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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Stochastic resolution of identity second-order Matsubara Green’s function theory

We create a stochastic resolution of identity representation to the second-order Matsubara Green’s function (sRI-GF2) theory. Using a stochastic resolution of the Coulomb integrals, the second order Born self-energy in GF2 is decoupled and reduced to matrix products/ contractions, which reduces the computational cost from O(N 5 ) to O(N 3 ) (with N being the number of atomic orbitals). Currently, the method can be viewed as an extension to our previous work on stochastic resolution of identity second order Møller-Plesset perturbation theory [T. Y. Takeshita et al., J. Chem. Theory Comput. 13, 4605 (2017)] and offers an alternative to previous stochastic GF2 formulations [D. Neuhauser et al., J. Chem. Theory Comput. 13, 5396 (2017)]. We show that sRI-GF2 recovers the deterministic GF2 results for small systems, is computationally faster than deterministic GF2 for N > 80, and is a practical approach to describe weak correlations in systems with 10 3 electrons and more.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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 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 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 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↗
DOE OSTI2024

Assessment of Cloud-based Applications for Enabling a Scalable Riskinformed Predictive Maintenance Strategy

The current light-water reactor fleet uses time-based maintenance strategies to achieve high-capacity factors. But to make nuclear more competitive in the energy market, these reactors could utilize emerging artificial intelligence (AI) and cloud computing technologies to achieve a cost-effective, predictive-maintenance strategy. This paper presents discussion and results on the application of cloud computing in the nuclear industry. The technical viability of cloud computing was analyzed using data from a boiling-water reactor’s safety relief valve. The models were hosted on three different systems: a local personal computer, Idaho National Laboratory’s high-performance computer system, and Microsoft Azure. The data were loaded and processed, and two types of models were trained in an A/B fashion. Based on the speed at which these actions were completed, it was determined that cloud computing affords adequate computing resources. Additionally, the computing power can scale with the demanded load. To enable cloud computing in the existing fleet, additional sensors, networks, and other requirements must be implemented to ensure a smooth transition from current maintenance strategies. However, the benefit is that the plants no longer need to manage their own servers, software, cybersecurity, and information technology support staff for in-house data analytics purpose. Many of these features can be offloaded to the cloud provider for a potential cost savings. Demonstrating how AI can improve the maintenance and operation of non-safety-related systems seems the likely path forward for implementing AI and cloud computing resources inside nuclear power plants.

azure↗
DOE OSTI2024

Scaling Ensembles of Data-Intensive Quantum Chemical Calculations for Millions of Molecules

Deep learning models are efficient computational tools that can accelerate the inverse design of molecules with desired functional properties by generating predictions at a fraction of the time required by traditional quantum chemical approaches. To ensure that a model maintains accuracy and transferability across broad regions of the chemical space explored during the inverse design, it must be trained on massively large volumes of simulation data. This requires running large-scale ensemble quantum chemical calculations on high-performance computing (HPC) systems for data collection. However, the efficient execution of such large ensemble calculations and the management of large volumes of output data require tools that can judiciously utilize computational resources and manage metadata overhead on the file system. Therefore, we present a high-performance, scalable, ensemble management framework for performing data-intensive quantum chemical electronic structure calculations for organic molecules. This framework provides abstractions to plug different ab initio, first principles, and first principles-based semi-empirical methods and executes them efficiently at large scale on HPC systems. It dynamically distributes tasks to resources and uses tiered storage for managing large collections of files. We employed this framework to process over ten million organic molecules and generate open-source datasets that provide UV-vis absorption spectra by running time-dependent density-functional tight-binding calculations. It is the largest database containing molecular optical spectra that were simulated with quantum chemical methods in a consistent manner.

Mehta, Kshitij↗
DOE OSTI2015

Computational and experimental study of laminar flames

During the past three years, our research has centered on an investigation of the effects of complex chemistry and detailed transport on the structure and extinction of hydrocarbon flames in coflowing axisymmetric configurations. We have pursued both computational and experimental aspects of the research in parallel on both steady-state and time-dependent systems. The computational work has focused on the application of accurate and efficient numerical methods for the solution of the steady-state and time-dependent boundary value problems describing the various reacting systems. Detailed experimental measurements were performed on axisymmetric coflow flames using two-dimensional imaging techniques. Previously, spontaneous Raman scattering, chemiluminescence, and laser-induced fluorescence were used to measure the temperature, major and minor species profiles. Particle image velocimetry (PIV) has been used to investigate velocity distributions and for calibration of time-varying flames. Laser-induced incandescence (LII) with an extinction calibration was used to determine soot volume fractions, while soot surface temperatures were measured with three-color optical pyrometry using a color digital camera. A blackbody calibration of the camera allows for determination of soot volume fraction as well, which can be compared with the LII measurements. More recently, we have concentrated on a detailed characterization of soot using a variety of techniques including time-resolved LII (TiRe-LII) for soot primary particles sizes, multi-angle light scattering (MALS) for soot radius of gyration, and spectrally-resolved line of sight attenuation (spec-LOSA). Combining the information from all of these soot measurements can be used to determine the soot optical properties, which are observed to vary significantly depending on spatial location and fuel dilution. Our goal has been to obtain a more fundamental understanding of the important fluid dynamic and chemical interactions in these flames so that this information can be used effectively in combustion modeling.

02 PETROLEUM↗
DOE OSTI2021

Test Points for Online Monitoring of Quantum Circuits

Noisy Intermediate-Scale Quantum (NISQ) computers consisting of tens of inherently noisy quantum bits (qubits) suffer from reliability problems. Qubits and their gates are susceptible to various types of errors. Due to limited numbers of qubits and high error rates, quantum error correction cannot be applied. Physical constraints of quantum hardware including the error rates are used to guide the design and the layout of quantum circuits. The error rates determine the selection of qubits and their operations. The resulting circuit is executed on the quantum computer. This study explores the risk of unexpected changes in the error rates of NISQ computers post-calibration. We show that unexpected changes in error rates can alter the output state of a quantum circuit. To detect these changes, we propose the insertion of test points into the quantum circuit to enable online monitoring of the physical qubit behavior. We utilize classical, superposition, and uncompute test points. Furthermore, we use a gate error coverage metric to assess the quality of the tests. We verify the effectiveness of the proposed scheme on different IBM quantum computers (IBM Q), in addition to a noisy simulation that shows the scalability of the proposed approach.

97 MATHEMATICS AND COMPUTING↗