We present a novel deep learning framework for flow field predictions in irregular domains
when the solution is a function of the geometry of either the domain or objects inside the …

Unsteady flow fields over a circular cylinder are used for training and then prediction using
four different deep learning networks: generative adversarial networks with and without …

Utilizing a neural network, individual down-axis images of combustion waves in the rotating
detonation engine (RDE) can be classified according to the number of detonation waves …

A fast prediction of blood flow in stenosed arteries with a hybrid framework of machine
learning and immersed boundary-lattice Boltzmann method (IB–LBM) is presented. The …

Cylindrical biomass (straw) particles play a crucial role in the numerical simulations of
biomass co-firing in coal-fired boilers, wherein the aerodynamic and heat transfer …

This paper presents a novel interface technique employing Artificial Neural Networks (ANN)
for efficient data transfer between incompressible fluid and deformable solid domains in a …

In recent years, the possibilities of higher thermodynamic efficiency and power output have
led to increasing interest in the field of pressure gain combustion (PGC). Currently, a …

Nonlinear finite element models (FEMs) are commonly used to perform analysis in the time
domain to simulate a limited number of stochastic loading scenarios that a slender marine …

Utilizing a neural network, individual down-axis images of combustion waves in a Rotating
Detonation Engine (RDE) can be classified according to the number of detonation waves …

The microstructures of materials determine their macroscopic properties. The traditional
bottom-up multi-scale approach provides a general strategy for studying the relationship …