Measurement of intelligent computing via Levenberg Marquardt algorithm (LMA) for accurate prediction of fluid forces in a transient non-Newtonian thermal flow

IF 4.4 2区 物理与天体物理 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY Results in Physics Pub Date : 2024-11-02 DOI:10.1016/j.rinp.2024.108032
Atif Asghar , Rashid Mahmood , Afraz Hussain Majeed , Ahmed S. Hendy , Mohamed R. Ali
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Abstract

Predicting precise results for the quantities of interest in time-dependent Computational Fluid Dynamics (CFD) simulations requires a significant investment of computational resources and time. To get around these issues, CFD simulations have been joined with Artificial Neural Networks (ANN). An optimally configured artificial neural network (ANN) is given the training and validation data sets produced by computational fluid dynamics (CFD). The flow around a cylinder, which is a well-known benchmark problem for incompressible flows, has been taken into consideration by the hybrid-CFD system. The mathematical model is based on the non-stationary Navier-Stokes and energy equations with viscosity. The basic architecture of the ANN model consists of 10 hidden layers, three output levels, and five input layers. Fast second-order LMA, a top-tier approach, was used to train the network. Both the Mean Square Error (MSE) and the coefficient of determination (R) provide statistical evidence that the ANN projected values for the drag and lift coefficients and average Nusselt number obtained from the finite element analysis are accurate. This analysis suggests that ANNs have the potential to significantly cut down on the amount of time and energy needed to run time-dependent simulations.
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通过 Levenberg Marquardt 算法(LMA)测量智能计算,准确预测瞬态非牛顿热流中的流体力
在随时间变化的计算流体动力学(CFD)模拟中,预测相关量的精确结果需要投入大量的计算资源和时间。为了解决这些问题,人们将 CFD 模拟与人工神经网络(ANN)结合起来。计算流体动力学(CFD)产生的训练和验证数据集被赋予一个优化配置的人工神经网络(ANN)。混合 CFD 系统考虑了圆柱体周围的流动,这是一个众所周知的不可压缩流动的基准问题。数学模型基于非稳态纳维-斯托克斯方程和带粘性的能量方程。ANN 模型的基本结构包括 10 个隐藏层、3 个输出层和 5 个输入层。网络训练采用了快速二阶 LMA(一种顶级方法)。平均平方误差(MSE)和决定系数(R)都提供了统计证据,证明从有限元分析中获得的阻力系数、升力系数和平均努塞尔特数的 ANN 预测值是准确的。这一分析表明,ANN 有可能大大减少运行随时间变化的模拟所需的时间和能量。
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来源期刊
Results in Physics
Results in Physics MATERIALS SCIENCE, MULTIDISCIPLINARYPHYSIC-PHYSICS, MULTIDISCIPLINARY
CiteScore
8.70
自引率
9.40%
发文量
754
审稿时长
50 days
期刊介绍: Results in Physics is an open access journal offering authors the opportunity to publish in all fundamental and interdisciplinary areas of physics, materials science, and applied physics. Papers of a theoretical, computational, and experimental nature are all welcome. Results in Physics accepts papers that are scientifically sound, technically correct and provide valuable new knowledge to the physics community. Topics such as three-dimensional flow and magnetohydrodynamics are not within the scope of Results in Physics. Results in Physics welcomes three types of papers: 1. Full research papers 2. Microarticles: very short papers, no longer than two pages. They may consist of a single, but well-described piece of information, such as: - Data and/or a plot plus a description - Description of a new method or instrumentation - Negative results - Concept or design study 3. Letters to the Editor: Letters discussing a recent article published in Results in Physics are welcome. These are objective, constructive, or educational critiques of papers published in Results in Physics. Accepted letters will be sent to the author of the original paper for a response. Each letter and response is published together. Letters should be received within 8 weeks of the article''s publication. They should not exceed 750 words of text and 10 references.
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