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On the dynamics and collisions of settling spheroidal particles 认领 引用 被引量：1
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作者 Xinyu Jiang Lihao Zhao Luca Brandt 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第1期49-63,共15页
In this study,we perform particle-resolved simulations of settling spheroidal particles,considering oblate and prolate spheroids and spheres,and investigate the shape effect on the particle dynamics in suspensions wit... In this study,we perform particle-resolved simulations of settling spheroidal particles,considering oblate and prolate spheroids and spheres,and investigate the shape effect on the particle dynamics in suspensions with volume fraction 1%and 5%.We first examine the single-point statistics of the translational and rotational motion of the settling particles.The horizontal velocity has a symmetrical distribution with standard deviation dependent on the particle shape.The greater horizontal velocity fluctuations of the non-spherical particles,compared to that of spheres,are attributed to the horizontal drift of settling spheroids with oblique orientations induced by the fluid-particle and particle-particle interactions.The fluctuation of particle vertical velocity,instead,is skewed under the effect of wake-induced hydrodynamic interactions.Further,we explore the particle pair statistics,which demonstrate the formation of column-like particle micro-structures for the lowest volume fraction considered.This clustering is more pronounced for spheroidal particles than spheres,due to the stronger attractions among vertically-aligned settling spheroids.Moreover,the particle pair statistics are directly related to the collision rate among the dispersed particles.The local accumulation of oblate/prolate spheroids serves as the major mechanism to promote the particle-particle collisions in dilute suspensions. 展开更多
关键词 Particle sedimentation Dynamics Collision rate Immersed boundary method
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An Eulerian-Lagrangian parallel algorithm for simulation of particle-laden turbulent flows 认领 引用 被引量：1
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作者 Harshal P.Mahamure Deekshith I.Poojary +1 位作者 Vagesh D.Narasimhamurthy Lihao Zhao 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第1期15-34,共20页
This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in ... This paper presents an Eulerian-Lagrangian algorithm for direct numerical simulation(DNS)of particle-laden flows.The algorithm is applicable to perform simulations of dilute suspensions of small inertial particles in turbulent carrier flow.The Eulerian framework numerically resolves turbulent carrier flow using a parallelized,finite-volume DNS solver on a staggered Cartesian grid.Particles are tracked using a point-particle method utilizing a Lagrangian particle tracking(LPT)algorithm.The proposed Eulerian-Lagrangian algorithm is validated using an inertial particle-laden turbulent channel flow for different Stokes number cases.The particle concentration profiles and higher-order statistics of the carrier and dispersed phases agree well with the benchmark results.We investigated the effect of fluid velocity interpolation and numerical integration schemes of particle tracking algorithms on particle dispersion statistics.The suitability of fluid velocity interpolation schemes for predicting the particle dispersion statistics is discussed in the framework of the particle tracking algorithm coupled to the finite-volume solver.In addition,we present parallelization strategies implemented in the algorithm and evaluate their parallel performance. 展开更多
关键词 DNS Eulerian-Lagrangian Particle tracking algorithm Point-particle Parallel software
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Generating airfoils from text:FoilCLIP,a novel framework for language-conditioned aerodynamic design 认领 引用 被引量：1
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作者 Mingcheng Lei Yufei Zhang 《Theoretical & Applied Mechanics Letters》 EI CAS CSCD 2025年第5期453-468,共16页
Recent advances in contrastive language-image pretraining(CLIP)models and generative AI have demonstrated significant capabilities in cross-modal understanding and content generation.Based on these developments,this s... Recent advances in contrastive language-image pretraining(CLIP)models and generative AI have demonstrated significant capabilities in cross-modal understanding and content generation.Based on these developments,this study introduces a novel framework for airfoil design via natural language interfaces.To the authors’knowledge,this study establishes the first end-to-end,bidirectional mapping between textual descriptions(e.g.,“low-drag supercritical wing for transonic conditions”)and parametric airfoil geometries represented by class-shape transformation parameters.The proposed approach integrates a CLIP-inspired architecture that aligns text embeddings with airfoil parameter spaces through contrastive learning,along with a semantically conditioned decoder that produces physically plausible airfoil geometries from latent representations.The experimental results validate the framework’s ability to generate aerodynamically plausible airfoils from natural language specifications and to classify airfoils accurately based on given textual labels.This research reduces the expertise threshold for preliminary airfoil design and highlights the potential for human-AI collaboration in aerospace engineering. 展开更多
关键词 Airfoil design Contrastive learning Natural language processing Generative model Class-shape transformation
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A fast transonic airfoil flow field prediction model based on a modified Fourier neural operator 认领 引用
4
作者 Weishao Tang Chenyu Wu +1 位作者 Yunjia Yang Yufei Zhang 《Science China(Physics,Mechanics & Astronomy)》 SCIE EI CSCD 2026年第1期36-53,共18页
Traditional aerodynamic optimization coupled with computational fluid dynamics is associated with a high computational cost.Surrogate models based on deep learning methods can rapidly predict flow fields from the grid... Traditional aerodynamic optimization coupled with computational fluid dynamics is associated with a high computational cost.Surrogate models based on deep learning methods can rapidly predict flow fields from the grid input but often suffer from poor accuracy and generalizability.This study introduces a modified Fourier neural operator for flow field prediction.Unlike most convolution-based models,the Fourier neural operator learns the solution operator directly in the function space,enhancing predictive accuracy and generalizability.The proposed model incorporates a shallow feature extractor,a boundary variable finetuner,and several physical priors,including the initial flow field and boundary conditions.The model is trained on uniformly parameterized algebraic grids to accelerate grid generation in aerodynamic optimization.The prediction error for the flow field and force coefficients on the validation and test sets is reduced by 70%to 90%compared with that of the previous convolutional model.The proposed model can make precise predictions for supercritical airfoils under typical working conditions,with a drag coefficient error of approximately 1 drag count on the validation set,and generalizes better than previous convolution-based methods do on extrapolative inflow conditions and airfoils. 展开更多
关键词 AI for computational fluid dynamics aerodynamic optimization Fourier neural operator
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