Special Issue "Applications of Artificial Intelligence Model of Heat and Mass Transfer"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 30 October 2021.

Special Issue Editors

Prof. Dr. Mohsen Sharifpur
E-Mail Website
Guest Editor
Department of Mechanical and Aeronautical Engineering, University of Pretoria, Pretoria 0002, South Africa.
Interests: convective nanofluids; heat transfer; CFD; multiphase flow
Dr. Mohammad Hossein Ahmadi
E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood, Iran
Interests: Artificial intelligence methods; Optimization; Heat transfer; Cogeneration Systems; Thermodynamics

Special Issue Information

Dear Colleagues,

An array of data are experimentally measured and presented in various heat and mass transfer-related area to demonstrate the road plan for scholars to develop novel heat transfer engineering applications. These huge data can be made more valuable by means of machine learning models, artificial intelligence techniques, and Big data focusing on different angels of heat transfer engineering such as nanofluid and natural convection, thermal systems, thermophysical properties, convective heat transfer in single-phase and multiphase flow, thermal energy storage, porous media, nanoscale heat transfer, Solar Energy, Fuel Cells and phase change materials. Heat transfer improvement is a requisite for developing cutting-edge engineering applications. Meanwhile, reducing energy consumption and improving energy savings activities can be achieved when heat transfer equipment working efficiently. In addition, improving heat transfer equipment can be led to reducing hazardous emissions such as CO2 and making them under control. Both industry and research zero in on heat transfer enhancement. However, there are a vast amount of experimental data which are not evaluated and investigated with newly presented evaluation techniques. Hence, to include both experimental and analysis aspects of heat transfer engineering applications in energy systems, we promote a new special collection and cordially invite all submissions which are related to heat and mass transfer investigations.

Prof. Dr. Mohsen Sharifpur
Dr. Mohammad Hossein Ahmadi
Guest Editors

Manuscript Submission Information

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Keywords

  • computational heat and mass transfer
  • convective porous media
  • phase change materials
  • mixed convection
  • turbulent transport
  • nanoscale heat transfer
  • nanofluids
  • thermal energy storage
  • solar energy
  • fuel cells
  • machine learning
  • artificial intelligence methods
  • deep learning
  • prediction
  • data preparation
  • multiphase flow

Published Papers (4 papers)

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Research

Article
Predicting Parameters of Heat Transfer in a Shell and Tube Heat Exchanger Using Aluminum Oxide Nanofluid with Artificial Neural Network (ANN) and Self-Organizing Map (SOM)
Sustainability 2021, 13(16), 8824; https://doi.org/10.3390/su13168824 - 06 Aug 2021
Viewed by 323
Abstract
This study is a model of artificial perceptron neural network including three inputs to predict the Nusselt number and energy consumption in the processing of tomato paste in a shell-and-tube heat exchanger with aluminum oxide nanofluid. The Reynolds number in the range of [...] Read more.
This study is a model of artificial perceptron neural network including three inputs to predict the Nusselt number and energy consumption in the processing of tomato paste in a shell-and-tube heat exchanger with aluminum oxide nanofluid. The Reynolds number in the range of 150–350, temperature in the range of 70–90 K, and nanoparticle concentration in the range of 2–4% were selected as network input variables, while the corresponding Nusselt number and energy consumption were considered as the network target. The network has 3 inputs, 1 hidden layer with 22 neurons and an output layer. The SOM neural network was also used to determine the number of winner neurons. The advanced optimal artificial neural network model shows a reasonable agreement in predicting experimental data with mean square errors of 0.0023357 and 0.00011465 and correlation coefficients of 0.9994 and 0.9993 for the Nusselt number and energy consumption data set. The obtained values of eMAX for the Nusselt number and energy consumption are 0.1114, and 0.02, respectively. Desirable results obtained for the two factors of correlation coefficient and mean square error indicate the successful prediction by artificial neural network with a topology of 3-22-2. Full article
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Article
Turbulent Flow Heat Transfer through a Circular Tube with Novel Hybrid Grooved Tape Inserts: Thermohydraulic Analysis and Prediction by Applying Machine Learning Model
Sustainability 2021, 13(6), 3068; https://doi.org/10.3390/su13063068 - 11 Mar 2021
Cited by 3 | Viewed by 464
Abstract
The present experimental work is performed to investigate the convection heat transfer (HT), pressure drop (PD), irreversibility, exergy efficiency and thermal performance for turbulent flow inside a uniformly heated circular channel fitted with novel geometry of hybrid tape. Air is taken as the [...] Read more.
The present experimental work is performed to investigate the convection heat transfer (HT), pressure drop (PD), irreversibility, exergy efficiency and thermal performance for turbulent flow inside a uniformly heated circular channel fitted with novel geometry of hybrid tape. Air is taken as the working fluid and the Reynolds number is varied from 10,000 to 80,000. Hybrid tape is made up of a combination of grooved spring tape and wavy tape. The results obtained with the novel hybrid tape show significantly better performance over individual tapes. A correlation has been developed for predicting the friction factor (f) and Nusselt number (Nu) with novel hybrid tape. The results of this investigation can be used in designing heat exchangers. This paper also presented a statistical analysis of the heat transfer and fluid flow by developing an artificial neural network (ANN)-based machine learning (ML) model. The model is trained based on the features of experimental data, which provide an estimation of experimental output based on user-defined input parameters. The model is evaluated to have an accuracy of 98.00% on unknown test data. These models will help the researchers working in heat transfer enhancement-based experiments to understand and predict the output. As a result, the time and cost of the experiments will reduce. Full article
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Article
Heat and Fluid Flow Analysis and ANN-Based Prediction of A Novel Spring Corrugated Tape
Sustainability 2021, 13(6), 3023; https://doi.org/10.3390/su13063023 - 10 Mar 2021
Cited by 3 | Viewed by 437
Abstract
A circular tube fitted with novel corrugated spring tape inserts has been investigated. Air was used as the working fluid. A thorough literature review has been done and this geometry has not been studied previously, neither experimentally nor theoretically. A novel experimental investigation [...] Read more.
A circular tube fitted with novel corrugated spring tape inserts has been investigated. Air was used as the working fluid. A thorough literature review has been done and this geometry has not been studied previously, neither experimentally nor theoretically. A novel experimental investigation of this enhanced geometry can, therefore, be treated as a new substantial contribution in the open literature. Three different spring ratio and depth ratio has been used in this study. Increase in thermal energy transport coefficient is noticed with increase in depth ratio. Corrugated spring tape shows promising results towards heat transfer enhancement. This geometry performs significantly better (60% to 75% increase in heat duty at constant pumping power and 20% to 31% reduction in pumping power at constant heat duty) than simple spring tape. This paper also presented a statistical analysis of the heat transfer and fluid flow by developing an artificial neural network (ANN)-based machine learning (ML) model. The model is evaluated to have an accuracy of 98.00% on unknown test data. These models will help the researchers working in heat transfer enhancement-based experiments to understand and predict the output. As a result, the time and cost of the experiments will reduce. The results of this investigation can be used in designing heat exchangers. Full article
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Article
Comparative Assessment to Predict and Forecast Water-Cooled Chiller Power Consumption Using Machine Learning and Deep Learning Algorithms
Sustainability 2021, 13(2), 744; https://doi.org/10.3390/su13020744 - 14 Jan 2021
Viewed by 661
Abstract
Over the last few decades, total energy consumption has increased while energy resources remain limited. Energy demand management is crucial for this reason. To solve this problem, predicting and forecasting water-cooled chiller power consumption using machine learning and deep learning are presented. The [...] Read more.
Over the last few decades, total energy consumption has increased while energy resources remain limited. Energy demand management is crucial for this reason. To solve this problem, predicting and forecasting water-cooled chiller power consumption using machine learning and deep learning are presented. The prediction models adopted are thermodynamic model and multi-layer perceptron (MLP), while the time-series forecasting models adopted are MLP, one-dimensional convolutional neural network (1D-CNN), and long short-term memory (LSTM). Each group of models is compared. The best model in each group is then selected for implementation. The data were collected every minute from an academic building at one of the universities in Taiwan. The experimental result demonstrates that the best prediction model is the MLP with 0.971 of determination (R2), 0.743 kW of mean absolute error (MAE), and 1.157 kW of root mean square error (RMSE). The time-series forecasting model trained every day for three consecutive days using new data to forecast the next minute of power consumption. The best time-series forecasting model is LSTM with 0.994 of R2, 0.233 kW of MAE, and 1.415 kW of RMSE. The models selected for both MLP and LSTM indicated very close predictive and forecasting values to the actual value. Full article
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