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Surrogate model of complex non-linear data for preliminary nacelle design

机译：用于机舱初步设计的复杂非线性数据的替代模型

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Most response surface methods typically work on isotropically sampled data to predict a single variable and fitted with the aim of minimizing overall error. This study develops a metamodel for application in preliminary design of aircraft engine nacelles which is fitted to full-factorial data on two of the eight independent variables, and a Latin hypercube sampling on the other six. The specific set of accuracy requirements for the key nacelle aerodynamic performance metrics demand faithful reproduction of parts of the data to allow accurate prediction of gradients of the dependent variable, but permit less accuracy on other parts. The model is used to predict not just the independent variable but also its derivatives, and the Mach number, an independent variable, at which a certain condition is met. A simple Gaussian process model is shown to be unsuitable for this task. The new response surface method meets the requirements by normalizing the input data to exploit self-similarities in the data. It then decomposes the input data to interpolate orthogonal aerodynamic properties of nacelles independently of each other, and uses a set of filters and transformations to focus accuracy on predictions at relevant operating conditions. The new method meets all the requirements and presents a marked improvement over published preliminary nacelle design methods. (C) 2018 The Authors. Published by Elsevier Masson SAS.
机译：大多数响应面方法通常在各向同性采样的数据上工作，以预测单个变量，并且其目的是使总误差最小。这项研究开发了一种用于飞机发动机机舱初步设计的元模型，该模型适用于八个独立变量中的两个的全要素数据，以及其他六个变量的拉丁超立方体采样。关键的机舱空气动力学性能指标的一组特定的精度要求，要求对数据的各个部分进行忠实的再现，以允许对因变量的梯度进行准确的预测，但对其他部分的准确性较低。该模型不仅用于预测自变量，而且还用于预测其导数以及满足特定条件的自变量马赫数。一个简单的高斯过程模型显示不适合此任务。新的响应面方法通过规范化输入数据以利用数据中的自相似性来满足要求。然后，它分解输入数据以相互独立地内插机舱的正交空气动力学特性，并使用一组过滤器和变换将准确性集中在相关操作条件下的预测上。新方法可以满足所有要求，并且与已发布的初步机舱设计方法相比有显着改进。 （C）2018作者。由Elsevier Masson SAS发布。

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