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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

SCILLA Scanning Electrical Mobility Spectrometer (SEMS) airborne data

The Scanning Electrical Mobility Spectrometer (SEMS) provides measurements of bulk aerosol size distributions, which are crucial for studying aerosol-cloud interactions. The instrument sampled through two different inlets: an isokinetic inlet and a counter-flow virtual impactor (CVI) inlet. Data were collected to help improve climate and earth systems models, characterize atmospheric conditions, and validate satellite observations and climate simulations.

Scanning Electrical Mobility Spectrometer↗
DOE OSTI2025

Advanced Sensors & Instrumentation FY24 Summary of Accomplishments

The Advanced Sensors and Instrumentation (ASI) program is the element of the Nuclear Energy Enabling Technologies (NEET) initiative dedicated to Instrumentation and Control (I&C) technology. This document collects in the form of summaries the research accomplishments presented at the program 2024 Annual Review meeting, which was held virtually between November 4 & 6-7, 2024.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2025

SCILLA mini-Aerosol Mass Spectrometer (mAMS) airborne data

Measurements of non-refractory, sub-micron aerosol composition, namely organic, sulfate, nitrate, chloride, and ammonium aerosols, by a mini- aerosol mass spectrometer (mAMS, Aerodyne Research, Inc.) are reported. The instrument sampled ambient air through 3 different inlets: unperturbed ambient air, a Counter-flow Virtual Impactor (CVI), and an oxidation flow reactor (OFR). Users should consult the Sampling Key for proper interpretation of the data.

and secondary aerosol in air",total_organics,nitra↗
DOE OSTI2020

Characterization of AGFA D4 and D3sc X-ray films in the 0.7 to 4.6 keV energy range

2020 High Temperature Plasma Diagnostic conference in Los Alamos, NM at LANL (Virtual conference) 12/14/2020-12/17/2020 web link: http://www.cvent.com/events/2020-high-temperatures-conference/event-summary-316fe078c3894ef5ab725d6bbdf69334.aspx This manuscript will appear in Review of Scientific Instruments part of the conference proceeding to the above conference

X-ray, Film, Characterization↗
DOE OSTI2025

Calculation of rf-induced temporal jitter in ultrafast electron diffraction

A significant contribution to the temporal resolution of an ultrafast electron diffraction (UED) instrument is arrival time jitter caused by amplitude and phase variation of radio-frequency (rf) cavities. In this paper, we present a semianalytical approach for calculating rf-induced temporal jitter from klystron and rf cavity parameters. Our approach allows fast estimation of temporal jitter for MeV-UED beamlines and can serve as a virtual timing tool when shot-to-shot measurements of rf amplitude and phase jitters are available. A simulation study for the SLAC MeV-UED instrument is presented, and the temporal resolution of several beamline configurations is compared.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

International Radiological/Nuclear Training for Emergency Response - Major Public Events Virtual Workshop: Nuclear Security Planning and Operations (Day 3) [Slides]

This module will provide a basic overview of radiological/nuclear search operations, including the application of radiological detection instrumentation and best practices for operational tactics, techniques and procedures. This module will introduce basic radiological/nuclear search operations and include the following: (1) A review of the principles of optimizing detection, (2) An understanding of radiation hazards and alarms, and (3) An examination of nuclear security measures best practices for buildings and stadium complexes, large areas, roadways and parking areas, and pedestrian and vehicle entrance portals.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

AGC-4 Experiment Irradiation Monitoring Data Qualification Final Report

The Graphite Technology Development Program ran a series of six experiments to quantify the effects of irradiation on nuclear grade graphite. This report focuses on the fourth experiment, Advanced Graphite Creep 4 (AGC 4). The Advanced Reactor Development (ARD) Technology Development Office (TDO) Program for research and development activities require documentation of qualified monitoring data to design and license the first high-temperature reactor nuclear plant. Qualified data meets the requirements for use as described in the experiment planning and quality assurance documents. Failed data do not meet the requirements and provide no useable information. Trend data may not meet all requirements but still provide some useable information. Use of Trend data requires assessment of how any deficiencies affect a particular use of the data. AGC-4 began with Advanced Test Reactor (ATR) Cycle 157D on May 30, 2015. After irradiating the graphite for two cycles, the capsule was removed from the reactor after ATR Cycle 158A, which ended on January 2, 2016, due to interference with another experiment. Irradiation was resumed with Cycle 162A on October 7, 2017 after the interfering experiment was removed from the reactor. AGC-4 irradiation completed by the end of Cycle 166B on January 10, 2020. Between Cycles 162A and 166B, AGC-4 capsule was removed from the reactor core during two Powered Axial Locator Mechanism (PALM) Cycles 163A and 165A to avoid overheating. All thermocouples (TCs) have functioned throughout the AGC-4 experiment. A total of 9,256,184 out of all 11,167,213 temperature records (or 82.9%) are Qualified for use by the ARD Program and 1,911,029 missing values are Failed. Argon, helium, and total gas flow data were within expected ranges, except only eight out-of-range values occurred during outages. A total of 21,240,627 out of all 22,288,5103 gas flow rates (or 92.8%) are Qualified for use by the ARD Program and 1,644,476 are Failed records mostly due to missing values. Discharge gas line moisture values were consistently low during full ATR power, except for 162B, when moisture content was increasing to more than 200 ppmv by the end of this cycle. During outages, moisture content reached as high as 700 ppmv. 1,089,941 out of a total of 1,107,331 moisture values (or 98.4%) are within the measurement range of the instrument and are Qualified for use by the ARD Program and 17,390 missing moisture values are Failed. Graphite creep specimens were subjected to one of three loads: 393, 491, or 589 lbf. For a brief period during Cycle 157D between 12:19 on June 2, 2015, and 08:23 on June 11, 2015, the load cells were wired incorrectly, resulting in missing stack load data. Missing stack loads were estimated from measured ram pressures using regression equations developed from the existing data from Cycle 157D. Estimated stack loads during this period are considered to be an accurate representation of actual load applied to the stacks. These loads deviate slightly from the planned loads. This deviation does not prevent the data from being Qualified for use but must be taken into account when analyzing the effect of load on creep. 6,095,728 out of a total of 6,403,985 moisture values (or 95.2%) are within the measurement range of the instrument and are Qualified for use by the ARD Program and 308,257 missing load values are Failed. Stack displacement increased consistently throughout the eight cycles, with total displacement reached highest value of 2.4 in by the end of irradiation. During ATR outages, a set of pneumatic rams raised the stacks of graphite creep specimens to ensure the specimens were not stuck within the test train. This stack raising was performed seven times throughout irradiation. All stacks were raised successfully each time. 4,744,974 out of a total of 6,094,513 displacement values (or 77.9%) are within the measurement range of the instrument and are Qualified for use by the ARD Program and 1,349,539 displacement values are Failed mostly due to missing values. Analyses were conducted on correlations between TCs to look for trends and step changes that might indicate instrument degradation or failure. Correlation analysis was used to identify instances when TCs form short circuits, referred to as virtual junctions, which result in TCs reporting temperatures from some location in the capsule other than the location where they were intended to read. No evidence of virtual junctions was found. Analyses were also conducted on control charts of temperature differences of between two TCs, which are expected to behave consistently throughout the entire irradiation period. Upward or downward trend over time indicates at least one TC in the pair was drifted. Examining control charts for all 66 possible pairs out of twelve TCs installed in the AGC-4 capsules reveal no clear drift failures occurred, except unstable behavior of two TCs in Zone 3, TC-7 and TC-8, over irradiation time. In conclus

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

VISION: a modular AI assistant for natural human-instrument interaction at scientific user facilities

Scientific user facilities, such as synchrotron beamlines, are equipped with a wide array of hardware and software tools that require a codebase for human-computer-interaction. This often necessitates developers to be involved to establish connection between users/researchers and the complex instrumentation. The advent of generative AI presents an opportunity to bridge this knowledge gap, enabling seamless communication and efficient experimental workflows. Here we present a modular architecture for the Virtual Scientific Companion by assembling multiple AI-enabled cognitive blocks that each scaffolds large language models (LLMs) for a specialized task. With VISION, we performed LLM-based operation on the beamline workstation with low latency and demonstrated the first voice-controlled experiment at an x-ray scattering beamline. The modular and scalable architecture allows for easy adaptation to new instruments and capabilities. Development on natural language-based scientific experimentation is a building block for an impending future where a science exocortex—a synthetic extension to the cognition of scientists—may radically transform scientific practice and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Label-based Virtual Directories In dCache

Traditional filesystems organize data in directories. These directories are typically a collection of files whose grouping is based on a single criterion, e.g., the starting date of an experiment, experiment name, beamline ID, measurement device, or instrument. However, each file in a directory can belong to several logical groups, such as a special event type, experiment condition, or a part of a selected dataset. dCache is a storage system developed to store large amounts of scientific data, used by many HEP and Photon Science experiments. With recent developments in dCache, we have introduced a concept of file tagging, which dynamically groups files with the same label into virtual directories. The file labels can be added, removed, renamed, and deleted through the admin interface or via REST API. The files in virtual directories are exposed through all protocols supported by dCache. This contribution will describe the details of the implementation for file tagging in dCache and present our future development plans on automatic metadata extractions, a feature that will significantly simplify data management. Additionally, we are exploring the future use of virtual directories as a way to translate scientific data catalogs into filesystem views for direct data analysis.

Sahakyan, Marina [DESY]↗
DOE OSTI2025

Robustness of Deep Learning Classification to Adversarial Input on GPUs: Asynchronous Parallel Accumulation Is a Source of Vulnerability

The ability of machine learning (ML) classification models to resist small, targeted input perturbations—known as adversarial attacks—is a key measure of their safety and reliability. We show that floating-point non associativity (FPNA) coupled with asynchronous parallel programming on GPUs is sufficient to result in misclassification, without any perturbation to the input. Additionally, we show that this misclassification is particularly significant for inputs close to the decision boundary and that standard adversarial robustness results may be overestimated up to 4.6 when not considering machine-level details. We first study a linear classifier, before focusing on standard Graph Neural Network (GNN) architectures and datasets used in robustness assessments. We develop a novel black-box attack using Bayesian optimization to discover external workloads that can change the instruction scheduling which bias the output of reductions on GPUs and reliably lead to misclassification. Motivated by these results, we present a new learnable permutation (LP) gradient-based approach to learning floating-point operation orderings that lead to misclassifications. The LP approach provides a worst-case estimate in a computationally efficient manner, avoiding the need to run identical experiments tens of thousands of times over a potentially large set of possible GPU states or architectures. Finally, using instrumentation-based testing, we investigate parallel reduction ordering across different GPU architectures under external background workloads, when utilizing multi-GPU virtualization, and when applying power capping. Our results demonstrate that parallel reduction ordering varies significantly across architectures under the first two conditions, substantially increasing the search space required to fully test the effects of this parallel scheduler-based vulnerability. These results and the methods developed here can help to include machine-level considerations into adversarial robustness assessments, which can make a difference in safety and mission critical applications.

Shanmugavelu, Sanjif [Maxeler Technologies, a Groq↗
DOE OSTI2025

Versatile system for ion energy measurements generated by pulsed laser ionization: Insights into electron-ion dynamics

Ion energy distributions generated by pulsed laser interactions with materials are essential for applications ranging from materials science to oncology. Ion energy characterization is particularly important for an emerging mass spectrometry technique called virtual-slit cycloidal mass spectrometry (VS-CMS). The ion energy distribution influences the design and performance of VS-CMS instruments, as well as the efficacy of laser-driven ionization methods in various fields. Several established techniques, including the retarding potential method, time-of-flight (TOF) analysis, and electrostatic energy analyzers, have been employed to measure ion energy distributions. The wide range of ion energies reported highlights the strong dependence of ion energy on laser parameters, target materials, and experimental conditions, as well as the necessity of making independent measurements of the ion energy distribution for specific laser systems and materials. This paper presents the design and characterization of a simple TOF-based apparatus for measuring ion energy distributions from pulsed laser ionization without external fields. This approach minimizes perturbation of electron-ion dynamics and enables simultaneous energy measurements at multiple spatial positions. Here, the apparatus was tested using a nanosecond pulsed Nd:YAG laser operating at 1064 nm, 532 nm, and 266 nm on solid copper sheets at various laser fluences. Simultaneous measurements at different distances provide new insights into ion-electron interactions post-ionization and demonstrate the influence of laser wavelength and fluence on ion energy distributions.

Ion energy↗
DOE OSTI2025

Twinac: A Universal Framework for Virtual Accelerator Controls

We propose a universal framework for a system of virtual accelerator controls (Twinac), a standard toolkit for research institutions to design, maintain, and use a real-time, end-to-end "digital twin" of their particle accelerator facility. This virtual counterpart will mirror any physical accelerator to provide (1) predictive maintenance; (2) surveillance of hidden environmental factors, such as seasonal temperature variations, which could impact performance of power supplies, magnets, and other instruments; and (3) a capability to model novel ways to operate the accelerator without risking equipment damage. Twinac is envisioned as accelerator facility agnostic, allowing institutions to share and reuse myriad simulation approaches (analytics-based, A.I.-driven, or combinations of the like) across facilities. The Twinac system lays the groundwork for a collaborative network of institutions to maintain and update this shared virtual accelerator technology.

Miceli, Tia M. [Fermilab] (ORCID:0000000265577789)↗
DOE OSTI2024

Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models

Accurate and continuous estimates of the thermodynamic structure of the lower atmosphere are highly beneficial to meteorological process understanding and its applications, such as weather forecasting. In this study, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval is used to retrieve temperature and humidity profiles from various combinations of input data collected by passive and active remote sensing instruments, in situ surface platforms, and numerical weather prediction models. Among the employed instruments are microwave radiometers (MWRs), infrared spectrometers (IRSs), radio acoustic sounding systems (RASSs), ceilometers, and surface sensors. TROPoe uses brightness temperatures and/or radiances from MWRs and IRSs, as well as other observational inputs (virtual temperature from the RASS, cloud-base height from the ceilometer, pressure, temperature, and humidity from the surface sensors) in a physical iterative retrieval approach. This starts from a climatologically reasonable profile of temperature and water vapor, with the radiative transfer model iteratively adjusting the assumed temperature and humidity profiles until the derived brightness temperatures and radiances match those observed by the MWR and/or IRS instruments within a specified uncertainty, as well as within the uncertainties of the other observations, if used as input. In this study, due to the uniqueness of the dataset that includes all the above-mentioned sensors, TROPoe is tested with different observational input combinations, some of which also include information higher than 4 km above ground level (a.g.l.) from the operational Rapid Refresh numerical weather prediction model. These temperature and humidity retrievals are assessed against independent collocated radiosonde profiles under non-cloudy conditions to assess the sensitivity of the TROPoe retrievals to different input combinations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Optical versus radiographic imaging and tomography: introduction to the ROADS feature issue

Optical imaging is an ancient branch of imaging dating back to thousands of years. Radiographic imaging and tomography (RadIT), including the first use of X-rays by Wilhelm Röntgen, and then, $γ$ -rays, energetic charged particles, neutrons, etc. are about 130 years young. The synergies between optical and radiographic imaging can be cast in the framework of these building blocks: Physics, Sources, Detectors, Methods, and Data Science, as described in Appl. Opt. 61, RDS1 (2022). Optical imaging has expanded to include three-dimensional (3D) tomography (including holography), due in to part the invention of optical (including infrared) lasers. RadIT are intrinsically 3D because of the penetrating power of ionizing radiation. Both optical imaging and tomography (OIT) and RadIT are evolving into even higher dimensional regimes, such as time-resolved tomography (4D) and temporarily and spectroscopically resolved tomography (4D + ). Further advances in OIT and RadIT will continue to be driven by desires for higher information yield, higher resolutions, and higher probability models with reduced uncertainties. Synergies in quantum physics, laser-driven sources, low-cost detectors, data-driven methods, automated processing of data, and artificially intelligent data acquisition protocols will be beneficial to both branches of imaging in many applications. These topics, along with an overview of the Radiography, Applied Optics, and Data Science virtual feature issue, are discussed here.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Emerging materials intelligence ecosystems propelled by machine learning

We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Preface to the Proceedings of the 23rd Topical Conference on High-Temperature Plasma Diagnostics

Measurements of phenomena advance our understanding of the physical universe. As our understanding grows, more precise measurements are required, as are measurements of new quantities. Diagnosis of a plasma requires developing new instruments, also called diagnostics, as well as new ways to analyze, interpret, synthesize, and correlate their measurements. Further, as computing power increases and multi-physics codes improve their accuracy, more precise measurements are needed to guide future code developments and to validate predictions of these simulation codes. The Topical Conference on High-Temperature Plasma Diagnostics is focused on presenting and documenting the most recent advances in these instrumentation areas for plasmas. The 23rd occurrence of this biennial conference took place on December 14–17, 2020, and was hosted by Los Alamos National Laboratory. Originally scheduled for June 2020 in Santa Fe, New Mexico, the conference was postponed and then held virtually due to the global COVID-19 pandemic. Conference times were adjusted and presentations were scheduled to meet the needs of attendees in Europe, the USA, and Asia.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗