Search Results (4,009)

Droplet-based microlens fabrication using Ultra Violet (UV) curable polymers demands the precise measurement of three-dimensional geometries, especially for non-axisymmetric shapes influenced by electric field deformation. In this work, we present a polar coordinate-based contour fitting method combined with Hermite interpolation to reconstruct 3D droplet geometries from two orthogonal shadowgraphy images. The image segmentation process integrates superpixel clustering with active contours to extract the droplet boundary, which is then approximated using a spline-based polar fitting approach. The two resulting contours are merged using a polar Hermite interpolation algorithm, enabling the reconstruction of freeform droplet shapes. We validate the method against both synthetic Computer-Aided Design (CAD) data and precision-machined reference objects, achieving volume deviations below 1% for axisymmetric shapes and approximately 3.5% for non-axisymmetric cases. The influence of focus, calibration, and alignment errors is quantitatively assessed through Monte Carlo simulations and empirical tests. Finally, the method is applied to real electrically deformed droplets, with volume deviations remaining within the experimental uncertainty range. This demonstrates the method’s robustness and suitability for metrology tasks involving complex droplet geometries. Full article

The large-scale integration of wind power into coastal power systems introduces significant challenges to reserve planning, especially under the threat of typhoons, which can cause extensive generation loss and threaten system security. Conventional reserve planning methods often fail to account for such extreme typhoon events. To fill the gap, this paper proposes a novel two-stage reserve planning framework that integrates economic optimization with operational security verification. In the first stage, a diverse set of high-impact typhoon scenarios are generated using a multivariate Markov chain Monte Carlo (MMCMC)–based path reconstruction method, which captures the dynamic evolution of key typhoon characteristics. In the second stage, the economically optimal reserve capacity is identified through cost-benefit analysis and then validated against the typhoon scenarios via N − 1 security verification. A case study on the modified IEEE RTS79 test system indicates that economically optimal reserve may be inadequate for ensuring security under severe typhoon conditions. However, a small increase in reserve capacity can effectively enhance system resilience with minimal additional cost. These results highlight the importance of incorporating typhoon scenario-based security verification into reserve planning especially for high-penetration wind power systems in coastal regions. Full article

A control chart is a crucial statistical process control (SPC) instrument for identifying method variances that may undermine product efficacy. The combined control chart has been utilized to enhance recognition capability. When testing a methodology, nonparametric statistics make a strong and compelling case when the distribution of a quality feature is uncertain. The primary focus of monitoring this work is to offer a novel control chart to support the surveillance of mean activities. This chart will incorporate a Tukey method, an extended exponentially weighted moving average control chart, and a moving average control chart called the Nonparametric EEWMA-MA chart. The Monte Carlo simulation facilitates assessments for evaluating system performance using average run lengths (ARL) based on zero-state. The comparison analysis demonstrates that the sensitivity of the suggested chart surpasses that of the conventional control chart (including the moving average (MA) chart, the extended exponentially weighted moving average (EEWMA) chart, and the mixed extended exponentially weighted moving average-moving average (EEWMA-MA) chart) in rapidly detecting changes that fluctuate with varying parameter settings by examining the minimal ARL. A simplified monitoring scenario using data on vinyl chloride can be employed to demonstrate the feasibility of the proposed technique. Full article

The High-Energy Ray Observatory (HERO) is a space-based experiment designed to measure the spectrum and composition of cosmic rays using an ionization calorimeter. The instrument’s effective geometric factor is at least 12 m²·sr for protons and 16 m²·sr or more for nuclei and electrons. Over an exposure period of approximately 5 to 7 years, the mission will enable high-resolution, element-by-element measurements of cosmic ray spectra in the energy range of 10¹² to 10¹⁶ eV per particle. A Monte Carlo simulation of the calorimeter—based on a scintillation detector with and without boron additives—was carried out using the GEANT4 software package. In this study, we examine the impact of boron additives in scintillator materials on energy resolution and their potential for discriminating between electromagnetic and hadronic components of cosmic rays. The primary objectives are to demonstrate that boron does not degrade detector characteristics and that it enables an additional timing-based method for cosmic-ray component rejection. The planned launch of the orbital experiment is scheduled for no earlier than 2029. Full article

To address the challenge of predicting lubrication states and reliability caused by the uncertainty of gear materials and structural parameters, a lubrication reliability analysis method considering the randomness of gear parameters is proposed. Firstly, a nonlinear dynamic model of a gear pair is established to derive the dynamic meshing force. The geometric and kinematic analyses are then performed to determine time-varying equivalent curvature radius and entrainment velocity. The minimum film thickness during meshing is further calculated. Considering gear parameters as random variables, a gear lubrication reliability model is formulated. Monte Carlo Simulation method is employed to accurately analyze the dynamic response, dynamic meshing force, equivalent curvature radius, entrainment velocity, probability distribution of minimum film thickness, and gear lubrication failure probability. Additionally, a specialized wear test device is designed to investigate the evolution of tooth surface roughness with wear and to forecast trends in gear lubrication failure probability as wear progresses. The results indicate that the uncertainty in gear parameters have minimal impact on the equivalent curvature radius and entrainment velocity, but significantly affect the dynamic meshing force. The gear speed and root mean square roughness are critical factors affecting lubrication reliability, and the early wear of the teeth enhances the lubrication reliability. The present work provides valuable insights for the design, maintenance, and optimization of high-performance gear systems in practical engineering applications. Full article

The coefficient of variation α is a critical parameter in the Brownian Passage Time (BPT) model, used to quantify the variability of large earthquake recurrence intervals. In this paper, a new estimation method is proposed for α based on paleoseismic sequences across multiple faults within a given tectonic region. By integrating Monte Carlo simulations with a Bayesian framework, the method assesses the probability distribution of α without assuming that the sample average recurrence interval equals the true mean μ, thereby avoiding epistemic bias. To validate the method, 1,000,000 simulations were conducted in two study areas of differing spatial scales. In the Western Qilian Mountains-Hexi Corridor, the posterior mean of α is 0.36 (without dating uncertainty) and 0.34 (with uncertainty). Expanding the analysis to 29 faults across western China, the estimated α increases to 0.39 (without dating uncertainty) and 0.36 (with uncertainty), with substantially reduced uncertainty bounds. The results reveal that increasing the number of paleoseismic sequences significantly reduces the uncertainty in estimating α, while considering dating uncertainty has only a minor impact. The methodology provides a robust framework for deriving region-specific recurrence variability parameters and proves particularly valuable for tectonically active regions where individual fault records are sparse but collectively form comprehensive datasets across multiple fault systems. Full article

The air transportation industry is challenged to address airport curbside delay problems that affect landside service quality and can potentially impact check-in operations. Methodological advances guided by industry requirements are needed to support curbside improvement studies. Existing methods require verification of assumptions prior to application or need expensive surveys to acquire data for use in microsimulations. A probability-based macrosimulation method is advanced for the evaluation of the level of service and capacity of the curbside processor. A key component of the method is the simulation of the stochastic balance of demand and available curb space for unloading/loading tasks using the Monte Carlo simulation model. The method meets the planning and operation requirements with the ability to analyze conditions commonly experienced at the curb area. Example applications illustrate the flexibility of the method in evaluating existing as well as planned facilities of diverse designs and sizes. The developed method can contribute to curbside processor delay reduction and due to the macroscopic nature of the method, the data requirements can be met by an airport authority without costly surveys. Full article

Distribution System Operators (DSOs) increasingly need planning tools that coordinate utility-influenced assets—such as electric-vehicle charging stations (EVCS) and voltage-support resources—with customer-sited distributed generation (DG). We present a Nash-equilibrium-based Iterative Best Response Algorithm (IBRA-NE) for joint planning of DG and EVCS in radial distribution networks. The framework supports two applicability modes: (i) a DSO-plannable mode that co-optimizes EVCS siting/sizing and utility-controlled reactive support (DG operated as VAR resources or functionally equivalent devices), and (ii) a customer-sited mode that treats DG locations as fixed while optimizing DG reactive set-points/sizes and EVCS siting. The objective minimizes network losses and voltage deviation while incorporating deployment costs and EV charging service penalties, subject to standard operating limits. A backward/forward sweep (BFS) load flow with Monte Carlo simulation (MCS) captures load and generation uncertainty; a Bus Voltage Deviation Index (BVDI) helps identify weak buses. On the EEU 114-bus system, the method reduces base-case losses by up to 57.9% and improves minimum bus voltage from 0.757 p.u. to 0.931 p.u.; performance remains robust under a 20% load increase. The framework explicitly accommodates regulatory contexts where DG siting is customer-driven by treating DG locations as fixed in such cases while optimizing EVCS siting and sizing under DSO planning authority. A mixed scenario with 5 DGs and 3 EVCS demonstrates coordinated benefits and convergence properties relative to PSO, GWO, RFO, and ARFO. Additionally, the proposed algorithm is also tested on the IEEE 69-bus system and results in acceptable performance. The results indicate that game-theoretic coordination, applied in a manner consistent with regulatory roles, provides a practical pathway for DSOs to plan EV infrastructure and reactive support in networks with uncertain DER behavior. Full article

We present new spectroscopic orbits for the bright binaries Mizar B, 3 Pup, $\nu$ Gem, 2 Lac, and $\varphi$ Aql. Our analysis is based on medium-resolution ($R\approx$ 12,000) échelle spectra obtained with the 0.81-m telescope and fiber-fed eShel spectrograph of the Three College Observatory (Greensboro, NC, USA) between 2015 and 2024. Orbital elements were inferred with an affine-invariant Markov-chain Monte-Carlo sampler; convergence was verified through the integrated autocorrelation time and the Gelman–Rubin statistic. Errors quote the 16th–84th-percentile credible intervals. Compared with previously published orbital solutions for the studied stars, our method improves the root-mean-square residuals by 25–50% and bring the 1$\sigma$ uncertainties on the radial velocity (RV) semi-amplitudes down to 0.02–0.15 km $s^{-1}$. These gains translate into markedly tighter mass functions and systemic RVs, providing a robust dynamical baseline for future interferometric and photometric studies. A complete Python analysis pipeline is openly available in a GitHub repository, ensuring full reproducibility. The results demonstrate that a Bayesian RV analysis with well-motivated priors and rigorous convergence checks yields orbital parameters that are both more precise and more reproducible than previous determinations, while offering fully transparent uncertainty budgets. Full article

The substation grounding grid, as the primary path for fault current dissipation, is crucial for ensuring the safe operation of the power system and requires regular inspection. The pulsed eddy current method, known for its non-destructive and efficient features, is widely used in grounding grid detection. However, during the parameter identification process, it is prone to local minima or no solution. To address this issue, this paper first develops a pulsed eddy current forward response model for the substation grounding grid based on the magnetic dipole superposition principle, with accuracy validation. Then, a variable dimensional Bayesian parameter identification method is introduced, utilizing the Reversible-Jump Markov Chain Monte Carlo (RJMCMC) algorithm. By using nonlinear optimization results as the initial model and introducing a dual-factor control strategy to dynamically adjust the sampling step size, the model enhances coverage of high-probability regions, enabling effective estimation of grounding grid parameter uncertainties. Finally, the proposed method is validated by comparing the forward response model with field test results, showing that the error is within 10%, demonstrating both the accuracy and practical applicability of the proposed parameter identification method. Full article

Maritime search and rescue (SAR) demands reliable sensing and communication under sea clutter. Emerging integrated sensing and communication (ISAC) technology provides new opportunities for the development and modernization of maritime radio communication, particularly in relation to search and rescue. This study investigated the dual-function capability of a phase-coded frequency modulated continuous wave (FMCW) system for search and rescue at sea, in particular for life signs detection in the presence of sea clutter. The detection capability of the FMCW system was enhanced by applying phase-modulated codes on chirps, and radar-centric communication function is supported simultaneously. Various phase-coding schemes including Barker, Frank, Zadoff-Chu (ZC), and Costas were assessed by adopting the peak sidelobe level and integrated sidelobe level of the ambiguity function of the established signals. The interplay of sea waves was represented by a compound K-distribution model. A multiple-input multiple-output (MIMO) architecture with the ZC code was adopted to detect multiple objects with a high resolution for micro-Doppler determination by taking advantage of spatial coherence with beamforming. The effectiveness of the proposed method was validated on the 4-transmit, 4-receive (4 × 4) MIMO system with ZC coded FMCW signals. Monte Carlo simulations were carried out incorporating different combinations of targets and user configurations with a wide range of signal-to-noise ratio (SNR) settings. Extensive simulations demonstrated that the mean squared error (MSE) of range estimation remained low across the evaluated SNR setting, while communication performance was comparable to that of a baseline orthogonal frequency-division multiplexing (OFDM)-based system. The high performance demonstrated by the proposed method makes it a suitable maritime search and rescue solution, in particular for vision-restricted situations. Full article

The Maxwell–Boltzmann (MB) distribution is fundamental in statistical physics, providing an exact description of particle speed or energy distributions. In this study, a discrete formulation derived via the survival function discretization technique extends the MB model’s theoretical strengths to realistically handle lifetime and reliability data recorded in integer form, enabling accurate modeling under inherently discrete or censored observation schemes. The proposed discrete MB (DMB) model preserves the continuous MB’s flexibility in capturing diverse hazard rate shapes, while directly addressing the discrete and often censored nature of real-world lifetime and reliability data. Its formulation accommodates right-skewed, left-skewed, and symmetric probability mass functions with an inherently increasing hazard rate, enabling robust modeling of negatively skewed and monotonic-failure processes where competing discrete models underperform. We establish a comprehensive suite of distributional properties, including closed-form expressions for the probability mass, cumulative distribution, hazard functions, quantiles, raw moments, dispersion indices, and order statistics. For parameter estimation under Type-II censoring, we develop maximum likelihood, Bayesian, and bootstrap-based approaches and propose six distinct interval estimation methods encompassing frequentist, resampling, and Bayesian paradigms. Extensive Monte Carlo simulations systematically compare estimator performance across varying sample sizes, censoring levels, and prior structures, revealing the superiority of Bayesian–MCMC estimators with highest posterior density intervals in small- to moderate-sample regimes. Two genuine datasets—spanning engineering reliability and clinical survival contexts—demonstrate the DMB model’s superior goodness-of-fit and predictive accuracy over eleven competing discrete lifetime models. Full article

Background: Port congestion contributes significantly to operational inefficiency and environmental impact in maritime logistics. With tightening EU regulations such as the Emissions Trading System (EU ETS) and FuelEU Maritime, understanding and mitigating the economic and environmental effects of vessel delays is increasingly critical. This study focuses on a single bulk cargo pier at Constanta Port (Romania), which has experienced substantial traffic fluctuations since 2021, and examines operational and environmental performance through a queuing-theoretic lens. Methods: The authors have applied an M/G/1/∞/FIFO/∞ queuing model to vessel traffic and service time data from 2021–2023, supplemented by Monte Carlo simulations to capture variability in maneuvering and service durations. Environmental impact was quantified in CO₂ emissions using standard fuel-based emission factors, and a Cold Ironing scenario was modeled to assess potential mitigation benefits. Economic implications were estimated through operational cost modeling and conversion of CO₂ emissions into equivalent EU ETS carbon costs. Results: The analysis revealed high berth utilization rates across all years, with substantial variability in waiting times and queue lengths. Congestion was associated with considerable CO₂ emissions, which, when expressed in monetary terms under prevailing EU ETS prices, represent a significant financial burden. The Cold Ironing scenario demonstrated a substantial reduction in at-berth emissions and corresponding cost savings, underscoring its potential as a viable mitigation strategy. Conclusions: Results confirm that operational congestion at the studied berth imposes substantial environmental and financial burdens. The analysis supports targeted interventions such as Just-In-Time arrivals, optimized berth scheduling, and Cold Ironing adoption. Recommendations are most applicable to single-berth bulk cargo operations; future research should extend the approach to multi-berth configurations and incorporate additional operational constraints for broader generalizability. Full article

Process designs based on deterministic simulations without considering parameter uncertainty or variability have a high probability of failing to meet specifications. In this work, uncertainty and global sensitivity analyses were applied to a biogas upgrading membrane process implemented in the COCO simulator (CAPE-OPEN to CAPE-OPEN), considering both controlled and non-controlled scenarios. A user-defined model code was developed to simulate gas separation membrane stages, and a preliminary study of membrane parameter uncertainty was performed. In addition, a unit generating combinations of uncertainty factors was developed to interact with the simulator’s parametric tool. Global sensitivity analyses were carried out using the Morris method and Sobol’ indices obtained by Polynomial Chaos Expansion, allowing for the ranking and quantification of the influence of feed variability and membrane parameter uncertainty on product streams and process utilities. Results showed that when feed variability was ±10%, its effect exceeded the uncertainty of the membrane parameters. Uncertainty analysis using the Monte Carlo propagation method provided lower and upper tolerance limits for the main responses. Relative gaps between tolerance limits and mean product flows were 8–9% at a feed variability of 5% and 14–18% at a feed variability of 10%, while relative tolerance gaps resulting from composition were smaller (0.4–1.2%). Full article

This study investigates the non-equilibrium radiation characteristics during the high-speed re-entry of a lunar-return-type capsule under rarefied atmospheric conditions. A line-by-line spectral model was developed to compute atomic emission and absorption coefficients for excited nitrogen and oxygen atoms. Coupled with the Direct Simulation Monte Carlo (DSMC) method, the Photon Monte Carlo (PMC) method was employed to solve the radiative energy transport equation. The model was validated against the FIRE II flight experiment at 1631 s and 1634 s, showing improved agreement with experimental heat flux data compared to previous numerical results. A detailed sensitivity analysis was conducted to examine the influence of spectral discretization and the number of emitted photons per computational cell. Results indicate that low spectral resolution can cause non-physical fluctuations in wall heat flux, while increasing the number of photons improves local smoothness. Optimal parameters were identified as 50,000 spectral points and 5000 photons per cell. The model was further applied to a lunar-return-type capsule re-ntering at 90 km and 95 km altitudes. It was found that radiative heating is spatially decoupled from aerodynamic heating and primarily governed by excited species concentration and line-of-sight geometry. At 90 km, radiative heating accounted for over $15.31\%$ of the aerodynamic heating, more than double that at 95 km. These results underscore the necessity of considering radiation effects in the design of thermal protection systems, particularly at high re-entry velocities and large angles of attack. Full article