Search Results (5,033)

This article examines the integrability of the combined KdV-mKdV equation, which provides an effective framework for modeling coherent structures in turbulent flows. We generate the explicit Darboux solutions for the combined KdV-mKdV equation using Wronskians. These results are further generalized to the K-th order and supplemented as the logarithmic derivative of the K-th order Wronskian that provides us with the multi-soliton solutions. We generate the exact explicit solution for one-, two-, and three-solitons. Graphical depictions of the soliton formations’ interactions, dynamical characteristics, and temporal evolution are used to support these conclusions. Furthermore, we generate the multi-wave and periodic cross-kink wave solutions by employing bilinear formulism. The graphical representations of these nonlinear excitations highlight their extensive dynamical activity and structural complexity. Full article

Downhole boosting technology breaks the physical limitations of conventional surface boosting by enhancing pressure at the wellbore bottom, with micro-axial compressors serving as its core compression module. However, traditional axial compressors, when miniaturized, suffer from severe end losses and easy instability, failing to adapt to downhole space constraints and the efficient pressurization demands of low-permeability, low-pressure, and small-flow reservoirs. To address this, this study designed a compact micro-axial compressor. CFturbo was used for parametric blade design and optimization, while ANSYS CFX 2025 (with the SST turbulence model) conducted numerical simulations. A “simulation–diagnosis–optimization–validation” closed-loop strategy was adopted to adjust the blade’s leading-edge shape, camber line, and thickness distribution, combined with grid independence verification and inter-stage matching optimization. The results show that at the design speed (60,000 rpm), the compressor achieves a pressure ratio of 1.57 and an isentropic efficiency of 83.6%. It also maintains stable performance at 55,000 rpm (off-design speed), with excellent inter-stage aerodynamic matching and controllable leakage losses. This compressor meets downhole operational needs, providing technical support for developing low-permeability, low-pressure, small-flow reservoirs. Full article

Effective management of reservoir water for irrigation is crucial in arid regions prone to drought and water shortages. However, evaporation losses from reservoirs remain poorly understood. Direct measurements typically quantify evaporation only at the measurement site rather than across the entire reservoir. This study introduces the Turbulent Exchange Approach for Reservoir Evaporation Estimation (TEAREE). The TEAREE is a simple model that integrates a bulk aerodynamic formulation with Landsat 8–9 satellite water-surface temperature data and meteorological observations to estimate spatially distributed daily reservoir evaporation. The TEAREE model was first evaluated at Elephant Butte and Caballo reservoirs in NM, USA, and subsequently applied across multiple reservoirs with diverse climatic conditions to demonstrate its applicability for estimating open-water evaporation. Daily evaporation was obtained by upscaling satellite overpass-time evaporation estimates using the daily-to-instantaneous vapor pressure deficit ratio (k_e) and wind speed. The model performed strongly across 12 lakes (R² = 0.91–0.99; RMSE = 0.27–0.85 mm/day) compared with the bulk aerodynamic (B_AER) method. Comparison with eddy covariance (EC) evaporation also showed good agreement. Monte Carlo analysis indicated moderate uncertainty associated with k_e variability, supporting the operational use of a constant k_e = 0.95 for daily upscaling. Full article

The dynamical tropopause layer pressure (DTLP) represents a key interface characterizing upper-tropospheric stratification and atmospheric dynamical structure. Its spatial morphology and gradient variations directly influence jet stream distribution as well as the intensity and location of clear-air turbulence (CAT). Over the Tibetan Plateau, complex terrain and pronounced dynamical variability result in a significantly lower tropopause height and enhanced horizontal gradients during winter. Aircraft cruising altitudes frequently approach or intersect the tropopause layer in this region, making accurate and fine-scale characterization of DTLP structures critically important for aviation safety. A deep learning-based DTLP retrieval model (Att-ResUNet_DEM) is developed by integrating terrain constraints and an attention mechanism. Using MERRA-2 reanalysis data as supervisory labels, the model incorporates a squeeze-and-excitation (SE) attention mechanism within a residual encoder–decoder framework, while a digital elevation model (DEM) is introduced as an additional input channel and fused with satellite brightness temperature data to explicitly account for terrain effects. A random forest (RF) model is implemented as a baseline for comparison. Compared with the RF model, the Att-ResUNet_DEM reduces the MAE and RMSE by 13.20% and 9.19%, respectively, while increasing the correlation coefficient to 0.76. Over the primary aviation corridors of the Tibetan Plateau, the Att-ResUNet_DEM model achieves a correlation coefficient(R) of 0.87, with markedly reduced gradient dispersion. A representative CAT case further confirms the model’s ability to capture the overall DTLP morphology and gradient enhancement zones. Overall, by combining a regionalized modeling strategy with terrain constraints, this study systematically improves DTLP retrieval accuracy and gradient consistency over complex terrain, providing a new technical pathway for high-resolution tropopause monitoring and aviation operation support. Full article

In the deep and narrow valleys of southwestern China, free-surface spillways are widely adopted as auxiliary flood-discharge structures in water conservancy projects. Owing to the high water head upstream, tunnels are often plagued by problems including excessive velocity, cavitation damage, and insufficient downstream energy dissipation. Previous studies have demonstrated that the installation of novel lateral deflectors in tunnels can effectively regulate local flow patterns while providing additional energy dissipation capacity. In this study, physical model experiments combined with numerical simulations were employed to further compare the energy dissipation characteristics of lateral deflectors. The turbulent characteristics, the energy dissipation process, and the evolution of vortex structures were systematically analyzed based on turbulent kinetic energy, turbulence dissipation rate, fluctuating pressure coefficient, and Hilbert–Huang transform (HHT) spectral analysis. The results show that the novel lateral deflector significantly enhances local turbulence intensity and turbulent kinetic energy, promoting the conversion of mean kinetic energy into turbulent kinetic energy and its rapid dissipation within a shorter distance. Spectral energy reaches its peak in the jet impingement region, accompanied by a marked increase in high-frequency components, indicating an intensified energy transfer from large-scale vortices to small-scale vortices. These findings suggest that the novel deflector can serve as an effective internal energy dissipator in free-surface tunnels with shorter turbulent region and more local turbulence. Full article

Free-space optical (FSO) communication is an attractive high-capacity wireless technology for terrestrial, aerial, and satellite links. However, FSO performance is strongly affected by multiple impairments, including path loss, turbulence attenuation, pointing errors, and equipment loss. Therefore, accurate performance evaluation requires channel modelling that accounts for both deterministic power losses and stochastic channel effects. This paper presents a comprehensive and structured review of FSO channel modelling, covering the transmission, propagation medium, and receiver sections. The composite channel response is represented using a mathematical formulation. Commonly used FSO models are reviewed and organised, including Beer–Lambert and geometrical loss, Kim and Kruse path loss models, Lognormal, Gamma–Gamma, K, and Málaga distributions, along with pointing-error and angle-of-arrival models. Each model is explained in terms of its physical meaning, assumptions, and applicable operating conditions. Lastly, a numerical example is presented to demonstrate how deterministic losses and stochastic channel effects can be combined in FSO performance evaluation. Full article

Accurate wind speed forecasting is essential for the efficient integration of wind energy into power systems, as it directly influences generation scheduling, grid stability, and energy market operations. Forecast errors can lead to significant economic losses, including increased balancing costs, inefficient dispatch of backup generation, and penalties in electricity markets. However, wind behaviour is highly complex due to the influence of synoptic weather systems, terrain variability, and turbulence, which makes accurate prediction particularly challenging. This paper proposes a hybrid modelling framework that combines a linear regression mean model with Gaussian process (GP) residual modelling to improve forecast accuracy. Monitoring stations were grouped based on geographic coordinates and elevation, with cluster validation using the Hopkins statistic and silhouette analysis. The results show that for high-elevation inland stations (cluster 2), GP residual modelling improves forecast accuracy by up to 16.3%. In contrast, for low-elevation coastal stations (cluster 1), the GP approach does not yield improvements, indicating that its effectiveness depends strongly on the underlying wind regime. Full article

Underwater wireless optical communication (UWOC) for autonomous underwater vehicles is severely compromised by the coupling of oceanic optical turbulence and platform motion. Traditional static statistical models fail to capture the temporal evolution of these stochastic processes, hindering effective real-time beam tracking. This paper proposes a unified dynamic framework and a hybrid intelligent control strategy to address beam misalignment in turbulent environments. First, a physically motivated stochastic differential equation (SDE) model is derived from the Radiative Transfer Equation via diffusion approximation. Validated by an inverse Fokker–Planck approach, this model accurately reconstructs drift fields for diverse channel conditions, serving as a dynamic generator for time-varying fading. Second, to maintain robust link alignment, a hybrid Fuzzy-Reinforcement Learning control strategy is developed. This approach integrates the interpretability of fuzzy logic with the adaptive optimization of Q-learning, incorporating a supervisor mechanism to handle deep fading events. Numerical simulations and hardware-in-the-loop (HIL) experiments demonstrate the system’s efficacy. The proposed controller achieves a median alignment error of 3.64 mm and reduces transient errors by over 80% compared to classical PID controllers during signal recovery. These results confirm that the proposed framework significantly enhances link stability and tracking robustness for AUVs in complex random media. Full article

Leakage flow interferes with the main flow movement and has a close relationship with the rotational stall phenomenon. To study the rotational stall characteristics of mixed-flow pumps under different tip clearances (rim clearances), numerical simulations of the internal flow field of the mixed-flow pump were carried out based on the SST k-ω turbulence model and hexahedral structured meshes, with the tip clearances set to 0.2 mm, 0.5 mm, and 0.8 mm respectively. The external characteristics, internal flow field under stall conditions, impeller surface pressure, and internal vorticity distribution of the mixed-flow pump were compared among the three different tip clearances. The research results show that when the tip clearance is 0.5 mm, the numerical simulation results are in good agreement with the experimental results, indicating the high reliability of the simulation. Under the three different tip clearances, the near-stall and deep-stall operating points on the external characteristic curves are consistent. When the tip clearance is 0.8 mm, the positive slope characteristic of the flow rate–head curve of the mixed-flow pump is the most obvious. From the small flow rate condition to the large flow rate condition, the influence of the tip clearance on the efficiency of the mixed-flow pump gradually increases. Under deep-stall conditions, with increasing tip clearance, the stall vortex at the flow passage outlet causes more intense disturbances to the inlet of the downstream flow passage and induces the formation of new stall vortices at the downstream passage inlet, thereby increasing internal flow losses. The increase in the tip clearance leads to changes in the morphology of the leakage vortex, a decrease in the impeller surface pressure, intensification of flow disorder, and enhancement of the leakage vortex intensity. Moreover, compared with the rated condition, the leakage vortex of the mixed-flow pump under stall conditions is more affected by the tip clearance. Full article

Financial time series in turbulent markets exhibit complex long-memory dynamics and multifractal features that traditional deep learning models fail to capture due to inherent exponential forgetting mechanisms. To address this, we propose Frac-Attn-GL, a novel Fractional-order Spatiotemporal Attention-based GRU-LSTM framework. Grounded in the Fractal Market Hypothesis, the model embeds Grünwald–Letnikov fractional-order operators into a dual-channel architecture (FracLSTM and FracGRU) to characterize long-range memory with rigorous power-law decay priors. Furthermore, an extreme-aware asymmetric loss function is designed to drive a dynamic spatiotemporal routing mechanism, enabling adaptive shifts between long-term macro trends and short-term micro shocks. Empirical tests on major U.S. stock indices reveal three significant findings. First, the Frac-Attn-GL framework substantially reduces prediction errors, achieving up to a 93.1% RMSE reduction on the highly volatile NASDAQ index compared to standard baselines. Second, the adaptively learned fractional-order parameters exhibit a consistent quantitative alignment with the market’s empirical multifractal singularity spectrum, supporting the physical interpretability of the model’s endogenous memory mechanism. Finally, hybrid residual multifractal diagnostics indicate that the framework effectively captures deep long-range correlations, reducing the Hurst exponent of the prediction residuals from ~0.83 to approximately 0.50, a level consistent with the absence of significant long-range dependence. Full article

Severe clear-air turbulence (CAT) remains a relevant hazard to aviation safety, often occurring without visible atmospheric indicators. This study presents a hybrid forecasting framework that integrates Global Forecast System outputs with machine-learning algorithms to predict severe CAT events over Southeast Brazil. To enhance predictive performance and reduce model complexity, a statistically grounded dimensionality reduction approach based on p-value filtering and false discovery rate control was applied, resulting in a compact set of physically interpretable predictors. Several machine-learning classifiers were then evaluated using receiver operating characteristic analysis to assess their predictive skill. The results show that relatively simple models can achieve strong discrimination when combined with rigorous feature selection, outperforming baseline turbulence diagnostics. These findings highlight the value of combining physically consistent diagnostics with data-driven approaches for regional severe CAT forecasting. Overall, the proposed framework provides an efficient and adaptable strategy that can support improved turbulence awareness and contribute to enhanced aviation safety. Full article

One of the engineering applications inspired by nature is bio-inspired wings. The aerodynamic properties and autorotation characteristics of samara wing models have been studied extensively using both experimental and numerical methods. However, the three-dimensional flow behavior and angle of attack interaction around a natural samara wing are not yet fully understood. This study investigates the flow behavior around a samara wing model, with the aim of underlying physics and qualitatively analyzing the flow field, as well as the aerodynamic forces and stresses. Since the samara wing and the flow around it are three-dimensional, the difficulty of experimental investigation was taken into account, and the numerical analysis was performed using Computational Fluid Dynamics techniques. The results obtained from the numerical solution of the governing equations for three-dimensional turbulent flow were verified with experimental data. The calculations were performed by varying the angle of attack of the model wing between 0 and 50 degrees at 10-degree intervals. Depending on the angle of attack, the velocity field around the wing, surface pressure, and stress distributions, vortex structures formed on the wing and streamlines were analyzed, and the results were presented. This study and its results on this model may lead to the development and optimization of the model and its use in turbines or air vehicles. Full article

Laser fiber-based collimation straightness measurement can eliminate the intrinsic drift of the laser source while offering a simple configuration and simultaneous measurement of straightness in two orthogonal directions. As a high-precision optoelectronic sensing method, it has been widely used for the measurement of straightness, parallelism, perpendicularity, and multi-degree-of-freedom geometric errors. However, two common issues remain in practical applications. One is the nonlinear response of the four-quadrant detector, the core position-sensitive sensor, which is caused by detector nonuniformity and the quasi-Gaussian distribution of the spot. The other is the degradation of measurement performance by atmospheric inhomogeneity and air turbulence along the optical path, particularly in long-distance measurements. To address these issues, a two-dimensional planar calibration method is first proposed to replace conventional one-dimensional linear calibration. A polynomial surface-fitting model is introduced to correct the nonlinear response and inter-axis coupling errors of the four-quadrant photoelectric sensor. Simulation and experimental results show that the proposed method significantly reduces the standard deviation of calibration residuals and improves measurement accuracy. In addition, based on our previously developed common-path beam-drift digital compensation method, comparative experiments were carried out on double-pass common-path and single-pass optical configurations employing corner-cube retroreflectors, and theoretical simulations were performed to analyze the influence of air-turbulence disturbances on measurement stability. Both theoretical and experimental results show that the double-pass common-path configuration exhibits more pronounced temporal drift. Therefore, a real-time digital compensation method for beam drift in long-distance single-pass common-path measurements is proposed. Experimental results demonstrate that the proposed method effectively suppresses drift induced by environmental air turbulence and thereby improving the accuracy and stability of long-travel geometric-error and related straightness measurement for machine-tool linear axes. Full article

Coastally Trapped disturbances (CTDs) are shifts in wind direction from the pre-dominant direction to equatorward to poleward for a period of time. These CTDs occur during the warm season off the California coast and impact coastal weather conditions and planned offshore wind plants. This study assesses the characteristics of CTD events as observed by lidar and other offshore buoys, then evaluates the ability of modeling systems to capture the correct characteristics, leveraging model output from the High-Resolution Rapid Refresh (HRRR) operational modeling system and the NOW-23 (National Offshore Wind) model dataset. CTDs were analyzed for October 2020 and May through to October of 2021, identifying 18 unique CTD events, confirmed by a nearby National Data Buoy Center (NDBC) buoy. The HRRR model captured most of these events, but the NOW-23 model output contained only 12 events. Composites of the wind, temperature, and pressure perturbations pre-, during, and post-event demonstrated the diminishment in wind speed, particularly for the alongshore component. Although the NOW-23 model captured the alongshore wind component and pressure perturbations well, the cross-shore wind component and temperature perturbations varied substantially. When the turbulent kinetic energy deviation and wind shear was positive across all levels pre-event, the NOW-23 modeling system was less likely to capture the CTD event. In contrast, the events that were captured by the model tended to have negative wind shear aloft pre-event. Full article

Artificial intelligence and machine learning techniques can be used for performing magnetic testing on spacecraft that has historically been difficult and risky to perform. Some of the difficulty arises from the need to take these measurements from within the turbulent near-field area of the spacecraft. Some methods of testing require the spacecraft to be hoisted in the air and swung while the measurements are being taken so that any magnetic signatures in the test area can be removed. These new artificial intelligence and machine learning techniques can be used to determine the magnetic torque of complex magnetic systems. Here we will describe a test method that collects such data and poses much less risk to the spacecraft. We will also show some combinations of hyper-parameters that can be used to increase the speed and accuracy of the models. Some models were able to achieve over 96.6% accuracy of multipole determination on simulated data and over a 99.99% accuracy of dipole moment determination on simulated data. Applications include attitude control systems (ACS), science instrument locations, and data analysis. Full article