Computer Simulation Research Articles

Shock petrographic and numerical modeling constraints on the morphology and size of the Morokweng impact structure, South Africa

AbstractThe 369 m deep M4 drill hole, located ~18 km NNW of the center of the 146 Ma Morokweng impact structure (MIS), intersects shocked Archean granitoid gneisses and subsidiary dolerite intrusions that are cut by faults, cataclasites and mm‐ to m‐wide suevitic and pseudotachylitic breccia dikes. The shock features in quartz in the gneisses and breccia dikes include decorated planar deformation features (PDFs), planar fractures, feather features, and toasting. Other minerals show features that may be shock‐related, such as multiple sets of planar features and alternate twin ladder structures in feldspars, kink bands in biotite, and planar features in titanite, apatite, and zircon; however, these are variably annealed and/or overprinted by hydrothermal alteration effects, and confirmation of their origin awaits further study. Universal Stage measurements of PDF sets in quartz from 12 gneissic target rocks and from lithic and mineral clasts in three suevitic and three pseudotachylitic breccia dikes reveal four dominant sets: (0001), {}, {} and {}. Based on these observations, the average peak shock pressure in these rocks is estimated at ≤16 GPa, which supports the original proximity (within one transient cavity radius) of these rocks to the point of impact. No discernible depth‐dependent shock attenuation was noted in the core. These shock levels and the elevated structural position of the rocks in the M4 core relative to the impact melt sheet intersected in drill holes closer to the center of the MIS suggest that the M4 lithologies represent part of the parautochthonous peak ring volume that subsequently experienced 1.5–2 km of post‐impact erosion before it was buried beneath younger sediments. Numerical modeling using the iSALE‐2D code suggests that the original Morokweng crater had a rim‐to‐rim diameter of between 70 and 80 km, and that the rocks in the M4 core were originally located at a depth of 7–8 km and a radial distance of 8–9 km from the point of impact.

Peculiarities of flexural wave propagation in a notched bar

The results of numerical modeling and experimental studies of the propagation of flexural elastic waves in a metal notched bar approximates the effect of an acoustic black hole are presented. The sample is a bar with notches, the depth of which increases according to the power law with an exponent equal to (4/3). It has been confirmed experimentally and with the simulation results, that such bars slow down the propagation of an elastic wave to the end of the bar. It is shown in such structures flexural waves have dispersion and their localization at the end of the bar is higher for some natural frequencies than that of a solid rod. The natural oscillations of the whole and notched bars are compared, i.e. the shape of the amplitude of the flexural wave along the rods. The dependence of the flexural wave length in a notched bar on the frequency is investigated as a wave propagates to the end of the bar.

Groundwater Model for Karst and Pelitic Aquifer Systems from a Semi-Arid Region Under Climate Change Scenarios: A Case Study in the Vieira River Watershed, Brazil

Water scarcity is a global issue, especially in semi-arid and arid regions where precipitation is irregularly distributed over time and space. Predicting groundwater flow in heterogeneous karst terrains, which are essential water sources, presents a significant challenge. This article integrates geology, hydrology, and water monitoring to develop a pioneering conceptual and numerical model of groundwater flow in the Montes Claros Region (Vieira River Watershed, Brazil). This model was evaluated under various climate change scenarios, considering changes in rainfall, groundwater consumption, and population growth over the current century. The results indicate that a decline in water table levels is inevitable, primarily driven by population growth and high pumping rates rather than rainfall fluctuations. This underscores the urgent need for improved monitoring, model upgrading, and more importantly, targeted water resource management for Montes Claros.

Self-centering rocking steel frame with column mid-height uplift: Experimental and numerical investigation

This paper proposes a self-centering rocking steel frame with column mid-height uplift (SCRSF-CMU) that exhibits high post-yield stiffness and energy dissipation capacity. The hysteretic performance of the SCRSF-CMU was investigated through cyclic loading tests. A numerical model of the SCRSF-CMU was established and validated against experimental results. Subsequently, the seismic performance of the SCRSF-CMU was compared with that of the self-centering rocking steel frame with column base uplift (SCRSF-CBU) using nonlinear time history analysis. Finally, the seismic responses of the SCRSF-CMU and SCRSF-CBU under varying earthquake intensities were analyzed using the endurance time analysis. The results indicate that the designed SCRSF-CMU demonstrates excellent lateral force resistance, energy dissipation, and self-centering capabilities. The rocking effect and the restoring force of the post-tensioned steel strands effectively controlled the residual lateral displacement of the specimens. The multi-scale numerical model of the SCRSF-CMU accurately captured its hysteretic behavior and deformation patterns. Compared to the SCRSF-CBU, the SCRSF-CMU exhibited superior rocking deformation capacity, post-yield stiffness, and hysteretic energy dissipation. Under MCE excitation, both SCRSF-CMU and SCRSF-CBU showed uniform inter-story drift distribution with minimal residual deformation, creating a satisfactory condition for the post-earthquake repair work if necessary. The high post-yield stiffness of the SCRSF-CMU was more effective in reducing both maximum and residual displacements. Endurance time analysis revealed that SCRSF-CMU and SCRSF-CBU exhibited relatively uniform inter-story drift distribution under SLE, DBE, and MCE earthquakes. Under FE excitation, the inter-story drift ratio of both structures were nearly identical; however, under DBE and MCE excitations, the maximum roof drift ratio and inter-story drift ratio of the SCRSF-CBU were larger, indicating that the SCRSF-CBU had higher deformation demands than the SCRSF-CMU.

Numerical Investigation of Burial Depth Effects on Tension of Submarine Power Cables

To protect submarine power cables from damage caused by anchoring and fishing, submarine power cables in shallow water areas are buried to a certain depth through a cable laying machine. However, limited attention has been paid to studying the stress behavior of submarine power cables while considering the effects of burial depth. In this research, static and dynamic analyses are carried out using three-dimensional numerical models performed by the OrcaFlex v11.0 to investigate the effects of burial depths on cable tension during the cable installation under various conditions. Numerical simulation results show that the peak tension of the submarine power cable increases linearly with the increase in burial depth. In addition, the burial depth can also change the tension state at the endpoint of the submarine power cable. The endpoint of the cable is in a compressed state when h < 2 m and the cable turns into a tensile state when h ≥ 2 m. Finally, genetic programming (GP) is used to analyze numerical simulation results to propose a prediction model that can be used to estimate the peak tension of the submarine power cable during cable installation under various burial depths in shallow sea areas. It should be noted that the proposed GP model is based on the analyses of numerical results; therefore, the GP model is open for further improvements as more experimental data become available.

On the accuracy of distance estimates by propagation time of sound signals on the arctic shelf

As part of numerical modeling, estimates are made of the accuracy of determining the distance between underwater sources and sound receivers located at a distance of several kilometers from each other in the Kara Sea in the autumn. It is assumed that the main source of possible errors in determining the distance is the lack of accurate data on the vertical profile of the sound speed along the propagation path of acoustic signals. Data from September and November were analyzed, in the interval between which significant changes in the profile take place, when the vertical gradient of sound speed changes from negative to positive values. Characteristic values of sound speed variations were obtained by statistical processing of hydrological data taken from the World Ocean Database. The results obtained are important for analyzing the capabilities of underwater acoustic navigation.

Numerical simulation of strengthening/deterioration and damage development of cementitious materials under sulfate attack environment

Cementitious materials exposed to sulfate environments often suffer from the combined effects of external sulfate attack and dry wet cycling. In order to predict the long-term performance of cement-based materials in sulfur rich environments, this paper aims to establish a numerical model that can reasonably characterize the degradation process of cementitious materials in sulfate environments. Using this model, we can address durability problems common to cementitious materials in real sulphate environments, such as determining the degree of corrosion, predicting the durability life of materials, and calculating the time to failure. The model consists of three parts: transportation, chemical reactions, and material damage. We establish the degradation model for cement-based materials in sulfate environments by considering ion diffusion, water convection, chemical reactions, accumulation of corrosion products, and the development of material strengthening/degradation. Compared with existing sulfate corrosion models, this model not only considers ettringite, but also takes into account the volume changes of gypsum formation, calcium leaching, and salt crystallization precipitation, which can more reasonably simulate the sulfate corrosion process in real environments. The model can reasonably simulate the distribution pattern of ion concentration, hydration products and corrosion products in the cementitious material, and on this basis calculate the porosity of the cementitious material. In order to make the simulation results more applicable to experimental and non-destructive testing results, the model uses easily obtainable relative dynamic elastic modulus to evaluate the degree of material strengthening/degradation and predict the durability life of the material.

A novel approach to detailed modeling and simulation of water-gap membrane distillation: Establishing a numerical baseline model

In water-gap membrane distillation (WGMD), natural convection within the water gap significantly impacts performance and thermal efficiency. Previous studies have used empirical or modified empirical correlation models to predict the Nusselt number within the water gap, investigating how natural convection affects WGMD performance and thermal efficiency. However, these models are specific to certain operating conditions, limiting their application in developing a comprehensive numerical model for the WGMD process. To address this limitation, we developed a numerical model by integrating a two-dimensional natural convection model within the water gap. Experimental investigations were conducted across a wide range of feed temperatures and water gap sizes to assess the influence of key operating parameters on performance. To validate the effectiveness of the proposed numerical model, the experimental results were compared with those from the proposed model and with results from numerical models used in previous studies. The proposed numerical model demonstrated a maximum deviation of 8.5% from the measured data, whereas the numerical models used in previous studies exhibited deviations of 22.9%. In addition, the flow characteristics within the water gap were analyzed through isotherms and streamlines, and the improved thermal efficiency of WGMD compared to direct contact membrane distillation (DCMD) was explored.

Advanced hybrid techniques for predicting discharge coefficients in ogee-crested spillways: integrating physical, numerical, and machine learning models

Abstract The primary objective of this work was to examine the flow characteristics over an ogee spillway using both a numerical model and the Machine Learning (ML) approach. A 3D computational fluid dynamics (CFD) model was employed to simulate the flow over an ogee spillway, utilizing the Reynolds averaged Navier–Stokes equations. The simulation encompassed a wide variety of head ratios, ranging from 0.1 to 6.0, to extend the rating curve of discharge coefficient (C) and head ratio (H e /H 0). The formation of the negative pressure zone rapidly occurred, and the maximum velocity area developed from toe to top of the spillway surface as the head ratio increased. Then, four ML models—RF, FNN, ADB, and KNN—were utilized to estimate the discharge coefficient of the spillway. Hyperparameter tuning using the Tree-Structured Parzen Estimator (TPE) and five-fold cross-validation ensured robust model performance. The ML model’s efficacy was assessed by conducting 200 random seed simulations. The RF and ADB models exhibited the highest predictive accuracy and consistency, with mean correlation coefficient (CC) values of 0.979 and 0.975, respectively. FNN and KNN also performed well but showed greater variability in their prediction. The results demonstrated reasonably good agreement between the physical, numerical, and ML models. Both numerical simulation methods and ML models, particularly RF, proved to be cost-efficient and reliable tools for designing and analyzing flow over an ogee spillway. These findings highlight the potential of integrating numerical simulations and advanced ML techniques to enhance the prediction and analysis of hydraulic structures, providing valuable insights for the design and management of spillway systems.

Numerical Modelling of Carrier Transport in Organic Field Effect Transistors

Background: Organic field effect transistors (OFETs), used in the fabrication of nanosensors, are one of the most promising devices in organic electronics because of their lightweight, flexibility, and low fabrication cost. However, the optimization of such OFETs is still in an early stage due to the minimal analytical and numerical models presented in the literature. Objective: This research presses to demonstrate a numerical carrier transport model based on the finite element method (FEM) to investigate the I-V characteristic of OFETs. Methods: Two various organic semiconductor materials have been included in the study, polyaniline and pentacene, where micro-scale, as well as nano-scale models have been presented. OFETs regarding channel length, dielectric thickness, and doping level impact have been studied. We nominated the threshold voltage, the on/off current ratio, the sub-threshold swing, and the field effect mobilities as the primary output evaluating parameters. Results: The numerical model has shown the criticality of the doping effect on tuning the device flowing drain current to exceed 300 μA saturation current, along with a threshold voltage of -0.1 V under a channel length of 30 nm. Conclusion: The study highlights the effectiveness of polyaniline over pentacene as nano-channel length OFET due to the boosted conductivity of polyaniline concerning pentacene.

Optimizing Well Integrity with Accurate Forecasting of Casing and Tubing Resistance Reduction Trends Through Innovative Numerical Modeling Techniques

Summary Well integrity is an essential aspect of well operations, and international standards are stringent on its application. Tubing and casing corrosion constitutes a major risk to well integrity and a challenge that must be analyzed preventively. Corrosion logs are programmed to verify the status of the barriers, but there is currently no methodology for planning future logs. In this paper, we propose a numerical modeling method to forecast tubular resistance reduction trends and estimate the barrier’s residual life. Corrosion logs are expensive and require the well to be shut in, which impacts the cost of the operation. Therefore, a numerical methodology for planning future logging is preferable to planning based on experience. Here we propose a methodology for predicting the point at which the downhole barriers will no longer satisfy the maximum allowable annular surface pressure (MAASP) criteria, using data from historical corrosion logs and a time-linear corrosion model. This innovative methodology recalculates MAASP for collapse and burst resistance of corroded pipes while considering well completion design and vertical depths resulting from the directional survey. The proposed methodology provides a comprehensive assessment of the corrosion progress, making it a more reliable tool for forecasting future corrosion trends. The methodology uses the last two corrosion logs for optimal prediction, but it also works when a single corrosion log is available. Numerical modeling is then used to calculate pipe resistance at each depth step of the LAS file, drawing a picture of the resistance vs. depth and time. The result of this new method is a chart that forecasts the progressive reduction of A-Annulus MAASP and the time at which it will cross the minimum criteria of the operator. Furthermore, the proposed methodology provides a probability window that takes into consideration the best- and worst-case scenarios. This is the first time that numerical modeling is used to recalculate MAASP and to forecast MAASP reduction trends in well barriers, based on corrosion/erosion measurements. The corrosion logging schedule can, hence, be programmed before MAASP reaches the operator’s limit, thus optimizing planning and costs for the operator. This, in turn, leads to a reduction in costs without affecting the overall operability and risking barrier failures.

Core temperature estimation of lithium-ion battery based on numerical model fusion deep learning

Temperature has a critical impact on the lifespan and safety of lithium batteries. This paper proposes a battery core temperature estimation method based on numerical model fused with long short-term memory (LSTM) neural network. The proposed technique extracts features from the numerical model, estimates the volume-averaged temperature by electrochemical impedance spectroscopy (EIS), uses an LSTM neural network to learn thermodynamic parameters and complex calculations, which takes advantage of the strengths of each method, and achieves accurate core temperature estimation. The effects of state of charge (SOC) and temperature on EIS are explored, impedance properties are selected on the criteria of robustness and rapidity, and the estimation of the volume-averaged temperature is achieved using the imaginary part of the impedance. The proposed method can achieve root mean squared error (RMSE) of less than 0.28 °C and mean absolute error (MAE) of less than 0.23 °C. The proposed method has advantages of high estimation accuracy and does not require an electrothermal model. It also considers the effect of ambient temperature and has a good generalization capability.

Evaporating capillary bridges of pure and binary liquids

We present a numerical and experimental study on the evaporation of microliter capillary bridges of both pure and binary liquids. Specifically, we focused on capillary bridges of a binary liquid composed of water and isopropanol confined between poly-dimethylsiloxane coated surfaces. We developed a finite-element method-based numerical model to solve Laplace equations for vapor diffusion of the two species present in the capillary bridge, considering quasi-steady and diffusion-limited evaporation. We applied a modified version of Raoult's law, incorporating activity coefficients for binary liquids. The Galerkin finite element method was employed in axisymmetric cylindrical coordinates. The numerical model was validated against in-house experiments of side visualization on an evaporating capillary bridge. We quantified the effect of confinement from the plates on slowing down the diffusion of liquid vapor. The volume evolution of the binary liquid capillary bridge was found to be nonlinear, strongly influenced by the initial concentration of isopropanol in the capillary bridge. This nonlinearity is attributed to the faster diffusion of isopropanol vapor compared to water vapor. We examined the effects of height, substrate radius, contact angle, and composition on the evaporation characteristics. We proposed a computationally efficient reduced-order model for determining evaporation kinetics, which yields predictions very close to those of the numerical model.

Performance and sustainability of BERT derivatives in dyadic data

In recent years, the Natural Language Processing (NLP) field has experienced a revolution, where numerous models –based on the Transformer architecture– have emerged to process the ever-growing volume of online text-generated data. This architecture has been the basis for the rise of Large Language Models (LLMs). Enabling their application to many diverse tasks in which they excel with just a fine-tuning process that comes right after a vast pre-training phase. However, their sustainability can often be overlooked, especially regarding computational and environmental costs. Our research aims to compare various BERT derivatives in the context of a dyadic data task while also drawing attention to the growing need for sustainable AI solutions. To this end, we utilize a selection of transformer models in an explainable recommendation setting, modeled as a multi-label classification task originating from a social network context, where users, restaurants, and reviews interact.

Spatially resolved CFD-DEM model with innovative experimental validation methods to improve understanding of sand retention in oil and gas wells with the consideration of filter-beds on standalone screens

Coupling CFD and DEM is commonly used to study particle-fluid flow in sand retention systems for oil and gas wells, addressing the limitations of laboratory experiments and reliance on empirical data. These numerical studies aid in optimising standalone sand screens, which are favoured over gravel-pack completions for cost-effectiveness. However, such studies overlook the critical role of the bulk filter-bed in retaining permeability for both particulate and fluid phases. This paper presents a robust numerical methodology using resolved CFD-DEM to model a sand retention system that accurately captures filter-bed permeability, which has a greater impact on sand production and retained fluid productivity than the screen itself from a long-term perspective. The numerical model's accuracy is validated through a novel experimental methodology, which involves benchmarking the numerically derived porosity and single-phase permeability against micro-CT imaging of the filter-bed. Results show strong consistency between the numerical model and micro-CT imaging of the laboratory-derived filter-bed. This validated model provides a solid foundation for running more accurate sand retention tests and improving standalone sand screen selection criteria. Future work will explore the effects of varying parameters on the filter-bed formation to determine optimal conditions for maximising sand retention while maintaining hydrocarbon productivity from a long-term perspective.

Computational multiphysics modeling of radioactive aerosol deposition in diverse human respiratory tract geometries

The evaluation of aerosol exposure relies on generic mathematical models that assume uniform particle deposition profiles over the human respiratory tract and do not account for subject-specific characteristics. Here we introduce a hybrid-automated computational workflow that generates personalized particle deposition profiles in 3D reconstructed human airways from computed tomography scans using Computational Fluid and Particle Dynamics simulations. This is the first large-scale study to consider realistic airways variability, where 380 lower and 40 upper human respiratory tract 3D geometries are reconstructed and parameterized. The data is clustered into nine groups using random forest regression. Computational fluid and particle dynamics simulations are conducted on these representative geometries using a realistic heavy-breathing respiratory cycle and radioactive iodine-131 as a source term. Monte Carlo radiation transport simulations are performed to obtain detailed energy deposition maps. Our findings emphasize the importance of personalized studies, as minor respiratory tract variations notably influence deposition patterns rather than global parameters of the lower airways, observing more than 30% variance in the mass deposition fraction.

Advancing the Seasonal Outlook of the Wet Seasons of Florida

Abstract In this study, we introduce an ensemble approach to provide a probabilistic seasonal outlook of the length and seasonal rainfall anomaly of the wet season over Florida using the observed variations of the onset date of the season at the granularity of ∼10-km grid resolution (which is the spatial resolution of the observed rainfall data used for this work). The time series of daily precipitation at the grid resolution of NASA’s Global Precipitation Mission is randomly perturbed 1000 times to account for the uncertainty of synoptic to mesoscale variations on the diagnosis of the onset and demise date of the wet season. The strong covariability of the onset date with the seasonal length and seasonal rainfall anomaly of the wet season is then leveraged to provide the seasonal outlooks by monitoring the onset date of the wet season. This simple seasonal outlook is effective in predicting extreme tercile and even extreme pentile anomalies across Florida. We suggest that the proposed approach to the seasonal outlook of the wet season of Florida provides a viable alternative in the absence of strong external forcing like ENSO or tropical Atlantic variability that potentially limits the predictability of numerical climate models used for seasonal prediction. Significance Statement Earlier studies have shown that the seasonal prediction from the numerical climate models even at zero lead time has very limited prediction skills over the summer in Florida, which also coincides with the wettest part of the year. Florida’s wet season exhibits significant interannual variations, which exert its influence on water management decisions for subsequent drier seasons of the year. Therefore, strategies to improve this skill are highly relevant. We propose in this study that by monitoring the onset of the wet season variations, we can usefully provide a probabilistic seasonal outlook of the season over Florida. This is done by leveraging the observed linear relationships between the onset date variations with the length and the seasonal rainfall anomaly of the season. Furthermore, the outlook is provided at the spatial resolution of the observed dataset, which in this case is at 10-km grid resolution.

The Evolution of Angular Momentum from Terrestrial Planets during their Planetary Formation

Abstract. It has been a long run on searching for the angular momentum development and its origin for the terrestrial planets, in contrast to a general theory or equation that can write the whole picture of the angular momentum development. Generally, planetary angular momentum is built up by numerous rocky or dust contributors fused together and affected by later interactions of different sources. In other words, it requires tremendous calculation on the demotion of each debris on a complex system, meaning various theories to study the physics and fit into present observational data. The case of terrestrial planets is studied by computer simulation with models that contain different strategies of interacting bodies inside the space, and these simulations are conducted under various initial conditions, for instance, the number of involving bodies and the sizes of the rocky debris. This article briefly gives a picture of the theories simulating and predicting the development and origin of terrestrial planets by literature view of listed references, aiming to summarize and discuss the differences of several simulations and their conditions.

Numerical Simulation and Experimental Analysis of Aerosol Deposition: Investigating Nozzle Effects on Particle Dynamics and Deposition

Aerosol deposition (AD), also known as vacuum kinetic spray (VKS), is used to deposit dense ceramic films onto a substrate surface at room temperature. One predominant factor influencing the overall deposition process is the nozzle geometry, which significantly impacts the quality, shape, and thickness of the coating. To evaluate the effect of nozzle geometry in AD, particle trajectory and impact velocity were investigated via computational simulation. A Eulerian-Lagrangian model was used to simulate the gas flow, particle in-flight behavior, as well as particle deposition characteristics on a flat substrate. Three common nozzle geometries in AD were utilized: a converging-diverging nozzle with a slit cross-section (CD-Slit), a converging-barrel nozzle with a slit cross-section (CB-Slit), and a converging-diverging round (CD-Round) nozzle. Moreover, suitable drag coefficient and Nusselt number correlations were used to account for compressibility and rarefaction effects on particle dynamics and heat transfer. The simulation results were compared to the deposition pattern obtained experimentally. The results demonstrate that shape of the nozzle has profound effect on the particle impact velocity and deposition pattern. The CD-Round nozzle provides uniform, thinner coatings ideal for extensive surfaces at a slower deposition rate. In contrast, the CB-Slit nozzle is optimized for maximum coating thickness in narrow, thick linear patterns. The CD-Slit nozzle achieves high deposition rates with uniform coatings and a distinctive cat-ear shaped pattern.

Biomimetic Gas Channel Constructed for Efficient CO2 Removal Based on Computational Simulations

Membrane oxygenator performs lung function in the Extra-corporeal Membrane Oxygenation (ECMO) system. In the clinical use, to ensure the balance of gas exchange of patient’s body, the gas-blood exchange membrane is required to possess CO2/O2 selectivity, which is suggested to be in the range of 10-12. In our work, the molecular dynamics simulation results displayed that the amino-functionalized Zr-MOFs provides fast transport channels for CO2 molecules because of the enhancement of the adsorption and diffusion ability for CO2. Therefore, we chose organic ligands with abundant CO2-philic groups to synthesize target amino-functionalized Zr-MOFs, and then dispersed them into medical-grade PDMS solution to study the separation performance of CO2/O2. On the basis of constructing anti-leakage coating layer, biomimetic gas channels were designed to increase the clearance rate of CO2. Experimental results exhibited that the CO2/O2 selectivity of (NH2)2-UiO-66/PDMS MMM was as high as 11.19, which was basically consistent with the simulation results. The obtained amino-functionalized Zr-MOF/PDMS coating layer displayed favorable biocompatibility and biosafety. Meanwhile, the construction of biomimetic channel solves the difficult problem of CO2 removal in PMP membrane, which verifies the feasibility of applying porous materials to gas-blood exchange membranes in the ECMO system.