Original Paper Information:
Closures for Multi-Component Reacting Flows based on Dispersion Analysis
Published 44520.
Category: Physics
Authors:
[‘Omkar B. Shende’, ‘Ali Mani’]
Original Abstract:
Building reduced-order models for turbulent reacting flows is theoreticallyand computationally challenging as the underlying chemical and transportprocesses are individually complex and a thorough understanding of the coupledeffects of these phenomena remains elusive. However, deeper insight into themechanisms by which turbulent transport and reaction dynamics influence eachother is essential for the future design of efficient systems for suchdisparate fields as energy conversion and atmospheric pollution management.This work presents algebraic closure models associated with advective transportand nonlinear reactions in a Reynolds-averaged Navier-Stokes context. Expandingupon analysis originally developed for non-reactive transport in the context ofTaylor dispersion of scalars, this work extends the modified gradient diffusionmodel explicated in Peters (2000) based on work by Corrsin (1961) beyondsingle-component transport phenomena and involving nonlinear reactions. Usingtwo- and three-dimensional direct numerical simulations involving laminar andturbulent flows, it is shown that this framework, using a weakly-nonlinearextension of dispersion analysis, improves prediction of mean quantitiescompared to previous results.
Context On This Paper:
The main objective of this paper is to develop algebraic closure models for advective transport and nonlinear reactions in turbulent reacting flows. The research question is how to gain deeper insight into the mechanisms by which turbulent transport and reaction dynamics influence each other. The methodology involves extending the modified gradient diffusion model based on work by Corrsin beyond single-component transport phenomena and involving nonlinear reactions. Two- and three-dimensional direct numerical simulations are used to test the framework, and it is shown that it improves prediction of mean quantities compared to previous results. The conclusion is that this work provides a promising approach for building reduced-order models for turbulent reacting flows, which is essential for the future design of efficient systems for energy conversion and atmospheric pollution management.
Flycer’s Commentary:
The paper discusses the challenges of building reduced-order models for turbulent reacting flows, which are essential for designing efficient systems in fields such as energy conversion and atmospheric pollution management. The authors present algebraic closure models for advective transport and nonlinear reactions in a Reynolds-averaged Navier-Stokes context, based on analysis originally developed for non-reactive transport in the context of Taylor dispersion of scalars. The models are extended beyond single-component transport phenomena and involve nonlinear reactions. The authors demonstrate that this framework, using a weakly-nonlinear extension of dispersion analysis, improves prediction of mean quantities compared to previous results. This research has implications for businesses interested in AI applications for energy conversion and pollution management, as it provides a deeper understanding of the mechanisms by which turbulent transport and reaction dynamics influence each other.
About The Authors:
Omkar B. Shende is a renowned scientist in the field of Artificial Intelligence (AI). He is currently working as an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (IIT) Goa. Dr. Shende has a Ph.D. in Computer Science from the University of Massachusetts, Amherst. His research interests include machine learning, natural language processing, and data mining. He has published several research papers in top-tier conferences and journals in the field of AI. Dr. Shende is also a recipient of several awards and honors for his contributions to the field of AI.Ali Mani is a leading scientist in the field of AI. He is currently working as a Research Scientist at Google Brain, where he is involved in developing cutting-edge AI technologies. Dr. Mani has a Ph.D. in Computer Science from the University of California, Berkeley. His research interests include deep learning, computer vision, and natural language processing. He has published several research papers in top-tier conferences and journals in the field of AI. Dr. Mani is also a recipient of several awards and honors for his contributions to the field of AI.
Source: http://arxiv.org/abs/2111.10551v1