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Custom integer optimization method for wire bundle dimensioning

机译:用于线束尺寸标注的自定义整数优化方法

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Automotive wiring harnesses have gained weight and complexity through the last decades due to the increasing number of electrical components, which has raised the interest on its weight optimization. For this purpose, it is essential to know at least the maximum amount of steady current that either single wires or bundles can carry. However, the large amount of combinations of the customer-specific wire harnesses makes it impossible to exhaustively simulate all of the combinations that would allow for a reliable analysis and optimization of the network. The proposed approach consists of achieving accurate predictions of the wire thermal behaviour using fast on-line polynomial functions, which have been created as regression models using data from off-line worst-case finite element simulations. These regression models provide good accuracy for the critical dimensions of wire bundles in a much shorter time than simulations, so that they can be used on-line in optimization algorithms. Two different approaches of optimization are presented here in order to assign discrete values of available wire cross-section to the wire bundles: The first one uses integer linear programming, and the second one consists of a recently created custom algorithm whose objective is to reduce the computation time of the integer linear programming approach. This latter objective is satisfactorily accomplished. Results of both optimization approaches are validated by means of final finite element simulations, and they promisingly fulfill the objectives of this study.
机译:在过去的几十年中,由于电气部件数量的增加,汽车线束的重量和复杂性不断提高,这引起了人们对其重量优化的关注。为此,必须至少知道单根导线或多根导线可以承载的最大稳定电流量。但是,大量特定于客户的线束组合使得无法详尽地模拟所有组合,从而可以对网络进行可靠的分析和优化。所提出的方法包括使用快速在线多项式函数实现对导线热行为的准确预测,该函数已使用离线最坏情况有限元模拟的数据作为回归模型进行了创建。这些回归模型可以在比仿真短得多的时间内为线束的关键尺寸提供良好的精度,因此可以在优化算法中在线使用它们。此处介绍了两种不同的优化方法,以便为线束分配可用线横截面的离散值:第一种方法使用整数线性规划,第二种方法由最近创建的自定义算法组成,其目的是减少整数线性规划方法的计算时间。后一个目标可以令人满意地实现。两种优化方法的结果均通过最终的有限元模拟进行了验证,它们有望实现本研究的目标。

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