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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Semi-Analytical Leakage Solutions for Aquifers (SALSA) v1

SALSA is a semi-analytical modeling tool that can provide assessments of pressure perturbations and brine leakage because of fluid injection and extraction activities in sedimentary basins. Sedimentary basins typically contain vertical sequences of near-horizontal aquifers separated by less-permeable aquitards. In the context of geologic carbon sequestration (GCS), while aquitards are relied upon to contain injected buoyant free-phase CO2 and limit inter-aquifer brine flow, the large number of leaky wells (e.g., unused/unsuccessful exploration wells, or improperly plugged water or oil/gas wells) that are present in sedimentary basins creates concerns for potential leakage and groundwater contamination. The mathematical theory and solution method included in SALSA is presented in our recent manuscript under review. SALSA is the first to account for the coupled leakage through aquitards and leaky wells, with geologic pressurization sources. SALSA based on semi-analytical solutions can be computationally very efficient for problems containing many injection and leaky wells in multilayered sedimentary systems, compared to any numerical simulation software requiring local mesh refinement around each well to obtain accurate results. Therefore, stakeholders (e.g., regulators, operators) for site screening, injection and post-injection pressure behavior and assessing leakage risks can use SALSA as a fast-predictive tool in GCS applications in multilayered aquifer systems.

Cihan, Abdullah↗
DOE OSTI2024

Implementation of the Glued Sphere Discrete Element Model for Non-Spherical Particles in MFiX Software

To enhance solver capabilities, simulation flexibility and model validation within the MFiX software, the U.S. Department of Energy (DOE) is funding efforts to develop and integrate the glued-sphere discrete element method into the latest version of MFiX as a dedicated computational module. The glued-sphere discrete element method is a numerical technique to depict the behavior of non-spherical particles in granular flows or particulate systems by representing them as a collection of component spheres. These spheres are bonded together to approximate the shape and mechanical/chemical properties of a more complex particle. The method effectively reuses the existing sphere-sphere collision algorithm, interphase momentum and heat transfer calculations utilized in the traditional discrete element method, extending these capabilities to non-spherical particles. Additionally, this method explicitly resolves intra-particle temperature and species distributions. The MFiX glued-sphere computational module includes tools for generating glued sphere configurations, a dedicated solver, and visualization capabilities in post-processing. More specifically within the computational module, collision detection and calculations were first performed on component spheres and then mapped onto non-spherical particles. The linear spring-dashpot model was utilized to simulate the sphere-sphere interactions.

Ke, Renjie↗
DOE OSTI2014

BISON Software V&V Plan

The primary vision for the BISON development team is to deliver a nuclear fuel performance simulation tool that is used to provide a researcher or fuel designer with best estimate calculations of the highly coupled and nonlinear phenomena that govern nuclear fuel behavior. Accurately simulating nuclear fuel behavior is a challenging computational undertaking and verification and validation (V&V) play an important role in realizing this vision. The purpose of this V&V plan is to express the BISON team’s definition of the terms verification and validation, document what we have done regarding V&V, and outline what we plan to do.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

OpenFlow arbitrated programmable network channels for managing quantum metadata

Quantum networks must classically exchange complex metadata between devices in order to carry out information for protocols such as teleportation, super-dense coding, and quantum key distribution. Demonstrating the integration of these new communication methods with existing network protocols, channels, and data forwarding mechanisms remains an open challenge. Software-defined networking (SDN) offers robust and flexible strategies for managing diverse network devices and uses. We adapt the principles of SDN to the deployment of quantum networks, which are composed from unique devices that operate according to the laws of quantum mechanics. We show how quantum metadata can be managed within a software-defined network using the OpenFlow protocol, and we describe how OpenFlow management of classical optical channels is compatible with emerging quantum communication protocols. We next give an example specification of the metadata needed to manage and control quantum physical layer (QPHY) behavior and we extend the OpenFlow interface to accommodate this quantum metadata. Here, we conclude by discussing near-term experimental efforts that can realize SDN’s principles for quantum communication.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Divertor Plasma Detachment Control Neural Network

DivControlNN is a state-of-the-art software tool that leverages advanced machine learning techniques to predict and control divertor plasma behavior in fusion reactors. Plasma, a highly energetic and electrically charged gas, requires meticulous management to protect reactor components and maintain optimal energy production. Conventional simulation methods, although extremely detailed, typically demand extensive computational time-making them unsuitable for real-time control scenarios. DivControlNN addresses this challenge by learning from tens of thousands of high-fidelity simulations, thereby creating a rapid surrogate model that can deliver near-instantaneous predictions. At the core of its functionality is a sophisticated technique known as latent space mapping, which condenses complex, high-dimensional plasma data into a compact, lower-dimensional representation. This streamlined representation enables the system to quickly forecast essential plasma properties and determine the precise conditions required for effective detachment. Detachment is a crucial process in which the plasma is cooled before reaching the divertor plates, thereby reducing heat loads and mitigating material erosion. In recent experiments conducted on the KSTAR tokamak in South Korea, DivControlNN successfully guided the detachment process without any fine-tuning-even when applied to a new tungsten divertor configuration. By achieving a computational speed-up of over one hundred million times compared to traditional simulation methods while maintaining low prediction errors, DivControlNN stands to significantly enhance real-time control and diagnostic capabilities in future fusion reactors. This breakthrough paves the way for safer, more reliable reactor operation and represents a major advancement toward realizing fusion energy as a practical, sustainable, and clean power source.

Xu, Xueqiao [Lawrence Livermore National Laborator↗
DOE OSTI2023

DLIO: A DATA-CENTRIC BENCHMARK FOR DEEP LEARNING APPLICATIONS

SF-22-136 Deep learning has been shown as a successful method for various tasks, and its popularity results in numerous open-source deep learning software tools. Deep learning has been applied to a broad spectrum of scientific domains such as cosmology, particle physics, computer vision, fusion, and astrophysics. Scientists have performed a great deal of work to optimize the computational performance of deep learning frameworks. However, the same cannot be said for I/O performance. As deep learning algorithms rely on big-data volume and variety to effectively train neural networks accurately, I/O is a significant bottleneck on large-scale distributed deep learning training. DLIO, is a novel representative benchmark suite built based on the I/O profiling of the selected workloads. DLIO can be utilized to accurately emulate the I/O behavior of modern deep learning applications. Using DLIO, application developers and system software solution architects can identify potential I/O bottlenecks in their applications and guide optimizations to boost the I/O performance leading to lower training times. The storage vendor can also use DLIO as a guide for designing and optimize the storage and filesystem targeting at deep learning application.

ZHENG, HUIHUO↗
DOE OSTI2023

Reposcanner

SAND2023-05455O Reposcanner provides a highly modular, extensible framework for defining routines for mining data from software repositories and performing analyses on that data to yield valuable insights on team behaviors. Reposcanner features seamless support for different version control platforms like GitHub, Gitlab, and Bitbucket; smart parsing of URLs; intelligent credential management capabilities; and a comprehensive test suite. Reposcanner is connected to the Exascale Computing Project and is intended for research purposes.

Mundt, Miranda↗
DOE OSTI2016

GridPACK™ : A Framework for Developing Power Grid Simulations on High-Performance Computing Platforms

This paper describes the GridPACK™ framework, which is designed to help power grid engineers develop modeling software capable of running on high performance computers. The framework makes extensive use of software templates to provide high level functionality while at the same time allowing developers the freedom to express whatever models and algorithms they are using. GridPACK™ contains modules for setting up distributed power grid networks, assigning buses and branches with arbitrary behaviors to the network, creating distributed matrices and vectors and using parallel linear and non-linear solvers to solve algebraic equations. It also provides mappers to create matrices and vectors based on properties of the network and functionality to support IO and to mana

Palmer, Bruce J.↗
DOE OSTI2022

Computer Science Research Needs for Parallel Discrete Event Simulation (PDES)

Historically, scientific computing efforts have demonstrated the clear need for, and effective use of, supercomputing with traditional time-stepped simulations. Nevertheless, there are several areas in the mission spaces of the U.S. Department of Energy and other agencies waiting to tap advanced computing research using a different, discrete event style of modeling, simulation, and analysis. These span a wide spectrum of applications including energy grid resilience, urban planning and policy, transportation science, building technologies, emergency response and planning, environmental impact analysis, computational epidemiology, Internet communications, cyber security, and cyber-physical systems, to name only a few. Even within traditional scientific applications, the role of discrete event modes of execution is increasing in the form of new event-based mathematical solvers such as quantized state integration methods and discrete-continuous hybrid system solvers. Co-design of advanced supercomputing hardware systems is another area that exploits discrete event simulation at its core for effective analyses. Complex systems, entity behaviors and interconnections play a significant role in all these applications, which are mapped to large-scale models with discrete event formulations. To make advancements in all the aforementioned scientific areas, many technical aspects need to be more thoroughly studied and deeply understood in parallel discrete event simulation (PDES). The unique dynamics inherent in a discrete event modeling approach, by their very nature, intersect and influence the entire stack of the computing system, including (a) the unique nature of the instruction sets exercised in PDES workloads without a predominance of high-precision floating point operations, (b) virtual time-constrained multi-threaded execution of many logical processes per processor, (c) extremely variable and difficult to predict network traffic characteristics, (d) interfaces and inter-dependencies with machine learning and artificial intelligence codes at higher software layers, and (e) highly challenging load balancing needs, especially in effectively accounting for accelerated/extremely heterogeneous computing in current and future high-performance computing systems. Efficient and accurate parallel execution of PDES workloads is also dominated by challenges in dealing with their asynchronous concurrency fundamentally present at the model level. Conservative synchronization, optimistic/speculative synchronization, and their hybrid schemes open new questions in fundamental computer science with respect to reversibility of computation and prediction (lookahead) of behaviors inherent within model codes. On the implementation front, there are relatively few scalable, general-purpose parallel discrete event simulators in the world, and even fewer have been studied on emerging hardware platforms. To enable scientific advances using PDES, the research needs in computer science must also be pursued and met in the intersection of the algorithmic and hardware-aware aspects of scalable PDES engines. This report is aimed at capturing a computer science-oriented view of this important area of research in PDES, presenting a sample of important applications with their inherent discrete event technology elements. Needs are outlined in core areas of parallel discrete event research as well as cross-cutting directions in computer science research that positively impact scientific advancements across several important application areas. A selection of priority research opportunities in advanced computing for PDES is identified to serve as reference for key research topics and their order of importance for scientific advancements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Stardust Network Measurement Components (esnet-stardust-components) v1

This software contains a set of components that allow us to measure and analyze the use and performance of computer networks using existing off the shelf Open Source components organized into a scalable and extensible system. The components were developed for and used within ESnet's Stardust Measurement Environment. The components are used to collect, process and display network measurement data in an architecture that employs a combination of Elasticsearch, Logstash and Grafana. The advantage of this approach is we can tailor system behavior to our analytical needs and expand its capability with new types of network measurement without waiting for a commercial vendor to support our fairly niche use cases. Additionally we have existing collaborations within our community that came about from prior research efforts that will benefit from the release of this software so that others can also contribute features.

Balas, Edward↗
DOE OSTI2016

An Application Development Platform for Neuromorphic Computing

Dynamic Adaptive Neural Network Arrays (DANNAs) are neuromorphic computing systems developed as a hardware based approach to the implementation of neural networks. They feature highly adaptive and programmable structural elements, which model arti cial neural networks with spiking behavior. We design them to solve problems using evolutionary optimization. In this paper, we highlight the current hardware and software implementations of DANNA, including their features, functionalities and performance. We then describe the development of an Application Development Platform (ADP) to support efficient application implementation and testing of DANNA based solutions. We conclude with future directions.

Dean, Mark↗
DOE OSTI2017

DYNAMIC - The Nonlinear Dynamical Network Simulator v. 0.1

The purpose of the DYNAMIC software system is to perform simulations to validate a new research concept called “Chaotic Logic” which we recently published. The problem to be solved is to simulate certain networks of nonlinearly-interacting dynamical variables, and collect statistics on their behavior, to show that they can reliably perform useful computations. The dynamical trajectories for such networks are computed by time integration of their Hamilton’s equations.

Frank, MichaelP.↗
DOE OSTI2020

Analyzing HM-5 data with NAIGEM v.2015.1.315 and NAIGEM v.2.1.4

Two HM-5 units were used to measure uranium items. For each measurement, the enrichment calibration was determined. The built-in software to determine the enrichment calibration was NAIGEM version 2015.1.315. The data were later analyzed by a standalone NAIGEM, version 2.1.4, in order to study the behavior of the NAIGEM software with the HM-5 data and the relationships between various parameters in the NAIGEM code and the HM-5.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Machine-Learning-aided Approach for Predicting the Thermal Expansion Behaviors in Advanced Test Reactor Capsules (NURETH-20 full paper)

Instrumented experiments at test reactors are essential to deploying new advanced reactor systems. Designing new experiments and generating data on specific conditions require both time and cost investment. A high-fidelity model of the experiment environment can be created using finite element analysis software to support the actual experiments, but computation time is still a concern in applying outcomes to real-time usage (e.g., a digital twin). This research proposes a machine-learning-aided approach to temperature and displacement predictions, based on the thickness of the outer gas gap on the experimental capsule used for the in-pile demonstration of a novel thermal conductivity probe in the Advanced Test Reactor. The capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. There were gas gaps between the fuel and rodlet and between the inner and outer capsule. The learning data consisted of an experimental capsule’s radial distributions of temperature and displacement, as obtained from Abaqus and the physical features. For the first step, temperature was predicted using three positional parameters. Then the displacement was predicted using six different positional parameters. Each physical feature was normalized to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement in all cases involving interpolation and extrapolation. Also, data similarity enhancement increased the similarity between training and target data increasing the predictive accuracy of machine-learning models. In some cases of extrapolation, the accuracy of the machine-learning model showed limited performance, but still data similarity enhancement improved the accuracy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Volumetric analysis and mesh generation of real and artificial microstructural geometries

Producing a viable finite element mesh of realistic microstructural structural geometry is a critical step in analyzing the thermo-mechanical behavior of complex multi-material composites. Advancements in imaging technology such as micro computed tomography have allowed modelers to access high resolution mesoscale geometries for direct numerical simulation. However, converting from voxel based 3D images to usable finite element meshes has been challenging. A robust method including algorithms and software scripts for generating finite element meshes from 3D imaged microstructures is presented in this paper. It includes a routine for inserting cohesive elements around material interfaces to enable modeling of interface properties including delamination and damage. The algorithms and procedures presented in this method leverage currently available software packages for processing surface based geometry into volume based meshes. In addition to converting real geometry from physical imaging systems, algorithms for producing numerically generated and statistically equivalent microstructural geometry are also included. These artificial microstructures can be a valuable resource for modelers when physical specimens do not exist or are limited in quantity: Method establishes a workflow from voxel data to viable finite element mesh including interface information, and Includes a method for synthetic geometry generation based on metrics from real microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2015

On a simple way to calculate electronic resonances for polyatomic molecules

We propose a simple method for calculation of low-lying shape electronic resonances of polyatomic molecules. The method introduces a perturbation potential and requires only routine bound-state type calculations in the real domain of energies. Such a calculation is accessible by most of the free or commercial quantum chemistry software. The presented method is based on the analytical continuation in a coupling constant model, but unlike its previous variants, we experience a very stable and robust behavior for higher-order extrapolation functions. Moreover, the present approach is independent of the correlation treatment used in quantum many-electron computations and therefore we are able to apply Coupled Clusters (CCSD-T) level of the correlation model. We demonstrate these properties on determination of the resonance position and width of the {sup 2}Π{sub u} temporary negative ion state of diacetylene using CCSD-T level of theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Transient Cold Flow Simulation of Fast Fluidized Bed Fuel Reactors for Chemical-Looping Combustion

Circulating fluidized bed in chemical-looping combustion (CLC) is a recent technology that provides great advantage for gas–solid interaction and efficiency. In order to obtain a thorough understanding of this technology and to assess its effectiveness for industrial scale deployment, numerical simulations are conducted. Computational fluid dynamics (CFD) simulations are performed with dense discrete phase model (DDPM) to simulate the gas–solid interactions. CFD commercial software ansysfluent is used for the simulations. Two bed materials of different particle density and diameter, namely the molochite and Fe100, are used in studying the hydrodynamics and particle behavior in a fuel reactor corresponding to the experimental setup of Haider et al. (2016, “A Hydrodynamic Study of a Fast-Bed Dual Circulating Fluidized Bed for Chemical Looping Combustion,” Energy Technol., 4(10), pp. 1254–1262.) at Cranfield University in the UK. Both the simulations show satisfactory agreement with the experimental data for both the static pressure and volume fraction at various heights above the gas inlet in the reactor. It is found that an appropriate drag law should be used in the simulation depending on the particle size and flow conditions in order to obtain accurate results. Furthermore, the simulations demonstrate the ability of CFD/DDPM to accurately capture the physics of circulating fluidized bed-based CLC process at pilot scale which can be extended to industrial scale projects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Progress Summary: Simulating stochastic and additively manufactured foams at large strains and high strain rates

Motivation: Understanding the mechanical response of polymer foams is essential in developing system material models and in designing replacements for legacy pads and cushions. Simulations can help elucidate structure-property relationships and compare local stress distributions to understand aging behavior and improve designs. In many applications,these materials experience significant loadings and computations need the capability to reproduce those conditions. Significance: In designing the compressive and shear response of additively manufactured foams, we expect the curvature of the substrate to influence their mechanical behavior. Progress:We have the capability of mapping planar structures to arbitrary curved surfaces and are currently developing the software and procedures to translate the mapped pattern into a geometrical model that can be imported to either finite element or material point software to evaluate the mechanical response. Figure 1 depicts a four-layer face-centered tetragonal pattern (FCT) mapped to a hemispherical surface. Because the mapping cannot exactly preserve both distances and angles, we expect some local variation in mechanical properties and porosity. These models will help determine the sensitivity of these variations to structure and curvature, optimize the mapping procedure, and inform the design of foams for non-planar surfaces.

36 MATERIALS SCIENCE↗