ADAPTIVE GRID METHOD BASED ON ARTIFICIAL INTELLIGENCE FOR COMPUTATIONAL FLUID DYNAMICS OF NEXT GENERATION

基于人工智能的下一代计算流体动力学自适应网格方法

• 批准号: 14550152
• 负责人: MATSUNO Kenichi
• 金额: $ 1.41万
• 依托单位: KYOTOINSTITUTE OF TECHNOLOGY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2002
• 资助国家: 日本
• 起止时间: 2002 至 2003
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-14550152/
• 关键词: shock wave; self-induced oscillation; compressible flow; supersonic flow; jet; numerical analysis; CFD; spectral analysis; 解適合格子形成法

The purpose of this project is to progress noticeably a adaptive-grid method by incorporating the approach developed in the field of the Artificial intelligence (AT). The project consists of two phases. At the first phase, the adaptive grid method using elliptic equation is incorporated into a new finite-volume method in moving grid system. At the same time, a neural network model of spatiotemporal visual processing has been applied to detect discontinuities, such as shock wave and contact surfaces, in a numerical solution of a supersonic flow. The present shock wave detection method using neural network approach has been successfully incorporated into the adaptive grid method since the shock waves are automatically detected by neural network and using the information of flow field.The present approach to detect discontinuities in the flow field has been proved surely a way to know the flow physics in the flow field. the method has been tried to extend in order to detect general flow properties, such as vortex, boundary layer and so on, and, however, it is still under studies since the neural network technology is also developing.

该项目的目的是通过结合人工智能(AT)领域开发的方法，显著地改进自适应网格方法。该项目包括两个阶段。在第一阶段，将基于椭圆方程的自适应网格方法引入到一种新的移动网格系统的有限体积方法中。同时，时空视觉处理的神经网络模型已被应用于超音速流动的数值解中的不连续性检测，如激波和接触面。本文提出的基于神经网络的激波检测方法成功地将神经网络方法应用于自适应网格方法中，利用流场信息对激波进行自动检测，为了解流场中的流动物理提供了一种可行的方法。为了检测一般的流动特性，如涡流、边界层等，该方法已被尝试扩展，但由于神经网络技术也在发展中，该方法仍处于研究阶段。

项目成果

井ノ本健, 松野謙一: "ニューラルネットワークを用いた流れ場の不連続面同定"日本機械学会論文集 B編. 掲載決定(印刷中). (2004)

Ken Inomoto、Kenichi Matsuno：“使用神经网络识别流场中的不连续表面”，日本机械工程师学会汇刊，B 卷。接受出版（印刷中）（2004 年）。

Kenichi Matsuno: "An Adaptively-Moving-Grid Finite-Volume Scheme with Boundary-Grid Ellimination/Addition"Computational Fluid Dynamics 2002, Springer. 447-452 (2002)

Kenichi Matsuno：“具有边界网格消除/加法的自适应移动网格有限体积方案”计算流体动力学 2002，施普林格。

T.Inomoto, K.Matsuno: "The Use of Neural Network for Detection of Flow Discontinuities"Transaction of the Japan Society of Mechanical Engineers (B). 70. (2004)

T.Inomoto、K.Matsuno：“使用神经网络检测流动不连续性”日本机械工程师学会会刊（B）。

K.Matsuno: "An Adaptively-Moving Grid Finite-Volume Scheme with Boundary-Grid Elimination/Addition"Computational Fluid Dynamics 2002, Springer. 447-452 (2002)

K.Matsuno：“具有边界网格消除/添加的自适应移动网格有限体积方案”计算流体动力学 2002，施普林格。

小林敏雄 編 (分担執筆): "数値流体力学ハンドブック"丸善株式会社. 723 (2003)

小林俊夫主编（合着）：《计算流体力学手册》丸善株式会社723（2003年）