Recent breakthroughs in computing power have made it feasible to use machine learning
and deep learning to advance scientific computing in many fields, including fluid mechanics …

Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …

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

Computational fluid dynamics (CFD) is an effective tool to investigate biomass fast pyrolysis
in fluidized bed reactor for bio-oil production, while it requires huge computational time …

The complex fluid-particle-fine (FPf) reacting flows have been widely practised in many
energy-intensive engineering processes, yet numerical methods capable of …

Coupling particle and reactor scale models is as essential as reactor fluid dynamics and
particle motion for accurate Computational Fluid Dynamic (CFD) simulations of biomass fast …

In this work, a convolutional neural network combined with a long short-term memory model
(CNN-LSTM) is employed to predict the mixing and segregation behaviors in a bidisperse …

Balancing the accuracy and efficiency is critical when employing the discrete particle
method to simulate particle-fluid systems in industrial reactors. This article systematically …

Computational fluid dynamics coupled with discrete element method (CFD‐DEM) has been
widely used to understand the complicated fundamentals inside gas–solid fluidized beds. To …

This study develops filtered two-fluid model (fTFM) closures by coupling computational fluid
dynamics (CFD) and deep learning algorithm (DL) for enabling coarse-grid simulations at …