Recent developments in DNS of turbulent combustion

IF 5.3 2区 工程技术 Q2 ENERGY & FUELS
Pascale Domingo, Luc Vervisch
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引用次数: 6

Abstract

The simulation of turbulent flames fully resolving the smallest flow scales and the thinnest reaction zones goes along with specific requirements, which are discussed from dimensionless numbers useful to introduce the generic context in which direct numerical simulation (DNS) of turbulent flames is performed. Starting from this basis, the evolution of the DNS landscape over the past five years is reviewed. It is found that the flow geometries, the focus of the studies and the overall motivations for performing DNS have broadened, making DNS a standard tool in numerical turbulent combustion. Along these lines, the emerging DNS of laboratory burners for turbulent flame modeling development is discussed and illustrated from DNS imbedded in Large Eddy Simulation (LES) and flow resolved simulation of bluff-body flames. The literature shows that DNS generated databases constitute a fantastic playground for developing and testing a large spectrum of promising machine learning methods for the control and the optimisation of combustion systems, including novel numerical approaches based on the training of neural networks and which can be evaluated in DNS free from sub-model artefacts. The so-called quasi-DNS is also progressively entering the optimisation loop of combustion systems, with the application of techniques to downsize real combustion devices in order to perform fully resolved simulations of their complex geometries. An example of such study leading to the improvement of an incinerator efficiency is reported. Finally, numbers are given relative to the carbon footprint of the generation of DNS databases, motivating the crucial need for community building around database sharing.

湍流燃烧的DNS研究进展
湍流火焰的模拟完全解决了最小的流动尺度和最薄的反应区,符合特定的要求,从无量纲数的角度进行讨论,有助于介绍进行湍流火焰直接数值模拟的一般背景。在此基础上,回顾了过去五年DNS格局的演变。研究发现,流动几何形状、研究重点和执行DNS的总体动机已经拓宽,使DNS成为数值湍流燃烧的标准工具。在此基础上,本文讨论了用于湍流火焰建模开发的实验室燃烧器的DNS,并从嵌入在大涡模拟(LES)中的DNS和陡体火焰的流动分解模拟中进行了说明。文献表明,DNS生成的数据库为开发和测试大量有前途的机器学习方法提供了一个绝佳的平台,用于控制和优化燃烧系统,包括基于神经网络训练的新型数值方法,这些方法可以在DNS中进行评估,而不需要子模型工件。所谓的准dns也正在逐步进入燃烧系统的优化循环,通过应用技术缩小真实燃烧装置的尺寸,以便对其复杂的几何形状进行完全解析的模拟。本文报告了一个通过这种研究提高焚化炉效率的实例。最后,给出了与生成DNS数据库的碳足迹相关的数字,这激发了围绕数据库共享建立社区的关键需求。
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来源期刊
Proceedings of the Combustion Institute
Proceedings of the Combustion Institute 工程技术-工程:化工
CiteScore
7.00
自引率
0.00%
发文量
420
审稿时长
3.0 months
期刊介绍: The Proceedings of the Combustion Institute contains forefront contributions in fundamentals and applications of combustion science. For more than 50 years, the Combustion Institute has served as the peak international society for dissemination of scientific and technical research in the combustion field. In addition to author submissions, the Proceedings of the Combustion Institute includes the Institute''s prestigious invited strategic and topical reviews that represent indispensable resources for emergent research in the field. All papers are subjected to rigorous peer review. Research papers and invited topical reviews; Reaction Kinetics; Soot, PAH, and other large molecules; Diagnostics; Laminar Flames; Turbulent Flames; Heterogeneous Combustion; Spray and Droplet Combustion; Detonations, Explosions & Supersonic Combustion; Fire Research; Stationary Combustion Systems; IC Engine and Gas Turbine Combustion; New Technology Concepts The electronic version of Proceedings of the Combustion Institute contains supplemental material such as reaction mechanisms, illustrating movies, and other data.
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