Numerical analysis of thermophoretic particle deposition on 3D Casson nanofluid: Artificial neural networks-based Levenberg–Marquardt algorithm

IF 1.8 4区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY
Amna Khan, Fahad Aljuaydi, Zeeshan Khan, Saeed Islam
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引用次数: 0

Abstract

The aim of this research is to provide a new computer-assisted approach for predicting thermophoresis particle decomposition on three-dimensional Casson nanofluid flow that passed over a stretched surface (thermophoresis particle decomposition on three-dimensional Casson nanofluid flow; TPD-CNF). In order to understand the flow behavior of nanofluid flow model, an optimized Levenberg–Marquardt learning algorithm with backpropagation neural network (LMLA-BPNN) has been designed. The mathematical model of TPD-CNF framed with appropriate assumptions and turned into ordinary differential equations via suitable similarity transformations are used. The bvp4c approach is used to collect the data for the LMLA-BPNN, which is used for parameters related with the TPD-CNF model controlling the velocity, temperature, and nanofluid concentration profiles. The proposed algorithm LMLA-BPNN is used to evaluate the obtained TDP-CNF model performance in various instances, and a correlation of the findings with a reference dataset is performed to check the validity and efficacy of the proposed algorithm for the analysis of nanofluids flow composed of sodium alginate nanoparticles dispersed in base fluid water. Statistical tools such as Mean square error, State transition dynamics, regression analysis, and error dynamic histogram investigations all successfully validate the suggested LMLA-BPNN for solving the TPD-CNF model. LMLA-BPNN networks have been used to numerically study the impact of different parameters of interest, such as Casson parameter, power-law index, thermophoretic parameter, and Schmidt number on flow profiles (axial and transverse), and energy and nanofluid concentration profiles. The range, i.e., 10−4–10−5 of absolute error of the reference and target data demonstrates the optimal accuracy performance of LMLA-BPNN networks.
三维卡松纳米流体上热泳粒子沉积的数值分析:基于人工神经网络的 Levenberg-Marquardt 算法
本研究旨在提供一种新的计算机辅助方法,用于预测通过拉伸表面的三维卡松纳米流体流上的热泳粒子分解(三维卡松纳米流体流上的热泳粒子分解;TPD-CNF)。为了理解纳米流体流动模型的流动行为,设计了一种优化的 Levenberg-Marquardt 学习算法和反向传播神经网络(LMLA-BPNN)。在适当的假设条件下建立 TPD-CNF 数学模型,并通过适当的相似性转换将其转化为常微分方程。使用 bvp4c 方法为 LMLA-BPNN 收集数据,用于与 TPD-CNF 模型相关的参数,控制速度、温度和纳米流体的浓度曲线。提议的 LMLA-BPNN 算法用于评估在各种情况下获得的 TDP-CNF 模型性能,并将评估结果与参考数据集进行关联,以检查提议的算法在分析由分散在基础流体水中的海藻酸钠纳米颗粒组成的纳米流体流动时的有效性和功效。均方误差、状态转换动力学、回归分析和误差动态直方图调查等统计工具都成功验证了所建议的 LMLA-BPNN 用于求解 TPD-CNF 模型。LMLA-BPNN 网络被用于数值研究不同相关参数(如卡森参数、幂律指数、热泳参数和施密特数)对流动剖面(轴向和横向)以及能量和纳米流体浓度剖面的影响。参考数据和目标数据的绝对误差范围为 10-4-10-5,这表明 LMLA-BPNN 网络具有最佳精度性能。
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来源期刊
Open Physics
Open Physics PHYSICS, MULTIDISCIPLINARY-
CiteScore
3.20
自引率
5.30%
发文量
82
审稿时长
18 weeks
期刊介绍: Open Physics is a peer-reviewed, open access, electronic journal devoted to the publication of fundamental research results in all fields of physics. The journal provides the readers with free, instant, and permanent access to all content worldwide; and the authors with extensive promotion of published articles, long-time preservation, language-correction services, no space constraints and immediate publication. Our standard policy requires each paper to be reviewed by at least two Referees and the peer-review process is single-blind.
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