相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。
Article
Mechanics
Zhiyuan Wang et al.
Summary: In recent years, machine learning methods such as deep neural networks have become a new approach to turbulence modeling. However, there are still limitations in dealing with high Reynolds numbers, including the lack of high-quality data and stability issues in the coupling process between turbulence models and Reynolds-averaged Navier-Stokes solvers. In this paper, an improved ensemble Kalman inversion method is proposed to address these challenges, integrating data assimilation and turbulence modeling into a unified approach. Experimental surface pressure coefficients are used to optimize the trainable parameters of the deep neural network, achieving high-fidelity turbulence models that agree well with experiments. The method is validated in cases of flows around S809 airfoil at high Reynolds numbers, effectively reducing errors in lift coefficients compared to traditional models.
Article
Computer Science, Interdisciplinary Applications
Xin-Lei Zhang et al.
Summary: Learning turbulence models from observation data is a significant interest in finding a unified model for practical flow applications. Direct and indirect sparse data are combined to train neural network-based turbulence models. The proposed ensemble-based method enables learning generalizable models from very sparse data by exploring the synergy between the two types of observation data in different observation spaces.
JOURNAL OF COMPUTATIONAL PHYSICS
(2023)
Article
Engineering, Aerospace
Chuanzhen Liu et al.
Summary: Wind-tunnel experiments were conducted to validate the potential advantages of the double-swept waverider in wide-speed performances. The results showed that the double-swept waveriders exhibited decreased lift-to-drag ratios and improved longitudinal stability with increasing Mach numbers. However, they showed a decreased lift-to-drag ratio in the supersonic state.
Review
Mechanics
Richard D. Sandberg et al.
Summary: This article presents the complex flow phenomena occurring in axial turbomachines and discusses how simulations contribute to their understanding and modeling. It explores the interaction between key aerodynamic features and unsteadiness, both deterministic and stochastic.
ANNUAL REVIEW OF FLUID MECHANICS
(2022)
Article
Mechanics
Zhaobin Li et al.
Summary: In this work, the effects of the side-to-side motion of a floating offshore wind turbine (FOWT) on wake dynamics were investigated. It was found that the motion frequency overlapping with the wake meandering frequency induced by the shear layer instability can lead to wake meandering with larger amplitudes. The onset of wake meandering is dominated by the inflow turbulence for high turbulence intensity. The probability density function of the spanwise instantaneous wake centers is non-Gaussian and closely related to the side-to-side motion. Linear stability analysis can accurately predict the least stable frequencies and the amplification factor for a certain motion amplitude, and nonlinearity affects the results when the motion amplitude increases.
JOURNAL OF FLUID MECHANICS
(2022)
Article
Engineering, Aerospace
Huiying Zhang et al.
Summary: This study focuses on the development and validation of a high-fidelity CFD solver for large-eddy simulations of combustion and reacting flows. The solver utilizes a high-resolution numerical scheme and a compressible flamelet formulation to capture both turbulent combustion and thermoacoustic effects. The accuracy of the solver is evaluated through comparisons with experimental data, and the results show that it can accurately predict combustion fields and fluctuation quantities, with comparable accuracy to state-of-the-art low-Mach solvers.
AEROSPACE SCIENCE AND TECHNOLOGY
(2022)
Article
Engineering, Aerospace
Wenhao Li et al.
Summary: This study conducts numerical simulations of corner separation flow and compares the predictive capabilities of various turbulence models. The results show that, with the modifications considering turbulent non-equilibrium transport characteristics and turbulence anisotropy, the SA-Helicity and SA-Helicity-QCR2013 models have more accurate predictions.
AEROSPACE SCIENCE AND TECHNOLOGY
(2022)
Article
Engineering, Aerospace
Zhen Li et al.
Summary: This study develops a quasi-wall-resolved large eddy simulation (LES) method for the analysis of laminar-turbulent transition in axial-flow compressors. The results show reasonable agreement between the predicted aerodynamic performance and the measured data, validating the QWRLES method and providing insights into the transition mechanism.
AEROSPACE SCIENCE AND TECHNOLOGY
(2022)
Article
Thermodynamics
Chongyang Yan et al.
Summary: This paper implements a data-driven Reynolds-averaged turbulence modeling approach using field inversion and machine learning to modify the Spalart-Allmaras model. The results show that the augmented model can reproduce the quantity of interest with relatively high accuracy and has a certain extent of generalization ability in similar flow conditions.
INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW
(2022)
Article
Thermodynamics
Xiao He et al.
Summary: This paper presents two methods to improve the explainability of machine learning models in the context of turbulence model development. The methods include reducing model complexity and explaining the correlation between inputs and outputs. The study focuses on using machine learning to improve the prediction accuracy of a specific turbulence model in transonic bump flows. The results show that these methods can provide valuable insights into the causal links between input features and the model outputs.
INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW
(2022)
Article
Mechanics
Xin-Lei Zhang et al.
Summary: In this work, an ensemble Kalman method is proposed to learn a nonlinear eddy viscosity model using a tensor basis neural network. By training the neural-network-based turbulence model with indirect observation data, the method proves to be effective in correctly learning the underlying turbulence models and predicting flows in similar configurations.
JOURNAL OF FLUID MECHANICS
(2022)
Review
Engineering, Mechanical
Steven L. Brunton
Summary: This paper provides a short overview of using machine learning to build data-driven models in fluid mechanics, breaking down the process into five stages and discussing embedding prior physical knowledge into each stage with specific examples.
ACTA MECHANICA SINICA
(2021)
Article
Mechanics
Xiang I. A. Yang et al.
Summary: This paper revisits the grid-point and time-step requirements for DNS and LES of turbulent boundary layers and establishes more general requirements. By considering the relationship between local grid spacing and the Kolmogorov length scale, accurate requirements for different simulation methods are proposed. According to the estimated costs, the computational costs of different simulation methods also vary.
Article
Engineering, Aerospace
Linyang Zhu et al.
Summary: This study combines turbulence big data with artificial intelligence to construct black-box algebraic models for turbulence simulation. By integrating scaling analysis and deep neural networks, a model with good generalization ability is successfully built, showing promising prospects for turbulence modeling using machine learning methods.
AEROSPACE SCIENCE AND TECHNOLOGY
(2021)
Article
Mechanics
Bing Cui et al.
Summary: This study numerically investigates internal flows of transonic compressor rotors and proposes a helicity-modified S-A model coupled with a transition prediction model to improve simulation reliability. The results show that this coupled model provides more accurate simulation results for transonic compressor rotors, indicating a great prospect for turbomachinery simulation.
Article
Physics, Fluids & Plasmas
Karthik Duraisamy
Summary: This paper reviews recent developments in using machine learning to enhance Reynolds-averaged Navier-Stokes (RANS) and large eddy simulation (LES) models of turbulent flows, emphasizing the importance of consistent ML augmentation in modeling. It discusses techniques for promoting model-consistent training and choosing the feature space based on physical and mathematical considerations, highlighting the potential of machine learning in turbulence modeling.
PHYSICAL REVIEW FLUIDS
(2021)
Article
Physics, Mathematical
Carlos A. Michelen Strofer et al.
Summary: This paper introduces the DAFI code as a flexible framework for data assimilation and field inversion problems. The code utilizes ensemble Kalman filters to solve the problems and offers built-in uncertainty quantification with Bayesian methods. Additionally, it provides tools and I/O utilities for integration with OpenFOAM and showcases its capabilities through several test cases.
COMMUNICATIONS IN COMPUTATIONAL PHYSICS
(2021)
Article
Mechanics
Zhaobin Li et al.
Summary: The study focuses on the similarity of wakes from wind turbines with different yaw angles and tip speed ratios under turbulent inflows. It finds that these wake characteristics overlap when properly normalized, suggesting the decomposition of yawed wind turbine wakes into streamwise and lateral components. Additionally, analytical expressions are proposed to relate instantaneous wake widths and centerline streamwise velocities, aiding in the development of physics-based dynamic wake models.
JOURNAL OF FLUID MECHANICS
(2021)
Article
Physics, Mathematical
Carlos A. Michelen Strofer et al.
Summary: This paper explores the use of ensemble approximation of the sensitivities of the RANS equations in training data-driven turbulence models with indirect observations. A deep neural network representing the turbulence model is trained using the network's gradients obtained by backpropagation and the ensemble approximation of the RANS sensitivities. Different ensemble approximations are explored, and it is found that the approximate nature of the ensemble gradient hinders further optimization of the underlying model once the sensitivity of the velocity to the underlying model becomes small.
COMMUNICATIONS IN COMPUTATIONAL PHYSICS
(2021)
Article
Thermodynamics
Martin Schmelzer et al.
FLOW TURBULENCE AND COMBUSTION
(2020)
Article
Computer Science, Interdisciplinary Applications
Yaomin Zhao et al.
JOURNAL OF COMPUTATIONAL PHYSICS
(2020)
Review
Thermodynamics
Richard D. Sandberg et al.
FLOW TURBULENCE AND COMBUSTION
(2019)
Article
Computer Science, Interdisciplinary Applications
Jin-Long Wu et al.
COMPUTERS & FLUIDS
(2019)
Article
Computer Science, Information Systems
Amina Adadi et al.
Article
Engineering, Mechanical
John D. Denton
JOURNAL OF TURBOMACHINERY-TRANSACTIONS OF THE ASME
(2017)
Article
Physics, Fluids & Plasmas
Jian-Xun Wang et al.
PHYSICAL REVIEW FLUIDS
(2017)
Article
Engineering, Aerospace
Anand Pratap Singh et al.
Article
Engineering, Aerospace
Weiwei Cui et al.
AEROSPACE SCIENCE AND TECHNOLOGY
(2016)
Article
Computer Science, Interdisciplinary Applications
Eric J. Parish et al.
JOURNAL OF COMPUTATIONAL PHYSICS
(2016)
Article
Computer Science, Interdisciplinary Applications
Jack Weatheritt et al.
JOURNAL OF COMPUTATIONAL PHYSICS
(2016)
Article
Mechanics
Julia Ling et al.
JOURNAL OF FLUID MECHANICS
(2016)
Article
Mechanics
Anand Pratap Singh et al.
Article
Engineering, Petroleum
Xiaodong Luo et al.
Article
Physics, Multidisciplinary
Yangwei Liu et al.
Review
Engineering, Aerospace
P. G. Tucker
PROGRESS IN AEROSPACE SCIENCES
(2011)
Article
Economics
K Fujimoto et al.
GAMES AND ECONOMIC BEHAVIOR
(2006)
