Automated generation of physical surrogate vehicle models for crash optimization
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Automated generation of physical surrogate vehicle models for crash optimization Michael Scha¨ffer . Ralf Sturm . Horst E. Friedrich
Received: 10 April 2018 / Accepted: 23 June 2018 Ó Springer Nature B.V. 2018
Abstract A challenge in the design and optimization of vehicle structures is the high computational costs required for crash analysis. In this paper an automated model generation for simplified vehicle crash models is presented. The considered crash load cases are the US NCAP (100%, 56 km/h), the Euro NCAP (40%, 64 km/h) and the IIHS Small Overlap (25%, 64 km/h). The generation of the physical surrogate vehicle models is based on different sub-steps which were automated using a process chain. With this process chain it is possible to evaluate very efficiently the influence of structural modifications on the global crash behavior. During the model generation the crash behavior of the surrogate model is directly compared with the full vehicle model to enable a direct assessment of the model quality. Since the interface, where the model is cut, is an important factor for the obtained correlation, different interface positions were analysed. With obtained solutions it is possible to identify the interface position, which fulfils the required correlation by a given computational time. Additionally, the interface discretisation is analyzed to identify the model configuration with the highest correlation. This investigation was performed for three different vehicle models.
M. Scha¨ffer (&) R. Sturm H. E. Friedrich Institute of Vehicle Concepts, German Aerospace Center (DLR), Pfaffenwaldring 38-40, 70569 Stuttgart, Germany e-mail: [email protected]
Keywords Crashworthiness Physical surrogates Simplified models Automated model generation Optimization Computational time
1 Introduction Vehicles body structures have to be lightweight with high crash safety to protect occupants and pedestrians. The development of a light and crash safe body structure is currently supported by simulations and structural optimizations. Due to the increased application of virtual methods the product development process for a new vehicle has been more than halved leading to shorter product life cycles (Duddeck 2008; Klaiber 2015). However, due to the high computational costs required for crash simulations, structural optimizations cannot be carried out with a full vehicle model. Especially since various crash load cases have to be considered for the development of a body structure. The influence of the crash load cases on the computational costs are described in Kodiyalam et al. (2004). In the case of a full vehicle optimization, the time required to obtain the optimization results can quickly increase to several weeks (Duddeck 2008). To counteract this problem, various countermeasures are described, such as increasing the number of CPUs or selecting a better optimization algorithm. Another option for reducing the computational time is through the use of surrogate models (Stein 2015).
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