arXiv subjects

Zhigang Yang

Publications and source records attributed to Zhigang Yang.

8 recordsLinked to original sources

arXiv2026

DrivAerRim: A large-scale full-vehicle CFD dataset for wheel rim aerodynamics

The wheel region contributes substantially to vehicle aerodynamic drag, and rim geometry strongly influences the flow in this region. However, publicly available automotive aerodynamics datasets offer limited variation in rim design. We present DrivAerRim, a large-scale dataset of full-vehicle computational fluid dynamics simulations covering 904 DeepWheel rim designs with diverse spoke layouts, opening areas and topologies. All cases use the same DrivAer Estateback, operating conditions and numerical setup, with rim geometry as the sole design variable. Wheel rotation is modelled using moving reference frame regions around the rims and rotating wall conditions on the deformed tyres. The numerical setup is assessed through mesh sensitivity analysis and comparisons with wind tunnel measurements. The resulting 9.32 TB release includes wheel geometries, vehicle and component force coefficients, accumulated drag, surface pressure, wall shear stress, and flow fields around the vehicle and wheels. Together, these data support surrogate model training and evaluation across diverse rim geometries, as well as aerodynamic optimisation and generative design to accelerate wheel rim development for vehicle drag reduction.

cs.CE↗

arXiv2026

Intra- and Cross-Scale Feature Interaction Network for Referring Remote Sensing Image Segmentation

Referring remote sensing image segmentation (RRSIS) is a challenging multimodal task that requires accurate localization and pixel-level segmentation of targets specified by natural-language expressions. Existing RRSIS datasets remain limited in image resolution, scene diversity, and category coverage, which restricts the generalization ability and practical applicability of current methods. To advance this field, we introduce NWPU-Refer, the largest and most diverse bilingual RRSIS dataset to date. It contains 15,003 high-resolution images (1024--2048 px) collected from more than 30 countries across five continents, together with 49,745 annotated targets covering single-object, multi-object, and no-target scenarios. We further propose the Multi-scale Referring Segmentation Network (MRSNet), a framework tailored to the characteristics of remote sensing imagery. Specifically, MRSNet incorporates an Intra-scale Feature Interaction Module (IFIM) to refine fine-grained features within each encoder stage and a Hierarchical Feature Interaction Module (HFIM) to integrate cross-scale information while preserving spatial structure. Extensive experiments on NWPU-Refer and RRSIS-D demonstrate that MRSNet achieves strong and competitive performance across multiple metrics, especially under strict IoU thresholds and IoU-based evaluation, validating the effectiveness of the proposed dataset and model. The dataset and code are publicly available at https://github.com/CVer-Yang/NWPU-Refer.

cs.CV↗

arXiv2026

Near-real-time, meter-scale 3D urban wind modeling for low-altitude micrometeorology: numerical verification of a GPU-accelerated lattice Boltzmann framework

This study presents a near-real-time, meter-scale three-dimensional urban wind simulation framework for low-altitude flight events in complex urban meteorological environments. It reconstructs high-resolution wind fields by combining sparse observations with efficient microscale flow modeling. The framework integrates lattice Boltzmann method large-eddy simulation (LBM-LES), high-fidelity urban morphology reconstruction that explicitly resolves real building details, and observation-driven boundary assimilation into a rapid end-to-end pipeline for realistic urban domains. Multi-site Doppler lidar measurements from dense urban Guangzhou, China, are used for evaluation. The system reconstructs three-dimensional wind fields at 5 m resolution over kilometer-scale domains within minutes. Robustness and accuracy are tested through controlled observation reduction, independent validation against withheld lidar stations, and sensitivity analyses of grid resolution and precursor domain extent. Results show stable reproduction of vertical wind structures and key local flow features under complex morphology and limited observations, providing a scalable pathway for near-real-time urban wind reconstruction.

physics.flu-dyn↗

arXiv2026

Multi-Granularity Conformal Prediction for Reliable Neural-Operator Automotive Aerodynamic Surrogates

High-fidelity computational fluid dynamics (CFD) provides detailed aerodynamic data for vehicle design, but its cost limits design iteration. Neural-operator surrogates reduce this cost, yet their deterministic predictions do not indicate when a geometry or surface region is reliable. This study develops a conformal-prediction framework for reliability-aware automotive aerodynamic surrogate modeling on the DrivAerML dataset. GeoTransolver is the main backbone, while Transolver assesses transfer across neural-operator architectures. For drag coefficient prediction, conformalized quantile regression constructs calibrated case-level intervals. For surface pressure and wall shear stress (WSS), point prediction is combined with residual-scale estimation and residual-normalized conformal calibration to obtain spatially adaptive intervals. Global absolute, point-adaptive normalized, and case-wise normalized calibration are compared under split and cross-validation-assisted out-of-fold protocols. All experiments target 90% nominal coverage. Conformal calibration corrects the under-coverage of raw drag-coefficient quantile intervals, while out-of-fold score aggregation reduces the Monte Carlo coverage standard deviation from 10.41 to 3.10 percentage points. For surface fields, point-adaptive normalized calibration yields the narrowest near-nominal intervals, reducing mean width by 22.68% for pressure and 25.35%--27.09% for WSS under the out-of-fold protocol. Case-wise normalized calibration is more conservative but improves vehicle-level reliability. Smoothness regularization reduces the residual-scale local-variation score by 74.29% and lowers interval widths without material coverage loss. The framework converts deterministic neural-operator outputs into calibrated reliability indicators for prioritizing uncertain vehicle geometries and surface regions in follow-up CFD verification.

physics.flu-dyn↗

arXiv2026

Urban Wind Effects on UAV Operations in Building-Dense Low-Altitude Airspace

Urban low-altitude uncrewed aerial vehicle (UAV) operations are shaped by building-modified wind and constrained terrain-building clearance. Spatially heterogeneous wind can perturb short-horizon motion, but its clearance consequence depends on local airspace: similar perturbations may remain benign in open areas yet become clearance-sensitive near building walls, roof edges, or elevated terrain. This study develops a common-basis method for early clearance assessment before route buffers or corridor alignments are fixed. For each position-height state, nominal and wind-affected vehicle-sized trajectory envelopes are generated with a shared heading and sampling design, separating fixed-support wind loading, initial clearance exposure, and wind-induced clearance change. Applied to Shanghai and Beijing, the method uses lattice-Boltzmann method (LBM) wind fields co-registered with 20 m terrain-building scene grids, height layers of 60-340 m, and a representative light UAV. Results show distinct spatial and vertical patterns in baseline wind loading and clearance exposure. Wind-affected envelopes produce little domain-wide median shift in clearance exposure but generate spatially concentrated upper-tail responses in lower-altitude layers on the normalized clearance-exposure scale. Kilometer-Tile-level analysis links these responses mainly to baseline clearance sensitivity and horizontal wind-gradient magnitude, with fixed-support wind loading and mean wind speed playing secondary roles. Low-altitude planning should distinguish wind-loaded locations from clearance-sensitive locations. As an upstream diagnostic, the method identifies units where nominal trajectory envelopes approach terrain-building margins and local wind variation may further alter clearance exposure, with increases concentrated in the upper tail.

physics.flu-dyn↗

arXiv2026

A Large-Scale Dataset and a New Method for RemoteSensing Traffic Object Segmentation

Remote sensing imagery plays a crucial role in evaluating regional transportation capacity. However, existing segmentation datasets often lack diversity in object categories and scenes, limiting the ability of models to comprehensively evaluate trans portation capacity in real-world scenes. To alleviate this gap, we construct a large-scale and diverse dataset for transportation object segmentation, named as NWPU-Traffic. This dataset encompass four traffic object categories (car, airplane, ship, and train) and a wide range of scenes from 49 cities across 7 countries, with instance-level annotations to ensure precise segmentation of individual objects, which bridges critical shortcomings in resolution and scene diversity in existing datasets. Leveraging this dataset, we establish a benchmark with several popular segmentation networks. Furthermore, we propose a novel segmentation method that leverages spatial-channel preserving feature interaction and an adaptive feature decoder, enabling robust segmentation across varying scales and complex environments. Extensive experiments and ablation studies validate the effectiveness of our approach. The dataset and code are publicly available at https://github.com/CVer-Yang/NWPU-Traffic.

cs.CV↗

arXiv2025

FeatureLens: A Highly Generalizable and Interpretable Framework for Detecting Adversarial Examples Based on Image Features

Although the remarkable performance of deep neural networks (DNNs) in image classification, their vulnerability to adversarial attacks remains a critical challenge. Most existing detection methods rely on complex and poorly interpretable architectures, which compromise interpretability and generalization. To address this, we propose FeatureLens, a lightweight framework that acts as a lens to scrutinize anomalies in image features. Comprising an Image Feature Extractor (IFE) and shallow classifiers (e.g., SVM, MLP, or XGBoost) with model sizes ranging from 1,000 to 30,000 parameters, FeatureLens achieves high detection accuracy ranging from 97.8% to 99.75% in closed-set evaluation and 86.17% to 99.6% in generalization evaluation across FGSM, PGD, CW, and DAmageNet attacks, using only 51 dimensional features. By combining strong detection performance with excellent generalization, interpretability, and computational efficiency, FeatureLens offers a practical pathway toward transparent and effective adversarial defense.

cs.CV↗

arXiv2020

Explorative gradient method for active drag reduction of the fluidic pinball and slanted Ahmed body

We address a challenge of active flow control: the optimization of many actuation parameters guaranteeing fast convergence and avoiding suboptimal local minima. This challenge is addressed by a new optimizer, called explorative gradient method (EGM). EGM alternatively performs one exploitive downhill simplex step and an explorative Latin hypercube sampling iteration. Thus, the convergence rate of a gradient based method is guaranteed while, at the same time, better minima are explored. For an analytical multi-modal test function, EGM is shown to significantly outperform the downhill simplex method, the random restart variant, Latin hypercube sampling, Monte Carlo iterations and the genetic algorithm. EGM is applied to minimize the net drag power of the two-dimensional fluidic pinball benchmark with three cylinder rotations as actuation parameters. The net drag power is reduced by $40\%$ employing direct numerical simulations at a Reynolds number of $100$ based on the cylinder diameter. This optimal actuation leads to $98\%$ drag reduction employing Coanda forcing for boat tailing and partial stabilization of vortex shedding. The price is an actuation energy corresponding to $58\%$ of the unforced parasitic drag power. EGM is also used to minimize drag of the $35^\circ$ slanted Ahmed body employing distributed steady blowing with 10 inputs. $17\%$ drag reduction are achieved using Reynolds-Averaged Navier-Stokes simulations (RANS) at the Reynolds number $Re_H=1.9 \times 10^5$ based on the height of the Ahmed body. The optimal actuation emulates boat tailing by inward-directed blowing with velocities which are comparable to the oncoming velocity. We expect that EGM will be employed as efficient optimizer in many future active flow control plants.

physics.flu-dyn↗