32 citations · 179 across the 21 of their papers we have counts for
22 papers
Physics-based reduced-order modeling of flash-boiling sprays in the context of internal combustion engines
Avijit Saha, Abhishek Y. Deshmukh, Temistocle Grenga +1
Flash-boiling injection is one of the most effective ways to accomplish improved atomization compared to the high-pressure injection strategy. The tiny droplets formed via flash-bo…
Efficient and accurate calculation of dispersion relations for intrinsically unstable premixed flames
Sofiane Al Kassar, Lukas Berger, Pasquale E. Lapenna +3
Premixed flames are susceptible to hydrodynamic and thermodiffusive instabilities that wrinkle the flame front and lead to complex multiscale patterns. They strongly impact the fla…
Dimensional Analysis of Vapor Bubble Growth Considering Bubble-bubble Interactions in Flash Boiling Microdroplets of Highly Volatile Liquid Electrofuels
Avijit Saha, Abhishek Y. Deshmukh, Temistocle Grenga +1
Electrofuels (e-fuels) produced from renewable electricity and carbon sources have gained significant attention in recent years as promising alternatives to fossil fuels for the tr…
Gradient Information and Regularization for Gene Expression Programming to Develop Data-Driven Physics Closure Models
Fabian Waschkowski, Haochen Li, Abhishek Deshmukh +5
Learning accurate numerical constants when developing algebraic models is a known challenge for evolutionary algorithms, such as Gene Expression Programming (GEP). This paper intro…
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data
Mathis Bode, Michael Gauding, Dominik Goeb +2
Models for finite-rate-chemistry in underresolved flows still pose one of the main challenges for predictive simulations of complex configurations. The problem gets even more chall…
Towards prediction of turbulent flows at high Reynolds numbers using high performance computing data and deep learning
Mathis Bode, Michael Gauding, Jens Henrik Göbbert +3
In this paper, deep learning (DL) methods are evaluated in the context of turbulent flows. Various generative adversarial networks (GANs) are discussed with respect to their suitab…