arXiv subjects

Shaoguang Zhang

Publications and source records attributed to Shaoguang Zhang.

6 recordsLinked to original sources

arXiv2026

Data-driven Augmentation of a Turbulence Model in Three dimensional Separated Flows

Classic turbulence models often struggle to accurately predict complex flows. Although data-driven techniques have addressed these shortcomings, most existing research has concentrated on two-dimensional (2D) cases. This study bridges this gap by enhancing a data-driven turbulence model, the SST-CND (shear stress transport-conditioned) model, which was originally trained on 2D separated flows, in 3D scenarios. An additional correction term, \b{eta}_3D, is introduced to account for 3D effects. The distribution of this term is determined through a 3D field inversion process using high-fidelity data obtained from the flow around a cube. An algebraic expression for \b{eta}_3D is then derived through symbolic regression and formulated to degrade to zero in 2D cases. The performance of the resulting SST-CND3D model is evaluated across a range of flows. In 2D flows, the SST-CND3D model performs identically to its 2D-trained predecessor. However, the model exhibits superior performance in 3D flows, such as the flow around the complex JAXA standard model high-lift configuration. These findings indicate that a sequential approach, constructing a 3D correction term that vanishes in 2D on top of a 2D-trained model, constitutes a promising method for developing data-driven turbulence models that perform accurately in 3D while preserving their effectiveness in 2D.

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arXiv2025

Numerical Simulation of Three-dimensional High-Lift Configurations Using Data-Driven Turbulence Model

Traditional Reynolds-averaged Navier-Stokes (RANS) equations often struggle to predict separated flows accurately. Recent studies have employed data-driven methods to enhance predictions by modifying baseline equations, such as field inversion and machine learning (FIML) with symbolic regression. However, data-driven turbulence models exhibit limited adaptability and are rarely applied to complex engineering problems. This study examines the application of data-driven turbulence models to complex three-dimensional high-lift configurations, extending their usability beyond previous applications. First, the generalizability of the SST-CND model, derived from conditioned field inversion and symbolic regression, is validated. Then, the spatially varying correction factor obtained through conditioned field inversion is transferred to the three-equation k-(v^2)-w model. The 30P30N three-element airfoil, the JAXA Standard Model (JSM), and the high-lift version of the NASA Common Research Model (CRM-HL) are numerically simulated. The results indicated that the SST-CND model significantly improves the prediction of stall characteristics, demonstrating satisfactory generalizability. The corrected k-(v^2 )-w-CND model accurately predicts the stall characteristics of CRM-HL, with a relative error of less than 5% compared to experimental results. This confirms the strong transferability of the model correction derived from conditioned field inversion across different turbulence models.

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arXiv2025

Data-driven Turbulence Modeling for Separated Flows Considering Non-Local Effect

This study aims to enhance the generalizability of Reynolds-averaged Navier-Stokes (RANS) turbulence models, which are crucial for engineering applications. Classic RANS turbulence models often struggle to predict separated flows accurately. Recently, Data-driven machine learning approaches for turbulence modeling have been explored to address this issue. However, these models are often criticized for their limited generalizability. In this study, we address this issue by incorporating non-local effects into data-driven turbulence modeling. Specifically, we introduce a transport equation for the correction term \b{eta} of the shear stress transport (SST) model to encode non-local information along the mean streamline. The coefficients of the equation are calibrated using high-fidelity data from the NASA hump and periodic hills. The resulting model, termed the \b{eta}-Transport model, demonstrates high accuracy across various separated flows outside the training set, including periodic hills with different geometries and Reynolds numbers, the curved backward-facing step, a two-dimensional bump, and a three-dimensional simplified car body. In all tested cases, the \b{eta}-Transport model yields smaller or equal prediction errors compared to those from classic local data-driven turbulence models using algebraic relations for \b{eta}. These results indicate improved generalizability for separated flows. Furthermore, the \b{eta}-Transport model shows similar accuracy to the baseline model in basic flows including the mixing layer and the channel flow. Therefore, the non-local modeling approach presented here offers a promising pathway for developing more generalizable data-driven turbulence models.

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arXiv2024

Development of a Generalizable Data-driven Turbulence Model: Conditioned Field Inversion and Symbolic Regression

This paper addresses the issue of predicting separated flows with Reynolds-averaged Navier-Stokes (RANS) turbulence models, which are essential for many engineering tasks. Traditional RANS models usually struggle with this task, so recent efforts have focused on data-driven methods such as field inversion and machine learning (FIML) to correct this issue by adjusting the baseline equations. However, these FIML methods often reduce accuracy in attached boundary layers. To address this issue, we developed a "conditioned field inversion" technique. This method adjusts the corrective factor \b{eta} (used by FIML) in the shear-stress transport (SST) model. It multiplies \b{eta} with a shield function f_d that is off in the boundary layer and on elsewhere. This maintains the accuracy of the baseline model for the attached flows. We applied both conditioned and classic field inversion to the NASA hump and a curved backward-facing step (CBFS), creating two datasets. These datasets were used to train two models: SR-CND (from our new method) and SR-CLS (from the traditional method). The SR-CND model matches the SR-CLS model in predicting separated flows in various scenarios, such as periodic hills, the NLR7301 airfoil, the 3D SAE car model, and the 3D Ahmed body, and outperforms the baseline SST model in the cases presented in the paper. Importantly, the SR-CND model maintains accuracy in the attached boundary layers, whereas the SR-CLS model does not. Therefore, the proposed method improves separated flow predictions while maintaining the accuracy of the original model for attached flows, offering a better way to create data-driven turbulence models.

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arXiv2024

Aerodynamic Prediction of a CRM High-lift Configuration using a modified three equation turbulence mode

Aerodynamic simulations were carried out in the study presented in this paper focusing on the stall performance of the High-Lift Common Research Model obtained from the fourth AIAA High-Lift Prediction Workshop. Various turbulence models of Reynolds-average Navier-Stokes simulations are analyzed. A modified version of the transitional k-(v^2 )-ω model developed to enhance stall prediction accuracy for high-lift configurations with a nacelle chine. The vortex generator, three-element airfoil, and high-lift model are numerically simulated. The results reveal that implementing a k-(v^2 )-ω model with separation shear layer fixed notably enhances the stall prediction behavior for both the three-element airfoil and high-lift configuration without affecting the prediction of the vortex strength of a vortex generator. Moreover, incorporating rotation correction into the SPF k-(v^2 )-ω model improves the prediction of vortex strength and further enhances stall prediction for the high-lift configuration. The relative error in predicting the maximum lift coefficient is less than 5% of the experimental data. The study also investigated the impact of the nacelle chine on the stall behavior of the high-lift configuration. The results demonstrate that the chine vortex can mitigate the adverse effects of the nacelle/pylon vortex system and increase the maximum lift coefficient.

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arXiv2021

Aerodynamic Prediction of High-Lift Configuration Using k-(v^2 )-ω Turbulence Model

The aerodynamic performance of the high-lift configuration greatly influences the safety and economy of commercial aircraft. Accurately predicting the aerodynamic performance of the high-lift configuration, especially the stall behavior, is important for aircraft design. However, the complex flow phenomena of high-lift configurations pose substantial difficulties to current turbulence models. In this paper, a three-equation k-(v^2)-ω turbulence model for the Reynolds-averaged Navier-Stokes equations is used to compute the stall behavior of high-lift configurations. A separated shear layer fixed function is implemented in the turbulence model to better capture the nonequilibrium characteristics of turbulence. Different high-lift configurations, including the two-dimensional multielement NLR7301 and Omar airfoils and a complex full-configuration model (JAXA Standard Model), are numerically tested. The results indicate that the effect of the nonequilibrium characteristics of turbulence is significant in the free shear layer, which is key to accurately predicting the stall behavior of high-lift devices. The modified SPF k-(v^2 )-ω model is more accurate in predicting stall behavior than the Spalart-Allmaras, shear stress transport, and original k-(v^2)-ω models for the full high-lift configuration. The relative errors in the predicted maximum lift coefficients are within 3% of the experimental data.

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