Computer Simulation Research Articles

Droplet collision with hydrophobic and superhydrophobic surfaces: Experimental studies and numerical modeling

The characteristics of the collision of water droplets with textured hydrophobic and superhydrophobic surfaces obtained using two techniques were studied experimentally. The surface processing techniques are based on laser modification of the surface layer and changes in its chemical composition. Weber numbers in the experiments were varied in the range of 10–200. Regimes of water droplet-surface collision, the critical conditions for the transition between regimes, as well as integral characteristics of the formed liquid fragments were identified. The main characteristics of the process under study, such as the droplet spreading diameter, the height of its rebound from the surface, the number of secondary fragments during its break up were obtained experimentally. A model with a two-dimensional axisymmetric formulation was used to predict the characteristics of water droplet-textured surface collision based on the phase field method. The results of experimental studies and numerical modeling are in satisfactory agreement (differences of no more than 3–5%). The conditions, when it is necessary to consider the values of static and dynamic contact angles during the modeling process, were determined.

A new experimental and numerical simulation method for measuring ash slag fluidity using VOF based on OpenFOAM

A numerical computational model of heated ash flow is proposed using the Volume of Fluid (VOF) method based on OpenFOAM. Established numerical model can accurately calculate the critical flow temperature (Tc) and critical flow velocity (Vc) of ash slag with the error of 1.5 % and 19 % from the experiment, respectively. Forty-five types of ash slag with widely varied properties in China were calculated based on the established model. Results show that high levels (>30 wt%) of Fe2O3 and CaO improve the flowability of the ash slag, and low levels of Al2O3 (10 wt%) also contribute to the flowability of the ash slag. Tc and Vc of the ash were distributed in the range of 1150 ∼ 1400 °C and 0.1 ∼ 1.0 mm/min, respectively. Tc and Vc change in opposite trends with the component content increases, indicating that a higher Tc corresponds to a lower Vc and fluidity reduction. Mathematical models for calculating the Tc and Vc of ash were obtained based on non-linear multiple regression and correlation coefficient, and prediction accuracy was within 10 % and 30 % for Tc and Vc, respectively. The proposed numerical simulation method and prediction model can provide an accurate reference for a wide range of ash fluidity. Also, it was demonstrated that elemental Na has a positive effect on ash flowability based on the model, which is important for measuring slag flowability in slag-tap furnaces.

Capacitive BaTiO3-PDMS hand-gesture sensor: Insights into sensing mechanisms and signal classification with machine learning

Flexible sensors have gained extensive interest because of their versatile applications in healthcare, robotics, and wearable devices. This study introduces a capacitive sensor utilizing barium titanate oxide (BaTiO3)-polydimethylsiloxane (PDMS) for bending sensing and addresses crucial performance parameters including sensitivity, repeatability, response time, and durability. The sensor exhibited a notable capacitance change of 42.85% in conjunction with fast (1 s) responses and recovery times, and minimal hysteresis (<2%). Its reliable performance across varying bending rates and durability through extensive cyclic tests underscore its applicability in real-world scenarios. Importantly, the sensor's capabilities were enhanced by integrating machine learning (ML), achieving an impressive accuracy of 97.11% in recognizing hand-sign language gestures. Furthermore, finite element analysis was employed to validate the correlation between the increase in compression-induced packing density and capacitance enhancement. This holistic integration of advanced materials, computational simulations, and ML not only extends the boundaries of sensor technology but also holds promise for revolutionizing human–machine interactions, aiding speech-impaired individuals, and enriching virtual reality experiences. This study represents a pivotal advancement in the field of flexible sensors and the unlocking of new dimensions of their applications.

MELCOR – DAKOTA coupling for uncertainty analyses in the SNAP environment/architecture

Uncertainty estimation to assess figures of merit characterizing evolution of a severe accident transient is a topic of current investigation in development of best-estimate plus uncertainty methodology. The probabilistic method to propagate input uncertainty is one of the methodologies used to develop Uncertainty Analyses (UAs). Using this methodology, UAs are performed by sampling probability distributions that describe the range of possible values that computer simulation model inputs can have. For each sample (or realization) of a set of uncertain input parameters, a computer simulation is performed. From the range of code simulation results obtained for each input realization, a distribution of code results is obtained. In this process, the distribution of input uncertainties is propagated to obtain a distribution of possible code results (i.e., the code output uncertainty). This probabilistic methodology is facilitated using Uncertainty Tools (UTs), which can be coupled with the accident analysis computer code to perform an UA. One of the UTs currently available is DAKOTA, developed by Sandia National Laboratories. DAKOTA is also provided as a SNAP plug-in. SNAP is a graphical user interface designed to support the use of USNRC codes (MELCOR, TRACE, etc). This paper is entirely derived from the NUREG/IA-532 issued by USNRC in April 2023 (Mascari et al., 2023) and has as a major target to summarize the main needs of UA in severe accident, the main element of the probabilistic method to propagate input uncertainty, and the workflow within SNAP to assist other interested analysts with their applications given they are members of the USNRC Cooperative Severe Accident Research Program (CSARP).

New empirical equations to assess energy efficiency of flow-dissipating vortex dropshaft

Vortex structures are generally considered the common energy-dissipating infrastructures in order to convey urban surface waters. During transferring wastewater in such urban networks, the major proportion of water energy is dissipated and additionally, the hydraulic performance of these infrastructures is evaluated on the basis of the dissipation of energy efficiency in the vortex dropshaft. Over the last decade, the literature review of vortex structures demonstrated that there is no reliable mathematical expression to predict energy efficiency although various experimental investigations were done to evaluate the hydraulics of vortex structures. In this research, robust numerical models based on conceptions of Artificial Intelligence (AI) methodologies (i.e., classification, non-parametric analysis, and evolutionary computing) were developed by using 144 experimental data extracted from a physical model of vortex dropshaft. Through analysis of experiments, three main factors, called flow Froude number (Fr), the ratio of sump height (Hs) to shaft diameter (D), and the ratio of drop total height (L) to shaft diameter (D) were determined to estimate the efficiency of flow energy dissipation. The numerical models were calibrated/trained by different algorithms such as multi/single-objective optimization strategies (e.g., Particle Swarm Optimization [PSO] and Multi-Objective Genetic Algorithm [MOGA]). Various statistical tests, known as quantitative assessments for training and testing stages of AI models, demonstrated that Evolutionary Polynomial Regression (EPR) had the best performance (Coefficient of Correlation [R] = 0.9736, Root Mean Square Error [RMSE] = 0.5923, Mean Absolute Percentage Error [MAPE] = 0.5035) and followed by MT (R = 0.9665, RMSE = 0.7039, MAPE = 0.6225), GEP (R = 0.9113, RMSE = 1.0803, MAPE = 0.9535), and MARS (R = 0.8337, RMSE = 1.6473, MAPE = 1.1010). Additionally, expressions given by AI models provided more accurate predictions of energy dissipation efficiency when compared with a regression-based equation (R = 0.9113, RMSE = 0.7039, MAPE = 0.6225) from the literature. Additionally, variations of three effective factors versus the predicted responses (values of energy dissipation efficiency given by AI models) were in coincidence with experimental observations. Overall, this study proved that the usability of mathematical expressions could provide new insights for experts to assess the hydraulic evaluation of vortex structures before their physical and prototype models are constructed.

Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis.

Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solve complex problems on unstructured networks, such as node classification, graph classification, or link prediction, with high accuracy. However, both inference and training of GNNs are complex, and they uniquely combine the features of irregular graph processing with dense and regular computations. This complexity makes it very challenging to execute GNNs efficiently on modern massively parallel architectures. To alleviate this, we first design a taxonomy of parallelism in GNNs, considering data and model parallelism, and different forms of pipelining. Then, we use this taxonomy to investigate the amount of parallelism in numerous GNN models, GNN-driven machine learning tasks, software frameworks, or hardware accelerators. We use the work-depth model, and we also assess communication volume and synchronization. We specifically focus on the sparsity/density of the associated tensors, in order to understand how to effectively apply techniques such as vectorization. We also formally analyze GNN pipelining, and we generalize the established Message-Passing class of GNN models to cover arbitrary pipeline depths, facilitating future optimizations. Finally, we investigate different forms of asynchronicity, navigating the path for future asynchronous parallel GNN pipelines. The outcomes of our analysis are synthesized in a set of insights that help to maximize GNN performance, and a comprehensive list of challenges and opportunities for further research into efficient GNN computations. Our work will help to advance the design of future GNNs.

Blood glucose-lowering activity of protocatechuic acid is mediated by inhibiting α-glucosidase

α-Glucosidase inhibitors are effective in controlling postprandial hyperglycemia, which play crucial roles in the management of type 2 diabetes. Protocatechuic acid (PCA) is one of phenolic acids existing not only in various plant foods but also as a major microbial metabolite of dietary anthocyanins in the large colon. The present study investigated the inhibitory mechanism of PCA on α-glucosidase in vitro and examined its effect on postprandial blood glucose levels in vivo. Results from in vitro experiments demonstrated that PCA was a mix-type inhibitor of α-glucosidase. Driven by hydrogen bonds and van der Waals interactions, PCA reversibly bound with α-glucosidase to form a stable α-glucosidase-PCA complex in a spontaneous manner. The computational simulation found that PCA could insert into the active cavity of α-glucosidase and establish hydrogen bonds with catalytic amino acid residues. PCA binding aroused the steric hindrance for substrates to enter active sites and caused the structural changes of interacted catalytic amino acid residues. PCA also exhibited postprandial hypoglycemic capacity in diabetic mice. This study may provide the theoretical basis for the application of PCA as an active ingredient of functional foods in dietary management of diabetes.

A Simplified Numerical Model as a Design Tool for Vertical Single U-Tube Ground Heat Exchangers

A Simplified Numerical Model as a Design Tool for Vertical Single U-Tube Ground Heat Exchangers

Blast mitigation effects of TPU polymers-based inflated membrane structures under external explosion

TPU polymers-based inflated membrane structures are a potential technology to protect pre-existing lightweight buildings, with the advantages of lightweight and simple building process. In this study, a series of field blast tests were performed for the structural response and blast load parameters to study the blast mitigation effect of the inflated membrane structures. The corresponding numerical models of the tests were then developed and validated to reproduce the fluid-structure interaction. Thin aluminum plates were used as the protected structure. Based on their maximum displacement, average plastic strain, depth of depression, and permanent deformation, damage factor was defined to evaluate their damage level. Both the experimental and numerical results show that inflated membrane structures can effectively mitigate blast loading and significantly reduce the permanent displacement of the aluminum plate by elastic compression deformation. The effects of the initial height and internal pressure of inflated membrane structures on the blast mitigation effectiveness were clarified by the parametric study. The overpressure-impulse equivalent damage curves were established by combining the damage factor and overpressure-impulse loads, which further confirmed the blast mitigation performance of inflated membrane structures. Our study has verified the blast mitigation effects of inflated membrane structures based on TPU polymers, which is potentially valuable in the field of protective engineering for lightweight construction.

Full-scale experimental, numerical, and theoretical research on the spatio-temporal evolution of the flow structure in longitudinal and semi-transversal ventilated tunnels

This paper presents full-scale experimental, numerical, and theoretical research on the spatial and temporal structure of airflow for different modes of ventilation system operation in a road tunnel. The tunnel under investigation is equipped with a semi-transverse ventilation system. There are 19 pairs of axial jet fans in each tunnel tube and 164 air supply vents on both sides of a tube located just above the road surface. A set of seven stand poles with mounted vane anemometers were arranged along the tunnel tube axis. Different configurations of axial jet fans and air supply vents were employed, and changes in airflow velocity were recorded. Since the main objective of the study is to achieve a deeper understanding of the examined phenomena, a numerical model of the flows in the tunnel was built, and the acquired data was used to validate it. Some theoretical considerations are provided and also validated. All of this together reveals a very uneven distribution of longitudinal air velocity along the tunnel axis. Then, time constants when switching the fans on and off are determined. It is shown that the time needed to achieve a stable flow after the fans are turned on is in the order of five minutes, and the decay time after the fans are turned off is much longer; it takes about 15 min to calm down the flow. Next, it is determined that the transverse air supply significantly changes the airflow structure: the average longitudinal air velocity at the inlet portal was up to 25% lower than without it, while values at the outlet portal were similar. Finally, it is indicated that axial fans that operate in the suction mode are similarly efficient to those in the pumping mode.

Numerical Modeling of Reaeration Coefficient for Lakes: A Case Study of Sawa Lake, Iraq

Numerical Modeling of Reaeration Coefficient for Lakes: A Case Study of Sawa Lake, Iraq

Computational assessment of carbon fabric reinforced polymer made prosthetic knee: Mechanics, finite element simulations and experimental evaluation.

A prosthetic knee is designed to replace the functionality of an anatomical knee in transfemoral amputees. The purpose of a prosthetic knee is to restore mobility and compensate amputees for their impairment. In the present research numerical modelling and simulation of a carbon fabric reinforced polymer made polycentric prosthetic knee with four-bar mechanism was performed. Virtual prototyping with computer-aided design and computer-aided engineering software ensured geometric and structural stability of the knee design. The linkage mechanism, instantaneous centre's location and trajectory were investigated using multibody dynamics and analytical formulations. Computational simulations with a non-linear finite element model were employed with joints, contact formulations and an orthotropic material model to predict the displacement, stress formulated and life of the knee prosthesis under static and cyclic loading conditions. Finite element analysis assessed the strength and durability of knee in accordance to standards. Maximum Principal stress of 155 MPa and life expectancy of 3.1 × 106 cycles were determined for the composite knee through numerical simulations ensuring a safe design. Experimental testing was also conducted as per standards and the percentage error was estimated to be 2.52%, thereby establishing the validity of the finite element model deployed. This type of simulation-based approach can be implemented to efficiently and affordably design and prototype a prosthetic knee with desired functioning criteria.

Influence of bank slope on sinuosity-driven hyporheic exchange flow and residence time distribution during a dynamic flood event

Abstract. This study uses a reduced-order two-dimensional (2-D) horizontal model to investigate the influence of the riverbank slope on the sinuosity-driven hyporheic exchange process along sloping alluvial riverbanks during a transient flood event. The deformed geometry method (DGM) is applied to quantify the displacement of the sediment–water interface (SWI) along the sloping riverbank during river stage fluctuation. This new modeling approach serves as the initial step focusing on the impact of the bank slope on the hyporheic exchange flux (HEF) and the residence time distribution (RTD) of pore water in the fluvial aquifer for a sinuosity-driven river corridor. Several controlling factors, including sinuosity, alluvial valley slope, river flow advective forcing and duration of flow, are incorporated into the model to investigate the effects of bank slope on aquifers of variable hydraulic transmissivity. Compared to simulations of a vertical riverbank, sloping riverbanks were found to increase the HEF. For sloping riverbanks, the hyporheic zone (HZ) encompasses a larger area and penetrated deeper into the alluvial aquifer, especially in aquifers with smaller transmissivity (i.e., due to increased hydraulic conductivity or reduced specific yield). Furthermore, consideration of sloping banks as compared to a vertical riverbank can lead to both underestimation and overestimation of the pore water travel time. The impact of bank slope on residence time was more pronounced during a flood event for high-transmissivity aquifer conditions, while it had a long-lasting influence after the flood event in lower-transmissivity aquifers. Consequently, the impact of bank slope decreases the travel time of water discharging into the river relative to base flow conditions. These findings highlight the need for (re)consideration of the importance of complex riverbank morphology conceptualization in numerical models when accounting for the HEF and RTD. The results have potential implications for river management and restoration and the management of river and groundwater pollution.

Unraveling the Indian monsoon’s role in fueling the unprecedented 2022 Marine Heatwave in the Western North Pacific

AbstractAn unprecedented marine heatwave (MHW) event occurred in the middle-high latitudes of the western North Pacific during the summer of 2022. We demonstrate that excessive precipitation thousands of kilometers away fuels this extreme MHW event in July 2022. In the upper atmosphere, a persistent atmospheric blocking system, forming over the MHW region, reduces cloud cover and increases shortwave radiation at the ocean surface, leading to high sea surface temperatures. Atmospheric perturbations induced by latent heat release from the extreme precipitation in the Indian summer monsoon region enhance this atmospheric blocking through the propagation of quasi-stationary Rossby waves. Our hypothesis is verified by using a numerical model that is forced with the observed atmospheric anomalous diabatic heating. This study sheds light on how a subtropical extreme event can fuel another extreme event at middle-high latitudes through an atmospheric teleconnection.

Heat transfer coefficient simulation and temperature distribution prediction of wire loops in the Stelmor air‐cooling system

AbstractDuring the Stelmor air‐cooling process, the temperature distribution has a significant impact on the wire loops' final mechanical properties. The temperature distribution during the air‐cooling process is accurately solved by establishing three‐dimensional model and numerical simulations. The heat transfer coefficient at the highly dense region is much smaller than that of wire loops at the low dense region, and changes periodically over time, according to a computational fluid dynamics simulation method. It is also found that the heat transfer coefficient on the cross‐section of the wire loop is very different, with a difference of 70–100 W/m2 K. Finally, the finite difference method is used to calculate the mathematical model of the temperature distribution during the Stelmor air‐cooling process. Comparing the results with the measurement data, the simulation results and measurement data match up well.

Structural response and mechanical properties of the hind wing of the beetle Protaetia brevitarsis.

The folding/unfolding mechanism and collision recovery effect of the beetle's hind wings can provide biomimetic inspiration for the optimization of wing deplorability and the investigation of collision prevention recovery mechanism of new amphibious morphing vehicle. In this study, a method is described to investigate the structural response and mechanical properties of the hind wings of the beetle Protetia brevitarsis under natural conditions. The specially processed test samples were conducted to tensile testing, which facilitates the evaluation of the mechanical properties of specific areas of the hind wing. The micro geometric morphological characteristics of the cross-section of the specimen after tensile fracture were observed by scanning electron microscopy. The three-dimensional morphology of the ventral and dorsal sides of the hind wing was characterized using three-dimensional scanning and reverse modeling methods. The finite element model of the hind wing is developed to investigate the structural deformation and modal response characteristics of its flapping. The uniformly distributed load on the hind wing surface is derived from the lift characteristics obtained from the computational fluid dynamics simulation of flapping wing motion. RESEARCH HIGHLIGHTS: Scanning electron microscope is used to observe the cross-sectional characteristics of the veins and membranes. The material properties of the wing membranes and veins of the hind wings were measured using the tensile testing system. The three-dimensional morphology of the hind wing was characterized using 3D scanning and reverse modeling methods. The finite element model of the hind wing is developed to investigate the structural deformation and modal response characteristics of its flapping.

Molecular-Sieving Separation of Methanol/Benzene Azeotrope by a Flexible Metal-Organic Framework.

Separation of methanol/benzene azeotrope mixtures is very challenging not only by the conventional distillation technique but also by adsorbents. In this work, we design and synthesize a flexible Ca-based metal-organic framework MAF-58 consisting of cheap raw materials. MAF-58 shows selective methanol-induced pore-opening flexibility. Although the opened pores are large enough to accommodate benzene molecules, MAF-58 shows methanol/benzene molecular sieving with ultrahigh experimental selectivity, giving 5.1 mmol g-1 high-purity (99.99%+) methanol and 2.0 mmol g-1 high-purity (99.97%+) benzene in a single adsorption/desorption cycle. Computational simulations reveal that the preferentially adsorbed, coordinated methanol molecules act as the gating component to selectively block the diffusion of benzene, offering a new gating adsorption mechanism.

Performance analysis of a gas turbine engine via intercooling and regeneration- Part 2

Abstract The current study aims to amplify the predictive ability of the numerical model developed for a gas turbine engine-based power plants by process of regeneration and intercooling. Artificial neural networks (ANN) and adaptive neuro-fuzzy interface systems (ANFIS) are the two techniques mainly concentrated in this study which were not properly implemented previously. The performance parameters namely, specific power (SP), thermal efficiency (η), and enthalpy based specific fuel consumption (EBSFC) of a Turboprop engine were predicted using thermodynamic parameters namely, pressure ratio (PR), nozzle pressure ratio (NPR), turbine inlet temperature (TIT), for constant regeneration (R), and intercooling (E) efficiencies. The results showed that a high regression result R 2 of 0.9831 and 0.9899 was found for the ANFIS model for η for training and testing, respectively. Also, the ANFIS model resulted in best performance of the performance characteristics when compared to ANN.

LES of autoignition process in a turbulent droplet-laden mixing layer – impact of an evaporation model and discretisation method

This paper focuses on the numerical modelling of the autoignition process in an ethanol-air mixture within a turbulent droplet-laden temporally evolving mixing layer, utilising the Large Eddy Simulation (LES) method. We compare results obtained by implementing two notably distinct discretisation methods (second-order Total Variation Diminishing – TVD, and fifth-order Weighted Essentially Non-Oscillatory – WENO) in the species and energy transport equations. Additionally, we examine three widely recognised evaporation models (the ‘ D 2 ’ model, the Abramzon–Sirignano model, and a non-equilibrium model based on the Langmuir-Knudsen law). For modelling the combustion process, we employ the Eulerian Stochastic Field method and a laminar chemistry model. The simulations encompass two droplet diameter distributions, one with low and the other with significant diameter non-uniformity. Furthermore, nine distinct initial conditions are considered. Our findings indicate that adjusting the discretisation scheme or the evaporation model can yield simulation outcomes differing to a substantial degree, similar to the impact of modifying the actual properties of the spray droplets or their initial distribution. This is particularly pronounced in the case of sprays featuring a narrowed droplet diameter distribution. We found that the Abramzon–Sirignano model results in the most rapid autoignition. The TVD scheme contributes to a more even spatial distribution of the evaporated fuel, consequently facilitating quicker evaporation and faster autoignition. Using the WENO scheme generates higher fuel vapor gradients near the droplets, leading to less intense evaporation and slower fuel transport in space.

A Dynamics-Weighted Principal Components Analysis of Dominant Atmospheric Drivers of Ocean Variability with an Application to the North Atlantic Subpolar Gyre

Abstract This paper describes a framework for identifying dominant atmospheric drivers of ocean variability. The method combines statistics of atmosphere–ocean fluxes with physics from an ocean general circulation model to derive atmospheric patterns optimized to excite variability in a specified ocean quantity of interest. We first derive the method as a weighted principal components analysis and illustrate its capabilities in a toy problem. Next, we apply our analysis to the problem of interannual upper ocean heat content (HC) variability in the North Atlantic Subpolar Gyre (SPG) using the adjoint of the MITgcm and atmosphere–ocean fluxes from the ECCOv4-r4 state estimate. An unweighted principal components analysis reveals that North Atlantic heat and momentum fluxes in ECCOv4-r4 have a range of spatiotemporal patterns. By contrast, dynamics-weighted principal components analysis collapses the space of these patterns onto a small subset—principally associated with the North Atlantic Oscillation—that dominates interannual SPG HC variance. By perturbing the ECCOv4-r4 state estimate, we illustrate the pathways along which variability propagates from the atmosphere to the ocean in a nonlinear ocean model. This technique is applicable across a range of problems across Earth system components, including in the absence of a model adjoint. Significance Statement While the oceans have absorbed 90% of the excess heat associated with human-forced climate change, the change in the ocean’s heat content is not steady, with peaks and troughs superimposed upon a general increase. These fluctuations come from chaotic changes in the atmosphere and ocean and can be hard to disentangle. We use this case of ocean heat content variability to introduce a new method for determining the patterns of weather and climate in the atmosphere that are most effective at generating fluctuations in the ocean. To do this, we combine the statistics of recent atmospheric activity with output from a state-of-the-art numerical ocean model that reveals physical processes driving changes in ocean quantities including ocean heat content. This approach suggests that the atmospheric patterns that stimulate the most energetic changes in ocean heat content in the northern North Atlantic are not necessarily the most energetic patterns present in the atmosphere. We test our findings by preventing these patterns from affecting the ocean in our numerical model and measure a strong reduction in ocean heat content fluctuations.