Transfer learning for predicting source terms of principal component transport in chemically reactive flow

The objective of this study is to evaluate whether the number of requisite training samples can be reduced with the use of various transfer learning models for predicting, for example, the chemical source terms of the data-driven reduced-order model that represents the homogeneous ignition process of a hydrogen/air mixture. Principal component analysis is applied to reduce the dimensionality of the hydrogen/air mixture in composition space. Artificial neural networks (ANNs) are used to tabulate the reaction rates of principal components, and subsequently, a system of ordinary differential equations is solved. As the number of training samples decreases at the target task (i.e.,for T0 > 1000 K and various phi), the reduced-order model fails to predict the ignition evolution of a hydrogen/air mixture. Three transfer learning strategies are then applied to the training of the ANN model with a sparse dataset. The performance of the reduced-order model with a sparse dataset is found to be remarkably enhanced if the training of the ANN model is restricted by a regularization term that controls the degree of knowledge transfer from source to target tasks. To this end, a novel transfer learning method is introduced, parameter control via partial initialization and regularization (PaPIR), whereby the amount of knowledge transferred is systemically adjusted for the initialization and regularization of the ANN model in the target task. It is found that an additional performance gain can be achieved by changing the initialization scheme of the ANN model in the target task when the task similarity between source and target tasks is relatively low.Comment: 41 pages, 14 figure

Autoignition of n-heptane in a turbulent co-flowing jet

N-heptane autoignition in turbulent co-flowing jets with preheated air is studied using the one-dimensional turbulence (ODT) model. The simulations are designed to investigate the effects of molecular and turbulent transports on the process of autoignition. Both homogeneous and jet configuration simulations are carried out. The jet configurations are implemented at different jet inlet Reynolds numbers and for two air preheat conditions. Statistics for the cases considered show that, while the onset of autoignition may be delayed by turbulence, the eventual evolution of the volumetric heat release rate indicates that turbulence enhances the post-ignition stages. Since different regions of the mixture can have different ignition delays and may be characterized by one- or two-stage ignition, the autoignition process can be accelerated by ignition kernel propagation or the role of heat dissipation may be reduced through the prevalence of one-stage and two-stage ignitions in different regions of the mixture.This paper was made possible by an NPRP award [NPRP 6-105-2-039] from the Qatar National Research Fund (a member of The Qatar Foundation)

Turbulence effects on the autoignition of DME in a turbulent co-flowing jet

Dimethyl ether (DME) autoignition in turbulent co-flowing jets with preheated air is studied using the one-dimensional turbulence (ODT) model. We investigate the effects of molecular and turbulent transport on the autoignition process at different jet Reynolds numbers and two air preheat conditions. Statistics for the cases considered show that the overall effects of turbulence and molecular transport can serve to delay or accelerate autoignition depending upon where ignition starts, the presence of 2-stage or single-stage ignition and the variations in ignition delay times in mixture fraction space. For the higher temperature air preheat cases, the classical view that autoignition is delayed by turbulence is established. For the lower preheat air temperature cases, we show that low-temperature chemistry associated with first-stage ignition can help accelerate the autoignition process and the transition to high-temperature chemistry. This acceleration can reduce the ignition delay time by as much as a factor of 2. Given this work and previous work by the authors based on a different fuel, n-heptane, we find that the ignition delay map based on homogeneous ignition for different mixture fractions can provide a preview of the ignition scenarios for the co-flowing jet configuration regardless of the choice of fuel considered.This paper was made possible by an NPRP award [NPRP 6-105-2-039] from the Qatar National Research Fund (a member of The Qatar Foundation)

Comparisons of Different Representative Species Selection Schemes for Reduced-Order Modeling and Chemistry Acceleration of Complex Hydrocarbon Fuels

The simulation of engine combustion processes, such as autoignition, an important process in the co-optimization of fuel-engine design, can be computationally expensive due to the large number of thermo-chemical scalars needed to describe the full chemical system. Yet, the inherent correlations between the different chemical species during oxidation can significantly reduce the complexity of representing this system. One strategy is to select a subset of representative species that accurately captures the combustion process at a fraction of the computational cost of the full system. In this study, we compare the performance of four different techniques to select these species. They include the two-step principal component analysis (PCA) approach, directed relation graphs (DRGs), the global pathway selection (GPS) approach, and the manifold-informed species selection method. A parametric study of the representative species selection is carried out on data from the simulation of homogeneous and perfectly stirred reactors by investigating seven cumulative variances and 47 different cut-off percentages for the two-step PCA, and 65 and 51 thresholds for the DRGs and GPS, respectively. Results show that these selection methods capture key important species that can accurately describe the chemical system and track each stage of oxidation. The two-step PCA is sensitive to the cumulative variance, and DRGs and GPS are sensitive to the choice of target variables. By selecting key representative species and reducing the number of thermo-chemical scalars, these three methods can be used to develop computationally efficient hybrid chemistry schemes