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Multiobjective robust design optimization of fatigue life for a truck cab

机译:卡车驾驶室疲劳寿命的多目标稳健设计优化

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Structural optimization for vehicle fatigue durability signifies an exciting topic of research to improve its long-term safety and performance with minimum cost. Nevertheless, majority of the existing studies has been dealing with deterministic optimization and has not involved uncertainties, which could lead to an unstable or even useless design in practice. In order to simultaneously enhance the performance and robustness of the fatigue life for a truck cab, a multiobjective optimization is proposed in this study. After validating the simulation model, different dual surrogate modeling (DSM) methods are attempted to overcome the limitation of classical dual response surface (DRS) method; and subsequently the most accurate model, namely dual Kriging (DKRG) in this case, is selected through a comparative study. Then, the multiobjective particle swarm optimization (MOPSO) algorithm is adopted to perform the optimization. Compared with traditional single objective optimization strategies which yield only one specific optimum, MOPSO allows producing a set of non-dominated solutions over the entire Pareto space for a non-convex problem, which provides designers with more insightful information. Finally, a multi-criteria decision making (MCDM) model, which integrates the techniques of order preference by similarity to ideal solution (TOPSIS) with grey relation analysis (GRA), is implemented to find a best compromise optimum from the Pareto set. The selected optimum demonstrated not only to improve the fatigue life of the truck cab, but also to enable the design less sensitive to presence of uncertainties.
机译:车辆疲劳耐久性的结构优化标志着一个激动人心的研究课题,即以最低的成本提高其长期安全性和性能。但是,大多数现有研究都在进行确定性优化,并且没有涉及不确定性,这在实践中可能导致不稳定甚至无效的设计。为了同时提高卡车驾驶室疲劳寿命的性能和鲁棒性,本研究提出了一种多目标优化方法。在验证了仿真模型之后,尝试使用不同的双重替代模型(DSM)方法来克服经典双重响应曲面(DRS)方法的局限性;然后通过比较研究选择了最准确的模型,在这种情况下,即双重克里格(DKRG)。然后,采用多目标粒子群算法(MOPSO)进行优化。与仅产生一个特定最优值的传统单目标优化策略相比,MOPSO可以针对非凸问题在整个Pareto空间中生成一组非支配解,从而为设计人员提供了更深入的信息。最后,实现了一个多准则决策模型(MCDM),该模型将顺序偏好技术与具有理想解决方案的相似性(TOPSIS)与灰色关联分析(GRA)相集成,从而从帕累托集中找到最佳折衷方案。所选的最优方案不仅可以提高卡车驾驶室的疲劳寿命,而且还可以使设计对不确定性不敏感。

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