Johnson, K L; Chowdhury, S; Lawrimore, W B; Mao, Y; Mehmani, A; Prabhu, R; Rush, G A; Horstemeyer, M F
Constrained topological optimization of a football helmet facemask based on brain response Journal Article
In: Materials and Design, vol. 111, pp. 108–118, 2016.
Abstract | Links | BibTeX | Tags: Accident prevention, ALGORITHMS, brain, Concussion, Constrained optimization, Design, Design optimization, finite element analysis, Finite element method, football helmet, Fuel additives, Genetic algorithms, Multiobjective optimization, Optimization, Safety devices, Shear strain, Sports, Surrogate model, Surrogate modeling, Topology, Traumatic Brain Injuries, traumatic brain injury
@article{Johnson2016a,
title = {Constrained topological optimization of a football helmet facemask based on brain response},
author = {Johnson, K L and Chowdhury, S and Lawrimore, W B and Mao, Y and Mehmani, A and Prabhu, R and Rush, G A and Horstemeyer, M F},
doi = {10.1016/j.matdes.2016.08.064},
year = {2016},
date = {2016-01-01},
journal = {Materials and Design},
volume = {111},
pages = {108--118},
abstract = {Surrogate model-based multi-objective design optimization was performed to reduce concussion risk during frontal football helmet impacts. In particular, a topological decomposition of the football helmet facemask was performed to formulate the design problem, and brain injury metrics were exploited as objective functions. A validated finite element model of a helmeted human head was used to recreate facemask impacts. Due to the prohibitive computational expense of the full scale simulations, a surrogate modeling approach was employed. An optimal surrogate model selection framework, called Concurrent Surrogate Model Selection, or COSMOS, was utilized to identify the surrogate models best suited to approximate each objective function. The resulting surrogate models were implemented in the Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization algorithm. Constraints were implemented to control the solid material fraction in the facemask design space, and binary variables were used to control the placement of the facemask bars. The optimized facemask designs reduced the maximum tensile pressure in the brain by 7.5% and the maximum shear strain by a remarkable 39.5%. This research represents a first-of-its-kind approach to multi-objective design optimization on a football helmet, and demonstrates the possibilities that are achievable in improving human safety by using such a simulation-based design optimization. © 2016 Elsevier Ltd},
keywords = {Accident prevention, ALGORITHMS, brain, Concussion, Constrained optimization, Design, Design optimization, finite element analysis, Finite element method, football helmet, Fuel additives, Genetic algorithms, Multiobjective optimization, Optimization, Safety devices, Shear strain, Sports, Surrogate model, Surrogate modeling, Topology, Traumatic Brain Injuries, traumatic brain injury},
pubstate = {published},
tppubtype = {article}
}
Johnson, K L; Chowdhury, S; Lawrimore, W B; Mao, Y; Mehmani, A; Prabhu, R; Rush, G A; Horstemeyer, M F
Constrained topological optimization of a football helmet facemask based on brain response Journal Article
In: Materials and Design, vol. 111, pp. 108–118, 2016.
@article{Johnson2016a,
title = {Constrained topological optimization of a football helmet facemask based on brain response},
author = {Johnson, K L and Chowdhury, S and Lawrimore, W B and Mao, Y and Mehmani, A and Prabhu, R and Rush, G A and Horstemeyer, M F},
doi = {10.1016/j.matdes.2016.08.064},
year = {2016},
date = {2016-01-01},
journal = {Materials and Design},
volume = {111},
pages = {108--118},
abstract = {Surrogate model-based multi-objective design optimization was performed to reduce concussion risk during frontal football helmet impacts. In particular, a topological decomposition of the football helmet facemask was performed to formulate the design problem, and brain injury metrics were exploited as objective functions. A validated finite element model of a helmeted human head was used to recreate facemask impacts. Due to the prohibitive computational expense of the full scale simulations, a surrogate modeling approach was employed. An optimal surrogate model selection framework, called Concurrent Surrogate Model Selection, or COSMOS, was utilized to identify the surrogate models best suited to approximate each objective function. The resulting surrogate models were implemented in the Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization algorithm. Constraints were implemented to control the solid material fraction in the facemask design space, and binary variables were used to control the placement of the facemask bars. The optimized facemask designs reduced the maximum tensile pressure in the brain by 7.5% and the maximum shear strain by a remarkable 39.5%. This research represents a first-of-its-kind approach to multi-objective design optimization on a football helmet, and demonstrates the possibilities that are achievable in improving human safety by using such a simulation-based design optimization. © 2016 Elsevier Ltd},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Johnson, K L; Chowdhury, S; Lawrimore, W B; Mao, Y; Mehmani, A; Prabhu, R; Rush, G A; Horstemeyer, M F
Constrained topological optimization of a football helmet facemask based on brain response Journal Article
In: Materials and Design, vol. 111, pp. 108–118, 2016.
Abstract | Links | BibTeX | Tags: Accident prevention, ALGORITHMS, brain, Concussion, Constrained optimization, Design, Design optimization, finite element analysis, Finite element method, football helmet, Fuel additives, Genetic algorithms, Multiobjective optimization, Optimization, Safety devices, Shear strain, Sports, Surrogate model, Surrogate modeling, Topology, Traumatic Brain Injuries, traumatic brain injury
@article{Johnson2016a,
title = {Constrained topological optimization of a football helmet facemask based on brain response},
author = {Johnson, K L and Chowdhury, S and Lawrimore, W B and Mao, Y and Mehmani, A and Prabhu, R and Rush, G A and Horstemeyer, M F},
doi = {10.1016/j.matdes.2016.08.064},
year = {2016},
date = {2016-01-01},
journal = {Materials and Design},
volume = {111},
pages = {108--118},
abstract = {Surrogate model-based multi-objective design optimization was performed to reduce concussion risk during frontal football helmet impacts. In particular, a topological decomposition of the football helmet facemask was performed to formulate the design problem, and brain injury metrics were exploited as objective functions. A validated finite element model of a helmeted human head was used to recreate facemask impacts. Due to the prohibitive computational expense of the full scale simulations, a surrogate modeling approach was employed. An optimal surrogate model selection framework, called Concurrent Surrogate Model Selection, or COSMOS, was utilized to identify the surrogate models best suited to approximate each objective function. The resulting surrogate models were implemented in the Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization algorithm. Constraints were implemented to control the solid material fraction in the facemask design space, and binary variables were used to control the placement of the facemask bars. The optimized facemask designs reduced the maximum tensile pressure in the brain by 7.5% and the maximum shear strain by a remarkable 39.5%. This research represents a first-of-its-kind approach to multi-objective design optimization on a football helmet, and demonstrates the possibilities that are achievable in improving human safety by using such a simulation-based design optimization. © 2016 Elsevier Ltd},
keywords = {Accident prevention, ALGORITHMS, brain, Concussion, Constrained optimization, Design, Design optimization, finite element analysis, Finite element method, football helmet, Fuel additives, Genetic algorithms, Multiobjective optimization, Optimization, Safety devices, Shear strain, Sports, Surrogate model, Surrogate modeling, Topology, Traumatic Brain Injuries, traumatic brain injury},
pubstate = {published},
tppubtype = {article}
}