3D Grid Research Articles

Expected 1.x Makespan-Optimal Multi-Agent Path Finding on Grid Graphs in Low Polynomial Time

Multi-Agent Path Finding (MAPF) is NP-hard to solve optimally, even on graphs, suggesting no polynomial-time algorithms can compute exact optimal solutions for them. This raises a natural question: How optimal can polynomial-time algorithms reach? Whereas algorithms for computing constant-factor optimal solutions have been developed, the constant factor is generally very large, limiting their application potential. In this work, among other breakthroughs, we propose the first low-polynomial-time MAPF algorithms delivering 1-1.5 (resp., 1-1.67) asymptotic makespan optimality guarantees for 2D (resp., 3D) grids for random instances at a very high 1/3 agent density, with high probability. Moreover, when regularly distributed obstacles are introduced, our methods experience no performance degradation. These methods generalize to support 100% agent density. Regardless of the dimensionality and density, our high-quality methods are enabled by a unique hierarchical integration of two key building blocks. At the higher level, we apply the labeled Grid Rearrangement Algorithm (GRA), capable of performing efficient reconfiguration on grids through row/column shuffles. At the lower level, we devise novel methods that efficiently simulate row/column shuffles returned by GRA. Our implementations of GRA-based algorithms are highly effective in extensive numerical evaluations, demonstrating excellent scalability compared to other SOTA methods. For example, in 3D settings, GRA-based algorithms readily scale to grids with over 370,000 vertices and over 120,000 agents and consistently achieve conservative makespan optimality approaching 1.5, as predicted by our theoretical analysis.

Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints

The self-consistent field (SCF) generation of the three-dimensional (3D) electron density distribution (ρ) represents a fundamental aspect of density functional theory (DFT) and related first-principles calculations, and how one can shorten or bypass the SCF loop represents a critical question in electronic structure theory from both practical and fundamental standpoints. Herein, a machine learning strategy, DeepSCF, is presented in which the map between the SCF ρ and the initial guess density (ρ0) constructed by the summation of neutral atomic densities is learned using 3D convolutional neural networks (CNNs). High accuracy and transferability of DeepSCF are achieved by first encoding ρ0 on a 3D grid and then expanding the input features to include atomic fingerprints beyond ρ0. The prediction of the residual density (δρ) rather than ρ itself is targeted, and given that δρ is indicative of chemical bonding information, a dataset of small-sized organic molecules featuring diverse bonding characters is adopted. The fidelity of DeepSCF is finally enhanced by subjecting the atomic geometries of the dataset to random rotations and strains. The effectiveness of DeepSCF is demonstrated using a complex carbon nanotube-based DNA sequencer model. This work evidences that the nearsightedness in electronic structure can be optimally represented via the spatial locality in CNNs, offering insight into the success of various machine learning-based atomistic materials simulations.

Decentralized Multi-Agent Search for Moving Targets Using Road Network Gaussian Process Regressions

Unmanned aerial vehicles (UAVs) can collaborate as teams to accomplish diverse mission objectives, such as target search and tracking. This paper introduces a method that leverages accumulated target-density information over the course of a UAV mission to adapt path-planning rewards, guiding UAVs toward areas with a higher likelihood of target presence. The target density is modeled using a Gaussian process, which is iteratively updated as the UAVs search the environment. Unlike conventional search algorithms that prioritize unexplored regions, this approach incentivizes revisiting target-rich areas. The target-density information is shared across UAVs using decentralized consensus filters, enabling cooperative path selection that balances the exploration of uncertain regions with the exploitation of known high-density areas. The framework presented in this paper provides an adaptive cooperative search method that can quickly develop an understanding of the region’s target-dense areas, helping UAVs refine their search. Through Monte Carlo simulations, we demonstrate this method in both a 2D grid region and road networks, showing up to a 26% improvement in target density estimates.

Effect of Interpolation Artifacts on Perceived Stability of Nearby Sources in a Navigable Reverberant Virtual Environment

Convolution with spatial Room Impulse Responses can achieve realistic auralizations. When combined with interpolation between spatially distributed RIRs, this technique can be used to create navigable virtual environments. This study explores the impact of various interpolation parameters on the perceived auditory stability of a nearby static sound source with listener movements in a reverberant environment. The auditory scene was rendered via third-order Ambisonic RIR convolution combined with magnitude-least-squares binaural decoding using nonindividualized head-related transfer functions. First, the estimated direction of arrival as a function of the listener’s position within a 2D grid of RIRs under various configurations is examined as an objective metric. The perceived stability of the auditory source is then assessed through a perceptual experiment. Participants freely explored a virtual scene reproduced over headphones and a tracked head-mounted display. They were asked to rate the stability of a nonvisual source under various conditions of RIR grid density, interpolation panning method, and reverberation time. Results indicate no need to use an RIR grid size finer than 1m to optimize source stability when using a three-nearest-neighbor interpolation scheme.

Robust and smooth couinaud segmentation via anatomical structure-guided point-voxel network

Precise Couinaud segmentation from preoperative liver computed tomography (CT) is crucial for surgical planning and lesion examination. However, this task is challenging as it is defined based on vessel structures, and there is no intensity contrast between adjacent Couinaud segments in CT images. To solve this challenge, we design a multi-scale point-voxel fusion framework, which can more effectively model the spatial relationship of points and the semantic information of the image, producing robust and smooth Couinaud segmentations. Specifically, we first segment the liver and vessels from the CT image and generate 3D liver point clouds and voxel grids embedded with the vessel structure. Then, our method with two input-specific branches extracts complementary feature representations from points and voxels, respectively. The local attention module adaptively fuses features from the two branches at different scales to balance the contribution of different branches in learning more discriminative features. Furthermore, we propose a novel distance loss at the feature level to make the features in the segment more compact, thereby improving the certainty of segmentation between segments. Our experimental results on three public liver datasets demonstrate that our proposed method outperforms several state-of-the-art methods by large margins. Specifically, in out-of-distribution (OOD) testing of LiTS dataset, our method exceeded the voxel-based 3D UNet by approximately 20% in Dice score, and outperformed the point-based PointNet2Plus by approximately 8% in Dice score. Our code and manual annotations of the public datasets presented in this paper are available online: https://github.com/xukun-zhang/Couinaud-Segmentation.

Subject-Agnostic Transformer-Based Neural Speech Decoding from Surface and Depth Electrode Signals.

This study investigates speech decoding from neural signals captured by intracranial electrodes. Most prior works can only work with electrodes on a 2D grid (i.e., Electrocorticographic or ECoG array) and data from a single patient. We aim to design a deep-learning model architecture that can accommodate both surface (ECoG) and depth (stereotactic EEG or sEEG) electrodes. The architecture should allow training on data from multiple participants with large variability in electrode placements and the trained model should perform well on participants unseen during training. We propose a novel transformer-based model architecture named SwinTW that can work with arbitrarily positioned electrodes by leveraging their 3D locations on the cortex rather than their positions on a 2D grid. We train subject-specific models using data from a single participant and multi-patient models exploiting data from multiple participants. The subject-specific models using only low-density 8×8 ECoG data achieved high decoding Pearson Correlation Coefficient with ground truth spectrogram (PCC=0.817), over N=43 participants, outperforming our prior convolutional ResNet model and the 3D Swin transformer model. Incorporating additional strip, depth, and grid electrodes available in each participant (N=39) led to further improvement (PCC=0.838). For participants with only sEEG electrodes (N=9), subject-specific models still enjoy comparable performance with an average PCC=0.798. The multi-subject models achieved high performance on unseen participants, with an average PCC=0.765 in leave-one-out cross-validation. The proposed SwinTW decoder enables future speech neuropros-theses to utilize any electrode placement that is clinically optimal or feasible for a particular participant, including using only depth electrodes, which are more routinely implanted in chronic neurosurgical procedures. Importantly, the generalizability of the multi-patient models suggests that such a model can be applied to new patients that do not have paired acoustic and neural data, providing an advance in neuroprostheses for people with speech disability, where acoustic-neural training data is not feasible.

Optimization of acoustic porous material absorbers modeled as rigid multiple microducts networks: Metamaterial design using additive manufacturing

This research presents a strategy to enhancing sound absorption in porous acoustic metamaterials through the optimization of micro-duct networks. The study employs a combination of analytical and numerical methods, systematically adjusting micro-duct diameters within a 2D grid to optimize absorption coefficients at specific frequency bands. The Finite Element Transfer Method (FETM) is utilized for modeling, supported by a multi-objective function influenced by the surface impedance of the network. This approach ensures practical applicability and manufacturability of the designed metamaterial. A significant aspect of the study is the development of an analytical mobility matrix for each microduct, integrated through the Transfer Matrix Method using the visco-thermal dissipation theory. This integration results in a physically coherent model, for which a semi-analytical sensitivity analysis related to the microduct diameters can be directly performed. The optimization employs the Method of Moving Asymptotes (MMA), effectively managing the complexity associated with a large number of design variables. Subsequently, the optimized structure is adapted into a 3D grid, facilitating prototype creation using Additive Manufacturing Technology (AMT) with a specific polymer material. The research methodology is validated through four distinct test cases, each demonstrating the efficacy and adaptability of the optimization tools and techniques. Experimental validation, conducted using an impedance tube, indicates a significant shift in the first absorption maximum from 2500 Hz to 1000 Hz, achieved without altering the material thickness. Additional validation through 3D Finite Element Method (FEM) modeling, which includes visco-thermal effects, further confirms the acoustic efficiency of the final designs. While the potential for achieving lower sub-wavelength conditions is recognized, the study opts for simpler structures, considering the current limitations of 3D printing technology. This study contributes to both theoretical understanding and practical application in acoustic material design, emphasizing the potential for customizing materials to specific frequency ranges. The integration of FETM modeling with MMA provides a systematic and effective approach for optimizing acoustic materials, particularly in enhancing absorption at lower frequencies.

Feasibility of Identifying Shale Sweet Spots by Downhole Microseismic Imaging

Several studies suggest that shale sweet spots are likely associated with a low Poisson’s ratio in the shale layer. Compared with conventional geophysical techniques with active seismic data, it is straightforward and cost-effective to delineate the distribution of 3D Poisson’s ratios using microseismic data. In this study, an alternating method is proposed to determine microseismic event locations, 3D P-wave velocity, and Poisson’s ratio models with data recorded from downhole monitoring arrays. The method combines the improved 3D traveltime tomography, which inverts P and S arrivals for 3D P-wave velocity and Poisson’s ratio structures simultaneously, and a 3D grid search approach for event locations in an iterative fashion. The traveltime tomography directly inverts the Poisson’s ratio structure instead of calculating the Poisson’s ratios from P- and S-wave velocities (i.e., Vp and Vs) that are inverted by conventional traveltime tomography separately. The synthetic results and analysis suggest that the proposed method recovers the true Poisson’s ratio model reasonably. Additionally, we apply the method to a field dataset, which indicates that it may help delineate the reservoir structure and identify potential shale sweet spots.

Process-integrated optimization and techno-economic analysis of membrane system for biogas upgrading: Effect of membrane performance from an economic perspective

It is useful to be able to predict the potential economic performance of new membrane materials and the associated configurations of equipment which would give the best possible performance with these materials. In this study we propose a systematic search of ranges of different possible membrane properties. The properties varied here include the CO2 permeance and the CO2/CH4 selectivity, and a 2D grid with different values of each property is investigated. For each individual combination of properties a superstructure optimization is used to find the configuration and operating pressures which minimize the cost of biogas upgrading. Applying this to every point in the grid gives a surface of upgrading costs across the ranges of possible CO2 permeances and selectivities. Subsequently a correlation equation is fitted to this data which can be used to predict the potential economic performance of future possible materials within this range. In general the upgrading costs reduce when either of these parameters are increased but for different reasons. Higher permeances allow smaller area of membranes to be used allowing lower membrane costs while higher selectivities reduce the need for recycling and associated compression costs. The selectivity, in particular, is found to strongly affect the optimal configuration with high selectivities allowing for designs without recycling while lower selectivities force the process to include recycling streams in order to meet recovery and purity targets. It is expected that the proposed correlation could be used to quickly evaluate the economic performance of new materials as they are being developed and tested.

Design and Analysis of Curved Steel Bridges Using 3D Shell Modelling

AbstractSteel bridge designers and erectors primarily rely on approximate methods, such as 1D line girder analyses and 2D grid analyses. For curved or skewed bridges, this can give erroneous indications of the structure's true behaviour. Refined methods of analysis, such as 3D shell modelling, aim to bring a significant change in the practice of bridge engineering. By moving the industry away from simplistic design assumptions, refined analysis methods lead to more accurate, less conservative results, which in turn brings true benefits such as improved structural safety and increased economy. In this paper, several case studies on curved plate girder and tub girder bridges are presented, which cover the full bridge life cycle, from erection of the steel structure and placement of the concrete deck, down to live load analyses using influence surfaces and load rating. The case studies will be modelled with mBrace3D, a dedicated FEA software for curved steel bridges.

Grid-Based DBSCAN Clustering Accelerator for LiDAR’s Point Cloud

Autonomous robots operate on batteries, rendering power efficiency essential. The substantial computational demands of object detection present a significant burden to the low-power cores employed in these robots. Therefore, we propose a grid-based density-based spatial clustering of applications with a noise (DBSCAN) clustering accelerator for light detection and ranging (LiDAR)’s point cloud to accelerate computational speed and alleviate the operational burden on low-power cores. The proposed method for DBSCAN clustering leverages the characteristics of LiDAR. LiDAR has fixed positions where light is emitted, and the number of points measured per frame is also fixed. These characteristics make it possible to impose grid-based DBSCAN on clustering a LiDAR’s point cloud, mapping the positions and indices where light is emitted to a 2D grid. The designed accelerator with the proposed method lowers the time complexity from O(n2) to O(n). The designed accelerator was implemented on a field programmable gate array (FPGA) and verified by comparing clustering results, speeds, and power consumption across various devices. The implemented accelerator speeded up clustering speeds by 9.54 and 51.57 times compared to the i7-12700 and Raspberry Pi 4, respectively, and recorded a 99% reduction in power consumption compared to the Raspberry Pi 4. Comparisons of clustering results also confirmed that the proposed algorithm performed clustering with high visual similarity. Therefore, the proposed accelerator with a low-power core successfully accelerated speed, reduced power consumption, and effectively conducted clustering.

Integrating Heuristic Methods with Deep Reinforcement Learning for Online 3D Bin-Packing Optimization.

This study proposes a method named Hybrid Heuristic Proximal Policy Optimization (HHPPO) to implement online 3D bin-packing tasks. Some heuristic algorithms for bin-packing and the Proximal Policy Optimization (PPO) algorithm of deep reinforcement learning are integrated to implement this method. In the heuristic algorithms for bin-packing, an extreme point priority sorting method is proposed to sort the generated extreme points according to their waste spaces to improve space utilization. In addition, a 3D grid representation of the space status of the container is used, and some partial support constraints are proposed to increase the possibilities for stacking objects and enhance overall space utilization. In the PPO algorithm, some heuristic algorithms are integrated, and the reward function and the action space of the policy network are designed so that the proposed method can effectively complete the online 3D bin-packing task. Some experimental results illustrate that the proposed method has good results in achieving online 3D bin-packing tasks in some simulation environments. In addition, an environment with image vision is constructed to show that the proposed method indeed enables an actual robot manipulator to successfully and effectively complete the bin-packing task in a real environment.

Generalizing feature preservation in iso-surface extraction from triple dexel models

We present a method to resolve visual artifacts of a state-of-the-art iso-surface extraction algorithm by generating feature-preserving surface patches for isolated arbitrarily complex, single voxels without the need for further adaptive subdivision. In the literature, iso-surface extraction from a 3D voxel grid is limited to a single sharp feature per minimal unit, even for algorithms such as Cubical Marching Squares that produce feature-preserving surface reconstructions. In practice though, multiple sharp features can meet in a single voxel. This is reflected in the triple dexel model, which is used in simulation of CNC manufacturing processes. Our approach generalizes the use of normal information to perfectly preserve multiple sharp features for a single voxel, thus avoiding visual artifacts caused by state-of-the-art procedures.

Deep Learning Soft-Decision GNSS Multipath Detection and Mitigation.

A technique is proposed to detect the presence of the multipath effect in Global Navigation Satellite Signal (GNSS) signals using a convolutional neural network (CNN) as the building block. The network is trained and validated, for a wide range of C/N0 values, with a realistic dataset constituted by the synthetic noisy outputs of a 2D grid of correlators associated with different Doppler frequencies and code delays (time-domain dataset). Multipath-disturbed signals are generated in agreement with the various scenarios encompassed by the adopted multipath model. It was found that pre-processing the outputs of the correlators grid with the two-dimensional Discrete Fourier Transform (frequency-domain dataset) enables the CNN to improve the accuracy relative to the time-domain dataset. Depending on the kind of CNN outputs, two strategies can then be devised to solve the equation of navigation: either remove the disturbed signal from the equation (hard decision) or process the pseudoranges with a weighted least-squares algorithm, where the entries of the weighting matrix are computed using the analog outputs of the neural network (soft decision).

O-220 Ovarify: a deep learning model for automatic calculation of ovarian morphological features in pelvic ultrasonography

Abstract Study question Can a deep learning model accurately characterize ovarian features on pelvic ultrasounds? Summary answer We developed a deep learning model, Ovarify, that can accurately classify and segment ovarian features on pelvic ultrasounds. What is known already Ovarian morphology, as detected using pelvic ultrasonography, is used widely in the assessment of gynecological health. Morphological metrics such as ovarian volume and antral follicle count have known diagnostic and prognostic value for a variety of gynecological outcomes, ranging from risk for ovulatory disorders to likelihood of response to assisted reproductive technologies. However, a lack of standardization in the interpretation of ovarian features, and the time-intensive nature of validated image analysis techniques (such as 2D grid analysis), limits their utility in clinical practice. Deep learning provides an opportunity to overcome these limitations. Study design, size, duration The training dataset for the model comprised 426 3D pelvic ultrasounds obtained using the GE Voluson ultrasound platform, yielding 56,538 individual frames with potential ovarian content. In addition, a similarly collected validation dataset consisted of 95 ultrasounds, yielding 11,304 frames. The held-out test set comprised 107 ultrasounds with 14,767 frames. All frames were manually labeled with the associated 2D ovarian contour, and 163 of the ultrasounds had an associated ovarian volume label. Participants/materials, setting, methods Ovarify consists of two stages: 1) a lightweight classifier that labels ultrasound frames as either positive or negative (having or not having ovarian content) and 2) a subsequent U-Net-inspired segmentation module with an Xception backbone and attention-based decoder that segments the ovary and antral follicles in all frames previously classified as positive. Classification performance was evaluated using F1-score and area under the receiver operating characteristic curve (AUROC). Segmentation performance was evaluated using the Jaccard index. Main results and the role of chance For the classification stage, Ovarify achieved an AUROC of 0.980±0.002 and 0.976±0.004 on the training and validation sets, respectively. On the held-out test set, the model achieved an AUROC of 0.971±0.005. In addition, the model achieved an F1-score of 0.825±0.006, 0.810±0.013, and 0.784±0.014 on the training, validation, and held-out testing sets, respectively. The high AUROC and F1-scores across both seen and unseen datasets support a high-performing classifier. In the segmentation stage, Ovarify reached a median Jaccard index of 0.815±0.004 on the unseen test set when tasked with segmenting the ovary, and a median Jaccard index of 0.657±0.018 on the unseen test set when segmenting antral follicles. High agreement between the ground truth ovary and follicle labels and the predicted segmented regions increases confidence in the downstream morphological analysis. For example, the Pearson correlation coefficient between the Ovarify-calculated ovarian volume and manually-validated ovarian volume was 0.813, indicating that an automatic ovarian volume calculation highly aligns with the ground truth. Ovarify was able to provide predictions in under one minute for the entirety of the held-out test set. Limitations, reasons for caution Ovarify was trained on images generated by the GE Voluson platform and has not been evaluated for generalizability across ultrasound images generated using machines whose output does not match the DICOM format standard. Wider implications of the findings The model developed is relevant for automatically calculating ovarian morphological features from pelvic ultrasounds. This approach has implications in removing user-specific bias and error when calculating biomarkers associated with female reproductive health, including but not limited to ovulatory disorders such as polycystic ovary syndrome (PCOS). Trial registration number not applicable

Multi-GPU 3D k-nearest neighbors computation with application to ICP, point cloud smoothing and normals computation

The k-Nearest Neighbors algorithm is a fundamental algorithm that finds applications in many fields like Machine Learning, Computer Graphics, Computer Vision, and others. The algorithm determines the closest points (d-dimensional) of a reference set R according to a query set of points Q under a specific metric (Euclidean, Mahalanobis, Manhattan, etc.). This work focuses on the utilization of multiple Graphical Processing Units for the acceleration of the k-Nearest Neighbors algorithm with large or very large sets of 3D points. With the proposed approach the space of the reference set is divided into a 3D grid which is used to facilitate the search for the nearest neighbors. The search in the grid is performed in a multiresolution manner starting from a high-resolution grid and ending up in a coarse one, thus accounting for point clouds that may have non-uniform sampling and/or outliers. Three important algorithms in reverse engineering are revisited and new multi-GPU versions are proposed based on the introduced KNN algorithm. More specifically, the new multi-GPU approach is applied to the Iterative Closest Point algorithm, to the point cloud smoothing, and to the point cloud normal vectors computation and orientation problem. A series of tests and experiments have been conducted and discussed in the paper showing the merits of the proposed multi-GPU approach.

Monitoring tendon breaks in concrete structures at different depths using distributed fiber optical sensors

Unannounced tendon breaks are a dreaded failure mechanism in concrete bridges. The failure of single wires or tendons does not necessarily cause cracking of the concrete. Thus, the damage cannot be detected by visual inspection. But, in the case of tendons bonded in concrete, re-anchoring causes characteristic local strain fields: tensile strains occur between the crack edges while three-dimensional compressive strains develop in the area around. On the surface, these strains are small and depend, e.g., on the concrete stiffness, the depth of the tendon inside the member and the anchorage length. Such strain fields can be measured by fiber optical sensors on the surface in two dimensional grids. By evaluating the backscatter of emitted light beams, minimal strain changes (uncertainty: ±1 µm/m) can be detected in quasi-continuous resolution (0.65 mm pitch). An experimental investigation on a prestressed concrete beam with bonded tendons is presented. Gradually, three tendons of 10.5 mm diameter were mechanically cut to simulate failure, while longitudinal strains were measured on a 2D grid at the surface. With a prestressing force of 64.5 kN and up to a depth of 25 cm breaks of the tendons could be detected. They caused strains about 10-15 µm/m at the surface at maximum. For varying tendon positions, the effect of depth on the strain is investigated by two sided measurements. As the distance between the break and the concrete surface increases, the strain significantly decreases but remains detectable. In the future, the results enable deeper analyses of the most influential parameters. The final aim remains to precisely detect the position and number of failed wires or tendons just from the strain signal at the surface.

Research on topology optimization of carbon fiber reinforced composite electric vehicle helmet based on deep learning

Abstract To study the topology optimization of carbon fiber composite helmets, a method of optimizing the three-dimensional (hereinafter referred to as 3D) model based on a deep learning network after dimensionality reduction is proposed. In this method, the 3D shell model is expanded and tiled into two-dimensional (hereinafter referred to as 2D) grids based on the one-step inverse forming method. The variable density method (Isotropic Material Penalty Density method) is used to collect the data set required for deep learning training, and the U-Net neural network is built to complete the training. To obtain the input tensor of the neural network, the mechanical analysis of the 3D model is carried out, and the physical properties are imported into the network for learning. In this way, the 2D optimization results of the helmet model are obtained and then reflected in the 3D model according to the unit topology relationship to obtain the final optimization results. Then, the ordinary helmet and the optimized helmet are simulated and compared under the same quality to test their mechanical properties. The results show that the stiffness performance and energy absorption effect of the optimized helmet are stronger when the quality of the two helmets is the same.

Fabrication of bimetallic-doped materials derived from two polymorphic Ni-Mo-based coordination polymers for enhanced dye removal and gaseous iodine capture

Two polymorphic Ni–Mo-based coordination polymers (CPs) based on the ligand N,N′-bis(4-pyridinecarboxamide)-1,2-cyclohexane (4-bpah) were created using hydrothermal synthesis. Each of these CPs (denoted as Ni–Mo-1 and Ni–Mo-2) has the similar main framework [Ni(4-bpah)2(Mo2O7)], but they each show different crystalline phases with different 2D grid windows. Moreover, the Ni–Mo-based CP-derived carbon-based composites (C–Ni–Mo-X-Y, X = 1 or 2 and Y = 400, 450, or 500) were prepared by thermal cracking and “low-temperature” calcination below 500 °C using two CPs as precursors. The results show that the Ni–Mo-based CP-derived carbon-based composites prepared at different temperatures exhibit irregular morphologies with different micron-sized or nano-sized loose structures and chemical compositions. Furthermore, the dye and gas iodine adsorption properties of the C–Ni–Mo-X-Y composites were investigated in detail.

Efficient and Portable Vectorized Sweep Kernels for the Transport Equation on 3D Cartesian Grids Using the Kokkos Framework

This paper describes the implementation of efficient and portable vectorized sweep kernels as part of the resolution of the neutron transport equation on three-dimensional Cartesian grids using the discrete ordinates ( S n ) method for the angular variable and the diamond differencing (DD) scheme for the spatial discretization. Vectorization is set up along the directions within the same octant and is independent of the spatial discretization order; therefore, the extension of this technique to high-order DD or discontinuous Galerkin schemes is immediate. Our implementation is written in C++17 and relies on the Kokkos performance portability framework. This library allows one to express shared-memory parallelism (including vectorization) in a machine-independent way and supports many backends including CUDA and OpenMP. Our vectorization procedure relies on the portable single instruction multiple data types provided by Kokkos. The method has been implemented for DD schemes up to order 2 and yields promising results on CPUs supporting standard vector instructions.