ANOVA-based Taguchi approach to optimise the rate of heat transport for the magnetised nanofluid across an exponential surface

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems Pub Date : 2025-07-24 DOI:10.1016/j.chemolab.2025.105494

J.K. Madhukesh , G.K. Ramesh

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Abstract

A reliable design optimization tool, the Taguchi method has been used to optimise system characteristics and boost performance in various kinds of engineering applications. In this study, we use the Taguchi technique to examine how to optimise nanofluid flow over an exponential surface. Additional force like magnetic field, radiation, pollutant dispersion and Smoluchowski temperature at the boundary are incorporated. Using the appropriate transformations, the modeled partial differential equations (PDEs) can be converted into dimensionless ordinary differential equations. Ordinary differential equations that are coupled have been numerically solved in their dimensionless form using the Adams-Bashforth-Moulton method. It has been thoroughly studied how the physical parameters affect temperature, velocity, and concentration. Visual displays are provided for the numerical findings for each of the pertinent physical parameters. Additionally, to optimise the system's heat transfer versus specific parameters, the Taguchi optimization technique is used in conjunction with Analysis of Varience (ANOVA) and multivariate regression analysis. It is noted that with an Signal-to-Noise Ratio (SNR) value of 11.3667, experimental number 13 has the highest Nusselt number (Nu) and the best conditions for heat transmission. Experimental number 16 has the lowest Nu, with an SNR of 2.6519.

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基于方差分析的田口方法优化磁化纳米流体在指数表面上的热传递率

田口方法是一种可靠的设计优化工具，在各种工程应用中已被用于优化系统特性和提高性能。在这项研究中，我们使用田口技术来研究如何优化纳米流体在指数表面上的流动。考虑了磁场、辐射、污染物弥散和边界处斯摩鲁霍夫斯基温度等附加力。通过适当的变换，可以将建模的偏微分方程转化为无因次常微分方程。用Adams-Bashforth-Moulton方法对耦合的常微分方程进行了无量纲形式的数值求解。对物理参数对温度、速度和浓度的影响进行了深入的研究。为每个相关物理参数的数值结果提供了可视化显示。此外，为了根据特定参数优化系统的传热，田口优化技术与方差分析（ANOVA）和多元回归分析相结合。实验数13的信噪比（SNR）为11.3667时，努塞尔数（Nu）最高，传热条件最佳。实验数16的Nu最小，信噪比为2.6519。

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来源期刊

Chemometrics and Intelligent Laboratory Systems 工程技术-分析化学

CiteScore

7.50

自引率

7.70%

发文量

169

审稿时长

3.4 months

期刊介绍： Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines. Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data. The journal deals with the following topics: 1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.) 2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered. 3) Development of new software that provides novel tools or truly advances the use of chemometrical methods. 4) Well characterized data sets to test performance for the new methods and software. The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.