1-20 of 24
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

Soot Particle Number Prediction in GDI Engines: A Comprehensive Grey-Box Machine Learning Approach

Available to Purchase

Khashayar Ebrahimi, Kevin Roggendorf, Hyuckmo Kwon, Jinsuk Kang
Proc. ASME. ICEF2024, ASME 2024 ICE Forward Conference, V001T06A008, October 20–23, 2024
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2024-140669
https://doi.org/10.1115/ICEF2024-140669
View Paper titled, Soot Particle Number Prediction in GDI Engines: A Comprehensive Grey-Box Machine Learning Approach
Open the PDF for Soot Particle Number Prediction in GDI Engines: A Comprehensive Grey-Box Machine Learning Approach in another window
Topics: Direct injection spark ignition engines, Machine learning, Particulate matter, Soot, Engines, Emissions, Air pollution control, Artificial neural networks, Physics, Stress
Proceedings Papers

Virtual Pressure Sensor Based on Ion Current Measurements Using Artificial Neural Networks

Available to Purchase

Ola Björnsson, Per Tunestål
Proc. ASME. ICEF2024, ASME 2024 ICE Forward Conference, V001T04A002, October 20–23, 2024
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2024-140748
https://doi.org/10.1115/ICEF2024-140748
View Paper titled, Virtual Pressure Sensor Based on Ion Current Measurements Using Artificial Neural Networks
Open the PDF for Virtual Pressure Sensor Based on Ion Current Measurements Using Artificial Neural Networks in another window
Topics: Artificial neural networks, Pressure sensors
Proceedings Papers

Multi-Fidelity Neural Network Regression for Efficient Training of Energy-Assisted Diesel Engine Control System

Available to Purchase

Ari Nejadmalayeri, Sai Ranjeet Narayanan, Suo Yang, Zongxuan Sun, Harsh Darshan Sapra, Randy Hessel, Sage Kokjohn, Kenneth S. Kim, Chol-Bum M. Kweon
Proc. ASME. ICEF2023, ASME 2023 ICE Forward Conference, V001T06A001, October 8–11, 2023
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2023-109750
https://doi.org/10.1115/ICEF2023-109750
View Paper titled, Multi-Fidelity Neural Network Regression for Efficient Training of Energy-Assisted Diesel Engine Control System
Open the PDF for Multi-Fidelity Neural Network Regression for Efficient Training of Energy-Assisted Diesel Engine Control System in another window
Topics: Artificial neural networks, Combustion, Control systems, Diesel engines
Proceedings Papers

Deep Learning for Surface Assessment of Cylinder Liners in Large Internal Combustion Engines

Available to Purchase

Matthias Schwab, Adéla Moravová, Christoph Angermann, Steinbjörn Jónsson, Christian Laubichler, Constantin Kiesling, Markus Haltmeier
Proc. ASME. ICEF2022, ASME 2022 ICE Forward Conference, V001T01A004, October 16–19, 2022
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2022-89893
https://doi.org/10.1115/ICEF2022-89893
View Paper titled, Deep Learning for Surface Assessment of Cylinder Liners in Large Internal Combustion Engines
Open the PDF for Deep Learning for Surface Assessment of Cylinder Liners in Large Internal Combustion Engines in another window
Topics: Artificial neural networks, Cylinders, Engines, Internal combustion engines, Wear, Reflection, Resolution (Optics), Automotive industry, Condition monitoring, Damage
Proceedings Papers

Accelerating Chemical Kinetics Calculations With Physics Informed Neural Networks

Available to Purchase

Ahmed Almeldein, Noah Van Dam
Proc. ASME. ICEF2022, ASME 2022 ICE Forward Conference, V001T06A007, October 16–19, 2022
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2022-90371
https://doi.org/10.1115/ICEF2022-90371
View Paper titled, Accelerating Chemical Kinetics Calculations With Physics Informed Neural Networks
Open the PDF for Accelerating Chemical Kinetics Calculations With Physics Informed Neural Networks in another window
Topics: Artificial neural networks, Chemical kinetics, Physics, Combustion, Engineering simulation, Modeling, Physical laws, Simulation, Architecture, Chemistry
Proceedings Papers

Predicting the Cetane Number, Sooting Tendency, and Energy Density of Terpene Fuel Additives

Available to Purchase

Travis Kessler, Amina SubLaban, J. Hunter Mack
Proc. ASME. ICEF2022, ASME 2022 ICE Forward Conference, V001T02A011, October 16–19, 2022
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2022-91163
https://doi.org/10.1115/ICEF2022-91163
View Paper titled, Predicting the Cetane Number, Sooting Tendency, and Energy Density of Terpene Fuel Additives
Open the PDF for Predicting the Cetane Number, Sooting Tendency, and Energy Density of Terpene Fuel Additives in another window
Topics: Density, Fuels, Carbon, Errors, Artificial neural networks, Chain, Combustion, Emissions, Engines, Heating
Proceedings Papers

Machine Learning and Genetic Algorithm Method for Powertrain Development: Rapid Generation of Engine Calibration Maps

Available to Purchase

Zachary L. Williams, Prathik Meruva, Daniel Christopher Bitsis, Jr.
Proc. ASME. ICEF2022, ASME 2022 ICE Forward Conference, V001T04A006, October 16–19, 2022
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2022-91169
https://doi.org/10.1115/ICEF2022-91169
View Paper titled, Machine Learning and Genetic Algorithm Method for Powertrain Development: Rapid Generation of Engine Calibration Maps
Open the PDF for Machine Learning and Genetic Algorithm Method for Powertrain Development: Rapid Generation of Engine Calibration Maps in another window
Topics: Artificial neural networks, Calibration, Engines, Genetic algorithms, Machine learning
Proceedings Papers

Evaluating Diesel/Biofuel Blends Using Artificial Neural Networks and Linear/Nonlinear Equations

Available to Purchase

Travis Kessler, Thomas Schwartz, Hsi-Wu Wong, J. Hunter Mack
Proc. ASME. ICEF2021, ASME 2021 Internal Combustion Engine Division Fall Technical Conference, V001T04A009, October 13–15, 2021
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2021-67785
https://doi.org/10.1115/ICEF2021-67785
View Paper titled, Evaluating Diesel/Biofuel Blends Using Artificial Neural Networks and Linear/Nonlinear Equations
Open the PDF for Evaluating Diesel/Biofuel Blends Using Artificial Neural Networks and Linear/Nonlinear Equations in another window
Topics: Artificial neural networks, Biofuel, Diesel, Nonlinear equations, Errors, Heating, Kinematics, Viscosity, Biomass, Combustion
Proceedings Papers

Predicting the Cetane Number, Yield Sooting Index, Kinematic Viscosity, and Cloud Point for Catalytically Upgraded Pyrolysis Oil Using Artificial Neural Networks

Available to Purchase

Travis Kessler, Thomas Schwartz, Hsi-Wu Wong, J. Hunter Mack
Proc. ASME. ICEF2020, ASME 2020 Internal Combustion Engine Division Fall Technical Conference, V001T02A006, November 4–6, 2020
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2020-2978
https://doi.org/10.1115/ICEF2020-2978
View Paper titled, Predicting the Cetane Number, Yield Sooting Index, Kinematic Viscosity, and Cloud Point for Catalytically Upgraded Pyrolysis Oil Using Artificial Neural Networks
Open the PDF for Predicting the Cetane Number, Yield Sooting Index, Kinematic Viscosity, and Cloud Point for Catalytically Upgraded Pyrolysis Oil Using Artificial Neural Networks in another window
Topics: Artificial neural networks, Kinematics, Pyrolysis, Viscosity, Diesel, Errors, Fuels, Biomass, Fuel gasification, Oxygen
Proceedings Papers

Study on Automatic Adaptation for a Control-Oriented Model of an Advanced Diesel Engine

Available to Purchase

S. Nishii, Y. Yamasaki
Proc. ASME. ICEF2020, ASME 2020 Internal Combustion Engine Division Fall Technical Conference, V001T04A008, November 4–6, 2020
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2020-3025
https://doi.org/10.1115/ICEF2020-3025
View Paper titled, Study on Automatic Adaptation for a Control-Oriented Model of an Advanced Diesel Engine
Open the PDF for Study on Automatic Adaptation for a Control-Oriented Model of an Advanced Diesel Engine in another window
Topics: Diesel engines, Artificial neural networks, Combustion, Engines, Algorithms, Combustion technologies, Heat, Ignition, Optimization algorithms, Physics
Proceedings Papers

Prediction of NO x Emissions for a Range of Engine Hardware Configurations Using Artificial Neural Networks

Available to Purchase

Nick Papaioannou, XiaoHang Fang, Felix Leach, Martin H. Davy
Proc. ASME. ICEF2020, ASME 2020 Internal Combustion Engine Division Fall Technical Conference, V001T06A001, November 4–6, 2020
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2020-2911
https://doi.org/10.1115/ICEF2020-2911
View Paper titled, Prediction of NO x Emissions for a Range of Engine Hardware Configurations Using Artificial Neural Networks
Open the PDF for Prediction of NO x Emissions for a Range of Engine Hardware Configurations Using Artificial Neural Networks in another window
Topics: Artificial neural networks, Emissions, Engines, Hardware, Nitrogen oxides, Diesel engines, Ejectors, Feedforward control, Fuels, Nozzles
Proceedings Papers

Screening Compounds for Fast Pyrolysis and Catalytic Biofuel Upgrading Using Artificial Neural Networks

Available to Purchase

Travis Kessler, Thomas Schwartz, Hsi-Wu Wong, J. Hunter Mack
Proc. ASME. ICEF2019, ASME 2019 Internal Combustion Engine Division Fall Technical Conference, V001T02A007, October 20–23, 2019
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2019-7170
https://doi.org/10.1115/ICEF2019-7170
View Paper titled, Screening Compounds for Fast Pyrolysis and Catalytic Biofuel Upgrading Using Artificial Neural Networks
Open the PDF for Screening Compounds for Fast Pyrolysis and Catalytic Biofuel Upgrading Using Artificial Neural Networks in another window
Topics: Artificial neural networks, Biofuel, Pyrolysis, Fuels, Diesel engines, Errors, Feedback, Biomass, Combustion, Fuel gasification
Proceedings Papers

Numerical Investigation of a Central Fuel Property Hypothesis Under Boosted Spark-Ignition Conditions

Available to Purchase

Pinaki Pal, Krishna Kalvakala, Yunchao Wu, Matthew McNenly, Simon Lapointe, Russell Whitesides, Tianfeng Lu, Suresh K. Aggarwal, Sibendu Som
Proc. ASME. ICEF2019, ASME 2019 Internal Combustion Engine Division Fall Technical Conference, V001T02A015, October 20–23, 2019
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2019-7284
https://doi.org/10.1115/ICEF2019-7284
View Paper titled, Numerical Investigation of a Central Fuel Property Hypothesis Under Boosted Spark-Ignition Conditions
Open the PDF for Numerical Investigation of a Central Fuel Property Hypothesis Under Boosted Spark-Ignition Conditions in another window
Topics: Fuels, Ignition, Flames, Engines, Artificial neural networks, Computational fluid dynamics, Computer simulation, Pressure, Regression models, Temperature
Proceedings Papers

Diesel Engine Acoustic Diagnostics With Machine Learning During Various Degradation Modes

Available to Purchase

Patrick Moore, Dianne Luning Prak, Len Hamilton, Jim Cowart
Proc. ASME. ICEF2019, ASME 2019 Internal Combustion Engine Division Fall Technical Conference, V001T05A001, October 20–23, 2019
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2019-7120
https://doi.org/10.1115/ICEF2019-7120
View Paper titled, Diesel Engine Acoustic Diagnostics With Machine Learning During Various Degradation Modes
Open the PDF for Diesel Engine Acoustic Diagnostics With Machine Learning During Various Degradation Modes in another window
Topics: Acoustics, Diesel engines, Machine learning, Engines, Combustion, Cylinders, Acoustic emissions, Air filters, Artificial neural networks, Errors
Proceedings Papers

Development of an Artificial Neural Network Model for the Performance Prediction of a Variable Compression Ratio Gasoline Spark Ignition Engine

Available to Purchase

Srinibas Tripathy, Sridhar Sahoo, Dhananjay Kumar Srivastava
Proc. ASME. ICEF2019, ASME 2019 Internal Combustion Engine Division Fall Technical Conference, V001T07A002, October 20–23, 2019
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2019-7118
https://doi.org/10.1115/ICEF2019-7118
View Paper titled, Development of an Artificial Neural Network Model for the Performance Prediction of a Variable Compression Ratio Gasoline Spark Ignition Engine
Open the PDF for Development of an Artificial Neural Network Model for the Performance Prediction of a Variable Compression Ratio Gasoline Spark Ignition Engine in another window
Topics: Artificial neural networks, Compression, Engines, Gasoline, Spark-ignition engine
Proceedings Papers

Diesel Engine Acoustic Emission Airflow Clogging Diagnostics With Machine Learning

Available to Purchase

Jim Cowart, Patrick Moore, Harrison Yosten, Leonard Hamilton, Dianne Luning Prak
Proc. ASME. ICEF2018, Volume 2: Emissions Control Systems; Instrumentation, Controls, and Hybrids; Numerical Simulation; Engine Design and Mechanical Development, V002T05A008, November 4–7, 2018
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2018-9601
https://doi.org/10.1115/ICEF2018-9601
View Paper titled, Diesel Engine Acoustic Emission Airflow Clogging Diagnostics With Machine Learning
Open the PDF for Diesel Engine Acoustic Emission Airflow Clogging Diagnostics With Machine Learning in another window
Topics: Acoustic emissions, Air flow, Diesel engines, Machine learning, Engines, Combustion, Acoustics, Cylinders, Air filters, Artificial neural networks
Proceedings Papers

Application of a Rectified Linear Unit (ReLU) Based Artificial Neural Network to Cetane Number Predictions

Available to Purchase

Travis Kessler, Gregory Dorian, J. Hunter Mack
Proc. ASME. ICEF2017, Volume 1: Large Bore Engines; Fuels; Advanced Combustion, V001T02A006, October 15–18, 2017
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2017-3614
https://doi.org/10.1115/ICEF2017-3614
View Paper titled, Application of a Rectified Linear Unit (ReLU) Based Artificial Neural Network to Cetane Number Predictions
Open the PDF for Application of a Rectified Linear Unit (ReLU) Based Artificial Neural Network to Cetane Number Predictions in another window
Topics: Artificial neural networks, Fuels, Errors, Databases, Testing
Proceedings Papers

Control Oriented Modelling of Engine IMEP Variation

Available to Purchase

Qilun Zhu, Robert Prucka, Shu Wang, Michael Prucka, Hussein Dourra
Proc. ASME. ICEF2016, ASME 2016 Internal Combustion Engine Division Fall Technical Conference, V001T05A001, October 9–12, 2016
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2016-9342
https://doi.org/10.1115/ICEF2016-9342
View Paper titled, Control Oriented Modelling of Engine IMEP Variation
Open the PDF for Control Oriented Modelling of Engine IMEP Variation in another window
Topics: Engines, Modeling, Combustion, Artificial neural networks, Turbulence, Actuators, Cycles, Cylinders, Engineers, Pressure
Proceedings Papers

Predicting the Cetane Number of Furanic Biofuel Candidates Using an Improved Artificial Neural Network Based on Molecular Structure

Available to Purchase

Travis Kessler, Eric R. Sacia, Alexis T. Bell, J. Hunter Mack
Proc. ASME. ICEF2016, ASME 2016 Internal Combustion Engine Division Fall Technical Conference, V001T02A010, October 9–12, 2016
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2016-9383
https://doi.org/10.1115/ICEF2016-9383
View Paper titled, Predicting the Cetane Number of Furanic Biofuel Candidates Using an Improved Artificial Neural Network Based on Molecular Structure
Open the PDF for Predicting the Cetane Number of Furanic Biofuel Candidates Using an Improved Artificial Neural Network Based on Molecular Structure in another window
Topics: Artificial neural networks, Biofuel, Fuels, Databases, Ignition, Errors, Methane, Testing, ASTM standards, Biomass
Proceedings Papers

Artificial Neural Network for Predicting Cetane Number of Biofuel Candidates Based on Molecular Structure

Available to Purchase

T. Sennott, C. Gotianun, R. Serres, M. Ziabasharhagh, J. H. Mack, R. Dibble
Proc. ASME. ICEF2013, Volume 2: Fuels; Numerical Simulation; Engine Design, Lubrication, and Applications, V002T02A009, October 13–16, 2013
Publisher: American Society of Mechanical Engineers
Paper No: ICEF2013-19185
https://doi.org/10.1115/ICEF2013-19185
View Paper titled, Artificial Neural Network for Predicting Cetane Number of Biofuel Candidates Based on Molecular Structure
Open the PDF for Artificial Neural Network for Predicting Cetane Number of Biofuel Candidates Based on Molecular Structure in another window
Topics: Artificial neural networks, Biofuel, Fuels, Diesel, Ethers (Class of compounds), Gasoline, Ignition, Testing