Comprehensive study and scientific process to increase the accuracy in estimating the thermal conductivity of nanofluids containing SWCNTs and CuO nanoparticles using an artificial neural network

IF 4.7 Q2 NANOSCIENCE & NANOTECHNOLOGY

Micro and Nano Systems Letters Pub Date : 2024-01-10 DOI:10.1186/s40486-023-00195-6

Mohammad Hemmat Esfe, Fatemeh Amoozad, Hossein Hatami, Davood Toghraie

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引用次数: 0

Abstract

This investigation aimed to evaluate the thermal conductivity ratio (TCR) of SWCNT-CuO/Water nanofluid (NF) using experimental data in the T range of 28–50 ℃ and solid volume fraction range of SVF = 0.03 to 1.15% by an artificial neural network (ANN). MLP network with Lundberg-Marquardt algorithm (LMA) was utilized to predict data (TCR) by ANN. In the best case, from the set of various structures of ANN for this nanofluid, the optimal structure was chosen, which consists of 2 hidden layers, the first layer with the optimal structure consisting of 5 neurons and the second layer containing 7 neurons. Eventually, for the optimal structure, the R² coefficient and MSE are 0.9999029 and 6.33377E-06, respectively. Based on all ANN information, MOD is in a limited area of − 3% < MOD < + 3%. Comparison of test, correlation yield, and ANN yield display that ANN evaluates laboratory information more exactly.

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利用人工神经网络提高含 SWCNT 和 CuO 纳米颗粒的纳米流体导热系数估算精度的综合研究和科学过程

本研究旨在利用人工神经网络（ANN），在 28-50 ℃ 的温度范围和 SVF = 0.03 至 1.15% 的固体体积分数范围内，使用实验数据评估 SWCNT-CuO/Water 纳米流体（NF）的导热率（TCR）。采用 Lundberg-Marquardt 算法（LMA）的 MLP 网络通过人工神经网络预测数据（TCR）。在最佳情况下，从适用于该纳米流体的各种人工神经网络结构集合中，选择了最佳结构，它由 2 个隐藏层组成，第一层的最佳结构由 5 个神经元组成，第二层包含 7 个神经元。最终，最优结构的 R2 系数和 MSE 分别为 0.9999029 和 6.33377E-06。根据所有 ANN 信息，MOD 处于 - 3% < MOD < + 3% 的有限区域。测试、相关收益率和 ANN 收益率的比较表明，ANN 对实验室信息的评估更为精确。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Micro and Nano Systems Letters Engineering-Biomedical Engineering

CiteScore

10.60

自引率

5.60%

发文量

审稿时长

13 weeks