Single-degree-of-freedom fluid–structure interaction model of dual synthetic jets actuator

Study on the deposition characteristics of crystallization fouling in rib channel based on CFD 3D dynamic mesh technology

Efficient mixing is a persistent bottleneck in agricultural and agrochemical processing, where rapid and uniform mixing must be achieved under laminar flow with low energy input and gentle shear. Inspired by peristaltic transport in biological systems, this study investigates a bio-inspired flexible-wall squeezing mixer and establishes a two-dimensional computational framework to quantify how periodic wall deformation governs scalar homogenization in a flexible conduit. An Arbitrary Lagrangian-Eulerian dynamic mesh approach is implemented to resolve moving boundaries and to prescribe actuation, enabling the systematic evaluation of the separate and coupled effects of peak wall-normal velocity amplitude A and actuation frequency f on mixing performance. Mixing effectiveness is quantified using a variance-based mixing index MI and a sustained-threshold mixing time ts, and response surface methodology is employed to map the A-f design space and interpret the roles of time-dependent shear, interfacial stretching and folding, and vortex intensification. Relative to a non-actuated baseline, a peak wall-normal velocity amplitude of 3 × 10-3 m s-1 at 2 Hz reduces ts by 21.3%. At fixed f = 3 Hz, increasing A from 1 × 10-3 to 4 × 10-3 m s-1 shortens ts by 10.2%, while at fixed A = 3 × 10-3 m s-1, raising f from 1 to 5 Hz further decreases ts by 6.6% with diminishing gains at the lowest frequencies. The response surface identifies an operating optimum at A = 4 × 10-3 m s-1 and f = 5 Hz, achieving a peak MI of 0.9557 and a minimum ts of 7.81 s. A periodically squeezed physical mixing loop was further examined using fluorescence imaging to assess outlet homogeneity trends. The stabilized outlet coefficient of variation (CV) decreased from about 0.65 without squeezing to 0.60 at 1 Hz and 10 mm s-1, 0.58 at 2 Hz and 10 mm s-1, and 0.54 at 2 Hz and 30 mm s-1, indicating that stronger and faster actuation improves outlet uniformity. The numerical and experimental results are therefore interpreted jointly as mechanistic and trend-level evidence, while a rigorous quantitative prediction for the cylindrical compliant device will require future three-dimensional, compliance-resolved simulations and broader experimental benchmarking.

Existing research primarily focuses on ordinary straight roads or curves; however, there is a notable lack of recent research on continuous curves. This research employs Computational Fluid Dynamics (CFD) dynamic mesh technology to numerically simulate the external flow field during vehicle overtaking on a continuous curve resembling a sine wave. This study conducts a numerical simulation to analyze the external flow field of vehicles during overtaking on a continuous curve, similar to a sine curve, using CFD. Using different initial velocities, the study analyzes lateral force on the vehicle body during overtaking. It investigates how dynamic changes in the external flow field affect vehicle dynamics by employing tetrahedral meshes, the SST k-ω turbulence model, and UDF programming. To address emergency overtaking scenarios during medical vehicle rescues, a four-factor orthogonal experimental design was employed to identify the safest overtaking condition: overtaking a small vehicle (5 m × 1.8 m) at 22 m per second with 1.5 times the vehicle width and no crosswind. Regression lines were fitted to the data, yielding a nonlinear regression equation that can predict road conditions, thereby providing theoretical support for intelligent driving systems.

We introduce Motion2VecSets, a 4D diffusion model for dynamic surface mesh generation from various ambiguous observations, including a sequence of RGB images, sparse and partial point clouds, and low-resolution voxel grids. While recent methods using neural field representations have shown success in modeling non-rigid objects, conventional feed-forward architectures struggle with noisy, partial, or sparse observations due to their deterministic nature. To address the inherent one-to-many mapping problem, we introduce a diffusion model that explicitly learns the shape and motion distribution of non-rigid objects through an iterative denoising process of compressed latent representations. The diffusion-based priors provide more plausible and diverse reconstructions under ambiguous conditions. Instead of relying on global latent codes, we represent 4D dynamics using latent sets. This novel 4D representation captures local shape and deformation patterns, leading to more accurate non-linear motion capture and significantly improving generalization capacity to unseen motions and identities. For temporally coherent tracking, we jointly denoise latent sets across frames and enable cross-frame information exchange. To reduce computational cost, we design an interleaved spatial-temporal attention block that alternately aggregates deformation latents along spatial and temporal dimensions. Extensive experiments on datasets of humans, animals, and articulated objects demonstrate that Motion2VecSets outperforms prior methods in reconstructing and tracking non-rigid deformations from various imperfect observations. Our implementation is available at https://vveicao.github.io/projects/Motion2VecSets/.

This paper presents a novel differential-type high-flow safety valve, with a rated flow and pressure of 3000 L/min and 50 MPa respectively, aimed at enhancing the impact resistance and stability of hydraulic supports. Based on the Ansys Fluent platform, dynamic mesh technology and User-Defined Functions (UDF) were employed to identify the optimal damping hole radius for the differential high-flow safety valve. The transient fluid characteristics throughout the opening process until stable unloading were simulated for valves featuring damping hole radii of 1 mm, 2 mm, and 3 mm. Based on the optimal damping hole radius, test samples were developed high flow safety valves and constructed a rapid dynamic loading shock test rig to evaluate their shock resistance characteristics. Results indicate that a damping hole radius of 2 mm achieves the best overall performance in both transient response characteristics and operational stability. The differential high-flow safety valve demonstrates rated flow and pressure of approximately 2996 L/min and 49.4 MPa respectively, with valve core opening time of under 2 ms, unloading time of under 5 ms, and pressure overshoot of below 20%. These findings validate the structural rationality of the differential high-flow safety valve and confirm its advantages in rapid unloading speed and excellent impact resistance.

Drifting fish aggregating devices (DFADs) are central to tropical tuna purse-seine fisheries, yet their hydrodynamic performance under realistic seas has not been adequately addressed, particularly for emerging eco-friendly designs. A three-dimensional framework based on computational fluid dynamics is developed to assess the motion response and mooring loads of full-scale DFADs comprising raft buoys, biodegradable cotton rope, and iron sinkers, using four buoy layouts (Models A to D). Unsteady Reynolds-averaged Navier–Stokes (URANS) simulations are performed with a realizable k–ε closure, volume of fluid (VOF) free-surface capturing, the Euler overlay method, dynamic overset meshes, and catenary mooring coupling. Regular waves representative of operational conditions (T = 1.40 to 2.40 s, H = 0.10 to 0.40 m) are imposed via a VOF wave-forcing technique, and mesh/time-step sensitivity analyses demonstrate the accurate reproduction of the first-order wave elevation (error < 0.8%). Surge drift per cycle and heave response amplitude operators, with the relative mooring force, are evaluated as functions of the relative wavelength (λ/La) and wave steepness (H/λ). The results reveal that the buoy layout exerts first-order control on DFAD dynamics, whereas short, steep waves dominate motion and line loads. The intermediate end-point sinker mass achieves a favorable balance between motion suppression and mooring load control, whereas distributing a fixed total sinker mass along the rope reduces heave response and mooring force by improving the tension redistribution and overall stability. Across all sea states, Models A and D reduced motion envelopes and mooring forces, indicating their suitability as robust, low-impact configurations. The proposed framework and design recommendations provide quantitative guidance for optimizing eco-DFAD geometry and deployment strategies, supporting safer and more sustainable DFAD-based tuna fisheries.

In this study, a three-dimensional simulation of a walking-beam reheating furnace was developed to improve the steel slab reheating process and reduce surface oxidation kinetics using computational fluid dynamics (CFD). Combustion, heat transfer, fluid dynamics, and chemical reaction models were integrated into the numerical framework of this study. In addition, dynamic mesh remeshing was coupled through user-defined functions (UDFs), enabling the simultaneous simulation of slab movement and evolution of the involved transport phenomena. Turbulence was modeled with the realizable k-ε formulation, combustion with the Eddy Dissipation model, and radiation with the P-1 model coupled with WSGGM to include CO2 and H2O gas radiation. Scale formation was modeled using customized functions based on Arrhenius-type kinetics and Wagner’s oxidation model, evaluating its growth as a function of time, temperature, and furnace atmosphere. The predicted thermal evolution inside the furnace was validated using industrial data, yielding an average deviation of 5%. Furthermore, the proposed operating conditions led to an average slab temperature of 1289.77 °C at the exit of the homogenization zone, which was 16 °C higher than that under the current operation but still within the target range (1250 ± 50 °C). The reduction in combustion air decreased energy losses and improved product quality, lowering the molar oxygen content in the furnace atmosphere from 4.9 × 102 mol to 6.7 × 101 mol. Additionally, annual savings of 4,793,472 kg of natural gas and 13,884 tons of steel were estimated owing to reduced oxidation losses. The proposed air–fuel adjustment led to estimated annual energy savings (equivalent to 4,793,472 kg of natural gas) and a reduction in material loss due to oxidation from 4.5% to 3.75% (an absolute reduction of 0.75 percentage points; relative reduction ≈ 16.7%), which has a significant industrial impact on metal conservation and descaling cost reduction. Although there are CFD studies on plate overheating and scale growth separately, this work presents three main contributions: (1) the integration, within a single numerical framework, of combustion, radiation, species transport, oxidation kinetics, and actual plate movement using a dynamic mesh; (2) validation against continuous industrial records (16 thermocouples) and quantification of operational benefits such as fuel savings and reduced material loss; and (3) a comparative analysis between actual and optimized conditions, which standardize the air–methane ratio.

Purpose This study aims to analyze the interplay and developmental dynamics of internal resources and capabilities within complex external environments shaped by market and technological interactions. It also seeks to clarify the dynamic interplay and dominant roles of market and technology, and to delineate the logical relationship between resource-capability configurations and corporate strategy. By adopting a resource orchestration and dynamic capabilities perspective, this research uncovers the intrinsic mechanisms, strategic logic and pathways of enterprise digital transformation, thereby offering theoretical and practical guidance for more organizations embarking on digital transformation. Design/methodology/approach This study uses a single-case research methodology combined with grounded theory to conduct a longitudinal analysis of century innovation, a leading traditional printing enterprise in China, as it undergoes its digital transformation journey. This paper has been refined for readability with the assistance of AI-powered language enhancement tools. Findings First, the digital transformation of enterprises follows a logical path of “driving context–strategic planning–digital transformation”, progressing through four developmental stages: digital element sedimentation, digital-intelligent formation, convergent network integration and smart engine leap. Second, technology and market forces operate as dual driving mechanisms, exerting a bidirectional push-and-pull influence that alternates in dominance. Specifically, market-driven stages prioritize resource mobilization, while technology-driven stages emphasize capability enhancement. Third, core to this process are resource orchestration and dynamic capabilities, which achieve a dynamic reciprocal meshing transmission. Specifically, resource orchestration evolves through “Fission-Incubation – Graft-Driven Growth – Synergistic-Coupling – Intelli-Control empowerment”, meshing with and concurrently activating dynamic capabilities of “Recognition and adoption – Learning and Internalization – Digital collaboration – Flexible extension”. Originality/value This study develops a “context–strategy–outcome” framework to elucidate the logic of enterprise digital transformation, uncovering the internal mechanisms, strategic logic and pathway processes driven by enterprise strategy. It articulates the bidirectional push–pull and alternating dominance of market and technology as external drivers, forming a “primary driver transition” mechanism. In addition, it clarifies resources and capabilities as core strategic elements, exhibiting meshing transmission and dynamic matching with “stage-coexistent” and “role-switching” characteristics – resource configuration dominates in market-driven phases, while capability evolution leads in technology-driven phases – offering a novel perspective on the gradual digital transformation of traditional enterprises.

Continuous rehabilitation monitoring outside clinics is critical for long-term recovery, yet existing modalities fall short. Vision- and wearable-based systems raise privacy and compliance concerns, while RF-based sensing, despite contactless, is fundamentally motion-dependent —blind to the stillness that characterizes balance, endurance, and postural control. We observe that conventional static clutter removal not only suppresses environmental reflections but also erases the body's involuntary micro-motions, such as breathing, heartbeat, and subtle sway. Our key insight is that these micro-motions are not noise but information, encoding physiological vitality even in apparent stillness. Realizing this shift—from detecting motion to perceiving life within stillness—introduces two fundamental challenges: (C1) the echoes of these micro-motions are orders of magnitude weaker than static clutter, spectrally overlap within the near-zero Doppler region and spatially co-located with dominant reflections; and (C2) mmWave reflections are inherently sparse and geometry-agnostic, lacking the structural priors required to recover body's shape and pose across users and environments. To address these, we design mmRehab, a transformative mmWave sensing system for rehabilitation, extending radar perception beyond motion to enable physiological interpretation even when users remain still. Within mmRehab, Micro-motion Feature Extraction addresses C1 through beamforming-based spatial isolation and micro-Doppler temporal discrimination, amplifying respiration- and posture-related cues; Geometry-aware Knowledge Transfer addresses C2 via depth-guided distillation, transferring structural priors from vision to radar representations for robust generalization. Extensive experiments on both dynamic and static rehabilitation tasks show that mmRehab reduces 3D reconstruction errors by over 24% and generalizes robustly to unseen users, distances, and orientations—demonstrating the feasibility of unified radar perception for motion and micro-motion rehabilitation monitoring.

Understanding particle deposition patterns in the pulmonary acinus is essential for early intervention and treatment in acinar diseases. This study numerically investigated the effects of respiratory modes and emphysematous alveolar wall ablation on airflow and particle deposition in a physiologically representative pulmonary acinar model. A heterogeneous acinar model was developed, incorporating alveolar expansion and contraction via the dynamic meshing method, and its validity was confirmed by comparison with published particle deposition data. Airflow and particle transport patterns were then analyzed under varying respiratory modes and degrees of alveolar wall ablation. For particles smaller than 1 μm, deposition decreased with higher breathing frequency and increased with larger tidal volume. Smaller particles penetrated deeper and deposited more uniformly due to strong airflow coupling. Compared with the normal acinus, the lesioned acinus exhibited reduced airflow variability, lower expansion capacity, and a decreased deposition fraction. Alveolar wall ablation impaired lung expansion and restricted distal airflow penetration, leading to localized particle deposition near the acinar entrance. As lesion severity increased, the deposition progressively declined due to altered flow patterns and a reduced surface-to-volume ratio. The particle deposition declined nonlinearly with lesion severity. A 30% wall ablation reduced total deposition by over 40%, whereas further increases to 60% and 90% caused only minor additional decreases, indicating a nonlinear response in which early structural damage disproportionately affects acinar particle deposition. These findings underscore the importance of early intervention to preserve alveolar drug deposition efficiency and improve therapeutic outcomes in patients with progressive pulmonary diseases such as emphysema.

This study addresses the impact-induced failure of drilling pump valves caused by uncontrolled disc–seat collisions by proposing a novel valve design incorporating a two-stage buffering mechanism. The design employs a wave spring as the primary buffer and an elastic sealing ring as the secondary buffer, effectively mitigating impact through staged energy dissipation. A nonlinear stiffness model of the wave spring, accounting for the transition between line and surface contact modes, was developed. Strong fluid–structure interaction transients were simulated using dynamic meshing and user-defined functions. A parametric study was conducted by systematically varying cylindrical spring stiffness (7.7–15 N/mm), preload (110–160 N), and wave spring type (D85 to D110). Results show that, compared to a conventional valve, the two-stage mechanism reduces impact velocity by 24.2%, accelerates opening response by 17.9%, and extends the closing phase by 0.28%. Increasing wave spring stiffness (from D85 to D110) decreases opening delay time by 98.7% and attenuates peak velocity by 44.4%. Optimized hybrid spring parameters can minimize closing delay height by 27.3%. By reducing seat erosion and suppressing vibration-induced failure, the two-stage buffering mechanism effectively extends valve service life and enhances operational reliability in high-cycle drilling operations.

A transient numerical framework incorporating dynamic mesh techniques was developed to simulate the launch process. On this basis, a thermal–fluid–structural multi-physics coupling paradigm was proposed to interpret the evolution of the flow field and the associated load response throughout the entire firing sequence. The results show that flow development follows a multi-stage dynamic pattern, comprising gas-impact filling, gap-jet formation, and subsequent free-jet expansion. A pronounced spatially heterogeneous phase lag was observed in the pressure–Mach number response. This phenomenon arises from a mismatch among the characteristic time scales of pressure-wave propagation, flow inertia, and shock–boundary-layer interaction. Quantitative analysis further indicates that the spatial superposition of high-temperature zones, high-Mach regions, and elevated-pressure areas activates a thermal–fluid–structural positive-feedback loop that drives the local peak temperature to approximately 2.5 × 103 K. The temperature response lags behind the pressure maximum by approximately 30–50 ms, reflecting the governing time scale of thermal inertia. In addition, vortical structures near the tube base account for nearly 15% of the total thrust. These findings provide a theoretical foundation for predicting transient peak loads in concentric cylindrical systems and for optimizing instantaneous thermal protection strategies.

4D head capture aims to generate dynamic facial meshes in the same topology with corresponding UV maps, which requires temporal correspondence between 3D head models. Existing pipelines either involve manual processing of artists or employ constraints such as landmark tracking and optical flow, failing to achieve a trade-off between accuracy and efficiency. To enhance this process, we propose Topo4D++, a novel framework for automatic geometry and texture reconstruction that optimizes densely aligned 4D heads and 8 K BRDF maps directly from calibrated multi-view videos. Our key insight is to represent facial models as a set of dynamic 3D Gaussians with fixed topology, where the Gaussian centers are bound to the mesh vertices. This enables tracking all vertices rather than sparse vertices on the face accurately by leveraging the inverse rendering capabilities of 3D Gaussian Splatting (3DGS), while also enabling ultra-high-resolution texture generation. To maintain face structure during dynamic 3DGS optimization, we propose to optimize geometry and texture alternatively under physical and topological constraints frame-by-frame and employ blendshape-based expression priors to address extreme expressions. Then, we propose to extract dynamic facial meshes in a regular wiring arrangement and high-fidelity textures with pore-level details from the learned Gaussians. Finally, we train a diffusion-based model to generate BRDF texture maps to achieve physically based rendering. Given the absence of a universal benchmark, we construct JHead, a novel benchmark for the comprehensive evaluation of 4D head capture methods. Extensive experiments on different datasets demonstrate that our method is generalized to different capture systems, identities, and expressions, outperforming current state-of-the-art head reconstruction methods in both mesh and texture qualitatively and quantitatively.

• Airflow and particle distribution using CFD-DPM, and dynamic mesh techniques. • Proposed Normalized Particle Exposure Index (NPEI) to assess contamination persistence. • Door speed and motion direction affect on transport and cleanroom integrity. • Variable-speed door (slow opening, fast closing) reduces contaminant levels by 21%. • Phased door movement improved contamination performance up to 17%. Maintaining optimal air quality in cleanrooms is critical for contamination control in sensitive industries, such as pharmaceuticals, electronics, and healthcare. Among the factors affecting cleanroom integrity, door movement plays a significant role in disturbing airflow and enabling contaminant intrusion. This study analyzed the hinged door motion effect on airflow dynamics and particle distribution employing computational fluid dynamics, discrete phase method and dynamic mesh approach. A novel index was proposed to quantitatively assess contamination transfer under varying operational scenarios. The simulation setup modeled a cleanroom connected to an airlock, with door opening and closing speeds ranging from π/8 to π/4 rad/s. Obtained numerical results were verified with literature experimental data to ensure the accuracy of the velocity field and particle concentration. The findings reveal that both the door speed and direction substantially influence contaminant transport. Based on these results, this study suggests optimal door operation strategies to minimize contamination risks, offering practical guidance for improved cleanroom design and protocols. A variable-speed door operation opening slowly and closing at twice the opening speed was shown to reduce the contaminant levels by 21%. Additionally, a new parameter, Normalized Particle Exposure Index (NPEI), was introduced to quantify contamination persistence, revealing that phased door movement improved performance by 17% compared to a 3-second opening and by 15% compared to a 2-second opening.

Unsteady flowfields are integral to high-speed applications, demanding precise modeling to accurately characterize their dynamic features. The simulation of unsteady supersonic and hypersonic flows is inherently computationally expensive, necessitating a highly refined mesh to capture these dynamic effects. While anisotropic metric-based adaptive mesh refinement has proven effective in achieving accuracy with much less complexity, current algorithms are primarily tailored for steady flowfields. This paper presents a novel approach to address the challenges of anisotropic grid adaptation of unsteady flows by leveraging a data-driven technique called dynamic mode decomposition (DMD). DMD has proven to be a powerful tool to model complex nonlinear flows, given its links to the Koopman operator and also its easy mathematical implementation. This research proposes the integration of DMD into the process of anisotropic grid adaptation to dynamically adjust the mesh in response to evolving flow features. The effectiveness of the proposed approach is demonstrated through numerical experiments on representative unsteady flow configurations, such as a cylinder in a subsonic flow and an oscillating cylinder in a supersonic channel flow. Results indicate that the incorporation of DMD enables an accurate representation of unsteady flow dynamics independently of the remeshing interval. Computational fluid dynamics results obtained with the dynamic anisotropic mesh adaptation achieved a fourfold reduction in drag error compared to static meshing methods.

How deformable Gurney flaps combined to droop nose leading edge affecting the output power of flapping wind turbine?

A review of multi-scale modeling strategies for metal additive manufacturing with emphasis on computational cost and time efficiency

CFD modeling of sloshing-induced pressure drop inside LH <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si11.svg" display="inline" id="d1e632"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> </mml:math> storage tanks used in maritime applications

Numerical simulation of cavity formation induced by supersonic combustion jet based on a dynamic mesh method