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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Conservative explicit local time-stepping schemes for the shallow water equations

Here we develop explicit local time-stepping (LTS) schemes with second and third order accuracy for the shallow water equations. The system is discretized in space by a C-grid staggering method, namely the TRiSK scheme adopted in MPAS-Ocean, a global ocean model with the capability of resolving multiple resolutions within a single simulation. The time integration is designed based on the strong stability preserving Runge–Kutta (SSP-RK) methods, but different time step sizes can be used in different regions of the domain through the coupling of coarse-fine time discretizations on the interface, and are only restricted by respective local CFL conditions. The proposed LTS schemes are of predictor–corrector type in which the predictors are constructed based on Taylor series expansions and SSP-RK stepping algorithms. The schemes preserve some important physical quantities in the discrete sense, such as exact conservation of the mass and potential vorticity and conservation of the total energy within time truncation errors. Moreover, they inherit the natural parallelism of the original explicit global time-stepping schemes. Extensive numerical tests are presented to illustrate the performance of the proposed algorithms.

58 GEOSCIENCES↗
DOE OSTI2016

Method, system and computer-readable media for measuring impedance of an energy storage device

Real-time battery impedance spectrum is acquired using a one-time record. Fast Summation Transformation (FST) is a parallel method of acquiring a real-time battery impedance spectrum using a one-time record that enables battery diagnostics. An excitation current to a battery is a sum of equal amplitude sine waves of frequencies that are octave harmonics spread over a range of interest. A sample frequency is also octave and harmonically related to all frequencies in the sum. A time profile of this sampled signal has a duration that is a few periods of the lowest frequency. A voltage response of the battery, average deleted, is an impedance of the battery in a time domain. Since the excitation frequencies are known and octave and harmonically related, a simple algorithm, FST, processes the time profile by rectifying relative to sine and cosine of each frequency. Another algorithm yields real and imaginary components for each frequency.

25 ENERGY STORAGE↗
DOE OSTI2021

Examination of Semi-Analytical Solution Methods in the Coarse Operator of Parareal Algorithm for Power System Simulation

With continuing advances in high-performance parallel computing platforms, parallel algorithms have become powerful tools for development of faster than real-time power system dynamic simulations. In particular, it has been demonstrated in recent years that parallel-in-time (Parareal) algorithms have the potential to achieve such an ambitious goal. Here, the selection of a fast and reasonably accurate coarse operator of the Parareal algorithm is crucial for its effective utilization and performance. This paper examines semi-analytical solution (SAS) methods as the coarse operators of the Parareal algorithm and explores performance of the SAS methods to the standard numerical time integration methods. Two promising time-power series-based SAS methods were considered; Adomian decomposition method and Homotopy analysis method with a windowing approach for improving the convergence. Numerical performance case studies on 10-generator 39-bus system and 327-generator 2383-bus system were performed for these coarse operators over different disturbances, evaluating the number of Parareal iterations, computational time, and stability of convergence. All the coarse operators tested with different scenarios have converged to the same corresponding true solution (if they are convergent) and the SAS methods provide comparable computational speed, while having more stable convergence to the true solution in many cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Scalability of high-performance PDE solvers

Performance tests and analyses are critical to effective high-performance computing software development and are central components in the design and implementation of computational algorithms for achieving faster simulations on existing and future computing architectures for large-scale application problems. In this article, we explore performance and space-time trade-offs for important compute-intensive kernels of large-scale numerical solvers for partial differential equations (PDEs) that govern a wide range of physical applications. We consider a sequence of PDE-motivated bake-off problems designed to establish best practices for efficient high-order simulations across a variety of codes and platforms. We measure peak performance (degrees of freedom per second) on a fixed number of nodes and identify effective code optimization strategies for each architecture. In addition to peak performance, we identify the minimum time to solution at 80% parallel efficiency. The performance analysis is based on spectral and p-type finite elements but is equally applicable to a broad spectrum of numerical PDE discretizations, including finite difference, finite volume, and h-type finite elements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defects

We numerically investigate an adaptive version of the parareal algorithm in the context of molecular dynamics. This adaptive variant has been originally introduced in [1]. We focus here on test cases of physical interest where the dynamics of the system is modelled by the Langevin equation and is simulated using the molecular dynamics software LAMMPS. In this work, the parareal algorithm uses a family of machine-learning spectral neighbor analysis potentials (SNAP) as fine, reference, potentials and embedded-atom method potentials (EAM) as coarse potentials. We consider a self-interstitial atom in a tungsten lattice and compute the average residence time of the system in metastable states. Our numerical results demonstrate significant computational gains using the adaptive parareal algorithm in comparison to a sequential integration of the Langevin dynamics. We also identify a large regime of numerical parameters for which statistical accuracy is reached without being a consequence of trajectorial accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Cross-field and parallel dynamics of SOL filaments in TCV

Using recently installed scrape-off layer diagnostics on the tokamak à configuration variable, we characterise the poloidal and parallel properties of turbulent filaments. We access both attached and detached divertor conditions across a wide range of core densities (f G ϵ [0.09, 0.66]) in diverted L-mode plasma configurations. With a gas puff imaging (GPI) diagnostic at the outer midplane we observed filaments with a monotonic increase in radial velocity (from 390 m s –1 to 800 m s –1 ) and cross-field radii (from 8.5 mm to 13.4 mm) with increasing core density. Interpreting the filament behaviour in the context of the two-region model, we find that they populate the ideal-interchange regime (C i ) in discharges at very low densities, and the resistive X (RX)-point regime for all other discharges. The scaling of filament velocity versus size shows good agreement with this interpretation. These results are discussed and compared with previous probe-based measurements for similar conditions, which mostly placed filaments in TCV in the resistive ballooning (RB) regime. In addition, for the first time in TCV, the parallel filament extension is studied by magnetically aligning the GPI measurements at the outboard midplane with a reciprocating probe in the divertor. In agreement with the filaments being in the ideal-interchange and the RX-point regimes, they are found to extend beyond the X-point into the outer divertor leg.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Toward active disruption avoidance via real-time estimation of the safe operating region and disruption proximity in tokamaks

This paper describes a real-time capable algorithm for identifying the safe operating region around a tokamak operating point. The region is defined by a convex set of linear constraints, from which the distance of a point from a disruptive boundary can be calculated. The disruptivity of points is calculated from an empirical machine learning predictor that generates the likelihood of disruption. While the likelihood generated by such empirical models can be compared to a threshold to trigger a disruption mitigation system, the safe operating region calculation enables active optimization of the operating point to maintain a safe margin from disruptive boundaries. The proposed algorithm is tested using a random forest disruption predictor fit on data from DIII-D. The safe operating region identification algorithm is applied to historical data from DIII-D showing the evolution of disruptive boundaries and the potential impact of optimization of the operating point. Real-time relevant execution times are made possible by parallelizing many of the calculation steps and implementing the algorithm on a graphics processing unit. Lastly, a real-time capable algorithm for optimizing the target operating point within the identified constraints is also proposed and simulated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

OpenMP Parallelization and Optimization of Graph-Based Machine Learning Algorithms

In this paper, we investigate the OpenMP parallelization and optimization of two novel data classification algorithms. The new algorithms are based on graph and PDE solution techniques and provide significant accuracy and performance advantages over traditional data classification algorithms in serial mode. The methods leverage the Nystrom extension to calculate eigenvalue/eigenvectors of the graph Laplacian and this is a self-contained module that can be used in conjunction with other graph-Laplacian based methods such as spectral clustering. We use performance tools to collect the hotspots and memory access of the serial codes and use OpenMP as the parallelization language to parallelize the most time-consuming parts. Where possible, we also use library routines. We then optimize the OpenMP implementations and detail the performance on traditional supercomputer nodes (in our case a Cray XC30), and test the optimization steps on emerging testbed systems based on Intel’s Knights Corner and Landing processors. We show both performance improvement and strong scaling behavior. Finally, a large number of optimization techniques and analyses are necessary before the algorithm reaches almost ideal scaling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Randomized Preconditioned Solvers for Strong Constraint 4D-Var Data Assimilation

The Strong Constraint 4D Variational (SC-4DVAR) data assimilation method is widely used in climate and weather applications. SC-4DVAR involves solving a minimization problem to compute the maximum a posteriori estimate, which we tackle using the Gauss-Newton method. The computation of the descent direction is expensive since it involves the solution of a large-scale and potentially ill-conditioned linear system, solved using the preconditioned conjugate gradient (PCG) method. Here, to address this cost, we efficiently construct scalable preconditioners using three different randomization techniques, which all rely on a certain low-rank structure involving the Gauss-Newton Hessian. The proposed techniques come with theoretical guarantees on the condition number, and at the same time, are amenable to parallelization. We also develop an adaptive approach to estimate the sketch size and choose between the reuse or recomputation of the preconditioner. We demonstrate the performance and effectiveness of our methodology on two representative model problems—the Burgers and barotropic vorticity equation—showing a drastic reduction in both the number of PCG iterations and the number of Gauss-Newton Hessian products after including the preconditioner construction cost.

Gauss-Newton↗
DOE OSTI2020

Shifting modes of iron sulfidization at the onset of OAE-2 drive regional shifts in pyrite δ 34 S records

We report total reduced inorganic S isotope ratios (δ 34 S CRS ) shift toward more negative values across much of the southern North Atlantic just before the onset of the Cenomanian-Turonian Ocean Anoxic Event (OAE-2). At the same time, there is no parallel isotopic change in the significantly larger pool of kerogen (organic) S, which indicates that the distribution and S-isotope composition of sulfide in the environment likely did not drive the change in δ 34 S CRS . Here, we investigate possible explanations for the negative shift in δ 34 S CRS values and their divergence from organic S by isolating iron sulfides for morphological identification and grain-specific isotopic analysis using secondary ion mass spectrometry (SIMS). In pre- and syn-OAE-2 sedimentary rocks from Demerara Rise, we find four distinct morphologies of iron sulfides: pyrite framboids (1–20 μm diameter), irregular pyrite aggregates (1–38 μm diameter), large cemented pyrite aggregates (~60 μm diameter), and irregular and cemented aggregates of the pyrite polymorph marcasite (1–45 μm diameter). These different textural groups have distinct S-isotopic compositions that are largely consistent through the onset of OAE-2. As such, the secular change in bulk δ 34 S CRS values likely reflects the changing proportions of these phases stratigraphically across OAE-2. All textural groups feature resolvable intra-grain δ 34 S variability, suggesting that the environments in which they formed were characterized by dynamic sulfide δ 34 S values and/or by partial closed-system distillation. We use grain-specific δ 34 S distributions to rule out shoaling of the chemocline within the sediments as a mechanism for the observed decrease in δ 34 S CRS . Instead, we propose that changes in the reactivity of the iron species delivered to Demerara Rise over the ~200 kyr leading up to the onset of OAE-2 impacted the relative contributions of pyrite with S-isotope signatures reflecting the water column, shallow sediments, and deeper sediments to the bulk sedimentary δ 34 S CRS value. Specifically, the change in iron reactivity at the onset of OAE-2 favored the production of 34 S-depleted large, cemented aggregates and framboids at the expense of more 34 S-enriched irregular aggregates. Our results underscore that bulk δ 34 S CRS measurements integrate multiple reduced phases that form via distinct reaction mechanisms and potentially in different parts of the depositional environment. Grain-specific SIMS analyses dramatically enrich our ability to interpret pyrite isotopic patterns in the geologic record.

58 GEOSCIENCES↗
DOE OSTI2019

Sparse, iterative simulation methods for one-dimensional laminar flames

Sparse, iterative simulation methods for one-dimensional laminar flames are proposed. The resulting steady and unsteady flame solvers exploit approximate Jacobians to greatly reduce the computational cost associated with matrix operations. The constant, non-unity Lewis number assumption is introduced to further reduce the computational cost. The solvers are also parallelized to reduce time-to-solution on distributed memory computer systems. Computed laminar flame speeds and species profiles for a range of chemical mechanisms (from 10 to 2878 species) are compared against a well-validated commercial code and are found to be consistent within solver tolerances. The computation times of both the unsteady and steady solutions increase only linearly with the number of species, which is a significant improvement over the quadratic or cubic scaling of existing steady-state flame solvers. For the largest mechanism tested, the steady-state flame solver is two orders of magnitude faster than commonly-used codes. The use of an approximate Jacobian is shown to reduce the rate of convergence for the steady-state solver, but does not significantly affect the domain of convergence. The steady-state solver with approximate Jacobian is thus well suited for computationally efficient laminar flame speed sweeps with large kinetic mechanisms.

42 ENGINEERING↗
DOE OSTI2026

Advancing specialized biofoundries via automated adaptive laboratory evolution

Adaptive laboratory evolution (ALE) is a powerful strategy for improving microbial phenotypes by harnessing natural selection under defined environmental conditions. Through applying selection regimes, beneficial mutations accumulate, enabling the generation of strains with enhanced properties. However, conventional ALE is labor-intensive and difficult to scale, limiting reproducibility and broader discovery of evolutionary principles. Recent advances in robotics, automation, and computational infrastructure are transforming ALE into a scalable, data-rich experimental paradigm. Automated platforms enable standardized and complex protocols, real-time monitoring, and highly parallel evolution campaigns, improving consistency while generating longitudinal datasets that reveal convergent adaptive mechanisms. Here, we discuss the role of specialized biofoundries in advancing automated ALE and enabling large-scale evolutionary engineering. We review major automated ALE formats and outline key design principles for effective ALE biofoundries, highlighting how automated ALE can support autonomous experimentation and AI-guided strain engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Domain decomposition in time for PDE-constrained optimization

Here, PDE-constrained optimization problems have a wide range of applications, but they lead to very large and ill-conditioned linear systems, especially if the problems are time dependent. In this paper we outline an approach for dealing with such problems by decomposing them in time and applying an additive Schwarz preconditioner in time, so that we can take advantage of parallel computers to deal with the very large linear systems. We then illustrate the performance of our method on a variety of problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Two-fluid and kinetic transport physics of Kelvin–Helmholtz instabilities in nonuniform low-beta plasmas

Hall-magnetohydrodynamic (Hall-MHD) theory, two-fluid simulations, and kinetic simulations are used in this work to investigate the cross-field transport properties of Kelvin–Helmholtz instabilities in nonuniform low-beta collisionless plasmas. Hall-MHD analysis shows how the linear properties of the instability are modified by density gradients and magnetization. High-order accurate two-fluid and kinetic simulations, with complete dynamics of finite-mass electrons and ions, are applied to a suite of parameter cases to systematically assess the effects of diamagnetic drift, magnetization, charge separation, and finite Larmor motion. Initialization of exact two-species kinetic equilibria facilitates the study of isolated physical effects and enables detailed cross-comparisons between two-fluid and kinetic simulations, including for cases where ion gyroradii are comparable to gradient scale lengths. For nonuniform plasmas with significant space charge, the results of two-fluid and kinetic simulations are found to disagree with Hall-MHD predictions. Kelvin–Helmholtz instability growth rates, per unit shear, are shown to be smaller when ion diamagnetic drift and $E \times B$ drift are parallel and larger when the two drifts are antiparallel. The effect is attributed to polarization drift in the shear layer, which leads to redistribution of charge, alters the electric field that drives plasma advection, and consequently modifies growth rates. Instability-induced mass transport for different parameters is characterized in terms of the flux across the shear layer and a simplified diffusion model. Distribution functions from kinetic simulations are shown to deviate substantially from Maxwellian reconstructions, indicating the importance of kinetic physics during the nonlinear phase of the instability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

A resistive MHD model and simulation on plasma flow evolution in the presence of resonant magnetic perturbation in a tokamak

Nonaxisymmetric magnetic fields such as the intrinsic error field and the externally applied resonant magnetic perturbation (RMP) in a tokamak are known to influence the plasma momentum transport and flow evolution through plasma response, which itself strongly depends on the plasma flow as well. The nonlinear interaction between plasma response and flow has been previously modeled in the conventional error field theory with the “no-slip” condition, which has been recently extended to allow the “free-slip” condition. In this work, we further target this specific process and numerically simulate the nonlinear plasma response and flow evolution in the presence of a single-helicity RMP in a circular-shaped model tokamak configuration, based on the full resistive MHD model in the initial-value code NIMROD. Time evolution of the parallel (to k) flow or “slip frequency” profile and its asymptotic steady state obtained from the NIMROD simulations are compared with both conventional and extended nonlinear response theories. Here, k is the wave vector of the propagating island. Good agreement with the extended theory with free-slip condition has been achieved for the parallel flow profile evolution in response to RMP in all resistive regimes, whereas the difference from the conventional theory with the no-slip condition tends to diminish as the plasma resistivity approaches zero.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Reversible to irreversible transitions for ac driven skyrmions on periodic substrates

Abstract Using atomistic simulations, we investigate the dynamical behavior of magnetic skyrmions in dimer and trimer molecular crystal arrangements, as well as bipartite lattices at 3/2 and 5/2 fillings, under ac driving over a square array of anisotropy defects. For low ac amplitudes, at all fillings reversible motion appears in which the skyrmions return to their original positions at the end of each ac drive cycle and the diffusion is zero. We also identify two distinct irreversible regimes. The first is a translating regime in which the skyrmions form channels of flow in opposing directions and translate by one substrate lattice constant per ac drive cycle. The translating state appears in the dimer and trimer arrangements, and produces pronounced peaks in the diffusivity in the direction perpendicular to the external drive. For larger ac amplitudes, we find chaotic irreversible motion in which the skyrmions can randomly exchange places with each other over time, producing long-time diffusive behavior both parallel and perpendicular to the ac driving direction.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multilevel Parareal Algorithm with Averaging for Oscillatory Problems

The present study is an extension of the work done by Peddle, Haut, and Wingate and Haut and Wingate, where a two-level Parareal method with mapping and averaging is examined. The method proposed in this paper is a multilevel Parareal method with arbitrarily many levels, which is not restricted to the two-level case. We give an asymptotic error estimate which reduces to the two-level estimate for the case when only two levels are considered. Introducing more than two levels has important consequences for the averaging procedure, as we choose separate averaging windows for each of the different levels, which is an additional new feature of the present study. The different averaging windows make the proposed method especially appropriate for nonlinear multiscale problems, because we can introduce a level for each intrinsic scale of the problem and adapt the averaging procedure such that we reproduce the behavior of the model on the particular scale resolved by the level. The method is applied to nonlinear differential equations. The nonlinearities can generate a range of frequencies in the problem. The computational cost of the new method is investigated and studied on several examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Dictionary Learning with Accumulator Neurons

The Locally Competitive Algorithm (LCA) uses local competition between non-spiking leaky integrator neurons to infer sparse representations, allowing for potentially real-time execution on massively parallel neuromorphic architectures such as Intel's Loihi processor. Here, we focus on the problem of inferring sparse representations from streaming video using dictionaries of spatiotemporal features optimized in an unsupervised manner for sparse reconstruction. Non-spiking LCA has previously been used to achieve unsupervised learning of spatiotemporal dictionaries composed of convolutional kernels from raw, unlabeled video. We demonstrate how unsupervised dictionary learning with spiking LCA (\hbox{S-LCA}) can be efficiently implemented using accumulator neurons, which combine a conventional leaky-integrate-and-fire (\hbox{LIF}) spike generator with an additional state variable that is used to minimize the difference between the integrated input and the spiking output. We demonstrate dictionary learning across a wide range of dynamical regimes, from graded to intermittent spiking, for inferring sparse representations of both static images drawn from the CIFAR database as well as video frames captured from a DVS camera. On a classification task that requires identification of the suite from a deck of cards being rapidly flipped through as viewed by a DVS camera, we find essentially no degradation in performance as the LCA model used to infer sparse spatiotemporal representations migrates from graded to spiking. We conclude that accumulator neurons are likely to provide a powerful enabling component of future neuromorphic hardware for implementing online unsupervised learning of spatiotemporal dictionaries optimized for sparse reconstruction of streaming video from event based DVS cameras.

artificial intelligence↗