Weiwei Zhang

• Professor
• Managing Director at Northwestern Polytechnical University

This study focuses on the numerical simulation of high Reynolds number separated flows and proposes a data-driven approach to improve the predictive capability of the SA turbulence model. First, data assimilation was performed on two typical airfoils with high angle-of-attack separated flows to obtain a high-fidelity flow field dataset. Based on th...

The Spalart–Allmaras (SA) model is widely used in engineering turbulence simulations. It has been calibrated using the logarithmic law and provides sufficient accuracy in zero pressure gradient (ZPG) turbulent boundary layer (TBL) but shows poor performance in the turbulence with adverse pressure gradient (APG), especially in separated flows. In th...

Physics-informed neural networks (PINNs) have shown remarkable prospects in solving forward and inverse problems involving partial differential equations (PDEs). However, PINNs still face the challenge of high computational cost in solving strongly nonlinear PDEs involving fluid dynamics. In this study, inspired by the input design in surrogate mod...

Physics-informed neural networks (PINNs) have recently emerged as popular methods for solving forward and inverse problems governed by partial differential equations. However, PINNs still face significant challenges when solving high-Reynolds-number flows with multi-scale phenomena. In our previous work, we proposed time-stepping-oriented neural ne...

Recent years have witnessed increasing research interests of physics-informed neural networks (PINNs) in solving forward, inverse, and parametric problems governed by partial differential equations (PDEs). Despite their promise, PINNs still face significant challenges in many scenarios due to ill-conditioning. Time-stepping-oriented neural network...

The data-driven aerothermal modeling method provides strong support for the hypersonic aircraft design. To reduce the training samples and improve the global geometric generalization ability, the Euler equation embedding double-series residual neural network (ED-ResNet) is presented. Firstly, the wall inviscid solution is approximated to the inform...

Passive modal controllers (PMC) are widely used in vibration control. However, the mechanism of PMC in galloping control is still unclear due to the limitation of the present quasi-steady theory. The motivation of this study is to reveal the dynamic mechanism of PMC in galloping control at a low Reynolds number () via linear stability analysis (LSA...

This study presents an active feedback control of the Kármán vortex shedding flow past a circular cylinder at low Reynolds numbers. The cylinder's rotational motion functions as the control actuator, while the transverse velocities of points along the wake axis serve as the feedback signals. First, using the autoregressive with exogenous input meth...

In recent years, Physics-Informed Neural Networks (PINNs) have become a representative method for solving partial differential equations (PDEs) with neural networks. PINNs provide a novel approach to solving PDEs through optimization algorithms, offering a unified framework for solving both forward and inverse problems. However, some limitations in...

Surrogate model can replace the parametric full-order model (FOM) by an approximation model, which can significantly improve the efficiency of optimization design and reduce the complexity of engineering systems. However, due to limitations in efficiency and accuracy, the applications of high-dimensional surrogate models are still challenging. In t...

Transonic aeroelasticity remains a significant challenge in aerospace. The coupling mechanism of aeroelastic problems involving the coexistence of fluid modes and multiple structural modes still needs further investigation. For this purpose, we analysed the dynamic characteristic of a two-degree-of-freedom (2DOF) NACA0012 airfoil in pre-buffet flow...

Supersonic internal flows often exhibit multiple reflected shocks within a limited distance. These shocks can interact with each other in a complex manner due to the characteristics of the shock wave–turbulent boundary layer interaction (STBLI), including flow distortion and the relaxing boundary layer. This study aims to characterise this type of...

Numerical simulation is dominant in solving partial differential equations (PDEs), but balancing fine-grained grids with low computational costs is challenging. Recently, solving PDEs with neural networks (NNs) has gained interest, yet cost-effectiveness and high accuracy remain a challenge. This work introduces a novel paradigm for solving PDEs, c...

Iterative methods are widely used for solving partial differential equations (PDEs). However, the difficulty in eliminating global low-frequency errors significantly limits their convergence speed. In recent years, neural networks have emerged as a novel approach for solving PDEs, with studies revealing that they exhibit faster convergence for low-...

In recent years, the synergy between artificial intelligence and turbulence big data has given rise to a new data-driven paradigm in turbulence research. Data-driven turbulence modeling has emerged as one of the forefront directions in fluid mechanics. Most existing studies focus on feature construction, selection, and the development of modeling f...

Transonic shock buffet is a significant self-excited shock oscillations and aerodynamic instability phenomenon induced by shock-boundary layer interaction, which limits the flight envelope and even causes flight accidents. The aviation industry has a significant interest in accurately predicting the shock buffet onset boundary, defined by a specifi...

Purpose The purpose of this study is to establish a novel airfoil icing prediction model using deep learning with geometrical constraints, called geometrical constraints enhancement neural networks, to improve the prediction accuracy compared to the non-geometrical constraints model. Design/methodology/approach The model is developed with flight v...

Physics-informed neural networks (PINNs) have shown remarkable prospects in solving the forward and inverse problems involving partial differential equations (PDEs). The method embeds PDEs into the neural network by calculating the PDE loss at a set of collocation points, providing advantages such as meshfree and more convenient adaptive sampling....

The flow around a circular cylinder is a typical case of unstable separated flow, and controlling its stability has long been a focus of flow control research. This study proposes an optimal control parameter design method based on resolvent analysis, which provides precise design criteria for jet position through effective gain. First, resolvent a...

By hinge moment, we mean the aerodynamic torque exerted on the rudder shaft by the airflow passing through the aircraft control surface, with obtaining high-precision results often relying on wind tunnel tests. Due to the complex aerodynamic balance insulation and installation errors that must be considered in cryogenic wind tunnels, the main metho...

With the development of high performance computer and experimental technology, the study of turbulence has accumulated a large number of high fidelity data. However, few general turbulence knowledge has been found from the data. So we use the symbolic regression(SR) method to find a new mixing length formula which is generally valid in wall-bounded...

Artificial intelligence techniques are considered an effective means to accelerate flow field simulations. However, current deep learning methods struggle to achieve generalization to flow field resolutions while ensuring computational efficiency. This paper presents a deep learning approach for rapid prediction of two types of subsonic flow fields...

Physics-informed neural networks (PINNs) have recently emerged as a novel and popular approach for solving forward and inverse problems involving partial differential equations (PDEs). However, achieving stable training and obtaining correct results remain a challenge in many cases, often attributed to the ill-conditioning of PINNs. Nonetheless, fu...

Considerable efforts have been devoted to the understanding of the small-scale characteristics in turbulent flows. While the universality of small-scale quantities has been established for incompressible flows, their extension to high-pressure transcritical flows remains an open area of research. To address this question, we investigate the real-fl...

The presentation and modeling of turbulence anisotropy are crucial for studying large-scale turbulence structures and constructing turbulence models. However, accurately capturing anisotropic Reynolds stresses often relies on expensive direct numerical simulations (DNS). Recently, a hot topic in data-driven turbulence modeling is how to acquire acc...

分离流及其诱导的流固耦合振动广泛存在于航空航天、风工程和海洋工程等领域，将严重影响重大装备的关键性能，甚至引起结构的破坏失效。利用和控制分离流动，能够达到趋利避害目的，特别是闭环控制，能够以较低的能耗实现理想的控制效果。然而对于复杂的非线性分离流及其诱导的流固耦合问题，很难建立准确的模型来描述其动力学特性，导致传统的基于模型的闭环控制方法失效。数据驱动控制策略，如无模型自适应控制、强化学习等方法，可以摆脱控制系统设计对物理机理模型的依赖。因此，发展数据驱动的控制方法研究分离流及其诱导的流固耦合动力学问题具有重要的理论意义和工程价值。 本文以低速绕圆柱的分离流动和跨声速抖振为主要研究对象，采用数据驱动控制方法抑制脉动载荷和弹性结构的振动，并研究了传感器布置、控制机构位置的影响，对比了不同控制律...

The rapid acquisition of high-fidelity flow field information is of great significance for the study of physical mechanisms such as flow separation and multi-field coupling, and is an indispensable technical means for the development of engineering problems such as aerodynamic optimization and fluid-structure interaction (FSI). A series of deep lea...

Theoretical analysis and numerical results have shown that frequency lock-in in vortex-induced vibration (VIV) is caused by the instability of the structural mode rather than a resonant response to external excitations. However, there is a lack of experimental evidence supporting relevant theoretical research findings. This study investigates VIV s...

Theoretical analysis and numerical results have shown that frequency lock-in in vortex-induced vibration (VIV) is caused by the instability of the structural mode rather than a resonant response to external excitations. However, there is a lack of experimental evidence supporting relevant theoretical research findings. This study investigates VIV s...

The rapid acquisition of high-fidelity flow field information is of great significance for engineering applications such as multi-field coupling. Current research in flow field modeling predominantly focuses on low Reynolds numbers and periodic flows, exhibiting weak generalization capabilities and notable issues with temporal inferring error accum...

The efficiency of adjoint-based aerodynamic shape optimization depends critically on the solution efficiency of adjoint equations. In this letter, we employ the Proper Orthogonal Decomposition (POD) method to analyze the adjoint field samples and project them from the physical space into a low-order modal space. Subsequently, the full-order adjoint...

The present study aims to investigate the influence of geometric nonlinearity on post-flutter responses by developing a full-mode coupled nonlinear flutter analysis method (frequency-domain method) and a time-dependent nonlinear analysis scheme (time-domain method). This approach integrates the three-dimensional (3D) nonlinear finite element model...

Physics-informed neural networks (PINNs) have recently become a new popular method for solving forward and inverse problems governed by partial differential equations. However, in the flow around airfoils, the fluid is greatly accelerated near the leading edge, resulting in a local sharper transition, which is difficult to capture by PINNs. Therefo...

The main buffet suppression techniques of aircrafts include active and passive flow control, aiming to reduce the impact of the fluctuating loads on the airfoil. These techniques are remedies after the airfoil design. The buffet suppression is not always considered in the aerodynamic optimization in the state of airfoil design because it is difficu...

The optimal control of flow and fluid–structure interaction (FSI) systems often requires an accurate model of the controlled system. However, for strongly nonlinear systems, acquiring an accurate dynamic model is a significant challenge. In this study, we employ the deep reinforcement learning (DRL) method, which does not rely on an accurate model...

Physics-informed neural networks (PINNs) have shown remarkable prospects in the solving the forward and inverse problems involving partial differential equations (PDEs). The method embeds PDEs into the neural network by calculating PDE loss at a series of collocation points, providing advantages such as meshfree and more convenient adaptive samplin...

Engineering problems often involve solving partial differential equations (PDEs) over a range of similar problem setups with various state parameters. In traditional numerical methods, each problem is solved independently, resulting in many repetitive tasks and expensive computational costs. Data-driven modeling has alleviated these issues, enablin...

The computational fluid dynamic/computational structural dynamic (CFD/CSD) coupling method plays an important role in static aeroelastic problems accounting for structural geometric nonlinearity. However, a huge computational burden comes from the CFD simulation, which also dominates the computational cost of the CFD/CSD coupling method. Therefore,...

Unsteady separated flow is a common flow condition causing many detrimental effects in aerospace and other fields. Open-loop control is a potential means to eliminate these drawbacks. At present, the unsatisfactory performance of open-loop control mainly attributes to the high-dimensional parameter optimization procedure and the lack of efficient k...

This paper presents a peculiar nodal-shaped oscillation in vortex-induced vibration (VIV). This response is distinct from the commonly observed limit cycle oscillation in VIV and other aeroelastic problems. To gain insight into the dynamics in nodal-shaped oscillation, we conduct wind tunnel tests to investigate the VIV response of a freely oscilla...

Aerodynamic surrogate modeling mostly relies only on integrated loads data obtained from simulation or experiment, while neglecting and wasting the valuable distributed physical information on the surface. To make full use of both integrated and distributed loads, a modeling paradigm is presented called the heteroge-neous data-driven aerodynamic mo...

Vortex-induced vibration is a typical nonlinear fluid–structure interaction phenomenon. Significant challenges to high-precision prediction by the prevalent methods rely on three complex nonlinear dynamic behaviors: nonlinear evolution (NE), vibration peak deviating from the resonance (PD), and nonlinear hysteresis. Although the semi-empirical mode...

Reynolds-averaged Navier–Stokes (RANS) models, which are known for their efficiency and robustness, are widely used in engineering applications. However, RANS models do not provide satisfactory predictive accuracy in many engineering-relevant flows with separation. Aiming at the difficulties of turbulence modeling for separated flows at high Reynol...

Flow-induced vibration (FIV) of bluff bodies can occur at subcritical Reynolds numbers (i.e., below the Re of the vortex shedding from fixed bodies). To analyze the mechanism of this subcritical FIV phenomenon, resolvent and dynamic mode analyses are introduced in this work. For laminar flow past a square cylinder, both resolvent and dynamic modes...

Physics-informed neural network (PINN) is a method for solving partial differential equations by encoding model equations into neural network, which fits solutions by simultaneously minimizing equation residuals and approximating definite solution conditions or observation data. Despite the fact that this approach has the benefits of being mesh-fre...

At transitional Reynolds numbers, an elastically supported airfoil oscillating in pitch can undergo laminar separation flutter (LSF), which is characterized by self-sustained small-amplitude oscillations. To gain insight into the mechanism of LSF, we conduct wind tunnel tests for [Formula: see text] to investigate the LSF response of a freely rotat...

Transonic buffet is a phenomenon of large self-excited shock oscillations caused by shock wave-boundary layer interaction, which is one of the common flow instability problems in aeronautical engineering. This phenomenon involves unsteady flow, which makes optimal design more difficult. In this paper, aerodynamic shape optimization design is combin...

By fusing aerodynamic data from multiple sources, multi-fidelity methods can well balance model accuracy and computational cost. To extend multi-fidelity models for predicting unsteady aerodynamics with uncertainty estimation, an improved modelling framework based on the Hierarchical Kriging (HK) is proposed for nonlinear aerodynamic reduced-order...

In the field of computational fluid dynamics, stability and convergence problems are often encountered when solving the governing equation. This paper studies the effect of the mode multigrid on the stability and convergence of iterative algorithms. By further analyzing the mechanism for accelerating the convergence of mode multigrid, a new adaptiv...

Vortex-induced vibration (VIV) and galloping are two typical flow-induced vibration (FIV) problems of bluff bodies, which often cause fatigue damage or destruction of structures. Therefore, the accurate and effective FIV prediction has become a requirement of the industry in order to prevent structural damage and guide design. Although the semi-emp...

Primary subarachnoid hemorrhage (SAH) is a type of acute stroke, accounting for approximately 10%of cases, with high disability and mortality rate. Early brain injury (EBI) is a critical factor in determining SAH mortality; however, there are no effective treatment interventions for EBI. Based on our results, the transmission of cyclic GMP-AMP (cGA...

The vortex-induced vibration (VIV) of circle cylinder can occur at subcritical Reynolds numbers as low as 20, which is called subcritical VIV. Recent numerical research suggests that the interaction of fluid mode and structural mode is the primary cause of its occurrence. Due to the limitations of experimental techniques and data analysis methods,...

Transonic buffeting can induce strong noise and reduce aircraft lifespan. In view of the complexity of transonic buffeting flow, this study combines the highly accurate Delayed-Detached Eddy Simulation (DDES) and Discrete Frequency Response (DFR) method to analyze the flow field and sound propagation law in different buffeting states, and also inve...

Transonic buffet is an aerodynamic phenomenon of self-sustained shock oscillations. The aeroelastic problem caused by it is very complex, including two different dynamic modes: forced vibration and frequency lock-in. The vibration of the structure has a negative influence on the fatigue life of the aircraft. Especially in the region of frequency lo...

Iterative steady-state solvers are widely used in computational fluid dynamics. Unfortunately, it is difficult to obtain steady-state solutions for unstable problems caused by physical instability and numerical instability. Optimization is a better choice for solving unstable problems because the steady-state solution is always the extreme point of...

The traditional method for obtaining the aerodynamic parameters of airfoils by solving Navier-Stokes (NS) equations is a time-consuming computing task.In this article, a novel data-driven deep attention network (DAN) is proposed for the reconstruction of the incompressible steady flow fields around airfoils. To extract the geometric represention of...

The static aeroelastic problem is concerned with those physical phenomena which involve significant mutual interaction between elastic and aerodynamic forces, which has dramatical influence on the overall flight performance and security of the aircraft. The computational fluid dynamics (CFD) and computational structural dynamics (CSD) coupling meth...

The frequency locking in phenomenon will occur in the transonic buffeting flow of the elastically supported airfoil. This phenomenon is usually accompanied by a large oscillation of the structure, eventually leading to the fatigue damage of the structure. Transonic aeroelasticity research based on computational fluid dynamics is extremely time-cons...

For complex flow problems such as wall-bounded turbulence with multi-scale and strongly nonlinear characteristics, the non-orthogonal and non-uniform meshes commonly used in numerical simulation limit the direct use of Convolutional Neural Networks (CNNs). The flow field is usually projected onto a uniform Cartesian mesh to use the convolution oper...

In recent years, machine learning methods represented by deep neural networks (DNN) have been a new paradigm of turbulence modeling. However, in the scenario of high Reynolds numbers, there are still some bottlenecks, including the lack of high-fidelity data and the stability problem in the coupling process of turbulence models and the Reynolds-ave...

Single-phase earth ground faults are the most frequent faults likely to occur but hard to identify in a distribution system, especially in a neutral ineffectively grounded system. Targeting on this goal, a novel AdaBoost-based single-phase earth ground fault identification model is put forward. First, after depicting the zero-sequence circuit of th...

Feature selection targets for selecting relevant and useful features, and is a vital challenge in turbulence modeling by machine learning methods. In this paper, a new posterior feature selection method based on validation dataset is proposed, which is an efficient and universal method for complex systems including turbulence. Different from the pr...

Allometric relationships between crown width (CW) and stem diameter at breast height (DBH) contribute in understanding forest dynamics and estimating forest biomass and carbon stocks. Nevertheless, the response of tree crown allometry to gap management and climate interactions remain unclear. We used 934 paired CW and DBH of Robinia pseudoacacia tr...

Iterative steady-state solvers are widely used in computational fluid dynamics. Unfortunately, it is difficult to obtain steady-state solution for unstable problem caused by physical instability and numerical instability. Optimization is a better choice for solving unstable problem because steady-state solution is always the extreme point of optimi...

In recent years, machine learning methods represented by deep neural networks (DNN) have been a new paradigm of turbulence modeling. However, in the scenario of high Reynolds numbers, there are still some bottlenecks, including the lack of high-fidelity data and the convergence and stability problem in the coupling process of turbulence models and...

In order to obtain the information about flow field, traditional computational fluid dynamics methods need to solve the Navier-Stokes equations on the mesh with boundary conditions, which is a time-consuming task. In this work, a data-driven method based on convolutional neural network and multi-head perceptron is used to predict the incompressible...

Wind tunnel test is an important means to obtain aerodynamic loads. However, due to the limited space location and experimental cost, it is usually difficult to arrange enough pressure taps on the complex model surface to obtain complete surface pressure distribution information. Hence, the accuracy of the lift and the pitching moment calculated by...

The vortex-induced vibration (VIV) problem is common in nature and engineering fields. Current passive or active control methods try to suppress VIV by changing the flow field or improving the flow stability based on the recognition that VIV is caused by resonance. But is VIV really caused by resonance? Tuned mass damper (TMD) has been used in VIV...

The environment of the moon is severe for sample drilling missions. To ensure mission success, it is necessary to carry out sufficient simulation research on the sampling conditions on Earth. Accordingly, a drill-soil interaction model was proposed, based on a virtual soil pile method. By considering the interaction relationship between the drill a...