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Simultaneous measurements, such as the combination of particle image velocimetry (PIV) for velocity fields with planar laser induced fluorescence (PLIF) for species fields, are widely used in experimental turbulent combustion applications for the analysis of a plethora of complex physical processes. Such physical analyses are driven by the interpretation of spatial correlations between these fields by the experimenter. However, these correlations also imply some amount of intrinsic redundancy; the simultaneous fields contain overlapping information content. The goal of this work lies in the quantitative extraction of this overlapping information content in simultaneous field measurements. Specifically, the amount of PIV information contained in simultaneously measured OH-PLIF fields in the domain of a swirl-stabilized combustor is sought. This task is accomplished using machine learning techniques based on artificial neural networks designed to optimize PLIF-to-PIV mappings. It was found that most of the velocity information content could be retrieved when considering linear combinations of neighborhoods of OH-PLIF signal spanning roughly two integral lengthscales (half of the considered domain), and that PLIF signal interactions contained in smaller, local regions (less than half of the domain) contained no PIV information. Further, by visualizing the coherent structures contained within the neural network parameters, the role of multi-scale interactions related to velocity field retrieval from the OH-PLIF signal became more apparent. Overall, this study reveals a useful pathway (in the form of overlapping information content extraction) to develop diagnostic tools that capture more information using the same experimental resources by minimizing redundancy.

A data-driven approach to classify combustion regimes in detonation waves is implemented, and a procedure for domain-localized source term modeling based on these classifications is demonstrated. The models were generated from numerical datasets of canonical detonation simulations. In the first phase, delineations of combustion regimes within the detonation wave structure were analyzed through a clustering procedure. The clustering output usefully illuminated distinctions between detonation, deflagration, and intermediary regimes within the wave structure. In the second phase, the resulting delineated fields from the clustering step were used to guide localized source term modeling via artificial neural networks (ANNs), enabling a type of classification-based regression approach for source term estimation. A comparison of the estimations obtained from the local ANNs (trained for a subset of the domain given by a particular cluster) with the global ANN counterparts (trained agnostic to the clustering) showed general improvement of estimations provided by the domain-localized modeling in most cases. Ultimately, this work illuminates the useful role of data-driven classification and regression techniques for both physical analysis of the complex wave structure and for the development of new models which may serve as suitable pathways for long-time simulations of complex combustion systems (such as rotating detonation combustors).

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., Preprint accepted to Combustion Theory and Modelling

For safety purposes, reliable reignition of aircraft engines in the event of flame blow-out is a critical requirement. Typically, an external ignition source in the form of a spark is used to achieve a stable flame in the combustor. However, such forced turbulent ignition may not always successfully relight the combustor, mainly because the state of the combustor cannot be precisely determined. Uncertainty in the turbulent flow inside the combustor, inflow conditions, and spark discharge characteristics can lead to variability in sparking outcomes even for nominally identical operating conditions. Prior studies have shown that of all the uncertain parameters, turbulence is often dominant and can drastically alter ignition behavior. For instance, even when different fuels have similar ignition delay times, their ignition behavior in practical systems can be completely different. In practical operating conditions, it is challenging to understand why ignition fails and how much variation in outcomes can be expected. The focus of this work is to understand relight variability induced by turbulence for two different aircraft fuels, namely Jet-A and a variant named C1. A detailed, previously developed simulation approach is used to generate a large number of successful and failed ignition events. Using this data, the cause of misfire is evaluated based on a discriminant analysis that delineates the difference between turbulent initial conditions that lead to ignition or failure. From the discriminant analysis, a compressed sensing algorithm is then applied to help pinpoint the locations of relevant turbulent features. Findings from the discriminant analysis are confirmed with the time history of near kernel properties. Next, a clustering strategy is used to identify ignition and misfire modes. With this approach, it was determined that the cause of ignition failure is different for the two fuels. While it was found that Jet-A is influenced by fuel entrainment, C1 was found to be more sensitive to small scale turbulence features. A larger variability is found in the ignition modes of C1, which can be subject to extreme events induced by kernel breakdown.

Time-resolved X-ray densitometry void fraction measurements and accompanying acoustic emissions have revealed that partial cavity shedding on a hydrofoil can be multimodal, with spontaneous changes in shedding sequence (referred to here as cavitation style) for fixed inlet flow conditions. These spontaneous, intermittent transitions between two physically different shedding styles are examined using data-based image analysis of the cavity flow fields in order to extract the associated physical mechanism leading to style transition. Three data-driven decomposition techniques are compared: proper orthogonal decomposition (POD), dynamic mode decomposition (DMD), and cluster-based reduced-order modeling (CROM), with a primary focus on the latter. The results highlight the utility of CROM over DMD and POD in the context of intermittent event analysis, both in terms of mode interpretability and transition mechanism identification. A frequency-based analysis of the CROM output revealed the existence of a shared harmonic between the different physical cavitation modes which gave further insight to the transition process. The data-based analysis ultimately illuminates the underlying flow mechanism that leads to the style transition, namely the key role of maximum cavity length buildup as it interacts with the vapor cloud collapse downstream of the hydrofoil.

This work utilizes data-driven methods to morph a series of time-resolved experimental OH-PLIF images into corresponding three-component planar PIV fields in the closed domain of a premixed swirl combustor. The task is carried out with a fully convolutional network, which is a type of convolutional neural network (CNN) used in many applications in machine learning, alongside an existing experimental dataset which consists of simultaneous OH-PLIF and PIV measurements in both attached and detached flame regimes. Two types of models are compared: 1) a global CNN which is trained using images from the entire domain, and 2) a set of local CNNs, which are trained only on individual sections of the domain. The locally trained models show improvement in creating mappings in the detached regime over the global models. A comparison between model performance in attached and detached regimes shows that the CNNs are much more accurate across the board in creating velocity fields for attached flames. Inclusion of time history in the PLIF input resulted in small noticeable improvement on average, which could imply a greater physical role of instantaneous spatial correlations in the decoding process over temporal dependencies from the perspective of the CNN. Additionally, the performance of local models trained to produce mappings in one section of the domain is tested on other, unexplored sections of the domain. Interestingly, local CNN performance on unseen domain regions revealed the models' ability to utilize symmetry and antisymmetry in the velocity field. Ultimately, this work shows the powerful ability of the CNN to decode the three-dimensional PIV fields from input OH-PLIF images, providing a potential groundwork for a very useful tool for experimental configurations in which accessibility of forms of simultaneous measurements are limited.