Simulation Domain Research Articles

Sim2Real: Generative AI to Enhance Photorealism through Domain Transfer with GAN and Seven-Chanel-360°-Paired-Images Dataset.

This work aims at providing a solution to data scarcity by allowing end users to generate new images while carefully controlling building shapes and environments. While Generative Adversarial Networks (GANs) are the most common network type for image generation tasks, recent studies have only focused on RGB-to-RGB domain transfer tasks. This study utilises a state-of-the-art GAN network for domain transfer that effectively transforms a multi-channel image from a 3D scene into a photorealistic image. It relies on a custom dataset that pairs 360° images from a simulated domain with corresponding 360° street views. The simulated domain includes depth, segmentation map, and surface normal (stored in seven-channel images), while the target domain is composed of photos from Paris. Samples come in pairs thanks to careful virtual camera positioning. To enhance the simulated images into photorealistic views, the generator is designed to preserve semantic information throughout the layers. The study concludes with photorealistic-generated samples from the city of Paris, along with strategies to further refine model performance. The output samples are realistic enough to be used to train and improve future AI models.

Insights into Performance and Degradation of Commercial Ni-Rich Cathodes: A Modeling Perspective

Li-ion batteries are widely used in consumer electronics due to their high energy density and currently gain further importance with regards to future mobility and their application in electric vehicles. While a high nickel content in stoichiometries like LiNi0.8Mn0.1Co0.1O2 (NMC811) increases the achievable capacity in a given voltage window, it also poses challenges to structural stability during cycling [1]. Therefore, application and further development of those materials requires a deep understanding of their performance limitations as well as their degradation. Since extensive testing is time-consuming and expensive, predictive simulation tools are needed that are able to describe the electrochemical behavior of the cell.In this contribution, we will present 3D microstructure-resolved electrochemical continuum simulations conducted in the simulation framework BEST. It is based on a thermodynamically consistent transport theory for mass and charge in the electrolyte and the active material [2]. Owing to the finite volume discretization of the governing equations, it is straightforward to use voxel-based image data obtained by focused ion beam - scanning electron microscopy (FIB-SEM) as the simulation domain. Previous studies have shown the importance of microstructural simulations with regards to battery operation [3] and contributed to a deeper insight into the cell aging [4]. This approach will be complemented by a pseudo-2D (p2D) model considering different aging modes, thus providing additional predictions on the cell aging.In our work we analyze commercial NMC811/graphite cells. We demonstrate a very good agreement at the beginning of life between our simulations and experimental data obtained in rate tests and electrochemical impedance spectroscopy (EIS). In the high-energy cathodes we found a very low content (3 vol-%) of carbon binder domain (CBD), which is insufficient to establish a conductive network. Hence, the electronic conductivity of the active material determines the effective conductivity of the electrode and limits the performance at high degrees of lithiation. Additionally, we evaluate the degradation of the NMC active material using our simulation approach.Combining the 3D microstructure-resolved simulations with an efficient p2D model provides a valuable toolchain to gain a comprehensive understanding of the performance limitations as well as the relevant aging modes in commercially available high-energy cathodes.Acknowledgement:This work has been funded by the ‘Bundesministerium für Bildung und Forschung’ within the project MiCha under the reference number 03XP0317D. The authors acknowledge support by the state of Baden-Württemberg through bwHPC (JUSTUS 2).

Molecular Dynamics Analysis of the Scattering Phenomena of Oxygen Molecules on an Ionomer Surface in Catalyst Layer of Fuel Cell

As energy demand increases and global warming progresses, expectations for fuel cells, which generate electricity through chemical reactions between hydrogen and oxygen, are rising. There are various types of fuel cells, which are classified according to the electrolyte. Polymer electrolyte fuel cells (PEFCs) are expected to be used in stationary power sources for home use and fuel cell vehicles because of their low operating temperature, short start-up time, and ease of miniaturization. The cost has been an issue in the widespread use of fuel cells. The amount of platinum used in the catalyst layer (CL) must be reduced to reduce costs. However, it increases the current density per unit area of platinum. There are three factors that contribute to voltage drop: ohmic polarization, activation polarization, and diffusion polarization. At high current densities, the voltage drop due to diffusion polarization is dominant. The main cause of diffusion polarization is the transport resistance of oxygen molecules in the CL, and improving the oxygen transport properties in the CL will lead to lower cost PEFCs. In this study, we analyzed the transport properties of oxygen molecules in the CLs of PEFCs using the Monte Carlo (MC) method. The effect of oxygen scattering on the overall oxygen transport properties was investigated by considering the behavior on the surface of the ionomer film (surface diffusion) into the MC method, which simulates the overall transport in the PEFC CL. The objective of this study is to reproduce accurate oxygen transport by comparing the results of MC and molecular dynamics (MD) methods. First, we used the MC method to analyze oxygen scattering phenomena. In this study, we used the 3D structure data of CL in the MC simulations. The three-dimensional structure of the sample was reconstructed from sequential slice images. Molecules were assumed to reflect specularly on the boundaries of the simulation domain. Oxygen molecules were randomly placed in the simulation system. The time step was set at 5 ps and the simulation time was set at 4 μs. In order to examine the effect of surface diffusion on the transport properties of oxygen molecules, the simulation was performed using various conditions of surface diffusion. We calculated the effective diffusion coefficient of oxygen in the CL as the transport property of oxygen molecules in the MC method. As the surface diffusion coefficient increases, the effective diffusion coefficient of oxygen increases. When the surface diffusion coefficient is small, the effective diffusion coefficient of oxygen increases due to a decrease in the time constant, which determines the probability distribution of the surface residence time. However, when the surface diffusion coefficient is large, a different trend emerges. As the time constant increases, the effective diffusion coefficient has a peak value and then decreases. Next, to verify the model of surface diffusion in the MC method, we analyzed the scattering phenomenon of oxygen molecules in a simulation system that simulates the pores in the CL using the MD method. We modeled the nano pores in the CL as a slit between the ionomer walls in the MD method. Nafion with the equivalent weight (EW) of 1146 was used as polymer model. The number of Nafion chains is set at 4, and the water content λ is set at 7. Three-dimensional periodic boundary conditions were applied to the simulation domain. After the system was equilibrated, 1 ns of NVT simulation was performed for production. The temperature was set at 297 K. The behavior of oxygen molecules impacting on the ionomer wall was analyzed. We analyzed the behavior of oxygen molecules colliding with the ionomer membrane using the MD method. The average residence time of surface diffusion on the ionomer thin film was of the order of 10-10s. When the calculation results were compared with the MC results, it was found that the average residence time obtained by the MD method was the value where the surface diffusion coefficient affects much on the effective diffusion coefficient. Comparing the results of the MD method with the surface diffusion model used in the MC method, there are some discrepancies between the results and the model. For example, the MC method used a model in which oxygen molecules reflect diffusively on the ionomer surface. However, in the MD method, it was found that oxygen molecules tend to reflect in the direction of travel (Fig. 1). Therefore, a more accurate model of the MC method needs to be developed in the future. Figure 1

Numerical Reconstruction of Proton Exchange Membrane Fuel Cell Gas Diffusion Layers

Flooding and dehydration reduce stability and power performance in Proton Exchange Membrane Fuel Cells (PEMFCs). The Gas Diffusion Layer (GDL) plays a crucial role in facilitating reactant gas transport and removing product water from the electrode. To suit various PEMFCs, GDLs with different shapes have been commercialized. The impact of the GDL structure on the surface-tension-driven water transport behavior remains poorly understood. However, this is one important aspect that can be controlled by proper design. In this study, the GDL performance is investigated by comparing curved and straight carbon fibers within the region. Specifically, an image-processing method extracts porosity, domain size, and fiber diameter from an experimental image-based GDL reconstruction. These parameters are utilized by in-house developed computer codes to stochastically reconstruct curved and straight carbon fiber GDLs, respectively. The real and reconstructed GDLs are compared in terms of pore size distribution, tortuosity, and permeability. Liquid transport in these GDLs and corresponding gas channels is simulated using a volume of fluid method in OpenFOAM 7.0.Figure 1(a) presents the T-shaped simulation domain and top view of three GDLs. Figure 1(b) displays the Cumulative Density Function (CDF) of the pore size distribution for the three GDLs, revealing that the main difference between the three GDLs lies in the pore diameter range of 10-30 µm. Upon completion of the research program, we aim to identify the influence of fiber shape on the GDL transport properties as well as the water behavior inside them. Figure 1

ActiveZero++: Mixed Domain Learning Stereo and Confidence-based Depth Completion with Zero Annotation.

Learning-based stereo methods usually require a large scale dataset with depth, however obtaining accurate depth in the real domain is difficult, but groundtruth depth is readily available in the simulation domain. In this paper we propose a new framework, ActiveZero++, which is a mixed domain learning solution for active stereovision systems that requires no real world depth annotation. In the simulation domain, we use a combination of supervised disparity loss and self-supervised loss on a shape primitives dataset. By contrast, in the real domain, we only use self-supervised loss on a dataset that is out-of-distribution from either training simulation data or test real data. To improve the robustness and accuracy of our reprojection loss in hard-to-perceive regions, our method introduces a novel self-supervised loss called temporal IR reprojection. Further, we propose the confidence-based depth completion module, which uses the confidence from the stereo network to identify and improve erroneous areas in depth prediction through depth-normal consistency. Extensive qualitative and quantitative evaluations on real-world data demonstrate state-of-the-art results that can even outperform a commercial depth sensor. Furthermore, our method can significantly narrow the Sim2Real domain gap of depth maps for state-of-the-art learning based 6D pose estimation algorithms.

Enhancing turbulence modeling: Machine learning for pressure-strain correlation and uncertainty quantification in the Reynolds stress model

This research study delves into the realm of enhancing the Reynolds stress model through the remarkable capabilities of machine learning. Focusing on two key aspects, we investigate the modeling of fluctuating pressure-rate-of-strain tensor and uncertainty quantification in turbulent fluid flows. In the first part of our investigation, a neural network is employed to predict the pressure-strain correlation term for the frictional Reynolds number, even in cases where it was not trained. Through the use of high-fidelity datasets, we surpass the limitations of traditional numerical methods, achieving more accurate results with reduced computational costs. Going beyond conventional analysis, we explore the isotropic and anisotropic components of the pressure-strain correlation term. Intriguingly, our findings unveil the significant influence of production and redistribution terms on the anisotropic component by 50% and viscous dissipation impacts the isotropic part by 65% in wall-bounded turbulent flows. The second part introduces a novel approach for uncertainty quantification in the Reynolds stress model. Leveraging a binary classification model, we identify error-prone points within the simulation domain with an impressive accuracy of up to 95%. This critical step paves the way for a deeper understanding of uncertainties in turbulent flows for more reliable predictions. By harnessing the power of machine learning, our study provides two compelling ways to enhance the Reynolds stress model, contributing to a more efficient and dependable prediction of turbulent fluid dynamics.

Unsupervised domain adaptation for SAR target classification based on domain- and class-level alignment: From simulated to real data

Synthetic Aperture Radar (SAR) target classification methods based on convolutional neural networks have attracted wide attention recently. Typically, these methods necessitate a substantial quantity of labeled data to train the deep model, which is an obstacle for SAR target classification since annotating SAR images is costly and time-consuming. Generating large-scale labeled data in simulation to train the deep model is a feasible alternative. However, cross-domain recognition is challenging due to significant differences in distribution between simulated and real domains. In this paper, we propose an unsupervised domain adaptation method for SAR target classification by aligning the distribution between the simulated and unlabeled real domains. Specifically, we encode the dependencies across different granularity perspectives including domain-, and class-levels simultaneously to align two domains finely. On the one hand, a gradient-weighted adversarial alignment method is proposed to align the global distributions at the domain-level. On the other hand, a novel class-level alignment method based on the prototypical network is proposed to align the finer class structure. Technically, we respectively model the cross-domain prototype-prototype relations and cross-domain prototype-instance relations with contrastive learning to achieve intra-class prototype compact as well as inter-class prototype separation among cross-domain categories, where the prototypes of the real domain are learned with pseudo labels. To improve the quality of pseudo-labels, a pseudo-label filtering strategy combined with the attribute scattering center is further designed. The experiments on two simulation-to-reality datasets indicate that the proposed method outperforms state-of-the-art unsupervised domain adaptive methods, such as pixelfeat (+4.45% and +7.6% in accuracy on SAMPLE and S2M datasets respectively). Code and data will be available in https://github.com/YuShi1213/Sim2Real-Unsupervised-SAR-Target-Classification.

The Random Ray Method Versus Multigroup Monte Carlo: The Method of Characteristics in OpenMC and SCONE

The Random Ray Method (TRRM) is a recently developed approach to solving neutral particle transport problems based on the Method of Characteristics. While the method previously has been implemented only in closed-source or limited-functionality codes, this work describes its implementation in two open-source Monte Carlo codes: OpenMC and SCONE. The random ray implementations required small modifications to the existing Multigroup Monte Carlo (MGMC) solvers, offering a rare venue for redundant, fine-grained, “apples-to-apples” speed and accuracy comparisons between transport methods. To this end, TRRM and MGMC solvers are evaluated against each other using each code’s native capabilities on reactor eigenvalue problems with different degrees of energy discretization. On the C5G7 benchmark (featuring only seven energy groups), TRRM achieves a maximum pin power error comparable to or lower than that of MGMC for a given run time. On a problem with 69 energy groups, MGMC is found to scale more efficiently, obtaining a lower pin power error for a given run time. However, the defining difference between the two transport methods is found to be their vastly different uncertainty distributions. Specifically, TRRM is found to maintain similar levels of accuracy and uncertainty throughout the simulation domain whereas MGMC can exhibit orders-of-magnitude greater errors in areas of the problem that feature low neutron flux. For instance, TRRM provided an up to 373 times speed advantage compared with MGMC for computing the flux in low-flux regions in the moderator surrounding the C5G7 core.

Reconnection Rate and Transition from Ion-coupled to Electron-only Reconnection

Standard collisionless magnetic reconnection couples with both electron and ion dynamics. Recently, a new type of magnetic reconnection, electron-only magnetic reconnection without ion outflow, has been observed, and its reconnection rate has been found to be much higher than that in ion-coupled reconnection. In this paper, using 2D particle-in-cell simulations, we find that when the ion gyroradius is much smaller than the size of the simulation domain, magnetic reconnection is standard with ion outflows. As the ion gyroradius increases, the ion response gradually weakens, and the reconnection rate becomes higher. Electron-only reconnection occurs when the ion gyroradius is comparable to the size of the simulation domain. This trend applies to both strong and weak guide field situations. Therefore, the key factor that controls the transition from ion-coupled reconnection to electron-only reconnection is the ratio between the ion gyroradius and the size of the simulation domain. We further show that, in electron-only reconnection, when the initial electron current sheet is thinner, the reconnection rate and the electron outflow speed are higher.

Assessing turbulence and mixing parameterizations in the gray-zone of multiscale simulations over mountainous terrain during the METEX21 field experiment

Multiscale numerical weather prediction models transition from mesoscale, where turbulence is fully parameterized, to microscale, where the majority of highly energetic scales of turbulence are resolved. The turbulence gray-zone is situated between these two regimes and multiscale models must downscale through these resolutions. Here, we compare three multiscale simulations which vary by the parameterization used for turbulence and mixing within the gray-zone. The three parameterizations analyzed are the Mellor-Yamada Nakanishi and Niino (MYNN) Level 2.5 planetary boundary layer scheme, the TKE-1.5 large eddy simulation (LES) closure scheme, and a recently developed three-dimensional planetary boundary layer scheme based on the Mellor-Yamada model. The simulation domain includes complex (i.e., mountainous) terrain in Nevada that was instrumented with meteorological towers, profiling and scanning lidars, a tethered balloon, and a surface flux tower. Simulations are compared to each other and to observations, with assessment of model skill at predicting wind speed, wind direction and TKE, and qualitative evaluations of transport and dispersion of smoke from controlled releases. This analysis demonstrates that microscale predictions of transport and dispersion can be significantly influenced by the choice of turbulence and mixing parameterization in the terra incognita, particularly over regions of complex terrain and with strong local forcing. This influence may not be apparent in the analysis of model skill, and motivates future field campaigns involving controlled tracer releases and corresponding modeling studies of the turbulence gray-zone.

Determination of a Numerical Surge Limit by Means of an Enhanced Greitzer Compressor Model

The surge limit of centrifugal compressors is a key parameter in the design process of modern turbochargers. Numerical methods like steady-state simulations are state-of-the-art methods for predicting the performance of the centrifugal compressor. In contrast to that, the determination of the surge limit with any numerical method is still an unsolved challenge. Since the extensive work of Greitzer and many other researchers in this field, it is well-known that surge is a system-dependent phenomenon. In the case of steady-state simulations, the simulation domain is chosen to be as small as possible due to the numerical cost. This simply implies that there is no system information included in the numerical model. Therefore, it is not possible to determine any system-dependent surge limit with today’s applied numerical methods. To overcome this issue, an enhanced Greitzer surge model, which has been developed at Tu Darmstadt, should act as a link between the simulation and the system in which the compressor will be operated. The focus of this paper will rather be on the methodology of determining the surge limit by means of numerical data than on the surge model itself. The methodology will be validated by experimental data of different systems.

Eco-friendly tool-based electrochemical polishing of additively manufactured metallic components

The elevated surface roughness of metal additive manufacturing (MAM) parts detrimentally affects wear resistance, diminishes fatigue strength, and hampers cooling efficiency, which mandates post-processing. Electrochemical polishing is a non-contact and non-thermal post-processing technique but uses non-ecofriendly acidic baths and is ineffective in removing unmolten or partially molten metal particles. To address these challenges, we propose a novel approach using a flat nano-polished cylindrical tool as the cathode and an eco-friendly electrolyte for finishing a MAM component fabricated via atomic diffusion additive manufacturing (ADAM). Our study evaluates the effectiveness of this approach through numerical simulation and optimises the process through experimental analysis. The numerical simulation developed in COMSOL incorporates the surface roughness data of the ADAM part as the anode surface profile in the 2D simulation domain. The current density distribution, viscous layer formation, reduction in surface roughness and mass material removal rate (MRRg) are analysed from the simulation results. The experimental optimisation of parameters, including polishing time, inter-electrode gap (IEG), electrolyte flow, and electrolyte composition, resulted in a substantial decrease in the average surface roughness (Ra) value of the ADAM component by 95.91 %. The polished surface is more levelled, exhibiting a glossy finish with no waviness and fewer surface cracks. The EDS and XRD analysis portrayed the presence of passive films, indicating improved corrosion resistance. Repeating the experiment for rolled and milled surfaces with the same process parameters resulted in a similar reduction in the Ra value by 97.59 % and 94.73 %, respectively. The comparative analysis, of ECP on MAM, rolled, and milled surfaces, indicates the potential to achieve a similar notable improvement in surface finish, irrespective of the process history of the manufactured part for the same ECP parameters. Comparing the anode surface profiles, Ra values, and mass of material dissolved showed a close fit between experimental and simulation results. Our study highlights the feasibility of ECP with the flat tool electrode and eco-friendly electrolytes to reduce the surface roughness of an MAM component significantly.

Flow morphology of a supersonic gravitating sphere

ABSTRACT Stars and planets move supersonically in a gaseous medium during planetary engulfment, stellar interactions, and within protoplanetary discs. For a nearly uniform medium, the relevant parameters are the Mach number and the size of the body, R, relative to its accretion radius, RA. Over many decades, numerical and analytical work has characterized the flow, the drag on the body, and the possible suite of instabilities. Only a limited amount of work has treated the stellar boundary as it is in many of these astrophysical settings, a hard sphere at R. Thus, we present new 3D athena++ hydrodynamic calculations for a large range of parameters. For RA ≪ R, the results are as expected for pure hydrodynamics with minimal impact from gravity, which we verify by comparing to experimental wind tunnel data in air. When RA ≈ R, a hydrostatically supported separation bubble forms behind the gravitating body, exerting significant pressure on the sphere and driving a recompression shock, which intersects with the bow shock. For RA ≫ R, the bubble transitions into an isentropic, spherically symmetric halo, as seen in earlier works. These two distinct regimes of flow morphology may be treated separately in terms of their shock stand-off distance and drag coefficients. Most importantly for astrophysical applications, we propose a new formula for the dynamical friction, which depends on the ratio of the shock stand-off distance to RA. That exploration also reveals the minimum size of the simulation domain needed to accurately capture the deflection of incoming streamlines due to gravity.

Large-scale streaks in a turbulent bluff body wake

A turbulent circular disk wake database (Chongsiripinyo & Sarkar, J. Fluid Mech., vol. 885, 2020) at Reynolds number $Re = U_\infty D/\nu = 5 \times 10^{4}$ is interrogated to identify the presence of large-scale streaks – coherent elongated regions of streamwise velocity. The unprecedented streamwise length – until $x/D \approx 120$ – of the simulation enables investigation of the near and far wakes. The near wake is dominated by the vortex shedding (VS) mode residing at azimuthal wavenumber $m=1$ and Strouhal number $St = 0.135$ . After filtering out the VS structure, conclusive evidence of large-scale streaks with frequency $St \rightarrow 0$ , equivalently streamwise wavenumber $k_x \rightarrow 0$ in the wake, becomes apparent in visualizations and spectra. These streaky structures are found throughout the simulation domain beyond $x/D \approx 10$ . Conditionally averaged streamwise vorticity fields reveal that the lift-up mechanism is active in the near as well as the far wake, and that ejections contribute more than sweeps to events of intense $-u'_xu'_r$ . Spectral proper orthogonal decomposition is employed to extract the energy and the spatiotemporal features of the large-scale streaks. The streak energy is concentrated in the $m=2$ azimuthal mode over the entire domain. Finally, bispectral mode decomposition is conducted to reveal strong interaction between $m=1$ and $St = \pm 0.135$ modes to give the $m=2$ , $St \rightarrow 0$ streak mode. Our results indicate that the self-interaction of the VS mode generates the $m=2$ , $St \rightarrow 0$ streamwise vortices, which leads to streak formation through the lift-up process. To the authors’ knowledge, this is the first study that reports and characterizes large-scale low-frequency streaks and the associated lift-up mechanism in a turbulent wake.

Abstract 13340: Contractility and Local Stress Patterns Depend on Directionality of Fibrosis Progression: Insights From Microscale Biomechanical Simulations

Introduction: Cardiac fibrosis, a common pathological process in heart disease, leads to reduced contractility and increased myocardial stiffness. However, it remains unclear how various cellular and subcellular events individually contribute to these changes. Hypothesis: Tissue-scale biomechanical effects resulting from cellular and subcellular changes, such as myocyte loss and increased ECM stiffness, are anisotropic and depend on the prevailing direction of fibrosis progression. Methods: We used a mechanical modeling framework allowing for explicit geometrical representation of individual myocytes embedded in ECM. We used a transversely isotropic hyperelastic formulation for the cells while assuming the ECM to be isotropic. Contraction was imposed in each cell using an active stress model. In a simulated tissue domain (6х12 cells), we explored different cellular configurations (Fig. 1A) representing longitudinal, transverse, and random fibrosis progression (i.e., myocyte loss). In each configuration, we examined tissue shortening and stress distributions subject to original and 10х increased ECM stiffness. Results: Myocyte loss perpendicular to the myofiber direction had the largest impact on contractility (Fig. 1B and 1C; as low as 10.8% shortening vs. 15.4% at baseline). In particular, transverse stresses were greatly perturbed in areas near where remaining cells met replacement matrix. Longitudinal and random fibrosis progression had less impact on contractility but led to dramatic heterogeneity in stress distributions. Increased matrix stiffness (Fig. 1C) led to increased magnitude of all stress values. Conclusions: Tissue-scale changes depend on both ECM stiffening and the directionality of myocyte loss. Our model predicts that transverse fibrosis progression has the greatest impact on myocardial contraction, while longitudinal and random progression induce higher local maxima in myocardial stress distributions.

Peridynamic modeling of the micromechanical response of snow under high strain rates

Modeling the mechanical response of snow is a challenge due to the wide range of properties and conditions that impact the mechanics, such as temperature, loading conditions, and the geometry of the microstructure. Current modeling approaches including the finite element and discrete element methods have made great strides in modeling the micromechanical behavior of snow, but they often make simplifications to the microstructure geometry and failure process. However, it has been shown that the geometry of individual snow grains and the sintered bonds that form between them, as well as the failure of these bonds, are important factors in the micromechanical behavior of snow. We investigate a modeling framework called peridynamics that has not been applied to snow problems previously, but has been successfully applied to material fracture problems. This approach can use segmented voxels from μCT scans of snow as the model input geometry, which allows for highly accurate representations of realistic snow microstructures within the model. We provide the first analyses to determine appropriate simulation domain sizes and the domain resolutions required to capture realistic snow behavior with peridynamics. We show that the approach is able to simulate realistic brittle behavior for snow at very high strain rates (≥0.1s−1). The model naturally produces highly damaged regions within the sintered bond regions of the microstructure without any preconditioning. We then discuss next steps for further validating peridynamics for snow applications.

Dissecting Earth's Magnetosphere: 3D Energy Transport in a Simulation of a Real Storm Event

AbstractWe present new analysis methods of 3D MHD output data from the Space Weather Modeling Framework during a simulated storm event. Earth's magnetosphere is identified in the simulation domain and divided based on magnetic topology and the bounding magnetopause definition. Volume energy contents and surface energy fluxes are analyzed for each subregion to track the energy transport in the system as the driving solar wind conditions change. Two energy pathways are revealed, one external and one internal. The external pathway between the magnetosheath and magnetosphere has magnetic energy flux entering the lobes and escaping through the closed field region and is consistent with previous work and theory. The internal pathway, which has never been studied in this manner, reveals magnetically dominated energy recirculating between open and closed field lines. The energy enters the lobes across the dayside magnetospheric cusps and escapes the lobes through the nightside plasmasheet boundary layer. This internal circulation directly controls the energy content in the lobes and the partitioning of the total energy between lobes and closed field line regions. Qualitative analysis of four‐field junction neighborhoods indicate the internal circulation pathway is controlled via the reconnection X‐line(s), and by extension, the interplanetary magnetic field orientation. These results allow us to make clear and quantifiable arguments about the energy dynamics of Earth's magnetosphere, and the role of the lobes as an expandable reservoir that cannot retain energy for long periods of time but can grow and shrink in energy content due to mismatch between incoming and outgoing energy flux.

Simulation of crude oil slick on ice infested sea water

A three-dimensional numerical model based on the smoothed particle hydrodynamics (SPH) method is utilized to model such interactions. The effects from air and water are well captured using suitable force components and without explicit inclusion of air and water phases. This reduces the four-phase SPH model into a two-phase model, significantly reducing computational costs and potentially enabling the use of this model for large-scale simulations. This study includes two simulation domain size. The results show that the remained oil slick in the icy domain is completely depend on properties of different crude oils and current velocity. Then we performed a numerical experiment to study the effects of different ice parameters (e.g., ice concentration, floe shape) on the drift of fresh and weathered oil. Ice fields with the size of 750 m × 1350 m are used in the experiment. The ice concentration varies between 30%, 50% and 70%. Quadrilateral and circular ice floes, in addition to real-shape floes digitize from an aerial image are all used in the experiment. Oil spills of 1257 m3 are released in the open water and later drift into the ice under the influence of steady current of 50 cms. The type of spilled crude oil varies from fresh Troll B, Troll B 250 °C, and Troll B 250 °C and 75%emuls. The behavior of oil spills while crossing through the ice infested area are investigated by measuring the thickness of crude oil, the average and maximum distance traveled by crude oil.

CME-EPC: A coarse-mechanism embedded error prediction and compensation framework for robot multi-condition tasks

While industrial robots are widely used in various fields owing to their large workspace and high flexibility, significant errors constrain their application in high-precision scenarios. Though there have been notable achievements in mechanism modeling for different working conditions, they are complex, work-dependent, and difficult to apply conveniently to multiple operating conditions. Therefore, a coarse-mechanism embedded error prediction and compensation (CME-EPC) framework for robot multi-condition tasks is proposed, combining knowledge-rich coarse mechanism models and intelligent algorithms. These modules are proposed in CME-EPC, including coarse mechanism embedded simulation domain construction, active learning-based labeling of few-shots, and clustering-guided balanced domain adaptation transfer learning. These modules perform jointly to achieve accurate prediction and reliable compensation of errors. The proposed framework is experimentally validated in four tasks under three conditions, achieving superior performance compared to the other six methods with a conventional coarse-mechanism model and 10 % of the labeled samples. The compensated error is significantly reduced compared with other methods, with a maximum reduction of 94.31 %. Further analysis revealed that the CME-EPC is efficient, stable, and robust against the uncertainty of the mechanism model, thus highlighting the future potential of the robot's high-precision applications.

Code verification of non‐linear immersed boundary simulations using the method of manufactured solutions

AbstractNon‐standard finite element technologies, such as immersed boundary approaches, are typically based on novel algorithms and advanced methods, which require reliable testing of the implemented code. For this purpose, the method of manufactured solutions (MoMS) offers a great framework, enabling an easy and straightforward derivation of closed‐form reference solutions. In this contribution, the focus is kept on non‐linear analysis via the finite cell method (FCM), which is typically based on an unfitted geometry discretization and higher‐order shape functions. The code verification via MoMS generally requires the application of boundary conditions to all boundaries of the simulation domain, which need to be enforced in a weak sense on the immersed boundaries. To avoid this, we propose a novel way of deriving manufactured solutions, for which the necessary constraints on the embedded boundaries are directly fulfilled. Thus, weak boundary conditions can be eliminated from the FCM simulation, and the simulation complexity is reduced when testing other relevant features of the immersed code. In particular, we focus on finite strain analysis of 3D structures with a Neo‐Hookean material model, and show that the proposed technique enables a reliable code verification approach for all load steps throughout the deformation process.