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# Lecture Slides: Lecture Advanced Simulation

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The lecture slides of lecture Advanced Simulation of the Modelling Course of Industrial Design of the TU Delft

The lecture slides of lecture Advanced Simulation of the Modelling Course of Industrial Design of the TU Delft

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### Transcript

• 1. G-L-7 Advanced Simulation Dr. Y. Song (Wolf) Faculty of Industrial Design Engineering Delft University of Technology Challenge the future 1
• 2. The real & virtual world Challenge the future 2
• 3. The real & virtual world Challenge the future 3
• 4. We need a bridge what are you studying? Case brief ‘touch’ your thoughts choices what exactly...? what do you foresee? Build abstract model System choices choices what does your model foresee? Thought simulation Experience Solve / simulate Compare did you both agree? everything you did/ saw/ felt/ remember to be true... Finish! Acceptable ? choices Revisit choices Experiment do you need more insights/ data? Courtesy of centech.com.pl and http://www.clipsahoy.com/webgraphics4/as5814.htm Challenge the future 4
• 5. For simple problems   water  Lengthbathtub  2  R 2  ( R  h(t )) 2  dh(t )  norifices  Cd   water  Aorifices  2  g  h(t ) dt Water Bathtu b Time for empty the bathtub Drain Ear th Discharge coefficient Diameter of the hole Challenge the future 5
• 6. NCAP car crash test: VW Golf 6 Challenge the future 6
• 7. NCAP car crash test: VW Golf 6 Challenge the future 7
• 8. The power of modelling Case study: PAM Crash ESI® PAMCRASH® Cost Safety Prediction Optimization Courtesy of http://www.esi-group.com/products/crash-impact-safety/pam-crash Challenge the future 8
• 9. Case study: The diving board Case brief: Design a jump-off diving board for the Olympic game. Requirement: When the athlete stands still at the tip of the board, the deformation should be between 7~15cm Challenge the future 9
• 10. Analysis Challenge the future 10
• 11. System System System consists of a set of interacting or interdependent system components (or subsystems) -Structure & interconnectivity -Boundary -Input & Output -Surroundings Challenge the future 11
• 12. The design System •Board •Support •Human Choices: to neglect Temperature differences Humidity Position of standing Non-uniform Material Supporting Structure … Challenge the future 12
• 13. Modelling Challenge the future 13
• 14. The purpose of models The purpose of models is not to fit the data but to sharpen the questions. Samuel Karlin National medal of science Challenge the future 14
• 15. Our model: Choices Phenomenon Statics Model Model simplification & adjustment Support Boundary conditions 1. Materials 2. Fixture 3. Force 4. Component interaction Fillet Force Challenge the future 15
• 16. Simulation Challenge the future 16
• 17. Analytical solution of a beam Force Elastic modulus Length Area momentum of inertia Challenge the future 17
• 18. Analytical solution of the beam Challenge the future 18
• 19. Introducing numerical solutions   water  Lengthbathtub  2  R 2  ( R  h(t )) 2  dh(t )  norifices  Cd   water  Aorifices  2  g  h(t ) dt Challenge the future 19
• 20. An example of numerical solution Using Euler method to solve an ODE 1 step 3 steps 6 steps 12 steps Challenge the future 20
• 21. The Finite Element Method (FEM) A numerical technique for finding approximate solutions of partial differential equations (PDE) Eliminating the differential equation or rendering the PDE into an ODE Widely adopted in CAE software as The de facto standard Ref. http://en.wikipedia.org/wiki/Finite_element_method Challenge the future 21
• 22. FEM Depending on the validity of the assumptions made in reducing the physical problem to a numerical algorithm, the computer output may provide a detailed picture of the true physical behavior or it may not even remotely resemble it. Ray W. Clough Founder of FEM National medal of science Challenge the future 22
• 23. CAD software CATIA Unigraphics Pro-Engineer Solidworks Challenge the future 23
• 24. Computer Aided Engineering – Leading Companies Courtesy of http://www.padtinc.com/blog/post/2011/08/26/CAE_Market_Size.aspx Challenge the future 24
• 25. The big players - Ansys
• 26. The big players - ESI group PAM Crash Product Usage PAM Comfort PAM Stamp Process Procast
• 27. The big players - COMSOL
• 28. Simulation @ Solidworks® works A s: Simulation s: Solidworks Flow Simulation 2012 B C
• 29. CAE software – The three phases eocessing ning the del and ironmental ors to be lied to it. Solving It is usually performed on high powered computers tify, Choose del Post processing The results visualization tools Learn Identify, choose Cause Evaluation Evaluate Solve/Simulate Learn Model Effects
• 30. Elements & Solvers 1 Selection of elements 2 3 FEM solution Meshing Solver
• 31. Elements & Solvers 1 FEM solution Selection of elements
• 32. The types of elements Flexibility / Precision
• 33. mplementation of nodes in Solidworks Draft quality High quality
• 34. Elements & Solvers 1 2 FEM solution Selection of elements Meshing
• 35. Mesh generation Mesh control Mesh type Beam ► Standard Shell ► Curvature Solid ► Transition Mixed ► Local mesh control
• 36. Typical implementation in Solidworks Beam Shell Solid
• 37. Solution of the “small” board Method Result Error Analytical 2.976 E-2 mm 0 Beam FEM 2.980 E-2 mm +0.13% Shell FEM 2.961 E-2 mm -0.5% Solid FEM 2.964 E-2 mm -0.4%
• 38. Case study: Mesh control h generation
• 39. The quality of mesh The quality of mesh Aspect ratio ► Taper ► Jacobian ratio ► Collapse ► Skew angle ► Warpage ► Twist ► … ► Aspect ratio Jacobian ratio
• 40. Aspect ratio – An illustration
• 41. Case study: the support oes matter
• 42. The Jacobian ratio he Jacobian calculation is done at the integration points of elements commonly known as Gauss oint. At each integration point, Jacobian Determinant is calculated, and the Jacobian ratio is found by e ratio of the maximum and minimum determinant value. = 1.0 = .398 = 1.0 J = .942 J = -.409 J = .883 J = -.130 J = .072 • Using Jacobian check at Nodes when using p-method •For high order shells, the Jacobian check uses 6 points located at the nodes •Empirical study indicates < 40
• 43. The solvers 1 2 3 FEM solution Selection of elements Meshing Solver
• 44. Solver selection FEPl us Direct parse Selection of solver Size of the problem Computer resources Material properties Size of the problem. In general, FFEPlus is faster in solving problems with degrees of freedom (DOF) over 100,000. It becomes more efficient as the problem gets larger.
• 45. Solver selection EPl us rect arse Selection of solver Size of the problem Computer resources Material properties Computer resources. The Direct Sparse solver in particular becomes faster with more memory available on your computer.
• 46. Solver selection EPl s ect rse Selection of solver Size of the problem Computer resources Material properties Material properties. When the moduli of elasticity of the materials used in a model are very different (like Steel and Nylon), then iterative solvers are less accurate than direct methods. The direct solver is recommended in such cases.
• 47. valuation
• 48. The complexity ools ignore complexity. Pragmatists suffer it. Some can avoid it. Geniuses remove it. Alan Perlis Computer scientist 1st ACM A.M. Turing Award (1966)
• 49. The influence of choice
• 50. Using sensitivity analysis to evaluate complicated problem ess l Deflection
• 51. S3 S4 S2 Input 1 S5 System S1 Input 2 Input 3 f ( p , p ,... p ),  Surroundings f ( p , p ,... p ), ,  Output 2 Output 3 S7 ent of the metric (f) w.r.t. inputs / parameters (p1,p2...,pn)  Output 1 S6 S10 S9 Boundary Parameter 3 Parameter 2 Parameter 1 The mathematical meaning of Sensitivity analysis f ( p , p ,... p )) S8 S1 Subsystem
• 52. Design parameters Evaluation Mesh parameters
• 53. Reflection
• 54. Reflection Can we optimize it? Can we correct the error? it OK? Next iteration Choices Mass Width Thickness Length Material
• 55. inear Dynamics
• 56. Transient and steady state Transient Settling time band Mp Steady-state error Time ependent Steady State
• 57. From Statics to Dynamics Mass Stiffness matrix Damping matrix Force vector Statics
• 58. Damping – Modal damping Modal Damping Modal damping is defined as a ratio of the critical damping
• 59. Modal damping ratio System Viscous Damping Ratio Metals (in elastic range) less than 0.01 Continuous metal structures 0.02 - 0.04 Metal structures with joints 0.03 - 0.07 Aluminum / steel transmission lines ~ 0.04 Small diameter piping systems 0.01 - 0.02 Large diameter piping systems 0.02 -0.03 Auto shock absorbers ~ 0.30 Rubber 0.05 Large buildings during earthquake 0.01 - 0.05 Prestressed concrete structures 0.02 -0.05 Reinforced concrete structures 0.04 -0.07
• 60. Case study: Slam dunk
• 61. Case study: Linear dynamics A Slam dunk Challenge the future 61
• 62. Non-linear analysis Challenge the future 62
• 63. Structural nonlinearities Courtesy of CAE associations: Snap through bulking Geometric Nonlinearities Contact Nonlinearities Material Nonlinearities Snap through bulking Challenge the future 63
• 64. Structural nonlinearities Courtesy of CAE associations: Snap through bulking Geometric Nonlinearities Contact Nonlinearities Material Nonlinearities Challenge the future 64
• 65. Material Nonlinearities Geometric Nonlinearities Contact Nonlinearities Material Nonlinearities Challenge the future 65
• 66. Material models Material model Linear Elastic Isotropic Orthotropic Nonlinear elastic Plasticity Hyperelasticity Viscoelasticity Creep Nitinol ... Challenge the future 66
• 67. Time dependent solution Challenge the future 67
• 68. Biomechanics Approach from MoM Complex Beam Theory • Straight Beam • Curved Beam • Composite Beam Courtesy of Daviddarling.info Challenge the future 68
• 69. Case study: Human Joint analysis Challenge the future 69
• 70. Case study: Bra Lingerie design Problem: 70% of women were wearing a bra that doesn't fit properly Designer: Dick Powell and Richard Seymour Technology: Arup's Advanced Technology LS-DYNA
• 71. Computing Fluid Dynamics
• 72. Computing Fluid Dynamics CFD branch of fluid mechanics that es numerical methods and algorithms to lve and analyze problems that involve fluid ws.
• 73. Case study: Air drag
• 74. Drag coefficient ag N] http://en.wikipedia.org/wiki/Automobile_drag_coefficient Area [m2] Drag Coefficient Density [kg/m3] 1 2 F     A  Cd  v 2 Velocity [m/s]
• 75. Drag coefficient http://en.wikipedia.org/wiki/Automobile_drag_coefficient Audi A4 B5 1995 BMW 7-series 2009 Honda Civic (Sedan) 2006 Peugeot 307 2001 Porsche 997 Turbo/GT3 2006 Volkswagen GTI Mk IV 1997 Nissan 370Z Coupe 2009 [16] (0.29 with sport package) Cd =0.26
• 76. Case study: Pointy or round?
• 77. Case study: Air drag – Low speed At 120 km/hour, which design is faster? P1 P2
• 78. Case study: Supersonic
• 79. Case study: Air drag – Supersonic At 350 m/s, which design is faster?
• 80. Case study: Drafting
• 81. What is drafting? Drafting Drafting or slipstreaming is technique where two ehicles or other moving bjects are caused to align n a close group reducing he overall effect f drag due to exploiting the ead object's slipstream.
• 82. Air drag High Pressure - Air is compressed Low pressure – a bit vacuum
• 83. To reduce air drag Reduce the pressure here Increase the pressure here
• 84. Drafting
• 85. Relations with in-between distance mm) Air Drag Cylinder 1 (N) Air Drag Cylinder 2 (N) 40 0.292 0.06 50 0.291 0.09 60 0.288 0.115 70 0.266 0.141 80 0.276 0.15 L 5 3 5 2 5 5 Drag of the cylinder (no drafting) Drag of the cylinder front Drag of the
• 86. Who taught swan goose aerodynamics?
• 87. atural convection
• 88. Natural convection eat wine by natural convection
• 89. ase study: Karman Vortex Street
• 90. Seattle
• 91. There is a bridge
• 92. Tacoma narrow bridge
• 93. Tacoma narrow bridge 1940
• 94. Case study: Karman Vortex Street epeating pattern of swirling vortices caused by the steady separation of flow of a fluid around blunt dies Theodore von Karman
• 95. Case study: Karman Vortex Street
• 96. Cell mesh in Flow Works
• 97. otation
• 98. Parrot AR Drone
• 99. Simulation
• 100. ase study: Fluid structure nteractions (FSI)
• 101. The Senz Mini model
• 102. The Senz Mini flow simulation
• 103. FSI in different ways Fluid Structure Time interval 1 Time interval 1 Time interval 2 Time interval 2 Time interval … Time interval … Time interval n Time interval n
• 104. SW - Think before we start Relative Stable Changed Time interval 1 Time interval 1 Time interval 2 Time interval 2 Time interval … Time interval … Time interval n Time interval n
• 105. Non-linear: Results Real Test
• 106. Theodore von Karman cientists study the world as it is, ngineers create the world that ver has been. Theodore von Karman National medal of science
• 107. At least we can ou can't make it good, at least ke it look good. Bill Gates
• 108. hank You! Dr. Y. Song (Wolf)