Crashworthiness Optimization Based on the Probability of Traumatic Brain Injury Accounting for Simulation Noise and Impact Conditions

Author(s):  
Seyed Saeed Ahmadisoleymani ◽  
Samy Missoum

Abstract Finite element-based crashworthiness optimization is nowadays extensively used to improve the safety of vehicles. However, the responses of a crash simulation are notoriously noisy. In addition, the actual or simulated responses during a crash can be highly sensitive to uncertainties. These uncertainties appear in various forms such as uncontrollable random parameters (e.g., impact conditions). To address these challenges, an optimization algorithm based on a Stochastic Kriging (SK) and an Augmented Expected Improvement (AEI) infill criterion is proposed. A SK enables the approximation of a response while accounting for the noise-induced aleatory variance. In addition, SK has the advantage of reducing the dimensionality of the problem by implicitly accounting for the influence of random parameters and their contribution to the overall aleatory variance. In the proposed algorithm, the aleatory variance is initially estimated through direct sampling and subsequently approximated by a regression kriging. This aleatory variance approximation, which is refined adaptively, is used for the computation of the infill criterion and probabilistic constraints. The algorithm is implemented on a crashworthiness optimization problem that involves a sled and dummy models subjected to an acceleration pulse. The sled model includes components of a vehicle occupant restraint system such as an airbag, seatbelt, and steering column. In all problems considered, the objective function is the probability of traumatic brain injury, which is computed through the Brain Injury Criterion (BrIC) and a logistic injury risk model. In some cases, probabilistic constraints corresponding to other types of bodily injuries such as thoracic injury are added to the optimization problem. The design variables correspond to the properties of the occupant restraint system (e.g., loading curve that dictates the airbag vent area versus pressure). In addition to the inherent simulation noise, uncertainties in the loading conditions are introduced in the form of a random scaling factor of the acceleration pulse.

Author(s):  
Seyed Saeed Ahmadisoleymani ◽  
Samy Missoum

Abstract Vehicle crash simulations are notoriously costly and noisy. When performing crashworthiness optimization, it is therefore important to include available information to quantify the noise in the optimization. For this purpose, a stochastic kriging can be used to account for the uncertainty due to the simulation noise. It is done through the addition of a non-stationary stochastic process to the deterministic kriging formulation. This stochastic kriging, which can also be used to include the effect of random non-controllable parameters, can then be used for surrogate-based optimization. In this work, a stochastic kriging-based optimization algorithm is proposed with an infill criterion referred to as the Augmented Expected Improvement, which, unlike its deterministic counterpart the Expect Improvement, accounts for the presence of irreducible aleatory variance due to noise. One of the key novelty of the proposed algorithm stems from the approximation of the aleatory variance and its update during the optimization. The proposed approach is applied to the optimization of two problems including an analytical function and a crashwor-thiness problem where the components of an occupant restraint system of a vehicle are optimized.


Author(s):  
Atsutaka Tamura ◽  
Junji Hasegawa ◽  
Takao Koide

A series of pedestrian sideswipe impacts were computationally reconstructed; a fast-walking pedestrian was collided laterally with the side of a moving vehicle at 25 km/h or 40 km/h, which resulted in rotating the pedestrian's body axially. Potential severity of traumatic brain injury (TBI) was assessed using linear and rotational acceleration pulses applied to the head and by measuring intracranial brain tissue deformation. We found that TBI risk due to secondary head strike with the ground can be much greater than that due to primary head strike with the vehicle. Further, an “effective” head mass, meff, was computed based upon the impulse and vertical velocity change involved in the secondary head strike, which mostly exceeded the mass of the adult head-form impactor (4.5 kg) commonly used for a current regulatory impact test for pedestrian safety assessment. Our results demonstrated that a sport utility vehicle (SUV) is more aggressive than a sedan due to the differences in frontal shape. Additionally, it was highlighted that a striking vehicle velocity should be lower than 25 km/h at the moment of impact to exclude the potential risk of sustaining TBI, which would be mitigated by actively controlling meff, because meff is closely associated with a rotational acceleration pulse applied to the head involved in the final event of ground contact.


2021 ◽  
pp. 1-15
Author(s):  
Seyed Saeed Ahmadisoleymani ◽  
Samy Missoum

Abstract Finite element-based crashworthiness optimization is extensively used to improve the safety of motor vehicles. However, the responses of crash simulations are characterized by a high level of numerical noise, which can hamper the blind use of surrogate-based design optimization methods. It is therefore essential to account for the noise-induced uncertainty when performing optimization. For this purpose, a surrogate, referred to as Non-Deterministic Kriging (NDK), can be used. It models the noise as a non-stationary stochastic process, which is added to a traditional deterministic kriging surrogate. Based on the NDK surrogate, this study proposes an optimization algorithm tailored to account for both epistemic uncertainty, due to the lack of data, and irreducible aleatory uncertainty, due to the simulation noise. The variances are included within an extension of the well-known expected improvement infill criterion referred to as Modified Augmented Expected Improvement (MAEI). Because the proposed optimization scheme requires an estimate of the aleatory variance, it is approximated through a regression kriging, which is iteratively refined. The proposed algorithm is tested on a set of analytical functions and applied to the optimization of an Occupant Restraint System (ORS) during a crash.


2019 ◽  
Vol 42 ◽  
Author(s):  
Colleen M. Kelley ◽  
Larry L. Jacoby

Abstract Cognitive control constrains retrieval processing and so restricts what comes to mind as input to the attribution system. We review evidence that older adults, patients with Alzheimer's disease, and people with traumatic brain injury exert less cognitive control during retrieval, and so are susceptible to memory misattributions in the form of dramatic levels of false remembering.


2020 ◽  
Vol 5 (1) ◽  
pp. 88-96
Author(s):  
Mary R. T. Kennedy

Purpose The purpose of this clinical focus article is to provide speech-language pathologists with a brief update of the evidence that provides possible explanations for our experiences while coaching college students with traumatic brain injury (TBI). Method The narrative text provides readers with lessons we learned as speech-language pathologists functioning as cognitive coaches to college students with TBI. This is not meant to be an exhaustive list, but rather to consider the recent scientific evidence that will help our understanding of how best to coach these college students. Conclusion Four lessons are described. Lesson 1 focuses on the value of self-reported responses to surveys, questionnaires, and interviews. Lesson 2 addresses the use of immediate/proximal goals as leverage for students to update their sense of self and how their abilities and disabilities may alter their more distal goals. Lesson 3 reminds us that teamwork is necessary to address the complex issues facing these students, which include their developmental stage, the sudden onset of trauma to the brain, and having to navigate going to college with a TBI. Lesson 4 focuses on the need for college students with TBI to learn how to self-advocate with instructors, family, and peers.


2019 ◽  
Vol 28 (3) ◽  
pp. 1363-1370 ◽  
Author(s):  
Jessica Brown ◽  
Katy O'Brien ◽  
Kelly Knollman-Porter ◽  
Tracey Wallace

Purpose The Centers for Disease Control and Prevention (CDC) recently released guidelines for rehabilitation professionals regarding the care of children with mild traumatic brain injury (mTBI). Given that mTBI impacts millions of children each year and can be particularly detrimental to children in middle and high school age groups, access to universal recommendations for management of postinjury symptoms is ideal. Method This viewpoint article examines the CDC guidelines and applies these recommendations directly to speech-language pathology practices. In particular, education, assessment, treatment, team management, and ongoing monitoring are discussed. In addition, suggested timelines regarding implementation of services by speech-language pathologists (SLPs) are provided. Specific focus is placed on adolescents (i.e., middle and high school–age children). Results SLPs are critical members of the rehabilitation team working with children with mTBI and should be involved in education, symptom monitoring, and assessment early in the recovery process. SLPs can also provide unique insight into the cognitive and linguistic challenges of these students and can serve to bridge the gap among rehabilitation and school-based professionals, the adolescent with brain injury, and their parents. Conclusion The guidelines provided by the CDC, along with evidence from the field of speech pathology, can guide SLPs to advocate for involvement in the care of adolescents with mTBI. More research is needed to enhance the evidence base for direct assessment and treatment with this population; however, SLPs can use their extensive knowledge and experience working with individuals with traumatic brain injury as a starting point for post-mTBI care.


ASHA Leader ◽  
2010 ◽  
Vol 15 (13) ◽  
pp. 38-38
Author(s):  
G. Gayle Kelley

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