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Journal Articles

A Transformative Approach for Breast Cancer Detection Using Physics-Informed Neural Network and Surface Temperature Data

Isaac Perez-Raya, Carlos Gutierrez, Satish Kandlikar

Publisher: ASME

J. Heat Mass Transfer. October 2024, 146(10): 101201.

Paper No: HT-23-1456

https://doi.org/10.1115/1.4065673

Published Online: June 17, 2024

View Article titled, A Transformative Approach for Breast Cancer Detection Using Physics-Informed Neural Network and Surface Temperature Data

Topics: Artificial neural networks, Cancer, Heat transfer, Modeling, Physics, Temperature, Tumors, Heat, Machine learning, Errors

Journal Articles

Accelerated Prediction of Photon Transport in Nanoparticle Media Using Machine Learning Trained With Monte Carlo Simulations

Daniel Carne, Joseph Peoples, Dudong Feng, Xiulin Ruan

Publisher: ASME

J. Heat Mass Transfer. May 2023, 145(5): 052502.

Paper No: HT-22-1557

https://doi.org/10.1115/1.4062188

Published Online: April 11, 2023

View Article titled, Accelerated Prediction of Photon Transport in Nanoparticle Media Using Machine Learning Trained With Monte Carlo Simulations

Topics: Absorption, Artificial neural networks, Machine learning, Nanoparticles, Photons, Reflectance, Simulation, Spectral sensitivity, Errors, Scattering (Physics)

Journal Articles

A Machine Learning Approach to Model Oxidation of Toluene in a Bubble Column Reactor

Raihan Tayeb, Yuwen Zhang

Publisher: ASME

J. Heat Mass Transfer. May 2023, 145(5): 052003.

Paper No: HT-22-1643

https://doi.org/10.1115/1.4057022

Published Online: March 20, 2023

View Article titled, A Machine Learning Approach to Model Oxidation of Toluene in a Bubble Column Reactor

Topics: Bubbles, Machine learning, Modeling, Oxidation, Boundary layers, Machinery, Simulation, Errors, Computational fluid dynamics

Journal Articles

Tree-Based Ensemble Learning Models for Wall Temperature Predictions in Post-Critical Heat Flux Flow Regimes at Subcooled and Low-Quality Conditions

Qingqing Liu, Yang Liu, Adam Burak, Joseph Kelly, Stephen Bajorek, Xiaodong Sun

Publisher: ASME

J. Heat Mass Transfer. April 2023, 145(4): 041604.

Paper No: HT-22-1448

https://doi.org/10.1115/1.4056763

Published Online: February 8, 2023

View Article titled, Tree-Based Ensemble Learning Models for Wall Temperature Predictions in Post-Critical Heat Flux Flow Regimes at Subcooled and Low-Quality Conditions

Topics: Critical heat flux, Errors, Film boiling, Flow (Dynamics), Heat flux, Subcooling, Wall temperature, Machine learning, Databases, Water

Journal Articles

Dimensional Analysis in Error Reduction for Prediction of Nucleate Boiling Heat Flux by Artificial Neural Networks for Limited Dataset

Sandipan Banerjee, Mark Sussman, Yongsheng Lian

Publisher: ASME

J. Heat Mass Transfer. June 2023, 145(6): 061602.

Paper No: HT-22-1383

https://doi.org/10.1115/1.4056539

Published Online: February 6, 2023

View Article titled, Dimensional Analysis in Error Reduction for Prediction of Nucleate Boiling Heat Flux by Artificial Neural Networks for Limited Dataset

Topics: Artificial neural networks, Dimensional analysis, Gravity (Force), Heat flux, Nucleate boiling, Machine learning, Errors, Boiling, Subcooling

Includes: Supplementary data

Journal Articles

Mapping the Chaotic Transitions of the Lorenz Equations With Unsupervised Machine Learning

Ben Tribelhorn, H. E. Dillon

Publisher: ASME

J. Heat Mass Transfer. January 2023, 145(1): 013301.

Paper No: HT-21-1833

https://doi.org/10.1115/1.4055937

Published Online: November 17, 2022

View Article titled, Mapping the Chaotic Transitions of the Lorenz Equations With Unsupervised Machine Learning

Topics: Dimensions, Geometry, Machine learning, Algorithms, Prandtl number

Journal Articles

An Expert Model Based on Physics-Aware Neural Network for the Prediction of Thermal Conductivity of Nanofluids

Bivas Bhaumik, Satyasaran Changdar, Soumen De

Publisher: ASME

J. Heat Mass Transfer. October 2022, 144(10): 103501.

Paper No: HT-21-1791

https://doi.org/10.1115/1.4055116

Published Online: August 18, 2022

View Article titled, An Expert Model Based on Physics-Aware Neural Network for the Prediction of Thermal Conductivity of Nanofluids

Topics: Artificial neural networks, Nanofluids, Physics, Thermal conductivity, Temperature, Nanoparticles, Algorithms, Fluids, Machine learning

Journal Articles

Status, Challenges, and Potential for Machine Learning in Understanding and Applying Heat Transfer Phenomena

Matthew T. Hughes, Girish Kini, Srinivas Garimella

Publisher: ASME

J. Heat Mass Transfer. December 2021, 143(12): 120802.

Paper No: HT-21-1518

https://doi.org/10.1115/1.4052510

Published Online: October 18, 2021

View Article titled, Status, Challenges, and Potential for Machine Learning in Understanding and Applying Heat Transfer Phenomena

Topics: Artificial neural networks, Heat transfer, Machine learning, Modeling, Flow (Dynamics), Algorithms, Physics

Journal Articles

Genetic Algorithm and Deep Learning to Explore Parametric Trends in Nucleate Boiling Heat Transfer Data

Emma R. McClure, Van P. Carey

Publisher: ASME

J. Heat Mass Transfer. December 2021, 143(12): 121602.

Paper No: HT-20-1709

https://doi.org/10.1115/1.4052435

Published Online: October 13, 2021

View Article titled, Genetic Algorithm and Deep Learning to Explore Parametric Trends in Nucleate Boiling Heat Transfer Data

Topics: Algorithms, Genetic algorithms, Gravity (Force), Heat flux, Nucleate boiling, Machine learning, Heat transfer, Errors, Pressure, Water

Journal Articles

Computational Fluid Dynamics—Machine Learning Prediction of Machinery Coupling Windage Heating and Power Loss

Ahmad Dawahdeh, Joseph Oh, Tianbo Zhai, Alan Palazzolo

Publisher: ASME

J. Heat Mass Transfer. August 2021, 143(8): 082201.

Paper No: HT-20-1501

https://doi.org/10.1115/1.4051351

Published Online: June 28, 2021

View Article titled, Computational Fluid Dynamics—Machine Learning Prediction of Machinery Coupling Windage Heating and Power Loss

Topics: Computational fluid dynamics, Machine learning, Simulation, Temperature

Journal Articles

Conjugate Thermal Optimization With Unsupervised Machine Learning

Reid Smith, Sandip Dutta

Publisher: ASME

J. Heat Mass Transfer. May 2021, 143(5): 052901.

Paper No: HT-20-1516

https://doi.org/10.1115/1.4049842

Published Online: March 19, 2021

View Article titled, Conjugate Thermal Optimization With Unsupervised Machine Learning

Topics: Boundary-value problems, Coolants, Flow (Dynamics), Machine learning, Optimization, Temperature, Transfer functions, Heat transfer

Journal citation

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Supplemental Material

A Transformative Approach for Breast Cancer Detection Using Physics-Informed Neural Network and Surface Temperature Data

Accelerated Prediction of Photon Transport in Nanoparticle Media Using Machine Learning Trained With Monte Carlo Simulations

A Machine Learning Approach to Model Oxidation of Toluene in a Bubble Column Reactor

Tree-Based Ensemble Learning Models for Wall Temperature Predictions in Post-Critical Heat Flux Flow Regimes at Subcooled and Low-Quality Conditions

Dimensional Analysis in Error Reduction for Prediction of Nucleate Boiling Heat Flux by Artificial Neural Networks for Limited Dataset

Mapping the Chaotic Transitions of the Lorenz Equations With Unsupervised Machine Learning

An Expert Model Based on Physics-Aware Neural Network for the Prediction of Thermal Conductivity of Nanofluids

Status, Challenges, and Potential for Machine Learning in Understanding and Applying Heat Transfer Phenomena

Genetic Algorithm and Deep Learning to Explore Parametric Trends in Nucleate Boiling Heat Transfer Data

Computational Fluid Dynamics—Machine Learning Prediction of Machinery Coupling Windage Heating and Power Loss

Conjugate Thermal Optimization With Unsupervised Machine Learning

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