Search Results (59)

High renewable penetration introduces stochastic variability in distribution-network operation, requiring probabilistic AC power-flow tools that remain accurate in the tails while avoiding the computational burden of large Monte Carlo simulation. This paper presents a fully reproducible non-intrusive polynomial chaos expansion (PCE) framework for uncertainty propagation through nonlinear Newton–Raphson AC power flow. The method uses sparse-grid quadrature to train PCE surrogates from deterministic power-flow evaluations and is benchmarked against high-fidelity Monte Carlo simulations. In the validation, the IEEE 33-bus feeder is evaluated using up to 50,000 Monte Carlo samples, 95% bootstrap confidence intervals, PCE orders 2–5, correlated uncertainty scenarios, realistic thermal-loading recalibration, reactive-power sensitivity of renewable injections, multi-feeder testing on IEEE 33-bus, CIGRE MV, CIGRE LV, and IEEE 118-bus networks, and a 365-snapshot full-year daily screening. For the base IEEE 33-bus case, third-order PCE required only 494 deterministic power-flow evaluations and reproduced the 50,000-sample Monte Carlo benchmark with relative mean errors of 0.014% for minimum voltage, 0.119% for active losses, and 0.113% for substation import. The corresponding wall-clock speed-up was 13.29×, while reducing deterministic evaluations by approximately 101×. Correlated load–PV uncertainty increased the upper tail of substation import from 6.06 MW to 6.30 MW, and realistic thermal recalibration revealed line-loading p99 values above 100% for the 60% target case, demonstrating the operational value of physically meaningful ampacity settings. The proposed workflow provides an open, scalable, and tail-aware basis for uncertainty-informed distribution-network planning under renewable variability. Full article

This study proposes a comprehensive methodology for the experimental characterization and non-linear dynamic modelling of Polycrystalline Diamond (PCD) bearings, establishing a high-fidelity digital twin approach for the condition monitoring of rotating machinery. The research addresses complex rotor–stator interactions through the development of a multibody numerical framework. A structural 1D Finite Element (FE) model of the stator assembly was first calibrated via experimental modal analysis, achieving a high correlation with the first four bending modes and a maximum frequency discrepancy of only 1.4%. This validated structure was integrated into a non-linear multibody environment to simulate transient rub-impact events at rotational speeds up to 5500 rpm across varying clearance configurations. The model successfully captures the transition from stable periodic orbital motion to the stochastic and chaotic regimes observed in high-clearance setups. Frequency-domain validation further confirms the model’s accuracy in identifying supersynchronous harmonics and energy distribution patterns. Quantitative analysis shows that high-clearance configurations generate impact forces exceeding 6000 N, providing critical data for structural health assessment. These results demonstrate that the proposed digital twin serves as a robust physical foundation for diagnostic systems, enabling the identification of contact-induced vibrational signatures that are essential for training prognostic algorithms. This approach facilitates the autonomous monitoring of critical rotating machinery in demanding industrial and subsea applications, supporting the transition toward active balancing and model-based vibration control strategies. Full article

Accurate prediction of post-construction settlement in high-speed railway (HSR) soft foundations is critical for operational safety yet challenging due to the non-equidistant and non-stationary nature of observation data. This study systematically evaluated the robustness and accuracy of settlement prediction models using a Monte Carlo simulation approach. A numerical model incorporating the permeability characteristics of soft foundations was established to simulate stochastic system responses. Furthermore, an innovative multi-metric evaluation framework was constructed using the entropy weight method, integrating goodness-of-fit, prediction accuracy (systematic error), and stability (random error). Four classical empirical models—Hyperbolic, Exponential Curve, Asaoka, and Hoshino—were assessed. The results indicate that: (1) The Hyperbolic Method significantly outperformed other models ($p<0.01$) in goodness-of-fit (mean correlation coefficient: 0.983 ± 0.006) and accuracy (systematic error: 3.2% ± 1.1%); (2) The Hoshino Method exhibited optimal stability, characterized by the lowest random error (3.8 ± 2.0 mm); and (3) Model performance showed a significant positive correlation with the permeability coefficient ($R^2>0.92$). Validated by five distinct engineering cases, the comprehensive performance ranking was determined as: Hyperbolic > Hoshino > Exponential Curve > Asaoka. These findings provide a scientific strategy for model selection under non-stationary conditions and offer theoretical support for refining railway deformation monitoring standards. Full article

The Mediterranean region has experienced pronounced hydroclimatic variability over recent decades, motivating a closer examination of the physical processes underlying these changes. This study analyzes ERA5 reanalysis data for 1950–2024 to investigate long-term trends and interrelations among temperature, precipitation, evaporation, wind, sensible heat, and column water, distinguishing between land and sea domains and multiple atmospheric layers. Results show a strong warming signal in the lower troposphere, with temperatures increasing by 0.03 °C year⁻¹ over land and 0.015 °C year⁻¹ over sea, and near-stagnancy in the upper troposphere, which indicates a steepening lapse rate. Unlike temperature, evaporation shows no strong long-term increase: over sea, it rises only slightly, and over land, it declines modestly, with both weak tendencies dominated by strong interannual variability and consistent with declining winds. Over land, sensible heat flux increases, while over the sea, it decreases, revealing divergent energy-partition regimes. Precipitation exhibits no significant long-term change, suggesting that the atmosphere has become warmer and slightly moister but less effective in converting vapor into rainfall. Correlation analyses indicate that wind speed exerts a stronger control on evaporation and precipitation than temperature across the whole region. The Hurst–Kolmogorov stochastic framework further reveals persistent long-term variability in Mediterranean hydroclimatic processes, underscoring that the region’s climate behavior is shaped by dynamic and complex interactions rather than by temperature trends alone. Full article

Wind-induced response poses a significant challenge to the stability of extra-large-span heavy steel box girders during synchronous lifting operations. This study adopted a method combining numerical simulation with on-site monitoring to investigate the aerodynamic characteristics the beam during the overall hoisting process of the Xiaotun Bridge. A high-fidelity finite element model was established using Midas NFX 2024 R1, and fluid–structure interaction (FSI) analysis was conducted, utilizing the RANS k-ε turbulence model to simulate stochastic wind fields. The results show that during the lifting stage from 3 m to 25 m, the maximum horizontal displacement of the steel box girder rapidly increases at wind angles of 90° and 60°, and the peak displacement is reached at 25 m. Under a strong breeze at a 90° wind angle and 25 m lifting height, the maximum lateral displacement was 42.88 mm based on FSI analysis, which is approximately 50% higher than the 28.58 mm obtained from linear static analysis. Subsequently, during the 25 m to 45 m lifting stage, the displacement gradually decreases and exhibits a linear correlation with lifting height. Concurrently, the maximum stress of the lifting lug of the steel box girder increases rapidly in the 3–25 m lifting stage, reaches the maximum at 25 m, and gradually stabilizes in the 25–45 m lifting stage. The lug stress under the same critical condition reached 190.80 MPa in FSI analysis, compared with 123.83 MPa in static analysis, highlighting a significant dynamic amplification. Furthermore, the detrimental coupling effect between mechanical vibrations from the lifting platform and wind loads was quantified; the anti-overturning stability coefficient was reduced by 10.48% under longitudinal vibration compared with lateral vibration, and a further reduction of up to 39.33% was caused by their synergy with wind excitation. Field monitoring validated the numerical model, with stress discrepancies below 9.7%. Based on these findings, a critical on-site wind speed threshold of 9.38 m/s was proposed, and integrated control methods were implemented to ensure construction safety. During on-site lifting, lifting lug stresses were monitored in real time, and if the predefined threshold was exceeded, contingency measures were immediately activated to ensure a controlled termination. Full article

This paper proposes an evolutionary modeling method of scattering clusters based on Geometric-Based Stochastic Modeling (GBSM). In the single-bounce scenario of vacuum pipeline maglev train communication, the dynamic generation and extinction process and statistical behavior of multiple clusters at high speed are analyzed. The model abstracts the multipath component into a cluster structure. By iteratively updating the channel state and the birth and death cluster information after initialization, a dynamic model of the evolution process of scattering clusters in time-varying channels is constructed, which depicts the time evolution process of multipath clusters. Under the framework of GBSM, the correlation statistical characteristics of the scattering cluster birth and death process are further derived, and analytical integral form expression of the channel time autocorrelation function (ACF) is theoretically solved. The simulation results reveal the inherent law of channel time-varying characteristics under the joint action of high-speed train operation and closed pipe structure, and the results show that the proposed method can effectively capture the transient dynamic characteristics and long-term statistical trends of multipath clusters. The proposed model provides a practical basis for channel modeling in vacuum tube maglev wireless communication systems. Full article

Recently, significant attention has been paid to the use of multirole materials in additive manufacturing (AM). Polyvinylidene fluoride (PVDF) is an ideal candidate material that has been selected for examination because of its unique characteristics. This study establishes a correlation between the macroscopic mechanical behavior and microscopic structural mechanisms, enabling the utilization of the deformation rate in tailoring the mechanical response of printed PVDF components. This research focuses on testing AM PVDF samples under different strain rates (10–300 mm/min), aiming to report their behavior under loading conditions compatible with the stochastic nature of real-life applications. The thermal (thermogravimetric analysis and differential scanning calorimetry) and rheological (viscosity and melt flow rate) properties were investigated along with their morphological characteristics (scanning electron microscopy). The response under combined dynamic and thermal loading was investigated through dynamic mechanical analysis, and the structural characteristics were investigated using spectroscopic techniques (Raman and energy-dispersive spectroscopy). The properties examined were the ultimate and yield strengths, modulus of elasticity, and toughness. Sensitivity index data are also provided. For completeness, the flexural strength, Charpy impact strength, and Vickers hardness were also evaluated, suggesting that the AM PVDF samples exhibit a resilient nature even when subjected to extremes regarding their strain rate versus their overall mechanical characteristics. PVDF exhibited a strain-hardening response with an increase in its strength of up to ~25% (300 mm/min) and a stiffness of ~15% (100 mm/min) as the loading speed of testing increased. Full article

The undifferenced and uncombined (UDUC) model preserves raw code and carrier-phase observations for each frequency, avoiding differencing or ionosphere-free combinations. This approach enables the direct estimation of atmospheric parameters. However, the stochastic characteristics of these parameters, particularly ionospheric delay, are often oversimplified or based on empirical assumptions, limiting the accuracy and convergence speed of Precise Point Positioning (PPP). To address this issue, this study introduces a stochastic constraint model based on the power spectral density (PSD) of ionospheric variations. The PSD describes the distribution of ionospheric delay variance across temporal frequencies, thereby providing a physically meaningful constraint for modeling their temporal correlations. Integrating this PSD-derived stochastic model into the UDUC framework improves both ionospheric delay estimation and PPP performance, especially under disturbed ionospheric conditions. This paper presents a BDS PPP/PPP-AR method that estimates the ionospheric power spectral density (IPSD) in real time. Vondrak smoothing is applied to suppress noise in ionospheric observations before IPSD estimation. Experimental results demonstrate that the proposed approach significantly improves convergence time and positioning accuracy. Compared to the empirical IPSD model, the PPP mode using the estimated IPSD reduced horizontal and vertical convergence times by 11.1% and 13.2%, and improved the corresponding accuracies by 15.7% and 12.6%, respectively. These results confirm that real-time IPSD estimation, coupled with Vondrak smoothing, establishes an adaptive and robust ionospheric modeling framework that enhances BDS PPP and PPP-AR performance under varying ionospheric conditions. Full article

Three-dimensional reconstruction programs are essential tools for understanding the behavior of asphalt mixtures. On the basis of accurate 3D models, it is convenient to identify the complex relationship between spatial structures and physical properties. In this work, we explore a low-cost and data-efficient way to create a collection of 3D asphalt mixture models. The core idea is to introduce a foundational segmentation program and stochastic modeling into the asphalt mixture reconstruction framework. First, our approach captures a 2D image to present spatial structures of the investigated sample. The integration of a smartphone camera and an image quilting method has been designed to understand fine-grained details and facilitate full coverage. Aiming at realizing high-quality segmentation, we propose the Segment Anything Model (SAM)-driven method to distinguish aggregate grains and asphalt binder. Second, Multiple-Point Statistics (MPS) is activated to build 3D models from 2D training images. To speed up the reconstruction step, we apply Nearest Neighbor Simulation (NNSIM) to improve pattern searching efficiency. Aiming at calculating 3D conditional probabilities, the probability aggregation framework is introduced into the asphalt mixture investigation. Third, our program focuses on the modeling evaluation procedure. Determination of a two-point correlation function, analysis of distance and a grain size distribution assessment are separately performed to check the reconstruction quality. The evaluation results indicate that our program not only preserves spatial patterns but also expresses uncertainty during the material production step. Full article

Wind turbine blades face significant challenges from stochastic wind loads, impacting structural integrity. Traditional analysis often isolates Computational Fluid Dynamics (CFD) from Building Information Modeling (BIM) in the design process. This study bridges this gap by integrating BIM forward design with CFD simulation. A universal BIM modeling framework is developed for rapid blade modeling, which is compatible with ANSYS Workbench 2022 R1 through intermediate format conversion. The influence of wind load on the blades under various wind speed conditions is analyzed, and the results indicate a significant correlation between wind load intensity and blade structural response. The maximum windward pressure reaches 4.96 kPa, while the leeward suction peaks at −6.28 kPa. The displacement at the tip and middle part of the blades significantly increases with the increase in wind speed. The growth rate of displacement between adjacent speeds rises from 1.20 to 1.94, and the overall increase rate within the entire range rises from 1.02 to 4.16. These results demonstrate the feasibility of using BIM forward design in accurate performance analysis, and also extends the value of BIM in wind energy. Furthermore, a bidirectional information flow is established, where BIM provides geometry for CFD, and simulation results will inform BIM design refinement. Full article

Wind speed is a critical parameter for both energy applications and climate studies, particularly under changing climatic conditions and has attracted increasing research interest from the scientific comunity. This parameter is of interest to both researchers interested in climate change and researchers working on issues related to energy production. Based on this, in this study, prospective analyses were made with various machine learning algorithms, the long-short term memory (LSTM), the artificial neural network (ANN), and the support vector machine (SVM) algorithms, and one of the stochastic methods, the seasonal autoregressive integrated moving average (SARIMA), using the monthly wind data obtained from Bodo. In these analyses, five different models were created with the assistance of cross-correlation. The models obtained from the analyses were improved with the wavelet transformation (WT), and the results obtained were evaluated for the correlation coefficient (R), the Nash–Sutcliffe model efficiency (NSE), the Kling–Gupta efficiency (KGE), the performance index (PI), the root mean standard deviation ratio (RSR), and the root mean square error (RMSE). The results obtained from this study unveiled that LSTM emerged as the best performance metric in the M04 model among other models (R = 0.9532, NSE = 0.8938, KGE = 0.9463, PI = 0.0361, RSR = 0.0870, and RMSE = 0.3248). Another notable finding obtained from this study was that the best performance values in analyses without WT were obtained with SARIMA. The results of this study provide information on forward-looking modeling for institutions and decision-makers related to energy and climate change. Full article

This study presents a comprehensive investigation into multi-directional fatigue damage characteristics of fixed offshore wind turbine tower roots through comparative analysis using FAST (3.5.0) and Bladed (4.3) software platforms. The research methodology encompasses three principal phases: First, a stochastic wind field model was developed through statistical analysis of historical wind speed measurements, achieving superior correlation (R² = 0.983) in goodness-of-fit tests. Subsequently, the rain flow counting technique was employed to characterize equivalent cyclic load spectra. Building upon these foundations, an integrated predictive fatigue life evaluation framework was formulated by synergistically combining S–N curve principles with Palmgren–Miner’s linear cumulative damage theory. The methodology was further validated through cross-platform verification with Bladed software, revealing only a 7.4% deviation in predicted fatigue lives between the two computational models, confirming the technical feasibility of the proposed simplified model. Full article

Background: Indonesia is an archipelagic country with 17,508 islands spread over the Pacific and Indian Oceans, with thousands of inter-island routes requiring a large and engaged fleet. The vast expanse of the country also leads to challenges related to optimal fleet coverage, routing, and oil distribution while maintaining cost-effectiveness and reliable supply. Methods: This study combined a mixed-integer linear-programming (MILP) model with a response surface methodology (RSM) approach to optimize vessel assignment, vessel routes, and inventory control simultaneously and comprehensively across three regional clusters (i.e., Western, Central, and Eastern Indonesia). The model takes into account a fleet of 28 vessels (13 medium range [MR] and 15 general purpose [GP]) that can distribute three oil products: gasoline, diesel, and kerosene. Results: The optimized solution yields 100% service reliability at an operational cost of $ 2.83 million per month—far lower than currently operating services. The model is robust against variations in demand (±20%), port congestion (±50%), and changing fuel prices (±50%), which is confirmed by a sensibility analysis. The close correlation coefficient (0.987) between the MILP and RSM results confirms the framework’s accuracy. At the same time, the critical performance factors were found to be vessel speed (13.5 knots), fleet size, and port operation time. Conclusions: The study offers a cost-efficient and data-intensive model that could be implemented as a maritime logistics framework, as well as potential areas for future work and insight for relevant stakeholders. Future research will have to integrate real-time data fusion, mainly due to the need for environmental and stochastic modeling methods to foster operational resilience in dynamic maritime business ecosystems. Full article

The growing significance of wind energy in supplying renewable electricity underlines the increasing importance of wind turbine efficiency. Hybrid towers, integrating steel and pre-stressed concrete in a stacked structure, address traditional limitations in nacelle height but face new vulnerabilities, exemplified by a collapse in September 2021. This highlights the crucial need for continuous monitoring, particularly of the tower structure’s tendons. This study introduces acoustic emission monitoring as a novel approach for the early detection of wire breaks within the highly stressed tendons of hybrid towers. The investigations described focus on evaluating the suitability of this method for the specific use case and developing a generalized monitoring approach. Accordingly, background noise in an operating wind turbine tower was recorded and analyzed over a year-long operational period. Correlation analyses of these data unveiled intricate relationships between operational parameters and noise levels, with wind speed, rotor speed, and blade pitch angle exerting influence. Laboratory experiments were conducted on a full-scale specimen, and wire breaks were artificially provoked to characterize the damage signal and assess its attenuation in relevant structural components. The experimental results were integrated into a stochastic model to determine feasible sensor distances, aiming for a 90% probability of detection at a 95% confidence level. Low attenuation along the tendon was identified, enabling reliable detection over significant distances. Nevertheless, practical considerations suggest a focus on tendon anchorages, with the potential for grouped monitoring in specific areas to optimize sensor deployment. The study proposes a sensor network configuration to enhance the safety and reliability of wind turbine structures. Full article

Accurately predicting the remaining useful life (RUL) of critical mechanical components is a central challenge in reliability engineering. Stochastic processes, which are capable of modeling uncertainties, are widely used in RUL prediction. However, conventional stochastic process models face two major limitations: (1) the reliance on strict assumptions during model formulation, restricting their applicability to a narrow range of degradation processes, and (2) the inability to account for potential variations in the degradation mechanism during modeling and prediction. To address these issues, we propose a novel mechanism-equivalence-based Tweedie exponential dispersion process (ME-based TEDP) for adaptive degradation modeling and RUL prediction of mechanical components. The proposed model enhances the original Tweedie exponential dispersion process (TEDP) by incorporating degradation mechanism equivalence, effectively capturing the correlation between model parameters. Furthermore, it improves prediction accuracy and interpretability by employing a dynamic testing–modeling–predicting strategy. Application of the ME-based TEDP model to high-speed rail bogie systems demonstrates its effectiveness and superiority over existing approaches. This study advances the theory of degradation modeling and significantly improves the precision of RUL predictions. Full article