Computational and Data-Driven Modeling of Turbulent Combustion and Engine Combustion Dynamics
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "I2: Energy and Combustion Science".
Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 21071
Special Issue Editor
Interests: turbulent combustion modeling; computational fluid dynamics; physics-informed machine learning; deep learning; multi-fidelity uncertainty quantification; high-order numerical methods; high-performance computing; combustion dynamics; low temperature combustion; alternative fuels; extreme combustion events; internal combustion engines; gas turbines; detonation engines; nanoparticle synthesis in flame sprays; engine design optimization
Special Issue Information
Dear Colleagues,
Combustion systems are ubiquitous in a wide range of power generation and propulsion (automotive, aviation, and spacecraft) applications. With the ever-increasing demand for higher fuel economy and reduction of pollutant emissions, the combustion community is striving toward the development of advanced combustion engines. As the efficiency of these engines keeps improving, their operating regime continues to be pushed closer to the boundary between stable and unstable combustion. In this operating regime, occurrence of stochastic combustion phenomena, governed by complex combustion dynamics, poses a severe challenge to engine performance and durability. Computational and data-driven approaches can play a major role in enabling a better understanding of combustion dynamics and development of reduced-order models to aid reliable and high-efficiency engine design.
In this context, this Special Issue is dedicated to combustion research advances, both fundamental and applied, in the area of numerical as well as data-driven modeling/analysis of combustion dynamics and stochastic combustion phenomena in reacting flow-based energy systems. Topics of interest include but are not limited to the application of computational fluid dynamics (CFD) and/or machine learning techniques for the investigation of cycle-to-cycle variability and knock in reciprocating engines, lean blow-out and combustion instabilities in gas turbine engines and rocket motors, and detonation engines.
Dr. Pinaki Pal
Guest Editor
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Keywords
- combustion dynamics
- turbulent combustion
- autoignition
- turbulent flames
- combustion modeling
- computational fluid dynamics
- numerical analysis
- modeling and simulation
- data-driven modeling
- machine learning
- deep learning
- internal combustion engines
- gas turbines
- rocket engines
- detonation engines
- abnormal combustion
- combustion instability
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