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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

SGEMP Algorithm Development and Demonstration for ICEPIC

Algorithms designed to facilitate calculation of gas-modified System-Generated Electromagnetic Pulse (SGEMP) phenomena have been developed for use in ICEPIC. Improvements and extensions of these and related models are considered for helping them be more widely applicable for SGEMP modeling. Confluent Sciences has developed a Buneman-Villasenor unit test tool for assessing particle merge algorithms and has applied that to several candidate algorithms. Statistical analysis of Maxwellian and near-Maxwellian velocity distribution functions has been used to understand limits and tradeoffs of particle merging. Collisional phenomena and plasma models that relieve the burden of a fully kinetic approach have also been developed and assessed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

RADAI: A Large-Scale Realistic Dataset for Radiation Detection Algorithm Development

Open, realistic datasets are essential for developing and benchmarking radiation detection algorithms, yet they remain scarce. The Radiological Anomaly Detection and Identification (RADAI) project was develop to create datasets that meet the training and testing needs for sophisticated radiation detection algorithms. The RADAI dataset is a large-scale synthetic resource that integrates high-fidelity Monte Carlo simulations with realistic urban scenarios to capture both background variability and source signatures. RADAI models construction-material NORM, people and vehicles, urban clutter, and dynamic environmental effects such as cosmic-ray and rain-induced transients, and they provide list-mode detector data with motion and response modeling suitable for algorithm training and evaluation. The RADAI project resulted in three publicly-released complementary datasets together with an online scoring portal for standardized performance assessment and an open software toolkit that supports data access, augmentation, model development, and evaluation. These resources enable reproducible comparisons across methods and promote rigorous studies at the scale required by contemporary machine learning. By grounding algorithm development in realistic, well-documented conditions, RADAI supports progress toward more robust detection, identification, and localization in complex urban environments.

Ghawaly, James M. [Division of Computer Science an↗
DOE OSTI2021

A Publicly Available, Annotated Dataset for Naturalistic Driving Study and Computer Vision Algorithm Development

Oak Ridge National Laboratory developed and implemented a data collection effort to create a dataset for use in evaluating and testing algorithms for analyzing driver behavior under controlled settings for support of the Federal Highway Administration’s Exploratory Advanced Research Program. This collection is called the ORNL Naturalistic Driving Study Sample (ONDSS). The dataset is designed to emulate aspects of the Second Strategic Highway Research Project (SHRP2), which contained a massive naturalistic driving study (NDS) with over 3000 drivers between 2010 and 2013 using their personal vehicles, with over 4300 person-years of data collected [HANKEY].

42 ENGINEERING↗
DOE OSTI2026

V1G Frequency Regulation: Algorithm Development, Validation & Analysis at Scale

Researchers at Argonne National Laboratory developed and validated a high-fidelity digital twin of a smart charging (V1G) ecosystem to model the participation of up to 1,000 unique electric vehicles (EVs) in the PJM frequency regulation market. Utilizing a discrete-event framework, the simulation models complex interactions, from dynamic grid signals (updated every 2 seconds) to individual EV charging dynamics. The simulation incorporates multiple EV models created from real-world lab test data. Researchers tested multiple control algorithms to balance the dual objectives of maximizing aggregator’s revenue and driver charging needs. Results demonstrate that aggregated EVs function as a controllable, highly effective grid resource, achieving high PJM Performance Scores (80–90%). Additionally, an optimized, market-aware bidding strategy was identified as key to profitability. The platform was shown to provide drivers with an average charging discount of nearly 50%. The algorithm was further validated in the lab using production EVs and charging stations to compare simulation results with real-world performance.

Manne, Nithin↗
DOE OSTI2022

The Global LAnd Surface Satellite (GLASS) evapotranspiration product Version 5.0: Algorithm development and preliminary validation

An accurate estimation of spatially and temporally continuous global terrestrial evapotranspiration (ET) is essential in the assessment of surface energy, water and carbon cycles. The Global LAnd Surface Satellite (GLASS) ET product Version 4.0 (v4.0) based on the Bayesian model averaging (BMA) method was generated to estimate global terrestrial ET. However, certain uncertainty for the GLASS ET product v4.0 limits its application. In this study, we introduced the deep neural networks (DNN) merging framework to improve terrestrial ET estimation for GLASS ET product Version 5.0 (v5.0) generation by integrating five satellite-derived ET products [Moderate Resolution Imaging Spectroradiometer (MODIS) ET product (MOD16), Shuttleworth–Wallace dual-source ET product (SW), Priestley–Taylor-based ET product (PT-JPL), modified satellite-based Priestley–Taylor ET product (MS-PT) and simple hybrid ET product (SIM)]. We compared the performance of DNN method against other merging methods, including GLASS ET algorithm v4.0 (BMA), the gradient boosting regression tree (GBRT) method and the random forest (RF) method, based on 195 global eddy covariance (EC) flux towers covering observations from 2000 through 2015. Validations indicated that the DNN had the highest accuracy among four merging methods across different land cover types, yielding the highest average determination coefficients (R 2 , 0.62), root-mean-squared-error (RMSE, 24.1 W/m 2 ) and Kling–Gupta efficiency (KGE, 0.77) with a of 99% confidence interval. Compared with GLASS ET algorithm v4.0, the DNN improved on the R 2 by approximately 7% (p < 0.01) and the KGE by 10%. Based on the DNN, we then generated 8-day GLASS ET product v5.0 globally with a 1 km spatial resolution from 2001 to 2015 driven by GLASS vegetation and surface net radiation (R n ) datasets and Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA2) datasets. Finally, this global terrestrial ET product provides a valuable dataset for monitoring regional and global water resources and environmental changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

High-order algorithmic developments and optimizations for large-scale GPU-accelerated simulations (Milestone CEED-MS36)

The goal of this milestone was to improve the high-order software ecosystem for CEED-enabled ECP applications by making progress on efficient matrix-free kernels targeting forthcoming ECP architectures. These kernels included matrix-free preconditioning and the development of new set of CEED solver bake-off problems. As part of this milestone, we also released the next version of the CEED software stack, CEED-4.0, reported on results from several application collaborations, and documented the efforts of porting to AMD GPUs for Frontier and other modern architectures, such as Fugaku. The specific tasks addressed in this milestone were: (1) Port and run CEED benchmarks/miniapps on Frontier EA systems; (2) Demonstrate performant libCEED integration in MFEM, Nek and applications; (3) Matrix-free preconditioning of high-order operators; (4) Benchmark problems for fast high-order solvers on GPU platforms; and (5) Public release of CEED-4.0. The artifacts delivered include the next version of the CEED software stack, CEED-4.0, the next libCEED release, libCEED-0.8, and a number of developments integrated within applications to improve their GPU and CPU performance and capabilities. See the CEED website, https://ceed.exascaleproject.org and the CEED GitHub organization, https://github.com/ceed for more details.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Integration of a DER Management System in Riverside. Final report

The tasks in this project covered various aspects, including algorithm development, algorithm integration into a commercial Active Network Management (ANM) platform, hardware-in-the-loop (HIL) testing in an industry-standard testing platform, pilot demonstration in Riverside, California, and also cost and benefit analysis. The DERMS platform in this project can host different algorithms developed on different platforms (e.g., MATLAB and Python) and it can interact with different hardware devices (e.g., different PV inverters, battery inverters, and different sensors). The DER control solution are based on an advanced model-free, layered, and clustered DER control paradigm. At the core of the DER control algorithms was the concept of Extremum Seeking (ES), which is a model-free probing-based control technique. The ES-based control algorithms were tested on major real-world inverters; both individually and in a cluster. It was shown that even legacy equipment (or when paired with a few additional advanced equipment) can support such advanced control. The monitoring algorithms utilize a heterogeneous set of legacy and advanced sensor measurements, such as behind-the-meter DER sensors, distribution-level Phase Measurement Units, distribution-substation Supervisory Control and Data Acquisition (SCADA), and line current sensors, with their limited availability; in order to infer practical network conditions. Sensor data are utilized to achieve resource forecasting, phase identification, and distribution system state estimation. The technology that was developed and demonstrated in this project could be transformational to utilities, including the smaller municipal utilities such as in Riverside, which may not have the resources to deploy advanced distribution system and DERMS solutions in order to support high penetration of solar power integration. This project created a real-world prototype to provide utilities with an assessment of smart grid monitoring and control technologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Nondestructive Assay Technical Infrastructure Program Mission and Vision

The Nondestructive Assay Program (NDAP) mission and vision is achieved by identifying and accomplishing a set of programmatic goals that correspond with eight broad technical program elements. The NDAP was created as a result of Defense Nuclear Facility Safety Board Recommendation 2007-1, entitled “Safety-Related In Situ Nondestructive Assay of Radioactive Materials,” which emphasized the need to improve in situ measurements of radioactive material at US Department of Energy (DOE) defense nuclear facilities. An NDAP five-year plan will define tasks to accomplish specific goals identified in the NDAP mission and vision. This mission and vision is applicable for eight technical program elements: Hardware/Software Development, Algorithm Development and Nuclear Data, Uncertainty Quantification, Nuclear Materials, Staffing, Personnel, And Training, Data Management, Requirement and Standards, and Information Preservation and Dissemination. An additional program element is technical support, which provides daily execution management support for the NDAP, site scope, and deliverable tracking via site task managers, along with technical advisement and support from the technical support group. The NDAP mission and vision provides attributes with specific goals for each program element, and some goals benefit multiple program elements. The goals and attributes defined herein are implemented via an NDAP five-year execution plan that defines site work scope, budget, and deliverables, all of which are updated annually. The NDAP is designed to benefit nondestructive assay (NDA) needs to support DOE nuclear criticality safety programs, ensuring that NDA technology is sufficiently capable of guaranteeing the safety of those who handle, store, process, or transport fissionable materials in the complex. Especially important for the NDAP is to maximize capabilities to identify, characterize, and manage in situ fissile material deposits in process equipment to ensure nuclear criticality safety at processing facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Addressing qubits with a software-defined radio FPGA (Full Technical Final Report)

Superconducting transmons can be configured as qubits and can also be used for weak signal axion searches. During a prior LDRD (17-ERD-006), a robust capability for system simulation and analysis, algorithm development, algorithm to FPGA workflow and experimental measurements was developed. During that project it was determined that a new software-defined radio FPGA would be a significant improvement in cost, simplicity, and software maintainability over the X6-1000M FPGA plus RF front-end system that had been used. In this Feasibility Study, we successfully developed the interface to the new NI USRP-2954R SDR platform, generated and optimized the VHDL of the existing algorithms, and experimentally tested the new FPGA system on real qubit in the laboratory. These tests showed it had the same SNR on weak measurements as the prior FPGA.

42 ENGINEERING↗
DOE OSTI2023

Correction of the baseline fluctuations in the GEM-based ALICE TPC

To operate the ALICE Time Projection Chamber in continuous mode during the Run 3 and Run 4 data-taking periods of the Large Hadron Collider, the multi-wire proportional chamber-based readout was replaced with gas-electron multipliers. As expected, the detector performance is affected by the so-called common-mode effect, which leads to significant baseline fluctuations. A detailed study of the pulse shape with the new readout has revealed that it is also affected by ion tails. Since reconstruction and data compression are performed fully online, these effects must be corrected at the hardware level in the FPGA-based common readout units. The characteristics of the common-mode effect and of the ion tail, as well as the algorithms developed for their online correction, are described in this paper. The common-mode dependencies are studied using machine-learning techniques. Toy Monte Carlo simulations are performed to illustrate the importance of online corrections and to investigate the performance of the developed algorithms.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

TEQUILA: a platform for rapid development of quantum algorithms

Variational quantum algorithms are currently the most promising class of algorithms for deployment on near-term quantum computers. In contrast to classical algorithms, there are almost no standardized methods in quantum algorithmic development yet, and the field continues to evolve rapidly. As in classical computing, heuristics play a crucial role in the development of new quantum algorithms, resulting in a high demand for flexible and reliable ways to implement, test, and share new ideas. In this paper, inspired by this demand, we introduce TEQUILA, a development package for quantum algorithms in PYTHON, designed for fast and flexible implementation, prototyping and deployment of novel quantum algorithms in electronic structure and other fields. TEQUILA operates with abstract expectation values which can be combined, transformed, differentiated, and optimized. On evaluation, the abstract data structures are compiled to run on state of the art quantum simulators or interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗