Search Results (519)

Mesh-agnostic models have advantages in processing flow field data with various topologies and densities, and they can easily incorporate partial differential equations. Beyond physics-informed neural networks, mesh-agnostic models have been studied for data-driven predictions of simple flows. In this study, a data-driven mesh-agnostic model is proposed to predict the transonic flow field of various supercritical airfoils. The model consists of two subnetworks, i.e., ShapeNet and HyperNet. ShapeNet is an implicit neural representation used to predict spatial bases of the flow field. HyperNet is a simple neural network that determines the weights of these bases. The input of ShapeNet is extended to ensure accurate prediction for different airfoil geometries. To reduce overfitting while capturing shock waves and boundary layers, a multi-resolution ShapeNet combining two activation functions is proposed. Additionally, a physics-guided loss function is proposed to enhance accuracy. The proposed model is trained and tested on various supercritical airfoils under different free-stream conditions. Results show that the model can effectively utilize airfoil samples with different grid sizes and distributions, and it can accurately predict the shock wave and boundary layer velocity profile. The proposed mesh-agnostic model can be used as a decoder in any conventional models, contributing to their application in complex and three-dimensional geometries. Full article

The three-dimensional borehole-to-surface transient electromagnetic (BSTEM) method plays a critical role in resolving subsurface conductivity structures under complex geological conditions. However, its application is often constrained by the high computational costs associated with large-scale simulations and fine temporal resolution. In this study, a time-parallel forward modeling strategy is employed by integrating the finite volume method (FVM) with the Multigrid Reduction-in-Time (MGRIT) algorithm. Maxwell’s equations are discretized in space using unstructured octree meshes, while the MGRIT algorithm enables parallelism along the time axis through coarse–fine temporal grid hierarchy and multilevel iterative correction. Numerical experiments on synthetic and field-scale models demonstrate that the MGRIT-based solver significantly reduces computational time compared to conventional direct solvers, particularly when a large number of processors are utilized. In a field-scale hematite mine model, the MGRIT-based solver reduces the total runtime by more than 40% while maintaining numerical accuracy. The method exhibits parallel scalability and is especially advantageous in problems involving a large number of time channels, where simultaneous time-step updates offer substantial performance gains. These results confirm the effectiveness and robustness of the proposed approach for large-scale 3D TEM simulations under complex conditions and provide a practical foundation for future applications in high-resolution electromagnetic modeling and imaging. Full article

This study investigates the performance of a high-resolution Weather Research and Forecasting with large-eddy simulation (WRF-LES) model in simulating the strong wind of a realistic typhoon (Jebi, 2018). Multiple domains are nested to downscale the grid resolution from 4.5 km to 33.3 m, and grid size sensitivity is tested in the innermost WRF-LES domain. The commonly used 1.5-order turbulent kinetic energy (TKE) subgrid-scale (SGS) model is excessively dissipative near the ground; this causes overshoot in the mean velocity profile compared with the expected log-law profile, a phenomenon slightly amplified by finer grids. Horizontal roll structures in the typhoon boundary can be effectively resolved with the 100 m horizontal grid size ($\mathrm{\Delta }x$). However, higher resolution is needed to capture small-scale turbulence, and the effective mesh resolution for resolved turbulence is about 5–9$\mathrm{\Delta }x$ near the ground. The nonlinear backscatter and anisotropy (NBA) model significantly reduces the overshoot, and the resolved velocity structures are insensitive to the SGS model except for the lowest model level. Full article

Texture mapping of weft-knitted fabrics plays a crucial role in virtual try-on and digital textile design due to its computational efficiency and real-time performance. However, traditional texture mapping techniques typically adapt pre-generated textures to deformed surfaces through geometric transformations. These methods overlook the complex variations in yarn length, thickness, and loop morphology during stretching, often resulting in visual distortions. To overcome these limitations, we propose Knit-Pix2Pix, a dedicated framework for generating realistic weft-knitted fabric textures directly from knitted unit mesh maps. These maps provide grid-based representations where each cell corresponds to a physical loop region, capturing its deformation state. Knit-Pix2Pix is an integrated architecture that combines a multi-scale feature extraction module, a grid-guided attention mechanism, and a multi-scale discriminator. Together, these components address the multi-scale and deformation-aware requirements of this task. To validate our approach, we constructed a dataset of over 2000 pairs of fabric stretching images and corresponding knitted unit mesh maps, with further testing using spring-mass fabric simulation. Experiments show that, compared with traditional texture mapping methods, SSIM increased by 21.8%, PSNR by 20.9%, and LPIPS decreased by 24.3%. This integrated approach provides a practical solution for meeting the requirements of digital textile design. Full article

Ultra-deep fault-controlled carbonate reservoirs exhibit strong heterogeneity, multi-scale fracture–cavity systems, and complex geological controls, which render conventional analytical well testing methods inadequate. This study proposes a geological model-based numerical well testing framework incorporating adaptive meshing, noise reduction, and machine-learning-assisted inversion. A multi-step workflow was established, including (i) single-well geological model extraction with localized grid refinement to capture near-wellbore flow behavior, (ii) pressure data denoising and preprocessing using low-pass filtering, and (iii) surrogate-assisted parameter inversion and sensitivity analysis using particle swarm optimization (PSO) to construct diagnostic type curves for different fracture–cavity control modes. The methodology was applied to different wells, yielding inverted fracture permeabilities ranging from approximately 140 to 480 mD and cavity permeabilities between about 110 and 220 mD. Results show that the numerical well testing method achieved an 85.7% interpretation accuracy, outperforming conventional approaches. Distinct parameter sensitivities were identified for single-, double-, and multi-cavity systems, providing a systematic basis for production allocation strategies. This integrated approach enhances the reliability of reservoir characterization and offers practical guidance for efficient development of ultra-deep carbonate reservoirs. Full article

This study provides a comprehensive three-dimensional Computational Fluid Dynamics analysis of airflow distribution in a surgical operating room under realistic occupancy and equipment conditions. Using integrated modelling in SolidWorks and a subsequent analysis in ANSYS Fluent, a full-scale Operating Room geometry was simulated to assess the effectiveness of a laminar airflow system. The model includes surgical staff mannequins, thermal loads from surgical lights, and medical equipment that commonly disrupt unidirectional flow patterns. A polyhedral mesh with over 2.8 million nodes was employed, and a grid independence study confirmed solution reliability. The realisable k–ε turbulence model with enhanced wall treatment was used to simulate steady-state airflow, thermal stratification, and pressure variation due to door opening. Results highlight significant flow disturbances and recirculation zones caused by the shear zone created by supply air, overhead lights and heat plumes, particularly outside the core laminar air flow zone. The most important area, 10 cm above the surgical site, shows a maximum velocity gradient of 0.09 s⁻¹ while the temperature gradient shows 6.7 K.m⁻¹ and the pressure gradient, 0.0167 Pa.m⁻¹. Streamline analysis reveals potential re-entrainment of contaminated air into the sterile field. Full article

Numerical modelling of airflow in underground mines is gaining importance in modern ventilation system design and safety assessment. Computational Fluid Dynamics (CFD) simulations enable detailed analyses of air movement, contaminant dispersion, and heat transfer, yet their reliability depends strongly on the accuracy of the geometric representation of excavations. Raw point cloud data obtained from laser scanning of underground workings are typically irregular, noisy, and contain discontinuities that must be processed before being used for CFD meshing. This study presents a methodology for automatic segmentation and regularization of large-scale point cloud data of underground excavation systems. The proposed approach is based on skeleton extraction and trajectory analysis, which enable the separation of excavation networks into individual tunnel segments and crossings. The workflow includes outlier removal, alpha-shape generation, voxelization, medial-axis skeletonization, and topology-based segmentation using neighbor relationships within the voxel grid. A proximity-based correction step is introduced to handle doubled crossings produced by the skeletonization process. The segmented sections are subsequently regularized through radial analysis and surface reconstruction to produce uniform and watertight models suitable for mesh generation in CFD software (Ansys 2024 R1). The methodology was tested on both synthetic datasets and real-world laser scans acquired in underground mine conditions. The results demonstrate that the proposed segmentation approach effectively isolates single-line drifts and crossings, ensuring continuous and smooth geometry while preserving the overall excavation topology. The developed method provides a robust preprocessing framework that bridges the gap between point cloud acquisition and numerical modelling, enabling automated transformation of raw data into CFD-ready geometric models for ventilation and safety analysis of complex underground excavation systems. Full article

This work investigates how the vehicle-to-tube suspension gap governs compressible flow physics and operating margins in Hyperloop-class transport at 10 kPa. To our knowledge, this is the first study to apply adjoint aerodynamic optimization to mitigate gap-induced choking and shock formation in a full pod–tube configuration. Using a steady, pressure-based Reynolds-averaged Navier-Stokes (RANS) framework with the GEnerlaized K-Omega (GEKO) turbulence model, a simulation for the cruise conditions was performed at M = 0.5–0.7 with a mesh-verified analysis (medium grid within 0.59% of fine) to quantify gap effects on forces and wave propagation. For small gaps, the baseline pod triggers oblique shocks and a near-Kantrowitz condition with elevated drag and lift. An adjoint shape update—primarily refining the aft geometry under a thrust-equilibrium constraint—achieves 27.5% drag reduction, delays the onset of choking by ~70%, and reduces the critical gap from d/D ≈ 0.025 to ≈0.008 at M = 0.7. The optimized configuration restores a largely subcritical passage, suppressing normal-shock formation and improving gap tolerance. Because propulsive power at fixed cruise scales with drag, these aerodynamic gains directly translate into operating-power reductions while enabling smaller gaps that can relax tube-diameter and suspension mass requirements. The results provide a gap-aware optimization pathway for Hyperloop pods and a compact design rule-of-thumb to avoid choking while minimizing power. Full article

The presented research is focused on identifying a cheap and environmentally friendly solution for recovering useful non-ferrous metals contained in used Ni-MH batteries—more specifically, in batteries that power medical equipment, i.e., portable defibrillators. The cathodic paste of Ni-MH batteries contains Ni(OH)₂ as an active material to which Zn, Co and Mn can be added. The paste is impregnated into a support mesh made of nickel. The anodic paste of Ni-MH batteries contains mixtures of rare earths capable of storing the released hydrogen. The paste is mixed with a binder and pressed onto a metal grid made of nickel alloy. After manual disassembly, the components of the Ni-MH batteries were analyzed by X-ray Fluorescence Spectroscopy (XRF) before and after the separation/recovery operation. To separate the cathode and anode paste from the metal supports (grids, metal meshes), an ultrasonic bath with appropriate solutions was used, and the optimal working parameters were established. The recovery of the anode paste was achieved by completely passing the rare earths into the citric acid solution used for ultrasonication; the nickel mesh was cleaned of the Ni(OH)₂ paste using water as the ultrasonication medium. After separation from the metal supports, the anode and cathode pastes were analyzed and characterized by XRF, optical and electron microscopy (SEM, EDX). The results obtained are of real interest for those who study the recycling of Ni-MH batteries; the use of ultrasound in a low-concentration citric acid environment for the purpose of recovering rare earths can be an economic and ecological alternative for battery recycling. Full article

Accurate sea surface temperature (SST) forecasting in coastal upwelling systems requires predictive models capable of representing complex oceanic geometries. This work revisits grid-to-mesh coupling strategies in Graph Neural Networks (GNNs) and analyzes how mesh topology and connectivity influence prediction accuracy and artifact formation. This standard coupling process is a significant source of discretization errors and spurious numerical artifacts that compromise the final forecast’s accuracy. Using daily Copernicus SST and 10 m wind reanalysis data from 2000 to 2020 over the Canary Islands and the Northwest African region, we evaluate four mesh configurations under varying grid-to-mesh connection densities. We analyze two structured meshes and propose two new unstructured meshes for which their nodes are distributed according to the bathymetry of the ocean region. The results show that forecast errors exhibit geometric patterns equivalent to order-k Voronoi tessellations generated by the k-nearest neighbor association rule. Bathymetry-aware meshes with $k=3$ and $k=4$ grid-to-mesh connections significantly reduce polygonal artifacts and improve long-term coherence, achieving up to 30% lower $\mathrm{RMSE}$ relative to structured baselines. These findings reveal that the underlying geometry, rather than node count alone, governs error propagation in autoregressive GNNs. The proposed analysis framework provides a clear understanding of the implications of grid-to-mesh connections and establishes a foundation for artifact-aware, geometry-adaptive learning in operational oceanography. Full article

The construction of a high-order shape function is a key and difficulty for unstructured grid mesh and sliding boundary problems. In this paper, a construction method of space-time absolute nodal coordinate formulation quadrilateral cable (SACQ) is proposed, and the accuracy of the SACQ element is studied and verified with three different applications. First, the shape function of SACQ is constructed with spatiotemporal reduction coordinates, and the action integral of SACQ is composed with the Lagrangian function and discrete with perspective transformation. Second, the numerical convergence region is discussed and determined with the Courant number. Furthermore, a space-time nodal dislocation and its relation with the Courant number are studied. The simulation and verification are focusing on some realistic problems. Finally, a one-sided impact, a free-flexible pendulum, a taut string with a sliding boundary and a deployable guyed mast under an impact transverse wave are simulated. In these problems, an unstructured grid meshed with SACQ has similar energy convergence and accuracy to a structured grid but shows better efficiency. Full article

As global warming accelerates, the Paris Agreement has emphasized the urgent need for technologies that reduce and manage carbon dioxide emissions. Consequently, carbon capture and storage (CCS) has emerged as a critical area of research. For the safe and efficient transportation of captured carbon dioxide in cryogenic tanks, the design must accurately account for the phase change behavior of liquefied carbon dioxide (LCO₂). This study proposes a numerical approach to evaluate the thermal insulation performance of cryogenic tanks by simulating the phase change process of LCO₂. The phase transition of LCO₂ was simulated in a horizontally oriented Type-C cryogenic tank using the open-source computational fluid dynamics (CFD) framework OpenFOAM (v2312). To validate the numerical methodology, the phase change in liquefied nitrogen (LN₂) inside a tank was first simulated and compared with available experimental data. A mesh-independence study was then conducted to determine the optimal grid resolution, and the effects of different equations of state (EOS) for both liquid and gaseous phases, as well as various turbulence models, were examined. The boil-off rate (BOR) and boil-off gas (BOG) generation within the tank were predicted, and variations in internal pressure and flow fields were analyzed. The simulation results over 5000 s showed that the internal tank pressure increased from 7.8 bar to 8.1 bar, and the average temperature rose by approximately 1.3 K. The total mass of LCO₂ decreased from 1439.3 kg to 1431.0 kg. Full article

This study addresses the trade-off between accuracy and efficiency in predicting the aerodynamics and wakes of large wind turbines. We developed a unified immersed boundary–actuator line framework with large-eddy simulation. The actuator line efficiently represents blade loading, while the immersed boundary method (IBM) with a wall model resolves near-blade turbulence. The solver uses a staggered Cartesian discretization and is accelerated by a hybrid CPU/GPU implementation. An implicit signed-distance geometry treatment and a ghost cell wall function based on Spalding’s law reduce near-wall grid requirements and eliminate body-fitted meshing. Flow past a three-dimensional cylinder at Re = 3900 validates the accuracy and good grid convergence of the IBM. For the wind turbine, three meshes show converged thrust and torque, with differences below 1% between the two finer grids. At the rated condition (U_∞ = 11.4 m/s), thrust and torque agree with STAR-CCM+ and FAST, with deviations of 6.3% and 1.2%, respectively. Parametric cases at 4–10 m/s show thrust and torque increasing nonlinearly with inflow, approximately quadratically, in close agreement with reference models. As wind speed rises, the helical pitch tightens, the wake broadens, and breakdown occurs earlier, consistent with stronger shed vorticity. The framework delivers high fidelity and scalability without body-fitted meshes, offering a practical tool for turbine design studies and extensible wind plant simulations. Full article

This paper investigates a physics-informed surrogate modeling framework for multi-phase flow in porous media based on the Fourier Neural Operator. Traditional numerical simulators, though accurate, suffer from severe computational bottlenecks due to fine-grid discretizations and the iterative solution of highly nonlinear partial differential equations. By parameterizing the kernel integral directly in Fourier space, the operator provides a discretization-invariant mapping between function spaces, enabling efficient spectral convolutions. We introduce a Dual-Branch Adaptive Fourier Neural Operator with a shared Fourier encoder and two decoders: a saturation branch that uses an inverse Fourier transform followed by a multilayer perceptron and a pressure branch that uses a convolutional decoder. Temporal information is injected via Time2Vec embeddings and a causal temporal transformer, conditioning each forward pass on step index and time step to maintain consistent dynamics across horizons. Physics-informed losses couple data fidelity with residuals from mass conservation and Darcy pressure, enforcing the governing constraints in Fourier space; truncated spectral kernels promote generalization across meshes without retraining. On SPE10-style heterogeneities, the model shifts the infinity-norm error mass into the ${10}^{-2}$ to ${10}^{-1}$ band during early transients and sustains lower errors during pseudo-steady state. In zero-shot three-dimensional coarse-to-fine upscaling from $30\times 110\times 5$ to $60\times 220\times 5$, it attains $R^2=0.90$, RMSE = $4.4\times {10}^{-2}$, and MAE = $3.2\times {10}^{-2}$, with more than 90% of voxels below five percent absolute error across five unseen layers, while the end-to-end pipeline runs about three times faster than a full-order fine-grid solve and preserves water-flood fronts and channel connectivity. Benchmarking against established baselines indicates a scalable, high-fidelity alternative for high-resolution multi-phase flow simulation in porous media. Full article

Direct Numerical Simulations (DNS) are widely employed to simulate thermo-fluid dynamics in packed bed reactors, offering high-fidelity insights into complex flow and heat transfer phenomena. However, recent studies have revealed notable differences in isothermal turbulent flow results across different DNS frameworks, leaving open the question of how conjugate heat transfer is affected. This study presents a comparison between DNS based on a finite volume method (FVM) and a lattice Boltzmann method (LBM) for predicting turbulent heat transfer in a low porosity face-centered cubic (FCC) packed unit. First, the methods are compared with respect to the required resolution and computational cost. Subsequently, global parameters for drag, heat transfer, and spatial as well as temporal variances are evaluated. The flow topology is further analyzed by examining the mean and fluctuating components of hydrodynamic and thermal fields. While good agreement between the methods is shown regarding time-averaged velocity and temperature profiles, more pronounced differences are observed when comparing the respective temporal variances between the two methods. Additionally, the FVM, which relies on a surface-fitted mesh, requires more degrees of freedom to obtain a grid-converged solution but delivers results of higher certainty than the LBM. These findings highlight important methodological considerations when selecting DNS approaches for resolving turbulent heat transfer in complex porous geometries. Full article