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Artificial Neural Network Modeling for Predicting Thermal Conductivity of EG/Water-Based CNC Nanofluid for Engine Cooling Using Different Activation Functions

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Frontiers in Heat and Mass Transfer 2024年第2期22卷 537-556页

作者： Munirul Hasan Mustafizur Rahman Mohammad Saiful Islam Wong Hung Chan Yasser M.Alginahi muhammad nomani kabir Suraya Abu Bakar Devarajan Ramasamy Faculty of Computing Universiti Malaysia Pahang Al-Sultan AbdullahPekanPahang26600Malaysia Faculty of Mechanical and Automotive Engineering Technology Universiti Malaysia Pahang Al-Sultan AbdullahPekanPahang26600Malaysia Department of Management and Information Technology St.Francis College179 Livingston StBrooklynNY11201USA Dell Technologies Data Analyticsand AutomationBukit TengahBukit Mertajam14000Malaysia Department of Computer Science Adrian CollegeAdrianMIUSA Department of Computer Science&Engineering United International University BangladeshUnited CityMadani AvenueBaddaDhaka1212Bangladesh

A vehicle engine cooling system is of utmost importance to ensure that the engine operates in a safe temperature *** most radiators that are used to cool an engine,water serves as a cooling *** performance of a radiator in terms of heat transmission is significantly influenced by the incorporation of nanoparticles into the cooling *** and uniformity of nanoparticle distribution are the two major factors for the practical use of *** shape and size of nanoparticles also have a great impact on the performance of heat *** researchers are investigating the impact of nanoparticles on heat *** study aims to develop an artificial neural network(ANN)model for predicting the thermal conductivity of an ethylene glycol(EG)/waterbased crystalline nanocellulose(CNC)nanofluid for cooling internal combustion *** implementation of an artificial neural network considering different activation functions in the hidden layer is made to find the bestmodel for the cooling of an engine using the *** of the model with different activation functions in artificial neural networks are analyzed for different nanofluid concentrations and *** artificial neural networks,Levenberg–Marquardt is an optimization approach used with activation functions,including Tansig and Logsig functions in the training *** findings of each training,testing,and validation phase are presented to demonstrate the network that provides the highest level of *** best result was obtained with Tansig,which has a correlation of 0.99903 and an error of 3.7959×10^(–8).It has also been noticed that the Logsig function can also be a good model due to its correlation of 0.99890 and an error of 4.9218×10^(–8).Thus ourANNwith Tansig and Logsig functions demonstrates a high correlation between the actual output and the predicted output.

关键词： Artificial neural network activation function thermal conductivity nanocellulose

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