[HTML][HTML] Machine learning for combustion
Combustion science is an interdisciplinary study that involves nonlinear physical and
chemical phenomena in time and length scales, including complex chemical reactions and …
chemical phenomena in time and length scales, including complex chemical reactions and …
Applications of machine learning to the analysis of engine in-cylinder flow and thermal process: A review and outlook
F Zhao, DLS Hung - Applied Thermal Engineering, 2023 - Elsevier
To adequately elucidate the complex in-cylinder flow structures and its underlying effects on
the thermal processes inside an internal combustion engine (ICE) has long been a daunting …
the thermal processes inside an internal combustion engine (ICE) has long been a daunting …
Towards a comprehensive optimization of engine efficiency and emissions by coupling artificial neural network (ANN) with genetic algorithm (GA)
In response to the stringent emission regulations, artificial neural network (ANN) coupled
with genetic algorithm (GA) is employed to optimize a novel internal combustion engine …
with genetic algorithm (GA) is employed to optimize a novel internal combustion engine …
Physics-integrated segmented Gaussian process (SegGP) learning for cost-efficient training of diesel engine control system with low cetane numbers
View Video Presentation: https://doi. org/10.2514/6.2023-1283. vid Control model training is
an essential step towards the development of an engine controls system. A robust controls …
an essential step towards the development of an engine controls system. A robust controls …
Colmena: Scalable machine-learning-based steering of ensemble simulations for high performance computing
Scientific applications that involve simulation ensembles can be accelerated greatly by
using experiment design methods to select the best simulations to perform. Methods that use …
using experiment design methods to select the best simulations to perform. Methods that use …
A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design
A novel design optimization approach (ActivO) that employs an ensemble of machine
learning algorithms is presented. The proposed approach is a surrogate-based scheme …
learning algorithms is presented. The proposed approach is a surrogate-based scheme …
An automated machine learning-genetic algorithm framework with active learning for design optimization
The use of machine learning (ML)-based surrogate models is a promising technique to
significantly accelerate simulation-driven design optimization of internal combustion (IC) …
significantly accelerate simulation-driven design optimization of internal combustion (IC) …
CFD optimization of the pre-chamber geometry for a gasoline spark ignition engine
In the present paper, an efficient optimization method based on Bayesian updating strategy
is developed for the design of a spark-ignition engine equipped with pre-chamber. 3D …
is developed for the design of a spark-ignition engine equipped with pre-chamber. 3D …
[HTML][HTML] Combining machine learning with 3D-CFD modeling for optimizing a DISI engine performance during cold-start
AC Ravindran, SL Kokjohn - Energy and AI, 2021 - Elsevier
This work presents a methodology for using machine learning (ML) techniques in
combination with 3D computational fluid dynamics (CFD) modeling to optimize the cold-start …
combination with 3D computational fluid dynamics (CFD) modeling to optimize the cold-start …
Deep-learning-based reduced-order modeling to optimize recuperative burner operating conditions
This study analyzed a recuperative burner system that is critical for energy efficiency and
pollutant reduction in the firing processes required in the manufacturing industries. We …
pollutant reduction in the firing processes required in the manufacturing industries. We …