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Influence of Forced Flow on the Dendritic Growth of Fe-C Alloy: 3D vs 2D Simulation
A 3D parallel cellular automaton-finite volume method (CA-FVM) model was used to simulate the equiaxed dendritic growth of an Fe-0.82 wt pct C alloy with xy-in-out and xyz-in-out type forced flows and the columnar dendritic growth with y-in-out type forced flow. In addition, the similarities and differences between the results of the 3D and 2D models are discussed and summarized in detail. The capabilities of the 3D and 2D CA-FVM models to predict the dendritic growth of the alloy with forced flow are validated through comparison with the boundary layer correction and Oseen–Ivanstov models, respectively. Because the forced flow can pass around perpendicular arms of the dendrites, the secondary arms at the sides upstream from the perpendicular arms are more developed than those on the upstream side of the upstream arms, especially at higher inlet velocities. In addition, compared to the xy-in-out case, the growth of the downstream arms is less inhibited and the secondary arms are more developed in the xyz-in-out case because of the greater lateral flow around their tips. Compared to the 3D case, the 2D equiaxed dendrites are more asymmetrical and lack secondary arms because of the thicker solute envelope. In the 3D case, the columnar dendrites on the upstream side (left one) are promoted, while the middle and downstream dendrites are inhibited in sequence. However, the sequential inhibition starts on the upstream side in the 2D case. This is mainly because the melt can pass around the upstream branch in 3D space. However, it can only climb over the upstream tip in 2D space. Additionally, the secondary arms show upstream development, which is more significant with increasing inlet velocity. The level of development of the secondary arms is also affected by the decay of the forced flow in the flow direction.
Scalable implementation of polynomial filtering for density functional theory calculation in PARSEC
In this work, we present an efficient implementation of polynomial filtering methods in PARSEC, a real-space pseudopotential based Kohn–Sham density functional theory solver. The implementation described here improves upon a Chebyshev-filtered subspace iteration algorithm used in the previous version of PARSEC. We present a hybrid polynomial filtering scheme that combines Chebyshev-filtered subspace iteration and a spectrum slicing method that partitions the spectrum into several spectral slices and uses bandpass-filtered subspace iteration to compute approximate eigenpairs within each interior slice simultaneously. We describe a procedure to partition a spectrum and construct polynomial filters. We also discuss a number of practical issues such as the use of appropriate data layouts for carrying out the computation on a two-dimensional process grid and how to achieve good load balance by allocating an appropriate number of process groups to each spectral slice. Numerical examples are presented to demonstrate the effectiveness of the hybrid polynomial filtering method as well as the superior parallel scalability of spectrum slicing in comparison to that of Chebyshev-filtered subspace iteration.
In situ characterization of charge rate dependent stress and structure changes in V 2 O 5 cathode prepared by atomic layer deposition
Here, the insertion/extraction of lithium into/from various host materials is the basic process by which lithium-ion batteries reversible store charge. This process is generally accompanied by strain in the host material, inducing stress which can lead to capacity loss. Therefore, understanding of both the structural changes and the associated stress – investigated almost exclusively separate to date – is a critical factor for developing high-performance batteries. Here, we report an in situ method, which utilizes Raman spectroscopy in parallel with optical interferometry to study effects of varying charging rates (C-rates) on the structure and stress in a V 2 O 5 thin film cathode. Abrupt stress changes at specific crystal phase transitions in the Li—V—O system are observed and the magnitude of the stress changes with the amount of lithium inserted into the electrode are correlated. A linear increase in the stress as a function of x in Li x V 2 O 5 is observed, indicating that C-rate does not directly contribute to larger intercalation stress. However, a more rapid increase in disorder within the Li x V 2 O 5 layers is correlated with higher C-rate. Ultimately, these experiments demonstrate how the simultaneous stress/Raman in situ approach can be utilized as a characterization platform for investigating various critical factors affecting lithium-ion battery performance.
Training data selection for accuracy and transferability of interatomic potentials
Abstract Advances in machine learning (ML) have enabled the development of interatomic potentials that promise the accuracy of first principles methods and the low-cost, parallel efficiency of empirical potentials. However, ML-based potentials struggle to achieve transferability, i.e., provide consistent accuracy across configurations that differ from those used during training. In order to realize the promise of ML-based potentials, systematic and scalable approaches to generate diverse training sets need to be developed. This work creates a diverse training set for tungsten in an automated manner using an entropy optimization approach. Subsequently, multiple polynomial and neural network potentials are trained on the entropy-optimized dataset. A corresponding set of potentials are trained on an expert-curated dataset for tungsten for comparison. The models trained to the entropy-optimized data exhibited superior transferability compared to the expert-curated models. Furthermore, the models trained to the expert-curated set exhibited a significant decrease in performance when evaluated on out-of-sample configurations.
Surrogate optimization of variational quantum circuits
Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications. However, the potential has not yet been realized, with few claims of quantum advantage and high resource estimates, especially due to the need for optimization in the presence of noise. Finding algorithms and methods to improve convergence is important to accelerate the capabilities of near-term hardware for VQE or more broad applications of hybrid methods in which optimization is required. To this goal, we look to use modern approaches developed in circuit simulations and stochastic classical optimization, which can be combined to form a surrogate optimization approach to quantum circuits. Using an approximate (classical CPU/GPU) state vector simulator as a surrogate model, we efficiently calculate an approximate Hessian, passed as an input for a quantum processing unit or exact circuit simulator. This method will lend itself well to parallelization across quantum processing units. We demonstrate the capabilities of such an approach with and without sampling noise and a proof-of-principle demonstration on a quantum processing unit utilizing 40 qubits.
Lattice Boltzmann approach for complex nonequilibrium flows
We present a lattice Boltzmann realization of Grad's extended hydrodynamic approach to nonequilibrium flows. Here, this is achieved by using higher-order isotropic lattices coupled with a higher-order regularization procedure. The method is assessed for flow across parallel plates and three-dimensional flows in porous media, showing excellent agreement of the mass flow with analytical and numerical solutions of the Boltzmann equation across the full range of Knudsen numbers, from the hydrodynamic regime to ballistic motion.
Tomosaic : efficient acquisition and reconstruction of teravoxel tomography data using limited-size synchrotron X-ray beams
X-rays offer high penetration with the potential for tomography of centimetre-sized specimens, but synchrotron beamlines often provide illumination that is only millimetres wide. Here an approach is demonstrated termed Tomosaic for tomographic imaging of large samples that extend beyond the illumination field of view of an X-ray imaging system. This includes software modules for image stitching and calibration, while making use of existing modules available in other packages for alignment and reconstruction. The approach is compatible with conventional beamline hardware, while providing a dose-efficient method of data acquisition. By using parallelization on a distributed computing system, it provides a solution for handling teravoxel-sized or larger datasets that cannot be processed on a single workstation in a reasonable time. Using experimental data, the package is shown to provide good quality three-dimensional reconstruction for centimetre-sized samples with sub-micrometre pixel size.
Combining Tensor Decompositions and Graph Analytics to Provide Cyber Situational Awareness at HPC Scale
This paper describes MADHAT (Multidimensional Anomaly Detection fusing HPC, Analytics, and Tensors), an integrated workflow that demonstrates the applicability of HPC resources to the problem of maintaining cyber situational awareness. MADHAT combines two high-performance packages: ENSIGN for large-scale sparse tensor decompositions and HAGGLE for graph analytics. Tensor decompositions isolate coherent patterns of network behavior in ways that common clustering methods based on distance metrics cannot. Parallelized graph analysis then uses directed queries on a representation that combines the elements of identified patterns with other available information (such as additional log fields, domain knowledge, network topology, whitelists and blacklists, prior feedback, and published alerts) to confirm or reject a threat hypothesis, collect context, and raise alerts. MADHAT was developed using the collaborative HPC Architecture for Cyber Situational Awareness (HACSAW) research environment and evaluated on structured network sensor logs collected from Defense Research and Engineering Network (DREN) sites using HPC resources at the U.S. Army Engineer Research and Development Center DoD Supercomputing Resource Center (ERDC DSRC). To date, MADHAT has analyzed logs with over 650 million entries.
Uncertainty Aware Deep Learning for Fault Prediction Using Multivariate Time Series Signals
The superconducting radio-frequency cavities are a crucial component of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. When a cavity faults, beam delivery to experimental end users is disrupted. Prediction of cavity faults prior to onset is essential to reduce operation and maintenance costs. In this work, a parallel long short-term memory (LSTM)-convolution neural network (CNN)-based deep learning (DL) model is proposed to predict impending faults using pre-fault signals. Further, we introduce an uncertainty quantification approach using Monte Carlo dropout with the LSTM-CNN model to ascertain confidence in the prediction. The model was tested using multivariate time series signals from stable cavity operations and before faults. Initial results show that on the test dataset, the model can identify impending faults before their onset with an average 10-fold cross validation accuracy of 97.39% and a standard deviation of 0.12% using a 100-ms time window. It is also observed that the model performs better as the prediction time moves closer to the fault onset. For additional context, we compare the performance of the model with three machine-learning-based (ML) fault prediction models. Our proposed parallel LSTM-CNN-based DL method shows better performance than the ML-based methods.
Energy Conservation Through Cloned Execution Of Simulations
High-performance computing facilities used for scientific computing draw enormous energy, some of them consuming many megawatt-hours. Saving the energy consumption of computations on such facilities can dramatically reduce the total cost of their operation and help reduce environmental effects. Here, we focus on a way to reduce energy consumption in many ensembles of simulations. Using the method of simulation cloning to exploit parallelism while also significantly conserving the computational and memory requirements, we perform a detailed empirical study of energy consumed on a large supercomputer consisting of hardware accelerator cards (graphical processing units, GPUs). We build on previous insights from mathematical analysis and implementation of cloned simulations that result in computational and memory savings by several orders-of-magnitude. Using instrumentation to track the power drawn by thousands of accelerator cards, we report significant aggregate energy savings from cloned simulations.
SU-E-T-778: Use of the 2D MatriXX Detector for Measuring Scanned Ion Beam Parameters
Purpose: The quality assurance (QA) procedure has to check the most relevant beam parameters to ensure the delivery of the correct dose to patients. Film dosimetry, which is commonly used for scanned ion beam QA, does not provide immediate results. The purpose of this work is to answer whether, for scanned ion beam therapy, film dosimetry can be replaced with the 2D MatriXX detector as a real-time tool. Methods: MatriXX, equipped with 32×32 parallel plate ion-chambers, is a commercial device intended for pre-treatment verification of conventional radiation therapy.The MatriXX, placed at the isocenter, and GAFCHROMIC films, positioned on the MatriXX entrance, were exposed to 131.44 MeV proton and 221.45 MeV/u Carbon-ion beams.The OmniPro-I’mRT software, applied for the data taking of MatriXX, gives the possibility of acquiring consecutive snapshots. Using the NI LabVIEW, the data from snapshots were logged as text files for further analysis. Radiochromic films were scanned with EPSON scanner and analyzed using software programs developed in-house for comparative purposes. Results: The field dose uniformity, flatness, beam position and beam width were investigated. The field flatness for the region covering 6×6 cm{sup 2} square field was found to be better than 2%. The relative standard deviations, expected to be constant over 2×2, 4×4 and 6×6 pixels from MatriXX measurement gives a uniformity of 1.5% in good agreement with the film results.The beam center position is determined with a resolution better than 200 µm for Carbon and less than 100 µm for proton beam.The FWHM determination for a beam wider than 10 mm is satisfactory, whilst for smaller beams the determination is uncertain. Conclusion: Precise beam position and fast 2D dose distribution can be determined in real-time using MatriXX detector. The results show that MatriXX is quick and accurate enough to be used in charged-particle therapy QA.
TH-AB-201-08: Ion Chamber Dose Measurements - Problems with the Temperature-Pressure Correction Factor
Purpose: To investigate the behavior of ionization chambers over a wide pressure range. Methods: Three cylindrical and two parallel-plate designs of ion chamber were investigated. The ion chambers were placed in vessel where the pressure was varied from atmospheric (101 kPa) down to 5 kPa. Measurements were made using 60Co and high-energy electron beams. The pressure was measured to better than 0.1% and multiple data sets were obtained for each chamber at both polarities to investigate pressure cycling and dependency on the sign of the charge collected. Results: For all types of chamber, the ionization current, corrected using the standard PTP, showed a similar behaviour. Deviations from the standard theory were generally small for Co-60 but very significant for electron beams, up to 20 % below P = 10 kPa. The effect was found to be always larger when collecting negative charge, suggesting a dependence on free-electron collection. The most likely source of such electrons is low-energy electrons emitted from the electrodes. This signal would be independent of air pressure within the chamber cavity. The data was analyzed to extract this signal and it was found to be a non-negligible component of the ionization current at atmospheric pressure. In the case of the parallel plate chambers, the effect was approximately 0.25 %. For the cylindrical chambers the effect was larger - up to 1.2 % - and dependent on the chamber type, which would be consistent with electron emission from different wall materials. For the electron beams, the correction factor was dependent on the electron energy and approximately double that observed in 60Co. Conclusion: Measurements have indicated significant deviations of the standard pressure correction that are consistent with electron emission from chamber electrodes. This has implications for both primary standard and reference ion chamber-based dosimetry.
Scaling Structured Multigrid to 500K+ Cores Through Coarse-Grid Redistribution
The effcient solution of sparse, linear systems resulting from the discretization of partial differential equations is crucial to the performance of many physics-based simulations. The algorithmic optimality of multilevel approaches for common discretizations makes them a good candidate for an efficient parallel solver. Yet, modern architectures for high-performance computing systems continue to challenge the parallel scalability of multilevel solvers. While algebraic multigrid methods are robust for solving a variety of problems, the increasing importance of data locality and cost of data movement in modern architectures motivates the need to carefully exploit structure in the problem. Robust logically structured variational multigrid methods, such as Black Box Multigrid (BoxMG), maintain structure throughout the multigrid hierarchy. This avoids indirection and increased coarse- grid communication costs typical in parallel algebraic multigrid. Nevertheless, the parallel scalability of structured multigrid is challenged by coarse-grid problems where the overhead in communication dominates computation. In this paper, an algorithm is introduced for redistributing coarse-grid problems through incremental agglomeration. Guided by a predictive performance model, this algorithm provides robust redistribution decisions for structured multilevel solvers. A two-dimensional diffusion problem is used to demonstrate the significant gain in performance of this algorithm over the previous approach that used agglomeration to one processor. In addition, the parallel scalability of this approach
Comparative Study of Finite Element Methods Using the Time-Accuracy-Size(TAS) Spectrum Analysis
We present a performance analysis appropriate for comparing algorithms using different numerical discretizations. By taking into account the total time-to-solution, numerical accuracy with respect to an error norm, and the computation rate, a cost-benefit analysis can be performed to determine which algorithm and discretization are particularly suited for an application. This work extends the performance spectrum model in for interpretation of hardware and algorithmic tradeoffs in numerical PDE simulation. As a proof-of-concept, popular finite element software packages are used to illustrate this analysis for Poisson’s equation.
Combining Sparse Approximate Factorizations with Mixed-precision Iterative Refinement
The standard LU factorization-based solution process for linear systems can be enhanced in speed or accuracy by employing mixed-precision iterative refinement. Most recent work has focused on dense systems. We investigate the potential of mixed-precision iterative refinement to enhance methods for sparse systems based on approximate sparse factorizations. In doing so, we first develop a new error analysis for LU- and GMRES-based iterative refinement under a general model of LU factorization that accounts for the approximation methods typically used by modern sparse solvers, such as low-rank approximations or relaxed pivoting strategies. We then provide a detailed performance analysis of both the execution time and memory consumption of different algorithms, based on a selected set of iterative refinement variants and approximate sparse factorizations. Our performance study uses the multifrontal solver MUMPS, which can exploit block low-rank factorization and static pivoting. We evaluate the performance of the algorithms on large, sparse problems coming from a variety of real-life and industrial applications showing that mixed-precision iterative refinement combined with approximate sparse factorization can lead to considerable reductions of both the time and memory consumption.
Exploring model complexity in machine learned potentials for simulated properties
Abstract Machine learning (ML) enables the development of interatomic potentials with the accuracy of first principles methods while retaining the speed and parallel efficiency of empirical potentials. While ML potentials traditionally use atom-centered descriptors as inputs, different models such as linear regression and neural networks map descriptors to atomic energies and forces. This begs the question: what is the improvement in accuracy due to model complexity irrespective of descriptors? We curate three datasets to investigate this question in terms of ab initio energy and force errors: (1) solid and liquid silicon, (2) gallium nitride, and (3) the superionic conductor Li $$_{10}$$ 10 Ge(PS $$_{6}$$ 6 ) $$_{2}$$ 2 (LGPS). We further investigate how these errors affect simulated properties and verify if the improvement in fitting errors corresponds to measurable improvement in property prediction. By assessing different models, we observe correlations between fitting quantity (e.g. atomic force) error and simulated property error with respect to ab initio values. Graphical abstract
Summer Internship Project: Set Topology [Slides]
Why a Set Topology? 1. Expand the array of tools given to physicists, educators, and students. 2. Through usages of PIC, MPM, and Monte Carlo. 3. My work at LANL was specified to handle requirements for the usage of MPM with FleCSI. 4. What we want it to do - phase change, simulating plasma, and other Multiphysics applications. 5. In FleCSI we already have mesh structures, we needed a way to represent those particles and certain behavior's of these particles that clients desire.