Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …

In terms of multiple temporal and spatial scales, massive data from experiments, flow field
measurements, and high-fidelity numerical simulations have greatly promoted the rapid …

Modal decomposition techniques are showing a fast growth in popularity for their wide range
of applications and their various properties, especially as data-driven tools. There are many …

In this paper, a novel reduced order model based on a convolutional auto-encoder with self-
attention (SACAE ROM) is proposed. The proposed model is a non-intrusive reduced order …

Counter-rotating axial compressor (CRAC) is a promising potential technology to improve
the thrust-to-weight ratio of aero-engines, but its special aerodynamic layout usually causes …

We develop a method for the prediction of flow fields based on local particle image
velocimetry (PIV) measurement. High spatial resolution can be achieved by focusing PIV on …

In order to reveal the dynamic characteristics and achieve rapid time series prediction of
ship maneuvering motion, a reduced-order dynamic mode decomposition (DMD) algorithm …

The decay of trailing vortices is a fundamental problem in fluid mechanics and constitutes
the basis of control applications that intend to alleviate the wake hazard. In order to …

In this paper, a novel method to construct non-intrusive reduced order model (ROM) is
proposed. The method is based on proper orthogonal decomposition and transformer neural …

Aerodynamic data can be obtained from different sources, which vary in fidelity, availability
and cost. As the fidelity of data increases, the cost of data acquisition usually becomes …