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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Modeling the Echelle Spectra Continuum with Alpha Shapes and Local Regression Fitting

Continuum normalization of echelle spectra is an important data analysis step that is difficult to automate. Polynomial fitting requires a reasonably high-order model to follow the steep slope of the blaze function. However, in the presence of deep spectral lines, a high-order polynomial fit can result in ripples in the normalized continuum that increase errors in spectral analysis. Here, we present two algorithms for flattening the spectrum continuum. The Alpha-shape Fitting to Spectrum algorithm is completely data driven, using an alpha shape to obtain an initial estimate of the blaze function. The Alpha-shape and Lab Source Fitting to Spectrum algorithm incorporates a continuum constraint from a laboratory source reference spectrum for the blaze function estimation. These algorithms are tested on a simulated spectrum, where we demonstrate improved normalization compared to polynomial regression for continuum fitting. We show an additional application, using the algorithms for mitigation of spatially correlated quantum efficiency variations and fringing in the charge-coupled device detector of the EXtreme PREcision Spectrometer.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Collaborative Computing Center (C3)

High Performance Computing (HPC) compliments theory and informs experimental processes in support of NE programs Big Data Analytics capabilities aid in the discovery of science-relevant relationships in data The C520 staff is made up of 19 Staff – Engineering, Basic Sciences, Computer Science, Statistics, Data Science, Technology Computer Computing Hardware Operations include: Falcon is fully utilized with an average 3X over subscription of waiting jobs over the past year Lemhi recently deployed in Feb 2019 Sawtooth procurement underway with Fall 2019 delivery 509 HPC accounts, lab, industry, university users 1.2 PB disk storage, 1.0 PB scratch storage, 0.8 PB disaster recovery storage Collaborative Computing Center nearing completion with approximately 200 staff -- Office space and 200 racks -- Data Center space for computer equipment

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

A hybrid computational strategy to address WGS variant analysis in >5000 samples

Abstract Background The decreasing costs of sequencing are driving the need for cost effective and real time variant calling of whole genome sequencing data. The scale of these projects are far beyond the capacity of typical computing resources available with most research labs. Other infrastructures like the cloud AWS environment and supercomputers also have limitations due to which large scale joint variant calling becomes infeasible, and infrastructure specific variant calling strategies either fail to scale up to large datasets or abandon joint calling strategies. Results We present a high throughput framework including multiple variant callers for single nucleotide variant (SNV) calling, which leverages hybrid computing infrastructure consisting of cloud AWS, supercomputers and local high performance computing infrastructures. We present a novel binning approach for large scale joint variant calling and imputation which can scale up to over 10,000 samples while producing SNV callsets with high sensitivity and specificity. As a proof of principle, we present results of analysis on Cohorts for Heart And Aging Research in Genomic Epidemiology (CHARGE) WGS freeze 3 dataset in which joint calling, imputation and phasing of over 5300 whole genome samples was produced in under 6 weeks using four state-of-the-art callers. The callers used were SNPTools, GATK-HaplotypeCaller, GATK-UnifiedGenotyper and GotCloud. We used Amazon AWS, a 4000-core in-house cluster at Baylor College of Medicine, IBM power PC Blue BioU at Rice and Rhea at Oak Ridge National Laboratory (ORNL) for the computation. AWS was used for joint calling of 180 TB of BAM files, and ORNL and Rice supercomputers were used for the imputation and phasing step. All other steps were carried out on the local compute cluster. The entire operation used 5.2 million core hours and only transferred a total of 6 TB of data across the platforms. Conclusions Even with increasing sizes of whole genome datasets, ensemble joint calling of SNVs for low coverage data can be accomplished in a scalable, cost effective and fast manner by using heterogeneous computing platforms without compromising on the quality of variants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Modular Subsurface Sensors and Integrated Software for Advanced Subsurface Characterization and Monitoring using Unoccupied Vehicles

The advent and subsequent proliferation of autonomous airborne, waterborne, and groundbased vehicles (i.e., “drones”) promises to broadly transform the geosciences and associated industries, including fossil energy exploration and development, mineral resource exploration and development, water-resource management, and environmental remediation. For geophysical characterization and monitoring, the prospect of programming highly repeatable and low-cost drone missions for subsurface imaging will allow for deployments in hazardous and previously inaccessible areas. Coupled with autonomous workflows for data processing, management, and visualization, drone-based geophysical characterization and monitoring will enable unprecedented, real-time insight into diverse subsurface properties and processes of scientific and engineering importance. Toward this end, the objectives of this Lab Directed Research and Development (LDRD) project were to develop new (1) instrumentation for dronebased electromagnetic induction (EMI) geophysical imaging, including separated transmitter and receivers and associated electronics, (2) software for real-time data telemetry, processing, management, and visualization. Although EMI has been previously deployed using unoccupied aerial systems (UASs), these applications failed to capitalize on the game-changing capabilities of drone platforms. Whereas drone-based data acquisition allows for collection of rich, three-dimensional (3D) multi-offset/multi-angle configurations between transmitters and receivers, past efforts have relied on conventional instrumentation that was designed for ground-based data collection with the transmitter and a single receiver housed in the same unit; nor did these previous applications demonstrate real-time delivery of results to support rapid management decisions in the field. In this 1-year project, we (1) designed and constructed new lightweight independent transmitter and receiver antenna platforms that communicate with a laptop computer; (2) developed software to control data acquisition, manage/transfer data, and visualize data as its collected; and (3) demonstrated the operation of the new hardware and software systems in a ground-based field test. Our work entails major technological advances for EMI and established a foundation on which to build a new drone-based, real-time geophysical EMI imaging capability to support diverse challenges facing the nation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2013

Demand Response Performance of GE Hybrid Heat Pump Water Heater

This report describes a project to evaluate and document the DR performance of HPWH as compared to ERWH for two primary types of DR events: peak curtailments and balancing reserves. The experiments were conducted with GE second-generation “Brillion”-enabled GeoSpring hybrid water heaters in the PNNL Lab Homes, with one GE GeoSpring water heater operating in “Standard” electric resistance mode to represent the baseline and one GE GeoSpring water heater operating in “Heat Pump” mode to provide the comparison to heat pump-only demand response. It is expected that “Hybrid” DR performance, which would engage both the heat pump and electric elements, could be interpolated from these two experimental extremes. Signals were sent simultaneously to the two water heaters in the side-by-side PNNL Lab Homes under highly controlled, simulated occupancy conditions. This report presents the results of the evaluation, which documents the demand-response capability of the GE GeoSpring HPWH for peak load reduction and regulation services. The sections describe the experimental protocol and test apparatus used to collect data, present the baselining procedure, discuss the results of the simulated DR events for the HPWH and ERWH, and synthesize key conclusions based on the collected data.

Demand Response↗
DOE OSTI2021

bnl/pub-Maffettone_2020_08

The application of the XCA package as first demonstrated in aXiv:2008.00283. ABSTRACT: The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time consuming, error prone, and impossible to scale. With the advent of autonomous robotic scientists or self-driving labs, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which output probabilistic classifications --- rather than absolutes --- to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering unprecedented time savings, and is demonstrated on a diverse set of organic and inorganic materials challenges. This innovation is directly applicable to inverse design approaches, robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

Maffettone, PhillipM [Brookhaven National Lab. (BN↗
DOE OSTI2011

Transition nucleon resonance electrocouplings from CLAS data on {pi}{sup +}{pi}{sup -}p electroproduction off protons

Electrocouplings of excited proton states with masses less than 1.8 GeV were determined for the first time from the CLAS data on {pi}{sup +}{pi}{sup -}p electroproduction at photon virtualities Q{sup 2}<1.5 GeV{sup 2}. Electrocouplings were obtained from a combined fit of all available observables within the framework of a phenomenological reaction model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Shallow EGS Regional Resource Potential and Map - Snake River Plain

SMU Geothermal Lab developed a methodology to estimate shallow (1 km to 4 km) Enhanced Geothermal Systems (EGS) resource potential using an approach that utilizes recent geology and geophysical research along with new well data to improve the thermal conductivity model, mitigate impacts from groundwater flow in the thermal model, and examine radioactivity data variations. By incorporating the results of the most recent projects with the SMU shallow methodology, we developed a more accurate, updated resource estimate for the Snake River Plain (SRP). The resulting maps and resource estimates can be used by the National Renewable Energy Lab (NREL), Bureau of Land Management (BLM), and the public to determine how best to move forward with future project development.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers

Liquid cooling is critical for thermal management in high-density data centers with the rising AI workloads. However, machine learning-based controllers are essential to unlock greater energy efficiency and reliability, promoting sustainability. We present LC-Opt, a Sustainable Liquid Cooling (LC) benchmark environment, for reinforcement learning (RL) control strategies in energy-efficient liquid cooling of high-performance computing (HPC) systems. Built on the baseline of a high-fidelity digital twin of Oak Ridge National Lab's Frontier Supercomputer cooling system, LC-Opt provides detailed Modelica-based end-to-end models spanning site-level cooling towers to data center cabinets and server blade groups. RL agents optimize critical thermal controls like liquid supply temperature, flow rate, and granular valve actuation at the IT cabinet level, as well as cooling tower (CT) setpoints through a Gymnasium interface, with dynamic changes in workloads. This environment creates a multi-objective real-time optimization challenge balancing local thermal regulation and global energy efficiency, and also supports additional components like a heat recovery unit (HRU). We benchmark centralized and decentralized multi-agent RL approaches, demonstrate policy distillation into decision and regression trees for interpretable control, and explore LLM-based methods that explain control actions in natural language through an agentic mesh architecture designed to foster user trust and simplify system management. LC-Opt democratizes access to detailed, customizable liquid cooling models, enabling the ML community, operators, and vendors to develop sustainable data center liquid cooling control solutions.

Naug, Avisek [Hewlett Packard Enterprise]↗
DOE OSTI2025

Efficient proximal subproblem solvers for a nonsmooth trust-region method

In [R. J. Baraldi and D. P. Kouri, Mathematical Programming, (2022), pp. 1-40], we introduced an inexact trust-region algorithm for minimizing the sum of a smooth nonconvex and nonsmooth convex function. The principle expense of this method is in computing a trial iterate that satisfies the so-called fraction of Cauchy decrease condition—a bound that ensures the trial iterate produces sufficient decrease of the subproblem model. In this paper, we expound on various proximal trust-region subproblem solvers that generalize traditional trust-region methods for smooth unconstrained and convex-constrained problems. We introduce a simplified spectral proximal gradient solver, a truncated nonlinear conjugate gradient solver, and a dogleg method. Finally, we compare algorithm performance on examples from data science and PDE-constrained optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Integral LOCA fragmentation test on high-burnup fuel

Increasing the fuel burnup limit in light water reactors is sought to enhance fuel cycle economics and requires establishing a technical basis. Experimental observations of severe fuel fragmentation under loss-of-coolant-accident conditions at Halden and Studsvik had raised the need for additional considerations during the development of this technical basis. These test data suggested that the burnup threshold for high burnup fuel fragmentation may be influenced by pre-transient power. Additional loss-of-coolant-accident test data is therefore valuable to complement these tests and enhance the current state of understating. Oak Ridge National Lab has developed the Severe Accident Test Station capable of examining the oxidation kinetics and accident response of irradiated fuel and cladding materials for design basis accident and beyond design basis accident scenarios. Severe Accident Test Station provides various temperature profiles, steam, and the thermal shock conditions necessary for integral loss of coolant accident testing, defueled oxidation quench testing, and high-temperature beyond design basis accident testing. Severe Accident Test Station has been successfully installed and demonstrated in the Irradiated Fuels Examination Laboratory at Oak Ridge National Lab. Furthermore, descriptions of the in-cell re-fabrication capabilities and assembly of the loss-of-coolant-accident test train are provided. Installation of the Severe Accident Test Station system and in-cell re-fabrication restores United States capability to examine postulated and extended loss-of-coolant-accident conditions on spent fuel and cladding and provides a platform for evaluating advanced fuel and accident-tolerant fuel cladding concepts. Lastly, three in-cell integral loss-of-coolant-accident test were performed in the Severe Accident Test Station and compared to the Nuclear Regulatory Commission sponsored loss-of-coolant-accident test as well as the original loss-of-coolant-accident test performed at Argonne National Lab. Finally, the results of these tests as well as all publicly available integral loss-of-coolant-accident test were used to validate a threshold for high burnup fuel fragmentation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2013

Overview of Spin Structure Program at Jefferson Lab

An extensive experimental program to study spin physics at low and moderate four - momentum transfer, Q{sup 2}, has been pursued by Jefferson Lab during the last 15 years, with complementary measurements taking place in all 3 experimental halls. Our inclusive data with high statistical precision and extensive kinematic coverage allow us to better constrain the polarized parton distributions, to accurately determine various moments of spin structure functions, to test the spin content of valent quarks, and to investigate the effects of resonance excitations and higher twist, dominant in this kinematic regime. Our recent semi-inclusive and exclusive measurements have given us new avenues of exploration, and inspired a new generation of experiments planned with 12 GeV beam. Highlights from 6 GeV experimental program are discussed, along with the 12 GeV data projections, and expected impact on the NLO global fits and phenomenological models of valent spin structure.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

DEPRECATED Haystack-Ruby [SWR-17-24]

DEPRECATED The open source Project Haystack initiative defines meta data and communication standards related to data from buildings and intelligent devices. The Project Haystack REST API defines standard formats and operations for exchanging Haystack tagged data over HTTP. The HaystackRuby gem wraps calls to this REST API to enable Ruby application to easily integrate data hosted on a Project Haystack compliant server. The HaystackRuby gem was developed at the National Renewable Energy Lab to support applications related to campus energy. We hope that this tool may be useful to others.

Source record↗
DOE OSTI2023

PACE

Performance Analytics for Computational Experiments (PACE) is an end-to-end performance monitoring infrastructure to summarize performance data collected from Department of Energy’s (DOE) climate model experiments to derive insights and present them through a web portal. PACE automatically captures provenance/timing data from simulations run on DOE supercomputers.

Sreepathi, Sarat [Oak Ridge National Lab. (ORNL), ↗
DOE OSTI2016

Tidal Current Cross-flow Turbine Wake ADV and PIV Data

Measurements in the wake of a high-solidity cross-flow turbine in a laboratory flume obtained using Acoustic Doppler Velocimetry and Particle Image Velocimetry for the purposes of characterizing the turbine wake and comparing the methods.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Measuring Q₀ in LCLS-II Cryomodules Using Helium Liquid Level

The nitrogen-doped cavities used in the Linac Coherent Light Source II (LCLS-II) cryomodules have shown an unprecedented high Q₀ in vertical and cryomodule testing compared with cavities prepared with standard methods. While demonstration of high Q₀ in the test stand has been achieved, maintaining that performance in the linac is critical to the success of LCLS-II and future accelerator projects. The LCLS-II cryomodules required a novel method of measuring Q₀, due to hardware incompatibilities with existing procedures. Initially developed at Jefferson Lab during cryomodule acceptance testing before being used in the tunnel at SLAC, we use helium liquid level data to estimate the heat generated by cavities. We first establish the relationship between the rate of helium evaporation from known heat loads using electric heaters, and then use that relationship to determine heat from an RF load. Here we present the full procedure along with the development process, lessons learned, and reproducibility while demonstrating for the first time that world record Q₀ can be maintained within the real accelerator environment.

43 PARTICLE ACCELERATORS↗
DOE OSTI2013

Search for New and Unusual Strangeonia using CLAS

We perform a survey of the proton, K + , K - -3 charged track data, taken by the CLAS detector for the HyCLAS experiment during the g12 run-period at Jefferson Lab. We aim to study the strong decay amplitudes, partial widths and production channels of strangeonia from the CLAS g12 dataset. HyCLAS was motivated by the experimental results for gluonic hybrid meson candidates, theoretical Lattice QCD, and Flux-tube Model calculations and predictions. The experiment was designed and conducted to search and observe new forms of hadronic matter through photoproduction.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2013

Validating the Melusine Gamma Spectroscopy Tool

This technical report describes testing to evaluate the gamma spectroscopy tool, Melusine, under development by Pacific Northwest National Laboratory. The goal was to verify that the software can successfully be used to provide accurate results and statistical uncertainties for the detection of isotopes of interest and their activities. Of special interest were spectra similar to those produced by radionuclide stations that contribute to the Comprehensive Nuclear Test Ban Treaty Organization’s International Monitoring System. Two data sets were used to test Melusine’s capabilities. The first was the result of a multi-lab calibration effort based on neutron activations produced at the University of California at Davis. The second was taken from the Proficiency Test Exercises conducted by the CTBTO directly in 2005. In 37 of 42 cases, Melusine produced results in agreement with the best answer presently available, in most cases with calculated uncertainties comparable to or better than competing analyses. In fact, Melusine technically provided one more result than CTBTO’s PTE analyses that agreed with the “book answer” (Monte Carlo simulation). Despite these promising results, the Melusine software is still under development. Effort is especially needed to simplify its analysis process, improve stability, and provide user documentation. Some significant analysis tasks require further vetting, such as those to address summing effects. However, our test results indicate that Melusine’s calculations as presently implemented are sound and can be used to reliably analyze spectra from the CTBTO’s radionuclide stations.

CTBTO↗