曲线特征线空间值估计新方法

IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
T. Ohkubo, E. Matsunaga
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引用次数: 0

摘要

数值计算用于各种情况。然而,要实现精确的数值计算,需要计算方法的精度和高空间分辨率的初值。因此,我们提出了一种考虑特征理论但不使用插值的空间值估计新方法。我们考虑了曲线特征线的处理,这意味着特征速度是局部改变的。在平均逆特征法(AICM)中,将局部变化的特征速度与前几步的特征速度平均。我们计算了用欧拉方程描述的激波管问题的空间值,并通过对比以往研究中提出的逆特征法(ICM)和传统插值方法的结果,检验了AICM的精度。与其他方法相比,AICM将所有参数的误差减小到1/10以下。我们从这些结果中确定,AICM准确地估计了特征速度发生显著变化的问题的空间分布。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New Spatial Value Estimation Method for Curved Characteristic Line
Numerical calculations are used in various situations. However, to achieve accurate numerical calculations, accuracy in the calculation method and initial values with high spatial resolution are necessary. Therefore, we propose a new method for estimating spatial values that considers characteristic theory but does not use interpolation. We consider the treatment of the curved characteristic line, which implies that the characteristic speed is altered locally. In the new method named averaging inverse characteristics method (AICM), the locally changing characteristic speed is averaged with the characteristic speed of the previous steps. We calculated the spatial values of the shock tube problem, described by the Euler equation, and examined the accuracy of the AICM by comparing the results of the inverse characteristics method (ICM) proposed in the previous study and the traditional interpolating methods. Compared to other methods, AICM reduced the error to less than 1/10 for all parameters. We determined from these results that the AICM accurately estimates the spatial distribution of problems where characteristic speed has significantly changed.
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来源期刊
CiteScore
1.50
自引率
14.30%
发文量
89
期刊介绍: JACIII focuses on advanced computational intelligence and intelligent informatics. The topics include, but are not limited to; Fuzzy logic, Fuzzy control, Neural Networks, GA and Evolutionary Computation, Hybrid Systems, Adaptation and Learning Systems, Distributed Intelligent Systems, Network systems, Multi-media, Human interface, Biologically inspired evolutionary systems, Artificial life, Chaos, Complex systems, Fractals, Robotics, Medical applications, Pattern recognition, Virtual reality, Wavelet analysis, Scientific applications, Industrial applications, and Artistic applications.
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