Zobrazeno 1 - 10
of 162
pro vyhledávání: '"Zhang Xin-lei"'
The development of a wall model using machine learning methods for the large-eddy simulation (LES) of separated flows is still an unsolved problem. Our approach is to leverage the significance of separated flow data, for which existing theories are n
Externí odkaz:
http://arxiv.org/abs/2409.00984
Publikováno v:
Open Medicine, Vol 19, Iss 1, Pp 273-84 (2024)
The aim of this study was to assess the impact of the external oblique intercostal block (EOIB) on early postoperative pain in patients who underwent laparoscopic cholecystectomy.
Externí odkaz:
https://doaj.org/article/9d0ca6438bfc470d843f57728533f635
Neural network-based turbulence modeling has gained significant success in improving turbulence predictions by incorporating high--fidelity data. However, the interpretability of the learned model is often not fully analyzed, which has been one of th
Externí odkaz:
http://arxiv.org/abs/2307.09058
Learning turbulence models from observation data is of significant interest in discovering a unified model for a broad range of practical flow applications. Either the direct observation of Reynolds stress or the indirect observation of velocity has
Externí odkaz:
http://arxiv.org/abs/2305.14759
This paper presents a neural network-based turbulence modeling approach for transonic flows based on the ensemble Kalman method. The approach adopts a tensor basis neural network for the Reynolds stress representation, with modified inputs to conside
Externí odkaz:
http://arxiv.org/abs/2305.06560
Publikováno v:
In Journal of Computational Physics 1 October 2024 514
In this work, we propose using an ensemble Kalman method to learn a nonlinear eddy viscosity model, represented as a tensor basis neural network, from velocity data. Data-driven turbulence models have emerged as a promising alternative to traditional
Externí odkaz:
http://arxiv.org/abs/2202.05122
Publikováno v:
In Journal of Computational Physics 15 July 2024 509
Training data-driven turbulence models with high fidelity Reynolds stress can be impractical and recently such models have been trained with velocity and pressure measurements. For gradient-based optimization, such as training deep learning models, t
Externí odkaz:
http://arxiv.org/abs/2104.07811
Reconstruction of turbulent flow based on data assimilation methods is of significant importance for improving the estimation of flow characteristics by incorporating limited observations. Existing works mainly focus on using only one observation dat
Externí odkaz:
http://arxiv.org/abs/2103.14923