Robust Design of Energy-Absorbing Crumple-Zone Structures Considering Uncertainties

考虑不确定性的吸能溃缩区结构的稳健设计

• 批准号: 311931593
• 负责人: Professor Dr.-Ing. Michael Hanss
• 金额: --
• 依托单位: Institut für Technische und Numerische Mechanik (ITM)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/311931593?language=en
• 关键词: Robust; Design; Energy; Absorbing; Crumple

The continually increasing performance and memory capacity of modern computers induces a comparably steady increase of complexity in the models used for numerical simulation. However, with regard to the computationally very costly optimization procedures to be performed, for example, in the framework of structural optimization, complex, high-dimensional models are not suitable, in particular if, additionally, uncertainties are to be taken into account to assure a robust optimized design. Against the background of passenger safety, which plays a vital role in modern automotive design, the overall objective of this research project is to develop a new, fuzzy arithmetical design strategy for an optimized energy-absorbing crumple-zone structure with robustness against uncertainties. The demands made on the robustness of this structure are twofold with respect to the polymorphic property of the uncertainties involved: First, the optimized structure shall be robust against the uncertainties of aleatoric type, introduced by the unpredictable, random conditions and environmental influences in real crash scenarios. Second, the structure shall account for the uncertainties of epistemic type, introduced by the loss of information due to the simplifications in modeling, the idealization of physical assumptions and the application of model order reduction techniques which are needed to achieve lower-dimensional models for an efficient optimization. To achieve the overall project objective, three major work tasks will be performed: After the derivation of different simplified crash models for the full-scale car structure, specific parameters of the simplified models will be considered as fuzzy-valued, for the purpose of covering the overall inherent uncertainty of the model in terms of parametric uncertainties. The values of these parameters will then be identified on the basis of an inverse fuzzy arithmetical strategy, whose implementation for high-dimensional simulation models will represent a core feature of this project. Using a novel criterion, developed by the applicant, for the quality assessment of fuzzy-parameterized models, the most appropriate simplified fuzzy-parameterized model will be selected, forming finally the basis for an effective design optimization of the crumple-zone structure with robustness against uncertainties. This new strategy of explicitly including both aleatoric and epistemic uncertainties in form of fuzzy-valued quantities already in the early stages of industrial design, as to be developed in this project, can significantly help to avoid costly and time-consuming re-engineering cycles, induced by disregarded uncertainties, in the final phases of prototype testing and certification.

现代计算机的性能和存储容量的不断增加导致了用于数值模拟的模型的复杂性相对稳定地增加。然而，例如在结构优化的框架中，对于要执行的计算非常昂贵的优化程序，复杂的高维模型是不合适的，特别是如果要另外考虑不确定性以确保稳健的优化设计的话。在现代汽车设计中，乘员安全起着至关重要的作用，本研究项目的总体目标是开发一种新的模糊算法设计策略，以优化吸能皱折区结构，并对不确定性具有鲁棒性。考虑到不确定性的多变性，对这种结构的稳健性提出了两方面的要求：首先，优化后的结构应该对实际碰撞场景中不可预测的随机条件和环境影响引入的任意类型的不确定性具有健壮性。其次，结构应考虑到认知型不确定性，这些不确定性是由于模型简化、物理假设的理想化和模型降阶技术的应用而导致的信息损失，这些都是实现低维模型以实现有效优化所必需的。为了实现项目的总体目标，将进行三项主要工作：在推导出不同的全尺寸轿车结构碰撞简化模型后，将简化模型的具体参数考虑为模糊值，以涵盖模型在参数不确定性方面的整体固有不确定性。然后，将根据逆模糊算术策略确定这些参数的值，该策略在高维模拟模型中的实施将代表该项目的核心特征。使用申请人开发的用于模糊参数模型质量评估的新准则，将选择最合适的简化模糊参数模型，最终形成对不确定具有鲁棒性的皱折区结构的有效设计优化的基础。本项目将开发的这种以模糊量的形式明确包括任意性和认知性不确定性的新策略，可以显著帮助避免在原型测试和认证的最后阶段由被忽视的不确定性引起的昂贵和耗时的再工程周期。