Mathematical Modelling and Analysis of Vehicle Frontal Crash using Lumped Parameters Models

Munyazikwiye, B. (2020). Mathematical Modelling and Analysis of Vehicle Frontal Crash using Lumped Parameters Models (Doctoral thesis). University of Agder, Kristiansand.

Abstract

A full-scale crash test is conventionally used for vehicle crashworthiness analysis. However, this approach is expensive and time-consuming. Vehicle crash reconstructions using different numerical modelling approaches can predict vehicle behavior and reduce the need for multiple full-scale crash tests, thus research on the crash reconstruction has received a great attention in the last few decades. Among modelling approaches, lumped parameters models (LPM) and finite element models (FEM) are commonly used in the vehicle crash reconstruction. This thesis focuses on developing and improving the LPM for vehicle frontal crash analysis. The study aims at reconstructing crash scenarios for vehicle-to-barrier (VTB), vehicleoccupant (V-Occ), and vehicle-to-vehicle (VTV), respectively.

In this study, a single mass-spring-damper (MSD) is used to simulate a vehicle to-barrier or a wall. A double MSD is used to model the response of the chassis and passenger compartment in a frontal crash, a vehicle-occupant, and a vehicle-tovehicle, respectively. A curve fitting, state-space, and genetic algorithm are used to estimate parameters of the model for reconstructing the vehicle crash kinematics. Further, the piecewise LPM is developed to mimic the crash characteristics for VTB, VO, and VTV crash scenarios, and its predictive capability is compared with the explicit FEM. Within the framework, the advantages of the proposed methods are explained in detail, and suggested solutions are presented to address the limitations in the study.

Has parts

Paper I: Munyazikwiye, B. B., Karimi, H. R. & Robbersmyr, K. G. (2013). Mathematical Modelling and Parameters Estimation of Car Crash Using Eigensystem Realization Algorithm and Curve-Fitting Approaches. Mathematical Problems in Engineering, 2013: 262196, 1-13. doi: https://doi.org/10.1155/2013/262196. Author´s accepted manuscript. Full-text is available in AURA as a separate file: .

Paper II: Munyazikwiye, B. B., Robbersmyr, K. G. & Karimi, H. R. (2014). A state-space approach to mathematical modelling and parameters identification of vehicle frontal crash. Systems Science & Control Engineering: An Open Access Journal, 2(1), 351-361. doi: https://doi.org/10.1080/21642583.2014.883108. Author´s accepted manuscript. Full-text is available in AURA as a separate file.

Paper III: Munyazikwiye, B. B., Karimi, H. R. & Robbersmyr, K. G. (2016). A Mathematical Model for Vehicle-Occupant Frontal Crash using Genetic Algorithm. In 2016 UKSim-AMSS 18th International Conference on Computer Modelling and Simulation. doi: https://doi.org/10.1109/UKSim.2016.12. Author´s accepted manuscript. Full-text is available in AURA as a separate file.

Paper IV: Munyazikwiye, B. B., Karimi, H. R. & Robbersmyr, K. G. (2017). Optimization of Vehicle-to-Vehicle Frontal Crash Model based on Measured Data using Genetic Algorithm. IEEE Access, 5, 3131-3138. doi: https://doi.org/10.1109/ACCESS.2017.2671357. Author´s accepted manuscript. Full-text is available in AURA as a separate file: http://hdl.handle.net/11250/2491210.

Paper V: Munyazikwiye, B. B., Karimi, H. R. & Robbersmyr, K. G. (2017). Application of Genetic Algorithm on Parameter Optimization of Three Vehicle Crash Scenarios. IFAC-PapersOnLine, 50(1), 3697-3701. doi: https://doi.org/10.1016/j.ifacol.2017.08.564. Author´s accepted manuscript. Full-text is available in AURA as a separate file. Published vetsion is shared in the terms of the Creative Commons CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).

Paper VI: Munyazikwiye, B. B., Vysochinskiy, D., Khadyko, M. & Robbersmyr, K. G. (2018). Prediction of Vehicle Crashworthiness Parameters Using Piecewise Lumped Parameters and Finite Element Models. Designs, 2(4): 43. doi: https://doi.org/10.3390/designs2040043. Author´s accepted manuscript. Full-text is available in AURA as a separate file: http://hdl.handle.net/11250/2597888.