Computer Simulation Research Articles

Physics-guided deep learning for skillful wind-wave modeling.

Modeling sea surface wind-waves is crucial for both scientific research and engineering applications. Nowadays, the most accurate wave models are based on numerical methods, which primarily concern the wave spectrum evolution by solving wave action balance partial differential equations. These methods are computationally expensive and limited by incomplete physical representations of wave spectral evolution. Here, we present a deep learning-based wave model trained using observation-merged wave hindcasts. Guided by the physics knowledge that waves are either generated by local current winds or by remote historical winds, this method can directly model significant wave height, bypassing the need for wave spectral information. This feature engineering effectively reduces the complexity of model inputs and outputs. The resulting artificial intelligence method can model 1 year of global significant wave heights at a 0.5° × 0.5° × 1-hour resolution within half an hour on a personal computer, achieving higher accuracy than state-of-the-art numerical wave models.

Numerical modelling of the surface plasmon modes of a circular cylindrical three-layer graphene waveguide

Numerical modelling of the surface plasmon modes of a circular cylindrical three-layer graphene waveguide

Developing a nearly automated open-source pipeline for conducting computational fluid dynamics simulations in anterior brain vasculature: a feasibility study

Intracranial aneurysms (IA) pose significant health risks and are often challenging to manage. Computational fluid dynamics (CFD) simulation has emerged as a powerful tool for understanding lesion-specific hemodynamics in and around IAs, aiding in the clinical management of patients with an IA. However, the current workflow of CFD simulations is time-consuming, complex, and labor-intensive and, thus, does not fit the clinical environment. To address these challenges, we have developed a semi-automated pipeline integrating multiple open-source software packages to streamline the CFD simulation process. Specifically, the study utilized medical angiography data from 18 patients. An in-house open-source DL image segmentation model (ARU-Net) was employed to generate 3D computer models of the anterior circulation. The segmented intracranial vasculature models, including IAs, were further refined using the Vascular Modeling Toolkit (VMTK), an open-source Python package. This step involved smoothing the surface of the models and extending the inlet and outlet regions to ensure a realistic representation of the vascular geometry. The refined vascular models were then converted into computational meshes using an open-source mesh generator known as TetGen. This process was nearly automated and required minimal user interaction(s). Blood flow simulations of the cerebral vascular models were performed using established SimVascular solvers (an open-source finite element platform for vascular applications) through an application programming interface (API). The CFD simulation process was also conducted using the manual workflow for comparative purposes. The initial assessment compared the geometries derived from manual and DL-based segmentation. The DL-based segmentation demonstrated reliable performance, closely aligning with manually segmented results, evidenced by excellent Pearson correlation coefficient (PCC) values and low relative difference (RD) values ranging from 3% to 10% between the computed geometrical variables derived from both methods. The statistical analysis of the computed hemodynamic variables, including velocity informatics and WSS-related variables, indicated good to excellent reliability for most parameters (e.g., ICC of 0.85–0.95). Given the data investigated, the proposed automated workflow streamlines the process of conducting CFD simulations. It generates results consistent with the current standard manual CFD protocol while minimizing dependence on user input.

Sensitivity of mesoscale modeling to urban morphological feature inputs and implications for characterizing urban sustainability

We examine the differences in meteorological output from the Weather Research and Forecasting (WRF) model run at 270 m horizontal resolution using 10 m, 100 m and 1 km resolution 3D neighborhood morphological inputs and with no morphological inputs. We find that the spatial variability in temperature, humidity, and other meteorological variables across the city can vary with the resolution and the coverage of the 3D urban morphological input, and that larger differences occur between simulations run without 3D morphological input and those run with some type of 3D morphology. We also find that the inclusion of input-building-defined roughness length calculations would improve simulation results further. We show that these inputs produce different patterns of heat wave spatial heterogeneity across the city of Washington, DC. These findings suggest that understanding neighborhood level urban sustainability under extreme heat waves, especially for vulnerable neighborhoods, requires attention to the representation of surface terrain in numerical weather models.

Two tests of variance homogeneity for clustered data where group size is informative

To evaluate variance homogeneity among groups for clustered data, Iachine et al. (Robust tests for the equality of variances for clustered data. J Stat Comput Simul 2010;80(4):365–377) introduced an extension of the well-known Levene test. However, this method does not account for informative cluster size (ICS) or informative within-cluster group size (IWCGS), which can occur in clustered data when cluster and group sizes are random variables. This article introduces two tests of variance homogeneity that are appropriate for data with ICS and IWCGS, one extending the Levene-style transformation method and one based on a direct comparison of estimates of variance. We demonstrate the properties of our tests in a detailed simulation study and show that they are resistant to the potentially biasing effects of ICS and IWCGS. We illustrate the use of these tests by applying them to a data set of x-ray diffraction measurements collected from a specimen of duplex steel.

Optimizing electrical properties and efficiency of copper-doped CdS and CdTe solar cells through advanced ETL and HTL integration: a comprehensive experimental and numerical study

Abstract Copper is a commonly preferred dopant for cadmium telluride based solar cells. Even though it is widely used as it enhances the electrical properties, it has the tendency to diffuse into the CdTe layer as well as the CdS/CdTe junction interface which adversely affects the performance of CdTe solar cells. In this experimental study copper doping of buffer cadmium sulfide layer has been performed to analyse its effect on structural, electrical, and optical properties of CdS and CdTe layers. While from the X-ray diffraction analysis it was observed that there was reduction in peak intensities and crystallite sizes of both the CdS and CdTe layers with the increase in amount of copper dopant, from the electrical properties it was found that there were improvements in carrier concentration, mobility, and conductivity of both the layers. To mitigate the losses due to Cu doping, enhance the efficiency and stability of CdTe solar cells an extensive numerical modelling approach was undertaken to employ electron transport layers (ETL) and hole transport layers (HTL) to the copper-doped CdS/CdTe solar cells. We obtained optimum results with titanium dioxide and copper barium thiostannate as ETL and HTL respectively. Finally, CdTe-based solar cells were modelled integrating copper-doped CdS as the buffer layers, TiO2 as ETL and CBTS as HTL respectively. The obtained experimental values of Cu-doped CdS and CdTe layers were implemented into this model. This superstrate configuration yielded impressive output parameters: open circuit voltage of 1.07 V, short-circuit current density of 29.32 mA cm−2, fill factor of 85.08 %, and efficiency of 26.67 %.

3D Numerical Modelling of Shallow Footing Resting on Tire-Derived Aggregate Backfill

3D Numerical Modelling of Shallow Footing Resting on Tire-Derived Aggregate Backfill

Capacity fade-aware parameter identification of zero-dimensional model for vanadium redox flow batteries

This study proposes a framework for evaluating the electrochemical performance of a vanadium redox flow battery (VRFB) system. First, a numerical solver for redox flow battery is constructed to represent the multi-physics system through systems of ordinary differential equations, which describe the mass conservation of existing vanadium ions. The present numerical model is validated regarding the voltage by comparing its results with the experimental results of previous studies. Second, we identify the parameters in the governing equations using a genetic algorithm with the present numerical model. We select seven parameters by considering the physical meaning of each parameter related to the electrochemical performance. The voltage for the first charging/discharging cycle and capacity fade data are used to identify the selected parameters. The voltage and capacity fade estimated by the parameters identified using the numerical model align with the previous studies. Finally, we analyze the global sensitivity of the identified parameters in terms of the voltage and capacity fade using the total Sobol’ indices because the high sensitivity confirms that the identified parameters have reliable values. As expected, voltage- and capacity-related parameters show high total Sobol’ indices for the voltage and discharging capacity, respectively. Furthermore, we predict the performance of the VRFBs using the identified parameter set and numerical model during 30-cycle operations. Additionally, the performance is compared according to the current density and vanadium concentration in the electrolyte. The proposed framework can be used to evaluate the electrochemical characteristics of developed VRFBs by identifying parameters related to physical performance, such as voltage and capacity fade. Moreover, the identified parameters can be utilized to predict voltage and capacity performance, enabling the optimization of operating conditions and configurations of VRFBs.

Numerical Modelling of the Punching Shear Response of Shearhead Connections between a CFT Column and a Flat Reinforced Concrete Slab

In this article, the punching shear response of shearhead connections between a Reinforced Concrete (RC) flat slab and interior Concrete-Filled steel Tubular (CFT) columns under static load is investigated by using a 3D nonlinear finite element modelling approach. The accuracy of the numerical model is verified against recent experimental results by comparing the results of the load–displacement relationship, failure models and cracking patterns. Based on validated numerical models, parametric investigations are conducted to examine the effects of slab thickness, concrete compressive strength, length of shearhead arm, size of shearhead cross section and flexural reinforcement ratio on the punching shear response of the connections. Then, some recommendations are given for the design of the effective parameters of shearhead connections, particularly focusing on the effective length and cross-section size of shearhear arms. Finally, a design equation considering the effect of the effective length of shearhead arms and flexural reinforcement ratio is proposed to predict the punching shear strength of shearhead connections. The comparison of punching shear strengths predicted by the proposed equation and some design codes shows that the proposed equation can predict the punching shear strength of shearhead connections more accurately than those from ACI 318-14 and Eurocode 2.

Active doping controls the mode of failure in dense colloidal gels

Mechanical properties of disordered materials are governed by their underlying free energy landscape. In contrast to external fields, embedding a small fraction of active particles within a disordered material generates nonequilibrium internal fields, which can help to circumvent kinetic barriers and modulate the free energy landscape. In this work, we investigate through computer simulations how the activity of active particles alters the mechanical response of deeply annealed polydisperse colloidal gels. We show that the "swim force" generated by the embedded active particles is responsible for determining the mode of mechanical failure, i.e., brittle vs. ductile. We find, and theoretically justify, that at a critical swim force the mechanical properties of the gel decrease abruptly, signaling a change in the mode of mechanical failure. The weakening of the elastic modulus above the critical swim force results from the change in gel porosity and distribution of attractive forces among gel particles, while below the critical swim force, the ductility enhancement is caused by an increase of gel structural disorder. Above the critical swim force, the gel develops a pronounced heterogeneous structure characterized by multiple pore spaces, and the mechanical response is controlled by dynamical heterogeneities. We contrast these results with those of a simulated monodisperse gel that exhibits a nonmonotonic trend of ductility modulation with increasing swim force, revealing a complex interplay between the gel energy landscape and embedded activity.

Study of fog dissipation in an internal boundary layer on Sable Island

AbstractAn interesting fog dissipation event was observed during the Fog and Turbulence Interactions in the Marine Atmosphere (FATIMA) Grand Banks field campaign, where a fog‐free region appeared immediately downstream of Sable Island as fog advected past it. This fog‐free region was predicted a priori by a high‐resolution numerical model that guided intensive operational periods of the FATIMA campaign, and its presence was adumbrated by GOES satellite observations. A comprehensive set of field observations shows that this fog‐free layer was due to the development of a (daytime) thermal internal boundary layer (IBL) that grew with distance from the leading shore line. The net incoming radiation following sunrise led to an increased air temperature and decreased relative humidity close to the ground, thus dissipating fog over the island. The height of the thermal IBL, as identified by the thickness of the superadiabatic layer, was found to be consistent with several available theoretical IBL formulae.

Numerical and experimental analysis of the ballistic performance of soft bulletproof vests for women

Abstract This article presents a comprehensive numerical and experimental analysis of the ballistic performance of soft bulletproof vests designed for women. The study involved a woman aged between 24 and 28 with a breast size of 85C. Two ballistic packages made from Twaron® CT 709 fabric were designed and constructed for her, featuring cut-and-sew formed breast cups and differing significantly in the number of layers (16 and 30 layers). The impact of the number of layers on breast deformation during the shooting was analyzed using numerical modeling and experiments, which included a Parabellum 9 mm × 19 mm FMJ® bullet and a Roma No. 1 plasticine substrate formed based on a plaster cast representing a woman’s figure. The research found that even significantly increasing the number of layers in the ballistic package did not lead to a substantial reduction in breast deformation during shooting. The likely reason for this is the cut-and-sew formed breast cups in the ballistic package, which easily undergo transverse deformation upon bullet impact. This suggests a need for further research to optimize the design of protective cups, which are crucial for proper force distribution and minimizing injuries during bullet impact. The conclusions drawn from this study could contribute to the development of more advanced and effective soft ballistic protection solutions for women.

Enhancing Plasma Torch Efficiency: Wet Steam Cooling

The efficient use of wet steam for plasma torch cooling and water plasma generation is important for reliable plasma generator design. Wet steam, due to phase transformation capability, improves heat removal at lower gas flow rates if compared to liquid water. To evaluate the wet steam cooling potential, a numerical model is proposed, incorporating governing equations (mass, momentum, energy, current, and Ampere's law) are expressed in the cylindrical coordinate system. The model is applied to investigate the wet steam cooling feasibility for the anode of a direct current plasma torch with non-transferred arc. Electric arc modeling examines anode spot location and anode surface temperature distribution for a 120 A arc current and gas flow rates of 42, 90, and 140 l/min under steady flow conditions. Analysis of spatial vapor content distribution in the refrigeration channel highlights unfavorable conditions when wet steam becomes dry.

Numerical simulation–based optimization of an integrated framework for the efficient development and sustainable utilization of geothermal resources: Application to the Bedugul geothermal field

Geothermal energy is a crucial renewable resource that can generate large power continuously but requires integrated and accurate assessments of its development and utilization for long-term and sustainable exploitation. Accordingly, this study develops an assessment framework considering all the influential factors for power generation: the potential capacity; silica scaling; type of power generation system; and temporal changes in the pressure, temperature, and fluid saturation in the reservoir. The applicability and effectiveness of the proposed method are demonstrated via a case study of the Bedugul geothermal two-phase system on Bali Island, Indonesia, using a calibrated numerical model and a stochastic resource assessment. The numerical model is a combination of wellbore, silica scaling, and thermodynamic power generation models. On the basis of the wellbore model by the Hagedorn and Brown pressure drop correlation, the calculated means of the production capacity and enthalpy from the liquid reservoir are 39.0 kg/s and 1340 kJ/kg, respectively. Using double-flash and flash-binary power generation systems over an exploitation period with a two-phase reservoir temperature of 260 °C, a reinjection temperature of 130 °C, and a silica scaling model, the predicted production from the liquid reservoir can sustain a power generation of 60 MWe, which is equivalent to 70 % of the power potential, according to a stochastic resource assessment using the Plackett–Burman design. The double-flash system is found to generate 1.0 MWe more power (a 1.6 % increase relative to the baseline capacity) than the flash-binary system using pentane as working fluid and to extend the lifetime of the make-up wells by 2.5 years. Consequently, it is vital, at an early stage of development, to understand the nature and properties of reservoirs and the thermodynamics of power generation systems via comprehensive research and reliable and accurate assessments of the power production capacity.

Evaluation of thermal parameters with geothermal energy recovery from a cold climate municipal solid waste landfill

This study presents finite element method (FEM) modeling with COMSOL Multiphysics to estimate the thermal properties of a waste landfill through back-analysis of temperature responses from buried thermistors during a full-scale multi-week active thermal response test. Field tests were conducted at the Loraas MSW landfill in Saskatoon, Canada, using instrumented equipment, including a vertical borehole heat exchanger and thermistor strings. The test consisted of two phases: a heat injection test during the summer and a heat extraction test in the winter. The study focused on a landfill 25 m deep, observing temperature changes at various depths over time. The effect of heat generation rate (HGR) on landfill temperature was investigated. Thermal conductivity and volumetric heat capacity increase with depth, with close alignment between summer and winter results, validating the tests. This research enhances the understanding of thermal parameters in MSW landfills by combining field tests and numerical modeling, offering insights into landfill behavior in cold climates. These findings hold significance for optimizing geothermal energy recovery from such landfills, calibrating thermal parameters for coupled models in landfills, and improving waste management practices.

Stabilizer Tensor Networks: Universal Quantum Simulator on a Basis of Stabilizer States

Efficient simulation of quantum computers relies on understanding and exploiting the properties of quantum states. This is the case for methods such as tensor networks, based on entanglement, and the tableau formalism, which represents stabilizer states. In this Letter, we integrate these two approaches to present a generalization of the tableau formalism used for Clifford circuit simulation. We explicitly prove how to update our formalism with Clifford gates, non-Clifford gates, and measurements, enabling universal circuit simulation. We also discuss how the framework allows for efficient simulation of more states, raising some interesting questions on the representation power of tensor networks and the quantum properties of resources such as entanglement and magic, and support our claims with simulations. Published by the American Physical Society 2024

Sustainable multiphase jet polishing of additively manufactured heat pipes utilizing recycled glass chips

Each year, a huge amount of waste glass chips is produced by machining, which is not recycled and has been treated as waste, resulting in the wasting of resources. However, the pristine physical properties of these micro-sized chips show great potential to be recycled as abrasives in jet polishing. In this work, machined glass chips were recycled and directly used as the abrasives in multiphase jet (MPJ) and abrasive water jet (AWJ) polishing for aluminum heat pipes of additive manufacturing with complex structures. Polishing performance of the recycled abrasives from waste glass was compared with those of commercial quartz abrasives with mesh sizes of 600, 800, 1000, 1200, and 3000. The results show that the recycled abrasives of glass chips have comparable performance to the commercial abrasives with mesh size of 1000. Computational fluid dynamic simulations were conducted on the two jet polishing to elucidate the fundamental material removal mechanisms. It is found that a higher drag force in the AWJ polishing could lead to uniform material removal and induce four times higher for material removal rate (MRR). The cost of setup, water and energy consumptions of MPJ polishing are only one tenth those of AWJ polishing. Energy dispersive spectroscopy reveals that there is no element contamination on the polished surface. This work proposes a novel approach of MPJ polishing for components produced by additive manufacturing with complicated structures. These findings provide new insights for the recycling use of waste chips generated in manufacturing, and suggest new solutions for saving energy and resources, as well as for clean and green production.

Estimating the Electric Fields Driving Lightning Dart Leader Development With BIMAP‐3D Observations

AbstractIn this paper, a numerical dart leader model has been implemented to understand the leader's development and the corresponding electric field changes observed by the 3D broadband mapping and polarization (BIMAP‐3D) system. The model assumes the extending leader channel is equipotential and has a linear charge distribution induced by an ambient electric field. The charge distribution induced by the ambient field can be used to model the electric field change at the ground. We then find the ambient electric field which best fits the field change measurements at the two BIMAP stations. The estimated ambient electric field decreases in the direction of dart leader propagation. Our observations and modeling results are consistent with our earlier hypothesis that dart leader speed is proportional to the electric field at the leader tip. The model also supports our earlier analysis that leader speed variations near branch junctions were due to previous charge deposits near the junctions. The modeled tip electric field is generally lower than the breakdown field unless the pre‐dart‐leader channel has a significant temperature of 3,000 K. This is consistent with the fact that dart leaders typically do not form new branches into the virgin air. Furthermore, the tip field is generally close to the negative streamer stability field at ambient temperatures, explaining the nature of the narrow and well‐defined channel structure. In addition to the charge distribution and the ambient and tip electric field, the development of the channel potential and current distribution are also presented.

A text-based, generative deep learning model for soil reflectance spectrum simulation in the solar range (400–2499 nm)

Soil spectral reflectance is a necessary input for land surface and radiative transfer models, and can be used to infer soil properties. Numerous soil reflectance inversion models have been developed based on mechanistic approaches, each with their own limitations. Mechanistic models based on radiative transfer theory are usually based on only a few input soil properties, whereas data-driven approaches are limited by high non-uniformity of available published datasets that severely limits the amount of data usable for model calibration. To address these limitations, a fully data-driven soil optics generative model (SOGM) for simulation of soil reflectance spectra from soil property inputs was developed based on the denoising diffusion probabilistic model (DDPM). The model was trained on an extensive dataset comprising nearly 180,000 soil spectra-property set pairs from 17 published datasets. The model generates soil reflectance spectra from text-based inputs describing soil properties and their values rather than only numerical values and labels in binary vector format, which means the model can handle variable formats for property reporting. Because the model is generative, it can simulate reasonable output spectra based on an incomplete set of available input properties, which becomes more reliable as the input property set becomes more complete. Two additional sub-models were also built to complement the SOGM: a spectral padding model that can fill in the gaps for spectra shorter than the target solar range (400 to 2499 nm), and a wet soil spectra model that can estimate the effects of water content on soil reflectance spectra given the dry spectrum predicted by the SOGM. It can also be easily integrated with other soil–plant radiation models used for remote sensing research such as PROSAIL and Helios 3D plant modeling software. The testing results of the SOGM on new datasets not included in model training demonstrated that the model can generate reasonable soil reflectance spectra based on available property inputs. Results also show soil clay/sand/silt fraction, organic carbon content, nitrogen content, and iron content tended to be important properties for spectra simulation. Inclusion of some trace minerals like nickel as model inputs decreased model performance because of their low concentrations and large propensity for ground-truth measurement error.

Angle of Attack Effects on the Induced Structural Loads of a Weapons Bay

AbstractAero-acoustic analysis was conducted on a weapons bay numerical model with doors, incorporating radar cross section reduction features. The effect of the angle of attack on the aero-acoustic response of the cavity was analysed at Mach numbers of 0.85 and 1.20. It was found that incidence influenced both mean-flow features and acoustic response. Further, linear and angular accelerations induced by the flow on the bay doors revealed potential adverse fluid–structure coupling when the results were compared with modal analysis. Again, the angle of attack did influence the aeroacoustic effects on the cavity door structure.