The paper introduces a panoramic aerodynamic performance prediction framework for turbomachinery cascades that first predicts basic Navier-Stokes parameters (temperature, pressure, density) and then derives key turbine stage performance metrics. A transformer‑enhanced neural operator (TNO) is trained on Rotor 37 blades to accurately predict transonic compressor blade performance, outperforming state‑of‑the‑art deep learning operators like FNO and DeepONet. The TNO also supports downstream tasks such as sensitivity analysis and optimization, achieving CFD‑like results while cutting computational cost by four orders of magnitude.
By Qineng Wang, Zhendong Guo, Liming Song, Tianyuan Liu
arXiv:2606. 24265v1 Announce Type: cross Abstract: Numerical simulations in industrial applications often require performing numerous high-precision computations parameterized by specific experimental conditions.
By Kazuto Ando, Rahul Bale, Akiyoshi Kuroda, Makoto Tsubokura
Numerical simulations in industrial applications often require performing numerous high-precision computations parameterized by specific experimental conditions. For instance, in vehicle body design, aerodynamic simulations are essential for evaluating the aerodynamic characteristics of various proposed body geometries.
arXiv:2609.06660v1 Announce Type: cross
Abstract: Accurate aerodynamic prediction is critical for designing fuel-efficient and safe transportation systems such as aircraft and automobiles, yet tradit...
By Wenxuan Jin, Jianguo Yao, Haibing Guan, Xijun Li
The paper explores using residual learning with an LSTM neural network to enhance unsteady aerodynamic load predictions for aeroelastic applications. By training the network on the difference between high‑fidelity CFD lift data and a physics‑based Wagner model for the NLR 7301 airfoil in transonic flow, the residual approach consistently outperforms a direct neural‑network model in most tests, especially in generalization scenarios. The study demonstrates that residual learning can effectively augment classical low‑order aerodynamic theories by learning a lower‑variance correction to the baseline physics model.
By Divya Sanghi, Carlos E. S. Cesnik
arXiv:2606. 16587v1 Announce Type: cross Abstract: Designing spray nozzles requires predicting how geometry shapes transient two-phase breakup, but high-fidelity volume-of-fluid (VOF) simulations with adaptive mesh refinement (AMR) are too expensive for iterative design exploration.
By Julius H Ramlau, Friedrich Hastedt, Tolga Birdal, Ehecatl-Antonio del R\'io Chanona, Nausheen S Basha, Omar K Matar
arXiv:2609.17160v1 Announce Type: new
Abstract: Machine-learning surrogate models offer a promising alternative to high-fidelity Computational Fluid Dynamics (CFD) simulations for aerodynamic analysi...
By Lionel Salesses, Caroline Sainvitu, Tariq Benamara
arXiv:2609.38638v1 Announce Type: cross
Abstract: Pickup trucks account for 14% of new light-duty vehicles produced in the United States, yet are among the least aerodynamic. Their open cargo bed add...
By Riddhiman Raut, Yin Yu, Aashwin Anand Mishra, Michael Emory, Thomas Economon, Peter Lyu, Juan J. Alonso
arXiv:2606. 07724v1 Announce Type: new Abstract: High-fidelity computational fluid dynamics (CFD) is crucial to vehicle aerodynamic analysis, but its cost still constrains early-stage design exploration.
By Kangkang Qi, Huiyu Yang, Keqi Ding, Yunpeng Wang, Yuntian Chen, Yuanwei Bin, Rikui Zhang, Jianchun Wang
The paper explores using residual learning with an LSTM neural network to enhance unsteady aerodynamic load predictions for aeroelastic applications. By training the network on the difference between high‑fidelity CFD lift data and a physics‑based Wagner model, the residual approach outperforms a direct neural‑network model in most cases, especially in generalization tests. The study demonstrates that aligning residual inputs with the baseline physics variables yields lower error and more consistent performance across varied motion scenarios.
The paper introduces LatentDDM, a neural operator framework that first pretrains on small subdomains and then adapts to new settings by training only a lightweight composition module. Experiments on steady Darcy flow and unsteady airfoil flow show that this approach reduces error by 36‑56% on larger domains and improves 20‑step rollouts, outperforming capacity‑matched full‑domain models. The study highlights co‑designed local pretraining and composition‑level transfer as a promising design principle for physical foundation models.
By Zituo Chen, Baiming Zhang, Sili Deng
arXiv:2606. 29628v1 Announce Type: cross Abstract: In this paper, two novel data-driven models based on kriging and neural networks (NN) are proposed to predict pressure losses across perforated plates with circular perforations in turbulent flows.
By Shuai Li