Hafiz Muhammad Shahbaz, Iftikhar Ahmad, Muhammad Asif Zahoor Raja, Hira Ilyas, Kottakkaran Sooppy Nisar, Muhammad Shoaib
{"title":"3D thermally laminated MHD non-Newtonian nanofluids across a stretched sheet: intelligent computing paradigm","authors":"Hafiz Muhammad Shahbaz, Iftikhar Ahmad, Muhammad Asif Zahoor Raja, Hira Ilyas, Kottakkaran Sooppy Nisar, Muhammad Shoaib","doi":"10.1007/s10973-024-13747-8","DOIUrl":null,"url":null,"abstract":"<div><p>The primary subject of this article is the study of the viscous flow of nanofluids consisting of copper-methanol and water in the presence of a three-dimensional stretched sheet, which is subjected to magnetohydrodynamic effects (3D-MHD-NF) by employing artificial recurrent neural networks that are optimized using a Bayesian regularization technique (ARNN-BR). The viscosity effect is recognized to be dependent on temperature, with methanol and water being used as the base fluid. The presented model is employed in the manipulation and creation of surfaces within the field of nanotechnology. Its applications include stretching, shrinking, wrapping, and painting devices. The Adams method was employed to generate a dataset for the 3D-MHD-NF model for four scenarios by varying the Hartmann number (<i>H</i>), volume fraction of nanoparticle (<span>\\(\\varphi\\)</span>), and viscosity parameter (<i>α</i>). The ARNN-BR technique employed a random selection of data 70% for training, 20% for testing, and 10% for validity. It has been found that boundary layer becomes thinner as the volume percentage of nanoparticle increases. Additionally, it is observed that augmentation in the viscosity parameter results in a proportional rise in temperature. Moreover, it is observed that increment in the variables <i>H</i>, <i>φ</i>, and <i>α</i> have an impact on the velocity boundary thickness in both the <i>x-</i> and <i>y</i>-directions. The newly introduced ARNN-BR technique's dependability, stability, and convergence were assessed using a fitness measure based on mean squares errors, histogram drawings, regression, input-error cross-correlation, and autocorrelation analysis for each scenario of the 3D-MHD-NF model.</p></div>","PeriodicalId":678,"journal":{"name":"Journal of Thermal Analysis and Calorimetry","volume":"150 1","pages":"479 - 504"},"PeriodicalIF":3.0000,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Thermal Analysis and Calorimetry","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s10973-024-13747-8","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0
Abstract
The primary subject of this article is the study of the viscous flow of nanofluids consisting of copper-methanol and water in the presence of a three-dimensional stretched sheet, which is subjected to magnetohydrodynamic effects (3D-MHD-NF) by employing artificial recurrent neural networks that are optimized using a Bayesian regularization technique (ARNN-BR). The viscosity effect is recognized to be dependent on temperature, with methanol and water being used as the base fluid. The presented model is employed in the manipulation and creation of surfaces within the field of nanotechnology. Its applications include stretching, shrinking, wrapping, and painting devices. The Adams method was employed to generate a dataset for the 3D-MHD-NF model for four scenarios by varying the Hartmann number (H), volume fraction of nanoparticle (\(\varphi\)), and viscosity parameter (α). The ARNN-BR technique employed a random selection of data 70% for training, 20% for testing, and 10% for validity. It has been found that boundary layer becomes thinner as the volume percentage of nanoparticle increases. Additionally, it is observed that augmentation in the viscosity parameter results in a proportional rise in temperature. Moreover, it is observed that increment in the variables H, φ, and α have an impact on the velocity boundary thickness in both the x- and y-directions. The newly introduced ARNN-BR technique's dependability, stability, and convergence were assessed using a fitness measure based on mean squares errors, histogram drawings, regression, input-error cross-correlation, and autocorrelation analysis for each scenario of the 3D-MHD-NF model.
本文的主要主题是研究由铜-甲醇和水组成的纳米流体在三维拉伸片存在下的粘性流动,该纳米流体受到磁流体动力学效应(3d - mmd - nf)的影响,采用使用贝叶斯正则化技术(ARNN-BR)优化的人工递归神经网络。粘度效应被认为是依赖于温度,甲醇和水被用作基础流体。所提出的模型用于纳米技术领域内表面的操纵和创造。它的应用包括拉伸、收缩、包裹和喷涂设备。通过改变哈特曼数(H)、纳米颗粒体积分数(\(\varphi\))和粘度参数(α),采用Adams方法生成4种情况下3D-MHD-NF模型的数据集。ARNN-BR技术采用随机选择的数据70% for training, 20% for testing, and 10% for validity. It has been found that boundary layer becomes thinner as the volume percentage of nanoparticle increases. Additionally, it is observed that augmentation in the viscosity parameter results in a proportional rise in temperature. Moreover, it is observed that increment in the variables H, φ, and α have an impact on the velocity boundary thickness in both the x- and y-directions. The newly introduced ARNN-BR technique's dependability, stability, and convergence were assessed using a fitness measure based on mean squares errors, histogram drawings, regression, input-error cross-correlation, and autocorrelation analysis for each scenario of the 3D-MHD-NF model.
期刊介绍:
Journal of Thermal Analysis and Calorimetry is a fully peer reviewed journal publishing high quality papers covering all aspects of thermal analysis, calorimetry, and experimental thermodynamics. The journal publishes regular and special issues in twelve issues every year. The following types of papers are published: Original Research Papers, Short Communications, Reviews, Modern Instruments, Events and Book reviews.
The subjects covered are: thermogravimetry, derivative thermogravimetry, differential thermal analysis, thermodilatometry, differential scanning calorimetry of all types, non-scanning calorimetry of all types, thermometry, evolved gas analysis, thermomechanical analysis, emanation thermal analysis, thermal conductivity, multiple techniques, and miscellaneous thermal methods (including the combination of the thermal method with various instrumental techniques), theory and instrumentation for thermal analysis and calorimetry.