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Proceedings Papers

Machine Learning Based Control of Multi-Temperature PCM Thermal Storage Assemblies - A Comparison of On/Off Versus Fully Modulating Valve Control

Alanna Y. Cooney, Van P. Carey
Proc. ASME. HT2022, ASME 2022 Heat Transfer Summer Conference, V001T06A011, July 11–13, 2022
Paper No: HT2022-86174
DOI: https://doi.org/10.1115/HT2022-86174
View Paper titled, Machine Learning Based Control of Multi-Temperature PCM Thermal Storage Assemblies - A Comparison of On/Off Versus Fully Modulating Valve Control
Click here to open pdf in another window PDF for Machine Learning Based Control of Multi-Temperature PCM Thermal Storage Assemblies - A Comparison of On/Off Versus Fully Modulating Valve Control
Topics: Machine learning, Temperature, Thermal energy storage, Valves, Flow (Dynamics), Manufacturing, Artificial neural networks, Optimization, Control equipment, Exergy
Proceedings Papers

Heat Transfer Coefficient Predictions of the Air-Cooled Condenser With Machine Learning Based on the Operation Big Data of the Power Plant

Kai Chen, Xin Xie, Yan Chu, Meng Leng, Jinyi Zhang, Zhenwei Xu, Feng Huang, Heming Zhang
Proc. ASME. HT2022, ASME 2022 Heat Transfer Summer Conference, V001T08A002, July 11–13, 2022
Paper No: HT2022-83767
DOI: https://doi.org/10.1115/HT2022-83767
View Paper titled, Heat Transfer Coefficient Predictions of the Air-Cooled Condenser With Machine Learning Based on the Operation Big Data of the Power Plant
Click here to open pdf in another window PDF for Heat Transfer Coefficient Predictions of the Air-Cooled Condenser With Machine Learning Based on the Operation Big Data of the Power Plant
Topics: Condensers (steam plant), Heat transfer, Heat transfer coefficients, Machine learning, Power stations
Proceedings Papers

Use of Genetic Algorithms to Extract Fundamental Heat Transfer Performance Parameters From Evaporative Cooler Test Data

Samuel Cabrera, Van P. Carey
Proc. ASME. HT2022, ASME 2022 Heat Transfer Summer Conference, V001T19A002, July 11–13, 2022
Paper No: HT2022-86172
DOI: https://doi.org/10.1115/HT2022-86172
View Paper titled, Use of Genetic Algorithms to Extract Fundamental Heat Transfer Performance Parameters From Evaporative Cooler Test Data
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Topics: Evaporative cooling, Genetic algorithms, Heat transfer, Flow (Dynamics), Heat exchangers, Drops, Sprays, Water, Air flow, Fins
Proceedings Papers

Determining Parametric Effects for Droplet Evaporation on Nanoporous Superhydrophillic Surfaces Using Machine Learning Techniques

Emma R. McClure, Van P. Carey
Proc. ASME. HT2021, ASME 2021 Heat Transfer Summer Conference, V001T11A005, June 16–18, 2021
Paper No: HT2021-63618
DOI: https://doi.org/10.1115/HT2021-63618
View Paper titled, Determining Parametric Effects for Droplet Evaporation on Nanoporous Superhydrophillic Surfaces Using Machine Learning Techniques
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Topics: Drops, Evaporation, Machine learning, Coatings, Metal surfaces, Sprays, Algorithms, Artificial neural networks, Columns (Structural), Cooling
Proceedings Papers

Machine Learning for Chaotic Rayleigh Transitions

Ben Tribelhorn, H. E. Dillon
Proc. ASME. HT2021, ASME 2021 Heat Transfer Summer Conference, V001T01A002, June 16–18, 2021
Paper No: HT2021-61158
DOI: https://doi.org/10.1115/HT2021-61158
View Paper titled, Machine Learning for Chaotic Rayleigh Transitions
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Topics: Machine learning, Geometry, Natural convection, Prandtl number, Bifurcation, Convection, Dynamic systems, Numerical analysis, Runge-Kutta methods, Spectral energy distribution
Proceedings Papers

Use of Machine Learning Tools to Assess Surface Dryout During Nucleate and Transition Boiling on Surfaces With Different Wetting and Substrate Properties

Ursan Tchouteng Njike, Samuel Cabrera, Emma R. McClure, Van P. Carey
Proc. ASME. HT2021, ASME 2021 Heat Transfer Summer Conference, V001T11A006, June 16–18, 2021
Paper No: HT2021-64012
DOI: https://doi.org/10.1115/HT2021-64012
View Paper titled, Use of Machine Learning Tools to Assess Surface Dryout During Nucleate and Transition Boiling on Surfaces With Different Wetting and Substrate Properties
Click here to open pdf in another window PDF for Use of Machine Learning Tools to Assess Surface Dryout During Nucleate and Transition Boiling on Surfaces With Different Wetting and Substrate Properties
Topics: Boiling, Machine learning, Wetting
Proceedings Papers

Application of Machine Learning for Enhancing the Transient Performance of Thermal Energy Storage Platforms for Supplemental or Primary Thermal Management

Nandan Shettigar, Debjyoti Banerjee, Meghan Truong, Ashok Thyagarajan, Alaba Bamido, Abigail Meza, Navin Kumar
Proc. ASME. HT2020, ASME 2020 Heat Transfer Summer Conference, V001T02A014, July 13–15, 2020
Paper No: HT2020-9167
DOI: https://doi.org/10.1115/HT2020-9167
View Paper titled, Application of Machine Learning for Enhancing the Transient Performance of Thermal Energy Storage Platforms for Supplemental or Primary Thermal Management
Click here to open pdf in another window PDF for Application of Machine Learning for Enhancing the Transient Performance of Thermal Energy Storage Platforms for Supplemental or Primary Thermal Management
Topics: Machine learning, Thermal energy storage, Thermal management, Transients (Dynamics), Subcooling, Algorithms, Melting, Phase change materials, Reliability, Artificial neural networks
Proceedings Papers

Use of Genetic Algorithms and Machine Learning to Explore Parametric Trends in Nucleate Boiling Heat Transfer Data

Emma R. McClure, Van P. Carey
Proc. ASME. HT2020, ASME 2020 Heat Transfer Summer Conference, V001T16A004, July 13–15, 2020
Paper No: HT2020-9077
DOI: https://doi.org/10.1115/HT2020-9077
View Paper titled, Use of Genetic Algorithms and Machine Learning to Explore Parametric Trends in Nucleate Boiling Heat Transfer Data
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Topics: Genetic algorithms, Gravity (Force), Heat transfer, Machine learning, Nucleate boiling
Proceedings Papers

A Novel Machine-Learning Aided Optimization Technique for Material Design: Application in Thin Film Solar Cells

Shima Hajimirza
Proc. ASME. HT2016, Volume 2: Heat Transfer in Multiphase Systems; Gas Turbine Heat Transfer; Manufacturing and Materials Processing; Heat Transfer in Electronic Equipment; Heat and Mass Transfer in Biotechnology; Heat Transfer Under Extreme Conditions; Computational Heat Transfer; Heat Transfer Visualization Gallery; General Papers on Heat Transfer; Multiphase Flow and Heat Transfer; Transport Phenomena in Manufacturing and Materials Processing, V002T15A016, July 10–14, 2016
Paper No: HT2016-7306
DOI: https://doi.org/10.1115/HT2016-7306
View Paper titled, A Novel Machine-Learning Aided Optimization Technique for Material Design: Application in Thin Film Solar Cells
Click here to open pdf in another window PDF for A Novel Machine-Learning Aided Optimization Technique for Material Design: Application in Thin Film Solar Cells
Topics: Design, Machine learning, Optimization, Solar cells, Thin films, Algorithms, Absorption, Energy conversion, Engineering systems and industry applications, Light