Accurate Simulation Research Articles

Shooting Simulator «Inhibitor»: Mathematical Support of Shot Special Effects

The algorithms for preparing shooters to perform an exercise, conducting its course with real-time informing, as well as the implementation of mathematical support of shot special effects (sounds, tracers, explosions, hits, and misses) for the optical-electronic shooting simulator 'Inhibitor' developed at Institute of Mechanics UdmFRC UB RAS and at facilities of Computer Department of Kalashnikov ISTU jointly with JSC «Kalashnikov» Concern» are described. A tactical and technical task is given for the visual and sound effects of a shot, such as tracers, grenade ruptures, misses, return fire, and controlled flight of PTURS. In addition, the manager must monitor the weapon simulator sensors during the preparation of the exercise (magazine, fire switch, cartridge inside the barrel and sight readings) and conduct the course of the exercise with real-time viewing of both targets and shots and altering the ammunition number and type (all ammunition can be made tracer ones). The arrows are selected from the department database for convenience. Viewing shooting errors and aiming along with hit points allows correction of trainees’ actions by means of commands and quick display service information for trainee independent classes on the projection screen. The study the factors affecting ballistic dispersion was carried out and the recommendations for its modeling were formulated. The literature review confirms the potential of further research and development of electronic rifle simulators via improvement of computational tools and the development of software libraries in order to increase the accuracy of shot process simulation and flexible display adjustment of current training exercise results. It is necessary to expand realistic opportunities of shot special effects and reduce the cost constantly, which means to increase the competitiveness of electronic shooting simulators.

A Composite Approach for Evaluating Operational Cloud Seeding Effect in Stratus Clouds

Robust water management is in intense demand in many water scarcity areas, such as arid and semi-arid regions in the world. As part of the regional water management strategy, rain enhancement is vital to replenish groundwater reservoirs, and the key challenge is how to assess its effectiveness. Some recent weather modification experiments attained cloud seeding effect through advanced in situ measurement coupled with accurate numerical simulation. However, there is still a lack of an objective and scientific approach to quantitatively evaluate the rain enhancement effect, especially for many non-randomized operational cloud seeding activities in China. In this study, we proposed a composite evaluation approach by analyzing two operational aircraft cloud seeding cases in stratus clouds in Shaanxi, China. By calculating the aircraft cloud seeding agent plumes, the target areas (as well as the control areas) of cloud seeding were dynamically and roughly determined. Physical properties, such as radar reflectivity and precipitation, were individually quantified in these areas. The cloud seeding effect was then evaluated by calculating the difference in parameter variation between target and control areas. This approach can be applied to qualitative analysis in a single aircraft cloud seeding operation and can also provide quantitative statistical results from multiple cloud seeding cases. We found that the average precipitation enhancement percentage of 18 operational aircraft cloud seeding cases is ~4.84%. Note that the homogeneity hypothesis of the seeding cloud, the error in the calculation of the target area, and the selection of control areas are the major uncertainties likely in the evaluation of the cloud seeding effect by this approach.

Comparative Assessment of Water Models in Protein-Glycan Interaction: Insights from Alchemical Free Energy Calculations and Molecular Dynamics Simulations.

Accurate computational simulations of protein-glycan dynamics are crucial for a comprehensive understanding of critical biological mechanisms, including host-pathogen interactions, immune system defenses, and intercellular communication. The accuracy of these simulations, including molecular dynamics (MD) simulation and alchemical free energy calculations, critically relies on the appropriate parameters, including the water model, because of the extensive hydrogen bonding with glycan hydroxyl groups. However, a systematic evaluation of water models' accuracy in simulating protein-glycan interaction at the molecular level is still lacking. In this study, we used full atomistic MD simulations and alchemical absolute binding free energy (ABFE) calculations to investigate the performance of five distinct water models in six protein-glycan complex systems. We evaluated water models' impact on structural dynamics and binding affinity through over 5.8 μs of simulation time per system. Our results reveal that most protein-glycan complexes are stable in the overall structural dynamics regardless of the water model used, while some show obvious fluctuations with specific water models. More importantly, we discover that the stability of the binding motif's conformation is dependent on the water model chosen when its residues form weak hydrogen bonds with the glycan. The water model also influences the conformational stability of the glycan in its bound state according to density functional theory (DFT) calculations. Using alchemical ABFE calculations, we find that the OPC water model exhibits exceptional consistency with experimental binding affinity data, whereas commonly used models such as TIP3P are less accurate. The findings demonstrate how different water models affect protein-glycan interactions and the accuracy of binding affinity calculations, which is crucial in developing therapeutic strategies targeting these interactions.

Daily NO2 Simulation Research Based on Automatic Machine Learning Ensemble Models

To understand the spatial distribution of NO2 near the surface, we utilized measured data from NO2 monitoring stations and combined it with column concentration data from the Tropospheric Monitoring Instrument （TROPOMI）, taking the Yangtze River Delta region as the study area. We considered the impact of factors such as population, elevation, and meteorological conditions on NO2 levels. We used automated machine learning to select five machine-learning algorithms with high simulation accuracy, namely ET, RF, XGBoost, LightGBM, and Catboost, and then integrated these five algorithms using the Stacking model to simulate the daily NO2 concentration in the Yangtze River Delta region from March 2020 to February 2021. The results indicated that the RMAE and MAE values of the Stacking ensemble model were 7.078 and 5.270, respectively, which outperformed the single algorithms of ET, RF, XGBoost, LightGBM, and Catboost. The spatial distribution of high NO2 concentrations in the Yangtze River Delta region, consisting of three provinces and one municipality, exhibited a U-shaped pattern with the convergence point located at the intersection of the three provinces, extending towards the southwest. Notably, urban pollution was particularly significant in the urban agglomerations centered around Shanghai, Hangzhou, Nanjing, and Hefei. There were 27 cities that exceeded the national standard daily limit. Changzhou was the city with the most serious NO2 pollution, with the NO2 concentration exceeding the standard for 14 d, followed by Shanghai, with 13 d. In terms of seasonal variation, the order of severity was as follows： winter, autumn, spring, and summer, with the least NO2 pollution occurring on July 9th during the summer, and the most severe NO2 pollution was observed on December 23rd during the winter.

Artificial intelligence‐driven sustainability: Enhancing carbon capture for sustainable development goals– A review

AbstractArtificial intelligence (AI) and environmental points are equally important components within the response to local weather change. Therefore, based on the efforts of reducing carbon emissions more efficiently and effectively, this study tries to focus on AI integration with carbon capture technology. The urgency of tackling climate change means we need more advanced carbon capture, and this is an area where AI can make a huge impact in how these technologies are operated and managed. It will minimize manufacturing emissions and improve both resource efficiency as well as our planet's environmental footprint by turning waste into something of value again. Artificial intelligence could be leveraged to analyze huge data sets from carbon capture plants, searching for optimal system settings and more efficient ways of identifying patterns in the available information at a larger scale than currently possible. In addition, AI incorporated sensors and monitoring mechanisms in the supply chain can identify any operational failure at reception itself allowing for timely action to protect those areas. Artificial intelligence also helps generative design for carbon capture materials, which allows researchers to explore new types of carbon‐absorbing material, including metal–organic frameworks and polymeric materials that are important in industrial CO2, such as moisture. In addition, it increases the accuracy of reservoir simulations and controls CO2 injection systems for storage or enhanced oil recovery. Through applying AI algorithms on reservoir geology, production performance and real‐time data this study would like to facilitate the optimization of injection processes as well as minimize CO2 emissions while assuring a maximum efficiency. Artificial intelligence integrates with renewable‐based carbon capture efforts that can be employed by AI‐driven smart grid systems to improve carbon capture methods.

Development of an improved mass transfer model for condensation with dynamic feedback regulation

In the field of condensation research, accurate and efficient numerical simulation models are highly desired. One of the widely used phase change mass transfer models is known as the Lee model. However, this model has limitations due to uncertainties in the mass transfer intensity factor, which can lead to simulation failures and impede large-scale engineering applications. This study presents an improved mass transfer model which employs a tuning function to dynamically and accurately modify the mass transfer intensity factor in each two-phase cell via feedback adjustment. This approach enables immediate and customized adjustment of the factor for each grid within the limit of the tuning function, significantly reducing the errors caused by repeated artificial adjustments and related uncertainties. The adjustment capabilities of the hyperbolic tangent and softsign functions are evaluated. The softsign function is superior in both regulation efficiency and accuracy. The model's accuracy is verified by comparing with previous experiments and simulations, as well as by the one-dimensional Stefan problem. The proposed model achieves high prediction accuracy, reduces the risk of simulation dispersion, and significantly enhances computational efficiency with the iteration number reduced by nearly half under certain conditions compared to the Lee model. Furthermore, the new model shows robustness as the prediction accuracy is insensitive to variations in critical parameters. The improved model is expected to provide valuable assistance for future research and applications in flow condensation with outstanding computational accuracy, efficiency, and stability.

Comparison and integration of physical and interpretable AI-driven models for rainfall-runoff simulation

Precise streamflow forecasting in river systems is crucial for water resources management and flood risk assessment. The Tagus Headwaters River Basin (THRB) in Spain is a key hydrological hub, providing regulated flow for agricultural, urban, and energy sectors, and facilitating water transfer to the Segura River Basin, a key semi-arid region. Given the basin's near-total allocation of water resources, accurate streamflow simulations are essential to optimize the socio-economic distribution and ensure sustainable management across both interconnected basins. This study conducts a comprehensive evaluation of the Soil and Water Assessment Tool (SWAT+), support vector regression (SVR), feed forward neural network (FFNN), and long short-term memory (LSTM) models in simulating the rainfall-runoff process at four gauging stations within the THRB. An ensemble machine learning technique is then applied to assess improvements in streamflow estimation. Results revealed that AI-based models significantly surpassed the SWAT+ model in performance. Furthermore, the application of an ensemble technique enhanced the precision of rainfall-runoff modeling by 18 to 26% during the calibration period and 4.1 to 9.2% during the validation period, compared to individual AI-based models. Additionally, the SWAT+ model's precision improved by 44 to 74% and 40 to 55% for the respective periods. The use of Shapley Additive Explanations (SHAP) methodology allowed the results of the ensemble with machine learning to be more interpretable by explaining how each model contributes to the prediction. This research offers significant contributions to hydrological modeling, highlighting the importance of ensemble techniques in elevating predictive accuracy for various river basins.

Modeling sediment movement in the shallow-water framework: A morpho-hydrodynamic approach with numerical simulations and experimental validation

This work presents a morpho-hydrodynamic model and a numerical approximation designed for the fast and accurate simulation of sediment movement associated with extreme events, such as tsunamis. The model integrates the well-established hydrostatic shallow-water equations with a transport equation for the moving bathymetry that relies on a bedload transport function. Subsequently, this model is discretized using the path-conservative finite volume framework to yield a numerical scheme that is not only fast but also second-order accurate and well-balanced for the lake-at-rest solution. The numerical discretization separates the hydrodynamic and morphodynamic components of the model but leverages the eigenstructure information to evolve the morphologic part in an upwind fashion, preventing spurious oscillations. The study includes various numerical experiments, incorporating comparisons with laboratory experimental data and field surveys.

Advancements in railway noise simulation: assessment of tools for virtual validation

In pursuit of more efficient and cost-effective measurement campaigns, the railway industry is embracing the prospect of virtual validation based on simulations. The primary obstacle lies in the need for noise simulation tools capable of accurately replicating complex sources, installation effects, noise propagation, ground reflection and, for some cases, the diffraction in the different train surfaces. This paper presents insights derived from European project Shift2Rail S2R-CFM-CCA-01-2019 Energy and Noise & Vibration WP6, concentrating on the validation and enhancement of two exterior noise simulation tools, tailored for railway applications. The paper covers four reference cases, simulating exterior noise at standstill with diverse noise sources and varying source locations and noise emission (i.e. electrical noise, cooling, etc.). A correlation with previously conducted measurements at typical receiver locations (7.5 meters from the track centerline) is carried out. The measurements were conducted in a "controlled" configuration, isolating as much as possible the analyzed noise source and gathering all the needed information about the ground and train geometry. The evaluation between measurements and simulations is done against a global and a 1/3rd octave band criteria with successful results. Finally, key features essential for achieving more accurate simulations are listed.

(G4-SEOPTIM): A GEANT4 application for shielded enclosure, designing and shielding optimizing: The case of a new Moroccan shielded enclosure

PurposeThis study aims to develop and validate a Geant4 simulation application using the latest G4RadioactiveDecayPhysics v5.1 library to optimize the shielding design of a Moroccan shielded enclosure. The goal is to ensure effective radiation protection by comparing simulation results with initial geometric calculations based on the attenuation law (Beer-Lambert Law) and implementing design optimizations to enhance safety and reduce costs. MethodsThe study began with a prototype design based on linear attenuation calculations, which were costly and offered limited patient safety. Using the Geant4 Monte Carlo toolkit, we simulated the decay of a Fluorine-18 source within the shielded enclosure to evaluate dose rate distributions. The simulation's accuracy was validated through comparison with experimental measurements, and the results informed the geometric optimization of the shielding enclosure. ResultsThe Geant4 simulations demonstrated that the current shielding design effectively reduces radiation doses to acceptable levels. However, the simulations also identified opportunities for further optimization of the enclosure's geometry, allowing for more precise and efficient use of shielding materials while maintaining safety standards. ConclusionThe study confirms the utility of Geant4 for optimizing radiation shielding designs. While the initial dimensions provided adequate protection, the simulation enabled more precise geometric optimization. This approach not only enhances radiation protection but also informs the design and construction of more efficient and cost-effective shielding enclosures. The adjustments made to the enclosure's faces significantly improved radiation protection, reducing the dose rate surrounding the shielded enclosure to acceptable levels.

Accurate carbon structure for prediction of reaction pathway during bio-coal production using torrefaction

The structural parameters of carbon were accurately determined through correction by employing a linear correlation method. Further, the nuclear Overhauser effect (NOE) was reduced in solid 13C-nuclear magnetic resonance spectra using a solid cross-polarization/magic angle spinning (CP/MAS) probe and the total suppression of sidebands sequence (TOSS). The reaction pathway during torrefaction was inferred from analysis of the accurate bond behavior and product distribution. The results show that torrefaction has a significant influence on the structural evolution of biomass. After correction, the relative deviations in the H/C ratio and aromaticity of the carbon structure decreased below 20 %. During torrefaction, the aliphatic carbon bonds were cleaved and aromatic carbon bonds were formed by dehydration, deoxygenation, oligomerization, aromatization, and Diels-Alder reaction. Torrefaction of biomass is divided into three stages. The first stage occurs below 220 °C, with disruption of the hydrogen bonds, a decrease in the number of Cal-O bonds, and the release of some CO2, CO, and furaldehyde. The second stage proceeds in the 220–260 °C range, where β-O-α bonds and glycoside bonds are broken, with the generation of ketones, furan, phenols, CO, and CO2. In the third stage between 260 and 300 °C, Cal-O and Cal-Cal bonds are broken. Bio-coal is composed of monomers of C20H14O3 with two aromatic rings linked by one aromatic ring. This significant, fundamental study enables accurate calculation and simulations for optimizing the industrial utilization of biomass.

Wind tunnel testing and modelling of a rod-airfoil setup: validation and 3D acoustic imaging

This work revisits the rod-airfoil benchmark experiment from Jacob et al. (2004) with the aim of performing a detailed numerical validation in the nearfield and far field as well as to perform 3D acoustic imaging. The setup consists of a rod near the leading edge of a NACA-0012 airfoil and is placed in a semi-open wind tunnel in anechoic conditions. In addition to a far-field microphone array, the setup is equipped with two planar microphone arrays as well as wall-mounted microphones on the closed wind tunnel section and on the airfoil. The setup is modelled using a second-order finite volume method, and particular attention is devoted to including installation effects and sensor response properties in order to guarantee simulation accuracy. Validation examples are provided, including single microphone spectra as well as the acoustic field reconstructed on the test object by means of 3D acoustic imaging using the planar microphone arrays. Finally, the experimentally-validated numerical model is used as a means to gain further insight into the tested object. For instance, the model allows to separate acoustical and turbulent effects present in the sensors located within the flow section.

The impact of heterogeneous accessibility to metro stations on land use changes in a bike-sharing context

The integration of urban rail transit and land use has been adopted as a crucial approach to fostering compact development in cities. Proximity to rail transit stations can increase the probability of land use changes, while few studies have analyzed the spatial heterogeneity of the impact of rail transit on land use changes. This study proposes a distance-decay function to delineate the spatial heterogeneity of metro station accessibility using bike-sharing data and examines the impact of metro stations accessibility on land use changes. Jiading New Town in Shanghai, China, is selected as our study case. By utilizing a non-linear distance-decay function to delineate metro station accessibility as a driving factor for training neural networks in a vector-based cellular automata model, an improvement in simulation accuracy is achieved compared to the model using a linear distance-decay function. This study could help establish more efficient strategies for promoting integrated development of rail transit and land use. The significance of this study lies in the generality of the optimized land use vector-based cellular automata model considering the spatial heterogeneity of metro station accessibility, which could also be applied to other situations, such as considering the accessibility to bus stops and road networks.

The Impact of Hydrogen on Flame Characteristics and Pollutant Emissions in Natural Gas Industrial Combustion Systems

To improve energy savings and emission reduction in industrial heating furnaces, this study investigated the impact of various molar fractions of hydrogen on natural gas combustion and compared the results of the Non-Premixed Combustion Model with the Eddy Dissipation Combustion Model. Initially, natural gas combustion in an industrial heating furnace was investigated experimentally, and these results were used as boundary conditions for CFD simulations. The diffusion flame and combustion characteristics of natural gas were simulated using both the non-premixed combustion model and the Eddy Dissipation Combustion Model. The results indicated that the Non-Premixed Combustion Model provided simulations more consistent with experimental data, within acceptable error margins, thus validating the accuracy of the numerical simulations. Additionally, to analyze the impact of hydrogen doping on the performance of an industrial gas heater, four gas mixtures with varying hydrogen contents (15% H2, 30% H2, 45% H2, and 60% H2) were studied while maintaining constant fuel inlet temperature and flow rate. The results demonstrate that the Non-Premixed Combustion Model more accurately simulates complex flue gas flow and chemical reactions during combustion. Moreover, hydrogen-doped natural gas significantly reduces CO and CO2 emissions compared to pure natural gas combustion. Specifically, at 60% hydrogen content, CO and CO2 levels decrease by 70% and 37.5%, respectively, while NO emissions increase proportionally; at this hydrogen content, NO concentration in the furnace chamber rises by 155%.

Dynamic fluid flow model for phosphate slurry pipeline: OCP main pipeline as case study

The transportation of phosphate slurry through large-scale pipelines presents significant challenges due to the complex behavior of multiphase flows, particularly with varying solid content, density, and dynamic viscosity. Efficient and accurate prediction of flow behavior is critical for optimizing the operation of such pipelines. This work aims to develop a dynamic computational model to simulate phosphate slurry flow in pipelines. The case study focuses on the OCP Group’s main slurry pipeline, which links the mining sites at Khouribga to the industrial plants at Jorf Lasfar, Morocco. This pipeline system spans a total length of 187.124 km, consisting of 5237 pipes with an inner diameter ranging from 0.8546 m to 0.8578 m, and features several elevation changes along its ground level. Using a section-averaged, dynamic approach and the Finite Volume scheme, the model computes essential flow parameters, including density, dynamic viscosity, and pressure, for the incompressible and non-Newtonian slurry and process water flows. The model’s accuracy is validated against on-site measured data, showing an average deviation of ±0.32% for outlet density, and below 10% for pressure along the pipeline, underscoring the model’s reliability. Additionally, a sensitivity analysis was conducted to illustrate the impact of key parameters on the predicted head losses and pressures along the pipeline. This analysis shows that the slurry viscosity is the most critical parameter, significantly influencing these predictions. This model provides high accuracy and reasonable CPU time for real-time simulation and monitoring, while also offering significant potential for optimizing pipeline operations and ensuring the reliability of phosphate transport.

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.

Optimization of an N2O Emission Flux Model Based on a Variable-Step Drosophila Algorithm

The application of intelligent process-based crop model parameter optimization algorithms can effectively improve both the model simulation accuracy and applicability. Based on measured values of soil N2O emission flux in wheat fields from 2020 to 2022, and meteorological data from 1971 to 2022, five parameters of the N2O emission flux module in the APSIM model were optimized using the variable step Fruit Fly algorithm (VSS-FOA). The optimized parameters were the soil nitrification potential, the range of concentrated KNH4 of ammonia and nitrogen at semi-maximum utilization efficiency, the proportion of nitrogen loss to N2O during the nitrification process, the denitrification coefficient, and the Power term P for calculating the denitrification water coefficient. Contrasting the optimized parameters using the VSS-FOA algorithm versus the default values supplied with the model substantially improved the goodness-of-fit to field measurements with the overall R2 increasing from 0.41 to 0.74, and a decrease in NRMSE from 17.1% to 11.4%. This work demonstrates that the VSS-FOA algorithm affords a straightforward mechanism for the optimization of parameters in models such as APSIM to enhance the accuracy of model N2O emission flux estimates.

Development of high-efficient asphalt pavement modeling software for digital twin of road infrastructure

To develop digital twin (DT) of road infrastructure, one critical element is computation of pavement responses (strains, stresses, and deflections) under traffic and environmental loading. This study aims to develop high-efficient asphalt pavement modeling software based on semi-analytical finite element method (SAFEM) for DT application. The algorithms address important aspects in vehicle-tire-pavement interaction modeling, such as dynamic vehicular loading, three-dimensional (3-D) non-uniform tire contact stress, viscoelastic behavior of asphalt material, and interface bonding condition. The simulation accuracy is verified by comparison with full-scale test and field measurements, and the relative differences are around 5 % to 20 %. Techniques including optimized discrete Fourier transform, parallel computing, graphics processing unit (GPU) acceleration, and sparse matrices are implemented for computation efficiency. As compared to the traditional 3-D FEM, SAFEM shows significant savings in computation time and storage usage. The high efficiency and accuracy make the software full of potential to be applied for DT of roadway infrastructure.

Comparison and Optimization of Light Use Efficiency-Based Gross Primary Productivity Models in an Agroforestry Orchard

The light-use efficiency-based gross primary productivity (LUE-GPP) model is widely utilized for simulating terrestrial ecosystem carbon exchanges owing to its perceived simplicity and reliability. Variations in cloud cover and aerosol concentrations can affect ecosystem LUE, thereby influencing the performance of the LUE-GPP model, particularly in humid regions. In this study, the performance of six big-leaf LUE-GPP models and one two-leaf LUE-GPP model were evaluated in a humid agroforestry ecosystem from 2018–2020. All big-leaf LUE-GPP models yielded GPP values consistent with that derived from the eddy covariance system (GPPEC), with R2 ranging from 0.66–0.73 and RMSE ranging from 1.81–3.04 g C m−2 d−1. Differences in model performance were attributed to the differences in the quantification of temperature (Ts) and moisture constraints (Ws) and their combination forms in the models. The Ts and Ws algorithms in the eddy covariance-light-use efficiency (EF-LUE) model well characterized the environmental constraints on LUE. Simulation accuracy under the common limitation of Ts and Ws (Ts × Ws) was higher than the maximum limitation of Ts or Ws (Min (Ts, Ws)), and the combination of the Ts algorithm in the Carnegie–Ames–Stanford Approach (CASA) and the Ws algorithm in the EF-LUE model was optimized in combination forms, thereby constraining LUE for GPP estimates (GPPBLO, R2 = 0.76). Various big-leaf LUE-GPP models overestimated or underestimated GPP on sunny or cloudy days, respectively, while the two-leaf LUE-GPP model, which considered the transmission of diffuse radiation and the difference in photosynthetic capacity of canopy leaves, performed well (R2 = 0.72, p < 0.01). Nevertheless, the underestimation/overestimation for shaded/sunlit leaves remained under different weather conditions. Then, the clearness index (Kt) was introduced to calculate the dynamic LUE in the big-leaf and two-leaf LUE-GPP models in the form of exponential or power functions, resulting in consistent performance even in different weather conditions and an overall higher simulation accuracy. This study confirmed the potential applicability of different LUE-GPP models and emphasized the importance of dynamic LUE on model performance.

High-resolution regional sea-ice model based on the discrete element method with boundary conditions from a large-scale model for ice drift

Abstract Understanding sea-ice dynamics at the floe scale is crucial to comprehend polar climate systems. While continuum models are commonly used to simulate large-scale sea-ice dynamics, they have limitations in accurately representing sea-ice behaviour at small scales. DEMs, on the other hand, are well-suited for modelling the behaviour of individual ice floes but face limitations due to computational constraints. To address the limitations of both approaches while combining their strengths, we explored the feasibility of using a DEM within a continuum model, where the latter provides boundary conditions for a rectangular high-resolution DEM domain. This paper presents a feasibility study where a discrete model of a 100 × 100 km2 icefield was created using high-resolution optical satellite imagery. Sea-ice dynamics were simulated in the DEM considering environmental forces and integrating large-scale ice-drift velocities as boundary conditions. Model predictions were compared with satellite observations for ice drift and deformation parameters. This numerical approach showed potential for offering accurate, high-resolution predictions of sea ice, particularly in coastal areas and near islands, and may find applications in ice navigation and climate studies. However, further development of the DEM, along with upgrades to the coupled ocean models providing data for the ice component, may be necessary. Additionally, challenges remain to develop a two-way coupling between the DEM and a continuum model, which may be needed to improve the accuracy of large-scale simulations.