Search Results (277)

Ocean current interference is a critical factor affecting the path planning of autonomous underwater vehicles (AUV). In ocean current environments, conventional path planning algorithms often exhibit significant limitations in complex flow fields without proper consideration of current interference. Therefore, to connect theoretical modeling with practical deployment while providing a systematic reference for developing current-resilient navigation strategies, this paper provides a comprehensive review of both environment modeling and algorithm evolution. Specifically, this survey first analyzes the impact of ocean current interference on AUV path planning and elaborates on the unique challenges arising in current-laden scenarios; it then reviews three categories of ocean current modeling methods (physics-based, historical-data-based, and real-time-observation-based approaches) in terms of their principles, advantages, limitations, and applicable scenarios; it next examines four mainstream categories of AUV path planning algorithms such as conventional graph-based methods, bio-inspired methods, intelligent learning-based methods and mathematical optimization under ocean current interference, where core improvement strategies and representative research achievements of each category are elaborated in detail; typical algorithms selected from these mainstream categories are then tested on a unified platform, and a comparative analysis of the results is presented; finally, current technical challenges and future research directions are summarized and discussed. Full article

Conventional air stripping for ammonia–nitrogen wastewater is often hampered by packing clogging and low mass transfer efficiency. To address these limitations, this study proposes a jet impact negative pressure reactor (JI-NPR) featuring an optimized scatter-pattern (D7) multi-orifice configuration. Computational Fluid Dynamics (CFD) simulations were employed to systematically investigate the effects of Reynolds number (Re = 5503.4~9651.0, corresponding to 2.76~4.84 m/s) on the hydrodynamic characteristics and deamination performance. Results indicate that increasing jet velocity significantly enhances the water volume fraction, resultant velocity, and pressure core intensity within the impact zone. Notably, these enhancements are maximized at the second row (z = 146 mm), attributed to reduced interference from the negative-pressure flash evaporation region. While a higher Re promotes interfacial renewal and vortex evolution, thereby enhancing mass transfer, it also intensifies energy dissipation and reduces the uniformity of the turbulent kinetic energy distribution. This work elucidates a critical trade-off between mass transfer enhancement and energy consumption, establishing a quantitative structure: the Re–flow field-performance relationship. The findings provide a theoretical foundation for the design and optimization of energy-efficient, high-performance wastewater treatment systems. Full article

To investigate the multi-stage inflation mechanism of a supersonic reefed parachute cluster, an ALE-based fluid–structure interaction method is applied. The canopy permeability is modeled using the Ergun equation, and the virtual structure contact method is employed to handle contact issues induced by large canopy deformation. A flow-domain time-step updating strategy is employed to perform finite-mass inflation simulation. The accuracy of the adopted method is validated by wind-tunnel test data. Full-stage simulations from supersonic to subsonic regimes are conducted to investigate the canopy deformation, flow-field structure, system attitude, and aerodynamic response of the parachute cluster–payload system. The results demonstrate that the parachute cluster maintains stable overall attitudes throughout multi-stage inflation, with axial translation dominating and lateral interference remaining negligible. A steady bow shock with strong inter-canopy shock interaction is formed in the first supersonic stage, followed by prominent vortex shedding in the second transonic stage, and full wake isolation with optimal deceleration efficiency achieved in the third subsonic stage. Quantitative comparison with a single-parachute system reveals stage-dependent interference: higher peak load and stronger oscillations in the supersonic stage, and approximately twice the load in the transonic/subsonic stages. The findings can provide critical theoretical guidance and technical support for engineering implementation of supersonic reefed parachute cluster deceleration systems. Full article

Existing DNAPL groundwater source inversion approaches are confronted with prominent bottlenecks: shallow surrogate models often fail to capture strong nonlinear multiphase flow relationships, traditional heuristic optimizers suffer from premature convergence, and ill-posed equifinality further degrades inversion reliability, together with prohibitive computational costs from repeated multiphase numerical simulation. Taking a typical chemical-contaminated site in Northeast China as the research object, this study establishes a multiphase flow numerical model that fully reproduces the migration and transformation mechanisms of chlorobenzene-based DNAPLs after systematic generalization of the site’s geological and hydrogeological conditions. To drastically cut the computational burden incurred during iterative inversion, high-quality datasets are generated via parameter sensitivity analysis and Latin hypercube sampling, based on which a deep convolutional neural network (DCNN)-driven high-fidelity surrogate model is constructed and embedded into the optimization framework as an equality constraint. A separated nonlinear programming model is formulated to independently quantify pollution source characteristics and hydrogeological parameters, with the objective of minimizing the residual error between field-measured and numerically simulated contaminant concentrations. A hybrid homotopy-particle swarm optimization (HH-PSO) algorithm is further proposed to address the limitations of conventional optimizers, including strong dependence on initial guesses and susceptibility to local optima. On this basis, a closed-loop feedback iteration scheme is developed, where source identification and parameter calibration are implemented alternately with bidirectional constraints and progressive correction to continuously refine and stabilize inversion outputs. This work presents distinct innovations in the methodology, algorithm, and practical application of DNAPL groundwater source inversion. Results from synthetic benchmark cases and on-site field applications demonstrate that the DCNN surrogate model achieves far higher fitting accuracy than shallow learning approaches (e.g., Kriging and support vector regression), with the coefficient of determination R² exceeding 0.99. After the feedback correction iteration procedure, the average relative error for retrieved source locations, release histories, and hydrogeological parameters drops to 3.72%, and the overall computational efficiency is elevated by approximately 99.84%. The integrated simulation–optimization inversion framework proposed in this work integrates monitoring signal denoising, multiphase numerical simulation, deep learning surrogate modeling, hybrid intelligent optimization, and feedback iterative correction. This integrated system effectively resolves core technical bottlenecks in DNAPL groundwater source inversion, such as nonlinear ill-posedness, equifinality induced by mutual interference between source terms and aquifer parameters, prohibitive computational costs of multiphase simulations, and premature convergence of traditional optimization algorithms. The established framework can serve as a robust theoretical foundation and technical tool for rapid, precise source tracing, pollution liability confirmation, and remediation design at complex contaminated sites. Full article

Mosquito-borne viruses, including dengue, Zika and chikungunya viruses, place a substantial burden on diagnostic services, especially where molecular laboratories are inaccessible or slow to return results. Point-of-care biosensors could reduce turnaround times and bring testing closer to patients in primary care, outbreak response, and field settings. This review critically examines nucleic acid amplification, CRISPR-assisted assays, lateral-flow platforms, microfluidic systems, electrochemical and optical biosensors, paper-based devices, and smartphone-enabled readouts. These technologies are evaluated in terms of sample preparation, analytical sensitivity and specificity, matrix interference, multiplexing, workflow integration, cost, and clinical validation. Overall, nucleic-acid-amplification and CRISPR-assisted platforms often achieve low reported detection limits under controlled conditions; lateral-flow and paper-based devices offer relatively simple and minimally instrumented workflows; and microfluidic, electrochemical, and smartphone-enabled systems support increasing levels of workflow integration, quantitative readout, and connectivity. However, few platforms currently integrate these advantages into a fully integrated and clinically validated “sample-to-result” workflow. Due to sample heterogeneity, viral strains, reference methods, assay conditions, and disparities in reporting practices, conducting meaningful cross-study comparisons remains challenging. Limited comparisons and insufficient prospective clinical and field validation further restrict the assessment of practical diagnostic utility. Therefore, strong analytical performance alone should not be interpreted as evidence of clinical validity. Priority directions include unified definitions of performance and reporting units, standardized validation protocols and external quality assessment, prospective multi-site evaluation using representative populations and specimens, and earlier consideration of manufacturing scalability, reagent stability, quality systems, and applicable regulatory requirements. Future platforms should integrate simplified sample preparation, multiplex detection, objective digital or AI-assisted interpretation, and secure connectivity while demonstrating measurable benefits for patient management and outbreak surveillance. Full article

In response to the severe wear of centrifugal pumps, Workbench workflow is utilized to adjust the blade inlet and outlet angles, aiming to reduce the wear of the impeller and volute of the centrifugal pump and optimize the pump’s efficiency and head. Orthogonal experiments are conducted by varying the inlet and outlet angles. The original sample points are expanded and optimized in combination with the support vector machine and grid search. The optimization results indicate that under the condition of spherical particles, the efficiency at the rated operating condition increases by 1.71%, and the head rises by 0.35%. The appropriate eddy currents formed by increasing the impeller inlet angle alleviate the particle deposition phenomenon in the centrifugal pump, resulting in a smoother particle flow. The wear of the centrifugal pump blades decreases from 40.76 × 10⁻⁷ mm to 7.77 × 10⁻⁷ mm. After optimization, the overall entropy generation rate of the volute decreases, while that of the blade suction surface and the impeller outlet area increases. Additionally, through empirical mode decomposition analysis, it is found that the optimized design reduces high–frequency interference and the pulsation amplitude, making the flow field more stable. The frequency distribution also shifts from being dominated by high–frequency components to concentrating energy in the medium- and low-frequency regions. Full article

To address the challenges associated with operational ventilation in high-altitude tunnels, this study investigates the distribution patterns of ventilation flow fields and optimizes the spatial layout parameters of jet fans to determine the most effective configuration. Using a case study from the Zhuohe Expressway tunnel, numerical simulations were conducted to analyze four key design parameters: the lateral clear distance (L) between two jet fans in a single group, the vertical distance (H) from the fan center to the tunnel lining, the axial distance (T) from the fan to the tunnel entrance, and the longitudinal spacing (S) between two groups of fans. The results indicate that for a single-fan group, when the parameter L is 1.25D (D is the fan diameter), pressure rise and comprehensive influence coefficients reach peak values of 20.090 Pa and 0.886, respectively. As well as the parameter H between 1.20 m and 1.25 m, the diffusion of the vertical wind field velocity is continuously reduced due to the constraint of the tunnel lining on Section BB of the tunnel fan’s symmetry axis, and the interference of the tunnel lining on the stable flow state of the fan’s outlet airflow is relatively small. Moreover, parameter T has a relatively low sensitivity impact on the increase in pressure and the variation of the influence coefficient. When the parameter T is within the range of 50 m to 100 m, the airflow at the entrance of the tunnel is smoothly connected with the airflow at the suction section of the fan. Additionally, the pressure rise and the influence coefficient increase by the parameter T. Both the fan’s pressure rise and the influence coefficient reach their maximum values when the parameter T is 100 m. Furthermore, in the case of two-fan groups, the gas is fully mixed in the tunnel when the parameter S is 150 m, and the fan pressure rise and the influence coefficient increase as well as parameter S. The gas between the two sets of fans has been fully mixed in the parameter S at 175 m, and the pressure rise and the coefficient influence reach their maximum values of 40.231 Pa and 0.887, respectively. In light of these findings, the following optimal parameters ranges are recommended for similar tunnel ventilation designs: parameter L is 1.25D for two jet fans within a single group, parameter H is between 1.20 m and 1.25 m, parameter T is 100 m from the tunnel entrance, and parameter S is between 150 m and 175 m for two groups of fans. Full article

Amphibious assault vehicles serve as core equipment for coastal defense and amphibious operations worldwide, with irreplaceable strategic value. Featuring outstanding comprehensive performance, modern amphibious assault vehicles can maintain stable navigation under Sea States 3–4 and adapt to complex nearshore hydrological environments, emerging as the primary platform for mechanized landing operations of the Marine Corps. Cluster navigation is an inevitable tactical form in the operational application of amphibious assault vehicles. When multiple vehicles sail in formation, the wave-making and water pressure effects induced by individual vehicles generate prominent wave interference drag within the formation, which significantly impacts the overall navigation efficiency and stability. Based on the nearshore combat background of amphibious landing, this paper investigates different formation layouts of amphibious assault vehicle clusters to determine the optimal configuration for group navigation. First, a numerical simulation and a physical experiment are combined; a certain type of amphibious assault vehicle is taken as the prototype for 3D geometric modeling via SOLIDWORKS. Then, adopting the CFD numerical simulation method, with navigation speed and optimal inter-vehicle spacing fixed, variables including formation layout and number of vehicles are controlled to simulate the flow field characteristics and total resistance of different cluster formations in calm water. Meanwhile, 3D printing technology is applied to manufacture scaled-down models for towing tank tests. The experimental results are in good agreement with numerical simulations, revealing the fundamental hydrodynamic laws of formation navigation. Under optimal inter-vehicle spacing, the longitudinal tandem formation achieves the best drag-reduction effect, while the double-column staggered formation (diamond/V formation) can effectively suppress wave interference drag and improve the overall hydrodynamic performance and tactical coordination. The research provides a solid theoretical basis and data support for optimizing formation sailing strategies, enhancing cluster navigation stability and safety, and improving maritime maneuver efficiency. It is also of universal reference value for the tactical deployment of amphibious combat equipment globally. Full article

With the increasing scale and density of low-altitude unmanned aerial vehicle (UAV) operations, safety separation between multirotor UAVs has become a critical parameter for low-altitude airspace management. Existing studies mainly consider aircraft geometry, navigation errors, trajectory deviations, and conventional collision risk models, while rotor-downwash-induced aerodynamic interference remains insufficiently addressed. This study proposes a safety separation assessment method for quadrotor UAVs by integrating computational fluid dynamics (CFD) with an improved Event collision model. A small-scale quadrotor UAV is analyzed, and its rotor downwash flow fields under vertical- and horizontal-motion conditions are simulated using the multiple reference frame method. Based on a 5 m/s crosswind-resistance capability threshold, aerodynamic-interference characteristic distances are extracted and used to construct a basic collision box. To better represent the actual aerodynamic hazard region, an I-shaped improved collision box is further developed and incorporated into the Event collision model. Under a target level of safety, the longitudinal, lateral, and vertical minimum safety separations are determined as 1.68 m, 1.72 m, and 1.08 m, respectively. The results show that the proposed CFD–Event coupled method can transform rotor downwash characteristics into collision risk parameters and provide a quantitative basis for safety separation assessment in dense low-altitude multirotor UAV operations. Full article

Remote sensing image super-resolution (SR) aims to enhance spatial resolution and recover image details, which typically enhances the quality of optical remote sensing imagery. However, interference from cloud haze cover and sensor noise often leads to distorted details and artifacts in reconstructed images of conventional deep learning SR approaches, significantly limiting reconstruction fidelity. To address these challenges, we propose a novel SR framework based on the flow matching paradigm and a diffusion transformer, named FlowT-SR, which achieves superior and reliable reconstruction quality by jointly mitigating sensor noise and thin cloud interference. First, an evolution path from low-resolution images to ground-truth images is constructed based on the optimal transport displacement interpolation mechanism, and the corresponding vector field that governs this evolution is employed as the supervision signal for subsequent model training. Then, a multi-scale interference suppression (MSIS) module is combined with a novel diffusion transformer network (DiTNet) to predict the vector field. The MSIS module performs preliminary denoising and captures the spatial distribution of thin cloud and haze in low-resolution images, providing degradation-aware feature representations for DiTNet. Subsequently, a DiTNet is presented to predict the evolution vector field obtained in the first stage, which consists of ten layers based on the diffusion transformer. By accurately predicting the vector field at any time step, the model effectively reduces the impact of cloud haze and noise interference to improve the reconstruction precision. Finally, driven by the predicted vector field along the evolution path, the SR remote sensing image is generated through solving the corresponding ordinary differential equation, yielding cloud-free and noise-reduced results. Extensive experiments on our dataset and the public CUHK Cloud Removal dataset demonstrate that FlowT-SR effectively suppresses cloud haze and noise interference, achieving superior reconstruction performance compared with current state-of-the-art methods in terms of both PSNR and SSIM. Full article

Background: Optical coherence tomography (OCT) and OCT angiography (OCTA) retinal imaging in inherited retinal dystrophies (IRD) has been limited to the posterior pole and central midperiphery (up to about 16.5 × 16.5 mm). Two novel commercially available swept-source (SS) OCT/-OCTA devices provide the possibility of wide-field (WF) evaluation of retinal and choroidal structures, including the vasculature, in a single examination. Methods: A limited consecutive series of 16 IRD patients were examined with a BMizar (400 kHz, 24 × 20 mm scan width) and a Dream OCT (200 kHz, 26 × 21 mm scan width) in addition to the normal clinical examination protocol. This series included patients with retinitis pigmentosa, cone-rod dystrophy, macular dystrophy and autosomal recessive bestrophinopathy. In addition, 12 healthy probands were examined. Results: WF-SS-OCT/-OCTA enabled the detection of retinal, choroidal and choriocapillaris alterations in the macular and midperiphery in a short, single examination session of up to 15 s. Even small foveal lesions and a small silent macular neovascularization were detected on WF screening. Regional alterations of choroidal and choriocapillaris flow patterns were identified. These were mostly in correspondence with areas that appeared clinically affected, but unexpected lesions were identified as well. Occlusion of peripheral retinal vessels was seen in retinitis pigmentosa, though flow was detected in retinal vessels, which were difficult to distinguish on fundus images. In one patient with nystagmus, WF-SS-OCT/-OCTA was performed, whereas standard OCT volume scan could not be obtained. The most frequent artifact were horizontal lines of misalignment, which did not interfere with the detection of pathologies. Conclusions: Both WF-SS-OCT/-OCTA devices provide detailed insights in structural and vascular retinal and choroidal alterations in a single, short examination. Larger series of IRD patients examined with WF-SS-OCT/-OCTA promise to provide novel insights into the pathology of IRDs. Full article

Accurate and rapid prediction of waterflood front propagation and volumetric sweep efficiency remains challenging in ultra-low-permeability reservoirs because of strong heterogeneity, threshold pressure gradients, reservoir anisotropy, complex well-pattern geometry, and layer-dependent flow interference. In this study, an improved 3D streamtube model was developed for waterflood-front tracking and volumetric sweep evaluation in ultra-low-permeability reservoirs. The model incorporates experimentally constrained threshold pressure gradients, anisotropic coordinate transformation, dynamic streamtube flow-rate allocation, Buckley–Leverett-based non-piston displacement, interlayer interference correction, and irregular well-pattern adaptability. A unified calculation framework was established for both injector–producer and injector–fracture streamtube units, enabling 3D integration of layer-specific swept areas into volumetric sweep efficiency. The proposed model was validated against a commercial numerical simulator using a representative well group from Block A of the Changqing Oilfield. The predicted streamtube architecture and sweep-efficiency evolution agree well with numerical simulation results, with an average relative error of approximately 3.1%, while reducing the computational time from 1043 s to 1.42 s for a 30-year simulation. Sensitivity analysis demonstrates that threshold pressure gradient, well spacing, and inter-well connectivity are the dominant controls on sweep efficiency, whereas well-pattern type, interlayer heterogeneity, and reservoir anisotropy exert secondary but non-negligible effects. Field application further reveals a strongly layer-dependent waterflood behavior: the upper sand body preferentially propagates eastward, whereas the lower sand body advances mainly southward, producing a vertically asynchronous and laterally misaligned sweep pattern. These results show that the proposed model provides an efficient and physically interpretable tool for rapid waterflood-front prediction, refined waterflood optimization, and targeted production enhancement in heterogeneous ultra-low-permeability oil reservoirs. Full article

Thrust vector control (TVC) technology significantly enhances the attitude control capability of aircraft. However, conventional multi-axis calibration is inherently cost-prohibitive and time-consuming, whereas static ground-bench calibration completely neglects the significant aerodynamic interference from external freestreams during actual flight. To resolve this operational bottleneck in engineering applications and calibration workflows, this paper proposes an agile, cost-effective hybrid modeling and process optimization methodology that seamlessly integrates low-cost ground experiments with high-fidelity numerical investigations. First, a customized one-axis force sensor test bench was developed to calibrate the baseline thrust vector performance under static ground conditions. Subsequently, computational fluid dynamics (CFD) simulations were conducted across the typical cruise speed range (30–60 m/s) of TVC aircraft to investigate the nonlinear interactions between the external freestream and the vectorized jet. The numerical results reveal that the interaction between the deflected jet exhaust and the external flow field significantly changes the flow structure and pressure distribution around the control surface. The momentum exchange, primarily governed by mass entrainment and cross-shear-layer mixing, broadens the control surface’s impact scope and enhances the injection effect, resulting in nonlinear variations in the control force under different operating conditions. Considering the influence of external flow and deflected jet, a dynamic control force model was established by integrating the ground static baseline model with simulation data. The fitting results demonstrate that this control model limits match error to within 3% across both experimental and numerical datasets, effectively suppressing loop uncertainties across the entire flight envelope while maintaining a minimalist sensing architecture. From a process engineering perspective, this “Hybrid Modeling”-driven approach substitutes expensive multi-degree-of-freedom testing hardware with an integrated simulation-experimental workflow, offering a highly scalable, simplified, and analyzable process method for TVC research and application. Full article

Fast and accurate resistance prediction is critical in early-stage ship design. While Michell’s thin-ship theory provides rapid evaluations, its linear assumptions limit accuracy, particularly as hull forms deviate from ideal slenderness. This paper introduces a physics-preserving neural calibration method that improves Michell’s theory without replacing the underlying solver. We train a two-dimensional convolutional encoder–decoder, conditioned on Froude numbers via global FiLM modulation, to predict a bounded correction to the geometric effective-slope field. Because the solver remains unchanged, the learned correction acts as an interpretable spatial perturbation rather than a black-box resistance map. Evaluated under a strict leave-one-family-out (LOFO) protocol on a fleet of five slender hull families (DTMB, NPL-4A, Wide-Light Canoe, Wigley, and Delft 372), the neural calibration achieves a mean absolute percentage error (MAPE) of 0.0741. This represents a 24% improvement over a reproduced 2020 baseline and a 7.9% improvement over the uncorrected Michell solver. The 2020 baseline is the rigid boundary-layer and phase-deflection correction of an earlier study by the present group, re-evaluated here on the present hulls at their measured attitudes. Ablation studies show that much of this aggregate gain is captured by a bounded global slope offset, indicating that a spatially uniform displacement correction accounts for most of the improvement on slender hulls, while the spatially varying field mainly adds per-family headroom. Finally, we map the physical boundaries of this approach. Dedicated recovery campaigns on fuller forms (KCS and Series 60) show that the model regresses compared to baselines. This confirms that while the correction successfully refines the linear source distribution for slender hulls, it cannot synthesize missing physics, such as stagnation pressure, separated flow, or wave interference, for fuller or unrelated geometries. Full article

Magnetically actuated microrobots have exhibited broad application prospects in biomedical fields. To advance their clinical application, extensive research has attempted to enhance the navigation robustness of microrobots in the body. In the vascular environment, microrobots are easily obscured by blood cells and disturbed by fluid flow, leading to the failure of external sensors and the formation of navigation blind zones. However, most existing navigation methods are based on ideal environment assumptions and struggle to address the challenges posed by navigation blind zones. The study proposes a navigation framework integrating Extended Kalman Filter (EKF) and a Proportional–Integral–Derivative (PID) controller. The EKF fuses sensor measurements and the microrobot kinematic model to sustain continuous state estimation when sensors fail inside blind zones. The simulation results show that this navigation framework achieves pixel-level positioning accuracy under ideal conditions and a 100% navigation success rate. In the presence of blind zone interference, this navigation framework can effectively suppress the divergence of position errors and significantly improve navigation robustness. The study proposes a theoretical framework for microrobot navigation in vascular blind zones. Further physical prototype experiments are required to verify its practical performance. Full article