Simulation Domain Research Articles

Simulation of dislocation slip and twin propagation in Mg through coupling crystal plasticity and phase field models

A numerical strategy to simulate plastic deformation in Mg alloys including dislocation slip and twin propagation is presented. Dislocation slip is included through a crystal plasticity model which is solved using the finite element method while twin propagation is taken into account by means of a phase field model which is solved using a fast Fourier transform algorithm. The coupled crystal plasticity and phase field equations were solved using different discretizations of the simulation domain using the same time step for both of them. The numerical strategy was used to simulate the deformation in compression of a Mg micro-pillar along the [101̄0] direction. The stress–strain curve predicted by the model as well as the dominant deformation mechanisms were in agreement with the experimental data in the literature and demonstrate the viability of the strategy to take explicitly into account twin propagation during the simulation of plastic deformation of Mg alloys. Finally, an example of slip/twin interaction in polycrystals was simulated to show the capabilities of the model.

Nowcasting of Wind in the Venice Lagoon Using WRF-FDDA

The Four-Dimensional Data Assimilation module (FDDA) is used in combination with the WRF model for the analysis of two case studies of high tide (on 4 April 2019 and on 12 November 2019) that affected the Venice Lagoon in the recent past. The system is implemented in the perspective of an operational use for nowcasting of 10 m wind, which will be part of a numerical system aimed at the forecast of the sea level height in the Venice Lagoon. The procedure involves the assimilation of data from meteorological surface stations distributed within the Venice Lagoon and in the open northern Adriatic Sea in front of the lagoon, as well asthe radiosonde profiles available within the simulation domain. The two cases were selected considering that the real-time forecasts missed their evolution, and the sea level height was significantly underpredicted. The comparison of the simulated wind with the observations shows a fairly good agreement over short time scales (1–2 h) in both cases; hence, the WRF-FDDA system represents a promising tool and a possibly valuable support to the decision makers in case of high tide in the Venice Lagoon.

Field emitter electrostatics: Efficient improved simulation technique for highly precise calculation of field enhancement factors

When solving the Laplace equation numerically via computer simulation, in order to determine the field values at the surface of a shape model that represents a field emitter, it is necessary to define a simulation box and, within this, a simulation domain. This domain must not be so small that the box boundaries have an undesirable influence on the predicted field values. A recent paper discussed the situation of cylindrically symmetric emitter models that stand on one of a pair of well-separated parallel plates. This geometry can be simulated by using two-dimensional domains. For a cylindrical simulation box, formulas have previously been presented that define the minimum domain dimensions (MDD) (height and radius) needed to evaluate the apex value of the field enhancement factor for this type of model, with an error-magnitude never larger than a “tolerance” εtol. This MDD criterion helps to avoid inadvertent errors and oversized domains. The present article discusses (in greater depth than previously) a significant improvement in the MDD method; this improvement has been called the MDD extrapolation technique (MDDET). By carrying out two simulations with relatively small MDD values, it is possible to achieve a level of precision comparable with the results of carrying out a single simulation using a much larger simulation domain. For some simulations, this could result in significant savings of memory requirements and computing time. Following a brief restatement of the original MDD method, the MDDET method is illustrated by applying it to the hemiellipsoid-on-plane and hemisphere-on-cylindrical-post emitter shape models.

Approach for Preservation and Reconstruction of Two-Dimensional Wave Spectra and Its Application to Boundary Conditions in Nested Wave Modeling

Typically, storing a two-dimensional wave spectrum could occupy more than one thousand storage units, making saving and reading boundary spectra computationally burdensome in nested wave simulations. This paper proposes a new approach for preservation of a wave spectrum that can reduce the required number of storage units to dozens. Using a corresponding reconstruction approach, the spectrum can then be rebuilt with intact spectral characteristics. Experimental application confirmed that the reconstructed spectra could be adopted as boundary conditions in nested wave modeling. The newly proposed approach for preservation and reconstruction of spectra allows long-term spectral information covering the entire simulated domain to be saved with more acceptable storage consumption, and such information can then be adopted as nesting conditions for nested-child simulations without the limitations of predefined boundaries. The above-mentioned properties of the new method could help support engineering projects concerning wave environments, research focused on wave climatology, and studies associated with wave energy assessment.

Kinetic Particle Simulations of Plasma Charging at Lunar Craters Under Severe Conditions

This paper presents fully kinetic particle simulations of plasma charging at lunar craters with the presence of lunar lander modules using the recently developed Parallel Immersed-Finite-Element Particle-in-Cell (PIFE-PIC) code. The computation model explicitly includes the lunar regolith layer on top of the lunar bedrock, taking into account the regolith layer thickness and permittivity as well as the lunar lander module in the simulation domain, resolving a nontrivial surface terrain or lunar lander configuration. Simulations were carried out to study the lunar surface and lunar lander module charging near craters at the lunar terminator region under mean and severe plasma environments. The lunar module’s position is also investigated to see its effect on the plasma charging relative to the craters. Differential surface charging was clearly resolved by the simulations. For the charging of a lunar lander module made of conducting materials, the results show a near-uniform potential close to that of its surrounding environment and moderate levels of local electric fields. Additionally, the risks associated with charging and discharging increase significantly under a more severe plasma charging environment as shown in the severe plasma environment cases.

A transfer learning approach for acoustic emission zonal localization on steel plate-like structure using numerical simulation and unsupervised domain adaptation

The detection and localization of damage in metallic structures using acoustic emission (AE) monitoring and artificial intelligence technology such as deep learning has been widely studied. However, a current challenge of this approach is the difficulty of obtaining sufficient labeled historical AE signals for the training process of deep learning models. This problem can be approached through the implementation of transfer learning. The innovation of this paper lies in the development of a transfer learning approach for AE source localization on a stainless-steel structure when no historical labeled AE signals are available for training. A finite element model is developed to generate numerical AE signals for the training. Unsupervised domain adaptation (UDA) technology is utilized to reduce the distribution difference between the numerical and the realistic AE signals and to derive the localization results of the unlabeled realistic AE signals. The results suggest that the proposed approach is capable of localizing AE signals with high accuracy in the absence of labeled training data.

HPC performance study of different collision models using the Lattice Boltzmann solver Musubi

Over the past decades, the lattice Boltzmann method (LBM) has become increasingly popular thanks to its capabilities in the domain of Large-Eddy Simulations (LES). Different collision schemes have been proposed to extend the scope of application to higher Reynolds number flows. This study compares the accuracy and the performance of some of these schemes on a D3Q27 lattice, including the original Multiple Relaxation Times (MRT) model, the Hybrid Recursive Regularized Bhatnagar–Gross–Krook (HRR) operator, as well as the Projected Recursive Regularized Bhatnagar–Gross–Krook (PRR) operator and the parametrized Cumulant collision scheme. For this purpose, the above-mentioned schemes are implemented in the HPC LBM solver Musubi and tested on a well-documented test-case describing the flow past a circular cylinder at a Reynolds number of 3900. Three different subgrid scale (SGS) models are used to account for the unresolved turbulence, i.e. the Smagorinsky model, the Wall-Adapting Local Eddy-viscosity (WALE) model, and the Vreman model. The Cumulant scheme uses an Implicit LES (ILES) subgrid scale model and shows the best agreement with the experimental data followed by MRT with WALE, and HRR with Vreman. The examined collision models are able to capture the second peak at f=3fvs of the power spectra density of the y velocity component first discovered in experiments.With respect to performance, the collision models are compared in terms of MLUPs/node and parallel efficiency for a strong scaling analysis. Again the Cumulant scheme outperforms the other collision models even when they are run on the reduced D3Q19 stencil. All the collision schemes show a strong scaling parallel efficiency above 60% on up to 16384 cores in our implementation.

Numerical Study of Aperture Shape Effects in Deep Cryogenic Etching of Silicon

Cryogenic etching of silicon in SF6/O2 plasma is the process of choice for fabrication of deep high aspect ratio structures with strict requirements for the sidewall angle, surface roughness and contamination, including MEMS, X-ray optics, and supercapacitors. The manufacturing of such structures, containing features of various size and shape, poses new challenges for the etching process optimization, since the key parameters of the resulting structures depend on the local aspect ratio and the shape of the mask aperture. As an aid in process optimization, we propose a three-dimensional Monte-Carlo simulator for the cryogenic etching of complex structures. It employs a surface kinetics model tuned to SF6/O2 process in 2.5D geometry and cubic voxel representations of the simulation domain. Systematic study on cryogenic etching of test structures with different mask shapes and aspect ratios is performed and principal mechanisms affecting etching results are identified.

Automated geometry and hexahedral mesh generation for kilometer-scale atmospheric flow simulations

This article introduces a methodology for automatic generation of geometries and meshes for kilometer-scale Atmospheric Boundary Layer (ABL) flow simulations, with topography and elevation. The proposed programmatic (hence automatable) \emph{template morphing approach} facilitates interpolation of scattered point cloud terrain data on a template geometry domain, morphing a high-quality quadrilateral template mesh for the interpolated geometry, and setup as well as execution of Computational Fluid Dynamics (CFD) flow simulation. The proposed method specifically addresses the previously reported problems of sustaining an ABL structure across the simulation domain by imposing the velocity and turbulence properties on all vertical surfaces. We present a validation study for the proposed method on an artificial Gaussian hill terrain. A real-world localized wind forecast application from the Turku Archipelago, Finland, is also presented, using open terrain data from National Land Survey of Finland. Such localized wind forecasts aim to assist ships in autonomous navigation and maneuvering in complex port or fairway environments, which is the motivation for this study.

Rheology of non-Brownian particle suspensions in viscoelastic solutions. Part 1: Effect of the polymer concentration

We study the effect of varying polymer concentration, measured by the dimensionless polymer viscosity partition function β, on the steady shear rheology of rigid particle suspensions using direct numerical simulation of the Oldroyd-B model. We compare the bulk rheology using immersed boundary simulations at Φ=2.5% and 5% to body-fitted single-particle simulations and find that the per-particle viscosity and first normal stress difference coefficient are always shear-thickening at all values of β considered. However, as β decreases, the polymer stress transforms the flow field near each particle from closed concentric streamlines to helical streamlines that advect stretched polymers away from the particle surface. At low β, the polymer stress is diffuse, where the distribution of the particle induced fluid stress (PIFS) caused by the stretched polymers is spread out in the simulation domain rather than concentrated near the particle surface. Therefore in multiparticle simulations, the polymer stress can be significantly affected by particle-particle interactions. The stress generated by a given particle is disrupted by the presence of particles in its vicinity, leading to a significantly lower PIFS than that of the single-particle simulation. In addition, at increased volume fractions and low values of β, the polymer stress distribution on the particle surface shifts so as to increase the magnitude of the polymer stress moments, resulting in a shear-thickening stresslet contribution to the viscosity that is not seen in single particle or high β simulations. This result indicates that for suspensions in highly viscoelastic suspending fluids that are characterized by a low β parameter, hydrodynamic interactions are significant even at modest particle concentrations and fully resolved multiparticle simulations are necessary to understand the rheological behavior.

Effects of grain size and small-scale bedform architecture on CO2 saturation from buoyancy-driven flow

Small-scale (mm-dm scale) heterogeneity has been shown to significantly impact CO2 migration and trapping. To investigate how and why different aspects of small-scale heterogeneity affect the amount of capillary trapping during buoyancy-driven upward migration of CO2, we conducted modified invasion percolation simulations on heterogeneous domains. Realistic simulation domains are constructed by varying two important aspects of small-scale geologic heterogeneity: sedimentary bedform architecture and grain size contrast between the matrix and the laminae facies. Buoyancy-driven flow simulation runs cover 59 bedform architecture and 40 grain size contrast cases. Simulation results show that the domain effective CO2 saturation is strongly affected by both grain size and bedform architecture. At high grain size contrasts, bedforms with continuous ripple lamination at the cm scale tend to retain higher CO2 saturation than bedforms with discontinuous or cross lamination. In addition, the “extremely well sorted” grain sorting cases tend to have lower CO2 saturation than expected for cross-laminated domains. Finally, both a denser CO2 phase and greater interfacial tension increase CO2 saturation. Again, variation in fluid properties seems to have a greater effect on CO2 saturation for cross-laminated domains. This result suggests that differences in bedform architecture can impact how CO2 saturation values respond to other variables such as grain sorting and fluid properties.

Comparison of travel-time and geostatistical inversion approaches for hydraulic tomography: Synthetic modeling study on data density and well configuration issues

Hydraulic tomography (HT) has been proven to be an effective approach in mapping the heterogeneity of hydraulic parameters. The travel-time based inversion (TTI) and geostatistical inversion (GI) approaches are two of several interpretation methods for HT data. In particular, GI is used to estimate heterogeneous hydraulic conductivity (K) and specific storage (Ss) tomograms, while TTI yields the diffusivity (D = K/Ss) tomogram. The main objective of this paper is to compare the performances of these two methods through a synthetic study. Two cases are designed based on different monitoring configurations. In each case, two independent scenarios are designed as: (1) providing the same dataset, and (2) providing datasets based on recommended strategies for each inversion approach. Then, the estimated tomograms are evaluated by direct comparison of estimated parameter distributions and assessment of model validation results. Comparison results show that the advantages of TTI are: (1) imaging of structural features representing high-D zones; and (2) requirement of less data for inverse modeling. In contrast, the advantages of GI are: (1) estimation of parameters throughout the simulation domain; (2) better characterization of low-D zones; and (3) generating the best estimated tomogram leading to best drawdown predictions. Collectively, our study suggests that TTI is suitable for rapid, coarse characterization of heterogeneity that could be utilized for providing hydrogeological structures for GI as an initial model. GI is more robust and preferable to applications that require higher accuracy in parameter estimation over a larger volume.

Multi-Sensor Data Fusion for Real-Time Multi-Object Tracking

Sensor data fusion is essential for environmental perception within smart traffic applications. By using multiple sensors cooperatively, the accuracy and probability of the perception are increased, which is crucial for critical traffic scenarios or under bad weather conditions. In this paper, a modular real-time capable multi-sensor fusion framework is presented and tested to fuse data on the object list level from distributed automotive sensors (cameras, radar, and LiDAR). The modular multi-sensor fusion architecture receives an object list (untracked objects) from each sensor. The fusion framework combines classical data fusion algorithms, as it contains a coordinate transformation module, an object association module (Hungarian algorithm), an object tracking module (unscented Kalman filter), and a movement compensation module. Due to the modular design, the fusion framework is adaptable and does not rely on the number of sensors or their types. Moreover, the method continues to operate because of this adaptable design in case of an individual sensor failure. This is an essential feature for safety-critical applications. The architecture targets environmental perception in challenging time-critical applications. The developed fusion framework is tested using simulation and public domain experimental data. Using the developed framework, sensor fusion is obtained well below 10 milliseconds of computing time using an AMD Ryzen 7 5800H mobile processor and the Python programming language. Furthermore, the object-level multi-sensor approach enables the detection of changes in the extrinsic calibration of the sensors and potential sensor failures. A concept was developed to use the multi-sensor framework to identify sensor malfunctions. This feature will become extremely important in ensuring the functional safety of the sensors for autonomous driving.

A Review on the Development of Two-Way Coupled Atmospheric-Hydrological Models

The past two decades have seen an intensive development in two-way coupled atmospheric and hydrological models, providing new opportunities to thoroughly understand hydrology–atmosphere coupling and improve hydrometeorological forecasting, which has not been possible before. This paper summarizes recent developments in hydrological presentation in land surface models (LSMs) and climate models, and the two-way coupling of atmospheric and hydrological models. The fully coupled models have been widely applied in identifying the impact of lateral surface and subsurface water transport in a land–atmosphere coupled system, and hydrometeorological simulations using techniques such as parameter calibration, data assimilation, and hydrology model structure revision have been used to improve the model accuracy. However, their applications still face major challenges, e.g., the complexity of hydrological parameter calibration, the lack of understanding of the physical mechanisms at high resolution, the parameterization of anthropogenic activities, and the limitations in simulation domain and period. Despite these difficulties, fully coupled atmospheric and hydrological models will gradually evolve into powerful tools to reproduce regional water cycles, offering significant potential for scientifically investigating water resources security issues affected by both climate change and human activities.

A Novel Point-Based Indexing Strategy for Biospeckle Sensors

In this letter, a novel point-based algorithm is presented for biospeckle sensors by combining random temporal history, robust normalization, and absolute value difference technique. Performance of the proposed strategy was evaluated and compared with existing strategies in simulation domain in terms of different analysis parameters (viz. frame invariance, correlation linearity, characterization of heterogeneity, and efficient computation). For experimental verification, biospeckle frames corresponding to coffee seeds ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coffea arabica</i> ) and maize seeds ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">zea maize</i> ) were utilized. From obtained results it can be inferred that, the proposed strategy possesses high accuracy in terms of all the analysis parameters. In future, the strategy can be widely utilized for industrial automation and quality inspection.

Characteristic Scales of Complexity and Coherence within Interplanetary Coronal Mass Ejections: Insights from Spacecraft Swarms in Global Heliospheric Simulations

Many aspects of the 3D structure and evolution of interplanetary coronal mass ejections (ICMEs) remain unexplained. Here, we investigate two main topics: (1) the coherence scale of magnetic fields inside ICMEs, and (2) the dynamic nature of ICME magnetic complexity. We simulate ICMEs interacting with different solar winds using the linear force-free spheromak model incorporated into the EUHFORIA model. We place a swarm of ∼20,000 spacecraft in the 3D simulation domain and characterize ICME magnetic complexity and coherence at each spacecraft based on the simulated time series. Our simulations suggest that ICMEs retain a lower complexity and higher coherence along their magnetic axis, but that a characterization of their global complexity requires crossings along both the axial and perpendicular directions. For an ICME of initial half angular width of 45° that does not interact with other large-scale solar wind structures, global complexity can be characterized by as little as 7–12 spacecraft separated by 25°, but the minimum number of spacecraft rises to 50–65 (separated by 10°) if interactions occur. Without interactions, ICME coherence extends for 45°, 20°–30°, 15°–30°, and 0°–10° for B, B ϕ , B θ , and B r , respectively. Coherence is also lower in the ICME west flank compared to the east flank due to Parker spiral effects. Moreover, coherence is reduced by a factor of 3–6 by interactions with solar wind structures. Our findings help constrain some of the critical scales that control the evolution of ICMEs and aid in the planning of future dedicated multispacecraft missions.

Development of an integrated machine-learning and data assimilation framework for NOx emission inversion

As major air pollutants, nitrogen oxides (NOx, mainly comprising NO and NO2) not only have adverse effects on human health but also contribute to the formation of secondary pollutants, such as ozone and particulate nitrate. To acquire reasonable NOx simulation results for further analysis, a reasonable emission inventory is needed for three-dimensional chemical transport models (3D-CTMs). In this study, a comprehensive emission adjustment framework for NOx emission, which integrates the simulation results of the 3D-CTM, surface NO2 measurements, the three-dimensional variational data assimilation method, and an ensemble back propagation neural network, was proposed and applied to correct NOx emissions over China for the summers of 2015 and 2020. Compared with the simulation using prior NOx emissions, the root-mean-square error, normalized mean error, and normalized mean bias decreased by approximately 40 %, 40 %, and 60 % in NO2 simulation using posterior NOx emissions corrected by the framework proposed in this work. Compared with the emissions for 2015, the NOx emission generally decreased by an average of 5 % in the simulation domain for 2020, especially in Henan and Anhui provinces, where the percentage reductions reached 24 % and 19 %, respectively. The proposed framework is sufficiently flexible to correct emissions in other periods and regions. The framework can provide reliable and up-to-date emission information and can thus contribute to both scientific research and policy development relating to NOx pollution.

ICON-Sapphire: simulating the components of the Earth system and their interactions at kilometer and subkilometer scales

Abstract. State-of-the-art Earth system models typically employ grid spacings of O(100 km), which is too coarse to explicitly resolve main drivers of the flow of energy and matter across the Earth system. In this paper, we present the new ICON-Sapphire model configuration, which targets a representation of the components of the Earth system and their interactions with a grid spacing of 10 km and finer. Through the use of selected simulation examples, we demonstrate that ICON-Sapphire can (i) be run coupled globally on seasonal timescales with a grid spacing of 5 km, on monthly timescales with a grid spacing of 2.5 km, and on daily timescales with a grid spacing of 1.25 km; (ii) resolve large eddies in the atmosphere using hectometer grid spacings on limited-area domains in atmosphere-only simulations; (iii) resolve submesoscale ocean eddies by using a global uniform grid of 1.25 km or a telescoping grid with the finest grid spacing at 530 m, the latter coupled to a uniform atmosphere; and (iv) simulate biogeochemistry in an ocean-only simulation integrated for 4 years at 10 km. Comparison of basic features of the climate system to observations reveals no obvious pitfalls, even though some observed aspects remain difficult to capture. The throughput of the coupled 5 km global simulation is 126 simulated days per day employing 21 % of the latest machine of the German Climate Computing Center. Extrapolating from these results, multi-decadal global simulations including interactive carbon are now possible, and short global simulations resolving large eddies in the atmosphere and submesoscale eddies in the ocean are within reach.

Numerical simulation of two-phase flow in gas diffusion layer and gas channel of proton exchange membrane fuel cells

Liquid water within the cathode Gas Diffusion Layer (GDL) and Gas Channel (GC) of Proton Exchange Membrane Fuel Cells (PEMFCs) is strongly coupled to gas transport properties, thereby affecting the electrochemical conversion rates. In this study, the GDL and GC regions are utilized as the simulation domain, which differs from previous studies that only focused on any one of them. A Volume of Fluid (VOF) method is adopted to numerically investigate the two-phase flow (gas and liquid) behavior, e.g., water transport pattern evolution, water coverage ratio as well as local and total water saturation. To obtain GDL geometries, an in-house geometry-based method is developed for GDL reconstruction. Furthermore, to study the effect of GDL carbon fiber diameter, the same procedure is used to reconstruct three GDL structures by varying the carbon fiber diameter but keeping the porosity and geometric dimensions constant. The wall wettability is introduced with static contact angles at carbon fiber surfaces and channel walls. The results show that the GDL fiber microstructure has a significant impact on the two-phase flow patterns in the cathode field. Different stages of two-phase flow pattern evolution in both cathode domains are observed. The liquid water in the GDL experiences water invasion, spreading, and rising, followed by the droplet breakthrough in the GDL/GC interface. In the GC, the water droplets randomly experience accumulation, combination, attachment, and detachment. Due to the difference in surface wettability, the water coverage of the GDL/GC interface is smaller than that of the channel side and top walls. It is also found that the water saturation inside the GDL stabilizes after the water breakthrough, while local water saturation at the interface keeps irregular oscillations. Last but not the least, a water saturation balance requirement between the GDL and GC is observed. In terms of varying fiber diameter, a larger fiber diameter would result in less water saturation in the GDL but more water in the GC, in addition to faster water movement throughout the total domain.

Angular momentum conservation in spin-lattice dynamics simulations

Kuzkin's angular momentum balance method is implemented in the LAMMPS SPIN package for atomistic spin-lattice dynamics, along with shifted-force exchange and N\'eel Hamiltonians parameterized to minimize energy drifts in the simulations. Angular momentum contributions arising from two mechanisms are quantified using this method: particle transport across the boundaries of a periodic simulation domain and external torques applied to the domain by periodic image atoms. When these mechanisms are accounted for, lattice angular momentum is exactly conserved in lattice systems and in spin-lattice systems with isotropic exchange interactions. The calculations show that spin-lattice angular momentum exchange only occurs when the N\'eel anisotropy energy is added to the exchange energy, and that with this addition, total angular momentum is approximately conserved in the magnetization direction but not in other directions. Inclusion of the N\'eel anisotropy increases the energy drifts observed in simulations of iron nanoparticles. These drifts are linearly proportional to the magnitude of the anisotropy energy and the simulation time step.