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Global Optimisation of Car Front-End Geometry to Minimise Pedestrian Head Injury Levels

Part of: Mobility

Published online by Cambridge University Press:  26 July 2019

Mohammed Reza Kianifar  and
Felician Campean

Abstract

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The paper presents a multidisciplinary design optimisation strategy for car front-end profile to minimise head injury criteria across pedestrian groups. A hybrid modelling strategy was used to simulate the car- pedestrian impact events, combining parametric modelling of front-car geometry with pedestrian models for the kinematics of crash impact. A space filling response surface modelling strategy was deployed to study the head injury response, with Optimal Latin Hypercube (OLH) Design of Experiments sampling and Kriging technique to fit response models. The study argues that the optimisation of the front-end car geometry for each of the individual pedestrian models, using evolutionary optimisation algorithms is not an effective global optimization strategy as the solutions are not acceptable for other pedestrian groups. Collaborative Optimisation (CO) multidisciplinary design optimisation architecture is introduced instead as a global optimisation strategy, and proven that it can enable simultaneous minimisation of head injury levels for all the pedestrian groups, delivering a global optimum solution which meets the safety requirements across the pedestrian groups.

Keywords

Computational design methodsOptimisationMultidisciplinary Design OptimisationComputer Aided Design (CAD)Passive Pedestrian Safety
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 2873 - 2882
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

References

Ben-Ari, E. N. and Steinberg, D. M. (2007), “Modeling Data from Computer Experiments: An Empirical Comparison of Kriging with MARS and Projection Pursuit Regression,” Qual. Eng., Vol. 19 No. 4, pp. 327–338. http://doi.org/10.1080/08982110701580930.Google Scholar
Braun, R. D., Moore, A. A. and Kroo, I. M. (1996a), “Use of the Collaborative Optimization Architecture for Launch Vehicle Design”, AIAA NASA and ISSMO Symposium on Multidisciplinary Analysis and Optimisation, AIAA Vol. 96 No. 4018, pp. 316–318, http://doi.org/10.2514/6.1996-4018.Google Scholar
Braun, R., Gage, P., Kroo, I. and Sobiesk, I. (1996b), “Implementation and Performance issues in Collaborative Optimization,” Proc. 6th AIAA/USAF/NASA/ISSMO Multidiscip. Anal. Optim. Symp., Vol. AIAA 1996. http://doi.org/10.1.1.45.6393.Google Scholar
Carter, E., Ebdon, S. and Neal-Sturgess, C. (2005), “Optimization of passenger car design for the mitigation of pedestrian head injury using a genetic algorithm,” in Proceedings of the 2005 conference on Genetic and evolutionary computation - GECCO ‘05, pp. 2113–2120. http://doi.org/10.1145/1068009.1068358.Google Scholar
Christensen, J., Bastien, C. and Blundell, M. V. (2012), “Effects of roof crush loading scenario upon body in white using topology optimisation,” Int. J. Crashworthiness, Vol. 17 No. 1, pp. 29–38. http://doi.org/10.1080/13588265.2011.625640.Google Scholar
De Lange, L., Rooij, V., Happee, R. and Liu, X. J. (2006), “Validation of Human Pedestrian Models Using Laboratory Data as well as Accident Reconstruction,” in Expert Symposium on Accident Research (ESAR).Google Scholar
Department for Transport (2018), “Reported road casualties in Great Britain: quarterly provisional estimates year ending June 2018”, available from https://www.gov.uk/transport/road-accidents-and-serious-accidents, accessed 24/11/2018.Google Scholar
Forrester, A., Sobester, D. A. and Keane, A. (2008), “Engineering Design via Surrogate Modelling: A Practical Guide”. John Wiley and Sons.Google Scholar
Gramacy, R. B. and Lian, H. (2012), “Gaussian Process Single-Index Models as Emulators for Computer Experiments,” Technometrics, Vol. 54 No. 1, pp. 30–41. http://doi.org/10.1080/00401706.2012.650527.Google Scholar
Hartmann, B., Baumann, W. and Nelles, O. (2013), “Axes-Oblique Partitioning of Local Model Networks for Engine Calibration,” in Design of Experiments (DoE) in Engine Development, pp. 92–106.Google Scholar
Joseph, V. R., Hung, Y. and Sudjianto, A. (2008), “Blind Kriging: A New Method for Developing Metamodels,” J. Mech. Des., Vol. 130 No. 3. http://doi.org/10.1115/1.2829873.Google Scholar
Kang, N., Kokkolaras, M. and Papalambros, P. (2012), “Optimal Design of Commercial Vehicle Systems Using Analytical Target Cascading,” 12th AIAA Aviat. Technol. Integr. Oper. Conf. 14th AIAA/ISSMO Multidiscip. Anal. Optim. Conf. http://doi.org/10.2514/6.2012-5524.Google Scholar
Kausalyah, V., Shasthri, S., Abdullah, K. A., Idres, M. M., Shah, Q. H. and Wong, S. V. (2014a), “Development of Economical Vehicle Model for Pedestrian Friendly Front End Profile Study,” Int. J. Simul. Model, Vol. 13 No. 4, pp. 419–432.Google Scholar
Kausalyah, V., Shasthri, S., Abdullah, K. A., Idres, M. M., Shah, Q. H. and Wong, S. V. (2014b), “Optimisation of vehicle front-end geometry for adult and pediatric pedestrian protection,” Int. J. Crashworthiness, Vol. 19 No. 2, pp. 153–160. http://doi.org/10.1080/13588265.2013.879506.Google Scholar
Khan, M. A. Z. (2011), “Transient engine model for calibration using two-stage regression approach,” PhD Thesis, Loughborough University.Google Scholar
Kroo, I. and Manning, V. (2000), “Collaborative Optimisation: Status and directions,” in 8th AIAA/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization.Google Scholar
Le Glatin, N. G. (2003), “Design of experiments analysis study of real world pedestrian-vehicle accident simulation scenarios”, School of Engineering, Coventry University.Google Scholar
Liu, X. and Yang, J. (2013), “Effects of vehicle impact velocity and front-end structure on dynamic responses of child pedestrians”, Traffic Inj. Prev., Vol. 4 No. 4, pp. 337–344. http://doi.org/10.1080/714040491.Google Scholar
Loeppky, J. L., Sacks, J. and Welch, W. J. (2009), “Choosing the Sample Size of a Computer Experiment: A Practical Guide”, Technometrics, Vol. 51 No. 4, pp. 366–376. http://doi.org/10.1198/TECH.2009.08040.Google Scholar
Martin, J. L., Lardy, A. and Laumon, B. (2011), “Pedestrian injury patterns according to car and casualty characteristics in france”, Ann. Adv. Automot. Med., Vol. 55, pp. 137–146.Google Scholar
Mendoza-Vázquez, M., Jakobsson, L., Davidsson, J., Brolin, K. and Östmann, M. (2014), “Evaluation of Thoracic Injury Criteria for THUMS Finite Element Human Body Model Using Real-World Crash Data”, in IRCOBI Conference 2014, pp. 528–541.Google Scholar
Oh, C., Kang, Y. and Kim, W. (2008), “Assessing the safety benefits of an advanced vehicular technology for protecting pedestrians”, Accid. Anal. Prev., Vol. 40 No. 3, pp. 935–942. http://doi.org/10.1016/j.aap.2007.10.010.Google Scholar
Ptak, M., Karliński, J. and Kopczyński, A. (2010), “Analysis of pedestrian passive safety with the use of numerical simulation”, J. Kones, Vol. 17 No. 1, pp. 337–342.Google Scholar
Rango, J., Schnorbus, T., Kwee, H., Beck, R., Kinoo, B., Arthozoul, S. and Zhang, M. (2013), “Comparison of Different Approaches for Global Modeling of Combustion Engines,” in Design of Experiments (DoE) in Engine Development, pp. 70–91.Google Scholar
Shen, J., Jin, X. L. and Zhang, X. Y. (2008), “Simulated evaluation of pedestrian safety for flat-front vehicles”, Int. J. Crashworthiness, Vol. 13 No. 3, pp. 247–254. http://doi.org/10.1080/13588260801933584.Google Scholar
Sun, G., Lv, X., Fang, J., Gu, X. and Li, Q. (2015), “Reliability-based design optimization of vehicle front-end structure for pedestrian lower extremity protection,” in 11th World Congress on Structural and Multidisciplinary Optimisation.Google Scholar
TNO Automotive-1 (2005), MADYMO Human Models Manual, V6.2.2, TNO Automotive, The Netherlands.Google Scholar
Untaroiu, C. D., Crandall, J. R., Takahashi, Y., Okamoto, M., Ito, O. and Fredriksson, R. (2010), “Analysis of running child pedestrians impacted by a vehicle using rigid-body models and optimization techniques”, Saf. Sci., Vol. 48 No. 2, pp. 259–267. http://doi.org/10.1016/j.ssci.2009.09.003.Google Scholar
Untaroiu, C. D., Meissner, M. U., Crandall, J. R., Takahashi, Y., Okamoto, M. and Ito, O. (2009), “Crash reconstruction of pedestrian accidents using optimization techniques”, Int. J. Impact Eng., Vol. 36 No. 2, pp. 210–219. http://doi.org/10.1016/j.ijimpeng.2008.01.012Google Scholar
Yao, J., Yang, J. and Otte, D. (2008), “Investigation of head injuries by reconstructions of real-world vehicle-versus-adult-pedestrian accidents”, Saf. Sci., Vol. 46 No. 7, pp. 1103–1114. http://doi.org/10.1016/j.ssci.2007.06.021.Google Scholar
Zhang, J., Chen, G. and Tang, H. (2011), “Parametric design and structural improvements to optimise frontal crashworthiness of a truck”, Int. J. Crashworthiness, Vol. 16 No. 5, pp. 501–509. http://doi.org/10.1080/13588265.2011.611395.Google Scholar
Zhao, Y, Rosala, G. F., Campean, I. F. and Day, A. J. (2010), “A response surface approach to front-car optimisation for minimising pedestrian head injury levels”, Int. J. Crashworthiness, Vol. 15 No. 2 pp. 143–150. http://doi.org/10.1080/13588260903094392.Google Scholar