Scientific Machine Learning (SciML) and Artificial Intelligence (AI) will have broad use and
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …

Kinetic model identification relies on accurate concentration measurements and physical
constraints to limit solution multiplicity. Not having these measurements and prior knowledge …

Three artificial neural network (ANN) models were developed to predict the fluid properties
of China RP-3 kerosene under supercritical pressure in replacement of the time-consuming …

Optimizing the distribution of heat release rate (HRR) is the key to improve the performance
of various combustors. However, limited by current diagnostic techniques, the spatial …

A dynamic zone flamelet model is proposed and validated in this study. The model divides
the whole computational domain into different zones and uses a local flamelet to describe …

High-performance computing communities are increasingly adopting Neural Networks (NN)
as surrogate models in their applications to generate scientific insights. Replacing an …

The more properties of biodiesel one wishes to represent, the more complex the surrogate
mixture becomes. Often the aim is to represent the H/C and O/C ratios and molecular weight …

Numerical simulations involving detailed kinetic combustion reaction mechanisms for long
chain fuels are associated with prohibitive computational costs. Thus, there is the need for …

To alleviate the huge computational cost in supersonic combustor modeling and to improve
the accuracy of traditional unsteady flamelet model, a zone flamelet is proposed. The main …

The computational treatment of detailed kinetic reaction mechanisms for combustion is
expensive, especially in the case of biodiesel fuels. In this way, great efforts in the search of …