Long-term predictions of turbulence by implicit U-Net enhanced Fourier neural operator

Physics of Fluids, Год журнала: 2023, Номер 35(7)

Опубликована: Июль 1, 2023

Long-term predictions of nonlinear dynamics three-dimensional (3D) turbulence are very challenging for machine learning approaches. In this paper, we propose an implicit U-Net enhanced Fourier neural operator (IU-FNO) stable and efficient on the long-term large-scale turbulence. The IU-FNO model employs recurrent layers deeper network extension incorporates U-net accurate prediction small-scale flow structures. is systematically tested in large-eddy simulations three types 3D turbulence, including forced homogeneous isotropic (HIT), temporally evolving turbulent mixing layer, decaying numerical demonstrate that more than other FNO-based models vanilla FNO, FNO (IFNO) (U-FNO), dynamic Smagorinsky (DSM) predicting a variety statistics velocity spectrum, probability density functions (PDFs) vorticity increments, instantaneous spatial structures field. Moreover, improves predictions, which has not been achieved by previous versions FNO. Besides, proposed much faster traditional LES with DSM model, can be well generalized to situations higher Taylor-Reynolds numbers unseen regime

Язык: Английский

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A novel method for predicting fluid–structure interaction with large deformation based on masked deep neural network

Physics of Fluids, Год журнала: 2024, Номер 36(2)

Опубликована: Фев. 1, 2024

Traditional fluid–structure interaction (FSI) simulation is computationally demanding, especially for bi-directional FSI problems. To address this, a masked deep neural network (MDNN) developed to quickly and accurately predict the unsteady flow field. By integrating MDNN with structural dynamic solver, an system proposed perform of flexible vertical plate oscillation in fluid large deformation. The results show that both field prediction structure response are consistent traditional system. Furthermore, method highly effective mitigating error accumulation during temporal predictions, making it applicable various deformation Notably, model reduces computational time millisecond scale each step regarding part, resulting increase nearly two orders magnitude speed, which greatly enhances speed

Язык: Английский

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Fast flow field prediction of three-dimensional hypersonic vehicles using an improved Gaussian process regression algorithm

Physics of Fluids, Год журнала: 2024, Номер 36(1)

Опубликована: Янв. 1, 2024

Conducting large-scale numerical computations to obtain flow field during the hypersonic vehicle engineering design phase can be excessively costly. Although deep learning algorithms enable rapid prediction with high-precision, they require a significant investment in training samples, contradicting motivation of reducing cost acquiring field. The combination feature extraction and regression also achieve high-precision fields, which is more suitable tackle three-dimensional small dataset. In this study, we propose reduced-order model (ROM) for utilizing proper orthogonal decomposition extract representative features Gaussian process improved automatic kernel construction (AKC-GPR) perform nonlinear mapping physical prediction. selection variables based on sensitivity analysis modal assurance criterion. underlying relationship unveiled between inflow conditions. ROM exhibits high predictive accuracy, mean absolute percentage error (MAPE) total less than 3.5%, when varying altitudes Mach numbers. During angle attack variations, only effectively reconstructs distribution by interpolation MAPE 7.02%. excellent small-sample fitting capability our AKC-GPR algorithm demonstrated comparing original AKC-GPRs maximum reduction 35.28%. These promising findings suggest that proposed serve as an effective approach accurate predicting, enabling its application analysis.

Язык: Английский

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Self-supervised learning based on Transformer for flow reconstruction and prediction

Physics of Fluids, Год журнала: 2024, Номер 36(2)

Опубликована: Фев. 1, 2024

Machine learning has great potential for efficient reconstruction and prediction of flow fields. However, existing datasets may have highly diversified labels different scenarios, which are not applicable training a model. To this end, we make first attempt to apply the self-supervised (SSL) technique fluid dynamics, disregards data pre-training The SSL embraces large amount (8000 snapshots) at Reynolds numbers Re = 200, 300, 400, 500 without discriminating between them, improves generalization Transformer model is pre-trained via specially designed pretext task, where it reconstructs complete fields after randomly masking 20% points in each snapshot. For downstream task reconstruction, fine-tuned separately with 256 snapshots number. models accurately reconstruct based on less than 5% random within limited window even 250 600, whose were seen phase. other prediction, 128 consecutive snapshot pairs corresponding then correctly predict evolution over many periods cycles. We compare all results generated by trained supervised learning, former unequivocally superior performance. expect that methodology presented here will wider applications mechanics.

Язык: Английский

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Aerodynamic shape optimization based on proper orthogonal decomposition reparameterization under small training sets

Aerospace Science and Technology, Год журнала: 2024, Номер 147, С. 109072 - 109072

Опубликована: Март 12, 2024

Язык: Английский

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A complete state-space solution model for inviscid flow around airfoils based on physics-informed neural networks

Journal of Computational Physics, Год журнала: 2024, Номер 516, С. 113285 - 113285

Опубликована: Июль 17, 2024

Язык: Английский

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Flow3DNet: A deep learning framework for efficient simulation of three-dimensional wing flow fields

Aerospace Science and Technology, Год журнала: 2025, Номер unknown, С. 109991 - 109991

Опубликована: Янв. 1, 2025

Язык: Английский

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Prediction of transonic flow over supercritical airfoils using geometric-encoding and deep-learning strategies

Physics of Fluids, Год журнала: 2023, Номер 35(7)

Опубликована: Июль 1, 2023

The Reynolds-averaged Navier-Stokes equation for compressible flow over supercritical airfoils under various conditions must be rapidly and accurately solved to shorten design cycles such airfoils. Although deep-learning methods can effectively predict fields, the accuracy of these predictions near sensitive regions their generalizability large-scale datasets in engineering applications enhanced. In this study, a modified vision transformer-based encoder-decoder network is designed prediction transonic addition, four are encode geometric input with information points performances compared. statistical results show that generate accurate complete field, mean absolute error on order 1e-4. To increase shock area, multilevel wavelet transformation gradient distribution losses introduced into loss function. This maximum typically observed area decreasing by 50%. Furthermore, models pretrained through transfer learning finetuned small improve applications. generated demonstrate yields comparable from reduced training time.

Язык: Английский

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Estimation of cavitation velocity fields based on limited pressure data through improved U-shaped neural network

Physics of Fluids, Год журнала: 2023, Номер 35(8)

Опубликована: Авг. 1, 2023

In marine applications, estimating velocity fields or other states from limited data are important as it provides a reference for active control. this work, we propose PVNet (Pressure-Velocity Network), an improved U-shaped neural network (UNet) combined with Transformer Modules and Multi-scale Fusion Modules, to predict pressure on the hydrofoil surface. To improve prediction accuracy, position encodings have been incorporated into input features. Tests cavitation dataset of NACA66 (National Advisory Committee Aeronautics) demonstrate that outperforms traditional models such shallow networks UNet. addition, conducted quantitative analysis impact features performance, providing guidance practical arrangement sampling points. Furthermore, by comparing different positional encodings, found reasonable can significantly accuracy.

Язык: Английский

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Fast and high-precision compressible flowfield inference method of transonic airfoils based on attention UNet

Physics of Fluids, Год журнала: 2024, Номер 36(3)

Опубликована: Март 1, 2024

Traditional numerical simulation methods for airfoil flowfields are complex and time-consuming, deep learning-based inference Reynolds-averaged Navier–Stokes equations (RANS) solutions of transonic airfoils have limitations in terms their robustness generalization. A novel data-driven method named as attention UNet (AU)-RANS is proposed efficient accurate prediction around with strong compressibility large-scale turbulent separation. First, to enhance the learning boundary flow information entire flowfield solution, an innovative data preprocessing convert physical quantities coordinate RANS into neural network spatial information. Second, mechanism introduced suppress feature responses irrelevant background regions sensitivity geometrical features input varying inflow conditions. The quantitative qualitative analyses AU-RANS results demonstrate that well-trained model can effectively infer accurately predict shock waves separation phenomena under high Mach number conditions a large angle attack.

Язык: Английский

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