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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Toward Resilient Heterogeneous Computing Workflow through Kokkos-DataSpaces Integration

With the growing number of applications designed for heterogeneous HPC devices, application programmers and users are finding it challenging to compose scalable workflows as ensembles of these applications, that are portable, performant and resilient. The Kokkos C++ library has been designed to simplify this cumbersome procedure by providing an intra-application uniform programming model and portable performance. However, assembling multiple Kokkos-enabled applications into a complex workflow is still a challenge. Although Kokkos enables a uniform programming model, the inter-application data exchange still remains a challenge from both performance and software development cost perspectives. In order to address this issue, we propose a Kokkos-DataSpaces Integration, with the goal of providing a virtual shared-space abstraction that can be accessed concurrently by all applications in an Kokkos workflow, thus extending Kokkos to support inter-application data exchange.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Performance of Coupled Physics Solvers for Multidisciplinary Hypersonic Flow Simulations on Several Classes of Computer Architectures

The application of hypersonic flow simulation tools to realistic flight scenarios will require the coupling of multiple physical effects to the baseline fluid dynamics. Such multiphysics effects can include the aerooelastic response of the airframe or engine components, dynamic transport of atmospheric particles, the deformation of solid-fluid interfaces that can ablate, pyrolyze, or erode, as well as a host of other processes, all of which are governed by unique sets of physical equations and models. Coupling multiple (and potentially disparate) physics solvers to a robust compressible flow solver poses additional challenges related to the stability, performance and scalability of the combined solver. The choices made during the software design process can therefore lead to a variation in simulation efficiency across different computer architectures. In this paper, we will consider two representative multiphysics hypersonic flow scenarios: the interaction of solid particulates with the flow field created by a hypersonic lifting body and the aerooelastic deformation of a model airframe under high-Mach-number flow conditions. For these simulations we explore the behavior of several hypersonic simulation tools, including Kestrel, FUN3D, US3D, and JENRE multiphysics framework, on several high performance computing systems containing various CPU and GPU architectures.

architecture↗
DOE OSTI2020

A caloritronics-based Mott neuristor

Abstract Machine learning imitates the basic features of biological neural networks at a software level. A strong effort is currently being made to mimic neurons and synapses with hardware components, an approach known as neuromorphic computing. While recent advances in resistive switching have provided a path to emulate synapses at the 10 nm scale, a scalable neuron analogue is yet to be found. Here, we show how heat transfer can be utilized to mimic neuron functionalities in Mott nanodevices. We use the Joule heating created by current spikes to trigger the insulator-to-metal transition in a biased VO 2 nanogap. We show that thermal dynamics allow the implementation of the basic neuron functionalities: activity, leaky integrate-and-fire, volatility and rate coding. This approach could enable neuromorphic hardware to take full advantage of the rapid advances in memristive synapses, allowing for much denser and complex neural networks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Solving the Bernstein-Vazirani problem using Majorana-based topological quantum algorithms

Executing quantum algorithms using Majorana zero modes—a major milestone for the field of topological quantum computing—requires a platform that can be scaled to large quantum registers, can be controlled in real time and space, and a braiding protocol that uses the unique properties of these exotic particles. Here, we demonstrate the first successful simulation of a Majorana-based, fault-tolerant quantum algorithm to solve the Bernstein-Vazirani problem in two-dimensional magnet-superconductor hybrid structures from initialization to read-out of the final many-body state. Utilizing the Majorana zero modes’ topological properties, we introduce an optimized braiding protocol for the algorithm and a scalable architecture for its implementation with an arbitrary number of qubits. We visualize the algorithm protocol in real time and space by computing the non-equilibrium density of states, which is proportional to the time-dependent differential conductance, and the non-equilibrium charge density, which assigns a unique signature to each final state of the algorithm.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Impact of High Performance Computing on the Detailed Simulation of Subsurface Flow and Contaminant Transport Using eSTOMP - 16341

The ability to model subsurface processes at the field-scale is essential to site closure and remedial action decisions. Field-scale models often require high-performance computing to meet the computational demands associated with complex systems, model calibration, and uncertainty quantification. eSTOMP, a parallel version of STOMP, is an analytical tool that meets the need for investigating coupled processes in the subsurface involving regional multi-fluid flow and biogeochemistry. The scalability of eSTOMP makes it ideal for simulating contaminant and geochemical reactive transport when long run times result from execution of a serial code. To demonstrate the impact of high-performance computation on subsurface analyses, the application of eSTOMP is presented for two sites. The first site involves uranium surface complexation within a highly dynamic, variably saturated flow field. The use of this model in conjunction with continued field monitoring is expected to provide a rigorous basis for developing operational strategies for field remediation and for defining defensible remediation endpoints. The second site involves the long-term dissolution of low-level radioactive glass emplaced within a near-surface repository. The waste form release rate is evaluated by modeling the basic physical and chemical processes that are known to control the waste form dissolution behavior, including pH and composition of the fluid contacting the glass. Simulations demonstrate that glass dissolution rates vary both in time and as a function of position within the disposal system, and that a 'leach rate' or radionuclide release rate parameter can only be estimated after identifying these impacts in a dynamic system. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

An overlapping domain decomposition method for large-scale problems

Discretization of dynamical models defined through partial differential equations leads to large-scale systems. Time-depending condition involves an iterative integration of such kind of systems. In this paper, a novel technique based on overlapped domain decomposition, without preconditioner and scalable, is presented. Due to the domain decomposition, subproblems are solved in parallel without communications, cutting off the computation time and optimizing the computational cost. This direct method describes an optimized parallel strategy to solve the initial problem, providing the exact solution, up to rounding errors. Moreover, it takes into account both physical nature of the problem and deriving numerical properties of the system. It is highly recommended in case of band matrices and a long time interval because of an increasing gain in terms of performance and computational cost with the number of integrations. A deep analysis of the computational cost concludes the paper.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A hybrid surrogate modeling framework for the Digital Twin of a Fluoride-salt-cooled High-temperature Reactor (FHR)

While nuclear energy is a non-greenhouse-gas emitting energy source, expensive operational costs due to the high-level of safety requirements decreases their competitiveness in the sustainable energy market. Advanced reactor concepts paired with Digital Twins aim to increase the commercialization gains of nuclear energy by reducing operational costs, increasing reactor reliability and enhancing power generation. To support Digital Twin tasks such as real-time autonomous control, proactive maintenance monitoring or optimizing power demand operations, a fast and accurate virtual representation of the Nuclear Power Plant (NPP) is required. The computational cost of high-fidelity, physics-based models are unsuitable for real-time analysis or scalability. Here, in this work, a hybrid surrogate modeling framework is developed fora Fluoride-salt-cooled High-temperature Reactor (FHR) that leverages physics-inspired models for key reactor components and uses data-driven methods for rapid system state space prediction. The Xenon reactivity feedback model is integrated to inform the surrogate model about the reactor core and the homologous pump theory model is the basis for representing pump degradation. Using a detailed, two dimensional thermal hydraulics model to generate data on the FHR, we train a network of Vectorized Autoregressive Moving-Average with eXogenous input (VARMAX) models to predict the remaining state values. The result is a surrogate model that provides a detailed reactor state representation of 41 system states and a pump degradation analysis. The framework is applied to Load Follows profiles, yielding high accuracy and a speedup that is more than 4000x faster compared to the higher- fidelity thermal hydraulics model, enabling real-time operational intelligence and applications in long horizon predictions. While the surrogate model framework is demonstrated for the particular case of FHR, the hybrid physical/data-driven modeling approach including the network of surrogates and the underlying modularity has the potential to be applied to other physical asset systems.

Digital Twins↗
DOE OSTI2015

Progress in fast, accurate multi-scale climate simulations

We present a survey of physical and computational techniques that have the potential to contribute to the next generation of high-fidelity, multi-scale climate simulations. Examples of the climate science problems that can be investigated with more depth with these computational improvements include the capture of remote forcings of localized hydrological extreme events, an accurate representation of cloud features over a range of spatial and temporal scales, and parallel, large ensembles of simulations to more effectively explore model sensitivities and uncertainties. Numerical techniques, such as adaptive mesh refinement, implicit time integration, and separate treatment of fast physical time scales are enabling improved accuracy and fidelity in simulation of dynamics and allowing more complete representations of climate features at the global scale. At the same time, partnerships with computer science teams have focused on taking advantage of evolving computer architectures such as many-core processors and GPUs. As a result, approaches which were previously considered prohibitively costly have become both more efficient and scalable. In combination, progress in these three critical areas is poised to transform climate modeling in the coming decades.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Atomistics of vapour–liquid–solid nanowire growth

Vapour–liquid–solid route and its variants are routinely used for scalable synthesis of semiconducting nanowires, yet the fundamental growth processes remain unknown. Here we employ atomic-scale computations based on model potentials to study the stability and growth of gold-catalysed silicon nanowires. Equilibrium studies uncover segregation at the solid-like surface of the catalyst particle, a liquid AuSi droplet, and a silicon-rich droplet–nanowire interface enveloped by heterogeneous truncating facets. Supersaturation of the droplets leads to rapid one-dimensional growth on the truncating facets and much slower nucleation-controlled two-dimensional growth on the main facet. Surface diffusion is suppressed and the excess Si flux occurs through the droplet bulk which, together with the Si-rich interface and contact line, lowers the nucleation barrier on the main facet. The ensuing step flow is modified by Au diffusion away from the step edges. Our study highlights key interfacial characteristics for morphological and compositional control of semiconducting nanowire arrays.

36 MATERIALS SCIENCE↗
DOE OSTI2015

How Much Higher Can HTCondor Fly?

The HTCondor high throughput computing system is heavily used in the high energy physics (HEP) community as the batch system for several Worldwide LHC Computing Grid (WLCG) resources. Moreover, it is the backbone of GlidelnWMS, the pilot system used by the computing organization of the Compact Muon Solenoid (CMS) experiment. To prepare for LHC Run 2, we probed the scalability limits of new versions and configurations of HTCondor with a goal of reaching 200,000 simultaneous running jobs in a single internationally distributed dynamic pool.In this paper, we first describe how we created an opportunistic distributed testbed capable of exercising runs with 200,000 simultaneous jobs without impacting production. This testbed methodology is appropriate not only for scale testing HTCondor, but potentially for many other services. In addition to the test conditions and the testbed topology, we include the suggested configuration options used to obtain the scaling results, and describe some of the changes to HTCondor inspired by our testing that enabled sustained operations at scales well beyond previous limits.

Fajardo, E. M.↗
DOE OSTI2021

The DAQ system of the 12,000 channel CMS high granularity calorimeter prototype

The CMS experiment at the CERN LHC will be upgraded to accommodate the 5-fold increase in the instantaneous luminosity expected at the High-Luminosity LHC (HL-LHC). Concomitant with this increase will be an increase in the number of interactions in each bunch crossing and a significant increase in the total ionising dose and fluence. One part of this upgrade is the replacement of the current endcap calorimeters with a high granularity sampling calorimeter equipped with silicon sensors, designed to manage the high collision rates. As part of the development of this calorimeter, a series of beam tests have been conducted with different sampling configurations using prototype segmented silicon detectors. In the most recent of these tests, conducted in late 2018 at the CERN SPS, the performance of a prototype calorimeter equipped with ≈12,000 channels of silicon sensors was studied with beams of high-energy electrons, pions and muons. This paper describes the custom-built scalable data acquisition system that was built with readily available FPGA mezzanines and low-cost Raspberry Pi computers.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Merged-Element Transmon

Transmon qubits are ubiquitous in the pursuit of quantum computing using superconducting circuits. However, they have some drawbacks that still need to be addressed. Most importantly, the scalability of transmons is limited by the large device footprint needed to reduce the participation of the lossy capacitive parts of the circuit. In this work, we investigate and evaluate losses in an alternative device geometry, namely, the merged-element transmon (mergemon). To this end, we replace the large external shunt capacitor of a traditional transmon with the intrinsic capacitance of a Josephson junction and achieve an approximately 100 times reduction in qubit dimensions. We report the implementation of the mergemon using a sputtered Nb–amorphous-Si–Nb trilayer film. In an experiment below 10 mK, the frequency of the readout resonator, capacitively coupled to the mergemon, exhibits a qubit-state-dependent shift in the low-power regime. The device also demonstrates the single-photon and multiphoton transitions that represent a weakly anharmonic system in two-tone spectroscopy. The transition spectra are explained well with master-equation simulations. Here, a participation-ratio analysis identifies the dielectric loss of the amorphous-Si tunnel barrier and its interfaces as the dominant source for qubit relaxation. We expect the mergemon to achieve high coherence for relatively small device dimensions when implemented with a low-loss, epitaxially grown, and lattice-matched trilayer.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Efficient Data Management in Neutron Scattering Data Reduction Workflows at ORNL

Oak Ridge National Laboratory (ORNL) experimental neutron science facilities produce 1.2 TB a day of raw event-based data that is stored using the standard metadata-rich NeXus schema built on top of the HDF5 file format. Performance of several data reduction workflows is largely determined by the amount of time spent on the loading and processing algorithms in Mantid, an open-source data analysis framework used across several neutron sciences facilities around the world. The present work introduces new data management algorithms to address identified input output (I/O) bottlenecks on Mantid. First, we introduce an in-memory binary-tree metadata index that resemble NeXus data access patterns to provide a scalable search and extraction mechanism. Second, data encapsulation in Mantid algorithms is optimally redesigned to reduce the total compute and memory runtime footprint associated with metadata I/O reconstruction tasks. Results from this work show speed ups in wall-clock time on ORNL data reduction workflows, ranging from 11% to 30% depending on the complexity of the targeted instrument-specific data. Nevertheless, we highlight the need for more research to address reduction challenges as experimental data volumes increase.

Godoy, William↗
DOE OSTI2025

SigTime: Learning and Visually Explaining Time Series Signatures

Understanding and distinguishing temporal patterns in time series data is essential for scientific discovery and decision-making. For example, in biomedical research, uncovering meaningful patterns in physiological signals can improve diagnosis, risk assessment, and patient outcomes. However, existing methods for time series pattern discovery face major challenges, including high computational complexity, limited interpretability, and difficulty in capturing meaningful temporal structures. Here, to address these gaps, we introduce a novel learning framework that jointly trains two Transformer models using complementary time series representations: shapelet-based representations to capture localized temporal structures and traditional feature engineering to encode statistical properties. The learned shapelets serve as interpretable signatures that differentiate time series across classification labels. Additionally, we develop a visual analytics system—SigTime—with coordinated views to facilitate exploration of time series signatures from multiple perspectives, aiding in useful insights generation. We quantitatively evaluate our learning framework on eight publicly available datasets and one proprietary clinical dataset. Additionally, we demonstrate the effectiveness of our system through two usage scenarios along with the domain experts: one involving public ECG data and the other focused on preterm labor analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Integrating and Characterizing HPC Task Runtime Systems for hybrid AI-HPC workloads

Scientific workflows increasingly involve both HPC and machine-learning tasks, combining MPI-based simulations, training, and inference in a single execution. Launchers such as Slurm’s srun constrain concurrency and throughput, making them unsuitable for dynamic and heterogeneous workloads. We present a performance study of RADICAL-Pilot (RP) integrated with Flux and Dragon, two complementary runtime systems that enable hierarchical resource management and high-throughput function execution. Using synthetic and production-scale workloads on Frontier, we characterize the task execution properties of RP across runtime configurations. RP+Flux sustains up to 930 tasks/s, and RP+Flux+Dragon exceeds 1,500 tasks/s with over 99.6% utilization. In contrast, srun peaks at 152 tasks/s and degrades with scale, with utilization below 50%. For IMPECCABLE.v2 drug discovery campaign, RP+Flux reduces makespan by 30–60% relative to srun/Slurm and increases throughput more than four times on up to 1,024. These results demonstrate hybrid runtime integration in RP as a scalable approach for hybrid AI-HPC workloads.

HPC-AI↗
DOE OSTI2012

MOCFE-Bone: The 3D-MOC Mini-Application for Exascale Research

MOCFE-Bone is a mini-application that simulates the main procedures in a 3D method of characteristics (MOC) code for numerical solution of the steady state neutron transport equation. 3D MOC was chosen as a specific methodology of interest in exascale computing for nuclear reactor applications because of its heterogeneous geometry capability, high degree of accuracy, and potential for scalability. In developing the mini-app, we focused on emulating the behavior of a production 3D MOC tool without requiring the input overhead associated with such a code. The MOCFE code at Argonne National Laboratory was used to investigate and characterize the behavior of the mini-app; the results of are presented here. Note that the existing MOCFE code resulted from research forays into the potential of 3D-MOC for large scale parallelism and is not a production level tool nor is it optimally implemented. Parts of the mini-app therefore reflect lessons learned and represent how a production-level tool would be written from this point forward rather than simply duplicating MOCFE, which has known scalability issues (i.e. parallelization in energy).

Wolters, E. R.↗
DOE OSTI2013

Kalman-filtered compressive sensing for high resolution estimation of anthropogenic greenhouse gas emissions from sparse measurements.

The estimation of fossil-fuel CO2 emissions (ffCO2) from limited ground-based and satellite measurements of CO2 concentrations will form a key component of the monitoring of treaties aimed at the abatement of greenhouse gas emissions. The limited nature of the measured data leads to a severely-underdetermined estimation problem. If the estimation is performed at fine spatial resolutions, it can also be computationally expensive. In order to enable such estimations, advances are needed in the spatial representation of ffCO2 emissions, scalable inversion algorithms and the identification of observables to measure. To that end, we investigate parsimonious spatial parameterizations of ffCO2 emissions which can be used in atmospheric inversions. We devise and test three random field models, based on wavelets, Gaussian kernels and covariance structures derived from easily-observed proxies of human activity. In doing so, we constructed a novel inversion algorithm, based on compressive sensing and sparse reconstruction, to perform the estimation. We also address scalable ensemble Kalman filters as an inversion mechanism and quantify the impact of Gaussian assumptions inherent in them. We find that the assumption does not impact the estimates of mean ffCO2 source strengths appreciably, but a comparison with Markov chain Monte Carlo estimates show significant differences in the variance of the source strengths. Finally, we study if the very different spatial natures of biogenic and ffCO2 emissions can be used to estimate them, in a disaggregated fashion, solely from CO2 concentration measurements, without extra information from products of incomplete combustion e.g., CO. We find that this is possible during the winter months, though the errors can be as large as 50%.

Ray, Jaideep↗