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Stress-strain evaluation of structural parts using artificial neural networks

机译:人工神经网络结构零件应力 - 应变评价

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The last decades have been driven by significant progress in the computational capacity, which have been supporting the development of increasingly realistic and detailed simulations. However, despite these improvements, several problems still do not have an effective solution, due to their numerical complexity. As a result, the answer to these problems can be improved by using techniques that enable the description of phenomena with less resolution, but with lower computational costs, which is the case of the reduced order models. The main objective of this article is the presentation of a new approach for reduced order model development and application in the design and optimization of structural parts. The selected method is the artificial neural networks. Artificial neural networks allow the prediction of certain variables based on a given dataset. Two typical case studies are addressed: the first is a fixed plate subjected to uniformly distributed pressure and the second is a reinforced panel also subjected to internal pressure, with regular reinforcements to improve the specific strength. With this method, a substantial reduction in the simulation time is observed, being, approximately, 40 times faster than the solution obtained with Ansys. The developed neural network has a relative average difference of about 20 %, which is considered satisfactory given the complexity of the problem and considering it is a first application of these networks in this domain. In conclusion, this research made it possible to highlight the potential of reduced order model: including the shorter response time, the less computational resources, and the simplification of problems in detriment of less resolution in the description of structural behaviour. Given these advantages, it is expected that these models will play a key role in future applications, as in digital twins.
机译:过去几十年来推动了计算能力的重大进展,这一直是支持发展越来越真实和详细的模拟。然而,尽管有这些改进,但由于其数值复杂性,仍有几个问题仍然没有有效的解决方案。结果,通过使用能够描述具有较少分辨率的技术的技术可以提高这些问题的答案,但是具有较低的计算成本,这是降低订单模型的情况。本文的主要目标是介绍一项新方法,用于减少秩序模型开发和在结构部件的设计和优化中的应用。所选方法是人工神经网络。人工神经网络允许基于给定的数据集预测某些变量。解决了两个典型的案例研究:首先是经过均匀分布的压力的固定板,第二个是增强面板,也经受内部压力,具有规则的增强,以提高特定强度。利用这种方法,观察到模拟时间的显着减小,比用ANSYS获得的溶液快大约40倍。发达的神经网络具有约20%的相对平均差异,这被认为是鉴于问题的复杂性并考虑到该域中的这些网络的第一次应用。总之,这项研究使得能够突出阶数模型的潜力:包括较短的响应时间,计算资源较少,以及在结构行为描述中损害较少分辨率的问题。鉴于这些优势,预计这些模型将在未来的应用中发挥关键作用,如数字双胞胎。

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