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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

A computationally efficient P 1 radiation model for modern combustion systems utilizing pre-conditioned conjugate gradient methods

The iterative convergence of the P 1 radiation model can be slow in optically thin scenarios when employing classical iterative methods. In order to remedy this shortcoming, an in-house P 1 radiation model was interfaced with high performance, scalable, linear solver libraries. Next, the accuracies of P 1 radiation model calculations was assessed by comparing its predictions against discrete ordinates (DO) model calculations for prototypical problems representative of modern combustion systems. Corresponding benchmark results were also included for comparison. Utilizing Pre-Conditioners (PC) to the Conjugate Gradients (CG) method, the convergence time of the P 1 radiation model reduced by a factor of 30 for modest problem sizes and a factor of 70 for larger sized problems when compared against classical Gauss Seidel sweeps. Additionally, PC provided 50% computational savings compared to employing CG in a standalone mode. The P 1 model calculation times were about 25–30% of the DO model calculation time. The time to solution also scaled linearly with an increase in problem size. The weighted sum of gray gases model employed in this study in conjunction with the P 1 model provided good agreement against benchmark data with L 2 error norms (defined relative to corresponding DO calculations) improving when isotropic intensities were prevalent.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Thermoelectricity in transition metal compounds: The role of spin disorder

Here, at room temperature and above, most magnetic materials adopt a spin-disordered (paramagnetic) state whose electronic properties can differ significantly from their low-temperature, spin-ordered counterparts. Yet computational searches for new functional materials usually assume some type of magnetic order. In the present work, we demonstrate a methodology to incorporate spin disorder in computational searches and predict the electronic properties of the paramagnetic phase. We implement this method in a high-throughput framework to assess the potential for thermoelectric performance of 1350 transition-metal sulfides and find that all magnetic systems we identify as promising in the spin-ordered ground state cease to be promising in the paramagnetic phase due to disorder-induced deterioration of the charge carrier transport properties. We also identify promising non-magnetic candidates that do not suffer from these spin disorder effects. In addition to identifying promising materials, our results offer insights into the apparent scarcity of magnetic systems among known thermoelectrics and highlight the importance of including spin disorder in computational searches.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Modeling Information Multiplexing in the Hippocampus

Modern computers are constantly faced with the challenge of processing ever-growing quantities of data that span a wide range of modalities. Of particular relevance to national security interests is the ability to integrate multimodal data for the purpose of fast decision making. The brain is a biological system that is specialized for high performance at this task, suggesting that understanding the mechanisms by which neural circuits integrate multimodal data may lead to improved man-made detection systems. This research focused on understanding these neural algorithms, and specifically tested the hypothesis that hippocampal neurons multiplex information from two different input streams. Specifically, we compare the spiking behavior of a computational model of hippocampal circuitry with neurophysiological data recorded from rodent hippocampus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Comparison of Deposition© software predictions to published experimental results for large (152 mm by 152 mm) square ducts

A comparison was performed between experimental aerosol deposition measurements in large air ducts, compared to predictions performed by the US Nuclear Regulatory Commission Deposition© software package. Although the Deposition© code was designed to calculate the transport of radioactive aerosols in small diameter sampling tubes, it was not certain that the software would adequately predict aerosol transport in large air ducts. Two sets of experimental data were used in this comparison, one set of results from small diameter (1.27 mm ID) tubes, and the other data set was from testing on large diameter (152 mm square) ventilation ducts. In general, the Deposition© software performs excellent predictions if the ductwork system has a computed aerosol percent penetration greater than 75% (with a 2.9% average error compared to experiments). For example, shorter duct systems have higher aerosol penetrations than a longer duct system of the same diameter. However, when the Deposition© software calculated an aerosol penetration less than about 75% for a particular duct system, the average percent error increased to 68.3% when compared to experimental measurements. The Deposition© software could therefore be used to ensure that an aerosol would have a high percent penetration through a ductwork system, but it should not be used to make (dosimetric) decisions about ductwork performance if the calculated percent penetration is less than 75%. This document includes a section of attached engineering formulas that explain the structure of the Excel spreadsheet calculations.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Resilience Enhancements through Deep Learning Yields

This report documents the Resilience Enhancements through Deep Learning Yields (REDLY) project, a three-year effort to improve electrical grid resilience by developing scalable methods for system operators to protect the grid against threats leading to interrupted service or physical damage. The computational complexity and uncertain nature of current real-world contingency analysis presents significant barriers to automated, real-time monitoring. While there has been a significant push to explore the use of accurate, high-performance machine learning (ML) model surrogates to address this gap, their reliability is unclear when deployed in high-consequence applications such as power grid systems. Contemporary optimization techniques used to validate surrogate performance can exploit ML model prediction errors, which necessitates the verification of worst-case performance for the models.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Pushing back the limit of ab-initio quantum transport simulations on hybrid supercomputers, In: SC '15 Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

The capabilities of CP2K, a density-functional theory package and OMEN, a nano-device simulator, are combined to study transport phenomena from first-principles in unprecedentedly large nanostructures. Based on the Hamiltonian and overlap matrices generated by CP2K for a given system, OMEN solves the Schrödinger equation with open boundary conditions (OBCs) for all possible electron momenta and energies. To accelerate this core operation a robust algorithm called SplitSolve has been developed. It allows to simultaneously treat the OBCs on CPUs and the Schrödinger equation on GPUs, taking advantage of hybrid nodes. Our key achievements on the Cray-XK7 Titan are (i) a reduction in time-to-solution by more than one order of magnitude as compared to standard methods, enabling the simulation of structures with more than 50000 atoms, (ii) a parallel efficiency of 97% when scaling from 756 up to 18564 nodes, and (iii) a sustained performance of 15 DP-PFlop/s.

Computer Science↗
DOE OSTI2018

Parallelized and Vectorized Tracking Using Kalman Filters with CMS Detector Geometry and Events

The High-Luminosity Large Hadron Collider at CERN will be characterized by greater pileup of events and higher occupancy, making the track reconstruction even more computationally demanding. Existing algorithms at the LHC are based on Kalman filter techniques with proven excellent physics performance under a variety of conditions. Starting in 2014, we have been developing Kalman-filter-based methods for track finding and fitting adapted for many-core SIMD processors that are becoming dominant in high-performance systems. This paper summarizes the latest extensions to our software that allow it to run on the realistic CMS-2017 tracker geometry using CMSSW-generated events, including pileup. The reconstructed tracks can be validated against either the CMSSW simulation that generated the hits, or the CMSSW reconstruction of the tracks. In general, the code's computational performance has continued to improve while the above capabilities were being added. We demonstrate that the present Kalman filter implementation is able to reconstruct events with comparable physics performance to CMSSW, while providing generally better computational performance. Further plans for advancing the software are discussed.

Cerati, Giuseppe↗
DOE OSTI2023

Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials

Optical metamaterials manipulate light through various confinement and scattering processes, offering unique advantages like high performance, small form factor and easy integration with semiconductor devices. However, designing metasurfaces with suitable optical responses for complex metamaterial systems remains challenging due to the exponentially growing computation cost and the ill‐posed nature of inverse problems. To expedite the computation for the inverse design of metasurfaces, a physics‐informed deep learning (DL) framework is used. A tandem DL architecture with physics‐based learning is used to select designs that are scientifically consistent, have low error in design prediction, and accurate reconstruction of optical responses. The authors focus on the inverse design of a representative plasmonic device and consider the prediction of design for the optical response of a single wavelength incident or a spectrum of wavelength in the visible light range. The physics‐based constraint is derived from solving the electromagnetic wave equations for a simplified homogenized model. The model converges with an accuracy up to 97% for inverse design prediction with the optical response for the visible light spectrum as input, and up to 96% for optical response of single wavelength of light as input, with optical response reconstruction accuracy of 99%.

Sarkar, Sulagna↗
DOE OSTI2018

Job Management and Task Bundling

High Performance Computing is often performed on scarce and shared computing resources. To ensure computers are used to their full capacity, administrators often incentivize large workloads that are not possible on smaller systems. Measurements in Lattice QCD frequently do not scale to machine-size workloads. By bundling tasks together we can create large jobs suitable for gigantic partitions. We discuss METAQ and mpi_jm, software developed to dynamically group computational tasks together, that can intelligently backfill to consume idle time without substantial changes to users’ current workflows or executables.

Berkowitz, Evan↗
DOE OSTI2019

Profiling the Usage of an Extreme-Scale Archival Storage System

Profiling the archival storage system in scientific computing environments has received much less attention compared to the parallel file system, but is equally important since it stores the final data products safely, for a long duration. In this paper, we analyze eight years worth of data transfer logs for accessing the archival file system (HPSS) in the Oak Ridge Leadership Computing Facility (OLCF), which has been hosting the world's largest supercomputers and file systems. Our analysis encompasses about 135 million data transfer activities to the 80 PB High Performance Storage System (HPSS), between 2010 and 2017. We analyze the logs from several dimensions, including studying the workload characteristics (e.g., access patterns, frequency of accesses and temporal behavior), file system characteristics (e.g., directory depth, file system scaling trends, file types), and scientific user behavior (e.g., domain-specific usage and organization). Based on the analysis, we derive insights into the future evolution of the archive in terms of provisioning, desired features and functionality from the archive software, role and right sizing of the archive tiers, quota management, and the importance of smart and efficient metadata and storage management. We believe our study will prove useful for both operating current archival storage and better provisioning future systems.

Sim, Hyogi↗
DOE OSTI2018

Umpire

The Umpire library consists of a collection of software components that provide memory and resource management capabilities that can be used by diverse high performance computing (HPC) applications. The current set of capabilities that Umpire provides Includes: • Flexible allocators to provision memory In the range of available memory resources on a target system. • An extensible allocation strategy model, allowing custom algorithms to be used to allocate memory resources in a scalable and high­performance fashion. • Memory-location aware operations that can be used to migrate and modify memory using specialized functions dependent on source and target data locations • A set of Introspection capabilities to allow the user to query the state of the different allocators and memory allocations.

ECP↗
DOE OSTI2017

Secure Enclaves: An Isolation-centric Approach for Creating Secure High Performance Computing Environments

High performance computing environments are often used for a wide variety of workloads ranging from simulation, data transformation and analysis, and complex workflows to name just a few. These systems may process data at various security levels but in so doing are often enclaved at the highest security posture. This approach places significant restrictions on the users of the system even when processing data at a lower security level and exposes data at higher levels of confidentiality to a much broader population than otherwise necessary. The traditional approach of isolation, while effective in establishing security enclaves poses significant challenges for the use of shared infrastructure in HPC environments. This report details current state-of-the-art in virtualization, reconfigurable network enclaving via Software Defined Networking (SDN), and storage architectures and bridging techniques for creating secure enclaves in HPC environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Scalable Analysis Methods and In Situ Infrastructure for Extreme Scale Knowledge Discovery

High performance computers have for many years been on a trajectory that gives them extraordinary compute power with the addition of more and more compute cores. At the same time, other system parameters such as the amount of memory per core and bandwidth to storage have remained constant or have barely increased. This creates an imbalance in the computer, giving it the ability to compute a lot of data that it cannot reasonably save out due to time and storage constraints. While technologies have been invented to mitigate this problem (burst buffers, etc.), software has been adapting to employ in situ libraries which perform data analysis and visualization on simulation data while it is still resident in memory. This avoids the need to ever have to pay the costs of writing many terabytes of data files. Instead, in situ enables the creation of more concentrated data products such as statistics, plots, and data extracts, which are all far smaller than the full-sized volume data. With the increasing popularity of in situ, multiple in situ infrastructures have been created, each with its own mechanism for integrating with a simulation. To make it easier to instrument a simulation with multiple in situ infrastructures and include custom analysis algorithms, this project created the SENSEI framework.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

A Unified Data-Driven Approach for Programming In Situ Analysis and Visualization: An Interim Report of Sandia Sub-Team Contributions

As we look ahead to next generation high performance computing platforms, the placement and movement of data is becoming the key-limiting factor on both performance and energy efficiency. Furthermore, the increased quantities of data the systems are capable of generating, in conjunction with the insufficient rate of improvements in the supporting I/0 infrastructure, is forcing applications away from the off-line post-processing of data towards techniques based on in ,situ analysis and visualization. Together, these challenges are shaping how we will both design and develop effective, performant and energy-efficient software. In particular, the challenges highlight the need for data and data-centric operations to be fundamental in the reasoning about, and optimization of, scientific workflows on extreme-scale architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Compare Mechanistic Predictions for Doped UO 2 Mechanical Response and Other Properties with Empirical Models and Experimental Measurements

The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation program develops predictive capabilities using computational methods for the analysis and design of advanced reactor and fuel cycle systems. This program has been supporting the development of BISON, a high-fidelity, high resolution fuel performance tool at the engineering scale. As part of its development, additional modeling capabilities and improvements have been developed for relevant fuel forms. In this work, a fuel creep deformation model for Cr-doped fuel has been implemented into BISON, along with improvements to the empirical UO 2 fuel creep model based on experimental data and improvements to the radial power factor calculation for doped fuels. This work allows for more accurate simulation analyses for both UO 2 and doped-UO 2 fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

On the Efficient Evaluation of the Exchange Correlation Potential on Graphics Processing Unit Clusters

The predominance of Kohn–Sham density functional theory (KS-DFT) for the theoretical treatment of large experimentally relevant systems in molecular chemistry and materials science relies primarily on the existence of efficient software implementations which are capable of leveraging the latest advances in modern high-performance computing (HPC). With recent trends in HPC leading toward increasing reliance on heterogeneous accelerator-based architectures such as graphics processing units (GPU), existing code bases must embrace these architectural advances to maintain the high levels of performance that have come to be expected for these methods. In this work, we purpose a three-level parallelism scheme for the distributed numerical integration of the exchange-correlation (XC) potential in the Gaussian basis set discretization of the Kohn–Sham equations on large computing clusters consisting of multiple GPUs per compute node. In addition, we purpose and demonstrate the efficacy of the use of batched kernels, including batched level-3 BLAS operations, in achieving high levels of performance on the GPU. We demonstrate the performance and scalability of the implementation of the purposed method in the NWChemEx software package by comparing to the existing scalable CPU XC integration in NWChem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

A Data Analysis Framework for Earth System Simulation within an In-Situ Infrastructure

This paper presents a generic procedure to implement a scalable and high performance data analysis framework for large-scale scientific simulation within an in-situ infrastructure. It demonstrates a unique capability for global Earth system simulations using advanced computing technologies (i.e ., automated code analysis and instrumentation), in-situ infrastructure (i.e ., ADIOS) and big data analysis engines (i.e ., SciKit-learn). This paper also includes a useful case that analyzes a globe Earth System simulations with the integration of scalable in-situ infrastructure and advanced data processing package. Finally, the in-situ data analysis framework can provides new insights on scientific discoveries in multiscale modeling paradigms.

97 MATHEMATICS AND COMPUTING↗