Search Results (620)

Recent studies have reported a rapid increase in short-duration, high-intensity rainfall over the Seoul Metropolitan Area (SMA), primarily associated with mesoscale convective systems (MCSs), highlighting the need for high-resolution and multi-platform observations for accurate forecasting. To address this challenge, the Korea Meteorological Administration (KMA) established the Korean Precipitation Observation Program (KPOP), an intensive observation network integrating radar, wind lidar, wind profiler, and storm tracker measurements. This study introduces the design and implementation of the KPOP network and evaluates its observational and forecasting value through a heavy rainfall event that occurred on 17 July 2024. Wind lidar data and weather charts reveal that a strong low-level southwesterly jet and enhanced moisture transport from the Yellow Sea played a key role in sustaining a quasi-stationary, line-shaped rainband over the metropolitan region, leading to extreme short-duration rainfall exceeding 100 mm h⁻¹. To investigate the impact of KPOP observations on numerical prediction, preliminary data assimilation experiments were conducted using the Korean Integrated Model-Regional Data Assimilation and Prediction System (KIM-RDAPS) with WRF-3DVAR. The results demonstrate that assimilating wind lidar observations most effectively improved the representation of low-level moisture convergence and spatial structure of the rainband, leading to more accurate simulation of rainfall intensity and timing compared to experiments assimilating storm tracker data alone. These findings confirm that intensive, high-resolution wind observations are critical for improving initial analyses and enhancing the predictability of extreme rainfall events in densely urbanized regions such as the SMA. Full article

This study addresses a key limitation in meso-scale discrete element modeling (DEM) of ultra-high performance concrete (UHPC). Most existing DEM frameworks rely on extensive macroscopic calibration and do not provide a clear, transferable pathway to derive contact law parameters from measurable micro-scale properties, limiting reproducibility and physical interpretability. To bridge this gap, we develop and validate a micro-indentation-informed, poromechanics-consistent calibration framework that links UHPC phase-level micromechanical measurements to a flat-joint DEM contact model for predicting uniaxial compression, direct tension, and flexural response. Elastic moduli and Poisson’s ratios of the constituent phases are obtained from micro-indentation and homogenization relations, while cohesion (c) and friction angle (α) are inferred through a statistical treatment of the indentation modulus and hardness distributions. The tensile strength limit (σₜ) is identified by matching the simulated flexural stress–strain peak and post-peak trends using a parametric set of (c, α, σₜ) combinations. The resulting DEM model reproduces the measured UHPC responses with strong agreement, capturing (i) compressive stress–strain response, (ii) flexural stress–strain response, and (iii) tensile stress–strain response, while also recovering the experimentally observed failure modes and damage localization patterns. These results demonstrate that physically grounded micro-scale measurements can be systematically upscaled to meso-scale DEM parameters, providing a more efficient and interpretable route for simulating UHPC and other porous cementitious composites from indentation-based inputs. Full article

Accurate prediction of the effective thermal conductivity (ETC) of nuclear fuels is essential for optimizing fuel performance and ensuring reactor safety. However, the experimental determination of ETC is often limited by cost and complexity, while high-fidelity simulations are computationally intensive. This study presents a novel hybrid framework that integrates experimental data, validated mesoscale finite element simulations, and machine-learning (ML) models to efficiently and accurately estimate ETC for advanced uranium-based nuclear fuels. The framework was demonstrated on three fuel systems: UO₂-BeO composites, UO₂-Mo composites, and U-10Zr metallic alloys. Mesoscale simulations incorporating microstructural features and interfacial thermal resistance were validated against experimental data, producing synthetic datasets for training and testing ML algorithms. Among the three regression methods evaluated, namely Bayesian Ridge, Random Forest, and Multi-Polynomial Regression, the latter showed the highest accuracy, with prediction errors below 10% across all fuel types. The selected multi-polynomial model was subsequently used to predict ETC over extended temperature and composition ranges, offering high computational efficiency and analytical convenience. The results closely matched those from the validated simulations, confirming the robustness of the model. This integrated approach not only reduces reliance on costly experiments and long simulation times but also provides an analytical form suitable for embedding in engineering-scale fuel performance codes. The framework represents a scalable and generalizable tool for thermal property prediction in nuclear materials. Full article

Powder bed fusion (PBF) is a widely adopted additive manufacturing (AM) process category that enables high-resolution fabrication across metals, polymers, ceramics, and composites. However, its inherent process complexity demands robust modeling to ensure quality, reliability, and scalability. This review provides a critical synthesis of advances in physics-based simulations, machine learning, and digital twin frameworks for PBF. We analyze progress across scales—from micro-scale melt pool dynamics and mesoscale track stability to part-scale residual stress predictions—while highlighting the growing role of hybrid physics–data-driven approaches in capturing process–structure–property (PSP) relationships. Special emphasis is given to the integration of real-time sensing, multi-scale modeling, and AI-enhanced optimization, which together form the foundation of emerging PBF digital twins. Key challenges—including computational cost, data scarcity, and model interoperability—are critically examined, alongside opportunities for scalable, interpretable, and industry-ready digital twin platforms. By outlining both the current state-of-the-art and future research priorities, this review positions digital twins as a transformative paradigm for advancing PBF toward reliable, high-quality, and industrially scalable manufacturing. Full article

Proton exchange membrane fuel cells (PEMFC) are recognized as promising next-generation energy technology. Yet, their performance is critically limited by inefficient gas transport and water management in conventional flow channels. Current rectangular gas channels (GC) restrict reactive gas penetration into the gas diffusion layer (GDL) due to insufficient longitudinal convection. At the same time, the complex multiphase interactions at the mesoscale pose challenges for numerical modeling. To address these limitations, this study proposes a novel cathode channel design featuring laterally contracted fin-shaped barrier blocks and develops a mesoscopic multiphase coupled transport model using the lattice Boltzmann method combined with the volume-of-fluid approach (LBM-VOF). Through systematic investigation of multiphase flow interactions across channel geometries and GDL surface wettability effects, we demonstrate that the optimized barrier structure induces bidirectional forced convection, enhancing oxygen transport compared to linear channels. Compared with the traditional straight channel, the optimized composite channel achieves a 60.9% increase in average droplet transport velocity and a 56.9% longer droplet displacement distance, while reducing the GDL surface water saturation by 24.8% under the same inlet conditions. These findings provide critical insights into channel structure optimization for high-efficiency PEMFC, offering a validated numerical framework for multiphysics-coupled fuel cell simulations. Full article

The Weather Research and Forecasting Model (WRF-ARW) version 4.5 was used to simulate the synoptic to mesoscale evolving atmosphere of Tropical Cyclone (TC) Hilary’s (2023) remnant passage over the southwestern United States. The atmospheric dynamic processes conducive to the precursor rain events were extensively studied to determine the effects of mid-level jetogenesis. Concurrently, the dynamics of mesoscale processes related to the interaction of TC Hilary over the complex topography of the western United States were studied with several sensitivity simulations on a nested 2 km × 2 km grid. The differential surface heating between the cloudy California coast and clear/elevated Great Basin plateau had a profound impact on the lower-mid-tropospheric mass field resulting in mid-level jetogenesis. Diagnostic analyses of the ageostrophic flow support the importance of both isallobaric and inertial advective forcing of the mid-level jetogenesis in response to differential surface sensible heating. This ageostrophic mesoscale jet ultimately transported tropical moisture in multiple plumes more than 1000 km poleward beyond the location of the extratropical transition of the storm, resulting in anomalous flooding precipitation within a massive arid western plateau. Full article

About 16% of typhoons making landfall in China strike Hainan Island, where near-surface extreme winds in dense urban areas exhibit a strong spatiotemporal heterogeneity that is difficult to capture with current observations and mesoscale models. Focusing on Haikou during Super Typhoon Yagi (2024)—the strongest autumn typhoon to hit China since 1949—we developed a multiscale ERA5–WRF–PALM framework to conduct 30 m resolution large-eddy simulations. PALM results are in reasonable agreement with most of the five automatic weather stations, while performance is weaker at the most sheltered park site. Mean near-surface wind speeds increased by 20–50% relative to normal conditions, showing a coastal–urban gradient: maps of weighted cumulative exposure to strong winds (≥Beaufort force 8) show much longer and more intense events along open coasts than within built-up urban cores. Urban morphology exerted nonlinear effects: wind speeds followed a U-shaped relation with both the open-space ratio and mean building height, with suppression zones at ~0.5–0.7 openness and ~20–40 m height, while clusters of super-tall buildings induced Venturi-like acceleration of 2–3 m s⁻¹. Spatiotemporal analysis revealed banded swaths of high winds, with open areas and islands sustaining longer, broader extremes, and dense street grids experiencing shorter, localized events. Methodologically, this study provides a rare, systematically evaluated application of a multiscale ERA5–WRF–PALM framework to a real typhoon case at 30 m resolution in a tropical coastal city. These findings clarify typhoon–city interactions, quantify morphological regulation of extreme winds, and support risk assessment, urban planning, and wind-resilient design in coastal megacities. Full article

Water diversion projects are a crucial measure for addressing eutrophication in shallow lakes worldwide. However, the impacts of different water diversion operation schemes on lake hydrodynamics and water quality can vary significantly, necessitating targeted, refined simulation assessments. This study focuses on Chaohu Lake, one of China’s most eutrophic lakes, and uses a mesoscale meteorological model coupled with a three-dimensional hydrodynamic and water quality model to conduct detailed numerical simulations. The study evaluates the effects of three water diversion operation scenarios and three subsurface flow guide dam scenarios during the ecological water replenishment period in Chaohu Lake from September to November. The simulation results indicate that all three water diversion operation scenarios improve the hydrodynamic conditions of Chaohu Lake, but there are significant differences in their effects on pollutant reduction in the lake. The retention of chemical oxygen demand (COD) in the water ranges from −36,812.1 to 472.8 tons, total nitrogen (TN) retention ranges from −22,637.2 to 3 tons, total phosphorus (TP) retention ranges from −4974 to 10.7 tons, and chlorophyll-a (Chl-a) retention ranges from −310.8 to −3.3 tons. Among the three subsurface flow guide dam schemes, all can promote the outflow of pollutants from Chaohu Lake. The combined subsurface flow guide dam scheme is the most effective, enabling an approximately 7.4% increase in pollutant export. The study demonstrates that diverting Huaihe River water through Paihe into Chaohu Lake, along with adding a combined subsurface flow guide dam in the West Lake area, can significantly improve the hydrodynamics and water quality in the West Lake area. This research provides essential technical support for the future operation of the Yangtze-to-Huaihe River Water Diversion Project and the layout of subsurface flow guide dams in Chaohu Lake, offering valuable insights for the ecological management of other shallow lakes. Full article

Hydrogen-based direct reduction (H-DR) represents an environmentally benign and energy-efficient alternative in ironmaking that has significant industrial potential. This study reviews the current status of H-DR shaft furnaces and accompanying hydrogen-rich reforming technologies (steam and autothermal reforming), assessing the three dominant numerical frameworks used to analyze these processes: (i) porous medium continuum models, (ii) the Eulerian two-fluid model (TFMs), and (iii) coupled computational fluid dynamics (CFD)–discrete element method (DEM) models. The respective trade-offs in terms of computational cost and model accuracy are critically compared. Recent progress is evaluated from an engineering standpoint in four key areas: optimization of the pellet bed structure and gas distribution, thermal control of the reduction zone, sensitivity analysis of operating parameters, and industrial-scale model validation. Current limitations in predictive accuracy, computational efficiency, and plant-level transferability are identified, and possible mitigation strategies are discussed. Looking forward, high-fidelity multi-physics coupling, advanced mesoscale descriptions, AI-accelerated surrogate models, and rigorous uncertainty quantification can facilitate effective scalable and intelligent application of hydrogen-based shaft furnace simulations. Full article

Observations of building-induced airflow disturbances arising from the new terminal building at the Hong Kong International Airport (HKIA) are documented in this paper. Two case studies are conducted: one involving turbulent flow downstream of the building and another involving a coherent “building-induced wave”. To capture these phenomena under realistic atmospheric forcing, we employ a coupled mesoscale–computational fluid dynamics modelling system. This approach integrates mesoscale boundary-layer conditions with building-resolving simulations for real airport disturbance analysis. The main features of the actual observation are largely captured by the simulations. As such, the simulated data are studied to find out the reason for the difference in the airflow behavior. The difference could be related to the stability of the “background” atmospheric boundary layer. This stability is influenced by a number of complicated factors, including the background mesoscale atmospheric stability, Foehn effect of the terrain, and solar heating of the sea/land surface. The study further discusses potential implications for runway operations using aviation-relevant indicators, including the 7-knot criterion and turbulence intensity. Full article

Local Climate Zones (LCZs) have become a foundational framework for urban climate modeling, yet their use across model families has not been systematically evaluated. Crucially, the LCZ framework itself has served as a developmental basis, revealing the progression of urban canopy parameterizations (UCP) from early models to the diverse model families currently in use. This evolution is exemplified by systems like the Weather Research and Forecasting (WRF) model, where the application of LCZ has fundamentally shifted from an experimental add-on to a basic, built-in feature of its urban-modeling capabilities. This review synthesizes a decade of LCZ-based studies to clarify how LCZ improves surface representation, enhances comparability, and supports multiscale modeling workflows. It provides a comprehensive overview of peer-reviewed work up to the end of 2024, offering a baseline for understanding the field’s rapid recent growth. Using a structured evidence-mapping approach, we categorize applications into three maturity stages: testing and measurement, operational and planning-oriented applications, and expansions beyond urban climate to chemistry, hydrology, and Earth-system modeling. The assessment covers various iterations of mesoscale systems (WRF, SURFEX/TEB, COSMO), local-scale climatologies (MUKLIMO-3, UrbClim), microscale models (ENVI-met, CFD), and supporting tools including SUEWS, SOLWEIG, RayMan, VCWG, and CESM-CLMU. Results show clear divisions of labor: WRF and SURFEX/TEB anchor process-rich regional simulations; MUKLIMO-3 and UrbClim offer computationally efficient long-horizon or multi-city assessments; ENVI-met and CFD provide design-scale insight when parameterized with LCZ archetypes. Across all families, model skill is strongly constrained by LCZ data quality and by inconsistencies in LCZ to UCP translation. We conclude that advancing LCZ-based urban climate modeling will depend on improved LCZ products, standardized parameter libraries, and formalized cross-scale model couplings that allow existing tools to interoperate more reliably under growing urban-climate pressures. Full article

This review highlights the growing relevance of ion-exchange nanofibrous membranes (IEX-NFMs) in membrane chromatography (MC) for protein purification, emphasising their structural advantages such as high porosity, tunable surface functionality, and low-pressure drops. While the adsorption of IEX-NFMs in MC is expanding due to their potential for high throughput and rapid mass transfer, a critical limitation remains: the precise binding capacity of these membranes is not well understood. Traditional experimental methods to evaluate protein–membrane interactions and optimise binding capacities are labour-intensive, time-consuming, and costly. Therefore, this review underscores the importance of computational modelling as a viable predictive approach to guide membrane design and performance prediction. Yet major obstacles persist, including the challenge of accurate representation of the complex and often irregular pore structures, as well as limited and/or oversimplified adsorption models. Along with molecular-scale simulations such as molecular dynamics (MD) simulations and quantum simulations, meso-scale simulations can provide insight into protein–fibre and protein–protein interactions under varying physicochemical conditions for larger time scales and lower computational burden. These tools can help identify key parameters such as binding accessibility, ionic strength effects, and surface charge density, which are essential for the rational design and performance prediction of IEX-NFMs. Moreover, integrating simulations with experimental validation can accelerate optimisation process while reducing cost. This technical review sets the foundation for a computationally driven design framework for multifunctional IEX-NFMs, supporting their use in next-generation chromatographic separations and broadening their applications in bioprocessing and analytical biotechnology. Full article

Reliable performance of TRISO (tristructural isotropic) nuclear fuel depends on the interplay between its multilayer architecture and the buffer-layer microstructure, which are difficult to isolate experimentally. We implement a multiscale, multiphysics model in the open-source MOOSE (Multiphysics Object-Oriented Simulation Environment) framework that couples particle-scale thermo-mechanical finite-element analysis with mesoscale phase-field fracture to link microstructure to effective stiffness and strength. The model resolves the combined influence of pore volume fraction, size, and aspect ratio and explicitly separates the effects of reduced load-bearing capacity from stress concentrations in the porous buffer. Simulations reveal substantial hoop stresses across coating layers under nominal thermal conditions due to material property mismatches and temperature gradients. In the buffer, stiffness and strength decrease with porosity; morphology is decisive: as aspect ratio decreases, strength degrades far more rapidly than stiffness, consistent with crack-like pores that amplify local stresses. The framework reproduces logarithmic trends with aspect ratio and explains the higher sensitivity of strength, providing parameters that can inform design and acceptance criteria (e.g., limits on pore elongation and porosity gradients). Implemented within MOOSE, the approach is readily extensible to irradiation-dependent kinetics, interface debonding, and uncertainty-quantified 3D analyses to support risk-informed TRISO fuel development. Full article

A multiscale model is developed to investigate the mechanical behavior and failure of in situ particle reinforced titanium matrix composites (PTMCs). Through the microstructural observation of the heterogeneous microscopic and mesoscopic structures in the in situ TiB/Ti55531 composites, multiscale heterogeneous models coupled to the finite element method are employed to simulate the mechanical behaviors and failures. In the atomic scale, molecular dynamics (MD) simulations are applied to determine the traction-separation (T-S) responses of the cohesive zone model (CZM) describing the Ti/TiB interface. Then, the mesoscale representative volume element (RVE) model with heterogeneous structure, including the Ti55531 matrix, the TiB particles, and their interfaces represented by the parameterized CZM, is established. The volume fraction and distribution morphology of TiB particles result from the microstructural analysis of titanium matrix composites. The simulation results show that the Young’s modulus, tensile strength and elongation of multiscale are in excellent agreement with experimental results. The stress transfer, damage evolution and fracture behavior of the TiB particles in the composites are also analyzed using this multiscale approach. Full article

The topological structure of fracture networks fundamentally controls the mechanical behavior and fluid-driven failure of brittle materials. However, a systematic understanding of how topology dictates hydraulic fracture propagation remains limited. This study conducted experimental investigations on granite specimens containing 10 different topological fracture structures using a self-developed visual hydraulic fracturing test system and an improved Digital Image Correlation (DIC) method. It systematically revealed the macroscopic control laws of topological nodes on crack initiation, propagation path, and peak pressure. The experimental results indicate that hydraulic crack initiation follows the “proximal-to-loading-end priority” rule. Macroscopically, the breakdown pressure shows a significant negative correlation with topological parameters (number of nodes, number of branches, normalized total fracture length). However, specific configurations (e.g., X-shaped nodes) can exhibit a configuration-strengthening effect due to dispersed stress concentration, leading to a higher breakdown pressure than simpler topological configurations. Discrete Element Method (DEM) simulations revealed the underlying mechanical essence at the meso-scale: the topological structure governs crack initiation behavior and initiation pressure by regulating the distribution of force chains and the mode of stress concentration within the rock mass. These findings advance the fundamental understanding of fracture–topology–property relationships in rock masses and provide insights for optimizing fluid-driven fracturing processes in engineered materials and reservoirs. Full article