A rotating detonation engine (RDE) is a realization of pressure-gain combustion, wherein a
traveling detonation wave confined in a chamber provides shock-based compression along …

Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …

Combustion science is an interdisciplinary study that involves nonlinear physical and
chemical phenomena in time and length scales, including complex chemical reactions and …

Solving for detailed chemical kinetics remains one of the major bottlenecks for
computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry …

Emerging supercomputing systems utilize a combination of central processing units (CPUs)
and graphics processing units (GPUs) in an effort to reach exascale capabilities while …

Rotating detonation engines (RDEs) offer increased thermal efficiencies and continuous
thrust in a compact design. While non-premixed RDEs, where fuel and oxidizer are injected …

A data-based reduced-order model (ROM) is developed to accelerate the time integration of
stiff chemically reacting systems by effectively removing the stiffness arising from a wide …

With the increase in computational power in the last decade and the forthcoming Exascale
supercomputers, a new horizon in computational modelling and simulation is envisioned in …

We propose a new modeling strategy to build efficient neural network representations of
chemical kinetics. Instead of fitting the logarithm of rates, we embed the hyperbolic sine …

We leverage the computational singular perturbation (CSP) theory to develop an adaptive
time-integration scheme for stiff chemistry based on a local, projection-based, reduced order …