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.

Large eddy simulation of pressure and dilution-jet effects on soot formation in a model aircraft swirl combustor, Combust. Flame 192 (2018) 452-472

The computational modeling of soot in aircraft engines is a formidable challenge, not only due to the multiscale interactions with the turbulent combustion process but the equally complex physical and chemical processes that drive the conversion of gas-phase fuel molecules into solid-phase particles. In particular, soot formation is highly sensitive to the gas-phase composition and temporal fluctuations in a turbulent background flow. In this work, a large-eddy simulation (LES) framework is used to study the soot formation in a model aircraft combustor with swirl-based fuel and air injection. Two different configurations are simulated: one with and one without secondary oxidation jets. Specific attention is paid to the LES numerical implementation such that the discrete solver minimizes the dissipation of kinetic energy. Simulation of the model combustor shows that the LES approach captures the two recirculation zones necessary for flame stabilization very accurately. Further, the model reasonably predicts the temperature profiles inside the combustor. The model also captures variation in soot volume fraction with global equivalence ratio. The structure of the soot field suggests that when secondary oxidation jets are present, the inner recirculation region becomes fuel lean, and soot generation is completely suppressed. Further, the soot field is highly intermittent suggesting that a very restrictive set of gas-phase conditions promotes soot generation.

In lean premixed combustors, flame stabilization is an important operational concern that can affect efficiency, robustness and pollutant formation. The focus of this paper is on flame lift-off and re-attachment to the nozzle of a swirl combustor. Using time-resolved experimental measurements, a data-driven approach known as cluster-based reduced order modeling (CROM) is employed to 1) isolate key flow patterns and their sequence during the flame transitions, and 2) formulate a forecasting model to predict the flame instability. The flow patterns isolated by the CROM methodology confirm some of the experimental conclusions about the flame transition mechanism. In particular, CROM highlights the key role of the precessing vortex core (PVC) in the flame detachment process in an unsupervised manner. For the attachment process, strong flow recirculation far from the nozzle appears to drive the flame upstream, thus initiating re-attachment. Different data-types (velocity field, OH concentration) were processed by the modeling tool, and the predictive capabilities of these different models are also compared. It was found that the swirling velocity possesses the best predictive properties, which gives a supplemental argument for the role of the PVC in causing the flame transition. The model is tested against unseen data and successfully predicts the probability of flame transition (both detachment and attachment) when trained with swirling velocity with minimal user input. The model trained with OH-PLIF data was only successful at predicting the flame attachment, which implies that different physical mechanisms are present for different types of flame transition. Overall, these aspects show the great potential of data-driven methods, particularly probabilistic forecasting techniques, in analyzing and predicting large-scale features in complex turbulent combustion problems.

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