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In this paper, we propose that thermally induced Knudsen forces in a rarefied gas can be exploited to achieve a tweezer-like mechanism that can be used to trap and grasp a micro-object without physical contact. Using the direct simulation Monte Carlo (DSMC) method, we showed that the proposed mechanism is achieved when a heated thin plate, mounted perpendicularly on a flat substrate, is placed close to a colder object; in this case, a beam. This mechanism is mainly due to the pressure differences induced by the thermal edge flows at the corners of the beam and the thermal edge flow at the tip of the thin plate. Specifically, the pressure on the top surface of the beam is smaller than that on its bottom surface when the thin plate is above the beam, while the pressure on the right side of the beam is smaller than that on its left side when the thin plate is located near the right side of the beam. These differences in pressure generate a force, which attracts the beam to the plate horizontally and vertically. Furthermore, this phenomenon is enhanced when the height of the beam is shorter, such that the horizontal and vertical net forces, which attract the beam to the plate, become stronger. The mechanism proposed here was also found to depend significantly on the height of the beam, the temperature difference between the thin plate and the beam, and the Knudsen number.

We propose an algorithm for fluid simulation in three-dimensional scenes with obstacles represented as implicit surfaces. The fluid simulation is performed based on the smoothed particle hydrodynamics method and the behavior of fluid particles in the vicinity of obstacles is defined by introducing a new model of particle motion specific to implicitly representation. For effective parallelization on the graphics processing units, we apply the grid of polynomial method to express the implicitly defined obstacles. The proposed method takes advantage of the properties of implicit representation such as smoothness, shape modelling and the expression of deforming objects.

Granular flows of 200 μm particles and the pile formation in a flat-bottomed hopper and bin in the presence of air and in a vacuum were predicted based on three-dimensional numerically empirical constitutive relations using Smoothed Particle Hydrodynamics and Computational Fluid Dynamics methods. The constitutive relations for the strain rate independent stress have been obtained as the functions of the Almansi strain including the large deformation by the same method as Yuu et al. [1]. The constitutive relations cover the elastic and the plastic regions including the flow state and represent the friction mechanism of granular material. We considered the effect of air on the granular flow and pile by the two-way coupling method. The granular flow patterns, the shapes of piles and the granular flow rates in the evolution are compared with experimental data measured under the same conditions. There was good agreement between these results, which suggests that the constitutive relations and the simulation method would be applicable for predicting granular flows and pile formation with complex geometry including free surface geometry. We describe the mechanisms by which the air decreases the granular flow rate and forms the convergence granular flow below the hopper outlet.

Changes in the resistance of tillage tools during cultivation are often investigated through field tests to reduce carbon emissions from agricultural machinery during tillage. Tillage tools are optimized using the test results to minimize energy loss. However, field tests typically face challenges such as high experimental costs, soil and climate limitations, and operational complexity. Numerical simulation, as a computer-aided research method, offers several advantages, including ease of use, cost-effectiveness, and resource conservation. This study employed the smoothed particle hydrodynamics (SPH) method and the coupled Eulerian-Lagrangian (CEL) method to examine the interaction between a biomimetic digging shovel and soil. In parallel, soil bin experiments were conducted, and the simulation results were compared to experimental data. The findings revealed that the simulated changes in cutting resistance were consistent with the experimental results, confirming the reliability of both models. Simulation results indicated that the CEL model required 6 h and 35 min to compute, while the SPH model required 7 h and 18 min. The relative error between the CEL model and the soil bin experiment was 8.81%, while that between the SPH model and the experiment was 13.76%. These results highlight the superior computational efficiency and higher computational accuracy of the CEL model. The validated CEL model was subsequently used to simulate the interactions between the digging shovel and soil under varying conditions.

Eddy-resolving turbulence simulations are essential for understanding and controlling complex unsteady fluid dynamics, with significant implications for engineering and scientific applications. Traditional numerical methods, such as direct numerical simulations (DNS) and large eddy simulations (LES), provide high accuracy but face severe computational limitations, restricting their use in high-Reynolds number or real-time scenarios. Recent advances in deep learning-based surrogate models offer a promising alternative by providing efficient, data-driven approximations. However, these models often rely on deterministic frameworks, which struggle to capture the chaotic and stochastic nature of turbulence, especially under varying physical conditions and complex, irregular geometries. Here, we introduce the Conditional Neural Field Latent Diffusion (CoNFiLD) model, a generative learning framework for efficient high-fidelity stochastic generation of spatiotemporal turbulent flows in complex, three-dimensional domains. CoNFiLD synergistically integrates conditional neural field encoding with latent diffusion processes, enabling memory-efficient and robust generation of turbulence under diverse conditions. Leveraging Bayesian conditional sampling, CoNFiLD flexibly adapts to various turbulence generation scenarios without retraining. This capability supports applications such as zero-shot full-field flow reconstruction from sparse sensor data, super-resolution generation, and spatiotemporal data restoration. Extensive numerical experiments demonstrate CoNFiLD’s capability to accurately generate inhomogeneous, anisotropic turbulent flows within complex domains. These findings underscore CoNFiLD’s potential as a versatile, computationally efficient tool for real-time unsteady turbulence simulation, paving the way for advancements in digital twin technology for fluid dynamics. By enabling rapid, adaptive high-fidelity simulations, CoNFiLD can bridge the gap between physical and virtual systems, allowing real-time monitoring, predictive analysis, and optimization of complex fluid processes. Available eddy-resolved simulations predict turbulent flows accurately but are too computationally demanding for widespread application. This study presents a Conditional Neural Field Latent Diffusion model that efficiently simulates complex spatiotemporal dynamics in turbulent systems, even within challenging 3D irregular domains.

The human nose serves vital physiological functions, including warming, filtration, humidification, and olfaction. These functions are based on transport phenomena that depend on nasal airflow patterns and turbulence. Accurate prediction of these airflow properties requires careful selection of computational fluid dynamics models and rigorous validation. The validation studies in the past have been limited by poor representations of the complex nasal geometry, lack of detailed airflow comparisons, and restricted ranges of flow rate. The objective of this study is to validate various numerical methods based on an anatomically accurate nasal model against published experimentally measured data under breathing flow rates from 180 to 1100ml/s. The numerical results of velocity profiles and turbulence intensities were obtained using the laminar model, four widely used Reynolds-averaged Navier-Stokes (RANS) turbulence models (i.e., k-ε, standard k-ω, Shear Stress Transport k-ω, and Reynolds Stress Model), large eddy simulation (LES) model, and direct numerical simulation (DNS). It was found that, despite certain irregularity in the flow field, the laminar model achieved good agreement with experimental results under restful breathing condition (180ml/s) and performed better than the RANS models. As the breathing flow rate increased, the RANS models achieved more accurate predictions but still performed worse than LES and DNS. As expected, LES and DNS can provide accurate predictions of the nasal airflow under all flow conditions but have an approximately 100-fold higher computational cost. Among all the RANS models tested, the standard k-ω model agrees most closely with the experimental values in terms of velocity profile and turbulence intensity.

This paper aims to study the difference of results in breakup state judgment, debris cloud and fragment characteristic parameter during hypervelocity impact (HVI) on large-scale complex spacecraft structures by various numerical simulation methods. We compared the results of the test of aluminum projectile impact on an aluminum plate with the simulation results of the smooth particle hydrodynamics (SPH), finite element method (FEM)-smoothed particle Galerkin (SPG) fixed coupling method, node separation method, and finite element method-smooth particle hydrodynamics adaptive coupling method under varying mesh/particle sizes. Then based on the test of the complex simulated satellite under hypervelocity impact of space debris, the most applicable algorithm was selected and used to verify the accuracy of the calculation results. It was found that the finite element method-smooth particle hydrodynamics adaptive coupling method has lower mesh sensitivity in displaying the contour of the debris cloud and calculating its characteristic parameters, making it more suitable for the full-scale numerical simulation of hypervelocity impact. Moreover, this algorithm can simulate the macro breakup state of the full-scale model with complex structure and output debris fragments with clear boundaries and accurate shapes. This study provides numerical simulation method options for the follow-up research on breakup conditions, damage effects, debris clouds, and fragment characteristics of large-scale complex spacecraft.

Particle-based methods have been increasingly attractive for solving biofluid flow problems, because of the ease and flexibility in modeling complex structure fluids afforded by the methods. In this review, we focus on popular particle-based methods widely used in red blood cell (RBC) simulations, including dissipative particle dynamics (DPD), smoothed particle hydrodynamics (SPH), and lattice Boltzmann method (LBM). We introduce their basic ideas and formulations, and present their applications in RBC simulations which are divided into three classes according to the number of RBCs in the simulation: a single RBC, two or multiple RBCs, and RBC suspension. Furthermore, we analyze their advantages and disadvantages. On weighing the pros and cons of the methods, a combination of the immersed boundary (IB) method and some forms of smoothed dissipative particle hydrodynamics (SDPD) methods may be required to deal effectively with RBC simulations.

The process of liquid droplet impinging upon the surface of particles entails complex dynamics and significant deformation. In this study, the smoothed particle hydrodynamics (SPH) method coupled with the discrete element method (DEM) is employed to investigate the motion process of liquid droplet impacting the particle surface. A surface tension model is introduced into the SPH motion equation to calculate the motion of the liquid droplet. In the SPH-DEM coupling module, the viscous force and capillary force between the liquid droplet and the particles are taken into account. The surface tension model is verified through two cases: the free deformation process of a stationary square liquid droplet and the impact of a liquid droplet on a hydrophobic wall. The accuracy of the DEM model is validated through experimental verification of dry particle collapse. And the experimental results validate the accuracy of the SPH-DEM model in simulating the liquid droplet impact on the particle surface. The simulation results are in good agreement with the experimental ones. Utilizing the SPH-DEM model, the influences of the droplet impact velocity and the particle diameter on the rebound phenomenon after the water droplet impacts the particle wall of the powder bed are respectively investigated. The results indicate that the higher the droplet impact velocity and the smaller the diameter of the powder bed particles, the faster the rebound rate of the droplet after impacting the powder bed layer.