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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 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Novel Deployment of Elpasolites as a Dual Neutron/Gamma-Ray Directional Detector

At a time when upholding national security has never been more important, there exists a need for the advancement of radiation detection technologies. Neutron and photon detectors are essential to fulfilling mission areas including detection and localization of missing, stolen or smuggled radiological or nuclear materials, quantification of the effects of a radiological or nuclear event, and supporting nonproliferation efforts. The aim of this study was to evaluate a new radiation detector based on the scintillation elpasolite compound Cs 2 LiYCl 6 :Ce (CLYC) for simultaneous measurements of neutron and photon flux and the localization of radiation sources. Previous studies performed on the CLYC scintillator indicate its potential for thermal neutron and gamma-ray measurements. This study is dedicated to the novel application of the CLYC as a dual neutron / photon detector and as part of a directional detection system. Both computational modeling and an experimental study were carried out within this research project. As part of the computational study, the response of a CLYC scintillator detector to gamma rays induced by thermal neutron interaction with Cl and 7 Li nuclei was investigated using the MCNP6 code. In addition, arrays of three and four CLYC detectors were modeled in order to evaluate the directional detection of both a thermal neutron source and a gamma-ray source. It was shown that little or no quality of source direction determination would be lost when three detectors were used in the array compared to four detectors. In the experimental study, the photon spectroscopy capabilities of the CLYC detectors were evaluated. A gamma-ray energy resolution of 4.9% was measured for the 662-keV peak of 137 Cs and 3.6% for the 1.33-MeV peak of 60 Co. Using a thermal neutron source, the pulse shape discrimination analysis was successfully performed for the CLYC detector signal waveforms. Thermal neutrons and gamma rays were separated with an exceptional figure of merit (FOM) of 2.3. An array of three CLYC detectors was assembled for the purpose of directional neutron / gamma-ray detection. The intrinsic peak efficiency of CLYC detectors was evaluated. The three-CLYC detector array was deployed for directional measurements with a single gamma-ray 137 Cs source, two gamma-ray sources of 137 Cs and 60 Co isotopes and a thermal neutron source designed using a 239PuBe neutron source supplied with a polyethylene moderator. Measurements were carried out using sources located in the longitude and latitude planes over the angles from 0° to 360°. The measured data were processed through a maximum likelihood estimation algorithm providing a possible direction for which the radioactive source in each case was positioned. The estimated directions were close if not exact matches for the actual directions to the radioactive source. The largest discrepancy in direction produced by the algorithm was approximately 11%. However, it was hypothesized that this percent error can be decreased by homogenizing the directional detection system to consist of scintillators of the same size and quality, identical photomultiplier tubes and identical aluminum housings. The feasibility of this hypothesis to decrease the percent error was confirmed by the zero percent error achieved in the directional measurements produced in the computational study utilizing a homogenous directional detection system. The results of computational and experimental studies completed within this research project provide means to propose the array of three CLYC scintillators as an efficient dual neutron / gamma-ray directional detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

SCALE 6.2 Continuous-Energy TSUNAMI-3D Capabilities

The TSUNAMI (Tools for Sensitivity and UNcertainty Analysis Methodology Implementation) capabilities within the SCALE code system make use of sensitivity coefficients for an extensive number of criticality safety applications, such as quantifying the data-induced uncertainty in the eigenvalue of critical systems, assessing the neutronic similarity between different systems, quantifying computational biases, and guiding nuclear data adjustment studies. The need to model geometrically complex systems with improved ease of use and fidelity and the desire to extend TSUNAMI analysis to advanced applications have motivated the development of a SCALE 6.2 module for calculating sensitivity coefficients using three-dimensional (3D) continuous-energy (CE) Monte Carlo methods: CE TSUNAMI-3D. This paper provides an overview of the theory, implementation, and capabilities of the CE TSUNAMI-3D sensitivity analysis methods. CE TSUNAMI contains two methods for calculating sensitivity coefficients in eigenvalue sensitivity applications: (1) the Iterated Fission Probability (IFP) method and (2) the Contributon-Linked eigenvalue sensitivity/Uncertainty estimation via Track length importance CHaracterization (CLUTCH) method. This work also presents the GEneralized Adjoint Response in Monte Carlo method (GEAR-MC), a first-of-its-kind approach for calculating adjoint-weighted, generalized response sensitivity coefficients—such as flux responses or reaction rate ratios—in CE Monte Carlo applications. The accuracy and efficiency of the CE TSUNAMI-3D eigenvalue sensitivity methods are assessed from a user perspective in a companion publication, and the accuracy and features of the CE TSUNAMI-3D GEAR-MC methods are detailed in this paper.

42 ENGINEERING↗
DOE OSTI2015

SCALE 6.2 Continuous-energy Tsunami- 3D capabilities - 14566

The TSUNAMI (Tools for Sensitivity and Uncertainty Analysis Methodology Implementation) capabilities within the SCALE code system make use of sensitivity coefficients for an extensive number of criticality safety applications, such as quantifying the data-induced uncertainty in the eigenvalue of critical systems, assessing the neutronic similarity between different systems, quantifying computational biases, and guiding nuclear data adjustment studies. The need to model geometrically complex systems with improved ease of use and fidelity and the desire to extend TSUNAMI analysis to advanced applications have motivated the development of a SCALE 6.2 module for calculating sensitivity coefficients using three-dimensional (3D) continuous-energy (CE) Monte Carlo methods: CE TSUNAMI-3D. This paper provides an overview of the theory, implementation, and capabilities of the CE TSUNAMI-3D sensitivity analysis methods. CE TSUNAMI contains two methods for calculating sensitivity coefficients in eigenvalue sensitivity applications: (1) the Iterated Fission Probability (IFP) method and (2) the Contribution-Linked eigenvalue sensitivity/Uncertainty estimation via Track length importance Characterization (CLUTCH) method. This work also presents the Generalized Adjoint Response in Monte Carlo method (GEAR-MC), a first-of-its-kind approach for calculating adjoint-weighted, generalized response sensitivity coefficients - such as flux responses or reaction rate ratios - in CE Monte Carlo applications. The accuracy and efficiency of the CE TSUNAMI-3D eigenvalue sensitivity methods are assessed from a user perspective in a companion publication, and the accuracy and features of the CE TSUNAMI-3D GEAR-MC methods are detailed in this paper. (authors)

42 ENGINEERING↗
DOE OSTI2010

Fault-tolerance for exascale systems.

Periodic, coordinated, checkpointing to disk is the most prevalent fault tolerance method used in modern large-scale, capability class, high-performance computing (HPC) systems. Previous work has shown that as the system grows in size, the inherent synchronization of coordinated checkpoint/restart (CR) limits application scalability; at large node counts the application spends most of its time checkpointing instead of executing useful work. Furthermore, a single component failure forces an application restart from the last correct checkpoint. Suggested alternatives to coordinated CR include uncoordinated CR with message logging, redundant computation, and RAID-inspired, in-memory distributed checkpointing schemes. Each of these alternatives have differing overheads that are dependent on both the scale and communication characteristics of the application. In this work, using the Structural Simulation Toolkit (SST) simulator, we compare the performance characteristics of each of these resilience methods for a number of HPC application patterns on a number of proposed exascale machines. The result of this work provides valuable guidance on the most efficient resilience methods for exascale systems.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Special Topic on High Performance Computing in Chemical Physics

Computational modeling and simulation have become indispensable scientific tools in virtually all areas of chemical, biomolecular, and materials systems research. Computation can provide unique and detailed atomic level information that is difficult or impossible to obtain through analytical theories and experimental investigations. In addition, recent advances in micro-electronics have resulted in computer architectures with unprecedented computational capabilities, from the largest supercomputers to common desktop computers. In conclusion, combined with the development of new computational domain science methodologies and novel programming models and techniques, this has resulted in modeling and simulation resources capable of providing results at or better than experimental chemical accuracy and for systems in increasingly realistic chemical environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence

Abstract Computational models of the Earth System are critical tools for modern scientific inquiry. Efforts toward evaluating and improving errors in representations of physical and chemical processes in these large computational systems are commonly stymied by highly nonlinear and complex error behavior. Recent work has shown that these errors can be effectively predicted using modern Artificial Intelligence (A.I.) techniques. In this work, we go beyond these previous studies to apply an explainable A.I. technique to not only predict model errors but also move toward understanding the underlying reasons for successful error prediction. We use XGBoost classification trees and SHapley Additive exPlanations analysis to explore the errors in the prediction of lightning occurrence in the NASA Goddard Earth Observing System model, a widely used Earth System Model. This explainable error prediction system can effectively predict the model error and indicates that the errors are strongly related to convective processes and the characteristics of the land surface.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Understanding shape entropy through local dense packing

Entropy drives the phase behavior of colloids ranging from dense suspensions of hard spheres or rods to dilute suspensions of hard spheres and depletants. Entropic ordering of anisotropic shapes into complex crystals, liquid crystals, and even quasicrystals was demonstrated recently in computer simulations and experiments. The ordering of shapes appears to arise from the emergence of directional entropic forces (DEFs) that align neighboring particles, but these forces have been neither rigorously defined nor quantified in generic systems. In this paper, we show quantitatively that shape drives the phase behavior of systems of anisotropic particles upon crowding through DEFs. We define DEFs in generic systems and compute them for several hard particle systems. We show they are on the order of a few times the thermal energy (k B T) at the onset of ordering, placing DEFs on par with traditional depletion, van der Waals, and other intrinsic interactions. In experimental systems with these other interactions, we provide direct quantitative evidence that entropic effects of shape also contribute to self-assembly. We use DEFs to draw a distinction between self-assembly and packing behavior. We show that the mechanism that generates directional entropic forces is the maximization of entropy by optimizing local particle packing. Finally, we show that this mechanism occurs in a wide class of systems and we treat, in a unified way, the entropy-driven phase behavior of arbitrary shapes, incorporating the well-known works of Kirkwood, Onsager, and Asakura and Oosawa.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Real-Time Stochastic Optimization of Complex Energy Systems on High-Performance Computers

A scalable approach computes in operationally-compatible time the energy dispatch under uncertainty for electrical power grid systems of realistic size with thousands of scenarios. The authors propose several algorithmic and implementation advances in their parallel solver PIPS-IPM for stochastic optimization problems. New developments include a novel, incomplete, augmented, multicore, sparse factorization implemented within the PARDISO linear solver and new multicore- and GPU-based dense matrix implementations. They show improvement on the interprocess communication on Cray XK7 and XC30 systems. PIPS-IPM is used to solve 24-hour horizon power grid problems with up to 1.95 billion decision variables and 1.94 billion constraints on Cray XK7 and Cray XC30, with observed parallel efficiencies and solution times within an operationally defined time interval. To the authors' knowledge, "real-time"-compatible performance on a broad range of architectures for this class of problems hasn't been possible prior to this work

Computer Science↗
DOE OSTI2023

A Reference Implementation for a Quantum Message Passing Interface

Practical applications of quantum computing are currently limited by the number of qubits that can be set with reasonable fidelities for each system. Therefore, a distributed quantum computing system with multiple quantum computers coherently connected is highly demanding. To realize the internode communication of quantum information, the software interface, Quantum Message Passing Interface (QMPI), leveraging the framework built for classical MPI but taking advantage of quantum teleportation to communicate between different quantum nodes was proposed. In this project, we develop the QMPI with point-to-point and collective operations in Qiskit and characterize its performance by demonstrating the application implementations. Moreover, we developed a new technique for optimizing collective communication of the distributed quantum programs with Multi-Controlled Toffoli gates. This technique beats the state-of-the-art in terms of fidelity and the number of remote EPR pairs consumed in both simulations and experiments.

Shi, Yue↗
DOE OSTI2022

Radiation Belt Remediation: A Complex Engineered System (RBR-ACES) (Institutional Computing Report)

We have performed the simulation campaigns in support of LDRD-DR project Radiation Belt Remediation: A Complex Engineered System (RBR-ACES). This project involves modeling wave generation and wave propagation in plasma environment and studying the effect of waves on the distribution of high energy electrons with the focus on electrons appeared after a high-altitude nuclear explosion (HANE). One of the major goal of this research is strong validation of the numerical models with laboratory experiments and space-based experiments and observations.

42 ENGINEERING↗
DOE OSTI2017

SymPy: symbolic computing in Python

SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Computational Analysis of Different Sparging Systems and their Influence in the Fluid-Dynamic Behavior of Bubble Column Reactors

Bubble column bioreactors are being actively considered for gas fermentation applications, specifically for CO2 utilization, and sugars to fuels conversion. Their main advantages include good mass transfer without any moving parts and low-cost of operation and maintenance. However, the design and scale-up of such reactors is challenging specifically for carbon capture applications where a mixture of gases (e.g. CO2/CO/H2) with variable solubilities is used. The overall performance of scaled-up bioreactors (e.g., mass transfer rate) is largely affected by gas holdup, bubble size distribution (BSD), and multiphase hydrodynamics. We investigate the effect of gas sparger designs on the performance of these large-scale bioreactors using computational fluid dynamics simulations in this work, so as to improve CO2 conversion at scale. The gas distribution systems in bubble column reactors not only determines operational regime, but also affects the evolution of the BSD, which in turn influences interfacial mass transfer and ultimately the efficiency of the gas-liquid exchange process. In addition to the BSD, uniformity in gas sparging affects gas holdup and bubble residence time which constitute important metrics of performance in gas-liquid systems. In this work, we use computational models to simulate high fidelity representations of different sparger designs and their effect on the operation of a bubble column reactor. Four different types of spargers have been selected for the computational study (Fig. 1): ladder, multi-ring, single-ring and toroidal. Their effect on superficial velocity, gas holdup mixing efficiency, and BSD will be evaluated in this work. The model uses a multiphase Eulerian framework similar to [1] and include a composition of mixtures of H2/CO/CO2 gases, common in fermentation applications.

BIOMASS FUELS,MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Novel type-II material system for laser applications in the near-infrared regime

The design and experimental realization of a type-II “W”-multiple quantum well heterostructure for emission in the λ > 1.2 μm range is presented. The experimental photoluminescence spectra for different excitation intensities are analyzed using microscopic quantum theory. On the basis of the good theory–experiment agreement, the gain properties of the system are computed using the semiconductor Bloch equations. Gain values comparable to those of type-I systems are obtained.

36 MATERIALS SCIENCE↗
DOE OSTI2014

A Runtime Environment for Supporting Research in Resilient HPC System Software & Tools

The high-performance computing (HPC) community continues to increase the size and complexity of hardware platforms that support advanced scientific workloads. The runtime environment (RTE) is a crucial layer in the software stack for these large-scale systems. The RTE manages the interface between the operating system and the application running in parallel on the machine. The deployment of applications and tools on large-scale HPC computing systems requires the RTE to manage process creation in a scalable manner, support sparse connectivity, and provide fault tolerance. We have developed a new RTE that provides a basis for building distributed execution environments and developing tools for HPC to aid research in system software and resilience. This paper describes the software architecture of the Scalable runTime Component Infrastructure (STCI), which is intended to provide a complete infrastructure for scalable start-up and management of many processes in large-scale HPC systems. We highlight features of the current implementation, which is provided as a system library that allows developers to easily use and integrate STCI in their tools and/or applications. The motivation for this work has been to support ongoing research activities in fault-tolerance for large-scale systems. We discuss the advantages of the modular framework employed and describe two use cases that demonstrate its capabilities: (i) an alternate runtime for a Message Passing Interface (MPI) stack, and (ii) a distributed control and communication substrate for a fault-injection tool.

Computer Science↗
DOE OSTI2011

Target Diagnostics Supports NIF's Path to Ignition

The physics requirements derived from the National Ignition Facility (NIF) experimental campaigns are leading to a wide variety of target diagnostics. Software development for the control and analysis of these diagnostics is included in the NIF Integrated Computer Control System, Diagnostic Control System and Data Visualization. These projects implement the configuration, controls, data analysis and visual representation of most of these diagnostics. To date, over 40 target diagnostics have been developed to support NIF experiments. In 2011 diagnostics were developed or enhanced to measure Ignition performance in a high neutron yield environment. Performance is optimized around four key variables: Adiabat (a) which is the strength and timing of four shocks delivered to the target, Velocity (V) of the imploding target, Mix (M) is the uniformity of the burn, and the Shape (S) of the imploding Deuterium Tritium (DT) hot spot. The diagnostics used to measure each of these parameters is shown in figure 1. Adiabat is measured using the Velocity Interferometer System for Any Reflector (VISAR) diagnostic consisting of three streak cameras. To provide for more accurate adiabat measurements the VISAR streak cameras were enhanced in FY11 with a ten comb fiducial signal controller to allow for post shot correction of the streak camera sweep non-linearity. Mix is measured by the Neutron Time of Flight (NTOF) and Radiochemical Analysis of Gaseous Samples (RAGS) diagnostics. To accommodate high neutron yield shots, NTOF diagnostic controls are being modified to use Mach Zehnder interferometer signals to allow the digitizers to be moved from near the target chamber to the neutron shielded diagnostic mezzanine. In December 2011 the first phase of RAGS diagnostic commissioning will be completed. This diagnostic will analyze the tracers that are added to NIF target capsules that undergo nuclear reactions during the shot. These gases are collected and purified for nuclear counting by the RAGS system. Three new instrument controllers were developed and commissioned to support this diagnostic. A residual-gas analyzer (RGA) instrument measures the gas content at various points in the system. The Digital Gamma Spectrometer instrument measures the radiological spectrum of the decaying gas isotopes. A final instrument controller was developed to interface to a PLC based Gas collection system. In order to support the implosion velocity measurements an additional Gated X-ray Detector (GXD) diagnostic was tested and commissioned. This third GXD views the target through a slit contained in its snout and allows the other GXD diagnostics to be used for measuring the shape on the same shot. In order to measure the implosion shape in a high neutron environment, Actide Readout In A Neutron Environment (ARIANE) and Neutron Imaging (NI) diagnostics were commissioned. The controls for ARIANE, a fixed port gated x-ray imager, contain a neutron shielded camera and micro channel plate pulser with its neutron sensitive electronics located in the diagnostic mezzanine. The NI diagnostic is composed of two Spectral Instruments SI-1000 cameras located 20M from the target and provides neutron images of the DT hot spot for high yield shots. The development and commissioning of these new or enhanced diagnostics in FY11 have provided meaningful insight that facilitates the optimization of the four key Ignition variables. In FY12 they will be adding three new diagnostics and enhancing four existing diagnostics in support of the continuing optimization series of campaigns.

42 ENGINEERING↗
DOE OSTI2010

Cyber security analysis testbed : combining real, emulation, and simulation.

Cyber security analysis tools are necessary to evaluate the security, reliability, and resilience of networked information systems against cyber attack. It is common practice in modern cyber security analysis to separately utilize real systems of computers, routers, switches, firewalls, computer emulations (e.g., virtual machines) and simulation models to analyze the interplay between cyber threats and safeguards. In contrast, Sandia National Laboratories has developed novel methods to combine these evaluation platforms into a hybrid testbed that combines real, emulated, and simulated components. The combination of real, emulated, and simulated components enables the analysis of security features and components of a networked information system. When performing cyber security analysis on a system of interest, it is critical to realistically represent the subject security components in high fidelity. In some experiments, the security component may be the actual hardware and software with all the surrounding components represented in simulation or with surrogate devices. Sandia National Laboratories has developed a cyber testbed that combines modeling and simulation capabilities with virtual machines and real devices to represent, in varying fidelity, secure networked information system architectures and devices. Using this capability, secure networked information system architectures can be represented in our testbed on a single, unified computing platform. This provides an 'experiment-in-a-box' capability. The result is rapidly-produced, large-scale, relatively low-cost, multi-fidelity representations of networked information systems. These representations enable analysts to quickly investigate cyber threats and test protection approaches and configurations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Towards Interactive, Reproducible Analytics at Scale on HPC Systems

The growth in scientific data volumes has resulted in a need to scale up processing and analysis pipelines using High Performance Computing (HPC) systems. These workflows need interactive, reproducible analytics at scale. The Jupyter platform provides core capabilities for interactivity but was not designed for HPC systems. In this paper, we outline our efforts that bring together core technologies based on the Jupyter Platform to create interactive, reproducible analytics at scale on HPC systems. Our work is grounded in a real world science use case-applying geophysical simulations and inversions for imaging the subsurface. Our core platform addresses three key areas of the scientific analysis workflow-reproducibility, scalability, and interactivity. We describe our implemention of a system, using Binder, Science Capsule, and Dask software. We demonstrate the use of this software to run our use case and interactively visualize real-Time streams of HDF5 data.

containers↗
DOE OSTI2024

Advanced Computing Annual Report 2023

In 2023, advanced computing saw the arrival of Kestrel, the National Renewable Energy Laboratory's (NREL's) newest high-performance computing (HPC) system. Kestrel will accelerate clean energy research at a pace and scale more than five times greater than Eagle, with approximately 44 petaflops of computing power. Kestrel's heterogeneous architecture - which includes both CPU-only and GPU-accelerated nodes - is designed to bring a much greater GPU capacity to EERE workloads compared to Eagle, enabling rapidly advancing applications in artificial intelligence and expanding research in new directions for computing. In Fiscal Year (FY) 2023, 333 projects utilized NREL's HPC system, advancing the U.S. Department of Energy's (DOE's) Office of Energy Efficiency and Renewable Energy (EERE) mission across 13 funding areas. Cross-disciplinary collaboration among researchers yielded more than 800 technical outputs, including 177 peer-reviewed journal articles in FY 2023. All this great work continues to advance the science of energy efficiency and renewable energy. This report highlights research that utilized HPC resources in FY 2023.

advanced computing↗