A machine learning framework is developed to compute the aerodynamic forces and
moment coefficients for a pitching NACA0012 airfoil incurring in light and deep dynamic …

This study introduces a deep learning surrogate model designed to predict the evolution of
the mean pressure coefficient on the back face of a Windsor body across a range of yaw …

Purpose Flow control has a great potential to contribute to a sustainable society through
mitigation of environmental burden. However, the high dimensional and nonlinear nature of …

Data from turbine cascade experiments typically exhibits low spatial–temporal resolution,
along with inevitable noise and local data missing. This paper aims to establish a super …

Surrogate models that combine dimensionality reduction and regression techniques are
essential to reduce the need for costly high-fidelity computational fluid dynamics data. New …

This paper presents an innovative approach by employing experimental and analytical
methods to examine the impact of temperature changes in flue gases following combustion …

A deep-learning-based closure model to address energy loss in low-dimensional surrogate
models based on proper-orthogonal-decomposition (POD) modes is introduced. Using a …

Convolutional autoencoders have proven to be an adequate tool to perform reduced-order
modeling for high-dimensional nonlinear dynamical systems. Their goal is to reduce …

Dynamic stall is a challenging fluid dynamics phenomenon occurring during rapid transient
motion of airfoils where the angle of attack exceeds the static stall angle. Understanding …

We present a method to increase the resolution of measurements of a physical system and
subsequently predict its time evolution using thermodynamics-aware neural networks. Our …