Search Results (2,505)

The widely applied empirical Darcy’s law in geotechnical engineering faces significant challenges in describing low-velocity flow processes in low-permeability porous media such as tight sandstones containing irreducible water. A deep understanding of low-velocity non-Darcy two-phase flow behavior in low-permeability porous media is essential for evaluating the development of ultra-low-permeability reservoirs. In this study, seven low-permeability three-dimensional digital cores with distinct pore structures were constructed based on realistic ultra-low-permeability sandstones. Using the lattice Boltzmann method, pore-scale investigations of water displacing oil were conducted. Low-velocity two-phase flow behavior under varying wettability conditions, pore structures, and fluid viscosities was simulated. The underlying mechanisms of low-velocity non-Darcy flow in ultra-low-permeability sandstones were examined, leading to a modified low-velocity non-Darcy flow equation. This improved model was subsequently applied to numerical simulations of ultra-low-permeability reservoirs. The results demonstrate that non-Darcy effects manifest primarily as nonlinearities in seepage curves, representing a marked departure from conventional Darcy’s law. Low-velocity non-Darcy (LVND) flow is predominantly constrained by the influence of complex pore-throat structures and capillary forces on fluid distribution. The dynamic equilibrium among capillary forces arising from residual water saturation, viscous forces, and pressure gradients constitutes the fundamental mechanism governing the onset of LVND flow. Enhanced nonlinear behavior is observed with increasing viscosity of the invading phase and elevated capillary forces. Substantial discrepancies in reservoir production dynamics are identified between LVND and classical Darcian regimes. Through pore-scale numerical simulations, this study systematically elucidates LVND behavior during bi-phasic flow in low-permeability porous media, while identifying critical controlling factors. These findings provide scientific rationale and technical support for addressing geological engineering challenges in tight sandstone formations. Full article

This study evaluates deterministic flood fatality models using a harmonised dataset of river and flash flood events in Europe (1980–2024). The objective is to quantify differences across data sources and critically assess the applicability of commonly used prediction models for hydrological floods, with particular emphasis on flash floods, which remain poorly represented in existing methodologies. The analysis integrates large-scale databases on flood fatalities (HANZE, EM-DAT) with detailed event-based studies containing hazard and other indicators, enabling a combined evaluation from different sources. Three model groups are assessed by comparing predicted and observed fatalities: Damage–Fatality, Depth–Fatality, and Depth–Velocity–Fatality approaches. Results confirm discrepancy between exposure and mortality: river floods dominate in terms of affected population (87%) and economic losses (71%), whereas flash floods account for nearly half of all fatalities despite affecting only 13% of people. All evaluated models show significant limitations for prediction of flash floods fatalities; single-parameter approaches perform poorly, while multi-parameter models remain highly sensitive to uncertain hydraulic inputs. The study demonstrates that current methods are not transferable to flash flood conditions and highlights the need for integrated, multi-variable approaches supported by consistent and high-quality datasets. The main contributions of the study are the first systematic validation of widely used models against historical river and flash flood events, revealing their uncertainties, and a comprehensive assessment of their robustness and sensitivity to key input indicators. Full article

In GNSS-denied environments, Micro-Electromechanical Systems–Inertial Navigation Systems (MEMS-INS) play a critical role in sustaining the autonomous operation of vehicles. However, the inherent accuracy constraints of MEMS inertial sensors often hinder their broader application. To address this limitation, this paper proposes a novel integrated navigation system based on a rotating MEMS architecture. Through a global observability analysis, the proposed method enables the effective separation of sensor biases, mitigates long-term error accumulation, and significantly enhances the estimation accuracy of both attitude and velocity. Simulation results indicate that a rotation scheme around the X-axis allows for the reliable estimation of sensor biases. Moreover, road test results reveal that when the vehicle experiences variations in angular velocity, the rotating configuration consistently outperforms its fixed counterpart, reducing heading, position, and velocity errors by 75.3%, 62.9%, and 76.0%, respectively. Full article

Cavitation damage poses a serious threat to the reliability of morning-glory spillways. This study aims to develop a reliability framework for predicting cavitation damage probability under uncertain operational conditions for the Haraz Dam spillway. Cavitation analysis in such structures exhibits inherent nonlinearity and uncertainty, complicating accurate damage prediction. This study incorporates model uncertainties to assess cavitation responses at multiple points on the Haraz Dam morning-glory spillway. Three-dimensional flow simulations were performed using Computational Fluid Dynamics (CFD) and validated against an experimental model from the Iran Water Research Institute, showing satisfactory agreement. Statistical parameters and probability density functions (PDFs) for key uncertainties were determined using the Shapiro–Wilk test. A total of 35 simulation runs, designed via the Central Composite Design (CCD) method, were conducted using Latin Hypercube Sampling (LHS). These simulations incorporated inter-uncertainty correlations and predicted cavitation damage responses at ten critical spillway locations through Response Surface Methodology (RSM). Both linear and second-order response functions were formulated based on interactions among model uncertainties. The results indicated a strong correlation (R² > 0.95) between numerical model outputs and RSM predictions, with the maximum RSM errors remaining within acceptable thresholds. Among the uncertainty factors, the inflow velocity demonstrated the highest contribution (>50%) to cavitation damage responses. These outcomes advance the understanding of cavitation mechanisms and provide a reliable methodology for evaluating damage risks in morning-glory spillways under uncertain operational conditions. Full article

Tensile and compressive stresses generated during the exploitation of wheel rims can lead to significant failures, posing risks to safety and the environment. Among non-destructive evaluation (NDE) methods, ultrasonic velocity measurement has become widely used for assessing stress states in critical rail vehicle components such as wheel rims. In this study, the relationship between ultrasonic wave velocity and applied compressive stresses in aluminum (EN AW-2011) and austenitic stainless steel (1.4301) specimens is investigated. The methodology integrates ultrasonic time-of-flight (TOF) measurements with controlled mechanical loading up to the elastic limit. The results show that ultrasonic velocity increases with applied compressive stress, with an average change of approximately 40 m/s between unloaded and maximum loading conditions. The material type was identified as the dominant factor, with velocity differences of up to 800 m/s between aluminum and steel, while the applied load contributed changes of approximately 200 m/s. Statistical analysis using Design of Experiments (DOE) and ANOVA confirmed the significance of all main factors (p < 0.0001). The findings demonstrate the sensitivity of ultrasonic velocity to elastic stress states and provide a quantitative basis for the development of reliable in situ ultrasonic stress monitoring systems in rail applications. Full article

The mixing process is a critical factor influencing the performance of concrete. As an effective method for enhancing mixing, vibratory stirring relies on the propagation characteristics of mechanical vibration within the concrete matrix. To investigate the propagation behavior of mechanical vibration in fresh steel fiber-reinforced concrete, a custom-developed mechanical vibration source and testing system was established. The results show that the vibration intensity attenuates to 50% at a distance of 5 cm from the source, to approximately 10% at 10 cm, and to less than 3% at 20 cm. A lower water-to-binder ratio facilitates the transmission of the vibration wave, while the presence of fibers and 0–5 mm coarse aggregates hinders vibration propagation. Based on these findings, an input–output energy conservation equation was developed to describe the transmission behavior of vibration energy. The numerical results were compared with experimentally measured vibration power and particle velocity displacement integrals, validating the effectiveness of the proposed energy conservation equation. Full article

Global Navigation Satellite Systems (GNSSs) provide essential position, velocity, and time (PVT) information worldwide. Accurate evaluation of GNSS signals and receiver performance requires realistic simulation environments, particularly for the carrier-to-noise-density ratio (C/N₀), a critical indicator reflecting signal quality dependent on satellite elevation angles. This paper presents the development of a GNSS testbed specifically designed to simulate and estimate C/N₀ values, focusing on GPS L1 C/A signals. The proposed testbed comprises three main components, a satellite simulator that controls signal power accurately according to satellite elevation angles, an up-/down-converter for RF/IF band conversion, and a signal receiver that estimates C/N₀ using the Narrowband–Wideband Power Ratio (NWPR) method. The performance of the proposed testbed was evaluated under four scenarios, namely static, dynamic, jamming, and real-signal. In the static scenario, the proposed system achieved a maximum C/N₀ estimation RMSE of 0.60 dB-Hz for satellites with elevation angles above 30° and 1.63 dB-Hz for those below 30°. In the dynamic scenario, the corresponding RMSE values were 0.68 dB-Hz and 0.86 dB-Hz, whereas under jamming conditions they increased to 2.08 dB-Hz and 2.12 dB-Hz, respectively. Furthermore, in the real-signal scenario, the C/N₀ values estimated by the proposed testbed exhibited trends consistent with those reported by a commercial u-blox receiver processing the same live-sky signals, thereby confirming its reliability under actual GNSS reception conditions. These results demonstrate that the proposed GNSS testbed enables reliable C/N₀ simulation and estimation for GNSS receiver performance evaluation. Full article

Efficient thermal management of lithium-ion batteries is critical for the safety, performance, and longevity of electric vehicles. This work numerically investigates a battery thermal management system (BTMS) for a LiFePO₄ battery, featuring a liquid-cooling plate with novel droplet-shaped turbulators and coolant with different nanofluids. Computational Fluid Dynamics (CFD) simulations were employed to analyze the effects of cooling channel geometry, nanofluid type, nanoparticle volume fraction, coolant inlet velocity, and battery discharge rate on the system’s thermal performance and pressure drop. Results show that the droplet-shaped channel reduces the maximum battery temperature by 1.64 K compared to a conventional straight channel, owing to enhanced turbulence and larger heat-transfer area. Among different coolants, the 6% Cu–water nanofluid demonstrated the highest cooling effectiveness due to its superior thermal conductivity. To balance competing objectives, a multi-objective optimization using Response Surface Methodology (RSM) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was performed. The optimal design was achieved with a coolant velocity of 0.097 m/s and a volume fraction of Cu nanoparticle of 3.85%, which maintained the maximum battery temperature of 299.7 K with a minimal pressure drop of 26.27 Pa at a 1.03 C discharge rate. These findings highlight that a BTMS combining droplet-shaped turbulators with a Cu–water nanofluid provides a highly effective and energy-efficient thermal management strategy. Full article

Avian wings enable autonomous control over flight trajectory and speed, and their swept-wing geometry inspires the application of sweep modifications to horizontal-axis wind turbine blades, an approach that is critical for improving aerodynamic performance. Hence, wind tunnel experiments were performed to evaluate the output power and wake features of a baseline straight-bladed and a swept-blade wind turbine. The experimental results demonstrate that inflow turbulence intensity (T.I.) affects the peak power coefficient of the swept-bladed turbine, with power coefficient gains being more significant when the tip speed ratio is greater than 3.0 and under yawed conditions. At a yaw angle of 20°, when the T.I. is 0.5%, 10.5%, and 19.0%, respectively, the corresponding increased values are 13.17%, 3.44%, and 4.68%. Cross-stream velocity in the near-wake region of the swept-bladed turbine is markedly higher than that for the baseline condition. The averaged T.I. in the wake velocity region of the swept-blade conditions is greater than that of the baseline condition at most measurement positions. Moreover, power spectral density (PSD) magnitudes behind the blade tip for the swept-blade configuration are higher than those of the baseline, particularly in the medium- and high-frequency domains. This work clarifies the aerodynamic characteristics of swept-blade wind turbines to varying levels of turbulent inflow. Full article

This research presents a two-dimensional transient thermo-hydraulic model designed to study how temperature and pressure change within a wellbore during CO₂ tubing fracturing. The model integrates one-dimensional axial compressible flow with radial heat transfer across the tubing, annulus, casing, cement sheath, and surrounding geological formation. Using the predicted temperature and pressure distributions, the phase behavior of the fracturing fluid along the wellbore is assessed. To enhance the accuracy of phase predictions, a visualization experiment is performed on a CO₂-based fracturing fluid containing 5 wt% of the thickener HPG. The critical transition conditions obtained experimentally are used to adjust the model accordingly. The study systematically examines the influence of key operational parameters such as injection rate, wellhead pressure, injection temperature, and the geothermal gradient of the formation. Findings reveal that injection conditions mainly govern the temperature and velocity fields, while heat transfer from the formation has a lesser impact during short-term injections. Pressure steadily decreases along the wellbore due to friction and fluid compressibility. A method based on density gradients is introduced to determine the depth at which phase transitions occur. Overall, this work offers a practical approach for predicting thermo-hydraulic behavior and phase changes during CO₂ fracturing processes. Full article

During the preliminary design of flight vehicles, i.e., missiles or guided rockets, propulsion system performance serves as a critical determinant of both maximum range and terminal velocity. However, complex grain configurations in solid rocket motors (SRMs) typically require geometric modeling software to obtain burning surface area, which severely constrains efficiency. To address this challenge, this study presents a neural network-enhanced rapid performance prediction and matching optimization framework for solid rocket motors (NN-SRM). In NN-SRM, neural networks are employed to simulate the evolution of key parameters during grain combustion, including burning surface area, grain volume, and moment of inertia. The zero-dimensional internal ballistics equations coupled with one-dimensional steady isentropic flow relations are incorporated into the framework to rapidly obtain thrust curves. A discrete–continuous mixed differential evolution algorithm is further employed to identify the optimal grain configuration that satisfies specific thrust requirements. Results demonstrate that, as for cylindrical, star, and finocyl grains, the neural network achieves R² exceeding 0.95. Finally, thrust matching optimization is conducted on three grains and achieves promising thrust solutions for the conditions of large thrust with short time and small thrust with long time, which demonstrates the effectiveness and practicality of the constructed NN-SRM. Full article

Background: Stretching exercises are strongly recommended as part of exercise training programs; however, their effects on blood pressure (BP) and other related cardiovascular parameters in adult individuals with elevated BP (pre-hypertension) or hypertension remain unclear. Methods: A systematic search was conducted in PubMed and databases accessed via the EBSCO platform up to 30 September 2025, following the PRISMA guidelines. An additional search of Scopus was performed on 8 April 2026. Studies eligible for inclusion were randomized controlled trials, randomized crossover trials, non-randomized clinical trials and single-arm trials investigating stretching interventions in adults with pre-hypertension and or hypertension. Risk of bias assessment was performed using RoB 2 for randomized trials and ROBINS-I for the non-randomized trials. A random-effect meta-analysis was performed when at least two studies reported sufficiently comparable BP outcomes. The quantitative synthesis was considered exploratory. Results: Eleven records published between 2014 and 2025 met the eligibility criteria and were included. All protocols used static stretching, although only a small number were clearly described as active stretching. The results were heterogeneous across the design, duration of intervention and outcomes. Chronic interventions more often reported favorable changes in indices of arterial stiffness, whereas acute interventions demonstrated more variable immediate BP responses. In the exploratory meta-analysis, the pooled estimate suggested a reduction in systolic blood pressure (SBP) in favor of stretching; however, this effect did not reach statistical significance (mean difference (MD) = −5.39 mmHg, 95% confidence interval (CI): −11.32 to 0.53; I² = 0%). For diastolic blood pressure (DBP), the pooled estimate favored stretching and reached statistical significance (MD = −3.93 mmHg, 95% CI: −7.25 to −0.60; I² = 0%). In sensitivity analyses including a third study, the pooled effects remained in favor of stretching for systolic BP (MD = −6.6 mmHg, 95% CI: −12.2 to −1.0; I² = 56%) and diastolic BP (MD = −5.4 mmHg, 95% CI: −7.1 to −3.7; I² = 8%). These pooled estimates should be interpreted with caution due to the small number of studies, heterogeneity in study design and participant characteristics, and overall limitations in methodological quality. Secondary findings suggested possible improvements in selected vascular parameters, including brachial–ankle pulse wave velocity, augmentation index, and cardio–ankle vascular index, whereas acute responses were more variable and protocol-dependent. Overall, the level of evidence was limited, with most randomized trials judged as having some concerns and non-randomized studies judged as having a critical risk of bias. Conclusions: Stretching interventions may improve BP and selected vascular parameters in adults with pre-hypertension and hypertension and may represent a practical adjunct within the non-pharmacological management of BP. However, the current evidence is limited by methodological heterogeneity, risk of bias, and the small number of studies available for quantitative synthesis. Therefore, the pooled findings should be considered exploratory and hypothesis-generating rather than definitive. Further high-quality randomized controlled trials are required to determine the optimal type, dose, and long-term clinical relevance of stretching interventions in this population. Full article

Understanding the unsteady aerodynamic behavior of quadrotor unmanned aerial vehicle (UAV) is critical for improving flight stability, control, and performance, particularly in complex operational environments. In closely spaced multirotor configurations, coherent tip vortices shed from each blade convect downstream and form helical vortex streets that interact with subsequent blades and neighboring rotors. These interactions induce rapid fluctuations in local inflow velocity and effective angle of attack, resulting in transient lift variations, increased vibratory loads, and elevated acoustic emissions. This study presents a comprehensive computational investigation of quadrotor rotor interactions and wake dynamics using a large-eddy simulation (LES). Detailed analyses reveal that the formation and evolution of tip vortices and blade–vortex interaction phenomena significantly influence lift fluctuations and aerodynamic loading. The simulations capture transient wake structures and their effects on neighboring rotors, highlighting unsteady aerodynamic mechanisms that are not adequately predicted by conventional RANS or URANS approaches. Parametric studies examining vortex-street offset distance demonstrate the sensitivity of wake-induced instabilities to design and operational parameters. The results provide new physical insights into multirotor wake dynamics and establish the LES as a predictive framework for quantifying unsteady aerodynamic loading in quadrotor drones. The findings provide insights into the complex flow physics of multirotor systems, offering guidance for more accurate modeling, rotorcraft design optimization, and the development of control strategies that mitigate adverse unsteady aerodynamic effects. This study provides new insights into rotor–vortex-street interactions, with applications to multirotor UAVs, by isolating multi-vortex coupling effects and quantifying the influence of horizontal vortex spacing on unsteady aerodynamic loading, complementing existing high-fidelity LES research. Full article

Understanding dry-out-induced weakening of reservoir and caprock rocks driven by gas displacement is critical for ensuring the operational safety and efficiency of underground gas storage (UGS). Using core samples from the Xiangguosi UGS collected from different regions and stratigraphic intervals, we quantify the evolution of dynamic elastic parameters during simulated downhole dry-out with a joint ultrasonic and nuclear magnetic resonance (NMR) monitoring system. The results show that as water saturation (S_w) decreases, the dynamic bulk modulus (K_d) and P-wave velocity (V_p) decline by varying degrees across specimens, with reductions ranging from 3.0% to 50.48% and from 1.34% to 17.56%, respectively, whereas the dynamic shear modulus (G_d) and S-wave velocity (V_s) show only minor variations throughout the process. These findings demonstrate that the sensitivity of dynamic parameters to dry-out is strongly specimen-dependent. Further analysis indicates that the dry-out response is highly variable and depends on a combination of petrophysical properties. Among these, the heterogeneity of the initial pore structure acts as an important factor, with its influence shaped by mineralogy and bulk frame rigidity. Cores with multimodal pore size distributions and well-developed macropores (long T₂ components) respond more strongly to dry-out, whereas higher clay mineral contents tend to mitigate modulus degradation by retaining water under stronger capillary confinement. Based on these observations, we propose a conceptual model of pore support and skeleton constraint. The model suggests that dry-out weakening arises from a progressive loss of pore fluid volumetric support to the rock skeleton as free water is preferentially displaced from meso- and macropores. These findings provide key experimental evidence and mechanistic insights for using geophysical methods to monitor dry-out zone expansion and to assess long-term formation stability in UGS. Full article

The diffusion model (DM) is a hot topic in deep generative models and is widely applied in image generation. In diffusion models, there are four main “secrets” that affect high-quality image generation: constructing the diffusion model, improving the sampling velocity, designing the diffusion process, and guiding diffusion models. How should one construct the diffusion model? How can one improve the sampling velocity? How should one design the diffusion process? How should one guide diffusion models? These questions are critical to enhancing diffusion model performance. However, most existing review papers focus on applications, while discussion of the four key technical aspects remains limited. In response, this paper summarizes four key technologies and six representative application directions. First, the basic principles of diffusion models are reviewed from three perspectives: denoising diffusion probabilistic models, noise conditional score network models, and stochastic differential equation models. Second, key techniques for improving sampling velocity are summarized from three perspectives: non-Markovian sampling, knowledge distillation sampling, and discrete optimization sampling. Third, the diffusion process design is summarized from three perspectives: latent space, Transformer-based diffusion, and non-Euclidean space. Fourth, guidance strategies are summarized from three perspectives: classifier guidance, classifier-free guidance, and multimodal guidance. Fifth, the advantages and applications of diffusion models are discussed in high-quality text-to-image generation, high-quality text-to-video generation, and high-quality image-to-image generation. Finally, this paper discusses the challenges faced by diffusion models in image generation. Overall, this review systematically discusses the four “secrets” of diffusion models for image generation and provides a useful reference for future research in this field. Full article