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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

National Spherical Torus eXperiment - Upgrade NSTX-U: Structural Analysis of Passive Plates Support Brackets

The objective of this analysis is to estimate and assess the stresses in the passive plates caused by a limiting plasma disruption. The objective of this calculation is also to address observed damage to mounting hardware after completion of NSTX operation. These observations initiated diagnostic efforts during the Upgrade project to monitor the severity of the disruptions that were causing the damage, and planned instrumentation to trend the behavior of the mounting hardware. The Recovery Project mission for the passive plate hardware is to appropriately reinforce the passive plates with stainless steel ribs mounted on the back of the plate to reduce their flexure and stress and reinforcements of the “biscuit” components of the bracketry to transfer loads without damage to bolts, biscuits, and bracket plates and welds. Mid-plane disruptions and quenches were manageable with NSTX vintage hardware. The Vertical Displacement Event (VDE) disruptions position the plasma closer to the passive plates and are more severe than the centered plasma. Earlier work pointed to the P1-P5 VDE as limiting. This has been confirmed by A. Brooks work in support of the load generation effort in reference. During vertical displacement large counter currents are generated in the plate as the plasma approaches it. - as would be expected from passive plates. When the plasma disrupts the currents in the plates are reversed, which also reverses the loading on the plates. This report documents the methodology and results of transient structural analysis on the plasma, passive plates support brackets. The loads induced by eddy and halo currents have been considered, and were generated using a separate electromagnetic analysis (NSTXU_1_11_2_1_CALC_051). The structural model also incorporates the effects from preload. Calculation was performed with the assumption that existing biscuits are not participating in the load bearing except the biscuits connected to jumper bolts, which are fully engaged. Results show that with the planned reinforcements, Passive plates and mounting structures satisfy static and fatigue criteria in all areas except welds, where separate analysis was performed. Both symmetric and asymmetric (with locating block) designs were analyzed, and although stresses were higher without locating block they still satisfy NSTX design criteria.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Development of a Scalable Risk-informed Predictive Maintenance Cloud-based Strategy at Nuclear Power Plants

The fact that light-water reactor operation and maintenance costs are prohibitively expensive and contribute to the premature decommissioning of nuclear power plants is partly due to how the equipment is monitored. In recent years, cloud computing has emerged as a dominant technology, as its low cost, computing and storage adaptability, and ability to host applications across numerous virtual infrastructures potentially make it a cost-effective alternative to onsite storage and diagnostics. In this paper, a technological assessment is carried out on a provisional cloud deployment architecture for a nuclear power plant predictive monitoring system. This cloud-based monitoring system would enable maintenance and diagnostic analysts and other authorized plant users to remotely monitor equipment functionality, thus enabling early fault detection and effective predictive maintenance practices. To provide data processing and storage, sensor device networking, and database management, the Microsoft Azure cloud platform is utilized as part of the proposed cloud architecture; however, this analysis could be extended to other cloud computing service providers as well. The focus of this paper is on application of cloud resources for enabling predictive maintenance, identification of technological hurdles associated with moving to a cloud-computing-based architecture, and potential benefits from moving to a centralized cloud system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Development of a Cloud-based Application to Enable a Scalable Risk-informed Predictive Maintenance Strategy at Nuclear Power Plants

Light-water reactor operations and maintenance (O&M) costs are prohibitively high, thus contributing to the premature decommissioning of nuclear power plants (NPPs). This is partly due to how the equipment is monitored. In recent years, cloud computing has emerged as a dominant technology by virtue of its low costs, computing and storage adaptability, and ability to host applications over numerous types of virtual infrastructures. Cloud computing can be a cost-effective alternative to onsite storage and diagnostics. This paper conducts a techno-economic assessment of a provisional cloud deployment architecture for a NPP predictive monitoring (PdM) system. The cloud-based monitoring system would enable maintenance and diagnostics (M&D) analysts and other authorized plant users to remotely monitor equipment functionality so as to enable PdM practices and early detection of faults. The Microsoft Azure cloud platform is included in the proposed cloud architecture to provide data processing and storage, sensor device networking, and database management; however, this analysis could be extended to other cloud computing service providers as well. For the techno-economic assessment, technical feasibility is measured in terms of network performance metrics such as response time, latency, and throughput, whereas economic feasibility is measured in terms of operational costs and capital expenditures. Finally, this report covers certain regulatory and security aspects that may concern licensees looking to implement cloud computing. The report focuses on the integration of sensor database storage, the application of cloud resources to PdM, and the identification of technological and economic hurdles associated with moving to a cloud-computing-based architecture.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Charge breeders: Development of diagnostic tools to probe the underlying physics

Charge breeders were developed more than 20 years ago in the context of radioactive ion beam (RIB) production. The main goal is to boost the charge state of a singly charged RIB to a higher value matching the A/Q of a post-accelerator. In that way, the RIB produced at some tens of keV can efficiently be accelerated to energies in a range of few MeV/u up to several tens of MeV/u, which is of interest for nuclear structure and nuclear astrophysics experiments. Two families of charge breeders have been developed: one based on Electron Cyclotron Resonance Ion Sources (ECRISs) suitable for Continuous Wave (CW) operation and another built around Electron Beam Ion Sources (EBISs) used for pulsed post-accelerators. For many years, experimental studies have been carried out to enhance the charge breeding process and improve the extracted beam quality, i.e., purity, intensity, emittance, and time structure. The evolution of the charge breeders is also closely related to the emergence of new facilities delivering even more exotic beams. Diagnostic tools, such as Faraday cups, beam profile monitors, and emittance scanners, are mandatory to tune the machines and evaluate their performances. However, to go beyond that, a better understanding of plasma physics (in ECRIS) and electron-ion interactions (in EBIS), as well as the 1+ beam characteristics, for instance, energy spread transverse emittance, is needed. Therefore, simulation codes modeling the physics phenomena in both type of charge breeders were developed to support the understanding of experimental outputs and to gain insights into non-observable parameters. Nevertheless, more sophisticated diagnostic tools are required to improve the charge breeding performance and to extend the potential applications toward new RIB facilities. This Review will present the two kinds of charge breeders and their technical evolutions. It will review the diagnostic tools and simulation codes employed for operation and evaluation of the internal physics processes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Monte Carlo Analysis of the Performance of the ITER Diagnostic Residual Gas Analyzer

The composition of exhausted gas is a key parameter in long-pulse plasma fusion experiments, and its evolution shall be monitored at timescales relevant to plasma dynamics and plasma-wall interactions. A diagnostic residual gas analyzer (DRGA) is a multisensor instrument particularly suited to these studies, and ITER will adopt DRGAs in the equatorial and in the divertor tokamak regions. In this work, we have revisited the design of the ITER divertor DRGA through simple vacuum analytical considerations supported by simulations conducted with Molflow+, a test particle Monte Carlo (TPMC) simulation code commonly used in the particle accelerator community. Here, starting with recommendations on the manufacturing of the vacuum piping of the DRGA, this work is followed by a complete vacuum characterization of the diagnostic vacuum setup (pressure profiles at base pressure and during sampling, orifice diameter, and length optimization), and finally, the in-vessel residence time of the most important gas species is simulated. These studies have allowed us to give insights into some experimental results recently found on the prototype DRGA installed in the Wendelstein W7-X stellarator.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Electrochemical Detection of Oxycodone and Its Main Metabolites with Nafion-Coated Single-Walled Carbon Nanotube Electrodes

Oxycodone is a strong opioid frequently used as an analgesic. Although proven efficacious in the management of moderate to severe acute pain and cancer pain, use of oxycodone imposes a risk of adverse effects such as addiction, overdose, and death. Fast and accurate determination of oxycodone blood concentration would enable personalized dosing and monitoring of the analgesic as well as quick diagnostics of possible overdose in emergency care. However, in addition to the parent drug, several metabolites are always present in the blood after a dose of oxycodone, and to date, there is no electrochemical data available on any of these metabolites. In this paper, a single-walled carbon nanotube (SWCNT) electrode and a Nafion-coated SWCNT electrode were used, for the first time, to study the electrochemical behavior of oxycodone and its two main metabolites, noroxycodone and oxymorphone. Both electrode types could selectively detect oxycodone in the presence of noroxycodone and oxymorphone. However, we have previously shown that addition of a Nafion coating on top of the SWCNT electrode is essential for direct measurements in complex biological matrices. Therefore, in this work, the Nafion/SWCNT electrode was further characterized and used for measuring clinically relevant concentrations of oxycodone in buffer solution. The limit of detection for oxycodone with the Nafion/SWCNT sensor was 85 nM, and the linear range was 0.5–10 μM in buffer solution. Furthermore, this study shows that the fabricated Nafion/SWCNT sensor has potential to be applied in clinical concentration measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Predictive Modeling of Local Film-Cooling Flow on a Turbine Rotor Blade

Abstract In the turbine section of a modern gas turbine engine, components exposed to the main gas path flow rely on cooling air to maintain hardware durability targets. Therefore, monitoring turbine cooling flow is essential to the diagnostic and prognostic efficacy of a condition-based operation and maintenance (CBOM) approach. This study supports CBOM goals by leveraging supervised machine learning to estimate relative changes to local film-cooling flowrate using surface temperature measured on the pressure side of a rotating turbine blade operating at engine-relevant aerothermal conditions. Throughout the lifetime of a film-cooled turbine component, characteristics of the film-cooling flow—such as film trajectory and cooling effectiveness—vary as degradation-driven geometry distortions occur, which ultimately affects the relationship between the model input and the model output—film-cooling flowrate predictions. The present study addresses this complication by testing a data-driven model on multiple turbine blades of the same nominal design, but with each blade exhibiting different localized film-cooling flow characteristics. By testing the model in this manner, strategies for mitigating the detrimental effects of film-cooling flow characteristic variations on model performance were investigated, and the corresponding flowrate prediction accuracy was quantified.

Engineering↗
DOE OSTI2022

Time-Resolved Beam Position Measurements for the Scorpius Multipulse Linear Induction Accelerator

Beam position monitors (BPMs) provide time-resolved measurements of the current and centroid position of high-current electron beams in linear induction accelerators (LIAs). One of the types of detectors used in BPMs is the B-dot loop, which generates a signal from the EMF due to the time varying magnetic flux through the loop. If some of the boundaries of the loop are composed of thick metal walls with finite conductivity, the resulting signal must be corrected for the magnetic field diffusion into the metal. The theoretically predicted flux due to diffusion is in remarkable agreement with experimental measurements. Although accurate BPM measurements of beam current require correction of magnetic field diffusion, accurate measurement of beam position requires no correction. In this note, we present a theoretical derivation and the experimental validation of this result.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Magnetic-Field Diffusion Effects in Beam Position Monitors III: Application to DARHT-II Beam Data

Beam position monitors (BPMs) provide time-resolved measurements of the current and centroid position of high-current electron beams in linear induction accelerators (LIAs). One of the types of detectors used in BPMs is the B-dot loop, which generates a signal from the EMF due to the time varying magnetic flux through the loop. If some of the boundaries of the loop are composed of thick metal walls with finite conductivity, the resulting signal must be corrected for the magnetic field diffusion into the metal. The theoretically predicted flux due to diffusion is in remarkable agreement with experimental measurements. Although accurate BPM measurements of beam current require correction of magnetic field diffusion, accurate measurement of beam position requires no correction. In this note, we present experimental validation of current and position results from a prototype detector employing finite conductivity sensing areas, based on experiments on the DARHT-II LIA.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

LCLS RF Station Phase Anomaly Candidate Dataset

A public anomaly detection dataset constructed from RF station faults for phase at SLAC's LCLS (Linac Coherent Light Source). We have compiled a dataset of the RF station diagnostic phase data and the beam-position monitor (BPM) signals, alongside the hand labels, for a labeled study period. The dataset consists of two HDF5 files (one for train and one for test) containing the raw data, two CSV files containing information about the candidates. The CSV file for the test dataset also contains the label.

Liang, Jia [Stanford Univ., CA (United States). In↗
DOE OSTI2025

Diagnostic Systems in the Muon $g-2$ experiment at Fermilab

The muon anomalous magnetic moment, $a_\mu=\frac{g-2}{2}$, is a low-energy observable which can be both measured and computed to high precision, making it a sensitive test of the Standard Model and a probe for new physics. This anomaly was measured with a precision of $0.20$~parts per million (ppm) by the Fermilab's Muon g-2 (E989) experiment. The final goal of the E989 experiment is to reach a precision of $0.14$~ppm. The experiment is based on the measurement of the muon spin anomalous precession frequency, $\omega_a$, based on the arrival time distribution of high-energy decay positrons observed by 24 electromagnetic calorimeters, placed around the inner circumference of a $14$~m diameter storage ring, and on the precise knowledge of the storage ring magnetic field and of the beam time and space distribution. Achieving this level of precision requires strict control over systematics, which is ensured through several diagnostic devices. At the accelerator level, these devices monitor the quality of the injected beam (e.g., verifying that it has the correct momentum), while at the detector level, they track both the magnetic field and the gain of the calorimeters. In this work the devices and techniques used by the E989 experiment will be presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Proton beam position measurement in air using a BPM

A Beam Position Monitor (BPM) is potentially useful to measure the position and phase of the beam in air in a non-destructive way. An air-gap BPM in experiments, such as beam-induced radioactive waste management and dynamic radiography applications, where a so-called air gap is needed, can be utilized to measure the beam position and phase. In this study, a stripline BPM was used in the air-gap of an 800 MeV proton beam transport line. The downstream end of the primary beamline exit window was made of a thin aluminum plate and allowed the beam to travel 1.2 m in ambient air before re-entering into a vacuum drift section. Such a configuration was arranged to examine the BPM effectiveness in atmospheric temperature and pressure where ionization of air occurs. In this study, a high energy (800 MeV), high current (0.6 A beam peak current/pulse) proton beam of 5 mm radius was transported in the air. The beam position relative to the axis was measured by detecting the signature of the beam in a nanosecond scale. This nanosecond scale detection ability was useful to identify other signals such as plasma effects. The BPM signals were processed at a frequency of 201 MHz; thus, one gets a stronger response in a stripline pattern as it was used in this study instead of a dot-type BPM. Experimental data show that the BPM works well in air, but ionization of air or plasma formation could not be measured over the BPM signal. The design, construction, and performance of a BPM in air environment are presented.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Multi-contrast machine learning improves schistosomiasis diagnostic performance

Schistosomiasis currently affects over 250 million people and remains a public health burden despite ongoing global control efforts. Conventional microscopy is a practical tool for diagnosis and screening ofSchistosoma haematobium, but identification of eggs requires a skilled microscopist. Here we present a machine learning (ML)-based strategy for automated detection ofS. haematobiumthat combines two imaging contrasts, brightfield (BF) and darkfield (DF), to improve diagnostic performance. We collected BF and DF images of urine samples, many of them containingS. haematobiumeggs, during two different field studies in Côte d’Ivoire using a mobile phone-based microscope, the SchistoScope. We then trained separate egg-detection ML models and compared the patient-level performance of BF and DF models alone to combinations of BF and DF models, using annotations from trained microscopists as the gold standard. We found that models trained on DF images, and almost all BF and DF combinations, performed significantly better than models trained on BF images only. When models were trained on images from the first field study (n = 349 patients, 748 images of each contrast), patient-level classification performance on patient images from the second study (n = 375 patients, 752 images of each contrast) met the WHO Diagnostic Target Product Profile (TPP) sensitivity and specificity for the monitoring and evaluation use case (sensitivity for all models and combinations was >75% when evaluated at a confidence score threshold that resulted in specificity >96.5%). When we used images from both field studies for the training set, performance of the models was improved. Overall, this work shows that the use of DF and BF increases the performance of ML models on images from devices with low-cost optics, while retaining the portability, power, and time-to-results of the WHO’s diagnostic TPP. DF requires no additional sample preparation and does not increase the complexity of the imaging system. It thus offers a practical means to improve performance of automated diagnostics forS. haematobiumas well as other microscopy-based diagnostics.

Infectious Diseases↗
DOE OSTI2024

Direct mechanistic connection between acoustic signals and melt pool morphology during laser powder bed fusion

Various nondestructive diagnostic techniques have been proposed for in situ process monitoring of laser powder bed fusion (LPBF), including melt pool pyrometry, whole-layer optical imaging, acoustic emission, atomic emission spectroscopy, high speed melt pool imaging, and thermionic emission. Correlations between these in situ monitoring signals and defect formation have been demonstrated with acoustic signals having been shown to predict pore formation with especially high confidence in recent machine learning studies. Here, in this work, time-resolved acoustic data are collected in both the conduction and keyhole welding regimes of LPBF-processed Ti-6Al-4V alloy. A non-dimensionalized Strouhal number analysis, used in whistle aeroacoustics, is applied to demonstrate that the acoustic signals recorded in the keyhole regimes can be directly associated with the vapor depression morphology. This mechanistic understanding developed from whistle aeroacoustics shows that acoustic monitoring during the LPBF process can provide a direct probe into the vapor depression dynamics and defect occurrence, especially in the keyhole regimes relevant to printing and defect formation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Learnings from the Marcellus Shale Energy and Environmental Lab (MSEEL) Using Fiber Optic Tools and Geomechanical Modeling

The study focuses on the MIP and Boggess pads of the MSEEL (Marcellus Shale Energy and Environmental Laboratory), a public-private partnership with a mandate to publicly release data for scientists and engineers to engage with. Multiple diagnostic tools are used to characterize the formation and monitor fracture treatment and propagation. Geomechanical modeling is used to understand the insitu stresses, microseismic to describe half-lengths and heights, and fiber optics to characterize offset well. Fracture Driven Interactions (FDI’s) and interstage communication. Recent publications covering the MSEEL MIP-3H and MIP-5H wells are reviewed and discussed. A preview of the findings from the Boggess pad (first production in November 2019) is also shared here

03 NATURAL GAS↗
DOE OSTI2023

Use of Convolutional Neural Network Image Classification and High-Speed Ion Probe Data Toward Real-Time Detonation Characterization in a Water-Cooled Rotating Detonation Engine

As rotating detonation engines (RDEs) progress in maturity, the importance of monitoring advancements toward development of active control becomes more critical. Experimental RDE data processing at time scales which satisfy real-time diagnostics will likely require the use of machine learning. This study aims to develop and deploy a novel real-time monitoring technique capable of determining detonation wave number, direction, frequency, and individual wave speeds throughout experimental RDE operational windows. To do so, the diagnostic integrates image classification by a convolutional neural network (CNN) and ionization current signal analysis. Wave mode identification through single-image CNN classification bypasses the need to evaluate sequential images and offers instantaneous identification of the wave mode present in the RDE annulus. Here, real-time processing speeds are achieved due to low data volumes required by the methodology, namely one short-exposure image and a short window of sensor data to generate each diagnostic output. The diagnostic acquires live data using a modified experimental setup alongside Pylon and PyDAQmx libraries within a python data acquisition environment. Lab-deployed diagnostic results are presented across varying wave modes, operating conditions, and data quality, currently executed at 3–4 Hz with a variety of iteration speed optimization options to be considered as future work. These speeds exceed that of conventional techniques and offer a proven structure for real-time RDE monitoring. The demonstrated ability to analyze detonation wave presence and behavior during RDE operation will certainly play a vital role in the development of RDE active control, necessary for RDE technology maturation toward industrial integration.

42 ENGINEERING↗