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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Performance of Julia for High Energy Physics Analyses

We argue that the Julia programming language is a compelling alternative to currently more common implementations in Python and C++ for common data analysis workflows in high energy physics. We compare the speed of implementations of different workflows in Julia with those in Python and C++. Furthermore, our studies show that the Julia implementations are competitive for tasks that are dominated by computational load rather than data access. For work that is dominated by data access, we demonstrate an application with concurrent file reading and parallel data processing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Robotics for HVAC applications: A critical review and future perspectives

Recent advances in artificial intelligence (AI), enhanced computational capabilities, and innovations in sensors and hardware have driven the increasing development and application of robots in heating, ventilation, and air conditioning (HVAC) systems. We selected and reviewed 101 studies published between 2005 and 2025, sourced from IEEE Xplore, Scopus, Web of Science, and the ACM Digital Library. To analyze these works, we developed a five-dimensional analytical framework (morphology, sensing, navigation, task execution, and system integration), inspired by the Springer Handbook of Robotics and tailored specifically for robotic applications in HVAC. Based on the reviewed studies, six distinct tasks spanning the entire HVAC lifecycle have been identified. Among the six tasks, inspection and maintenance dominate (59 %), followed by indoor monitoring and auditing (21 %), whereas leakage detection, comfort support, and installation/retrofit remain less explored. To address the identified gaps, this review proposes future research directions including investigating robot-aware HVAC design principles, developing multimodal HVAC sensing and data fusion techniques, enhancing robot training and hardware capabilities, and expanding robotic applications beyond Maintenance and Operations (M&O). The findings from this review inform future robotics research for HVAC applications and ultimately enhance system affordability, energy efficiency, resilience or reliability, and occupant environmental comfort. Moreover, it seeks to inspire researchers to explore the intersections of robotics, computer science, building science, and HVAC engineering fostering advancements in this multidisciplinary field.

AI↗
DOE OSTI2015

Guided basin-hopping search of small boron clusters with density functional theory

The search for the ground state structures of Boron clusters has been a difficult computational task due to the unique metalloid nature of Boron atom. Previous research works had overcome the problem in the search of the Boron ground-state structures by adding symmetry constraints prior to the process of locating the local minima in the potential energy surface (PES) of the Boron clusters. In this work, we shown that, with the deployment of a novel computational approach that incorporates density functional theory (DFT) into a guided global optimization search algorithm based on basin-hopping, it is possible to directly locate the local minima of small Boron clusters in the PES at the DFT level. The ground-state structures search algorithm as proposed in this work is initiated randomly and needs not a priori symmetry constraint artificially imposed throughout the search process. Small sized Boron clusters so obtained compare well to the results obtained by similar calculations in the literature. The electronic properties of each structures obtained are calculated within the DFT framework.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Parameter extraction for a SPICE model of an hTron superconducting thermal switch

Efficiently simulating large circuits is crucial to the development of superconducting nanowire-based electronics. However, current simulation tools for this technology are not adapted to the scaling of circuit size and complexity. We focus on the multilayered heater-nanocryotron (hTron), a promising superconducting nanowire-based switch used in applications such as superconducting nanowire single-photon detector readout. Previously, the hTron was modeled using traditional finite-element methods, which fall short in simulating systems at a larger scale. An empirical-based method would be better adapted to this task, enhancing both simulation speed and agreement with experimental data. In this work, we perform switching current and activation delay measurements on 17 hTron devices. We then develop a method for extracting physical fitting parameters used to characterize the devices. We build a SPICE behavioral model that reproduces the static and transient device behavior using these parameters, and validate it by comparing its performance to a model developed in prior work, showing an improvement in simulation time by several orders of magnitude. Furthermore, our model provides circuit designers with a tool to help understand the hTron’s behavior during all design stages, thus enabling broader use of the hTron across various new areas of application.

Caloritronics↗
DOE OSTI2018

Revealing the role of electron-electron correlations by mapping dissociation of highly excited D 2 + using ultrashort XUV pulses

Understanding electron-electron correlations in matter ranging from atoms to solids represents a grand challenge for both experiment and theory. Here, these correlations occur on attosecond timescales and have only recently become experimentally accessible. In the case of highly excited systems, the task of understanding and probing correlated interactions is even greater. In this work, we combine state-of-the-art light sources and advanced detection techniques with ab initio calculations to unravel the role of electron-electron correlation in D 2 photoionization by mapping the dissociation of a highly excited D 2 + molecule. Correlations between the two electrons dictate the pathways along which the molecule dissociates and lead to a superposition of excited ionic states. Using 3D Coulomb explosion imaging and electron-ion coincidence techniques, we assess the relative contribution of competing parent ion states to the dissociation process for different orientations of the molecule with respect to the laser polarization, which is consistent with a shake-up ionization process. As a step toward observing coherent superposition experimentally, we map the relevant nuclear potentials using Coulomb explosion imaging and show theoretically that such an experiment could confirm this coherence via two-path interference.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Application of machine learning in the determination of impact parameter in the 132 Sn+ 124 Sn system

Here, 132 Sn + 124 Sn collisions at a beam energy of 270 MeV/nucleon were performed at the Radioactive Isotope Beam Factory (RIBF) in RIKEN to investigate the nuclear equation of state. Reconstructing the impact parameter is one of the important tasks in the experiment as it relates to many observable. In this work, we employ three commonly used algorithms in machine learning, the artificial neural network (ANN), the convolutional neural network (CNN), and the light gradient boosting machine (LightGBM), to determine the impact parameter by analyzing either the charged particle spectra or several features simulated with events from the ultrarelativistic quantum molecular dynamics (UrQMD) model. To closely imitate experimental data and investigate the generalizability of the trained machine learning algorithms, incompressibility of nuclear equation of state and the in-medium nucleon-nucleon cross sections are varied in the UrQMD model to generate the training data. The mean absolute error Δb between the true and the predicted impact parameter is smaller than 0.45 fm if training and testing sets are sampled from the UrQMD model with the same parameter set. However, if training and testing sets are sampled with different parameter sets, Δb would increase to 0.8 fm. The generalizability of the trained machine learning algorithms suggests that these machine learning algorithms can be used reliably to reconstruct the impact parameter in experiment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Accelerating Scientific Workflows on HPC Platforms with In Situ Processing

Scientific workflows drive most modern large-scale science breakthroughs by allowing scientists to define their computations as a set of jobs executed in a given order based on their data dependencies. Workflow management systems (WMSs) have become key to automating scientific workflows-executing computational jobs and orchestrating data transfers between those jobs running on complex high-performance computing (HPC) platforms. Traditionally, WMSs use files to communicate between jobs: a job writes out files that are read by other jobs. However, HPC machines face a growing gap between their storage and compute capabilities. To address that concern, the scientific community has adopted a new approach called in situ, which bypasses costly parallel filesystem I/O operations with faster in-memory or in-network communications. When using in situ approaches, communication and computations can be interleaved. In this work, we leverage the Decaf in situ dataflow framework to accelerate task-based scientific workflows managed by the Pegasus WMS, by replacing file communications with faster MPI messaging. We propose a new execution engine that uses Decaf to manage communications within a sub-workflow (i.e., set of jobs) to optimize inter-job communications. We consider two workflows in this study: (i) a synthetic workflow that benchmarks and compares file- and MPI-based communication; and (ii) a realistic bioinformatics workflow that computes mu-tational overlaps in the human genome. Experiments show that in situ communication can improve the bioinformatics workflow execution time by 22% to 30% compared with file communication. Our results motivate further opportunities and challenges for bridging traditional WMSs with in situ frameworks.

Decaf↗
DOE OSTI2022

Exploring Tradeoffs in Federated Learning on Serverless Computing Architectures

Federated learning is driving the development of new techniques to efficiently and securely use data across multiple sites while using diverse resources. One of these techniques is the use of the serverless computing paradigm to abstract away resource specific configurations, allowing federated learning across heterogeneous environments. However, deploying federated learning across edge resources, the cloud, and traditional HPC sites will require specialized approaches in order to best account for the weaknesses and strengths of each resource. In this work, we explore the new tradeoffs presented by managing a federated learning task across heterogeneous resources and demonstrate these tradeoffs with experiments using a serverless federated learning framework.

Baughman, Matt↗
DOE OSTI2022

Sparse Deep Neural Network Inference using different Programming Models

Sparse deep neural networks have gained increasing attention recently in achieving speedups on inference with reduced memory footprints. However, the real-world applications are little shown with specialized optimizations, yet a wide variety of DNN tasks remain dense without exploiting the advantages of sparsity in networks. Recent work presented by MIT/IEEE/Amazon GraphChallenge has demonstrated significant speedups and various techniques. Still, we find that there is limited investigation of the impact of various Python and C\slash C++ based programming models to explore new opportunities in general cases. In this work, we provide performance evaluation through different programming models using CuPy, cuSPARSE, and OpenMP to discuss the advantages and disadvantages of our sparse implementations on single-GPU and multiple GPUs of NVIDIA DGX-A100 40GB/80GB platforms.

machine learning, HPC↗
DOE OSTI2024

Transmission Data-Driven User-Defined Model for Inverter-based and Conventional Power Plants

Recent events in Odessa [1], [2] have shed light on the complexities of integrating large Inverter-Based Resource (IBR) plants with the transmission system, prompting NERC to stress continuous performance monitoring by transmission operators. Challenges such as plant control updates, IBR model revisions, Phase-locked loop loss of synchronism, and protection events have been identified, underscoring the need for enhanced monitoring protocols by regulatory bodies. The recent FERC 901 order underscores the importance of accurate data exchange regarding IBRs for reliability studies. However, limited access to IBR plant-related data hampers effective decision-making for transmission operators (TOP). This paper proposes a method for constructing data-driven User-Defined dynamic Models (UDM) for power plants for validating multiple-event data using field measurements from interconnection bus locations. The problem is formulated as a power plant model identification problem and a multi-task learning approach under partial input observability assumptions is proposed in this work. This approach aims to predict aggregated responses of conventional and IBR power plants during various dynamic physical events which is useful for planning studies under diverse disturbance conditions. Ultimately, this methodology emphasizes the importance of plant visibility to operators in addressing power system challenges, facilitating improved planning and operational studies.

Mahapatra, Kaveri [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2016

Quantifying element incorporation in multispecies biofilms using nanoscale secondary ion mass spectrometry image analysis

Elucidating nutrient exchange in microbial communities is an important step in understanding the relationships between microbial systems and global biogeochemical cycles, but these communities are complex and the interspecies interactions that occur within them are not well understood. Phototrophic consortia are useful and relevant experimental systems to investigate such interactions as they are not only prevalent in the environment, but some are cultivable in vitro and amenable to controlled scientific experimentation. Nanoscale secondary ion mass spectrometry (NanoSIMS) is a powerful, high spatial resolution tool capable of visualizing the metabolic activities of single cells within a biofilm, but quantitative analysis of the resulting data has typically been a manual process, resulting in a task that is both laborious and susceptible to human error. In this work, the authors describe the creation and application of a semiautomated image-processing pipeline that can analyze NanoSIMS-generated data, applied to phototrophic biofilms as an example. The tool employs an image analysis process, which includes both elemental and morphological segmentation, producing a final segmented image that allows for discrimination between autotrophic and heterotrophic biomass, the detection of individual cyanobacterial filaments and heterotrophic cells, the quantification of isotopic incorporation of individual heterotrophic cells, and calculation of relevant population statistics. The authors demonstrate the functionality of the tool by using it to analyze the uptake of 15 N provided as either nitrate or ammonium through the unicyanobacterial consortium UCC-O and imaged via NanoSIMS. The authors found that the degree of 15N incorporation by individual cells was highly variable when labeled with 15 NH 4 + , but much more even when biofilms were labeled with 15 NO 3 – . In the 15 NH 4 + -amended biofilms, the heterotrophic distribution of 15 N incorporation was highly skewed, with a large population showing moderate 15 N incorporation and a small number of organisms displaying very high 15 N uptake. The results showed that analysis of NanoSIMS data can be performed in a way that allows for quantitation of the elemental uptake of individual cells, a technique necessary for advancing research into the metabolic networks that exist within biofilms with statistical analyses that are supported by automated, user-friendly processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

TU-C-BRE-09: Performance Comparisons of Patient Specific IMRT QA Methodologies Using ROC Analysis

Purpose: To evaluate the ability of a selection of patient-specific QA methods to accurately classify IMRT plans as acceptable or unacceptable based on a multiple ion chamber (MIC) phantom. Methods: Twenty-four IMRT plans were selected (20 previously failed the institutional QA), and were measured on a MIC phantom to assess their dosimetric acceptability. These same plans were then measured using film (Kodak EDR2) and ion chamber (Wellhofer cc04), ArcCheck (Sun Nuclear), and MapCheck (Sun Nuclear) (delivered AP field-by-field, AP composite, and with original gantry angles). All gamma analyses were performed at 2%/2mm, 3%/3mm, and 5%/3mm. By using the MIC results as a gold standard, the sensitivity and specificity were calculated across a range of cut-off thresholds (% pixels passing for gamma analysis, and % dose difference for ion chamber), and were used to form ROC curves. Area under the curve (AUC) was used as a metric to quantify the performance of the various QA methods. Results: Grouping device’s AUC’s revealed two statistically significant different groups: ion chamber (AUC of 0.94), AP composite MapCheck (AUC of 0.85), ArcCheck (AUC of 0.84), and film (AUC of 0.82) were in the better performing group, while original gantry angles and AP field-by-field MapCheck (AUC of 0.65 and 0.66, respectively) matched less well with the gold standard results. Optimal cut-offs were also assessed using the ROC curves. We found that while often 90% of pixels passing is used as a criteria, the differing sensitivities of QA methods can lead to device and methodology-based optimal cutoff thresholds. Conclusion: While many methods exist to perform the same task of patient-specific IMRT QA, they utilize different strategies. This work has shown that there are inconsistencies in these methodologies in terms of their sensitivity and specificity to dosimetric acceptability. This work was supported by Public Health Service grants CA010953, CA081647, and CA21661 awarded by the National Cancer Institute, United States Department of Health and Human Services.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Quantum Machine Learning Applications in High-Energy Physics

Some of the most significant achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and operating large-scale experiments like the Large Hadron Collider or the Tevatron. In these facilities, the ultimate theory to describe matter at the most fundamental level is constantly probed and verified. These experiments often produce large amounts of data that require storing, processing, and analysis techniques that continually push the limits of traditional information processing schemes. Thus, the High-Energy Physics (HEP) field has benefited from advancements in information processing and the development of algorithms and tools for large datasets. More recently, quantum computing applications have been investigated to understand how the community can benefit from the advantages of quantum information science. Nonetheless, to unleash the full potential of quantum computing, there is a need to understand the quantum behavior and, thus, scale up current algorithms beyond what can be simulated in classical processors. In this work, we explore potential applications of quantum machine learning to data analysis tasks in HEP and how to overcome the limitations of algorithms targeted for Noisy Intermediate-Scale Quantum (NISQ) devices.

Delgado, Andrea↗
DOE OSTI2023

2023 Data Science Challenge Notebooks

THE DATA SCIENCE INSTITUTE WILL BE INVITING STUDENTS FROM UC MERCED AND UC RIVERSIDE TO PARTICIPATE IN A DATA SCIENCE CHALLENGE AT LLNL. IN THE CHALLENGE, THE STUDENTS WILL EXPLORE A DATA-DRIVEN APPROACH TO RECONSTRUCTING ELECTROANATOMICAL MAPS OF THE HEART AT CLINICALLY RELEVANT RESOLUTIONS, COMBINING INPUT FROM THE STANDARD 12-LEAD ECG WITH ADVANCED MACHINE LEARNING TECHNIQUES. THE DATA THE STUDENTS WILL HAVE ACCESS TO HAS BEEN IM REVIEWED FOR EXTERNAL RELEASE. (LLNL-MI-835833) THESE NOTEBOOKS WILL PROVIDE THE STUDENTS WITH INSTRUCTIONS OF HOW TO LOAD THE DATA AND WORK WITH THE DATA, ALONG WITH TUTORIALS TO DO BASIC MACHINE LEARNING TASKS LIKE LOGISTIC REGRESSION AND IMAGE CLASSIFICATION.

Landajuela Larma, Mikel↗
DOE OSTI2012

Research Toward Zero Energy Homes: Budget Periods One, Two and Three (January 1, 2008 ‐ December 31, 2010)

This final report was compiled from the detailed annual reports that were submitted for efforts in 2008 and 2009, and from individual task reports from 2010. Reports, case studies, and presentations derived from this work are available through the Building America website. The BIRA team is led by ConSol, a leading provider of energy solutions for builders since 1983. In partnership with over fifty builders, developers, architects, manufactures, researchers, utilities, and agencies, research work was performed in California, Colorado, Utah, New Mexico, Washington, Oregon, and Hawaii and five (5) climate regions (Hot-Dry, Marine, Hot-Humid, Cold, and Hot/Mixed Dry). In addition to research work, the team provided technical assistance to our partners whose interests span the entire building process. During the three year budget period, the BIRA team performed analyses of several emerging technologies, prototype homes, and high performance communities through detailed computer simulations and extensive field monitoring to meet the required climate joule milestone targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2012

Long-Term Demonstration of Hydrogen Production from Coal at Elevated Temperatures: Year 6 - Activity 1.12 - Development of a National Center for Hydrogen Technology

The Energy & Environmental Research Center (EERC) has continued the work of the National Center for Hydrogen Technology® (NCHT®) Program Year 6 Task 1.12 project to expose hydrogen separation membranes to coal-derived syngas. In this follow-on project, the EERC has exposed two membranes to coal-derived syngas produced in the pilot-scale transport reactor development unit (TRDU). Western Research Institute (WRI), with funding from the State of Wyoming Clean Coal Technology Program and the North Dakota Industrial Commission, contracted with the EERC to conduct testing of WRI’s coal-upgrading/gasification technology for subbituminous and lignite coals in the EERC’s TRDU. This gasifier fires nominally 200–500 lb/hour of fuel and is the pilot-scale version of the full-scale gasifier currently being constructed in Kemper County, Mississippi. A slipstream of the syngas was used to demonstrate warm-gas cleanup and hydrogen separation using membrane technology. Two membranes were exposed to coal-derived syngas, and the impact of coal-derived impurities was evaluated. This report summarizes the performance of WRI’s patent-pending coal-upgrading/ gasification technology in the EERC’s TRDU and presents the results of the warm-gas cleanup and hydrogen separation tests. Overall, the WRI coal-upgrading/gasification technology was shown to produce a syngas significantly lower in CO 2 content and significantly higher in CO content than syngas produced from the raw fuels. Warm-gas cleanup technologies were shown to be capable of reducing sulfur in the syngas to 1 ppm. Each of the membranes tested was able to produce at least 2 lb/day of hydrogen from coal-derived syngas.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2014

FY13 Annual Progress Report for SECA Core Technology Program

This progress report covers technical work performed during fiscal year 2013 at PNNL under Field Work Proposal (FWP) 40552. The report highlights and documents technical progress in tasks related to advanced cell and stack component materials development and computational design and simulation.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

The CMSN team working on computational-design-Fe-based-superconductors (Final Report)

This CMSN collaboration comprises four nodes. Rutgers (G. Kotliar K. Haule) , U Arizona(M. Van Schilfgaarde), UCDavis (S. Savrasov W. Pickett) and Ames National Labs-Iowa State University (J. Schmalian- K.M. Ho- V. Antropov). The goal of this CMSN was to develop an unified methodology for electronic structure calculations to estimate from first principles the physical properties of iron pnictides which should serve as a test of the methodologies and the codes. This goals requires the integration of methodologies to treat electron electron interactions ( which can be treated by DMFT GW and Gutzwiller methods ) and electron phonon interactions and magnetic interactions (to be treated in linear response) which were developed by the different members of this CMSN team. I summarize below the collaborative work carried out until now below stressing the difficult but all important task of joint code development. In this area significant advances have taken place during the last year. These include a) an integration of the DMFT code of the Rutgers group with the Gutzwiller codes developed in Iowa. The result is the best available code to carry out LDA+G and is having impact on correlated materials with f electrons. and b) A Wannier interface between the QP GW code developed in Arizona and the Rutgers DMFT codes. These developments will be continued and will open new capabilities to treat correlated materials in general, and will be applied to the iron pnictides in the final year of this CMSN.

Kotliar, Gabriel↗