About this Research Topic
Understanding the behavior of reacting flow fields in turbulent combustors is highly complex due to the cumulative interactions between various variables. The nonlinear interactions among these variables contribute to the unique behavior of combustors. It is crucial to study and report these behaviors to enable engine manufacturers to make informed modifications in the future. However, researchers often struggle to find effective methods for understanding combustor systems. Discrepancy regarding combustor behavior and influence of flow parameters call for continued and further research, with a clearer understanding of these dynamics being essential for successful future designs.
When it comes to selecting the proper method, physics-inspired data-driven techniques can be highly effective for predicting the dynamics of combustors. However, the limited understanding of these techniques has restricted their application in several areas, including real-time prediction of turbulent combustor behavior, combustor modeling, and understanding the flow physics of industrial combustors.
This Research Topic is focused on highlighting recent advancements in combustion and exploring the integration of physics-based learning and data-driven approaches, to enhance the understanding of the dynamics in turbulent combustors. We hope this Research Topic will stimulate discussion in the field and encourage future collaborations.
Areas within the scope of this collection include, but are not limited to:
• Understanding the dynamics of turbulent combustors
• Real-time data-driven prediction of abnormal combustor behavior
• Developing data-driven techniques for combustor flow dynamics
• Modeling reacting flows to aid in the design of future combustors
• Applying machine learning to detect combustion system behavior
• Investigating turbulence in reacting combustor flows using physics-inspired approaches
• Analyzing the thermal behavior of industrial burners
• Developing theoretical and numerical models for turbulent combustors.
When it comes to selecting the proper method, physics-inspired data-driven techniques can be highly effective for predicting the dynamics of combustors. However, the limited understanding of these techniques has restricted their application in several areas, including real-time prediction of turbulent combustor behavior, combustor modeling, and understanding the flow physics of industrial combustors.
This Research Topic is focused on highlighting recent advancements in combustion and exploring the integration of physics-based learning and data-driven approaches, to enhance the understanding of the dynamics in turbulent combustors. We hope this Research Topic will stimulate discussion in the field and encourage future collaborations.
Areas within the scope of this collection include, but are not limited to:
• Understanding the dynamics of turbulent combustors
• Real-time data-driven prediction of abnormal combustor behavior
• Developing data-driven techniques for combustor flow dynamics
• Modeling reacting flows to aid in the design of future combustors
• Applying machine learning to detect combustion system behavior
• Investigating turbulence in reacting combustor flows using physics-inspired approaches
• Analyzing the thermal behavior of industrial burners
• Developing theoretical and numerical models for turbulent combustors.
Keywords: combustion, data-driven techniques, flow physics, turbulent combustors, physics-based learning, combustor flow dynamics, machine learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
