of the following parts: • Floor pan • Muffler • Inlet and Outlet Pipes • Manifold • The model is created using Rhino3d and imported into MuSES as a wavefront (.obj) file • The maximum temperature is assigned as 250 degree Celsius • The simulation is for a period of 30 minutes • The thermal properties and boundary conditions are assigned in MuSES

  21. Results Exhaust system simulation result • The heat distribution is studied • The radiation and conduction effects is visible • The inlet pipe is at the maximum temperature

  22. Car Model • Under body • Engine • Muffler • Transmission • Inverter • Radiator • Catalytic converter • Starter Motor • Batteries • Intake and Exhaust Manifold • Wheels • Upper body

  23. Simulation Result of Car Model

  24. Construction of model Reasonable temperatures Medium complexity Tolerance slope of solution convergence - 5e-007 Assumptions Construction of the model

  25. Solution Justification Simulation result of Upper Body & Under body

  26. Temperature Curves Catalytic converter Engine Muffler

  27. Thermal Images of Upper Body Car Upper body Infrared prediction View 1 View 2

  28. Thermal Image of Under Body Infrared Prediction of Car Under body

  29. Simulation of Laser Scanned 3d Models Model of a Waterneck System • The water neck is connected between the radiator and the engine block • The water neck is a part of the cooling system • The maximum temperature is assigned to the engine block as 200 F and the radiator box is assigned 100 F

  30. Result of Simulation Waterneck model simulation result • The simulation duration : 10 minutes • The conduction occurs at the end of the water neck embedded on the engine block

  31. Scanning and Reconstruction of Muffler Range image of a car muffler Original color image of a car muffler Experimental setup of IVP range scanner Front view Back view Superquadric fitted 3D model of the car muffler Courtesy: Umayal

  32. Result for Scanned Muffler Real time heat distribution of a Muffler

  33. Toyota Tundra Undercarriage Source: http://www.3dcadbrowser.com

  34. Simulation Result

  35. Simulated Thermal Images

  36. Toyota Tundra Engine Source: http://www.3dcadbrowser.com

  37. Toyota Engine Model • The model had 1104 parts when downloaded • With the help of Yohan, the 1104 parts were extracted out as VRML files • The VRML files were merged according to the various part definitions • Meaningful parts like exhaust manifold, cylinder blocks and heads, piston, injectors were formed

  38. Engine Model Oil pan Toyota Engine Model Exhaust Manifold Crank

  39. Bioheat transfer • Transportation of thermal energy in a living tissue is a complex process involving • Conduction • Convection • Radiation • Metabolism • Evaporation • Phase change • Various methods developed to determine the thermal conductivity and diffusivity of biomaterials Head Model Source: http://imaging.utk.edu/~rangan/3D%20Database/iris.htm

  40. Bioheat Transfer • Human thermal model • Passive System: Described by equations resultant from the application of heat and mass balances to a tissue control volume. • Temperature control system:Responsible for the maintenance of the human body’s temperature. Head Model in MuSES

  41. Heat Transfer within the Tissue • Heat transfer equation (Pennes) •  tissue density (kg/m3) • c  tissue specific heat (J.kg-1.oC-1) • T tissue temperature (oC) • t time (s) • k tissue heat conductivity (W.m-1.oC-1) • b blood density (kg/m3) • b blood perfusion rate (s-1) • Cb  blood specific heat (J.kg-1.oC-1) • Tar,i arterial blood temperature inside the ith cylinder (oC) • q is metabolic heat production (W/m3)

  42. Example of Simulation http://www.thermoanalytics.com/services/biothermal.html

  43. Outline • Personal facts • Simulation of Thermal Images • Objective • Introduction to MuSES • Models used for simulations and simulation results • Segmentation of Thermal Images • Objective • Idea of segmentation • Some preliminary results from Spring 2003 • Conclusion

  44. Objective • Segment meaningful automotive parts from thermal images of under vehicle scene • Review some of the common segmentation procedures • Implement the algorithms for thermal image segmentation

  45. Watershed Transformation • Visualize an image in three dimensions: two spatial coordinates versus gray levels. We consider three types of points: • Points belonging to a regional minimum • Points at which a drop of water, if placed at the location of any of those points, would fall with certainty to a single minimum --- Catchment basin/Watershed • Points at which water would be equally likely to fall to more than one such minimum --- Divide lines/Watershed lines Source: Digital image processing Gonzalez and Woods

  46. Watershed Transformation Original Image Topographic Image Watershed transformation Segmented Image Source: http://cmm.ensmp.fr/~beucher/wtshed.html

  47. Preliminary Results From Matlab Original Image Segmented Image Original Image Segmented Image Source: http://imaging.utk.edu/safer

  48. Watershed Transformation • The algorithm is decomposed into two steps: • The sorting step: Initial sorting of the pixels in the increasing order of their gray values • The flooding step: Involves the progressive flooding of the catchment basins of the image • Currently developing the algorithm in C++ • Results available by the end of this semester Source: Watersheds in digital spaces: an efficient algorithm based on immersion simulations – L. Vincent and P.Soille

  49. Conclusion • Simulation of thermal images eases the analysis of the automotive parts under different temperature conditions • Limitations: • Meshing • Simulation duration • Segmentation of thermal images helps in highlighting the under vehicle automotive part • Future work is to concentrate a particular method to segment the thermal images

  50. Thank You

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