Exploring the potential of machine learning in reducing the computational time/expense and improving the reliability of engine optimization studies
Published 2018 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Exploring the potential of machine learning in reducing the computational time/expense and improving the reliability of engine optimization studies
Authors
Keywords
-
Journal
International Journal of Engine Research
Volume -, Issue -, Pages 146808741880894
Publisher
SAGE Publications
Online
2018-11-02
DOI
10.1177/1468087418808949
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Computational optimization of the combustion system of a heavy duty direct injection diesel engine operating with dimethyl-ether
- (2018) Jesús Benajes et al. FUEL
- Computational optimization of a reactivity controlled compression ignition (RCCI) combustion system considering performance at multiple modes simultaneously
- (2017) Chaitanya Kavuri et al. FUEL
- Gaussian process regression for forecasting battery state of health
- (2017) Robert R. Richardson et al. JOURNAL OF POWER SOURCES
- A comparison of Reactivity Controlled Compression Ignition (RCCI) and Gasoline Compression Ignition (GCI) strategies at high load, low speed conditions
- (2016) Chaitanya Kavuri et al. ENERGY CONVERSION AND MANAGEMENT
- Blending the benefits of reactivity controlled compression ignition and gasoline compression ignition combustion using an adaptive fuel injection system
- (2016) Chaitanya Kavuri et al. International Journal of Engine Research
- High Load (21 Bar IMEP) Dual Fuel RCCI Combustion Using Dual Direct Injection
- (2014) Jae Hyung Lim et al. JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME
- Reactivity controlled compression ignition and conventional diesel combustion: A comparison of methods to meet light-duty NOx and fuel economy targets
- (2013) Sage L Kokjohn et al. International Journal of Engine Research
- Development of an n-heptane-n-butanol-PAH mechanism and its application for combustion and soot prediction
- (2012) Hu Wang et al. COMBUSTION AND FLAME
- An Analytical Jacobian Approach to Sparse Reaction Kinetics for Computationally Efficient Combustion Modeling with Large Reaction Mechanisms
- (2012) Federico Perini et al. ENERGY & FUELS
- Fuel reactivity controlled compression ignition (RCCI): a pathway to controlled high-efficiency clean combustion
- (2011) S L Kokjohn et al. International Journal of Engine Research
- Optimization of a heavy-duty compression–ignition engine fueled with diesel and gasoline-like fuels
- (2010) Yu Shi et al. FUEL
- Assessment of Multiobjective Genetic Algorithms With Different Niching Strategies and Regression Methods for Engine Optimization and Design
- (2010) Yu Shi et al. JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME
- Numerical Parametric Study of Diesel Engine Operation with Gasoline
- (2009) Youngchul Ra et al. COMBUSTION SCIENCE AND TECHNOLOGY
- Optimization study of the effects of bowl geometry, spray targeting, and swirl ratio for a heavy-duty diesel engine operated at low and high load
- (2008) Y Shi et al. International Journal of Engine Research
- Reduction of Numerical Parameter Dependencies in Diesel Spray Models
- (2008) Neerav Abani et al. JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreFind the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
Search