Accurate Simulation Research Articles

Bridging the gap: an interpretable coupled model (SWAT-ELM-SHAP) for blue-green water simulation in data-scarce basins

Blue water (BW) and green water (GW) are crucial components of the hydrological cycle, but their accurate simulation and interpretation remain challenging in data-scarce basins. We propose the SWAT-ELM-SHAP model, coupling the Soil and Water Assessment Tool (SWAT), Ensemble Learning Model (ELM), and Shapley Additive Explanations (SHAP) method. This novel approach bridges the gap between a physically-based hydrological model, a data-driven machine learning (ML) model, and a holistically-interpreted SHAP method, offering accurate blue-green water simulation and holistic result interpretation for improved water resources management in data-scarce basins. We took the transfer simulation of blue-green water from the Xiangjiang River Basin (source basin, 82,900km²) to the Zishui River Basin (target basin, 28,142km²) as a case study to test and evaluate the feasibility of the coupled model during 1991-2022. The model performance results indicate that the simulation accuracy of our new coupled model is improved in data-scarce basins. In combination with hydrological response features generated by SWAT and meteorological features as the ELM input, our model enhances the daily blue-green water simulation. The Nash-Sutcliffe Efficiency coefficient (NSE) for BW, Green water flow (GWF), and Green water storage (GWS) consistently exceeds 0.77 during the calibration period (1991-2010) and exceeds 0.8 during the testing period (2011-2022). The interpretation results of coupled model demonstrate that SHAP holistic interpretation provides good interpretability for blue-green water simulation results in data-scarce basins. In general, the SWAT-ELM-SHAP offers a referenced approach that can reliably and efficiently simulate blue-green water in data-scarce basins, but more importantly, can further our understanding of the potential causal relationships, influence mechanisms, and variation mechanisms of blue-green water under changing environmental conditions.

Cross-Seasonal Effect on Tropical Pacific Precipitation: Implication of South Pacific Quadrupole Simulation in CMIP6

Abstract The tropical Pacific convergence zone plays a crucial role in the global climate system. Previous research studies emphasized the cross-seasonal influence of the South Pacific quadrupole (SPQ) mode on the tropical Pacific climate. This study assesses the relationship between austral summer SPQ and austral winter tropical precipitation in phase 6 of the Coupled Model Intercomparison Project (CMIP6) models. The analysis emphasizes the historical experiments conducted within this time frame, spanning from 1979 to 2014. Our findings reveal that the SPQ is accurately represented in all CMIP6 models, but the connection between SPQ and precipitation is inadequately simulated in most models. To investigate the reasons behind these intermodel differences in reproducing SPQ-related processes, we categorize models into two groups. The comparisons demonstrate that the fidelity of model simulations in replicating the SPQ–tropical precipitation relationship hinges significantly on their capacity to reproduce the positive wind–evaporation–sea surface temperature (WES; SST) feedback over both the southwestern Pacific (25°–10°S; 150°E–160°W) and the southeastern Pacific (30°–10°S; 140°–80°W). This positive WES feedback propagates the SPQ signal into the tropics, intensifying the meridional gradient of SST anomaly in the tropical western-central Pacific, which consequently amplifies convection and rainfall in that area. In the group of models that failed to simulate this relationship accurately, the weakened WES feedback can be traced back to biases in wind speed and its variation. Furthermore, this WES feedback establishes a connection between SPQ and El Niño–Southern Oscillation (ENSO). A better rendition of the SPQ–tropical rainfall connection tends to result in a better simulation of the onset of SPQ-related ENSO events. As a result, this study advances our comprehension of extratropical impacts on the tropics, with the potential to enhance the accuracy of tropical climate simulation and prediction. Significance Statement Tropical rainfall plays an important role in the global climate system. Beyond the well-known influence of El Niño–Southern Oscillation (ENSO) on the tropical rainfall, the sea surface temperature (SST) anomaly in the South Pacific has a cross-seasonal impact on the precipitation over the tropical Pacific via air–sea coupled processes. Such SST anomaly pattern shows a quadrupole structure in the extratropical South Pacific, known as the South Pacific quadrupole (SPQ) mode. However, the relationship between SPQ and tropical precipitation remains poorly simulated in most state-of-the-art climate models. One primary reason for this gap between observed and simulated relationships is the underestimation of wind speed and its variation over the south tropical Pacific in these models. This limitation undermines their ability to accurately represent the air–sea interactions that drive tropical precipitation patterns, leading to inaccuracies in simulations. Our study aims to bridge this knowledge gap by enhancing our understanding of the extratropical effects on the tropical Pacific. By exploring the mechanisms underlying the SPQ–precipitation connection, we expect to improve the simulation and prediction capabilities of tropical climate models, thereby enhancing our ability to forecast and adapt to future climatic changes.

Developing the optimized control scheme for digital light processing 3D printing by combining numerical simulation and machine learning-guided temperature prediction

Digital light processing (DLP) 3D printing has attracted significant attention for its rapid printing speed, high accuracy, and diverse applications. However, the continuous DLP printing process releases substantial heat, resulting in a swift temperature rise in the curing area, which may lead to printing failures. Due to the lack of effective means to measure real-time temperature changes of the curing surface during continuous DLP 3D printing, the prevailing approach is to predict temperature variations during printing via numerical simulation. Nevertheless, temperature prediction methods relying solely on numerical simulation tend to be slow and overlook heat exchange dynamics during printing, potentially resulting in prediction inaccuracies, particularly for complex models. To address these issues, this paper proposes a method to combine numerical simulation and a machine learning approach for temperature prediction in the DLP 3D printing process, along with a printing control scheme generation method. Firstly, the m+nth order autocatalytic kinetic model considering the light intensity and the Beer–Lambert law are employed to formulate the heat calculation equation for the photopolymer resin curing reaction. Subsequently, a heat exchange calculation equation is established based on Fourier heat conduction law and Newton’s cooling equation. A numerical simulation model for temperature changes during the printing process is then developed by integrating the heat calculation equation, heat exchange calculation equation, and measurement data from Photo-DSC. Furthermore, a temperature measurement device for the printing process is designed to validate the accuracy of the numerical simulation. Following this, an improved Long Short-term Memory (LSTM) network is proposed, using temperature change data generated by the numerical simulation model to train the network for rapid (2×10−4s/layer) prediction of temperature changes during printing. Finally, aiming for the shortest printing time, an optimized control scheme planning algorithm and a target function are designed based on the model’s temperature change data and the monomer’s flash point to ensure the temperature remains below this threshold. This algorithm can automatically generate the optimal printing control scheme for any model. Experimental results demonstrate that the proposed temperature prediction method can predict temperature variation accurately. Based on this, the generated printing control scheme can guarantee efficient and high-quality manufacturing for any model.

Fast thermal simulation of WAAM processing: toward manufacturing strategy evaluation

Managing the quality of parts produced by the Wire Arc Additive Manufacturing (WAAM) process presents a significant challenge, particularly due to the complexity of thermal control. Effective thermal management is crucial for minimizing defects, making fast and accurate thermal simulations essential for testing and optimizing various manufacturing strategies. This article proposes a rapid simulation that decouples the calculation of heat conduction from convection and radiation. The proposed simulation is described and validated against experimental data. The influences of spatial and temporal discretization are examined. In conclusion, this developed approach provides a fast and efficient simulation of a manufacturing strategy for improvement.

Visualized generator for the simulation of wastewater quality and quantity variations in the sewer system

Data generators are imperative to support design, management, scenario simulation, risk assessment, and regulatory compliance. Hybrid sewer systems struggle with accurate water quality and quantity monitoring due to variable flow patterns, missing connections, limited monitoring capacity. To accurately regenerate operational data for hybrid sewer system along the sewer shed, a visualized generator was developed to simulate wastewater quantity and quality variations within different scales in the sewer system. The generator was constructed using a multi-level, tree-structured model incorporating various modules, including domestic, industrial, WWTP, and pump stations, to simulate time series variations. A novel instantaneous unit pollutant-hydrograph modeling associated with wastewater conductivity monitoring data was proposed in the generator. The validated generation data of flow, COD, ammonia nitrogen, and phosphate were well-fitted with the full-scale measured data in residential areas, pump stations, and WWTPs. The proposed generator could be used to predict and simulate the dynamic flow and wastewater quality variations at different scale regions in sewer network-wide to support the operation and management of pump stations and WWTPs. The generator modules enable accurate simulation and visualization of water quality and quantity in hybrid sewer system, enhancing the understanding of infiltration, inflow, and pollutant dynamics, especially under challenging conditions like simultaneous RDII and overflow.

Optimizing parameter estimation in hydrological models with convolutional neural network guided dynamically dimensioned search approach

Hydrological model calibration plays a crucial role in estimating optimal parameters for accurate simulation. Estimation of parameters is inevitable in hydrological modeling due to the challenge of directly measuring them, as most parameters are conceptual descriptions of physical processes. Modellers commonly employ optimization algorithms for calibrating hydrological models. However, these algorithms often pose computational challenges, especially when dealing with complex physics-based and distributed models. To address these challenges, our study introduces a novel approach called hydroCNN+DDS. By leveraging the strengths of Convolutional Neural Networks (CNN) and the Dynamically Dimensioned Search (DDS) algorithm, hydroCNN+DDS simplifies the model calibration process in complex physics-based models. This approach enables to capture the general patterns and relationships between discharge time series and parameters without compromising the underlying physics. We use hydroCNN+DDS to estimate parameters in the highly parameterized hydrological model, Structure for Unifying Multiple Modeling Alternatives (SUMMA) using hourly observed discharge. Notably, hydroCNN quickly generates sub-optimal parameters, serving as a good initial solution for DDS. This initialization aids DDS in converging faster towards an optimal solution. One of the notable advantages of the hydroCNN+DDS approach is its potential for spatial and temporal transferability. This feature proves valuable in dynamic systems and regions with limited historical data, expanding the applicability of the methodology. Furthermore, our proposed methodology is versatile and can be applied to any simple or complex models, accommodating any variables of interest. The best practices of good model calibration are followed in our approach.

Benchmarking numerical simulation of induced fault slip with semi-analytical solutions

AbstractPore pressure fluctuation in subsurface reservoirs and its resulting mechanical response can cause fault reactivation. Numerical simulation of such induced seismicity is important to develop reliable seismic hazard and risk assessments. However, modeling of fault reactivation is quite challenging, especially in the case of displaced faults, i.e., faults with non-zero offset. In this paper, we perform a systematic benchmarking study to validate two recently developed numerical methods for fault slip simulation. Reference solutions are based on a semi-analytical approach that makes use of inclusion theory and Cauchy-type singular integral equations. The two numerical methods both use finite volume discretizations, but they employ different approaches to represent faults. One of them uses a conformal discrete fault model (DFM) while the other employs an embedded (non-conformal) fault model. The semi-analytical test cases cover a vertical frictionless fault, and inclined displaced faults with constant friction and slip-weakening friction. It was found that both numerical methods accurately represent pre-slip stress fields caused by pore pressure changes. Moreover, they also successfully cope with a vertical frictionless fault. However, for the case with an inclined displaced fault with a constant friction coefficient, the embedded method can not converge for the post-slip phase, whereas the DFM successfully coped with both constant and slip-weakening friction coefficients. In its current implementation, the DFM is therefore the model of choice when accurate simulation of local faulted systems is required.

Miniaturized on-chip optical differentiator based on 2F-structured metasurfaces.

Analog optical computing based on Fourier optics has attracted ever-growing attention, offering unprecedented low power consumption and high parallelism computation at the speed of light. Typically, classical optical 4F systems have been widely employed as one of the most common approaches for analog optical computing. However, most existing schemes replicate the original architecture relying on two Fourier transforms and one spatial-frequency filtering, resulting in bulky size and complex structure. Here, we propose a novel, to the best of our knowledge, on-chip 2F structure that achieves ultra-miniaturized optical analog computing. Taking advantage of the exceptional design flexibility of metasurfaces, we reduce the optical path length through a combination of phase compensation and complex amplitude modulation, thereby significantly simplifying the system structure without sacrificing accuracy compared to the traditional 4F system. As a proof-of-concept demonstration, we design and fabricate an on-chip optical differentiator on a silicon-on-insulator platform, achieving 84.01% and 79.81% differentiation accuracy in simulation and experiment, respectively.

Algorithm for simulating calligrapher’s stroke features using neural networks

In the field of calligraphy art, simulating the stroke features of calligraphers has always been a challenging task. Traditional methods often rely on manually designed rules or feature extraction algorithms, which are difficult to accurately capture calligraphy details and time-consuming. This article aimed to explore a more effective method to simulate the stroke features of calligraphers using neural network technology. This article mainly explored the simulation algorithm of calligrapher stroke features based on neural networks, including calligrapher stroke feature statistics, calligrapher stroke feature abstraction, and calligrapher stroke feature extraction. By using Convolutional Neural Network (CNN) to learn and analyze calligraphy works, a better performance neural network model was established to achieve automatic recognition and classification of calligrapher stroke features. By continuously optimizing model parameters, the accuracy of calligrapher stroke feature simulation can be improved. The average similarity rates for imitating stroke features of five calligraphers (regular script, cursive script, clerical script, seal script, and running script) using Artificial Neural Network (ANN) were 0.72, 0.62, 0.40, 0.33, and 0.53, respectively. The average similarity rates for imitating stroke features of 5 calligraphers (regular script, cursive script, clerical script, seal script, and running script) using CNN were 0.93, 0.78, 0.87, 0.67, and 0.80, respectively. The research results of this article promoted the inheritance and innovation of calligraphy art, and expanded the expression forms and application fields of calligraphy art.

Simulating Real-Time Molecular Electron Dynamics Efficiently Using the Time-Dependent Density Matrix Renormalization Group.

Compared to ground-state electronic structure optimizations, accurate simulations of molecular real-time electron dynamics are usually much more difficult to perform. To simulate electron dynamics, the time-dependent density matrix renormalization group (TDDMRG) has been shown to offer an attractive compromise between accuracy and cost. However, many simulation parameters significantly affect the quality and efficiency of a TDDMRG simulation. So far, it is unclear whether common wisdom from ground-state DMRG carries over to the TDDMRG, and a guideline on how to choose these parameters is missing. Here, in order to establish such a guideline, we investigate the convergence behavior of the main TDDMRG simulation parameters, such as time integrator, the choice of orbitals, and the choice of matrix-product-state representation for complex-valued nonsinglet states. In addition, we propose a method to select orbitals that are tailored to optimize the dynamics. Lastly, we showcase the TDDMRG by applying it to charge migration ionization dynamics in furfural, where we reveal a rapid conversion from an ionized state with a σ character to one with a π character within less than a femtosecond.

Computation Models for Tsunami Wave Propagation using Extended Cellular Automata

Using traditional models to simulate the propagation of large-scale tsunami waves leads to low accuracy and low efficiency. To ensure higher timing accuracy of tsunami wave spread, built an enhanced model that connects cellular automata with an existing model of tsunami waves. To determine the ideal time step value, our approach considers how time steps affect simulation accuracy. The spread of tsunami waves using a two-dimensional cellular automata model was used to test the model. Extending cellular automata (CA) models for tsunami wave propagation involves incorporating additional complexity and realism to better simulate the dynamics of these natural phenomena. The findings indicate that 1/4 of the time required for all of the cellular material to be traversed is the ideal time step for the tsunami wave spread global cellular automata simulation program. With a mean accuracy of 86.78% and a mean Kappa coefficient of 0.6443, this model demonstrated strong temporal and spatial consistency when compared to historical tsunami wave data from NOAA. Combining the Kappa coefficient with extended cellular automata (ECA) can be beneficial in various applications, particularly those involving classification tasks or spatial modeling. The earliest arrival tsunami wave spread can be predicted and simulated using this approach.

Influence of a City Block on ES-CFD Coupled Analysis

Coupled analysis using the complementary methods of energy simulation (ES) and computational fluid dynamics (CFD) can improve the calculation accuracy of thermal environment simulations. However, existing studies on ES-CFD coupled analyses that consider the effects of solar radiation and surrounding conditions have been insufficient. In practice, net solar radiation fluctuates, owing to the influence of urban blocks, and the solar radiation incident on the interior determines the heating range of the interior, which results in fluctuations in the convective heat transfer coefficient. This study conducted an ES-CFD coupled analysis to examine differences in the convective heat transfer coefficients due to the different insolation conditions and the surroundings of target buildings. The risk of condensation was evaluated using the dew point temperature in the analysis model, and a neutral insulation performance was employed in the set cases with the presence or absence of urban streets as a variable. Buildings within urban city blocks were observed to have a lower dew point temperature and a higher risk of condensation, which is a reasonable assessment. The results of this study will contribute significantly to the development of comprehensive simulation technologies.

The Distributed Xin’anjiang Model Incorporating the Analytic Solution of the Storage Capacity Under Unsteady-State Conditions

Developing a functional linkage between hydrological variables and easily accessible terrain and soil information is a novel concept for distributed hydrological models. This approach aims to address limitations imposed by data scarcity and high computational demands. The model hypothesizes that the relationship between the evaporation flux and the absolute value of the matric potential follows a power exponential pattern. Analytic solutions for the groundwater depth, the evaporation capacity, and the storage capacity are derived with respect to the topographic index, considering the relationship between the groundwater depth and the topographic index and the influence of setting off. Subsequently, a distributed Xin’anjiang Model using the analytic solution of the storage capacity under unsteady-state conditions is constructed. This new model is employed to simulate soil moisture and discharge in the Tarrawarra Watershed. The simulation results for soil moisture and discharge are compared with those from the Storage Capacity Model and the DHSVM. Additionally, the computational speeds of all three models are compared. The findings indicate that the simulation accuracy of the new model for soil moisture and discharge surpasses that of the Storage Capacity Model and the DHSVM. Meanwhile, the computational speed of the new model is significantly faster than the DHSVM and slightly slower than the Storage Capacity Model. It offers a balance between computational efficiency, predictive accuracy, and physical mechanism representation. The data requirements of the new model are minimal and easy to procure, and it requires less computational effort. Moreover, it accurately captures the spatial and temporal dynamics of soil moisture and the discharge process of the watershed.

Three-dimensional quasi-complete information numerical simulation of gravity anomalies and parallel computing on CPU-GPU

Abstract In order to improve the efficiency and accuracy of numerical simulation of gravity anomalies in large-scale complex models, this paper proposes a method for three-dimensional numerical simulation of gravity anomalies under arbitrary terrain, and implements its CPU-GPU parallel acceleration scheme. By performing a two-dimensional Fourier transform along the horizontal direction, three-dimensional partial differential satisfied by gravitational potential are transformed into one-dimensional ordinary differential equations in different wavenumbers, reducing computational and storage requirements. Moreover, the ordinary differential equations in different wavenumbers are independent of each other and have good parallelism. By retaining the vertical direction as the spatial domain and introducing traveling wave decomposition, the effects caused by upper and lower boundaries are eliminated. The vertical grid is arbitrary. A two-dimensional quasi-complete information Fourier transform is utilized to enhance both the accuracy and efficiency of the Fourier transform process, allowing both uniform and non-uniform flexible sampling. Based on the high parallelism, one-dimensional ordinary differential equations are solved in parallel using CPU, and quasi-complete information Fourier transform are computed in parallel using GPU. An abnormal sphere is designed to analyze the wavenumber spectral distribution characteristics of the anomalous potential and summarize the wavenumber sampling rules. The logarithmic uniform sampling in the wavenumber domain has high accuracy. Compared with Gauss-FFT, the quasi-complete information Fourier transform selects fewer wavenumbers and exhibits higher accuracy. The computational efficiency is greatly enhanced through the use of single-precision CPU-GPU parallel scheme, and a model with tens of millions of nodes only takes a few seconds. Two terrain models are designed to verify the algorithm’s adaptability to arbitrary complex terrains. This is significant for achieving fine inversion imaging and interpretation of gravity anomalies under large-scale complex conditions.

Internal physiological drivers of leaf development in trees: Understanding the relationship between non‐structural carbohydrates and leaf phenology

Abstract Plant phenology is crucial for understanding plant growth and climate feedback. It affects canopy structure, surface albedo, and carbon and water fluxes. While the influence of environmental factors on phenology is well‐documented, the role of plant intrinsic factors, particularly internal physiological processes and their interaction with external conditions, has received less attention. Non‐structural carbohydrates (NSC), which include sugars and starch essential for growth, metabolism and osmotic regulation, serve as indicators of carbon availability in plants. NSC levels reflect the carbon balance between photosynthesis (source activity) and the demands of growth and respiration (sink activity), making them key physiological traits that potentially influence phenology during critical periods such as spring leaf‐out and autumn leaf senescence. However, the connections between NSC concentrations in various organs and phenological events are poorly understood. This review synthesizes current research on the relationship between leaf phenology and NSC dynamics. We qualitatively delineate seasonal NSC variations in deciduous and evergreen trees and propose testable hypotheses about how NSC may interact with phenological stages such as bud break and leaf senescence. We also discuss how seasonal variations in NSC levels, align with existing conceptual models of carbon allocation. Accurate characterization and simulation of NSC dynamics are crucial and should be incorporated into carbon allocation models. By comparing and reviewing the development of carbon allocation models, we highlight the shortcomings in current methodologies and recommend directions to address these gaps in future research. Understanding the relationship between NSC, source–sink relationships, and leaf phenology poses challenges due to the difficulty of characterizing NSC dynamics with high temporal resolution. We advocate for a multi‐scale approach that combines various methods, which include deepening our mechanistic understanding through manipulative experiments, integrating carbon sink and source data from multiple observational networks with carbon allocation models to better characterize the NSC dynamics, and quantifying the spatial pattern and temporal trends of the NSC‐phenology relationship using remote sensing and modelling. This will enhance our comprehension of how NSC dynamics impact leaf phenology across different scales and environments. Read the free Plain Language Summary for this article on the Journal blog.

Numerical simulation and validation of heavy rainfall flood inundation in small watersheds in undocumented mountainous areas

ABSTRACT With global warming and the increase in extreme precipitation events, floods are becoming more frequent in mountainous areas, and the safety of lives and property of people is seriously threatened. However, understanding of the flooding process in uninformative mountainous areas is limited due to the lack of high-quality hydrometeorological data. Hence, this study adopts the MIKE21 model to simulate flood inundation in the Shadai River basin in the Qilian Mountain region of the northern Tibetan Plateau as an example. This validates the model-simulated flow and inundation extent using the flow data obtained from the calculation of the flood trace points, extent of inundation, and high-resolution remote sensing images. The results show that the flash flood inundation mainly occurs at 12:00–01:00 AM on 18 August 2022, and the simulated and actual maximum inundation areas are 7.9 and 9.5 km2, respectively. The fitted F-statistic value is 0.81, and the relative error between the calculated flow rate of the flood trace point and the model-simulated flow rate is 8%, indicating good consistency. Furthermore, an in-depth exploration of the model parameter sensitivity reveals that the use of distributed Manning's roughness coefficient value has higher simulation accuracy.

The concept of big data and predictive analytics in reservoir engineering: The future of dynamic reservoir models

Reservoir engineering is a critical discipline in the oil and gas industry, aimed at optimizing the extraction and management of subsurface resources. Traditionally, reservoir models have relied on static data and conventional simulation methods, which often fall short in capturing the dynamic complexities of reservoirs. With the advent of big data and predictive analytics, there is a significant opportunity to revolutionize reservoir modeling by integrating real-time data for continuous updates and more accurate predictions. This review introduces a theoretical exploration of how big data and predictive analytics can transform dynamic reservoir models, highlighting their potential to improve reservoir management and hydrocarbon recovery. Big data technologies enable the collection, storage, and processing of vast amounts of structured and unstructured data from multiple sources, including well logs, seismic surveys, drilling operations, and production data. Predictive analytics, through machine learning and statistical techniques, can extract actionable insights from these datasets to predict reservoir behavior, enhance decision-making, and optimize production strategies. This integration facilitates real-time monitoring and adaptive reservoir management, allowing engineers to respond to changing reservoir conditions and uncertainties more effectively. The review further explores the implications of big data-driven predictive models for enhanced hydrocarbon recovery techniques, such as water flooding and gas injection. By automating data processing and integrating real-time field data, predictive analytics can improve the accuracy of reservoir simulations, reduce downtime, and increase operational efficiency. Ultimately, the future of dynamic reservoir modeling lies in the seamless integration of big data and predictive analytics, providing a pathway to smarter, more sustainable resource management and maximized recovery factors in increasingly complex reservoir environments. Keywords: Big Data, Predictive Analytics, Reservoir Engineering, Dynamic Reservoir, Models.

Numerical Simulations for Calibration Setup for Dynamic Contrast-Enhanced Ultrasonography Imaging Protocol.

This study presents an investigation of an innovative microfluidic flow separator using both numerical and experimental approaches to calibrate contrast-enhanced ultrasound scanners. Numerical simulations were conducted using Lagrangian particles tracking and passive scalar transport methodologies using the OpenFOAM software. The experimental validation confirmed the accuracy of the numerical simulations, particularly at an imposed total pressure of , showing an excellent agreement in particle distributions. The study emphasizes the computational efficiency and modeling of passive scalar transport, providing valuable understanding into the behavior of scalar quantities in microfluidic systems. An optimized diffusion coefficient value of was identified, showing its critical role in achieving accurate simulation results and optimizing the performance of microfluidic flow separators for contrast-enhanced ultrasound scanner calibration.

Microscopic Modeling Techniques for Steel Fiber Reinforced Concrete: Applications and Future Directions

This article reviews micro-modeling methods for steel fiber-reinforced concrete (SFRC), including finite element, discrete element, and computational fluid dynamics approaches, to analyze its mechanical and structural behavior. The models offer predictive insights on stress response, crack propagation, and durability in SFRC, particularly in high-stress structural applications. This study discusses the limitations of current modeling practices, such as high computational demand and parameter determination challenges, and suggests future improvements to enhance simulation accuracy and scalability. This review aims to support the advancement of SFRC in structural engineering applications.

Finite element modeling of the influence of FRP techniques on the seismic behavior of an arch stone bridge of historical interest

In this paper, a masonry bridge is simulated in order to assess both its structural and seismic vulnerability. Therefore, the present study aimed to approximately analyze the real behavior of Mikron Bridge structure. The modeling was conducted by combining the finite element method (FEM) and discrete element method (DEM) using the ABAQUS® software. By comparing the results of numerical and experimental modal analyses, the accuracy of simulation was verified. To simulate the seismic behavior of the Mikron Bridge, the component of Erzincan earthquake occurred in 1992 was used. The results extracted from the seismic analysis show that some parts of the bridge structure are damaged but not destroyed.