1-20 of 30
Artificial neural networks
Close
Save search
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Proceedings Papers

Machine Learning Algorithms for Predicting Condensation Pressure Drop in Mini/Micro Channels

Available to Purchase

Farshad Barghi Golezani, Jiayuan Li, Logan Pirnstill, Chirag Kharangate
Proc. ASME. HT2024, ASME 2024 Heat Transfer Summer Conference, V001T03A009, July 15–17, 2024
Publisher: American Society of Mechanical Engineers
Paper No: HT2024-131476
https://doi.org/10.1115/HT2024-131476
View Paper titled, Machine Learning Algorithms for Predicting Condensation Pressure Drop in Mini/Micro Channels
Open the PDF for Machine Learning Algorithms for Predicting Condensation Pressure Drop in Mini/Micro Channels in another window
Topics: Algorithms, Condensation, Heat transfer, Machine learning, Microchannels, Pressure drop, Thermal systems, Two-phase flow, Artificial neural networks, Heat
Proceedings Papers

Inferring Flow Boiling Interfacial Shear Stress Using Physics Informed Neural Networks From Control Volume Models

Available to Purchase

Logan M. Pirnstill, Chirag Kharangate
Proc. ASME. HT2024, ASME 2024 Heat Transfer Summer Conference, V001T03A006, July 15–17, 2024
Publisher: American Society of Mechanical Engineers
Paper No: HT2024-131314
https://doi.org/10.1115/HT2024-131314
View Paper titled, Inferring Flow Boiling Interfacial Shear Stress Using Physics Informed Neural Networks From Control Volume Models
Open the PDF for Inferring Flow Boiling Interfacial Shear Stress Using Physics Informed Neural Networks From Control Volume Models in another window
Topics: Artificial neural networks, Boiling, Flow (Dynamics), Heat transfer, Modeling, Physics, Shear stress
Proceedings Papers

A Convolution Neural Network Design for Combined Image and Sensor Data Analysis to Determine Droplet Vaporization Regime and Heat Transfer Performance

Available to Purchase

Ursan Tchouteng Njike, Anisa Silva, Van P. Carey
Proc. ASME. HT2024, ASME 2024 Heat Transfer Summer Conference, V001T03A005, July 15–17, 2024
Publisher: American Society of Mechanical Engineers
Paper No: HT2024-131218
https://doi.org/10.1115/HT2024-131218
View Paper titled, A Convolution Neural Network Design for Combined Image and Sensor Data Analysis to Determine Droplet Vaporization Regime and Heat Transfer Performance
Open the PDF for A Convolution Neural Network Design for Combined Image and Sensor Data Analysis to Determine Droplet Vaporization Regime and Heat Transfer Performance in another window
Topics: Artificial neural networks, Design, Drops, Heat transfer, Sensors, Boiling, Heat conduction, Imaging, Nucleate boiling, Data acquisition systems
Proceedings Papers

Optimized Design and Control of a Multi-Temperature, Multi-Module Thermal Energy Storage Ensemble

Available to Purchase

Alanna Y. Cooney, Van P. Carey
Proc. ASME. HT2023, ASME 2023 Heat Transfer Summer Conference, V001T16A007, July 10–12, 2023
Publisher: American Society of Mechanical Engineers
Paper No: HT2023-107406
https://doi.org/10.1115/HT2023-107406
View Paper titled, Optimized Design and Control of a Multi-Temperature, Multi-Module Thermal Energy Storage Ensemble
Open the PDF for Optimized Design and Control of a Multi-Temperature, Multi-Module Thermal Energy Storage Ensemble in another window
Topics: Design, Temperature, Thermal energy storage, Artificial neural networks, Exergy, Optimization, Phase change materials, Control equipment, Energy storage, Fluid dynamics
Proceedings Papers

Use of Artificial Intelligence Techniques, Correlation of Thermal Images and the Concept of Thermal Impedance Aiming to Estimate the Location and Size of Breast Tumours

Available to Purchase

Jefferson G. Nascimento, Gabriela L. Menegaz, Gilmar Guimaraes
Proc. ASME. HT2023, ASME 2023 Heat Transfer Summer Conference, V001T15A004, July 10–12, 2023
Publisher: American Society of Mechanical Engineers
Paper No: HT2023-107545
https://doi.org/10.1115/HT2023-107545
View Paper titled, Use of Artificial Intelligence Techniques, Correlation of Thermal Images and the Concept of Thermal Impedance Aiming to Estimate the Location and Size of Breast Tumours
Open the PDF for Use of Artificial Intelligence Techniques, Correlation of Thermal Images and the Concept of Thermal Impedance Aiming to Estimate the Location and Size of Breast Tumours in another window
Topics: Artificial intelligence, Artificial neural networks, Cancer, Tumors, Diseases, Geometry, Heat, Imaging, Phantoms, Shapes
Proceedings Papers

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

Available to Purchase

Alanna Y. Cooney, Van P. Carey
Proc. ASME. HT2022, ASME 2022 Heat Transfer Summer Conference, V001T06A011, July 11–13, 2022
Publisher: American Society of Mechanical Engineers
Paper No: HT2022-86174
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
Open the PDF for Machine Learning Based Control of Multi-Temperature PCM Thermal Storage Assemblies - A Comparison of On/Off Versus Fully Modulating Valve Control in another window
Topics: Machine learning, Temperature, Thermal energy storage, Valves, Flow (Dynamics), Manufacturing, Artificial neural networks, Optimization, Control equipment, Exergy
Proceedings Papers

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

Available to Purchase

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

Application of the Neural Networks in Prediction of the Thermal Flow on the Jiu River

Available to Purchase

Victorita Radulescu
Proc. ASME. HT2020, ASME 2020 Heat Transfer Summer Conference, V001T04A005, July 13–15, 2020
Publisher: American Society of Mechanical Engineers
Paper No: HT2020-9043
https://doi.org/10.1115/HT2020-9043
View Paper titled, Application of the Neural Networks in Prediction of the Thermal Flow on the Jiu River
Open the PDF for Application of the Neural Networks in Prediction of the Thermal Flow on the Jiu River in another window
Topics: Artificial neural networks, Flow (Dynamics), Rivers, Temperature, Water, Water temperature, Errors, Chemical properties, Computer simulation, Cooling towers
Proceedings Papers

Application of Artificial Neural Network As a Near-Real Time Technique for Solving Non-Linear Inverse Heat Conduction Problems in a One-Dimensional Medium With Moving Boundary

Available to Purchase

Hamidreza Najafi, Obinna Uyanna, Jian Zhang
Proc. ASME. HT2020, ASME 2020 Heat Transfer Summer Conference, V001T02A013, July 13–15, 2020
Publisher: American Society of Mechanical Engineers
Paper No: HT2020-9054
https://doi.org/10.1115/HT2020-9054
View Paper titled, Application of Artificial Neural Network As a Near-Real Time Technique for Solving Non-Linear Inverse Heat Conduction Problems in a One-Dimensional Medium With Moving Boundary
Open the PDF for Application of Artificial Neural Network As a Near-Real Time Technique for Solving Non-Linear Inverse Heat Conduction Problems in a One-Dimensional Medium With Moving Boundary in another window
Topics: Artificial neural networks, Heat conduction
Proceedings Papers

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

Available to Purchase

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
Publisher: American Society of Mechanical Engineers
Paper No: HT2020-9167
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
Open the PDF for Application of Machine Learning for Enhancing the Transient Performance of Thermal Energy Storage Platforms for Supplemental or Primary Thermal Management in another window
Topics: Machine learning, Thermal energy storage, Thermal management, Transients (Dynamics), Subcooling, Algorithms, Melting, Phase change materials, Reliability, Artificial neural networks
Proceedings Papers

Significance of Delta Winglets on the Air Side Performance of Flat Finned Versus Wavy Finned-Tube Heat Exchangers

Available to Purchase

Tariq Amin Khan, Nasir Mehdi Gardezi, Wei Li, Yang Zhou, Zahid Ayub
Proc. ASME. HT2020, ASME 2020 Heat Transfer Summer Conference, V001T02A006, July 13–15, 2020
Publisher: American Society of Mechanical Engineers
Paper No: HT2020-8991
https://doi.org/10.1115/HT2020-8991
View Paper titled, Significance of Delta Winglets on the Air Side Performance of Flat Finned Versus Wavy Finned-Tube Heat Exchangers
Open the PDF for Significance of Delta Winglets on the Air Side Performance of Flat Finned Versus Wavy Finned-Tube Heat Exchangers in another window
Topics: Heat exchangers, Genetic algorithms, Heat transfer, Artificial neural networks, Computational fluid dynamics, Computer simulation, Flow (Dynamics), Friction, Heat transfer coefficients, Pareto optimization
Proceedings Papers

Inverse Model for Fire Heat Release Rate Using Deep Neural Networks

Available to Purchase

Luyao Kou, Xinzhi Wang, Hui Zhang, Rui Yang, Yi Liu
Proc. ASME. HT2020, ASME 2020 Heat Transfer Summer Conference, V001T06A003, July 13–15, 2020
Publisher: American Society of Mechanical Engineers
Paper No: HT2020-9110
https://doi.org/10.1115/HT2020-9110
View Paper titled, Inverse Model for Fire Heat Release Rate Using Deep Neural Networks
Open the PDF for Inverse Model for Fire Heat Release Rate Using Deep Neural Networks in another window
Topics: Artificial neural networks, Fire, Heat, Emergency response, Modeling, Sensors, Time series
Proceedings Papers

Optimal Position of Rectangular Vortex Generator for Heat Transfer Enhancement in a Flat-Plate Channel

Available to Purchase

Tariq Amin Khan, Wei Li, Zhengjiang Zhang, Jincai Du, Sadiq Amin Khan, Fu Liu
Proc. ASME. HT2017, Volume 1: Aerospace Heat Transfer; Computational Heat Transfer; Education; Environmental Heat Transfer; Fire and Combustion Systems; Gas Turbine Heat Transfer; Heat Transfer in Electronic Equipment; Heat Transfer in Energy Systems, V001T02A005, July 9–12, 2017
Publisher: American Society of Mechanical Engineers
Paper No: HT2017-4904
https://doi.org/10.1115/HT2017-4904
View Paper titled, Optimal Position of Rectangular Vortex Generator for Heat Transfer Enhancement in a Flat-Plate Channel
Open the PDF for Optimal Position of Rectangular Vortex Generator for Heat Transfer Enhancement in a Flat-Plate Channel in another window
Topics: Flat plates, Generators, Heat transfer, Vortices, Pressure drop, Computer simulation, Artificial neural networks, Flow (Dynamics), Fluid dynamics, Genetic algorithms
Proceedings Papers

Application of Genetic Algorithm-Polynomial Neural Network for Modelling Heat Transfer and Fluid Flow Characteristics of a Double-Pipe Heat Exchanger

Available to Purchase

Mehdi Mehrabi, Tuhid Pashaee, Mohsen Sharifpur, Josua P. Meyer
Proc. ASME. HT2013, Volume 1: Heat Transfer in Energy Systems; Thermophysical Properties; Theory and Fundamental Research in Heat Transfer, V001T01A015, July 14–19, 2013
Publisher: American Society of Mechanical Engineers
Paper No: HT2013-17194
https://doi.org/10.1115/HT2013-17194
View Paper titled, Application of Genetic Algorithm-Polynomial Neural Network for Modelling Heat Transfer and Fluid Flow Characteristics of a Double-Pipe Heat Exchanger
Open the PDF for Application of Genetic Algorithm-Polynomial Neural Network for Modelling Heat Transfer and Fluid Flow Characteristics of a Double-Pipe Heat Exchanger in another window
Topics: Algorithms, Artificial neural networks, Fluid dynamics, Heat exchangers, Heat transfer, Modeling, Pipes, Polynomials, Design, Heat transfer coefficients
Proceedings Papers

Application of Artificial Neural Networks to Predict Heat Transfer From Buried Pipe for Ground Source Heat Pump Applications

Available to Purchase

Hakan Demir, Ş. Özgür Atayılmaz, Özden Agra, Ahmet Selim Dalkılıç
Proc. ASME. HT2013, Volume 4: Heat and Mass Transfer Under Extreme Conditions; Environmental Heat Transfer; Computational Heat Transfer; Visualization of Heat Transfer; Heat Transfer Education and Future Directions in Heat Transfer; Nuclear Energy, V004T14A030, July 14–19, 2013
Publisher: American Society of Mechanical Engineers
Paper No: HT2013-17729
https://doi.org/10.1115/HT2013-17729
View Paper titled, Application of Artificial Neural Networks to Predict Heat Transfer From Buried Pipe for Ground Source Heat Pump Applications
Open the PDF for Application of Artificial Neural Networks to Predict Heat Transfer From Buried Pipe for Ground Source Heat Pump Applications in another window
Topics: Artificial neural networks, Heat pumps, Heat transfer, Pipes, Soil, Temperature, Fluids, Heat exchangers, Density, Design
Proceedings Papers

A Critical Review on the Determination of Convective Heat Transfer Coefficient During Condensation in Smooth and Enhanced Tubes

Available to Purchase

Ahmet Selim Dalkiliç, Ali Celen, Mohamed M. Awad, Somchai Wongwises
Proc. ASME. HT2013, Volume 2: Heat Transfer Enhancement for Practical Applications; Heat and Mass Transfer in Fire and Combustion; Heat Transfer in Multiphase Systems; Heat and Mass Transfer in Biotechnology, V002T07A007, July 14–19, 2013
Publisher: American Society of Mechanical Engineers
Paper No: HT2013-17565
https://doi.org/10.1115/HT2013-17565
View Paper titled, A Critical Review on the Determination of Convective Heat Transfer Coefficient During Condensation in Smooth and Enhanced Tubes
Open the PDF for A Critical Review on the Determination of Convective Heat Transfer Coefficient During Condensation in Smooth and Enhanced Tubes in another window
Topics: Condensation, Convection, Heat exchangers, Artificial neural networks, Heat transfer coefficients, Numerical analysis, Computational fluid dynamics, Flow (Dynamics), Refrigerants, Refrigeration
Proceedings Papers

A Critical Review on the Determination of Pressure Drop During Condensation in Smooth and Enhanced Tubes

Available to Purchase

Ahmet Selim Dalkiliç, Ali Celen, Mohamed M. Awad, Somchai Wongwises
Proc. ASME. HT2013, Volume 2: Heat Transfer Enhancement for Practical Applications; Heat and Mass Transfer in Fire and Combustion; Heat Transfer in Multiphase Systems; Heat and Mass Transfer in Biotechnology, V002T07A008, July 14–19, 2013
Publisher: American Society of Mechanical Engineers
Paper No: HT2013-17567
https://doi.org/10.1115/HT2013-17567
View Paper titled, A Critical Review on the Determination of Pressure Drop During Condensation in Smooth and Enhanced Tubes
Open the PDF for A Critical Review on the Determination of Pressure Drop During Condensation in Smooth and Enhanced Tubes in another window
Topics: Condensation, Pressure drop, Heat exchangers, Artificial neural networks, Heat transfer coefficients, Numerical analysis, Computational fluid dynamics, Flow (Dynamics), Refrigerants, Refrigeration
Proceedings Papers

Classification of Condensing Heat Exchangers Performance Data by Gaussian Mixtures

Available to Purchase

Arturo Pacheco-Vega, Gabriela Avila
Proc. ASME. HT2009, Volume 1: Heat Transfer in Energy Systems; Thermophysical Properties; Heat Transfer Equipment; Heat Transfer in Electronic Equipment, 737-745, July 19–23, 2009
Publisher: American Society of Mechanical Engineers
Paper No: HT2009-88627
https://doi.org/10.1115/HT2009-88627
View Paper titled, Classification of Condensing Heat Exchangers Performance Data by Gaussian Mixtures
Open the PDF for Classification of Condensing Heat Exchangers Performance Data by Gaussian Mixtures in another window
Topics: Heat exchangers, Artificial neural networks, Algorithms, Condensation, Film condensation
Proceedings Papers

Factor Analysis for Forced and Mixed Convection Laminar Heat Transfer in a Horizontal Tube Using Artificial Neural Network

Available to Purchase

L. M. Tam, A. J. Ghajar, H. K. Tam, S. C. Tam
Proc. ASME. HT2007, ASME/JSME 2007 Thermal Engineering Heat Transfer Summer Conference, Volume 1, 1013-1020, July 8–12, 2007
Publisher: American Society of Mechanical Engineers
Paper No: HT2007-32155
https://doi.org/10.1115/HT2007-32155
View Paper titled, Factor Analysis for Forced and Mixed Convection Laminar Heat Transfer in a Horizontal Tube Using Artificial Neural Network
Open the PDF for Factor Analysis for Forced and Mixed Convection Laminar Heat Transfer in a Horizontal Tube Using Artificial Neural Network in another window
Topics: Artificial neural networks, Heat transfer, Mixed convection, Rayleigh number, Forced convection, Heat flux, Laminar flow, Prandtl number, Reynolds number, Turbulent heat transfer
Proceedings Papers

Turbulent Forced Convection Correlation for the In-Tube Flow of Binary Gas Mixtures With 0.1

Available to Purchase

Gerardo Diaz, Antonio Campo
Proc. ASME. HT2007, ASME/JSME 2007 Thermal Engineering Heat Transfer Summer Conference, Volume 1, 389-394, July 8–12, 2007
Publisher: American Society of Mechanical Engineers
Paper No: HT2007-32355
https://doi.org/10.1115/HT2007-32355
View Paper titled, Turbulent Forced Convection Correlation for the In-Tube Flow of Binary Gas Mixtures With 0.1<Pr<0.7
Open the PDF for Turbulent Forced Convection Correlation for the In-Tube Flow of Binary Gas Mixtures With 0.1<Pr<0.7 in another window
Topics: Flow (Dynamics), Forced convection, Turbulence, Artificial neural networks, Gases, Interpolation, Testing, Trains