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beta-NTF reduction and fast kriging simulation of optimal engine configurations

机译：最佳发动机配置的β-NTF减少和快速克里格模拟

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In an optimization process, models are applied to simulate different design behaviors in order to determine the most suitable one. However, this requires the use of a structured methodology to correctly explore the design space and truly converge to the best solution. It is therefore necessary to test and validate the optimal design. For engines, two ways are essentially used: building and testing a real cylinder, or simulating the new design with Computational-Fluid-Dynamics (CFD) models. These two techniques are both expensive and time consuming. An alternative way is proposed to test new designs with a fast simulation based on a kriging method. The exploration of the design space is based on 27 cylinder configurations and the results of their CFD models. It converged to an optimal design depending on the objective function. A kriging method was used to interpolate the behavior of the optimal design just found. In this paper we present the beta-NTF model reduction (to define the data set used by the kriging method) and the principle of the kriging technique. We then briefly discuss the results. The results underline the method's advantages despite the small gap between the expected results and those for kriging.
机译：在优化过程中，应用模型来模拟不同的设计行为，以便确定最合适的设计行为。但是，这需要使用结构化方法来正确探索设计空间并真正收敛到最佳解决方案。因此，有必要测试和验证最佳设计。对于发动机，主要使用两种方式：建造和测试真正的圆柱体，或使用计算流体动力学（CFD）模型模拟​​新设计。这两种技术既昂贵又耗时。建议采用基于Kriging方法测试新设计的替代方法。设计空间的探索基于27个气缸配置和其CFD模型的结果。它根据目标函数融合到最佳设计。使用Kriging方法来插入刚刚发现最佳设计的行为。在本文中，我们介绍了Beta-NTF模型减少（以定义Kriging方法使用的数据集）和Kriging技术的原理。然后我们简要讨论结果。尽管预期的结果与Kriging的差距差距小，但结果强调了该方法的优势。

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