Mesoscale Numerical Model Research Articles

Recommended coupling to global meteorological fields for long-term tracer simulations with WRF-GHG

Abstract. Atmospheric transport models are often used to simulate the distribution of greenhouse gases (GHGs). This can be in the context of forward modeling of tracer transport using surface–atmosphere fluxes or flux estimation through inverse modeling, whereby atmospheric tracer measurements are used in combination with simulated transport. In both of these contexts, transport errors can bias the results and should therefore be minimized. Here, we analyze transport uncertainties in the commonly used Weather Research and Forecasting (WRF) model coupled with the greenhouse gas module (WRF-GHG), enabling passive tracer transport simulation of CO2 and CH4. As a mesoscale numerical weather prediction model, WRF's transport is constrained by global meteorological fields via initialization and at the lateral boundaries of the domain of interest. These global fields were generated by assimilating various meteorological data to increase the accuracy of modeled fields. However, in limited-domain models like WRF, the winds in the center of the domain can deviate considerably from these driving fields. As the accuracy of the wind speed and direction is critical to the prediction of tracer transport, maintaining a close link to the observations across the simulation domain is desired. On the other hand, a link that is too close to the global meteorological fields can degrade performance at smaller spatial scales that are better represented by the mesoscale model. In this work, we evaluated the performance of strategies for keeping WRF's meteorology compatible with meteorological observations. To avoid the complexity of assimilating meteorological observations directly, two main strategies of coupling WRF-GHG with ERA5 meteorological reanalysis data were tested over a 2-month-long simulation over the European domain: (a) restarting the model daily with fresh initial conditions (ICs) from ERA5 and (b) nudging the atmospheric winds, temperatures, and moisture to those of ERA5 continuously throughout the simulation period, using WRF's built-in four-dimensional data assimilation (FDDA) in grid-nudging mode. Meteorological variables and simulated mole fractions of CO2 and CH4 were compared against observations to assess the performance of the different strategies. We also compared planetary boundary layer height (PBLH) with radiosonde-derived estimates. Either nudging or daily restarts similarly improved the meteorology and GHG transport in our simulations, with a small advantage of using both methods in combination. However, notable differences in soil moisture were found that accumulated over the course of the simulation when not using frequent restarts. The soil moisture drift had an impact on the simulated PBLH, presumably via changing the Bowen ratio. This is partially mitigated through nudging without requiring daily restarts, although not entirely alleviated. Soil moisture drift did not have a noticeable impact on GHG performance in our case, likely because it was dominated by other errors. However, since the PBLH is critical for accurately simulating GHG transport, we recommend transport model setups that tie soil moisture to observations. Our method of frequently re-initializing simulations with meteorological reanalysis fields proved suitable for this purpose.

Mesoscale models for effective elastic properties of carbon-black/ultra-high-molecular-weight-polyethylene nanocomposites

In this paper, we apply mesoscale numerical modeling to predict the effective elastic properties of conductive carbon-black/ultra-high-molecular-weight-polyethylene nanocomposites. The models are based on X-ray microcomputed tomography images. The images show that for the considered range of carbon additive weight fractions, the conductive carbon black (CB) particles are distributed around the ultra-high-molecular-weight-polyethylene (UHMWPE) granules forming a carbon-containing layer of a thickness on the order of 1–2 μm.Finite element models of representative volume elements (RVE), incorporating the CB-containing layer, are developed. The RVEs are generated based on the size and shape statistics extracted from processed microcomputed tomography images with further incorporation of the CB-containing layer by a custom image processing code. The layer is modeled analytically as a 2-phase composite consisting of spherical CB inclusions distributed in the UHMWPE matrix. Elastic moduli predicted in the models are compared to experimental data. Results show that the numerical simulations predict effective elastic moduli within the confidence intervals of the experimental measurements up to 7.5 wt % of CB inclusions.

Numerical investigation on plastic hinge behavior of hybrid steel-FRP reinforced concrete columns

This paper presents a numerical study on the plastic hinge behavior and rotation capacity of concrete columns reinforced with a combination of steel bars and fiber-reinforced polymer (FRP) bars. A meso-scale numerical model, which considers the internal heterogeneity of concrete and bond behavior between concrete and longitudinal reinforcements, was developed and validated. The failure modes, lateral load-displacement responses, and the physical plastic hinge lengths of hybrid steel-FRP reinforced concrete (SFRC) columns were investigated and compared with those of conventional steel-reinforced concrete columns. The results indicated that the steel yielding zone and curvature localization zone of SFRC columns with FRP longitudinal bars were enlarged, due to the relatively low modulus of FRP bars. After that, a parametric analysis was performed to study the plastic hinge length of SFRC columns with respect to the nominal area ratio of FRP longitudinal bars, shear span-to-depth ratio, axial load ratio, and longitudinal reinforcement ratio. A new empirical model was proposed to predict the equivalent plastic hinge length, which was subsequently integrated into an analytical method for predicting the ultimate rotation. The comparison of predictions with simulation and test results demonstrated the accuracy of both the proposed empirical model and analytical method.

AI-Driven Forecasting for Morning Fog Expansion (Sea of Clouds)

Abstract This study attempted to forecast the morning fog expansion (MFE), commonly referred to as the “sea of clouds,” utilizing an artificial intelligence (AI) algorithm. The radiation fog phenomenon that contributes to the sea of clouds is caused by various weather conditions. Hence, the MFE was predicted using datasets from public meteorological observations and a mesoscale numerical model (MSM). In this study, a machine learning technique, the gradient-boosting method, was adopted as the AI algorithm. The Miyoshi basin in Japan, renowned for its MFE, was selected as the experimental region. Training models were developed using datasets from October to December 2018–21. Subsequently, these models were applied to forecast MFE in 2022. The model employing the upper-atmospheric prediction data from the MSM demonstrated the highest robustness and accuracy among the proposed models. For untrained data in the fog season during 2022, the model was confirmed to be sufficiently reliable for forecasting MFE, with a high hit rate of 0.935, a low Brier score of 0.119, and a high area under the curve (AUC) of 0.944. Furthermore, the analysis of the importance of the features elucidated that the meteorological factors, such as synoptic-scale weak wind, temperatures close to the dewpoint temperature, and the absence of middle-level cloud cover at midnight, strongly contribute to the MFE. Therefore, the incorporation of upper-level meteorological elements improves the forecast accuracy for MFE. Significance Statement An AI-driven forecasting model for predicting morning fog expansion (MFE), sea of clouds, which often affects local livelihoods, was constructed. Fog forecasting machine learning techniques were utilized in the Japanese region famous for the morning fog. This study revealed that more accurate forecasting models incorporate numerically predicted weather elements sourced from the public routine system rather than real-time observed weather elements. Notably, the upper-level wind speed reflecting synoptic-scale dynamics, surface dewpoint depression, and middle-level cloud cover play significant roles in governing MFE. Therefore, incorporating upper-level meteorological elements into the features to machine learning is crucial for improving the forecasting accuracy of MFE.

Coupling WRF with HEC-HMS and WRF-Hydro for flood forecasting in typical mountainous catchments of northern China

Abstract. The atmospheric–hydrological coupling systems are essential to flood forecasting because they allow for more improved and comprehensive prediction of flood events with an extended forecast lead time. Achieving this goal requires a reliable hydrological model system that enhances both rainfall predictions and hydrological forecasts. This study evaluates the potential of coupling the mesoscale numerical weather prediction model, i.e., the weather research and forecasting (WRF) model, with different hydrological modeling systems to improve the accuracy of flood simulation. The fully distributed WRF-Hydro modeling system and the semi-distributed Hydrological Engineering Center Hydrological Modeling System (HEC-HMS) were coupled with the WRF model, and the lumped HEC-HMS model was also adopted using the observed gauge precipitation as a benchmark to test the model uncertainty. Four distinct storm events from two mountainous catchments in northern China characterized by varying spatial and temporal rainfall patterns were selected as case studies. Comparative analyses of the simulated flooding processes were carried out to evaluate and compare the performance of the coupled systems with different complexities. The coupled WRF–HEC-HMS system performed better for long-duration storm events and obtained optimal performance for storm events uniformly distributed both temporally and spatially, as it adapted to more rapid recession processes of floods. However, the coupled WRF–HEC-HMS system did not adequately capture the magnitude of the storm events as it had a larger flow peak error. On the other hand, the fully distributed WRF–WRF-Hydro system performed better for shorter-duration floods with higher flow peaks as it can adapt to the simulation of flash floods. However, the performance of the system became poor as uniformity decreased. The performance of the lumped HEC-HMS indicates some source of uncertainty in the hydrological model when compared with the coupled WRF–HEC-HMS system, but a larger magnitude error was found in the WRF output rainfall. The results of this study can help establish an adaptive atmospheric–hydrologic coupling system to improve flood forecasting for different watersheds and climatic characteristics.

X-ray micro-computed tomography for mechanical behaviour analysis of Automated Fiber Placement (AFP) laminates with integrated gaps and overlaps

Automated Fiber Placement (AFP) is a manufacturing technique widely used for the serial production of aerospace parts. A deep understanding of the effect of lay-up defects is crucial for part and lay-up design. Currently, numerical models for structural simulation lack a precise representation of the internal structure of AFP laminates, which is crucial for understanding the impact of defects on mechanical properties. This paper presents a novel approach based on high-resolution micro-computed tomography (micro-CT) scans from specimens manufactured with AFP, which automatically creates a mesoscale numerical model incorporating as-fabricated defect morphologies. The hexahedral mesh, generated from the segmented plies of the micro-CT volume, accounts for ply thickness and out-of-plane fiber orientation. This approach is verified with mechanical testing and digital image correlation (DIC) under tensile loading. The simulation results align closely with experimental testing and accurately illustrate the influence of fiber waviness in various defect configurations, such as gaps and overlaps. The study shows that lay-up defects can lead to knockdown factors of up to 12% in tensile properties, with each defect creating a distinct pattern in the local strain. This model can serve as a benchmark for further numerical simulations and surrogate models of defect configurations under varying loading conditions.

Numerical study for atmospheric transport of radioactive materials for hypothetical severe nuclear accident under different meteorological conditions

A study for atmospheric transport is essential for the consequence assessment of severe nuclear accidents since radionuclides could be released from the nuclear facility into the atmosphere and cause radioactive pollution in the environment. Atmospheric transport behaviors are strongly related with meteorological conditions, which can obviously influence the transport and diffusion characteristics of radioactive materials in the atmosphere; thus, it is meaningful to investigate the coupling effects between meteorological processes and transport behaviors of radioactive materials. To evaluate the influence of meteorological conditions on atmospheric transport, meteorological parameters for different seasons were first acquired by the weather research forecast model. Furthermore, atmospheric transport behaviors of radioactive materials were simulated by the meso-scale numerical model under different meteorological conditions, and numerical analyses were conducted toward transport and deposition behaviors of radioactive materials. In addition, the influence of FDDA (four-dimensional data assimilation) on meteorological parameters and atmospheric transport behaviors was researched. The present study is important for strengthening consequence assessment for severe nuclear accident and made it possible to apply the data assimilation technology in further research works.

Quantifying urban climate response to large-scale forcing modified by local boundary layer effects

Over the past two decades, the joint manifestation of global warming and rapid urbanization has significantly increased the occurrence of heatwaves and the formation of urban heat islands in temperate cities. Consequently, this synergy has amplified the frequency and duration of periods with tropical nights (TNs) in these urban areas. While the occurrences of such extreme events demonstrate irregular and nonlinear annual patterns, they consistently manifest a discernible rising decadal trend in local or regional climatic data. In urban regions situated amidst hilly or mountainous landscapes, changing wind directions—often associated with uphill or downhill thermal flows—profoundly impact the spread and dispersion of heat-related pollution, creating unique natural ventilation patterns. Using the Lausanne/Pully urban area in Switzerland as examples of hilly and lakeshore temperate cities, this study explores the influence of wind patterns and natural urban ventilation on the nonlinearity of recorded climatic data within an urban environment. This study integrates a mesoscale numerical weather prediction model (COSMO-1), a microscale Computational Fluid Dynamics (CFD) model, field observations, variational mode decomposition technique, and statistical analysis to investigate how wind speed and direction critically influence the nonlinearity of recorded long-term trends of extreme events, specifically focusing on the frequency and duration of TNs in lakeshore and hilly cities. The results strongly indicate a direct correlation between the frequency of TNs and the occurrence of specific moderate wind patterns. These wind patterns are exclusively captured by the microscale CFD model, unlike the mesoscale model, which neglects both urban morphology and complex hilly terrains. The impact of temporal and spatial variability of the wind field on long-term observations at fixed measurement stations suggests that caution should be exercised when relying on limited spatial measurement points to monitor and quantify long-term urban climate trends, particularly in cities located in complex terrains.

Mesoscale modeling of recycled aggregate concrete: A parametric analysis of the quality and shape of aggregate

AbstractRecycled aggregate concrete (RAC) is a multi‐phase material, and it is meaningful to investigate the effect of the properties of each phase. However, a comprehensive parametric analysis of these properties is still lacking, which limits a full understanding of the behavior of RAC. This paper uses numerical mesoscale models and contributes to knowledge on this topic. It focuses on the effects of the quality and shape of aggregate since they are relevant to the behavior of concrete but have scarcely been studied for RAC. Their effects on the mechanical response and fracture behavior of RAC were analyzed based on a validated two‐dimensional numerical model. In addition, this paper also justified the choice of the range of the parameters as well as benchmarked the numerical results with the state‐of‐the‐art. The main findings of the paper are: (1) stiffer aggregates decrease the tensile (by up to 29%) and compressive strength of RAC (by up to 7%); (2) aggregate shape moderately influences these properties by up to 10% and 8%; (3) the modulus of elasticity of RAC is considerably influenced by the stiffness of the aggregates (15%), while it is almost unaffected by the shape of the aggregates; (4) both stiffer and elongated aggregates tend to cause micro‐cracking in the interface and premature failure of concrete.

Mesoscale multilayer multitrack modeling of melt pool physics in laser powder bed fusion of lattice metal features

This paper reports on the development of a mesoscale multiphysics numerical model for predicting the dimensions of melt pool zones in Laser Powder Bed Fusion process, in a multilayer and multitrack application dedicated to the manufacturing of lattice metal features. In such context, a clear need emerges to study laser-matter interaction regarding the persisting questions surrounding the comprehension of melt pool. An experimental campaign involving thin pillars made of the nickel-based superalloy IN718 is presented, highlighting the complexity introduced by the thin tracks superposition. Then, a continuous mesoscale numerical model, considering heat transfer, melt pool flow, and vaporization phenomena, and its extension to multilayer-multitrack simulation is detailed. Some discussions about the numerical approach and its ability to predict the global morphology and dimensions of melt pool zones and resulting tracks after solidification are proposed. Finally, comparisons between the experiments and the numerical model show good agreement, with a maximum relative error of 8 % observed for remelted zone depth. This study demonstrates the capability of the present approach to help in understanding the influence of process parameters on melt pool shape and, thereafter, to determine process parameters to optimize for lattice features building.

Mesoscale modeling of new-to-old concrete interface under combined shear and compressive loads

A mesoscale model of new-to-old concrete interface under combined shear and compressive loads is established, and a parameter study is conducted to evaluate the influence of interface roughness, bond strength, and fracture energy. The mesoscale model is numerically generated using a random aggregate technique, in which the number of aggregates is calculated using the Fuller grading curve and Walraven formula and the aggregates are randomly placed using the Monte Carlo simulation. The concrete damaged plasticity model is employed to simulate the mechanical behaviors of new and old mortars, as well as the interfacial transition zone between the mortar and aggregate. The linear elastic model is utilized to simulate the behavior of aggregates, while the cohesive-friction model is employed to represent the behavior of new-to-old concrete interface. The effect of interface roughness, bond strength, and fracture energy on the interface shear strength and post-peak shear strength using the double-shear test specimens is investigated. The results indicate that increasing the roughness of old concrete surface leads to significant increases in both the interface shear strength and post-peak shear strength, but the larger interface roughness is not necessarily better. As the interface bond strength increases, the interface shear strength remains the same, whereas the post-peak shear strength shows a large increase with the lower interface roughness. Moreover, the interface fracture energy imparts the minimal influence on the interface shear strength and post-peak shear strength, but the higher interface fracture energy can improve the overall constitutive behavior of new-to-old concrete interface if the roughness of old concrete surface is low. The numerical mesoscale model presented can provide the theoretical guidance and technical support for effective design and construction of new-to-old concrete interface and structures.

Fatigue life prediction for CA mortar in CRTS II railway slab track subjected to combined thermal action and vehicle load by mesoscale numerical modelling

The fatigue life of the cement asphalt (CA) mortar in the slab track of the China Railway Track System (CRTS) II is crucial to the smooth and safe operation of high-speed railways. To investigate the fatigue life of the CA mortar under the combined thermal action and vehicle loads, this paper develops a new coupled thermal-mechanical finite element (FE) model for the concrete slab track at the mesoscale. First, the meteorology and heat transfer principle are adopted to simulate the temperature distribution of the concrete slab track. The heterogeneous characteristics of the concrete of the precast slab are modelled by the three-phase composite material at the mesoscale using the new developed random aggregate algorithm. Then, the energy-based exponential cohesive zone model is adopted to simulate the interface between the CA mortar and the precast concrete slab. The proposed mesoscale numerical model can provide reliable predictions for the temperature distribution and the deformation of the precast concrete slab under thermal action, which are confirmed by the relevant field measurements. Finally, the fatigue life of the CA mortar is estimated by the nonlinear damage cumulative method associated with the stress time history under the combined thermal action and vehicle load. From the obtained results for the numerical example, the thermal action can decrease severely the fatigue life of the CA mortar, and the fatigue life varies significantly at different positions, with the most vulnerable zone located near but not directly below the rails.

Can mesoscale models capture the effect from cluster wakes offshore?

Long wakes from offshore wind turbine clusters can extend tens of kilometers downstream, affecting the wind resource of a large area. Given the ability of mesoscale numerical weather prediction models to capture important atmospheric phenomena and mechanisms relevant to wake evolution, they are often used to simulate wakes behind large wind turbine clusters and their impact over a wider region. Yet, uncertainty persists regarding the accuracy of representing cluster wakes via mesoscale models and their wind turbine parameterizations. Here, we evaluate the accuracy of the Fitch wind farm parameterization in the Weather Research and Forecasting model in capturing cluster-wake effects using two different options to represent turbulent mixing in the planetary boundary layer. To this end, we compare operational data from an offshore wind farm in the North Sea that is fully or partially waked by an upstream array against high-resolution mesoscale simulations. In general, we find that mesoscale models accurately represent the effect of cluster wakes on front-row turbines of a downstream wind farm. However, the same models may not accurately capture cluster-wake effects on an entire downstream wind farm, due to misrepresenting internal-wake effects.

Mesoscale numerical modeling of unreinforced masonry wall response to underground dynamic loads: A comparative study utilizing FEM and DEM

This study investigates the response of unreinforced masonry walls to dynamic loading induced by underground explosions. A mesoscale numerical modeling approach is employed to capture the nonlinear behavior of the masonry wall. The Menetrey-Willam yield criterion is utilized to represent the wall's nonlinear behavior at the mesoscale level, while the cohesive zone model (CZM) is employed to simulate the behavior of mortar, thereby overcoming the limitations of linear elasticity. The soil is characterized using the modified Mohr-Coulomb model. After calibration against material characterization tests, a numerical model is developed to obtain results that closely match experimental data. Given the disturbed nature of the crater soil, both finite element and discrete element methods are utilized for soil modeling. Comparison between these methods reveals that the discrete element method yields more realistic results in terms of displacement and strain. The study's findings enhance our understanding of the response of unreinforced masonry walls to dynamic loading and offer valuable insights for future structural designs and safety assessments.

Formation mechanism and development potential of wind energy resources on the Tibetan plateau

It is important to understand whether the wind energy resources on the Tibetan Plateau have power potential suitable for development. To this end, we conducted sodar wind measurement experiments in areas of typical terrain, analyzed radiosonde and surface meteorological observations, and performed mesoscale numerical model simulations to study the characteristics and formation mechanisms, and to assess the technical potential, of the wind energy resources on the Tibetan Plateau. Results showed that the wind energy resources on the Tibetan Plateau are very abundant and of high quality with enormous development potential. The total technical potential of the wind energy resources at 100-m height above ground level is 1.02 billion kW, accounting for 26 % of the total technical potential of China's wind energy resources. The technical potential of the wind energy resources at very rich and rich levels accounts for 63 % of the total. The strengthening effect of the superposition of valley wind circulations and the synoptic-scale background wind field is the fundamental formation mechanism of the abundant wind energy resources on the Tibetan Plateau.

3D meso-scale modeling of bond behavior between helical strand and concrete

Excellent interfacial bonding is the foundation for the strand and concrete working together. Helical structure of strand causes its rotation during the pull-out process, which makes it difficult to quantify the bond-slip at the interface. A three-dimensional (3D) meso-scale numerical model is first established to investigate the bond behavior at strand-concrete interface in this study. A refined modeling method is proposed to build a helical strand with the longitudinal helix shape and transverse flower-like shape. The heterogeneous concrete composed of three-phase composite material is built based on the random aggregate model. The interaction between strand and concrete is implemented by the surface-to-surface contact strategy. The rationality of the proposed model is verified by the experimental results. The effects of the helical structure of strand, the lay length of strand, aggregate content and eccentricity of strand on interfacial bond behavior are discussed. The results indicate that considering the helical structure of strand and heterogeneity of concrete can more accurately describe the stress distribution at the strand -concrete interface. The proportion of twisting slip is lower than that of longitudinal slip in the whole pull-out process, and its proportion increases first and then decreases. The interfacial bond strength increases first and then decreases with the increase of lay length of strand and aggregate content, and it reaches the best when the lay length is 14 time of diameter with the aggregate content of 45 %.

A Comparative Study on Shear Size Effect of Steel-And BFRP-RC Shear Walls Under Cyclic Lateral Loads

ABSTRACT This paper presents a comparative study on the size effect behaviors of shear performances of concrete shear walls reinforced by steel bars and Basalt Fiber Reinforced Polymer (BFRP) bars. All the works are conducted through a two-dimensional meso-scale numerical simulation model representing the concrete shear wall. The horizontal reinforcement ratio and the sectional size are the main parameters concerned by the present study. Both the seismic behaviors under cyclic loading and the size effect behaviors of the shear performances were simulated and investigated. It can be concluded from the simulation results, (1) the use of steel bars or BFRP bars as horizontal reinforcement does not change the shear failure modes of concrete shear walls, (2) the size effect on the nominal shear strength of BFRP reinforced concrete (BFRP-RC) shear wall is more significant compared to that for steel-RC shear wall, (3) the increase of horizontal reinforcement ratio improves the shear capacity of the concrete shear wall, while weakens the size effect on the nominal shear strength, and (4) the size effect law for the nominal shear strength of steel-RC shear wall is applicable to BFRP-RC shear wall. Finally, the importance of considering size effect in the evaluation of shear performance of steel-/BFRP-RC shear wall is manifested by comparing the predictive results of several popular design codes with the simulation results.

A 3D Meso-Scale Model and Numerical Uniaxial Compression Tests on Concrete with the Consideration of the Friction Effect.

Achieving the real mechanical performance of construction materials is significantly important for the design and engineering of structures. However, previous researchers have shown that contact friction performs an important role in the results of uniaxial compression tests. Strong discreteness generally appears in concrete-like construction materials due to the random distribution of the components. A numerical meso-scale finite-element (FE) method provides the possibility of generating an ideal material with the same component percentages and distribution. Thus, a well-designed meso-FE model was employed to investigate the effect of friction on the mechanical behavior and failure characteristics of concrete under uniaxial compression loading. The results showed that the mechanical behavior and failure profiles of the simulation matched well with the experimental results. Based on this model, the effect of friction was determined by changing the contact friction coefficient from 0.0 to 0.7. It was found that frictional contact had a slight influence on the elastic compressive mechanical behavior of concrete. However, the nonlinear hardening behavior of the stress-strain curves showed a fairly strong relationship with the frictional contact. The final failure profiles of the experiments showed a "sand-glass" shape that might be expected to result from the contact friction. Thus, the numerical meso-scale FE model showed that contact friction had a significant influence on both the mechanical performance and the failure profiles of concrete.

On rapid compaction of granular materials: Combining experiments with in-situ imaging and mesoscale modeling

Grain and pore kinematics are important features of the response of granular materials to impact loading and rapid compaction. These kinematics and the associated material-phase stresses control solidification processes in shock-driven manufacturing and ignition in energetic materials. Diagnostics used in traditional gas-gun experiments cannot resolve spatially-heterogeneous grain and pore kinematics during granular compaction. Similarly, continuum models of the granular compaction process do not account for this spatial heterogeneity, making predictions of solidification or ignition challenging. Here, we propose a method of accessing spatially-heterogeneous grain and pore behaviors during rapid compaction which involves x-ray tomography, in-situ x-ray phase contrast imaging, and mesoscale numerical modeling. We use this method to study heterogeneous grain and pore kinematics and local stresses in a ductile aluminum powder impacted at velocities up to 800 m/s. We first validate the mesoscale model by comparing its predictions with x-ray measurements from an impact experiment on a sample that was used to generate a numerical microstructure. We then quantify the evolution of variables such as 3D pore sizes and local stresses. We comment on the role of microstructure on the granular material’s response and the sensitivity of the material response to changes in structure, impact velocity, and sample size.

Urban Flood Analysis and Early Warning Solution for Disaster Resilience

The objective of this work is to present the experimental findings of an urban flood early warning system developed by combining a mesoscale numerical weather prediction model (WRF) forecast with a distributed hydrologic modelling system. Hydrodynamic models have been used in the simulation of intricate and interrelated dynamic systems, such as urban watersheds and urban water infrastructure. The use of these models in assessing the flood hazard, vulnerability, and risk is well-established. On the other hand, the Numerical weather prediction (NWP) such as WRF model output are crucial to increase the lead time for the flood forecasting. The Weather Research and Forecasting (WRF) model is employed for the purpose of generating precipitation forecasts, which are subsequently utilized to conduct hydrologic and hydrodynamic simulations. In accordance with the climatology of the study area, the WRF model forecast is juxtaposed with the observed rainfall data for days 1, 2, and 3. Statistical indicators of accuracy assessment suggest acceptable degree of correlation with the observed precipitation. The experiment successfully designed and implemented a comprehensive web-based urban flood forecast and early warning system. The system is able to predict possible inundation areas within the catchment due to projected rainfall. Additionally, the study develops a web-based Flood Management Information System (MIS) that is fully automated and disseminate early warning with a lead time of 72 hours.