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Nonlinear Optimization of Vehicle Safety Structures : Modeling of Structures Subjected to Large Deformations.

By: Contributor(s): Material type: TextTextPublisher: San Diego : Elsevier Science & Technology, 2015Copyright date: ©2016Edition: 1st edDescription: 1 online resource (488 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780124173095
Subject(s): Genre/Form: Additional physical formats: Print version:: Nonlinear Optimization of Vehicle Safety StructuresDDC classification:
  • 629.231
LOC classification:
  • TL242
Online resources:
Contents:
Cover -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Chapter | one - Vehicle Architectures, Structures, and Safety Requirements -- 1.1 - Introduction -- 1.2 - Legislative requirements -- 1.3 - Occupant injuries -- 1.3.1 - The crash test dummy families (or the tools to capture injury criteria) -- 1.3.2 - Typical injury criteria -- 1.3.2.1 - Head injury criteria (HIC) -- 1.3.2.2 - Head injury criteria for free motion head form (HIC(d)) -- 1.3.2.3 - Neck injury criteria (Nij) -- 1.3.2.4 - TI (Tibia index) -- 1.3.3 - Surrogate impactors -- 1.3.4 - Human computer models -- 1.4 - Typical vehicle architectures and scope for optimization -- 1.4.1 - Ladder frame -- 1.4.2 - Tubular structures -- 1.4.3 - Integral structures -- 1.4.4 - Shape and size -- 1.4.5 - Materials and manufacture -- 1.5 - Holistic approach to vehicle design -- 1.5.1 - Overall architecture design for structural instruction limitation -- 1.5.2 - Local shape and sizing for legal and other desirable structural requirements -- 1.6 - Conclusions and opportunities -- References -- Chapter | two - Numerical Techniques for Structural Assessment of Vehicle Architectures -- 2.1 - Introduction to finite element analysis (FEA) -- 2.2 - Theory of elasticity -- 2.3 - Elements -- 2.3.1 - One-dimensional elements -- 2.3.2 - Two-dimensional elements -- 2.3.3 - Three-dimensional elements -- 2.3.4 - Zero-dimensional elements -- 2.3.5 - Meshing strategy -- 2.3.6 - Element type -- 2.3.7 - Element shape -- 2.3.8 - Element size -- 2.4 - Fundamental explicit and implicit finite element analysis -- 2.5 - Nonlinear explicit finite element analysis -- 2.5.1 - Understanding the need for explicit FEA in connection with vehicle safety assessment -- 2.6 - Explicit FEA applied to vehicle safety assessment -- 2.6.1 - Standard explicit equations and convergence criteria.
2.6.2 - Stress wave propagation and timestep -- 2.6.3 - Relating the timestep to explicit FEA for vehicle safety assessment -- 2.6.4 - Critical element length -- 2.6.5 - Summation of factors influencing the timestep magnitude -- 2.6.6 - Importance of consistent mesh size -- 2.6.7 - Manipulating timestep magnitude -- 2.7 - Contacts -- 2.7.1 - Panel-to-panel contacts -- 2.7.2 - Tied contacts -- 2.8 - Example convergence study of explicit FEA -- 2.8.1 - Contact forces -- 2.8.2 - Kinetic energy -- 2.8.3 - Internal energy -- 2.8.4 - Total energy -- 2.8.5 - Summation of convergence study -- References -- Chapter | three - Introduction to General Optimization Principles and Methods -- 3.1 - What is structural optimization? -- 3.2 - How are optimization problems generally solved? -- 3.3 - General optimization methods and principles -- 3.4 - The curse of dimensionality -- 3.5 - Convex programming and optimization -- 3.5.1 - Linear programming -- 3.5.2 - The Simplex method -- 3.5.3 - Application to real-world engineering problems -- 3.5.4 - Sequential linear programming -- 3.6 - Gradient-based methods and line search methods -- 3.6.1 - Gradient descent method -- 3.6.2 - MatLab example of gradient descent method -- 3.6.3 - Newton-Raphson method -- 3.6.4 - MatLab example of Newton-Raphson method -- 3.6.5 - Steepest descent method -- 3.6.6 - MatLab example of steepest descent method -- 3.6.7 - Conjugate gradient method -- 3.6.8 - MatLab example of conjugate gradient method -- 3.6.9 - Comparison of gradient-based methods and line search methods -- 3.7 - Additional mathematical optimization methods -- 3.7.1 - Lagrangian multipliers -- 3.7.2 - Karush-Kuhn tucker optimality conditions -- 3.7.3 - Trust regions -- 3.8 Additional aspects of structural optimization -- 3.8.1 - Design space and design volume -- 3.8.2 - Uniqueness of solution.
3.8.3 - Design sensitivity and robustness -- 3.8.4 - Multiobjective optimization -- 3.8.5 - Multidisciplinary optimization and multiphysics optimization -- References -- Chapter | four - Introduction to Structural Optimization and Its Potential for Development of Vehicle Safety Structures -- 4.1 - Topology optimization -- 4.1.1 - The variable density method -- 4.1.2 - The SIMP interpolation scheme -- 4.1.3 - SRV constraint -- 4.1.4 - Design sensitivity analysis -- 4.1.5 - Homogenization-based optimization -- 4.2 - Shape optimization -- 4.2.1 - The Lagrangian approach to shape optimization -- 4.2.2 - The Eulerian approach to shape optimization -- 4.2.3 - Shape optimization using morphing -- 4.3 - Metamodeling -- 4.3.1 - Response surface methodology -- 4.4 - Point selection methods for metamodeling -- 4.4.1 - Factorial design -- 4.4.2 - Koshal design -- 4.4.3 - D-optimal -- 4.4.4 - Space-filling designs -- 4.5 - Optimization strategies for metamodel-based optimization -- 4.5.1 - Single-stage optimization -- 4.5.2 - Sequential stage optimization -- 4.5.3 - Sequential stage optimization with domain reduction -- 4.5.4 - Model suitability and analysis of variance -- References -- Chapter | five - Applications of Linear Optimization to Concept Vehicle Safety Structures -- 5.1 - Introduction -- 5.2 - Full vehicle structure topology optimization -- 5.2.1 - Introduction -- 5.2.2 - Crashworthiness criteria and design volume -- 5.2.3 - Discretization -- 5.2.3.1 - Packaging of essential components -- 5.2.4 - Boundary conditions -- 5.2.4.1 - Single point constraints (SPC) -- 5.2.4.2 - Inertia relief (IR) -- 5.2.4.3 - Runtime differences between single point constraints and inertia relief -- 5.2.5 - Post processing of optimization for SPC and IR -- 5.2.6 - Realism of SPC and IR boundary conditions in linear topology optimization in vehicle architecture definition.
5.2.7 - IR models sensitivity study -- 5.2.7.1 - Angle sensitivity study -- 5.2.7.2 - Stiffness sensitivity study -- 5.2.7.3 - Combined angle and stiffness sensitivity study -- 5.2.8 - Discussion -- 5.2.9 - Conclusions and limitations -- 5.3 - From topology optimization to computer-aided design (CAD) model -- 5.3.1 - Introduction -- 5.3.2 - Method 1: a holistic BIW sizing methodology -- 5.3.2.1 - 1D beam model -- 5.3.2.2 - 2D shell model -- 5.3.2.3 - Validity of method 1 using a crash model -- 5.3.2.4 - Conclusion of method 1: a holistic BIW sizing methodology -- 5.3.3 - Method 2: detailed frontend design -- 5.3.4 - Method 3: development of manufacturable body panel geometry -- 5.3.4.1 - Segment model -- 5.3.4.2 - Optimization of vehicle roof structure cross sections -- 5.4 - Conclusions: applications of linear optimization to concept vehicle safety structures -- References -- Chapter | six - Complications of Nonlinear Structural Optimization -- 6.1 - Equivalent static load method -- 6.2 - Initial optimization study -- 6.2.1 - Initial model setup -- 6.2.2 - Results of initial optimization study -- 6.2.3 - Parameter sensitivity study -- 6.3 - Revised optimization study -- 6.3.1 - Penalization factor p -- 6.3.2 - Load step type (LST) -- 6.3.3 - Nonlinear material parameters (NMP) -- 6.3.4 - External load angles (ELA) -- 6.3.5 - Scalability of ESLM -- 6.3.6 - Overall conclusion of revised optimization study -- 6.4 - ESLM versus linear static topology optimization -- References -- Chapter | seven - Heuristic and Meta-Heuristic Optimization Algorithms -- 7.1 - Mathematical algorithms -- 7.2 - Heuristic and meta-heuristic algorithms -- 7.3 - Evolutionary algorithms -- 7.3.1 - Evolutionary structural optimization (ESO) -- 7.3.2 - Additive evolutionary structural optimization (AESO) -- 7.3.3 - Bidirectional evolutionary structural optimization (BESO).
7.4 - Requirements for optimization of structures exposed to large (nonlinear) deformations -- 7.5 - Hybrid cellular automata -- 7.6 - Combinatory optimization problems -- 7.7 - Ant colony optimization -- 7.8 - Stochastic hill climbing -- 7.9 - Tabu search -- 7.10 - Simulated annealing -- 7.11 - Particle swarm optimization -- 7.12 - Neural networks -- 7.13 - General principles -- 7.13.1 - Robust optimization -- 7.13.2 - Fuzzy logic -- 7.13.3 - Monte Carlo simulation -- 7.14 - Entropy -- References -- Chapter | eight - Definition, Implementation, and Partial Validation of a Nonlinear Topology Optimization Algorithm -- 8.1 - Algorithm definition -- 8.1.1 - Calculate sensitivity numbers -- 8.1.2 - Average sensitivity numbers and determine target volume -- 8.1.3 - Construct new design -- 8.1.4 - Tabu search implementation -- 8.1.5 - Simulated annealing implementation -- 8.2 - Algorithm implementation and software development -- 8.2.1 - Selection of programming language and software platform -- 8.2.2 - Overview of BEETS PowerShell software -- 8.3 - Linear topology optimization case studies -- 8.3.1 - BEETS case study 1 -- 8.3.2 - BESO case study 1 -- 8.3.3 - VDM-SIMP case study 1 -- 8.3.4 - BEETS case study 2 -- 8.3.5 - BESO case study 2 -- 8.3.6 - VDM-SIMP case study 2 -- 8.3.7 - BEETS case study 3 -- 8.3.8 - BESO case study 3 -- 8.3.9 - VDM-SIMP case study 3 -- 8.4 - Nonlinear topology optimization case studies -- 8.4.1 - BEETS case study 4 -- 8.4.2 - BEETS case study 5: effects of ER and ARMAX magnitudes -- 8.4.3 - BEETS case study 6: cooling function influence -- 8.4.4 - BEETS case study 7: neighborhood influence -- 8.4.5 - BEETS case study 8: Tabu search influence -- 8.4.6 - BEETS case study 9: effects of MEP -- 8.4.7 - BEETS case study 10: combined effect of BEETS -- 8.5 - Conclusion of the potential of BEETS for nonlinear topology optimization.
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Cover -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Chapter | one - Vehicle Architectures, Structures, and Safety Requirements -- 1.1 - Introduction -- 1.2 - Legislative requirements -- 1.3 - Occupant injuries -- 1.3.1 - The crash test dummy families (or the tools to capture injury criteria) -- 1.3.2 - Typical injury criteria -- 1.3.2.1 - Head injury criteria (HIC) -- 1.3.2.2 - Head injury criteria for free motion head form (HIC(d)) -- 1.3.2.3 - Neck injury criteria (Nij) -- 1.3.2.4 - TI (Tibia index) -- 1.3.3 - Surrogate impactors -- 1.3.4 - Human computer models -- 1.4 - Typical vehicle architectures and scope for optimization -- 1.4.1 - Ladder frame -- 1.4.2 - Tubular structures -- 1.4.3 - Integral structures -- 1.4.4 - Shape and size -- 1.4.5 - Materials and manufacture -- 1.5 - Holistic approach to vehicle design -- 1.5.1 - Overall architecture design for structural instruction limitation -- 1.5.2 - Local shape and sizing for legal and other desirable structural requirements -- 1.6 - Conclusions and opportunities -- References -- Chapter | two - Numerical Techniques for Structural Assessment of Vehicle Architectures -- 2.1 - Introduction to finite element analysis (FEA) -- 2.2 - Theory of elasticity -- 2.3 - Elements -- 2.3.1 - One-dimensional elements -- 2.3.2 - Two-dimensional elements -- 2.3.3 - Three-dimensional elements -- 2.3.4 - Zero-dimensional elements -- 2.3.5 - Meshing strategy -- 2.3.6 - Element type -- 2.3.7 - Element shape -- 2.3.8 - Element size -- 2.4 - Fundamental explicit and implicit finite element analysis -- 2.5 - Nonlinear explicit finite element analysis -- 2.5.1 - Understanding the need for explicit FEA in connection with vehicle safety assessment -- 2.6 - Explicit FEA applied to vehicle safety assessment -- 2.6.1 - Standard explicit equations and convergence criteria.

2.6.2 - Stress wave propagation and timestep -- 2.6.3 - Relating the timestep to explicit FEA for vehicle safety assessment -- 2.6.4 - Critical element length -- 2.6.5 - Summation of factors influencing the timestep magnitude -- 2.6.6 - Importance of consistent mesh size -- 2.6.7 - Manipulating timestep magnitude -- 2.7 - Contacts -- 2.7.1 - Panel-to-panel contacts -- 2.7.2 - Tied contacts -- 2.8 - Example convergence study of explicit FEA -- 2.8.1 - Contact forces -- 2.8.2 - Kinetic energy -- 2.8.3 - Internal energy -- 2.8.4 - Total energy -- 2.8.5 - Summation of convergence study -- References -- Chapter | three - Introduction to General Optimization Principles and Methods -- 3.1 - What is structural optimization? -- 3.2 - How are optimization problems generally solved? -- 3.3 - General optimization methods and principles -- 3.4 - The curse of dimensionality -- 3.5 - Convex programming and optimization -- 3.5.1 - Linear programming -- 3.5.2 - The Simplex method -- 3.5.3 - Application to real-world engineering problems -- 3.5.4 - Sequential linear programming -- 3.6 - Gradient-based methods and line search methods -- 3.6.1 - Gradient descent method -- 3.6.2 - MatLab example of gradient descent method -- 3.6.3 - Newton-Raphson method -- 3.6.4 - MatLab example of Newton-Raphson method -- 3.6.5 - Steepest descent method -- 3.6.6 - MatLab example of steepest descent method -- 3.6.7 - Conjugate gradient method -- 3.6.8 - MatLab example of conjugate gradient method -- 3.6.9 - Comparison of gradient-based methods and line search methods -- 3.7 - Additional mathematical optimization methods -- 3.7.1 - Lagrangian multipliers -- 3.7.2 - Karush-Kuhn tucker optimality conditions -- 3.7.3 - Trust regions -- 3.8 Additional aspects of structural optimization -- 3.8.1 - Design space and design volume -- 3.8.2 - Uniqueness of solution.

3.8.3 - Design sensitivity and robustness -- 3.8.4 - Multiobjective optimization -- 3.8.5 - Multidisciplinary optimization and multiphysics optimization -- References -- Chapter | four - Introduction to Structural Optimization and Its Potential for Development of Vehicle Safety Structures -- 4.1 - Topology optimization -- 4.1.1 - The variable density method -- 4.1.2 - The SIMP interpolation scheme -- 4.1.3 - SRV constraint -- 4.1.4 - Design sensitivity analysis -- 4.1.5 - Homogenization-based optimization -- 4.2 - Shape optimization -- 4.2.1 - The Lagrangian approach to shape optimization -- 4.2.2 - The Eulerian approach to shape optimization -- 4.2.3 - Shape optimization using morphing -- 4.3 - Metamodeling -- 4.3.1 - Response surface methodology -- 4.4 - Point selection methods for metamodeling -- 4.4.1 - Factorial design -- 4.4.2 - Koshal design -- 4.4.3 - D-optimal -- 4.4.4 - Space-filling designs -- 4.5 - Optimization strategies for metamodel-based optimization -- 4.5.1 - Single-stage optimization -- 4.5.2 - Sequential stage optimization -- 4.5.3 - Sequential stage optimization with domain reduction -- 4.5.4 - Model suitability and analysis of variance -- References -- Chapter | five - Applications of Linear Optimization to Concept Vehicle Safety Structures -- 5.1 - Introduction -- 5.2 - Full vehicle structure topology optimization -- 5.2.1 - Introduction -- 5.2.2 - Crashworthiness criteria and design volume -- 5.2.3 - Discretization -- 5.2.3.1 - Packaging of essential components -- 5.2.4 - Boundary conditions -- 5.2.4.1 - Single point constraints (SPC) -- 5.2.4.2 - Inertia relief (IR) -- 5.2.4.3 - Runtime differences between single point constraints and inertia relief -- 5.2.5 - Post processing of optimization for SPC and IR -- 5.2.6 - Realism of SPC and IR boundary conditions in linear topology optimization in vehicle architecture definition.

5.2.7 - IR models sensitivity study -- 5.2.7.1 - Angle sensitivity study -- 5.2.7.2 - Stiffness sensitivity study -- 5.2.7.3 - Combined angle and stiffness sensitivity study -- 5.2.8 - Discussion -- 5.2.9 - Conclusions and limitations -- 5.3 - From topology optimization to computer-aided design (CAD) model -- 5.3.1 - Introduction -- 5.3.2 - Method 1: a holistic BIW sizing methodology -- 5.3.2.1 - 1D beam model -- 5.3.2.2 - 2D shell model -- 5.3.2.3 - Validity of method 1 using a crash model -- 5.3.2.4 - Conclusion of method 1: a holistic BIW sizing methodology -- 5.3.3 - Method 2: detailed frontend design -- 5.3.4 - Method 3: development of manufacturable body panel geometry -- 5.3.4.1 - Segment model -- 5.3.4.2 - Optimization of vehicle roof structure cross sections -- 5.4 - Conclusions: applications of linear optimization to concept vehicle safety structures -- References -- Chapter | six - Complications of Nonlinear Structural Optimization -- 6.1 - Equivalent static load method -- 6.2 - Initial optimization study -- 6.2.1 - Initial model setup -- 6.2.2 - Results of initial optimization study -- 6.2.3 - Parameter sensitivity study -- 6.3 - Revised optimization study -- 6.3.1 - Penalization factor p -- 6.3.2 - Load step type (LST) -- 6.3.3 - Nonlinear material parameters (NMP) -- 6.3.4 - External load angles (ELA) -- 6.3.5 - Scalability of ESLM -- 6.3.6 - Overall conclusion of revised optimization study -- 6.4 - ESLM versus linear static topology optimization -- References -- Chapter | seven - Heuristic and Meta-Heuristic Optimization Algorithms -- 7.1 - Mathematical algorithms -- 7.2 - Heuristic and meta-heuristic algorithms -- 7.3 - Evolutionary algorithms -- 7.3.1 - Evolutionary structural optimization (ESO) -- 7.3.2 - Additive evolutionary structural optimization (AESO) -- 7.3.3 - Bidirectional evolutionary structural optimization (BESO).

7.4 - Requirements for optimization of structures exposed to large (nonlinear) deformations -- 7.5 - Hybrid cellular automata -- 7.6 - Combinatory optimization problems -- 7.7 - Ant colony optimization -- 7.8 - Stochastic hill climbing -- 7.9 - Tabu search -- 7.10 - Simulated annealing -- 7.11 - Particle swarm optimization -- 7.12 - Neural networks -- 7.13 - General principles -- 7.13.1 - Robust optimization -- 7.13.2 - Fuzzy logic -- 7.13.3 - Monte Carlo simulation -- 7.14 - Entropy -- References -- Chapter | eight - Definition, Implementation, and Partial Validation of a Nonlinear Topology Optimization Algorithm -- 8.1 - Algorithm definition -- 8.1.1 - Calculate sensitivity numbers -- 8.1.2 - Average sensitivity numbers and determine target volume -- 8.1.3 - Construct new design -- 8.1.4 - Tabu search implementation -- 8.1.5 - Simulated annealing implementation -- 8.2 - Algorithm implementation and software development -- 8.2.1 - Selection of programming language and software platform -- 8.2.2 - Overview of BEETS PowerShell software -- 8.3 - Linear topology optimization case studies -- 8.3.1 - BEETS case study 1 -- 8.3.2 - BESO case study 1 -- 8.3.3 - VDM-SIMP case study 1 -- 8.3.4 - BEETS case study 2 -- 8.3.5 - BESO case study 2 -- 8.3.6 - VDM-SIMP case study 2 -- 8.3.7 - BEETS case study 3 -- 8.3.8 - BESO case study 3 -- 8.3.9 - VDM-SIMP case study 3 -- 8.4 - Nonlinear topology optimization case studies -- 8.4.1 - BEETS case study 4 -- 8.4.2 - BEETS case study 5: effects of ER and ARMAX magnitudes -- 8.4.3 - BEETS case study 6: cooling function influence -- 8.4.4 - BEETS case study 7: neighborhood influence -- 8.4.5 - BEETS case study 8: Tabu search influence -- 8.4.6 - BEETS case study 9: effects of MEP -- 8.4.7 - BEETS case study 10: combined effect of BEETS -- 8.5 - Conclusion of the potential of BEETS for nonlinear topology optimization.

References.

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