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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Study of Shock Formation Parameters With Drive Conditions in Magnetically Accelerated Plasma Flows

We present experimental data regarding the formation of high-energy-density shocks in magnetically accelerated plasma flows using pulsed power drivers. We quantify the flow velocity and temperature of the ablated plasma using optical Thomson scattering and gated emission imaging across two different generators. We show that, regardless of the drive parameters, the plasma flows show continuous acceleration over centimeter spatial scales, in line with trends in published simulation work. When stationary targets are placed in these supersonic flows, bow-shock formation is observed at all drive parameters in a range of materials. In the higher density flow generated on the 1-MA COBRA generator at Cornell University, heating of the upstream flow ahead of the shock is observed and quantified, which is not observed at the lower density flow on the 0.2-MA Bertha driver at UC San Diego. Here, when combined with previous work on the XP generator at Cornell, we can show that these three experimental setups allow control of the effect of radiation loss and upstream absorption on the formation of the bow shock.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Livewire: A Model Platform for Data Quality Assessment and AI Readiness Across DOE Missions

High-quality, well-governed data is essential for accelerating discovery and achieving operational excellence across DOE and national laboratory missions. The Livewire Data Platform is a DOE-supported platform that offers automated assessments of data quality, standardization, provenance, and Artificial Intelligence (AI) readiness. It allows researchers and data practitioners to systematically and easily evaluate datasets against established governance criteria and prepare them for advanced analytics. Livewire addresses critical challenges in DOE's data ecosystem with integrated capabilities for metadata validation, provenance tracking, and schema alignment. This platform's automated workflows assist users in identifying data quality gaps, enhancing interoperability between datasets collected from various stakeholders, and ensuring compliance with DOE data standards, all while reducing manual curation efforts. Additionally, we will discuss its AI readiness framework, which is being developed to prepare datasets for training models, developing advanced analytic tools, and machine learning applications. Using some of the more than one hundred tabular datasets on Livewire, processed with this open-source methodology, we will demonstrate how Livewire can serve as a model for scalable, standards-driven data management. This approach provides a pathway to leverage existing and future datasets within the DOE, boosting innovation and efficiency across national laboratories.

33 - ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Accelerating Lossy and Lossless Compression on Emerging BlueField DPU Architectures

Data compression has become a crucial technique in addressing performance bottlenecks caused by increasing data volumes in High-Performance Computing (HPC), Big Data, and Deep Learning (DL). Despite its potential to boost system performance, recent studies have identified significant challenges with existing compression methods, mainly due to their high computational demands amidst continuously growing data sizes. Concurrently, the advent of Data Processing Units (DPUs), equipped with programmable System-on-Chip (SoC) and specialized compression accelerators, offers a promising opportunity to alter the landscape of data compression. This paper explores the complexities and potential of leveraging NVIDIA BlueField DPUs to accelerate lossy and lossless compression. Towards this, we introduce PEDAL, an innovative library that leverages the hardware capabilities of DPUs to unify and optimize data compression designs. Moreover, we seamlessly co-design PEDAL with the popular MPICH MPI library, demonstrating up to 101x speedup in compression time and 88x decrease in communication latency. Drawing on these achievements, we share our experience with various research communities about accelerating data compression on DPUs in communication-oriented HPC scenarios.

Li, Yuke↗
DOE OSTI2025

Rad-hard readout system for Timepix3 Hybrid Pixel Detectors

The Beam Gas Ionisation (BGI) profile monitor, located in the Proton Synchrotron (PS) and Super Proton Synchrotron (SPS) at CERN, requires a radiation-tolerant readout system to transfer data from the challenging accelerator surroundings to the back-end for processing. The system needs to control and acquire data from four Timepix3 Hybrid Pixel Detectors (HPDs) located directly inside the beam pipe, a highly radioactive environment. It must ensure reliability given limited hardware access and preserve signal integrity for the high-speed data (32 channels at 320 MHz). However, due to the unavailability of a suitable rad-hard Timepix3 readout, the Beam Instrumentation PiXeL (BIPXL) readout system was designed to meet these requirements. This system employs radiation-hardened components such as the GBTx and the FEASTMP, both developed at CERN. It will be compatible with forthcoming hybrid pixel detector initiatives in similarly harsh radiation conditions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

pixelvar79/ESGAN-Flowering-Detection-paper

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

Varela, Sebastian↗
DOE OSTI2026

Genesis Data Card Schema, Template and Supporting Tools

Genesis Data Cards provide a standardized template and schema for documenting scientific datasets in support of discovery, access, interoperability, reusability, governed use, and AI usability. This release of the Genesis Data Card repository includes a versioned Markdown template, a LinkML schema with generated Pydantic and JSON artifacts, schema documentation, and example completed data cards. Validation tooling is provided to ensure that completed data cards conform to the schema prior to submission. Accompanying documentation for the structured metadata is provided as a Field Reference Guide. The schema and accompanying template provided in this repository address the call for actionable context that enables humans and AI systems to find, access, interpret, cite, and reuse data, and, when appropriate, integrate it into AI and machine learning workflows. The data card is intended to serve as a common metadata artifact intended to support standardized, cross-program dataset documentation across Department of Energy (DOE)-aligned efforts, including but not limited to Genesis Mission-related implementations, the Office of Science, National Nuclear Security Administration (NNSA), and Advanced Simulation and Computing (ASC) data governance and stewardship initiatives.

data card↗
DOE OSTI2025

Data Qualification Report: SRNL Glass Composition-Properties (ComPro) Database

The Savannah River National Laboratory Glass Composition-Properties (ComPro) database is an extensive database containing pertinent composition and durability data to support the accelerated clean-up mission at the Defense Waste Processing Facility. The activities described in this data qualification report were performed to support the information contained in the database. There were two objectives of the original data qualification process. The first objective was to review supporting documentation to determine if DOE/RW-0333P Quality Assurance Requirements and Description had been implemented during the original work. If the DOE/RW-0333P Quality Assurance Requirements and Description had not been directly implemented during the original work, the second objective was to determine if the controls that were used were adequate to meet the intent of the DOE/RW-0333P Quality Assurance Requirements and Description. The results of these two objectives and the activities performed to support these decisions are described in this document. An assessment of each dataset was made to determine if the data were RW-0333P Compliant, RW-0333P Equivalent or Non-RW-0333P Compliant. The original data qualification was performed in accordance with E7, Conduct of Engineering Manual, Procedure 3.70, Revision 4, Qualification of Data. The specific method that was used was Equivalent Controls as described in E7, 3.70. Revision 2 of this document adds supporting information for the RW-0333P Compliant datasets added to Revision 3 of the database.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Design and analysis of CXL performance models for tightly-coupled heterogeneous computing

Truly heterogeneous systems enable partitioned workloads to be mapped to the hardware that nets the best performance. However, current practice requires that inter-device communication between different vendors' hardware use host memory as an intermediary step. To date, there are no widely adopted solutions that allow accelerators to directly transfer data. A new cache-coherent protocol, CXL, aims to facilitate easier, fine-grained sharing between accelerators. In this work we analyze existing methods for designing heterogeneous applications that target GPUs and FPGAs working collaboratively, followed by an exploration to show the benefits of a CXL-enabled system. Specifically, we develop a test application that utilizes both an NVIDIA P100 GPU and a Xilinx U250 FPGA to show current communication limitations. From this application, we capture overall execution time and throughput measurements on the FPGA and GPU. We use these measurements as inputs to novel CXL performance models to show that using CXL caching instead of host memory results in a 1.31X speedup, while a more tightly-coupled pipelined implementation using CXL-enabled hardware would result in a speedup of 1.45X.

Cabrera, Anthony↗
DOE OSTI2023

GUI Control System for the Mu2e Electrostatic Septum High Voltage at Fermilab

The Mu2e Experiment has stringent beam structure requirements; namely, its proton bunches with a time structure of 1.7 $\mu$s in the Fermilab Delivery Ring. This beam structure will be delivered using the Fermilab 8-GeV Booster, the 8-GeV Recycler Ring, and the Delivery Ring. The 1.7-$\mu$s period of the Delivery Ring will generate the required beam structure by means of a third order resonant extraction system operating on a single circulating bunch. The electrostatic septum (ESS) for this system is particularly challenging, requiring mechanical precision in a ultra high vacuum of 1 x 10$^-8$ Torr to generate 100 kV across 15 mm. This paper describes a graphical user interface that has been developed to automate the conditioning and commissioning process for the electrostatic septa. It is based on an interface to the Fermilab ACNET system using the ACSys Python Data Pool Manager (DPM) Client produced and maintained by Fermilab Accelerator Controls. Network interfacing between data pool managers made by the application and ACNET devices introduce an inherent (approximately 1 s) latency in throughput of the readouts. This delay is utilized to process and graph incoming data events of devices crucial to conditioning of a electrostatic septum (ESS). 'Ramping' and 'Monitoring' modes adjust settings of the power supply based on internal logic to efficaciously increase and maintain the high voltage (HV) in the ESS, easing the voltage setting on incidence of sparking or other possibly damaging events. A timestamped log file is produced as the application runs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Developing Multi-Gene CRISPRa/I Programs to Accelerate DBTL Cycles in ABF Hosts Engineered for Chemical Production (CRADA 468)

Bacterial metabolism is comprised of large and complex gene networks that can produce valuable chemical products. Sophisticated organism engineering efforts are required to optimize production of high-value compounds from these networks. In principle, synthetic multi-gene transcriptional programs could be constructed to reengineer these networks for efficient industrial chemical production. In practice, however, our incomplete ability to understand and model the underlying networks, combined with our limited ability to predictably control the expression of multiple genes makes achieving this goal difficult. To overcome these challenges, we will combine new CRISPR-Cas multi-gene expression programs with computational modeling, machine learning, and multi-omics data to enhance the efficacy of design-build-test-learn (DBTL) cycles. For industrially promising microorganisms in early stages of development, creating technologies for rapidly engineering complex multi-gene programs could be transformative for accelerating data- and model-driven strain design. New CRISPR-Cas tools allow programmable gene activation (CRISPRa) or repression (CRISPRi) at multiple genes simultaneously, using the catalytically inactive Cas9 protein (dCas9) with guide RNAs that recognize DNA targets through predictable Watson-Crick base pairing. To enable accelerated DBTL cycles, we will combine these technologies with advanced Agile BioFoundry (ABF) capabilities for multi-omics data collection and machine learning. We will demonstrate the immediate applicability of these tools by rapidly improving the production of an industrial aromatic in multiple ABF organisms. We recently identified and optimized new transcriptional activators that can be linked to programmable CRISPR-Cas DNA binding domains to activate gene expression in E. coli. We can now use these CRISPRa tools as generalizable trans-acting regulators for combinatorial multi-gene expression tuning that can be easily transferred to new pathways and networks without additional genome engineering. We anticipate these tools will also transfer to new hosts. We have recently found that CRISPRa systems developed in E. coli can be readily ported to Pseudomonas putida, suggesting that multi-gene CRISPRa/i programs for diverse ABF organisms may be within reach.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Uncertainty quantification for deep learning in particle accelerator applications

With the advent of increased computational resources and improved algorithms, machine learning-based models are being increasingly applied to complex problems in particle accelerators. However, such data-driven models may provide overly confident predictions with unknown errors and uncertainties. For reliable deployment of machine learning models in high-regret and safety-critical systems such as particle accelerators, estimates of prediction uncertainty are needed along with accurate point predictions. In this investigation, we evaluate Bayesian neural networks (BNN) as an approach that can provide accurate predictions along with reliably quantified uncertainties for particle accelerator problems, and compare their performance with bootstrapped ensembles of neural networks. We select three accelerator setups for this evaluation: a storage ring, a photoinjector, and a linac. The problems span different data volumes and dimensionalities (e.g., scalar predictions as well as image outputs). It is found that BNN provide accurate predictions of the mean along with reliable estimates of predictive uncertainty across the test cases. In this vein, BNN may offer an attractive alternative to deterministic deep learning tools to generate accurate predictions with quantified uncertainties in particle accelerator applications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Involving the new generations in particle physics endeavours

Since 1984 INFN and University of Pisa scientists performing experiments at Fermilab have been running a two-month summer training program for Italian students at the lab. In 1984 the program involved only a few physics students from the University of Pisa, but it was later extended to other INFN groups and to engineering students. Since 2004 the program has been supported in part by the US Department of Energy (DOE) in the frame of an exchange agreement with INFN and has been run by the Cultural Association of Italians at Fermilab (CAIF). In 2007 the Sant’Anna School of Advanced Studies (Pisa) established an agreement with Fermilab to share the cost of four engineering students each year. In the almost 40 years of its history, the program has hosted at Fermilab approximately 550 Italian students from more than 20 Italian universities and from some non-Italian universities. In addition, in the years 2010-2019, with the support of the Italian National Institute of Astrophyics (INAF), the Italian Space Agency (ASI), and CAIF, 30 students were hosted in other US laboratories and universities. The Fermilab training programs spanned from data analysis to design and construction of particle detectors and accelerator components, R/D on superconductive elements, theory of accelerators, and analysis of astrophysical data. At the other US laboratories the offered training was on Space Science. In 2015 the University of Pisa endorsed the program as one of its own Summer Schools. The interns are enrolled as Pisa students for the duration of the internship. They are required to write summary reports published in the Fermilab and University of Pisa web pages. Upon positive evaluation by a University board, students are acknowledged 6 ECTS credits. The entire program is expected to expand further under CAIF management. An agreement has been signed between ASI and CAIF, for ASI to support yearly three two-months fellowships in US space science. In the following we inform on student recruiting, training programs, and final evaluation

Barzi, E.↗
DOE OSTI2020

Toward fusion plasma scenario planning for NSTX-U using machine-learning-accelerated models

One of the most promising devices for realizing power production through nuclear fusion is the tokamak. To maximize performance, it is preferable that tokamak reactors achieve advanced operating scenarios characterized by good plasma confinement, improved magnetohydrodynamic (MHD) stability, and a largely non-inductively driven plasma current. Such scenarios could enable steady-state reactor operation with high \emph{fusion gain} --- the ratio of produced fusion power to the external power provided through the plasma boundary. Precise and robust control of the evolution of the plasma boundary shape as well as the spatial distribution of the plasma current, density, temperature, and rotation will be essential to achieving and maintaining such scenarios. The complexity of the evolution of tokamak plasmas, arising due to nonlinearities and coupling between various parameters, motivates the use of model-based control algorithms that can account for the system dynamics. In this work, a learning-based accelerated model trained on data from the National Spherical Torus Experiment Upgrade (NSTX-U) is employed to develop planning and control strategies for regulating the density and temperature profile evolution around desired trajectories. The proposed model combines empirical scaling laws developed across multiple devices with neural networks trained on empirical data from NSTX-U and a database of first-principles-based computationally intensive simulations. The reduced execution time of the accelerated model will enable practical application of optimization algorithms and reinforcement learning approaches for scenario planning and control development. An initial demonstration of applying optimization approaches to the learning-based model is presented, including a strategy for mitigating the effect of leaving the finite validity range of the accelerated model. The approach shows promise for actuator planning between experiments and in real-time.

machine learning↗
DOE OSTI2025

MRCI Subtask 2.3: Developing Industrial Partnerships and Regional Technical Collaboration Final Technical Summary Report

Under the objective of regional data collection and helping accelerate deployment, MRCI collaborated with industrial stakeholders in their project planning, characterization, and analysis. Some examples of these collaborations are given below. The data and information shared by the industrial collaborations added to the regional CCS framework development and were incorporated into the overall datasets, while addressing any proprietary data requirements. Three examples of collaborative partnerships with industry that have provided geologic characterization data relevant and beneficial to the MRCI program are discussed below, including: the UIC Class II Injection Facility in Eastern Ohio, the Core Energy CO2-EOR (enhanced oil recovery) operation in Otsego County Michigan, and the Marquis ethanol plant in Hennepin Illinois.

CCS,CCUS,MRCI,Midwest USA,Technical Challenges,inj↗
DOE OSTI2024

A Co-Registered In-Situ and Ex-Situ Dataset of Electrical, Acoustic, and CT Characteristics from Wire Arc Additive Manufacturing Process

Recent progress in sensing techniques and data analytics tools have significantly accelerated the development of Wire Arc Additive Manufacturing (WAAM) systems. This data centric approach emphasizes leveraging available data throughout the production process to optimize performance. Integration of extensive data analysis provides the opportunity to improve precision, reduce waste, and enhance the quality of produced parts. This method relies on AI/ML models and optimization techniques, which are developed using the data collected from various sources, including in-situ sensors, ex-situ imaging, and manufacturing process parameters. The quality and diversity of this data, along with the alignment between different data streams (achieved through spatiotemporal registration) are critical for the successful development of AI/ML and optimization models. In this work, we present a spatiotemporally registered dataset generated during the WAAM process of deposition of a rectangular block. The dataset includes the comprehensive description of deposition process, process parameters, in-situ collected welding characteristics, acoustic data, and X-Ray Computed Tomography analysis data for the build. Dataset A Co-Registered In-Situ and Ex-Situ Dataset of Electrical, Acoustic, and CT Characteristics from Wire Arc Additive Manufacturing Process has arisen under UT-Battelle, LLC’s Prime Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy (DOE) to manage and operate the Oak Ridge National Laboratory. UT-Battelle, LLC will not assert any rights under United States law or under the Prime Contract it has in the dataset against any user of the dataset, including any copyrights or patent rights. UT-Battelle, LLC requests that attribution to the dataset is provided as academically appropriate.

42 ENGINEERING↗
DOE OSTI2023

A Data-driven, Physics-based Transport Model of Solar Energetic Particles Accelerated by Coronal Mass Ejection Shocks Propagating through the Solar Coronal and Heliospheric Magnetic Fields

Abstract In an effort to develop computational tools for predicting radiation hazards from solar energetic particles (SEPs), we have created a data-driven physics-based particle transport model to calculate the injection, acceleration, and propagation of SEPs from coronal mass ejection (CME) shocks traversing through the solar corona and interplanetary magnetic fields. The model runs on an input of corona and heliospheric plasma and magnetic field configuration from a magnetohydrodynamic model driven by solar photospheric magnetic field measurements superposed with observed CME shocks determined from coronagraph images. SEP source particles are injected at the shock using the result of diffusive shock acceleration formulation from a characteristic obliquity-dependent injection from a heated solar wind thermal tail population. With several advanced computation techniques involving stochastic simulation and integration, the model obtains the particle intensity at any location in interplanetary space through the rigorous solution to the time-dependent 5D focus transport equation in the phase space that includes perpendicular diffusion. We apply the model to the 2011 November 3 CME event. The calculation results reproduce multispacecraft SEP observations at Earth and STEREO-B reasonably well without normalization of particle flux. The observations at STEREO-A can be reproduced by rescaling particle energy or modified energy dependence of particle diffusion coefficients. This circumsolar SEP event seen by spacecraft at Earth, STEREO-A, and STEREO-B at widely separated longitudes can be explained by diffusive shock acceleration by a single CME shock with a moderate speed.

79 ASTRONOMY AND ASTROPHYSICS↗