引入集成自组织图和径向基函数网络,准确预测水-Fe3O4 纳米流体粘度与固体体积分数和温度的关系

IF 16.3 1区 工程技术 Q1 ENERGY & FUELS
Lei Chang , Wenqing Yang , Mohamad Khaje Khabaz , S. Ali Eftekhari , Tamim Alkhalifah , Yasmin Khairy
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引用次数: 0

摘要

纳米流体是纳米颗粒在基础流体中的胶体悬浮液,有望提高导热性和能源效率。纳米流体的粘度受温度和纳米粒子浓度等因素的影响,预测纳米流体的粘度对于优化性能至关重要。这项研究引入了一种结合自组织图(SOM)和径向基函数(RBF)网络的创新方法,以准确预测粘度。自组织图指导 RBF 中心位置,以提高准确性,并通过水-Fe3O4 纳米流体数据进行了验证。温度对粘度的影响很大,温度从 10 ℃ 调整到 50 ℃ 时,粘度从 5 mPa.s 左右下降到 2 mPa.s 以下。这种新颖的网络组合提高了纳米流体应用的效率,推动了纳米流体工程的预测建模,促进了更广泛的工业和科学创新。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Introducing an integrated self-organizing map and radial basis function network for accurate prediction of water- Fe3O4 nanofluid viscosity versus solid volume fraction and temperature
Nanofluids, colloidal suspensions of nanoparticles in base fluids, promise improved thermal conductivity and energy efficiency. Predicting nanofluid viscosity, influenced by factors like temperature and nanoparticle concentration, is crucial for optimizing performance. This research introduces an innovative approach combining Self-Organizing Map (SOM) and Radial Basis Function (RBF) networks to accurately predict viscosity. The SOM guides RBF center placement for enhanced accuracy, validated with water-Fe3O4 nanofluid data. Temperature significantly affects viscosity, with adjustments from 10 to 50 °C showing viscosity decreases from around 5 to lower than 2 mPa.s. The integrated SOM-RBF model achieves high precision (max absolute error 0.1078 mPa.s, 4 % relative error), suggesting potential for further enhancement. This novel neural network combination enhances efficiency in nanofluid applications, advancing predictive modeling in nanofluid engineering for broader industrial and scientific innovation.
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来源期刊
Renewable and Sustainable Energy Reviews
Renewable and Sustainable Energy Reviews 工程技术-能源与燃料
CiteScore
31.20
自引率
5.70%
发文量
1055
审稿时长
62 days
期刊介绍: The mission of Renewable and Sustainable Energy Reviews is to disseminate the most compelling and pertinent critical insights in renewable and sustainable energy, fostering collaboration among the research community, private sector, and policy and decision makers. The journal aims to exchange challenges, solutions, innovative concepts, and technologies, contributing to sustainable development, the transition to a low-carbon future, and the attainment of emissions targets outlined by the United Nations Framework Convention on Climate Change. Renewable and Sustainable Energy Reviews publishes a diverse range of content, including review papers, original research, case studies, and analyses of new technologies, all featuring a substantial review component such as critique, comparison, or analysis. Introducing a distinctive paper type, Expert Insights, the journal presents commissioned mini-reviews authored by field leaders, addressing topics of significant interest. Case studies undergo consideration only if they showcase the work's applicability to other regions or contribute valuable insights to the broader field of renewable and sustainable energy. Notably, a bibliographic or literature review lacking critical analysis is deemed unsuitable for publication.
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