A novel cost-efficient deep learning framework for static fluid–structure interaction analysis of hydrofoil in tidal turbine morphing blade

L Wang, J Xu, Z Wang, B Zhang, Z Luo, J Yuan… - Renewable Energy, 2023 - Elsevier
A tidal turbine can benefit from exquisitely designed morphing blades with a flexible trailing
edge by mitigating up to 90% of the load fluctuation in harsh ocean environments, which …

40th Anniversary of the first international topical meeting on nuclear reactor thermal-hydraulics: Highlights of thermal-hydraulics research in the past four decades

E Merzari, FB Cheung, SM Bajorek… - Nuclear Engineering and …, 2021 - Elsevier
The year 2020 marks the 40th anniversary of the first International Topical Meeting on
Nuclear Reactor Thermal-hydraulics (NURETH-1). Hosted by the thermal-hydraulics division …

End-to-end brain tumor detection using a graph-feature-based classifier

M Hu, J Wang, CW Chang, T Liu… - Medical Imaging 2023 …, 2023 - spiedigitallibrary.org
Brain tumors are caused by abnormal cell growth and can cause pain and reduced survival
rates. The early detection of brain tumors is pivotal in improving outcomes. Recently …

Review of physics-based and data-driven multiscale simulation methods for computational fluid dynamics and nuclear thermal hydraulics

AS Iskhakov, NT Dinh - arXiv preprint arXiv:2102.01159, 2021 - arxiv.org
Modeling of fluid flows requires corresponding adequate and effective approaches that
would account for multiscale nature of the considered physics. Despite the tremendous …

A Perspective on Data-Driven Coarse Grid Modeling for System-Level Thermal Hydraulics

AS Iskhakov, CK Tai, IA Bolotnov… - Nuclear Science and …, 2023 - Taylor & Francis
In the future, advanced reactors are expected to play an important role in nuclear power.
However, their development and deployment are hindered by the absence of reliable and …

Data-driven RANS closures for improving mean field calculation of separated flows

Z Chen, J Deng - Frontiers in Physics, 2024 - frontiersin.org
Reynolds-averaged Navier-Stokes (RANS) simulations have found widespread use in
engineering applications, yet their accuracy is compromised, especially in complex flows …

Aerodynamic surrogate model based on deep long short-term memory network: An application on high-lift device control

Y Zhang, S Wang, G Sun, J Mao - Proceedings of the …, 2022 - journals.sagepub.com
An unsteady aerodynamic surrogate model based on the deep LSTM (long short-term
memory) network is proposed for predicting unsteady aerodynamic coefficients. Deflection …

An improved neural network for modeling airfoil's unsteady aerodynamics in transonic flow

Y Pan, X An, Y Lei, C Ji - Physics of Fluids, 2024 - pubs.aip.org
Understanding the aerodynamic hysteresis loop phenomenon is essential when assessing
aerodynamic performance and designing aircraft control systems. This phenomenon is a …

Physics-integrated machine learning: embedding a neural network in the Navier-Stokes equations. Part I

AS Iskhakov, NT Dinh - arXiv preprint arXiv:2008.10509, 2020 - arxiv.org
In this paper the physics-(or PDE-) integrated machine learning (ML) framework is
investigated. The Navier-Stokes (NS) equations are solved using Tensorflow library for …

[HTML][HTML] Thermal stratification prediction in reactor system based on CFD simulations accelerated by a data-driven coarse-grid turbulence model

Z Liu, P Zhao, BA Florin, X Cheng - Nuclear Engineering and Technology, 2024 - Elsevier
Thermal stratification in large enclosures is an integral phenomenon to nuclear reactor
system safety. Currently, the effective model for thermal stratification utilizes a multi-scale …