Article
Thermodynamics
Yiwu Kuang, Fei Han, Lijie Sun, Rui Zhuan, Wen Wang
Summary: This study constructed a database of liquid hydrogen flow boiling heat transfer, utilized an Artificial Neural Network to identify key parameters, and proposed a new correlation for predicting the Nusselt number of liquid hydrogen flow boiling, achieving accurate predictions.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2021)
Article
Thermodynamics
Nicolo Mattiuzzo, Marco Azzolin, Arianna Berto, Stefano Bortolin, Davide Del Col
Summary: This study investigates the condensation process of two mixtures, R513A and R516A, in channels with different diameters. It is found that condensation at low mass flux is rare but important for chillers and heat pumps with variable speed compressors. The high-speed camera is used to observe the two-phase flow in the 3.38mm channel and compare it with flow pattern maps. Additionally, the heat transfer coefficient data are compared with three semi-empirical models and a specifically developed Artificial Neural Network model.
INTERNATIONAL JOURNAL OF THERMAL SCIENCES
(2023)
Article
Thermodynamics
Adel Bouali, Salah Hanini, Brahim Mohammedi, Mouloud Boumahdi
Summary: The study used boiling water data in an inclined channel to develop an artificial neural network model, achieving high accuracy in predicting heat transfer coefficient. The comparison with experimental data and empirical correlations showed good agreement.
Article
Thermodynamics
Chuanshuai Dong, Takashi Hibiki
Summary: The aim of this study is to develop a robust and theoretically supported heat transfer coefficient correlation for downward two-component two-phase flow in inclined pipes. A new correlation was proposed based on an extensive literature review and analysis of experimental data. This new correlation is of practical importance in understanding the two-component two-phase heat transfer characteristics.
APPLIED THERMAL ENGINEERING
(2023)
Article
Thermodynamics
Yongnam Lee, Myeonggi Cha, Hyungrae Kim, HangJin Jo
Summary: This study experimentally investigated the film condensation heat transfer of low-mass-flux high-pressure steam in inclined tubes, and analyzed the effects of different parameters on the heat transfer coefficient. The study also discussed the local heat transfer variations due to different flow patterns.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2022)
Article
Thermodynamics
Fatemeh Zakeri, Mohammad Reza Sarmasti Emami
Summary: In this study, a water-based graphene oxide nanofluid was used in a counter-current flow double pipe heat exchanger to evaluate its thermohydraulic performance. The results showed that an increase in flow rate and nanoparticle concentration improved the heat transfer coefficient, while the temperature of the hot fluid had a minimal effect on the coefficient. The friction factor and pressure drop of the nanofluid were higher than those of the basefluid, and increased with concentration. The RBFNN model exhibited higher accuracy in predicting the coefficient compared to the MLPNN model. The nanofluid outperformed the basefluid in terms of HTC, with an improvement of up to 85% due to the high thermal conductivity of graphene nanoparticles.
INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER
(2023)
Article
Thermodynamics
Carlos A. Dorao, Maria Fernandino
Summary: The heat transfer mechanisms during flow condensation of binary mixtures inside pipes have been extensively studied, and it has been found that the heat transfer coefficient follows a similar scaling law for single and binary component fluids. This discovery reduces the complexity of the model and suggests that the dominant heat transfer resistance is located in the conductive sublayer, unaffected by the flow pattern, liquid film thickness, or mass transfer resistance.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2022)
Article
Thermodynamics
Mohamed T. Abdelghany, Samir M. Elshamy, M. A. Sharafeldin, O. E. Abdellatif
Summary: This article presents a novel model of an artificial neural network that predicts friction factor and Nusselt number for pulsating flow in conically coiled tubes. Experimental results demonstrate that the best heat transfer performance is achieved at a pulsating frequency of 4Hz. Comparison with experimental data shows that a feed-forward neural network can accurately predict the Nusselt number and friction factor.
APPLIED THERMAL ENGINEERING
(2023)
Article
Nuclear Science & Technology
Binbin Qiu, Qingchuan Yang, Xiaobing Yu, Tingshan Ma, Jiping Liu
Summary: This work investigates direct contact condensation of steam-water in vertical upward and downward directions, with a focus on bubble condensation pressure oscillation frequency and heat transfer coefficient. The study finds that there is a positive correlation between the steam mass flux, water subcooling degree, bubble heat transfer coefficient, and pressure oscillation frequency in both directions. An artificial neural network model is proposed to accurately predict the bubble heat transfer coefficient, providing a convenient method for obtaining the coefficient of heat transfer.
PROGRESS IN NUCLEAR ENERGY
(2023)
Article
Thermodynamics
Adnan Berber, Mehmet Guerdal
Summary: In this study, the effect of curved fin geometry and winglet angle of attack on heat transfer is estimated using a machine learning method. Experimental studies are conducted to investigate airflow in a rectangular channel under specific conditions, and an artificial neural network is used to predict heat transfer. The results show a high accuracy in predicting heat transfer compared to experimental data.
THERMAL SCIENCE AND ENGINEERING PROGRESS
(2023)
Article
Thermodynamics
Nurlaily Agustiarini, Hieu Ngoc Hoang, Jong-Taek Oh, Jong Kyu Kim
Summary: Existing prediction models of flow boiling heat transfer coefficient provide an applicable method to obtain the closest to the true value. Heat transfer coefficient data are collected through an experimental study of R1234yf. A machine-learning prediction model is proposed to improve the prediction accuracy by feeding the program with a factor from heat transfer coefficient data. Alternative prediction method and heat transfer coefficient correlation are proposed.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2023)
Article
Thermodynamics
Feng Nie, Haocheng Wang, Yanxing Zhao, Qinglu Song, Shiqi Yan, Maoqiong Gong
Summary: This study presents machine learning methods for predicting flow condensing heat transfer coefficients inside horizontal tubes based on an assembled database, and demonstrates excellent predictive performances of the designed ML models. A new universal correlation is developed, showing reliable predictions for all data points in the database.
INTERNATIONAL JOURNAL OF THERMAL SCIENCES
(2023)
Article
Engineering, Multidisciplinary
Xinlin He, Maawiya Ould Sidi, N. Ameer Ahammad, Mohamed Abdelghany Elkotb, Samia Elattar, A. M. Algelany
Summary: This research evaluates various methods to enhance the performance of microchannels using Artificial Neural Network (ANN) and lattice Boltzmann method (LBM). The study combines LBM with ANN to investigate the impact of magneto hydro dynamic (MHD) on slip velocity of nanofluid flow inside microchannels. The efficiency of the cooling system is increased by using multiple fins and applying a magnetic field to reduce thermal boundary layer and enhance heat transfer.
ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS
(2022)
Article
Mechanics
Feroz Ahmed Soomro, Mahmoud A. Alamir, Shreen El-Sapa, Rizwan Ul Haq, Muhammad Afzal Soomro
Summary: In this work, the behavior of magnetohydrodynamic fluid flow over a permeable surface was studied using similarity transformation technique and artificial neural network models. The study revealed a decrease in heat transfer rate with increasing first- and second-order slip parameters. The neural network models showed high accuracy in predicting skin friction coefficients and heat transfer rates, reducing the time required for numerical predictions.
ARCHIVE OF APPLIED MECHANICS
(2022)
Article
Thermodynamics
Ghaem Taghipour Kani, Amirreza Ghahremani
Summary: The purpose of this study is to predict the thermal performance of heat pipes using artificial intelligence methods. Various prediction models with different inputs have been employed, and a dataset of 1196 experimental data has been collected for training. Machine learning regression methods and artificial neural network (ANN) models have been used, with the random forest regressor method achieving the best performance. ANN models have shown acceptable accuracy for a wide range of working fluids. A new correlation has also been proposed, but the results of using methods based on artificial intelligence were found to be more accurate. The optimal ANN model significantly improved the prediction accuracy from 0.60 to 0.95. The choice of the best method should consider the trade-off between accuracy and simplicity.
INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER
(2023)
Article
Thermodynamics
Sadra Azizi, Ebrahim Ahmadloo
APPLIED THERMAL ENGINEERING
(2016)
Article
Thermodynamics
Ebrahim Ahmadloo, Sadra Azizi
INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER
(2016)
Article
Mechanics
Sadra Azizi, Ebrahim Ahmadloo, Mohamed M. Awad
INTERNATIONAL JOURNAL OF MULTIPHASE FLOW
(2016)
Article
Mechanics
Sadra Azizi, Mohamed M. Awad, Ebrahim Ahmadloo
INTERNATIONAL JOURNAL OF MULTIPHASE FLOW
(2016)
Article
Mechanics
E. Ahmadloo, A. A. Gharehaghaji, M. Latifi, N. Mohammadi, H. Saghafi
ENGINEERING FRACTURE MECHANICS
(2017)
Article
Engineering, Mechanical
E. Ahmadloo, A. A. Gharehaghaji, M. Latifi, H. Saghafi, N. Mohammadi
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
(2019)
Article
Polymer Science
Ebrahim Ahmadloo, Najmeh Sobhanifar
JOURNAL OF APPLIED POLYMER SCIENCE
(2014)
Article
Polymer Science
Hajir Karimi, Fakhri Yousefi, Ebrahim Ahmadloo, Jamaledin Dastranj
JOURNAL OF POLYMER ENGINEERING
(2014)
Article
Polymer Science
Ebrahim Ahmadloo, Najmeh Sobhanifar, Fatemeh Sadat Hosseini
JOURNAL OF POLYMER ENGINEERING
(2014)
Correction
Polymer Science
Ebrahim Ahmadloo, Najmeh Sobhanifar
Article
Polymer Science
Ebrahim Ahmadloo, Najmeh Sobhanifar
Article
Engineering, Manufacturing
Seyyed Hamed Mousavi Azam, Ebrahim Ahmadloo
MATERIALS AND MANUFACTURING PROCESSES
(2016)