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The Role of Constitutive Model in Traumatic Brain Injury Prediction

Kacker, Shubhra
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563874757653453

Abstract Details

2019, MS, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
Traumatic brain injury is a major cause of fatalities in the United Sates. To understand the correct mechanics and develop protective gear, computational methods such as finite element analysis of human head have been used extensively in the past. In modeling such a complex biomechanical phenomenon, constitutive equations describing the material behavior are used. Due to inevitable variability in soft tissue experimental data, uncertainty in the material parameters are observed. To model this variability accurately, the uncertainty in the material parameters needs to be propagated to quantities of interest in the simulation, such as injury criteria. This in turn requires the proper selection of the constitutive models. This work gives an insight into the role of these constitutive models in traumatic brain injury prediction. In this thesis, a Bayesian framework is used for the estimation of material model parameters based on the nested sampling algorithm MULTINEST. A non-linear visco-hyperelastic material model is considered for brain tissue and is implemented in the finite element software LS-DYNA. Various hyperelastic models are considered to understand the role of these models on traumatic brain injury prediction. Finite element analysis of the SIMon human head model is performed to simulate an impact loading causing traumatic brain injury and a maximum principal strain-based injury criterion is considered to quantify the severity of the injury. To non-intrusively propagate the uncertainty in the material parameters, a Gaussian process surrogate model is used in order to avoid high computational expense. Based on this, the distribution of the injury criterion is obtained for all the material models considered. The results obtained show that the constitutive model plays a major role in propagating the uncertainty to the injury criterion. Some material models are very sensitive to the material parameters compared to others. The analyst should be extra cautious when deciding the constitutive model to be used in such uncertainty propagation problems.
Kumar Vemaganti, Ph.D. (Committee Chair)
Woo Kyun Kim, Ph.D. (Committee Member)
Sandeep Madireddy, Ph.D. (Committee Member)
72 p.
Mechanical Engineering
Uncertainty quantification; Metamodeling; Traumatic brain injury; Uncertainty propagation; Gaussian process; Hyperelastic costitutive models

Recommended Citations

Citations

  • Kacker, S. (2019). The Role of Constitutive Model in Traumatic Brain Injury Prediction [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563874757653453

    APA Style (7th edition)

  • Kacker, Shubhra. The Role of Constitutive Model in Traumatic Brain Injury Prediction. 2019. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563874757653453.

    MLA Style (8th edition)

  • Kacker, Shubhra. "The Role of Constitutive Model in Traumatic Brain Injury Prediction." Master's thesis, University of Cincinnati, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563874757653453

    Chicago Manual of Style (17th edition)

ucin1563874757653453
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© 2019, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.