Emerging trends in numerical simulations of combustion systems

Abstract

Numerical simulations have played a vital role in the design of modern combustion systems. Over the last two decades, the focus of research has been on the development of the large eddy simulation (LES) approach, which leveraged the vast increase in computing power to dramatically improve predictive accuracy. Even with the anticipated increase in supercomputing capabilities, the use of LES in design is limited by its high computational cost. Moreover, to aid decision making, such LES computations have to be augmented to estimate underlying uncertainties in simulation components. At the same time, other changes are happening across industries that build or use combustion devices. While efficiency and emissions reduction are still the primary design objectives, reducing cost of operation by optimizing maintenance and repair is becoming an important segment of the enterprise. This latter quest is aided by the digitization of combustors, which allows collection and storage of operational data from a host of sensors over a fleet of devices. Moreover, several levels of computing including low-power hardware present on board the combustion systems are becoming available. Such large data sets create unique opportunities for design and maintenance if appropriate numerical tools are made available. As LES revolutionized computing-guided design by leveraging supercomputing, a new generation of numerical approaches is needed to utilize this vast amount of data and the varied nature of computing hardware. In this article, a review of emerging computational approaches for this heterogeneous data-driven environment is provided. A case is made that new but unconventional opportunities for physics-based combustion modeling exist in this realm.

Computational Modeling of Boundary Layer Flashback in a Swirling Stratified Flame Using a LES-Based Non-Adiabatic Tabulated Chemistry Approach

“…In this work, the large eddy simulation (LES) approach is used to model turbulent flame propagation. It is now well-established that LES can capture transient and unsteady effects in turbulent combustion [ 29 , 30 ]. However, LES is not particularly suited for modeling near-wall flows, where the local anisotropy of turbulence renders sub-filter models inaccurate.…”

“…In the LES approach, the flow field is decomposed into resolved and unresolved fields using a spatial filtering operation. Governing equations are solved for the resolved variables, while the effect of the unresolved scales is modeled using statistical closures [ 29 , 30 , 44 ]. The filtered equation for mass conservation is given by: where denotes filtered variable, while denotes Favre or density-weighted filtered variable, is the density, and denotes the velocity in the j -th direction.…”

A Neural Network-Inspired Matrix Formulation of Chemical Kinetics for Acceleration on GPUs

“…Computational modeling of reacting flows has become an integral part of the design and analysis of complex propulsion concepts [1,2]. While a wide variety of tools constitute the modeling pathways, simulations for reacting flows require detailed models for chemical reactions.…”

“…While a wide variety of tools constitute the modeling pathways, simulations for reacting flows require detailed models for chemical reactions. In most applications, the multi-physics behavior stemming from chemical reactions induces a highly complex Arrhenius-based expression for the species source terms, the nonlinearity of which produces an extremely stiff set of equations for the time evolution of the reacting chemical species [1][2][3]. The wide range of timescales in this setting must be resolved to accurately represent the characteristic turbulence-chemistry interactions.…”

Using Machine Learning to Construct Velocity Fields from OH-PLIF Images

“…Ultimately, the utilization of simultaneous measurements allows the practitioner to deduce the physical causes of instantaneous spatial (or spatio-temporal) correlations between these measurements, leading to a better understanding of the underlying complex turbulence-flame interactions. In a broader sense, much of the physical inductions and analysis based on the correlations implied by simultaneous measurements 1) facilitate combustor design, 2) validate/parametrize models used in numerical simulations, and more recently, 3) provide the foundation for data-driven predictive models [9].…”