Mesoscale Numerical Model Research Articles

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.

Performance evaluation of lightning potential index and flash count using WRF microphysical parameters over Rajasthan and West Bengal, India

Accurate lightning prediction stands as a pressing global challenge, demanding robust solutions for safeguarding lives and valuable assets. In this study, we employ the mesoscale numerical model, Weather Research and Forecasting (WRF-ARW, version 4.0.3), to conduct numerical simulations of lightning occurrences during the 2021 monsoon season in Hooghly (on 07 June) and Jaipur (on 11 July). These events resulted in 31 and 11 casualties, respectively. The WRF model is integrated at horizontal resolutions of 9 km and 3 km for both regions, utilizing six-hourly NCEP-FNL datasets at a 0.25º resolution. The primary objective of this inquiry is to identify the most suitable microphysics scheme among WSM-6, NSSL-2, and MORRISON for a comprehensive assessment of lightning activity. Model performance is meticulously evaluated through skill scores, including Equitable Threat Score (ETS), False Alarm Rate (FAR), Accuracy (ACC), and Probability of Detection (POD), focusing specifically on hourly rainfall. Furthermore, a comprehensive spatial evaluation assesses the Lightning Potential Index and lightning flash count using the McCaul Method. The model-simulated results effectively depict lightning conditions in both regions, showing slight spatial and temporal discrepancies compared to observational datasets. Validation of the simulated lightning flash count is accomplished using data from the Lightning Detection Network (LDN) operated by the Indian Institute of Tropical Meteorology (IITM). The NSSL-2 microphysical scheme demonstrates noteworthy efficacy in identifying lightning occurrences in both regions. Rainfall representations correspond remarkably well with Indian Monsoon Data Assimilation and Analysis (IMDAA) data, indicating precipitation levels of 20–40 mm and 70–80 mm in Jaipur and Hooghly, respectively. The NSSL-2 microphysics scheme exhibits commendable proficiency, with consistently high model skill scores (ACC and POD ∼0.9) for lightning events. This study signifies a significant step toward the development of an operational lightning warning system, offering the potential to substantially reduce the risks associated with lightning occurrences. Consequently, such a system has the capacity to enhance safety and preparedness measures for regions affected by lightning phenomena.

Mesoscale numerical investigation of dynamic spalling fracture in toughness concrete

This study presents a two-phase mesoscale numerical model to investigate the dynamic spalling fracture behaviour of hybrid fibre ultra-high toughness cementitious composites (UHTCC). The model simulated the presence of random steel fibres and ductile UHTCC independently, and accurately incorporated the fibre-matrix bond-slip relationship, with carefully considering the influence of the inclination angle of the steel fibres on the pullout behaviour. Validation of the numerical model involved a meticulous comparison between numerical outcomes and results from spallation tests, encompassing strain-time and velocity-time histories, failure modes, and locations of cracking or fracture. Subsequently, the developed model was employed to analyse the impact of fibre inclination, stress wave amplitude, and waveform (based on the impulse equivalence principle) on the dynamic spalling fracture behaviour of hybrid fibre UHTCC. Evaluation of spalling fracture performance was based on both maximum velocity and pullback velocity of resulting fragments or free surfaces. Findings revealed that fibres parallel to the stress wave propagation direction exhibited optimum performance. It was also observed that stress waveforms with rectangular and sinusoidal shapes presented the highest risk of spalling fracture, while exponentially attenuated waves were comparatively safer. Additionally, stress waveforms with either rise time or duration at maximum pressure were deemed hazardous. Further, the hybrid fibre UHTCC demonstrated multiple spalling cracking/fracture behaviours under all stress waves except rectangular ones. This comprehensive exploration provided insights for the utilization of hybrid fibre UHTCC in impact engineering.

Effects of Concrete Strength and CFRP Cloth Ratio on the Shear Performance of CFRP Cloth Strengthened RC Beams

A three-dimensional meso-scale numerical model, which considers (1) the concrete meso-structures, (2) the bond slip between concrete internal structures and steel bars, and (3) the stripping behavior of carbon fiber reinforced polymer (CFRP) cloth from the surface of the concrete component, is established to investigate the effects of concrete strength and the CFRP cloth ratio on the shear performance of reinforced concrete (RC) beams. On the basis of verifying the rationality of the shear failure model and the feasibility of the CFRP reinforcement simulation method, 30 orthogonally designed numerical models of six kinds of concrete strength and five kinds of CFRP cloth ratios were designed and established. Based on the numerical simulation results, this paper analyzes the damage evolution of the CFRP–concrete interface, the variation trend of CFRP strain along the height direction of beam, the failure mode, and the load-displacement curve. Results show the following: (1) With the increase of concrete strength grade and CFRP cloth ratio, the shear strength of RC beams strengthened with increases in CFRP cloth ratio, the influence range of concrete strength grade is 24–50%, and the influence range of the CFRP cloth ratio is 10–25%; (2) The improvement range decreases, and the improvement range of the concrete strength grade decreases, and the reduction range is about 20%; (3) Based on the simulation results, influences of concrete strength and CFRP cloth ratio on the shear strength of CFRP cloth-strengthened RC beams are quantitatively considered.

Study on pure-torsional size effect of CFRP-strengthened RC beams using a 3D meso-scale numerical model

In this paper, a 3D meso-scale numerical model for pure-torsional failure analysis of carbon fiber reinforced plastic (CFRP)-strengthened reinforced concrete (RC) beams is established. Three main aspects regarding the establishment of the numerical model are thoroughly considered, including the heterogeneity of concrete, the bond-slip relationship between steel bars and concrete, and the nonlinear interaction between CFRP and concrete. Effects of CFRP fiber ratio and span-height ratio on pure-torsional performances and size effect behaviors of CFRP-strengthened RC beams are analyzed. With the increase of CFRP fiber ratio, the cracks spacing decreases, the number of cracks increases, and the strain of CFRP decreases. With the increase of the span-height ratio, the inclination angle of the oblique crack decreases, and the strain of CFRP increases. It is found that CFRP-strengthened RC beams have an obvious torsional size effect. Furthermore, CFRP fiber ratio and span-height ratio not only have a coupling enhancement effect on the nominal torsional strength, but also have a coupling weakening effect on the size effect behavior. Based on the simulation results, a CFRP-related Torsional size effect law (SEL), which can quantitatively describe the influences of CFRP fiber ratio and span-height ratio on the size effect of nominal torsional strength of CFRP-strengthened RC beams, is established.

Preparing the assimilation of the future MTG‐IRS sounder into the mesoscale numerical weather prediction AROME model

AbstractThe infrared sounder (IRS) instrument is an infrared Fourier‐transform spectrometer that will be on board the Meteosat Third Generation series of the future European Organization for the Exploitation of Meteorological Satellite's geostationary satellites. It will measure the radiance emitted by the Earth at the top of the atmosphere using 1,960 channels. The IRS will provide high spatial‐ and temporal‐frequency four‐dimensional information on atmospheric temperature and humidity, winds, clouds, and surfaces, as well as on the chemical composition of the atmosphere. The assimilation of these new observations represents a great challenge and opportunity for the improvement of numerical weather prediction (NWP) forecast skill, especially for mesoscale models such as the Applications de la Recherche à l'Opérationnel à Méso‐Echelle (AROME) at Météo‐France. The objectives of this study are to prepare for the assimilation of the IRS in this system and to evaluate its impact on the forecasts when added to the currently assimilated observations. By using an observing system simulation experiment constructed for a mesoscale NWP model. This observing system simulation experiment framework makes use of synthetic observations of both IRS and the currently assimilated observing systems in AROME, constructed from a known and realistic state of the atmosphere. The latter, called the “nature run”, is derived from a long and uninterrupted forecast of the mesoscale model. These observations were assimilated and evaluated using a 1 hr update cycle three‐dimensional variational data assimilation system over 2‐month periods, one in the summer and one in the winter. This study demonstrates the benefits that can be expected from the assimilation of IRS observations into the AROME NWP system. The assimilation of only 75 channels over oceans increases the total amount of observations used in the AROME three‐dimensional variational data assimilation system by about 50%. The IRS impact in terms of forecast scores was evaluated and compared for the summer and winter periods. The main findings are as follows: (a) over both periods the assimilation of these observations leads to statistically improved forecasts over the whole atmospheric column; (b) for the summer‐season experiment, the forecast ranges up to 48 hr are improved; (c) for the winter‐season experiment, the impact on the forecasts is globally positive but is smaller than the summer period and extends only to 24 hr. Based on these results, it is foreseen that the addition of future IRS observations in the AROME NWP systems will significantly improve mesoscale weather forecasts.

Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA

Observed by satellites for more than a decade, surface soil moisture (SSM) is an essential component of the Earth system. Today, with the Sentinel missions, SSM can be derived at a sub-kilometer spatial resolution. In this work, aggregated 1 km × 1 km SSM observations combining Sentinel-1 (S1) and Sentinel-2 (S2) data are assimilated for the first time into the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model using the global Land Data Assimilation System (LDAS-Monde) tool of Meteo-France. The ISBA simulations are driven by atmospheric variables from the Application of Research to Operations at Mesoscale (AROME) numerical weather prediction model for the period 2017–2019 for two regions in Southern France, Toulouse and Montpellier, and for the Salamanca region in Spain. The S1 SSM shows a good agreement with in situ SSM observations. The S1 SSM is assimilated either alone or together with leaf area index (LAI) observations from the PROBA-V satellite. The assimilation of S1 SSM alone has a small impact on the simulated root zone soil moisture. On the other hand, a marked impact of the assimilation is observed over agricultural areas when LAI is assimilated, and the impact is larger when S1 SSM and LAI are assimilated together.

Prediction of Convective Available Potential Energy and Equivalent Potential Temperature using a Coupled WRF and Deep Learning for Typhoon Identification

To predict typhoons in the western North Pacific Ocean, it is required to predict the determinants of typhoon activities. The formation of the typhoon can be controlled by Convective Available Potential Energy (CAPE) and Equivalent Potential Temperature (theta-e). To predict the variables, a mesoscale numerical model of Weather Research and Forecasting (WRF) can be used. However, the output of WRF needs to improve to obtain a more accurate CAPE and theta-e prediction. This study uses a coupled WRF model and Deep Learning (DL) Multilayer Perceptron Regressor approach to increase CAPE and theta-e prediction skills. Simulation with dataset scenarios with WRF outputs as predictors and sounding data as predictors are developed and tested to obtain the most appropriate package of deep learning simulation. The study found that coupled models provide increased mean accuracy of theta-e and CAPE, namely 16.6% and 32.0% higher than using original WRF, respectively. This study also shows the difference of skill scores in the spatial distribution of CAPE and theta-e of WRF result and its coupled model.

Phase segmentation in X-ray CT images of concrete with implications for mesoscale modeling

X-ray computed tomography (XRCT) is a valuable tool for characterizing the microstructure of concrete and for developing 3D mesoscale numerical models directly from experimental data. However, the results of imaging and subsequent modeling are reliable only if individual phases can be identified and segmented accurately. Siliceous aggregates and cement paste are difficult to separate in XRCT images because of their similar X-ray attenuation coefficients. This work examines the quality of aggregate phase segmentation in XRCT images using (1) a standard deviation thresholding approach and (2) a random forest classification. Both approaches were validated with ground truth data for concrete samples with different aggregate volume fractions. Our findings show that either approach may successfully be used to segment aggregate phases if appropriate post-processing is performed. However, our results emphasize the critical need to preserve both aggregate size and shape during post-processing as illustrated through mesoscale modeling.

Enhancement of Cruise Boat Resilience to Strong Convective Gusts with Global Model Cumulus Variable Prediction

Ship pilots and maritime safety administration have an urgent need for more accurate and earlier warnings for strong wind gusts. This study firstly investigated the “Oriental Star” cruise ship capsizing event in 2015, one of the deadliest shipwreck events in recent years, and explored all related hydro-meteorological components in a global mesoscale model. It was found that rather than the missing signal in raw surface-wind prediction, the cumulus precipitation variable (CP) increased dramatically during the accident occurrence, which significantly corresponds to a sub-grid strong wind gust. The effective lead time could be extended from 24 h (deterministic model) to 48 h (ensemble model). This finding was then verified in another two recent deadly cruise boat accidents. The introduction of the new variable aims to improve the current maritime safeguard system in predicting sub-grid strong wind gusts for small-sized cruise boats offshore and in inland rivers. Finally, an automatic response system was developed to provide economical convection prediction via INMARSAT email communication, aiming to explore operational severe convective gust early warning and appropriate numerical mesoscale model applications.

Compressive stress-strain behavior and model for geometrical-similar BFRP RC square columns

This paper presents experimental, numerical and analytical investigations to evaluate the compressive behavior and size effect of square concrete columns internally reinforced with basalt fiber-reinforced polymer (BFRP) bars and ties. Nine BFRP reinforced concrete (RC) square columns were tested under axial compression. The main parameters included cross-sectional heights, transverse reinforcement ratios, and configuration of transverse BFRP ties. The compressive behaviors of BFRP RC columns were discussed in terms of axial load-strain response, confined strength, and ductility index. The results revealed that there was significant size effect on the confined efficiency and ductility. With the increasing cross-sectional height, the maximum decreases of confined efficiency and ductility index were 16.0% and 18.9% respectively. Moreover, a meso-scale numerical model considering the heterogeneity of concrete was developed and verified using the test results. A parametric analysis was then performed to evaluate the effects of cross-sectional height and transverse reinforcement ratio on the compressive performance of BFRP tie-confined concrete. Finally, a design-oriented stress-strain model was proposed considering the effect of cross-sectional height and transverse reinforcement ratio. The proposed model was validated using the test results in the present study and literature.

Comprehensive Efficiency Evaluation of Aircraft Artificial Cloud Seeding in Hunan Province, China, Based on Numerical Simulation Catalytic Method

Aircraft cloud seeding refers to the use of equipment on aircraft to release chemicals into clouds, changing their physical and chemical properties to increase rainfall or snowfall. The purpose of precipitation enhancement is to alleviate drought and water scarcity issues. Due to the complexity of the technology, the precise control of factors such as cloud characteristics and chemical release amounts is necessary. Therefore, a scientific evaluation of the potential of aircraft cloud seeding can help to improve the effectiveness of the process, and is currently a technical challenge in weather modification. This study used the mesoscale numerical model WRF coupled with a catalytic process to simulate and evaluate the seven aircraft cloud seeding operations conducted in Hunan Province in 2021. The results show that WRF can effectively evaluate the effectiveness of cloud seeding. When the water vapor conditions are suitable, the airborne dispersion of silver iodide (AgI) can significantly increase the content of large particles of high-altitude ice crystals, snow, and graupel, resulting in an increase in low-level rainwater content and, correspondingly, an increase in ground precipitation. When the water vapor conditions are insufficient, the dispersion of AgI does not trigger effective precipitation, consistent with the results of station observations and actual flight evaluations. This study provides an effective method for scientifically evaluating the potential and effectiveness of aircraft cloud seeding operations.

Cyclic compressive behavior of hook-end steel and macro-polypropylene hybrid fiber reinforced recycled aggregate concrete

One widely accepted strategy is to add fiber into recycled aggregate concrete (RAC) to improve compressive strength. Few existing investigations have focused on the cyclic compressive behavior of hybrid fiber reinforced recycled aggregate concrete (HFRRAC). A total of eighteen groups of prismatic specimens with different fiber combinations and recycled coarse aggregate (RCA) replacement rates were conducted under cyclic compression to investigate the mechanical properties of hooked-end steel and macro-polypropylene hybrid fiber reinforced recycled aggregate concrete. Results indicate that specimens under cyclic loading fail similarly to those subjected to monotonic loading. The envelope curve of the cyclic stress-strain curve for HFRRAC closely resembles the corresponding monotonic stress-strain curve. RCA replacement rates and fiber combinations do not significantly affect plastic strain and stress deterioration. Fibers added to specimens mitigate stiffness degradation and improve hysteretic energy dissipation. The proposed constitutive equations can be used to describe the cyclic compressive behavior of HFRRAC. The fibers improve the density and structure of mortar and aggregate-paste interfaces from the microstructure morphologies. The fracture mechanisms of HFRRAC were also studied using mesoscale numerical models under uniaxial compression loading. The cohesive zone model is suitable for simulating the compressive behavior of HFRRAC. Numerical specimens fail in mixed fracture modes, primarily mode II fractures.

Experimental and mesoscale analysis on wave propagation induced by impact in dry silica sand

When investigating the propagation of stress waves in sand-like materials, the materials are generally modeled as homogeneous parts despite their complex structures comprising numerous particles of varying sizes and shapes. However, this approach fails to consider the non-continuum nature of sand-like materials. To address this issue, a mesoscale numerical model of silica sand particles with random shapes and three sizes is employed in this study to numerically analyze wave propagation. Experimental data from an upgraded split Hopkinson pressure bar test are utilized to validate the accuracy of the numerical model. Using the validated numerical model, we investigate the impact of relative densities, incident wavelengths, and wave amplitudes on stress wave attenuation in dry silica sand. Empirical equations are derived from summarizing the stress wave attenuation law induced by impact loading. The study shows that relative density and wavelength are critical factors affecting the residual peak stress ratio in silica sand, while the effect of loading wave amplitude is limited.

Study on the Damage Characteristic of Ununiform Corroded SFRC by 3D Mesoscale Numerical Simulation

Steel fiber reinforced concrete (SFRC) will appear ununiform corrosion under chloride environment, resulting in corrosion-induced crack. However, the mechanism of corrosion-induced crack of ununiform corroded SFRC is not clear. In this paper, the 3D mesoscale numerical model of SFRC structure is established, the generation algorithm of round straight steel fiber is provided, and the corrosion-induced crack are simulated by XFEM, at the end the damage characteristic of ununiform corroded SFRC are studied. The result shows with the increase of rebar corrosion area, the number of corrosion-induced cracks increase gradually. The width of each corrosion-induced crack decreases linearly with the thickness of the protective layer, and the width of the penetrate crack decrease to 0.08 mm~1.2 mm.

Investigating the effect of aligned Ag nanorods on para-aramid woven fabric and anisotropy in inter-yarn friction

Inter yarn friction, measured in terms of yarn pull-out force, is a major mechanism of energy absorption in soft body armor. Increasing inter-yarn friction of para-aramid fabric to improve ballistic performance without adding weight is challenging. This work reports the fabrication of aligned Ag nanorods (AgNRs) on the para-aramid (Kevlar) fabric surface using the glancing angle deposition technique. A single yarn pull-out test examines Kevlar fabric treatment. The AgNRs-coated para-aramid fabric retains its flexibility and weight while increasing yarn pull-out force by 130%. Anisotropy in inter-yarn friction depends on the yarn's sliding direction relative to Ag nanorod tilt direction, indicating direction-dependent yarn pull-out force. The periodic character of pull-out response post-peak force is replicated by modeling the yarn pull-out phenomenon by incorporating a mesoscale level numerical model of woven fabric. The numerical model confirms the experimental results that formation of nanorods significantly increases inter-yarn friction and pull-out force.

Investigating the microplastic behavior of hierarchical polycrystalline [formula omitted]-TiAl microstructures

Hierarchical, nano-twinned microstructures are a promising route to optimize the strength and deformability of metals and alloys. The present paper investigates the role of grain and twin boundaries and the effect of twin spacing on the evolution of plasticity in a twinned, polycrystalline γ-TiAl microstructure. Via atomistic simulations of uniaxial compression tests it is possible to disentangle these influencing factors, and the results show that the onset of plasticity is governed by the sub-grain structure of the samples, while the Schmid factor does not play a prominent role. At high strains in uniform or only slightly twinned models, grain boundary sliding is one of the major deformation mechanisms, whereas twin-boundary-mediated plastic deformation dominates the highly twinned structures. Moreover, based on analyzing the degree of localization parameter, it is inferred that upon decreasing the lamellae size, higher uniformity of shear strain in the samples can be achieved. These results can be used to inform mesoscale numerical modeling of hierarchical TiAl microstructures.

Dissimilarity of Turbulent Transport of Momentum and Heat Under Unstable Conditions Linked to Convective Circulations

AbstractThe dissimilarity between the turbulent transport of momentum and heat under unstable conditions and its physical mechanisms are investigated in this study, based on the multiple‐level turbulence observation from the Tianjin 255‐m meteorological tower. The transport dissimilarity is observed from the surface layer to the lower part of mixed layer as atmospheric instability increases. Although the transport dissimilarity is accompanied by the development of plumes and thermals under unstable conditions, plumes and thermals can produce intense transport both of momentum and heat simultaneously. It is convective circulations induced by vigorous thermals that cause transport dissimilarity. The horizontal divergence generated by convective circulations imposes a dominant large‐scale reduction in the along‐wind velocity component near the surface, which is related to increased counter gradient transport of momentum, while the temporal variations in temperature mainly reflect the role of plumes and thermals and thus the transport of heat is predominantly down‐gradient. This difference in respective physical processes subsequently leads to dissimilar transport between momentum and heat under unstable conditions. Therefore, it is of great interest to represent the influence of convective circulations on the momentum‐flux estimation in future investigations, aiming to improve the boundary‐layer parameterization schemes for mesoscale numerical weather models.

Using observational mean‐flow data to drive large‐eddy simulations of a diurnal cycle at the SWiFT site

AbstractReproducing realistic date‐ and site‐specific unsteady wind conditions in large‐eddy simulations is becoming increasingly useful in wind energy. How to run a large‐eddy simulation to match observed conditions, however, remains an open research question. One approach that has received considerable attention is mesoscale‐to‐microscale coupling, in which information about the mesoscale weather, most commonly acquired from a mesoscale numerical weather model, is passed on to a microscale model. In this paper, we demonstrate how the recently developed profile‐assimilation technique, a form of mesoscale‐to‐microscale coupling, can be used to drive large‐eddy simulations solely based on observed mean‐flow profiles at a single location, bypassing the need for auxiliary mesoscale simulations. The new approach is evaluated for a diurnal cycle at the Scaled Wind Farm Technology site. Observed mean‐flow profiles from the ground up to a height of 2 km are reconstructed by aggregating measurements from multiple instruments, and gaps in the data are infilled with natural neighbor interpolation. We perform nine simulations using various forcing approaches to deal with data limitations. The results show that it is indeed possible to drive microscale large‐eddy simulation with observations using the profile‐assimilation technique, notwithstanding large gaps in virtual potential temperature measurements. However, profile assimilation with vertical smoothing of the error between the desired and actual profiles is required. Without that smoothing, the microscale simulations develop unrealistically high turbulence levels under many situations. Finally, we show that simulated mesoscale data can account for missing observations, although care is needed as both data sources are not necessarily compatible.

Fracture behavior and size effect of UHPFRC: Experimental and meso-scale numerical investigation

In this research, the fracture characteristics and size effect of ultra-high performance fiber reinforced concrete (UHPFRC) are investigated by notched three-point bending beam test and meso-scale numerical simulation. Four series of geometrically similar UHPFRC notched-beams with different sizes were tested at quasi-static condition. Crack patterns during the fracture process were obtained by digital image correlation and the phenomenon of crack initiation, multiple cracking and crack localization were well captured. The macro-scale fracture properties are analyzed by double-K fracture model (DKFM) from the perspective of composite toughening. Results show that the initial fracture toughness evaluates the toughness of matrix and the increment from initial fracture toughness to unstable fracture toughness evaluates the toughening due to fiber bridging. The initial fracture toughness and unstable fracture toughness have no size effect and can be treated as material inherent. Experimental results also show a slight decreasing tendency of strength with the increasing of specimen size, which can be approximately fitted by size effect law (SEL). Then a meso-scale numerical model was developed by explicit representation of fibers and directly considering bond-slip relationship of fiber–matrix. Simulation results show that the meso-scale numerical model developed in this research can well simulate the fracture of UHPFRC in terms of crack patterns, fracture process zone, mechanical response and size effect. Parametric study was also conducted based on the meso-scale numerical model and the effects of fiber content, fiber orientation and interfacial bond strength on fracture performance and size effect are analyzed.