Search Results (772)

Achieving a balance between accuracy and computational efficiency in solar energy flux estimation models remains a key challenge in atmospheric radiative transfer research. Given the high computational cost of spectral models, a widely used simplification strategy consists of parameterizing atmospheric spectral transmittances through wavelength-averaging formulations. This study introduces a Clear-Sky Multivariable (CSMV) broadband parametric model derived from the Leckner spectral model for estimating the three components of solar irradiance under clear-sky conditions: direct normal irradiance (DNI), diffuse irradiance (Gd), and global irradiance (G). The model development follows a two-stage procedure. First, discrete broadband transmittances are obtained by applying an independent spectral integration scheme to the transmittances of the source spectral model. In the second stage, these discrete values are fitted with analytical functions expressed solely in terms of atmospheric state parameters, yielding wavelength-independent broadband formulations. While the overall development framework follows a classical parameterization approach, the calculation of the diffuse component introduces a novel way of estimating the fraction of aerosol scattering directed toward the ground. The model was tested against data collected from eight radiometric stations distributed across six continents and benchmarked against two well-established reference models. Overall, the results indicate a high level of accuracy and demonstrate the practical applicability of the model. Full article

The thermal safety and longevity of lithium-ion batteries are critically constrained by excessive temperature rise and spatial thermal non-uniformity, particularly during high-rate discharges. Most existing numerical investigations rely on simplified heat generation models that fail to capture the spatiotemporal heterogeneity of electrochemical heat sources, leading to compromised predictive accuracy. To address this deficiency, this study develops a comprehensive three-dimensional electrochemical–thermal coupled framework, integrating the Newman pseudo-two-dimensional (P2D) electrochemical model with conjugate heat transfer and laminar flow dynamics. The predictive robustness of this framework is rigorously validated against experimental data across multiple discharge rates (3 C and 5 C). The validated model is then deployed to evaluate a water-cooled microchannel cold plate designed for prismatic $\mathrm{L}\mathrm{i}\mathrm{M}{\mathrm{n}}_2{\mathrm{O}}_4$/graphite cells under a demanding 5 C discharge. A systematic parametric investigation is conducted to quantify the effects of ambient temperature (293–343 K), microchannel number (2–6), and coolant inlet velocity (0.1–0.6 m/s) on the maximum battery temperature (${\mathrm{T}}_{\mathrm{m}\mathrm{a}\mathrm{x}}$) and temperature difference ($\mathsf{\Delta }\mathrm{T}$). Results demonstrate that the proposed system exhibits exceptional environmental robustness: over a 50 K ambient temperature span, ${\mathrm{T}}_{\mathrm{m}\mathrm{a}\mathrm{x}}$ increases by merely 2.0 K, remaining safely below the 323 K industry limit. Densifying the channel count from 2 to 6 further reduces ${\mathrm{T}}_{\mathrm{m}\mathrm{a}\mathrm{x}}$ by 1.55 K and narrows $\mathsf{\Delta }\mathrm{T}$ to 4.25 K, successfully satisfying the strict 5 K temperature uniformity standard. Furthermore, the thermal benefit of elevating inlet velocity exhibits a pronounced diminishing-return trend governed by the asymptotic reduction in bulk coolant temperature rise, dictating a critical trade-off against the quadratically escalating pumping power. Ultimately, these findings provide robust theoretical guidelines for the rational design of safe and energy-efficient battery thermal management systems. Full article

Polyurethane O-ring seals are vital for the service life and sealing reliability of hydraulic systems, yet internal hysteresis heat generation under reciprocating motion causes localized temperature rise, altering contact pressure distribution and impairing sealing performance. This study aimed to clarify the coupled effects of reciprocating motion parameters on O-ring hysteresis heat generation and sealing performance. A unified hysteresis heat generation rate expression was derived by combining the time–temperature superposition principle with the Maier–Göritz model, and the heat source model was integrated into a thermo-mechanically coupled finite element analysis (FEA) framework, validated by matching simulated and experimental temperature rise histories. Under baseline conditions, hysteresis heating causes the O-ring’s peak contact pressure to decrease by approximately 0.4 MPa during the outward stroke. Parametric analysis revealed that elevated operating parameters increase contact pressure to maintain effective sealing, but simultaneously intensify hysteresis heating. Quantitatively, the maximum O-ring temperature was highly sensitive to operating conditions, reaching 63.6 °C at 8 MPa hydraulic pressure, 60.0 °C at a 90 Hz reciprocating frequency, and up to 81.5 °C for a friction coefficient of 0.2. Although the current framework is limited by the exclusion of interfacial frictional heating, it enables the reliable quantitative prediction of thermal loads. Ultimately, this study provides a robust method for assessing sealing safety margins and offers theoretical guidance for the structural optimization of hydraulic sealing systems. Full article

With the rapidly growing global demand for clean energy, offshore wind power has become an important renewable energy source. To clarify how the principal dimensions affect the performance of a 15 MW-class floating wind turbine platform in 100 m water depth, this paper proposes a steel-concrete hybrid semi-submersible platform and systematically performs a parametric sensitivity analysis. The platform adopts a three-column configuration with heave tanks. The upper columns and cross braces are made of steel, while the lower hexagonal columns, pontoons, and heave tanks are constructed from concrete, significantly reducing steel consumption while satisfying structural and stability requirements. Focusing on three key design variables—draft, column spacing, and column diameter—this study establishes a unified normalized sensitivity analysis framework. It quantitatively evaluates their influence on platform mass, intact stability, natural periods, and fully coupled dynamic responses (including surge, heave, pitch motions, and mooring line tensions) under both operational and extreme conditions. The results reveal distinct roles of the principal dimensions in governing the platform dynamics: column spacing is the most sensitive parameter for tuning pitch response, restoring stiffness, and stability; increasing draft effectively suppresses heave and pitch responses but has only a limited effect on low-frequency surge motions; and column diameter strongly affects the natural periods of heave and pitch. Notably, dynamic responses exhibit significant nonlinear characteristics with variations in column diameter. When the diameter exceeds 110–120% of the baseline value, the peak pitch response under extreme sea states shows a deteriorating inflection point, accompanied by an accelerated surge in peak mooring loads. This indicates that excessive increases in column diameter may cause wave excitation forces to become dominant, thereby compromising the overall dynamic safety of the system. This paper identifies the governing geometric parameters for different motion modes and their control boundaries, providing a quantifiable and generalizable basis for the multi-objective collaborative design and cost reduction optimization of 15 MW steel-concrete hybrid semi-submersible floating wind turbine platforms. Full article

Shaped charge is widely used in petroleum drilling, yet the inherent parametric uncertainty of oil shale introduces significant uncertainties that affect perforation outcomes. The complex coupling of oil shale constitutive parameters under extreme strains poses challenges for uncertainty quantification. A coupled algorithm integrating an improved material point method (MPM) and polynomial chaos expansion (PCE) is presented, and polynomial chaos expansion (PCE) is used to systematically analyze the uncertainty and sensitivity of shaped charge jet penetration depth. Mechanical parameters from oil shale samples at Checun Coal Mine well No. 1 were tested to define key parameter ranges and establish a reliable uncertainty space. A benchmark simulation of a single isolated shaped charge jet validated the algorithm, and Sobol’ global sensitivity analysis identified internal friction angle, density, and Poisson’s ratio as strongly sensitive parameters, while tensile strength, Young’s modulus, and cohesion showed weak sensitivity, supporting surrogate model dimensionality reduction. Composite detonation models of three and five charges further revealed the effects of multi-projectile blast wave coupling on jet dynamics, providing new theoretical insights into cluster effects under high-energy, high-pressure, and extreme-strain conditions. Sensitivity and uncertainty analyses based on surrogate models emphasized the critical influence of internal friction angle alongside Poisson’s ratio and density. A reliable numerical framework is established for multi-physics coupled simulations of geomechanical responses under complex multi-source explosive loading. Full article

Recently, distributed DC microgrids have gained prominence due to their modular design, scalability, and seamless integration with renewable energy sources. However, ensuring robust operation of distributed secondary control schemes remains challenging, particularly in the presence of unavoidable communication disruptions and parametric uncertainties encountered in practice. Most existing control strategies either assume ideal communication networks or address fault tolerance and communication constraints separately, which limits their applicability in realistic networked environments. This paper proposes an event-triggered fault-tolerant distributed secondary control framework for DC microgrids operating under communication faults. An embedded averaged model is incorporated to support fault-tolerant decision-making and to guide event-triggered communication updates. In addition, an auxiliary recovery mechanism is introduced, enabling neighboring converters to cooperatively compensate for information loss during communication interruptions without centralized supervision. Lyapunov-based stability analysis establishes boundedness and practical convergence of the closed-loop system under event-triggered updates and bounded disturbances while explicitly excluding Zeno behavior. The simulation results under communication fault scenarios demonstrate that the proposed approach achieves accurate DC bus voltage regulation with steady-state deviations below 1% while restoring proportional power sharing with an averaged error within 5%. The embedded model error remains bounded throughout the fault interval, and fault-tolerant control actions are triggered sparsely with well-separated inter-event times on the order of tens of milliseconds, thereby significantly reducing the communication burden. These results confirm the effectiveness and robustness of the proposed framework for the resilient operation of distributed DC microgrids under practical communication constraints. Full article

Lago Argentino and Lago Viedma are large lakes fed by glaciers in Southern Patagonia, characterized by extraordinarily strong, persistent westerly winds and sharp gradients in regional relief, climate, and gravity field. We present operational models of spatio-temporal lake-level variations that represent instantaneous ellipsoidal lake-surface height as the superposition of three components: (i) a time-averaged lake-level topography derived from geoid modeling and ICESat-2 residuals, (ii) temporally varying water-volume changes in the lake estimated from tide gauge time series corrected for atmospherically driven perturbations, and (iii) a static hydrodynamic response to wind stress and air-pressure forcing. The atmospheric response is parametrized through empirically derived transfer functions obtained by regressing instantaneous lake-level anomalies against ERA5 wind and pressure fields, capturing wind-driven tilting. Standard deviations of ICESat-2 ATL13 elevations amount to 106 cm and 70 cm over Lago Argentino and Lago Viedma, respectively. The subtraction of our models reduces these standard deviations to 8 cm (Argentino) and 14 cm (Viedma). Surface waves incompletely averaged out within ICESat-2’s narrow footprint are identified as a principal source for the residual variability. A standard deviation of ATL13 elevations below 2 cm on calm days demonstrates ICESat-2’s unprecedented capability of monitoring water resources from space in a region of sparse hydrological infrastructure. Full article

The safe integration of Large Language Models in clinical environments requires strict adherence to verified medical evidence. As part of the PARROT AI project, this study provides a systematic evaluation of how prompting strategies affect the reliability of Retrieval-Augmented Generation (RAG) pipelines using the MedQA USMLE benchmark ($N=500$). Four prompting strategies were examined: Baseline (zero-shot), Neutral, Expert Chain-of-Thought (Expert-CoT) with structured clinical reasoning, and StrictCitations with mandatory evidence grounding. The experiments covered six modern model architectures: Command R (35B), Gemma 2 (9B and 27B), Llama 3.1 (8B), Mistral Nemo (12B), and Qwen 2.5 (14B). Evaluation was conducted using the Deterministic RAG Evaluator, providing an objective assessment of grounding through the Unsupported Sentence Ratio (USR) based on TF-IDF and cosine similarity. The results indicate that structured reasoning in the Expert-CoT strategy significantly increases USR values (reaching 95–100%), as models prioritize internal diagnostic logic over verbatim context. In contrast, the StrictCitations strategy, while maintaining high USR due to the conservative evaluation threshold, achieves the highest level of verifiable grounding and source adherence. The analysis identifies a statistically significant Verbosity Signal ($r=0.81,p<0.001$), where increased response length serves as a proxy for model uncertainty and parametric leakage, a pattern particularly prominent in Llama 3.1 and Gemma 2. Overall, the findings demonstrate that prompting strategy selection is as critical for clinical reliability as model architecture. This work delivers a reproducible framework for the development of trustworthy medical AI assistants and highlights citation-enforced prompting as a vital mechanism for improving clinical safety. Full article

Operationally, energy transition takes place at the local level, that is, in cities and rural municipalities. Its effectiveness is, therefore, dependent on individual actions undertaken in enterprises and households. It also constitutes a particularly challenging task for industrial regions with centuries-old mining traditions. Meanwhile, the opinions of residents living in mining cities receive little attention in the literature. For these reasons, this study used survey research conducted in 19 Silesian cities with county rights and on a representative sample of 1863 residents. In this way, answers were sought to the following research questions: (1) How do urban residents in a developing economy in a post-mining region assess their knowledge regarding environmental protection and energy transition? (2) How do they evaluate local authorities’ actions concerning the replacement of non-ecological heating sources in households? The analysis of results employed descriptive statistics and non-parametric statistical tests, identifying differences in respondents’ assessments according to gender, age, education, and place of residence. The analyses conducted indicate that residents assess their environmental awareness as average. They also rate their knowledge of the energy transition below average, despite being in the midst of it. The assessments of men, older individuals, and those with vocational and secondary education are higher in both cases than the assessments of women, younger generations, and respondents with primary, post-secondary, and higher education. Respondents also rate financial and informational–educational support for heating source replacement as average. Importantly, however, these actions are noticed and appreciated. They meet the expectations of less formally educated individuals (formal education level: primary, vocational, and secondary). However, they do not generate enthusiasm among those with post-secondary and higher education, whose environmental needs and expectations may be higher. The level of financial support, and to a lesser extent informational–educational support, differs significantly among the studied cities, indicating the absence of a coherent regional policy. This may also result in deepening environmental disparities and inequalities in quality of life among the studied urban centers. The two-dimensional assessment reveals that the majority of the examined cities fall into the stagnator category, exhibiting average levels of both environmental awareness and institutional support for energy transition. The most favorable prospects for effective energy transition are observed in Gliwice and Żory, while Zabrze, Świętochłowice, and Jastrzębie-Zdrój—post-mining cities burdened by limited development potential and financial constraints—demonstrate the least promising outlook. The conclusions and recommendations derived from this article directly align with the implementation of Sustainable Development Goal 7—Affordable and Clean Energy (SDG 7)—which addresses energy transition, including the adoption of clean heat sources. They also support the development of sustainable cities, thereby contributing to the achievement of Sustainable Development Goal 11—Sustainable Cities and Communities (SDG 11). Full article

Plant-derived extracellular vesicles (PDEVs), engineered phytosomes, bioinspired polymeric plant-based nanoparticles (PBNPs), hybrid phyto-inorganic nanocomposites, green-synthesized metal nanoparticles, self-assembled nanoarchitectures, and multifunctional composites represent a rapidly advancing class of sustainable, nature-inspired nanocarriers. These platforms combine exceptional biocompatibility, negligible immunogenicity, and renewable sourcing with tunable drug loading, targeted delivery, and controlled release properties. This review synthesizes translational advances from 2020 to 2026, covering scalable isolation/bioprocessing (bioreactors, elicitation), multi-parametric physicochemical/multi-omics characterization, rational engineering/hybridization, and rigorous in vitro/in vivo assessments of uptake, biodistribution, pharmacokinetic (PK), and efficacy. Phytosomes and PBNPs markedly enhance oral bioavailability and targeted delivery of lipophilic phytochemicals, while PDEVs offer unique immunomodulatory, anti-inflammatory, and gene-regulatory activities. Hybrid and green-synthesized systems provide structural stability, redox modulation, and synergistic effects, and self-assembled/multifunctional composites address solubilization barriers with stimuli-responsive design. Early-phase human studies on grapefruit-, ginger-, turmeric-, and ginseng-derived PDEVs report excellent short-term safety, favorable PK, and preliminary bioactivity signals, with no observed immunogenicity or dose-limiting toxicities; however, these trials remain exploratory, constrained by small sample sizes and safety-focused endpoints. Despite challenges, including methodological heterogeneity, variable yields, long-term safety uncertainties (notably for inorganic hybrids), and regulatory ambiguities, emerging strategies such as clustered regularly interspaced short palindromic repeats (CRISPR)-engineered plant line; artificial-intelligence-driven process optimization; standardized guidelines, and integrated clinical, intellectual property, and commercialization frameworks are progressively addressing these barriers. Collectively, these advances position plant-derived nanocarriers as immunologically privileged, eco-friendly alternatives to synthetic and mammalian platforms, laying the foundation for a sustainable era of precision phytomedicine. Full article

This paper describes a new methodology implemented in the ESTE decision support system for evaluating the source term resulting from a nuclear weapon detonation. The methodology is based on a model of a stabilized radioactive mushroom cloud, parameterized as the source term for a Lagrangian particle dispersion model. It includes radionuclide composition, spatial distribution of aerosol and gaseous particles, and particle size distribution. This method is designed for rapid assessment of radiological impacts primarily at medium- and long-range distances, for example, in neighboring countries. The parametrization has been calibrated and adjusted using data from historical nuclear tests, and its performance is evaluated in terms of impacted area, range, and spatial overlap of fallout regions. A comparison is presented between ESTE calculations and field measurements obtained after the British nuclear tests conducted in the 1950s at the Maralinga Range (Australia), using historical ERA5 meteorological reanalyses from ECMWF. Full article

Similar to other municipal facilities, landfills are a substantial source of emissions of various biological pollutants. Numerous sustainability challenges result from the extremely high variability of emissions of harmful biological agents, which necessitates precise detection of microbiological emissions from these municipal facilities. This study aimed to assess whether a municipal waste landfill impacts indicator microorganisms and bacterial endotoxins occurring in soils within the landfill’s zone of influence. The research was conducted directly at the landfill site and in the surrounding area. Soil samples were collected monthly from eight sites over three years. Microbiological analyses included determination of total Salmonella counts and bacteria of the coliform group, Clostridium spp., Clostridium perfringens, and bacterial endotoxin concentrations. Results revealed a significant effect of the landfill on soil sanitary quality, indicating that adverse impacts depended mainly on the distance from the active waste sector of the landfill. The results also confirmed the usefulness of bacterial endotoxins as indicators of soil contamination with microorganisms within the municipal landfill and surroundings. Parametric statistical analyses effectively characterised contamination levels, and the Newman–Keuls multiple comparison test proved to be a rapid and reliable tool for assessing exceedances of established sanitary standards. Findings indicate that fresh waste is a critical source of microbiological contamination in soils, and they emphasise the value of combined microbial and endotoxin monitoring for sustainable landfill environmental assessment and management. While the current study focuses on soil contamination, future research should evaluate the impact of landfill on indicator microorganisms and bacterial endotoxins in air and water. Full article

The prevalence of high-rack warehouses and large-space facilities with high ceilings poses significant challenges to traditional automatic sprinkler systems, which often exhibit activation delays and limited suppression efficacy. This study investigates the spatio-temporal evolution and distribution characteristics of fire-induced thermal smoke flow through a hybrid approach combining full-scale fire experiments and numerical simulations. A physical hypothesis is proposed: the ceiling temperature field approximately follows a two-dimensional Gaussian distribution. Through parametric numerical simulations under varied ambient temperatures, fire identification criteria were calibrated, encompassing a sustained increase in the average temperature rise within high-temperature zones, the attainment of a predefined threshold, and the spatial stabilization of the Gaussian distribution center. Subsequently, a precise algorithm for rapid fire identification and source localization was developed. Experimental validation demonstrates that the proposed algorithm significantly outperforms traditional passive-activation closed sprinklers, advancing fire detection by 46–67 s. Furthermore, the fire source localization error is maintained within half of the sprinkler spacing. The algorithm also exhibits robust environmental adaptability and generalizability across a wide ambient temperature range, providing a technical foundation for active-actuation fire suppression. Full article

The increasing penetration of renewable energy sources in power systems has intensified the need for accurate modelling of generation units under transient conditions. Despite the widespread adoption of the IEC 61400-27 generic wind turbine models, their parametrization remains a critical challenge. Classical optimization-based approaches are time-consuming, prone to convergence to local minima in the high-dimensional non-convex parameter space and require substantial expert knowledge. To address this gap, this paper proposes a machine learning- and deep learning-based methodology for estimating the key mechanical parameters of Type III wind turbines. A synthetic database of 10,000 active power responses was generated using DIgSILENT PowerFactory via its Python Application Programming Interface, covering a wide range of voltage dip conditions and mechanical parameter combinations. A comparative analysis of eight machine learning and deep learning algorithms for this task is performed. Validation is performed on both the synthetic dataset and two real manufacturer-validated wind turbine models. The results demonstrate that the proposed methodology enables fast and accurate identification of the mechanical parameters of wind turbines, maintaining reliable estimation performance even in the presence of measurement noise, thereby supporting its applicability in power system stability studies. Full article

Gas Metal Arc Welding (GMAW) is widely adopted in automated manufacturing industries where the accurate prediction of thermal fields and welding-induced distortions is essential to ensure joint integrity of the parts; however, finite element modeling, as the most reliable non-destructive predictive approach, remains time-consuming and highly user-specialized. This work presents an automated and low computational cost thermo-mechanical finite element methodology implemented in Ansys Parametric Design Language (APDL) for the parametric analysis of GMAW T-joints, integrating automated geometry generation, meshing, heat source implementation, and thermo-mechanical modeling for different beam and weld seam dimensions under continuous or intermittent single-pass configurations. A volume element selection strategy is introduced to limit heat input calculations to the active weld pool region, achieving up to a 50% computational time reduction while maintaining high predictive accuracy, in contrast with conventional and partial selection methods. Overall script performance was validated through temperature and displacement comparisons between the numerical and experimental results of two T-joint configurations using SM490A structural steel specimens. The results demonstrate that the developed macro provides a useful tool for automated thermo-mechanical welding analysis, significantly reducing model preparation effort while enabling the evaluation of parametric T-joint geometries and welding conditions with a low computational cost focus. Full article