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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Developing Load Cell Monitoring on the Mock UF 6 Feed and Withdrawal System

A proof-of-concept load cell monitoring system has been developed for the Mock Feed and Withdrawal (F&W) System at Oak Ridge National Laboratory (ORNL). This system leverages industry-standard commercial off-the-shelf (COTS) hardware and software in a modular and scalable design. The Mock F&W System was designed to simulate operations at a uranium hexafluoride (UF 6 ) gas centrifuge enrichment plant F&W area. It provides a test bed for process-monitoring technologies. Water, used as a surrogate for UF 6 , is pumped from three feed tanks, through a process surge tank, and is then cut between two tails tanks and three product tanks. The weight of the each tank is continuously monitored using a load cell monitoring system. In addition to the F&W stations, large and small accountancy stations are monitored. The Mock F&W System allows ORNL to test and evaluate capabilities of industry-standard process monitoring hardware and software. A variety of commonly used weighing indicators are connected to the load cells to monitor the weight at each station. These weighing indicators are representative of the types likely found in operating facilities. A data center modeled after those currently used for international safeguards was installed to collect and manage the weight data. This data center provides a robust and flexible test bed for testing and developing software solutions. COTS software packages that leverage industry-standard communication protocols provide a data flow architecture that is modular, interoperable, and scalable. One software package provides communication drivers to collect the raw weight data from the weighing indicators. A data historian is used to store the raw data. Custom in-house algorithms have been developed using MATLAB to analyze the raw historical data and to automatically identify process information. This information includes the number of cycles completed and the mass of material transferred from stations to the process. The process information identified by the analysis is stored in a Microsoft SQL Server database. Visualization concepts have been developed that display the analyzed process information alongside visually compelling plots and animations of the raw data. The load cell monitoring system developed on the Mock F&W System was designed as a proof of concept that could be scaled to a field trial at an operating facility. This system, built with industry standard hardware and software, has allowed ORNL to develop capabilities that provide confidence in the ability to leverage existing operator load cell monitoring equipment with minimal impact on operations. The international safeguards community is evaluating the use of load cell monitoring as an additional safeguards measure; this system demonstrates a practical solution to collect, store, analyze, and visualize load cell weight data.

commercial off the shelf↗
DOE OSTI2018

Method of Determining the Reliability of Real-Time In-reactor Monitoring of VVER

A methodological approach and the basic tenets for evaluating the reliability of in-reactor monitoring of VVER are presented. The method is based on the statistical analysis of indications in real time and includes methods for evaluating the admissible maximum error of the measurement channels used for monitoring as well as the average value and admissible deviation of indications in groups of parallel monitoring channels, as well as a method of evaluating reliability by comparing the values of a parameter with the regime values. In comparing the indications of a monitoring channel with the indications of other parallel channels, the median value is proposed as the baseline value. The developed method is implemented in the Diagnostika SVRK special software, which has been successfully tested during the commissioning of the No. 1 unit at the Novovoronezh NPP-2.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Development of a Data Acquisition System for Kinetic Inductance Detectors: Wide Dynamic Range and High Sampling Rate for Astronomical Observation

Microwave kinetic inductance detectors have a variety of potential applications in astronomical observations. We built a data acquisition system for kinetic inductance detectors combining a dedicated analog board and a commercially available digital board to meet the requirements of astronomical measurements, such as observation of the cosmic microwave background. This paper reports the status of the development of the data acquisition system. We have already achieved simultaneous readout through 120 channels using a direct down-conversion method to decode the signal. A variety of software has been developed and tested using the functionalities of the system and actual detectors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Scalable branching on dual decomposition of stochastic mixed-integer programming problems

In this work, we present a scalable branching method for the dual decomposition of stochastic mixed-integer programming. Our new branching method is based on the branching method proposed by Caroe and Schultz that creates branching disjunctions on first-stage variables only. We propose improvements to the process for creating branching disjunctions, including (1) branching on the optimal solutions of the Dantzig-Wolfe reformulation of the restricted master problem and (2) using a more comprehensive (yet simple) measure for the dispersions associated with subproblem solution infeasibility. We prove that the proposed branching process leads to an algorithm that terminates finitely, and we provide conditions under which globally optimal solutions can be identified after termination. We have implemented our new branching method, as well as the Caroe-Schultz method and a branch-and-price method, in the open-source software package DSP. Using SIPLIB test instances, we present extensive numerical results to demonstrate that the proposed branching method significantly reduces the number of node subproblems and solution times.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Accuracy of automated measurement and verification (M&V) techniques for energy savings in commercial buildings

Trustworthy savings calculations are critical to convincing investors in energy efficiency projects of the benefit and cost-effectiveness of such investments and their ability to replace or defer supply-side capital investments. However, today’s methods for measurement and verification (M&V) of energy savings constitute a significant portion of the total costs of efficiency projects. They also require time-consuming manual data acquisition and often do not deliver results until years after the program period has ended. The rising availability of “smart” meters, combined with new analytical approaches to quantifying savings, has opened the door to conducting M&V more quickly and at lower cost, with comparable or improved accuracy. These meter- and software-based approaches, increasingly referred to as “M&V 2.0”, are the subject of surging industry interest, particularly in the context of utility energy efficiency programs. Program administrators, evaluators, and regulators are asking how M&V 2.0 compares with more traditional methods, how proprietary software can be transparently performance tested, how these techniques can be integrated into the next generation of whole-building focused efficiency programs. This paper expands recent analyses of public-domain whole-building M&V methods, focusing on more novel M&V2.0 modeling approaches that are used in commercial technologies, as well as approaches that are documented in the literature, and/or developed by the academic building research community. We present a testing procedure and metrics to assess the performance of whole-building M&V methods. We then illustrate the test procedure by evaluating the accuracy of ten baseline energy use models, against measured data from a large dataset of 537 buildings. The results of this study show that the already available advanced interval data baseline models hold great promise for scaling the adoption of building measured savings calculations using Advanced Metering Infrastructure (AMI) data. Median coefficient of variation of the root mean squared error (CV(RMSE)) was less than 25% for every model tested when twelve months of training data were used. With even six months of training data, median CV(RMSE) for daily energy total was under 25% for all models tested. Finally, these findings can be used to build confidence in model robustness, and the readiness of these approaches for industry uptake and adoption

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

An automated QC station for the calibration of the Mu2e calorimeter readout units

The Mu2e calorimeter will employ Readout Units, each made of two Silicon Photomultipliers arrays and two Front End Electronics boards. To calibrate them, we have designed, assembled and put in operation an automated Quality Control (QC) station. Gain, collected charge and photon detection efficiency are evaluated for each unit. Here, in this paper, the QC Station is presented, in its hardware and software aspects, summarizing also the tests performed on the ROUs and the first measurement results.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Three-dimensional flow contrast imaging of deep tissue using noncontact diffuse correlation tomography

This study extended our recently developed noncontact diffuse correlation spectroscopy flowmetry system into noncontact diffuse correlation tomography (ncDCT) for three-dimensional (3-D) flow imaging of deep tissue. A linear array of 15 photodetectors and two laser sources connected to a mobile lens-focusing system enabled automatic and noncontact scanning of flow in a region of interest. These boundary measurements were combined with a finite element framework for DCT image reconstruction implemented into an existing software package. This technique was tested in computer simulations and using a tissue-like phantom with anomaly flow contrast design. The cylindrical tube-shaped anomaly was clearly reconstructed in both simulation and phantom. Recovered and assigned flow contrast changes in anomaly were found to be highly correlated: regression slope = 1.00, R{sup 2} = 1.00, and p < 10{sup −5} in simulation and regression slope ≥ 0.97, R{sup 2} ≥ 0.96, and p < 10{sup −3} in phantom. These results exhibit promise of our ncDCT technique for 3-D imaging of deep tissue blood flow heterogeneities.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Transforming microseismic clouds into near real-time visualization of the growing hydraulic fracture

SUMMARY Microseismic observations during unconventional reservoir stimulation are typically seen as a proxy for clusters of hydraulic fractures and the extent of the stimulated reservoir. Such straightforward interpretation is often misleading and fails to provide a physically reasonable image of the fracturing process. This paper demonstrates the application of a physics-based machine learning algorithm which enables a rapid and accurate fracture mapping from the microseismic data. Our training and validation data set relies on a history-matched geomechanical modelling workflow implemented in GEOS software for the Hydraulic Fracturing Test Site 1 (HFTS-1) project. For this study we augmented the simulated fracture growth through geostatistical modelling of induced seismicity, so that the synthetic microseismic catalogue matches the main statistical properties of the field observations. We formulated the problem of mapping the actual fracture in the clutter of events to parallel common video segmentation workflows: several past video frames (microseismic density snapshots) are passed through a deep convolutional network to classify whether a given voxel is associated with a fracture or intact rock. We found that for accurate fracture mapping, the network’s input and architecture must be augmented to incorporate the fluid injection parameters (pressure, rate, concentration of proppant, and location of the perforation within the cluster). The error rate for the network reached as little as 10 per cent of the fracture area, while a conventional microseismic interpretation approach yielded ∼300 per cent. Our approach also yields must faster predictions than conventional methods (minutes instead of weeks), and could enable engineers to make rapid decisions regarding engineering parameters (pumping rate, viscosity) in real time during stimulation.

58 GEOSCIENCES↗
DOE OSTI2026

Throughput Estimation of Data Transport Networks From Digital Twin Measurements

Digital twins of networked infrastructures, known as Virtual Infrastructure Twins (VITs), are increasingly used for software development, pre-deployment testing, and design space exploration. While VITs avoid the costs and potential disruptions associated with experiments on operational networks, their throughput measurements are typically not sufficiently accurate for performance profiling of wide-area networks that they emulate. Here, machine learning (ML) methods are developed to transform these inaccurate VIT network throughput measurements to closely match in peak and overall profile of those from a physical testbed or production network. First, a micro kernel network reflecting a physical network is utilized to collect one-time measurements on a host to support this ML transformation. Then, a generic multi-modal ML method is developed to learn a map that transforms measurements from subsequent VITs on the same host to match past, current and follow-on testbed and cloud networks. ML generalization equations are derived to establish its correctness and probabilistically guarantee its generalization accuracy. Experimental results are presented for a variety of VIT hosts with target testbed and cloud networks; they include a case study of a four-site science ecosystem wherein inaccurate convex VIT measurement profiles are transformed into accurate concave profiles of target networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Software-Defined Data Center Network Architecture using VXLAN-based BGP EVPN for Dynamic Workflows in a Supercomputing Environment (VXLAN-based BGP EVPN Fabric for HPC) v1

This software repository automates the deployment of a multi-vendor VXLAN-based BGP EVPN architecture, leveraging Containerlab to instantiate a stretched CLOS topology. It integrates Linux, Nokia SR Linux, and Arista cEOS, using BGP for underlay, overlay, and topology extension. The software enables rapid prototyping and testing of advanced network configurations. Its key advantage lies in providing a dynamic, programmable environment for research and development of critical technologies supporting dynamic workflows within supercomputing environments, surpassing the limitations of static, vendor-locked alternatives by fostering interoperability and agility.

Kumar, Ronal [Lawrence Berkeley National Laborator↗
DOE OSTI2018

Advanced TidGen Power System - ProteusDS Version 2.43.5 Files

The TidGen Power System generates emission-free electricity from tidal currents and connects directly into existing grids using smart grid technology. The power system consists of three major subsystems: shore-side power electronics, mooring system, and turbine generator unit (TGU) device. ProteusDS is a full featured dynamic analysis software capable of simulating vessels, structures, lines, and technologies in harsh marine environments. This simulation software that was used to test the Advanced TidGen Power System. This submission includes the supporting Proteus simulation files.

16 TIDAL AND WAVE POWER↗
DOE OSTI2013

Global Field Aligned Mesh and Gyrokinetic Field Solver in a Tokamak Edge Geometry (Final Technical Report)

This project was a collaboration between researchers at the California Institute of Technology and the University of California, Irvine to investigate the utility of a global field-aligned mesh and gyrokinetic field solver for simulations of the tokamak plasma edge region. Mesh generation software from UC Irvine was tested with specific tokamak edge magnetic geometry scenarios and the quality of the meshes and the solutions to the gyrokinetic Poisson equation were evaluated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

An open source platform for multi-scale spatially distributed simulations of microbial ecosystems

The goal of this project was to develop a tool for facilitating simulation, validation and discovery of multiscale dynamical processes in microbial ecosystems. This led to the development of an open-source software platform for Computation Of Microbial Ecosystems in Time and Space (COMETS). COMETS performs spatially distributed time-dependent flux balance based simulations of microbial metabolism. Our plan involved building the software platform itself, calibrating and testing it through comparison with experimental data, and integrating simulations and experiments to address important open questions on the evolution and dynamics of cross-feeding interactions between microbial species.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

TREAT Modeling and Simulation Strategy

This report summarizes a four-phase process used to describe the strategy in developing modeling and simulation software for the Transient Reactor Test Facility. The four phases of this research and development task are identified as (1) full core transient calculations with feedback, (2) experiment modeling, (3) full core plus experiment simulation and (4) quality assurance. The document describes the four phases, the relationship between these research phases, and anticipated needs within each phase.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Extreme Temperature Sample Environment for Materials Research Using Neutron Scattering

Extreme temperatures and non-equilibrium materials are both central to Department of Energy’s (DOE) mission of addressing grand challenges in emerging energy and advanced materials technologies. The goal of this project is to develop and perfect a system for studying materials in extreme and non-equilibrium conditions using neutron scattering. Neutron scattering has many advantages in the study of materials structure including the ability to use isotopic substitution to determine partial structure factors. When used at a pulsed neutron source, the potential for event-based detection can enable a stroboscopic technique to interrogate very short lived metastable states in supercooled liquids. The work performed in this Phase I project included: (i) literature reviews and discussions with beamline scientists to establish key requirements for the instrument, (ii) laboratory-based research at Materials Development, Inc. (MDI) to test design concepts, modify control software and model designs, (iii) feasibility experiments and measurements on a prototype sample at the Spallation Neutron Source, and (iv) analysis of the results and design of the proposed Phase II instrument. The results of the work show that the proposed approach is viable and provides a strong basis for the Phase II instrument design and Phase III commercialization. MDI participated in the DOE Commercialization Assistance Program with the Larta Institute (Larta) to accelerate commercial sales of the instruments. The market research identified a served available market of ~13M including sales to beamlines, university research laboratories, and industrial glass and materials businesses. Over the ca. 10 year product life cycle, MDI expects to sell instruments with a cumulative net present value profit of approximately 5M.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Multirate Time Integration for ODEs and DAEs (Final Report)

This project investigated multirate time integration methods. These methods evolve a differential equation system in time by advancing parts of the problem with fast time scales at small time steps and parts of the problem with slow time scales with larger time steps. The project developed new methods and theory as well as a set of prototype software implementations. Multirate methods were tested on combustion and climate applications. A series of five presentations were given on project results and two papers are planned.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Application of Gaussian Bayes classifier to differentiate chlorine-based chemical agents

The Portable Isotopic Neutron Spectroscopy (PINS) is a commercialized system developed by Idaho National Laboratory (INL) to examine chemical warfare agents (CWA) non-destructively, utilizing Prompt Gamma Neutron Activation Analysis (PGNAA) techniques. The PINS system takes advantage of a high-resolution gamma-ray spectrum from a mechanically-cooled high-purity germanium (HPGe) detector. One of the difficult technical challenges is to discriminate the chlorine-based chemical agents. Especially, CN, CNB, CNS and CG have similar chemical compositions to make it hard to discriminate them with a higher confidence. Current identification algorithms for PINS systems with 252-Cf sources have been improved and updated continuously as more field data became available, and new algorithms was studied to complement the current algorithms by adopting the Gaussian Bayes classifier. These new algorithms were intended to be applied to a subset of chlorine-based chemical agents, and their main goal is discriminate CN, CNB, CNS and CG with their ratios of the chlorine neutron inelastic 1763keV peak to the chlorine thermal neutron capture 1959keV peak, which is referred to as the “Cl i/c” or “clic” ratio in this study. The Cl i/c ratios were assumed to follow Gaussian distributions with the means and the standard deviations unique to their corresponding chemical agents. The prior probabilities of these four chemical agents were optimized with a collection of field data to achieve the best performance in terms of precision or positive predictive value (PPV). Finally, their posterior probabilities as functions of the Cl i/c ratio were implemented in the current version of PINS analysis software in order to be tested with more field data.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Joint Analysis of Program Data Representations using Machine Learning for Improved Software Assurance and Development Capabilities

We explore the use of multiple deep learning models for detecting flaws in software programs. Current, standard approaches for flaw detection rely on a single representation of a software program (e.g., source code or a program binary). We illustrate that, by using techniques from multimodal deep learning, we can simultaneously leverage multiple representations of software programs to improve flaw detection over single representation analyses. Specifically, we adapt three deep learning models from the multimodal learning literature for use in flaw detection and demonstrate how these models outperform traditional deep learning models. We present results on detecting software flaws using the Juliet Test Suite and Linux Kernel.

97 MATHEMATICS AND COMPUTING↗