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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Expression of Interest in Contributions to the Electron-Ion Collider: Forward Silicon Vertex/Tracker Developments

The LANL EIC team would like to contribute to the EIC silicon vertex/tracking detector design, construction, commissioning and operation. Our primary focus is for a proposed forward silicon tracker with pseudorapidity coverage from 1 to 3.5 in the nucleon/nuclei beam going (forward) direction at IP-6 of the EIC. LANL LDRD is currently supporting this effort from FY20-FY22 with a funding of $5M. Meanwhile, we are open to collaborate on the other EIC detector sub-systems such as the central, backward silicon vertex/tracking detectors and/or a precision timing detector based on the LGAD technology.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Advanced Instrumentation Testbed (AIT) - antiNeutrino Experiment One (NEO) (Q1 FY2021)

In anticipation of the planned June 2021 design review, conceptual design, costing, and documentation efforts continued. The quarter was highlighted by the completion of three significant milestones: 1) 50% conceptual design and costing of the AIT facility, 2) bottom-up cost estimates for NEO, and 3) parametric NEO performance estimates (via the Big Trade Study). Because cost estimates exceed available funding levels, the project team started (late Dec 2020) a value engineering/pivot exercise to align costs with available funds. This exercise is anticipated to consume the project team for most of Q2. The end-result expectation (of the exercise) is a programmatically feasible and scientifically relevant AIT-NEO conceptual design that is aligned with available funds.

42 ENGINEERING↗
DOE OSTI2021

Big Data For Operation and Maintenance Cost Reduction

The purpose of this research is to develop a first-of-a-kind framework for integrating Big Data capability into the daily activities of our current fleet of nuclear power plants. Big Data is traditionally defined as data sets with high volume, velocity, and heterogeneity, and the existing Big Data analytics capabilities are now widely popular in fields such as finance, weather, e-commerce, healthcare and sports. In the nuclear industry, while the volume and velocity of data may present computational challenges for existing analytics capabilities, data heterogeneity are seen to present the major challenge. This research project mainly focuses on incorporating the wide range of data heterogeneities in nuclear power plants into an integrated Big Data Analytics capability. The primary end-product of this project is a Big Data framework that is capable of dealing with the large volume and heterogeneity of the data found in nuclear power plants to extract timely and valuable information on equipment performance. The framework can generate system insights that are actionable relations between measurable impacts and the corresponding maintenance action plans and enable optimization of plant operation and maintenance based on the extracted information. The developed framework is capable of handling heterogeneous data including both image data and time-series sensor data. Specifically, this developed framework includes the following components. The first component is an overarching maintenance ontology which includes system insights required by maintenance optimization. The maintenance ontology interacts with other components in the developed framework. The second component handles Piping & Instrumentation Diagram (P&ID) data. It can be used to extract system components and their relations automatically from the P&IDs. This extracted information is stored in the first component, i.e., maintenance ontology, and is also used as input to the third component, i.e., a tool for generating the fault tree for the corresponding system. The generated fault tree in turn is stored in the ontology for assessing risk that is used as a criterion in maintenance policy optimization. The fourth component is a tool for inferring the parameters in the Markov degradation model for a nuclear system. It uses basic information from the ontology. The fifth component is a tool for assessing the degradation level using sensor measurement data, for example, pressure, flowrate. This tool can be used for determining corrective maintenance actions. The results obtained from components four and five are returned to the ontology. The sixth component of the framework is a tool for optimizing the maintenance policy for a nuclear system of interest. It takes certain basic information from the ontology, e.g., costs of maintenance actions and system failures, as input, and returns the optimal maintenance policy to the ontology. This tool can be used for determining predictive maintenance actions. A set of experiments have also been conducted to verify the algorithms developed in this project for nuclear system degradation monitoring. The experiments are based on four solenoid valves, similar to the ones used in nuclear power plants. The analyses based on the experimental data using two algorithms, i.e., the Randomized Window Decomposition (RWD) algorithm and the particle filtering algorithm, and the results are introduced in the report. The Big Data framework developed in this project can be used as a support tool in daily activities of plant operation and maintenance and will reduce current costs while maintaining or improving safety levels. Overall, the project will not only benefit existing reactors, however it will open new frontiers to realize the long overdue value of Big Data Analytics in the nuclear sphere.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

A Massive, Position-Resolving, High-Energy-Resolution Detector for Non-Accelerator Cosmic and Intensity Frontier Particle Physics

We proposed to take the first steps in the development of a detector that promises energy resolution of tens of eV FWHM combined with robust nuclear-recoil discrimination and high fidelity, mm-precision position reconstruction for applications in non-accelerator particle physics at the Cosmic and Intensity Frontiers. The detector would obtain these excellent resolutions by sensing athermal phonons produced by particle interactions in crystalline, dielectric targets using a sensitive, highly multiplexable superconducting phonon sensor, the kinetic inductance detector (KID). This detector would be applicable to: the search for low-mass particle dark matter candidates with masses below 5~GeV via direct detection of scattering of dark matter particles with terrestrial nuclei; detection of coherent elastic neutrino-nucleus scattering to test for new physics such as a non-standard value of the weak nuclear charge, non-standard neutrino interactions (perhaps driven by a neutrino magnetic moment), or the existence of sterile neutrinos; and, searches for neutrinoless double-beta decay. During the funding period, we demonstrated scaling up of the detector concept from a 22-mm by 22-mm by 1-mm, 1-g prototype with 0.9~keV FWHM energy resolution to a 75-mm diameter by 1-mm, 9-g prototype while improving the inferred energy resolution to 0.7~keV~FWHM. In the process, we solved many problems associated with scaling device fabrication to large wafers and vastly reduced the fraction of the surface occupied by inactive but energy-absorbing metal. We also demonstrated a new technique that substantially simplifies the process of characterizing a new detector. These results provide a good foundation for future work scaling up the design to 4-mm thickness substrates and improving the resolution to reach 0.035~keV~FWHM, yielding a detector with compelling potential for the above applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Physics Opportunities in the ORNL Spallation Neutron Source Second Target Station Era

The Oak Ridge National Laboratory (ORNL) Spallation Neutron Source (SNS) First Target Station (FTS), used by the COHERENT experiment, provides an intense and extremely high-quality source of pulsed stopped-pion neutrinos, with energies up to about 50 MeV. Upgrades to the SNS are planned, including a Second Target Station (STS), which will approximately double the expected neutrino flux while maintaining quality similar to the FTS source. Furthermore, additional space for ten-tonne scale detectors may be available. We describe here exciting opportunities for neutrino physics, other particle and nuclear physics, and detector development using the FTS and STS neutrino sources.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

A Search for Sterile Neutrino Dark Matter (Final Report)

Decades of dark matter searches with increasingly higher sensitivity have so far not been able to detect the most-favored hypothetical dark matter particles. This has increased interest in sterile neutrinos with masses in the kilo-electronvolt (keV) range as possible dark matter candidates. We have established a and new and world-leading approach to search for keV-scale sterile neutrinos with superconducting radiation detectors. For this, we have implanted radioactive beryllium-7 (Be-7) nuclei into our superconducting tunnel junction (STJ) detectors and measured the recoil energy of the lithium-7 (Li-7) daughter nucleus with high accuracy. If the associated electron neutrino wave function contains some admixture of a keV sterile flavor, the Li-7 recoil energy will be shifted and produce an additional peak in the spectrum whose energy depends on the sterile neutrino mass. While we have not observed any evidence for sterile neutrinos in these precision decay experiments, the experiment has increased the sensitivity of sterile neutrino searches by a factor of ~10 over competing approaches in the mass range from ~10 to ~800 keV. The next generation of STJ detectors that we have developed as part of this project will be able further increase the sensitivity of the sterile neutrino search by at least another two orders of magnitude.

42 ENGINEERING↗
DOE OSTI2022

Snowmass Neutrino Frontier NF10 Topical Group Report: Netrino Detectors

We discuss here future neutrino detectors with physics goals ranging from the eV to the EeV scale. The focus is on future enabling technologies for such detectors, rather than existing detectors or those under construction. The report includes methodologies across the broad spectrum of neutrino physics: liquid noble and other cryogenic detectors, including LAr and LXe TPCs; photon-based detectors including technologies enabling hybrid Cherenkov/scintillation detectors; low-threshold detectors which use a wide variety of technologies to probe physics like coherent neutrino-nucleus scattering or detection of cosmic background neutrinos; and ultra-high energy detectors including optical and radio detectors, as well as tracking detectors for use at the forward physics facility of the LHC.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Double DVCS Measurement with the CLAS12 High-Luminosity Upgrade [Slides]

CLAS12 has a diverse physics program, with its detector commissioned in 2018 and since then acquiring physics data. The detector performance is close to design after improvements from AI-assisted tracking, but luminosity upgrades will greatly help efficiently execute the existing physics program and facilitate new physics opportunities. Two phases of luminosity increases are planned. The first, a doubling to 2x10 35 cm -2 s -1 , is in progress with an additional, fast tracking layer and en route in the next 3 years. The second, larger, phase, to 10 37 cm -2 s -1 , converts CLAS12’s forward acceptance region into a muon detector. μCLAS12 in the 2 nd phase is one of only two facilities in the world that can measure DDVCS, extending access to GPDs into new kinematic space. It also can provide, for example, access to heavy quarkonium.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Development of selenium imaging detectors toward a definitive search for neutrinoless ββ decay

Imaging sensors made from an ionization target layer of amorphous selenium (aSe) coupled to a silicon complementary metal-oxide-semiconductor (CMOS) active pixel array for charge readout are a promising technology for neutrino physics. The high spatial resolution in a solid-state target provides unparalleled rejection of backgrounds from natural radioactivity in the search for the neutrinoless ββ decay of 82 Se. With this grant, we experimentally determined two crucial factors necessary for the realization of this novel technology: i) the intrinsic energy response of aSe to ionizing electrons, and ii) the feasibility to couple a target layer of aSe to a CMOS active pixel sensor to image electron tracks.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

A demonstrator for a real-time AI-FPGA-based triggering system for sPHENIX at RHIC

The RHIC interaction rate at sPHENIX will reach around 3 MHz in pp collisions and requires the detector readout to reject events by a factor of over 200 to fit the DAQ bandwidth of 15 kHz. Some critical measurements, such as heavy flavor production in pp collisions, often require the analysis of particles produced at low momentum. This prohibits adopting the traditional approach, where data rates are reduced through triggering on rare high momentum probes. We explore a new approach based on real-time AI technology, adopt an FPGA-based implementation using a custom designed FELIX-712 board with the Xilinx Kintex Ultrascale FPGA, and deploy the system in the detector readout electronics loop for real-time trigger decision.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

SSTDR and FDR Detection of Un-Energized and Energized Cable Anomalies Including Thermal Degradation Using Machine Learning

Historically, cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, continued use of these cables must shift to a performance-based approach since it is cost prohibitive to completely replace cables that are likely still capable of performing their design function. A variety of cable tests are available and are commonly applied during outages when the cables can be taken out of service. Frequency domain reflectometry (FDR) is one of these test methods that is being more broadly accepted and used because it not only detects anomalies along the cable with a low-voltage signal that does not stress the cable insulation, but the technique also locates the anomalies. This supports follow-up local inspection and local repair or partial replacement of a damaged cable segment. Currently, FDR testing is only applied to cables that are taken out of service since the test instrument would be damaged by operational voltages. A related technology that has found some acceptance in the aircraft and rail industry is spread spectrum time domain reflectometry (SSTDR). This technology has been implemented with a custom commercial instrument by LiveWire Innovation that is designed to operate on live cables up to 1000 volts and with a bandwidth of 48 MHz. Initial evaluation by the Pacific Northwest National Laboratory (PNNL) of the Live Wire system indicated that a broader bandwidth (BW) SSTDR may be better for many kinds of flaws. This led PNNL to develop an SSTDR laboratory instrument suitable for tests up to 500 MHz bandwidth. Testing on energized cables is also desirable for online monitoring systems so an inductive clamshell coupler was developed that allows energized cables to be tested up to at least 5 kV and likely higher voltage levels. Dielectric spectroscopy and tan delta testing plus various laboratory destructive tests were included in this data acquisition campaign directed to feed a machine learning (ML) study. With these kinds of developments, online energized cable tests may be possible with industrial adoption of such hardware advances but it will be completely impractical to have highly skilled data analysts continually examine these complex signals for indications of damage or compromised conditions. If online testing is to be implemented in new test hardware, it must be accompanied by software that can interpret the signals and alert plant operators of changing or degraded conditions. The thermally aged, shielded cable investigated here was separately treated for ML analysis. Visual analysis of electrical data showed generally increasing peaks where the cable entered and exited the oven. These peaks were not exactly aligned with expected locations, but these differences were attributed to velocity of propagation calibration errors. Only supervised ML was applied to the thermally aged data as this data was only available shortly before the committed publication date of this report. The supervised ML was structured to divide the 0 to 70-day responses as ‘normal’ from 0 to 35 days or ‘anomalous’ from 36 to 70 days, based on cable tensile elongation at break (EAB) insulation characterization. Using 80% of the data for training and 20% for testing, the supervised ML predicted normal versus anomalous was 70% accurate. Important conclusions include: • Accuracy to predict the presence of cable damage is improved from the 2023 effort by more training data. Weighted accuracies for comparisons among the instruments ranged from 67 to 89 % for unsupervised ML and 71 to 99% for supervised ML. • Based on the synthetic data tests, the unsupervised models are more generalizable to unseen anomalies. The Multi-Layer Perceptron classifier (MLP) model reported as high as 99.7% accuracy on the test data, but this dropped to 58.3% when tested on the synthetic data. In contrast, the unsupervised Pointwise model only achieved 89.7% accuracy on the experimental data but reported 78.3% accuracy on the synthetic data. • The best anomaly indicators are higher frequency (400 MHz BW) FDR data. Other tests may be interesting but for this study, this was the best predicter.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors

This R&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors. The limitations of a 15 kHz maximum trigger rate imposed by the calorimeters can be negated by intelligent use of streaming technology in the tracking system. The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions (3MHz), using Graph Neural Network (GNN) and High Level Synthesis for Machine Learning (hls4ml). Success at sPHENIX promises immediate benefits, minimizing resources and accelerating the heavy-flavor measurements. The approach is transferable to other fields. For the EIC, we develop a DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Contrastive Machine Learning with Gamma Spectroscopy Data Augmentations for Detecting Shielded Radiological Material Transfers

Data analysis techniques can be powerful tools for rapidly analyzing data and extracting information that can be used in a latent space for categorizing observations between classes of data. Machine learning models that exploit learned data relationships can address a variety of nuclear nonproliferation challenges like the detection and tracking of shielded radiological material transfers. The high resource cost of manually labeling radiation spectra is a hindrance to the rapid analysis of data collected from persistent monitoring and to the adoption of supervised machine learning methods that require large volumes of curated training data. Instead, contrastive self-supervised learning on unlabeled spectra can enhance models that are built on limited labeled radiation datasets. This work demonstrates that contrastive machine learning is an effective technique for leveraging unlabeled data in detecting and characterizing nuclear material transfers demonstrated on radiation measurements collected at an Oak Ridge National Laboratory testbed, where sodium iodide detectors measure gamma radiation emitted by material transfers between the High Flux Isotope Reactor and the Radiochemical Engineering Development Center. Label-invariant data augmentations tailored for gamma radiation detection physics are used on unlabeled spectra to contrastively train an encoder, learning a complex, embedded state space with self-supervision. A linear classifier is then trained on a limited set of labeled data to distinguish transfer spectra between byproducts and tracked nuclear material using representations from the contrastively trained encoder. The optimized hyperparameter model achieves a balanced accuracy score of 80.30%. Any given model—that is, a trained encoder and classifier—shows preferential treatment for specific subclasses of transfer types. Regardless of the classifier complexity, a supervised classifier using contrastively trained representations achieves higher accuracy than using spectra when trained and tested on limited labeled data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

A Threshold Helium Leakage Detection Switch with Ultra Low Power Operation

Detecting helium leakage is important in many applications, such as in dry cask nuclear waste storage systems. This work develops a helium detection system based on the relative permittivity (dielectric constant) difference between air and helium. This difference changes the status of an electrostatic microelectromechanical system (MEMS) switch. The switch is a capacitive-based device and requires a very negligible amount of power. Exciting the switch’s electrical resonance enhances the MEMS switch sensitivity to detect low helium concentration. This work simulates two different MEMS switch configurations: a cantilever-based MEMS modeled as a single-degree-freedom model and a clamped-clamped beam MEMS molded using the COMSOL Multiphysics finite-element software. While both configurations demonstrate the switch’s simple operation concept, the clamped-clamped beam was selected for detailed parametric characterization due to its comprehensive modeling approach. The beam detects at least 5% helium concentration levels when excited at 3.8 MHz, near electrical resonance. The switch performance decreases at lower excitation frequencies or increases the circuit resistance. The MEMS sensor detection level was relatively immune to beam thickness and parasitic capacitance changes. However, higher parasitic capacitance increases the switch’s susceptibility to errors, fluctuations, and uncertainties.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Supernova pointing capabilities of DUNE

The determination of the direction of a stellar core collapse via its neutrino emission is crucial for the identification of the progenitor for a multimessenger follow-up. A highly effective method of reconstructing supernova directions within the Deep Underground Neutrino Experiment (DUNE) is introduced. The supernova neutrino pointing resolution is studied by simulating and reconstructing electron-neutrino charged-current absorption on Ar 40 and elastic scattering of neutrinos on electrons. Procedures to reconstruct individual interactions, including a newly developed technique called “brems flipping,” as well as the burst direction from an ensemble of interactions are described. Performance of the burst direction reconstruction is evaluated for supernovae happening at a distance of 10 kpc for a specific supernova burst flux model. The pointing resolution is found to be 3.4 degrees at 68% coverage for a perfect interaction-channel classification and a fiducial mass of 40 kton, and 6.6 degrees for a 10 kton fiducial mass respectively. Assuming a 4% rate of charged-current interactions being misidentified as elastic scattering, DUNE’s burst pointing resolution is found to be 4.3 degrees (8.7 degrees) at 68% coverage.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Radiation Damage Studies on Titanium Alloys as High Intensity Proton Accelerator Beam Window Materials

A high-strength dual a+ß phase titanium alloy Ti–6Al–4V is utilized as a material for beam windows in several accelerator target facilities. However, relatively little is known about how material properties of this alloy are affected by high-intensity proton beam irradiation. With plans to upgrade neutrino facilities at J-PARC and Fermilab to over 1 MW beam power, the radiation damage in the window material will reach a few displacements per atom (dpa) per year, significantly above the ~0.3 dpa level of existing data. The international RaDIATE collaboration, Radiation Damage In Accelerator Target Environments, has conducted a high intensity proton beam irradiation of various target and window material specimens at Brookhaven Linac Isotope Producer (BLIP) facility, including a variety of titanium alloys. Post-Irradiation Examination (PIE) of the specimens in the 1st capsule, irradiated at up to 0.25 dpa, is in progress. Tensile tests in a hot cell at Pacific Northwest National Laboratory (PNNL) exhibited a clear signature of radiation hardening and loss of ductility for Ti–6Al–4V, while Ti–3Al–2.5V, with less ß phase, exhibited less severe hardening. Microstructural investigations will follow to study the cause of the difference in tensile behavior between these alloys. High-cycle fatigue (HCF) performance is critical to the lifetime estimation of beam windows exposed to a periodic thermal stress from a pulsed proton beam. The first HCF data on irradiated titanium alloys are to be obtained by a conventional bend fatigue test at Fermilab and by an ultrasonic mesoscale fatigue test at Culham Laboratory. Specimens in the 2nd capsule, irradiated at up to ~1 dpa, cover typical titanium alloy grades, including possible radiation-resistant candidates. These systematic studies on the effects of radiation damage of titanium alloy materials are intended to enable us not only to predict realistic lifetimes of current beam windows made of Ti–6Al–4V, but also to extend the lifetime by choosing a more radiation and thermal shock tolerant alloy with a preferable heat treatment, or even by developing new materials.

Titanium Alloy, Beam Window, Proton Beam, Radiatio↗
DOE OSTI2022

Theia: Summary of physics program. Snowmass White Paper Submission

Theia would be a novel, "hybrid" optical neutrino detector, with a rich physics program. This paper is intended to provide a brief overview of the concepts and physics reach of Theia. Full details can be found in the Theia white paper [1].

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗