Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “nuclear event monitoring”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Low Power, Radiation Resilient Synchronous Edge Processing for Remote Monitoring

Next-generation space remote sensing systems may be equipped with imaging arrays that sense data at a rate that outstrips the processing capability of any computing hardware that can operate within a satellite’s power budget. This project developed novel convolutional and recurrent neural networks to detect and estimate point-like events amid clutter, and investigated their efficient and accurate implementation on analog in-memory computing systems that are 10-1000× more energy-efficient than digital processors. This project leveraged two memory devices at different levels of technological maturity: a large-scale analog computing prototype using commercial SONOS charge-trap memory, and electrochemical memory (ECRAM) with intrinsic radiation hardness. We experimentally demonstrated end-to-end analog processing of our neural networks on SONOS and characterized the radiation response of both SONOS and ECRAM. We advanced the state-of-the-art in ECRAM precision and reliability, and developed co-design methods to enable accurate long-term operation of SONOS analog accelerators in space radiation environments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Seismic Signal Detection on International Monitoring System 3-Component Stations using PhaseNet

In this report we discuss training a deep learning seismic signal detection model on 3-component stations from the International Monitoring System (IMS) using the PhaseNet architecture. Using 14 years of associated signals from the International Data Centre’s (IDC) Late Event Bulletin (LEB), we auto-curated training data consisting of signal windows containing associated arrivals, and noise windows that contain no LEB-associated signals. We trained several models using different waveform window durations (30 seconds and 100 seconds), with and without bandpass filtering. We evaluated the effectiveness of our models using associated signals from the Unconstrained Global Event Bulletin (UGEB) and found that several of our models outperformed the signal detections from the IDC’s Selected Event List 3 (SEL3) arrival table. The SEL3 bulletin evaluated on the UGEB dataset with 100-second waveform windows registered a precision and recall of .15 and .48, respectively, versus .19 and .59 for our filtered-data model. For the 30-second waveform window dataset, the SEL3 bulletin achieved a precision and recall of .31 and .47, respectively, versus .32 and .60 for our filtered-data model. Finally, our models detected signals from all source-to-receiver distances, suggesting it is feasible to use a single PhaseNet model for the IMS network.

58 GEOSCIENCES↗
DOE OSTI2025

Evaluation of Station Performance of the Idaho National Laboratory Seismic Monitoring Network Using Network Detection Thresholds

The Idaho National Laboratory (INL) Seismic Monitoring Network is located in eastern Idaho and monitors a portion of the intermountain seismic belt. It has been in place for 50 yr and has undergone several major changes, the most recent of which has been the transition to the Antelope real‐time acquisition system and the implementation of automatic phase picking algorithms to aid in analysis. This study discusses the efforts to evaluate the performance of the INL seismic monitoring network (and other surrounding stations) using the new real‐time acquisition system. The method outlined by Wilson et al. (2021) is used to develop an empirical relationship between the observability of local earthquakes as a function of magnitude and distance. This relationship is used to produce detection thresholds for Pwaves for all stations of interest. The INL seismic network has two main goals: monitor tectonic‐and volcanic‐related events and measure ground motions for input into seismic hazard analysis. Because of these two overall objectives, several seismic stations have been installed near critical facilities and, therefore, are not as quiet as stations that are used primarily for earthquake detection. This is reflected in their detection thresholds, which are much smaller for stations away from facilities. This study shows that the INL Seismic Monitoring Network is able to detect earthquakes near INL facilities with M L > 1.2, with redundancies built in to ensure this sensitivity even if data became unavailable from some stations. This study also shows “holes” in the monitoring network where the detection of smaller earthquakes is highly dependent on sparsely placed seismic stations. In conclusion, the results of this study will be used to govern plans for expansion of earthquake monitoring in Idaho and the surrounding region and to fine‐tune the detection thresholds for individual stations.

58 - GEOSCIENCES↗
DOE OSTI2026

Advanced Test Reactor Safety Basis Update for Gas-Cooled Experiments

The Advanced Test Reactor (ATR) supports neutron irradiation of several types of experiments. One such experiment type is referred to as a gas leadout. Gas leadout experiments actively flow gas through the experiment which allows for active temperature control. It also allows for in-situ data of the experiment. For example, fission gas migration through a fuel sample can be monitored via activity of the sweep gas. Historically, ex-pile equipment and fission product monitors were housed in shielded ATR cubicles. Due to other facility updates, cubicle space is no longer available for gas leadout experiment equipment. To support continued operation of gas leadout experiments, ATR completed a safety basis update that supports a new housing for leadout equipment that may process potentially contaminated gas. In addition to the structure and associated equipment, technical safety requirements regarding handling and storage of experiments needed to be revised to support fueled gas leadout experiments and associated outage configurations. The safety basis update addressed the full lifecycle of these experiments, including experiment movement and interim storage, and credible abnormal events such as failures or leaks in contaminated gas tubing in occupied areas. This paper discusses the completed analyses performed to support the safety basis update associated with gas leadout experiments, including thermal-hydraulic evaluation, probabilistic analysis, and dose consequence analyses.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

DAQExpert the service to increase CMS data-taking efficiency

The Data Acquisition (DAQ) system of the Compact Muon Solenoid (CMS) experiment at the LHC is a complex system responsible for the data readout, event building and recording of accepted events. Its proper functioning plays a critical role in the data-taking efficiency of the CMS experiment. In order to ensure high availability and recover promptly in the event of hardware or software failure of the subsystems, an expert system, the DAQ Expert, has been developed. It aims at improving the data taking efficiency, reducing the human error in the operations and minimising the on-call expert demand. Introduced in the beginning of 2017, it assists the shift crew and the system experts in recovering from operational faults, streamlining the post mortem analysis and, at the end of Run 2, triggering fully automatic recovery without human intervention. DAQ Expert analyses the real-time monitoring data originating from the DAQ components and the high-level trigger updated every few seconds. It pinpoints data flow problems, and recovers them automatically or after given operator approval. We analyse the CMS downtime in the 2018 run focusing on what was improved with the introduction of automated recovery; present challenges and design of encoding the expert knowledge into automated recovery jobs. Furthermore, we demonstrate the web-based, ReactJS interfaces that ensure an effective cooperation between the human operators in the control room and the automated recovery system. We report on the operational experience with automated recovery.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Safety Testing of AGR-5/6/7 Compacts 2-2-2 and 2-2-4

Compacts 2-2-2 and 2-2-4 from the Advanced Gas Reactor (AGR) Fuel Development and Qualification Program’s final irradiation campaign (AGR-5/6/7) were each separately subjected to a thermal soak at 1600°C for 300 h under flowing helium to simulated conditions experienced during a postulated high-temperature gas-cooled reactor (HTGR) depressurization conduction cooldown event. The safety testing of the fuel compacts’ performance under accident conditions was conducted in the Oak Ridge National Laboratory (ORNL) Core Conduction Cooldown Test Facility (CCCTF), which utilizes a stand-alone hot cell at the Irradiated Fuels Examination Laboratory (IFEL). The CCCTF heats fuel to temperatures up to 1900°C in a non-oxidizing environment while continuously monitoring the sweep gas for radioactive 85 Kr to detect release levels associated with hermetic failure of the tristructural-isotropic (TRISO) coating surrounding each fuel kernel. In addition, certain metallic radionuclides that escape the fuel compact are collected on deposition cups that are periodically exchanged with a new cup to obtain information on the overall retention behavior of the TRISO coating layers. Because cesium can diffuse through intact pyrocarbon layers, abnormal degradation of the silicon carbide (SiC) layer in the absence of holistic TRISO coating failure is indicated by release of 134 Cs at levels equivalent to an individual particle inventory in the absence of significant 85 Kr release (Hunn et al. 2014). Additional description of the CCCTF system is provided in Appendix A.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Monte Carlo Dropout Uncertainty Quantification of Long Short-Term Memory Autoencoder Anomaly Detection in a Liquid Sodium Cold Trap

Advanced high-temperature fluid reactors, such as sodium-cooled fast reactors (SFRs) and molten salt–cooled reactors (MSCRs), require coolant purification systems to prevent fluid contamination and local freezing that can lead to plugging. Liquid sodium purification can be achieved with a cold trap, where the sodium temperature is reduced to a near-freezing point to precipitate out impurities. Automation of monitoring of the cold trap performance with machine learning algorithms can aid in early detection of incipient anomalies. An efficient approach to loss-of-coolant–type anomaly detection in a cold trap monitored with more than two dozen thermal-hydraulic sensors consists of a long short-term memory (LSTM) autoencoder. This work develops the uncertainty quantification of the LSTM autoencoder performance for cold trap anomaly detection using the Monte Carlo (MC) dropout method. The MC dropout methodology creates a distribution of sister distributions that all slightly differ from each other because of random neurons being turned off for testing. The variances of the sister network distributions are used to make an uncertainty interval. Our analysis shows that the uncertainty in the autoencoder performance is largest near the peak of the anomaly signal. Using the MC dropout method, we investigate the uncertainty in the anomaly detection with missing sensor inputs. This capability allows the reactor operator to evaluate resilience of the anomaly detection system and to make informed decisions about continuity of operation in the event of sensor failure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

First-ever deployment of a SiPM-on-tile calorimeter in a collider: a parasitic test with 200 GeV p p collisions at RHIC.

We describe the testing of a prototype SiPM-on-tile iron-scintillator calorimeter at the Relativistic Heavy Ion Collider (RHIC) during its 200 GeV p p run in 2024. The prototype, measuring 20 × 20 cm 2 and 24 radiation lengths in depth, was positioned in the STAR experimental hall, approximately 8 m from the interaction point and 65 cm from the beam line, covering a pseudorapidity range of about 3.1 < η < 3.4. By using the dark current of a reference SiPM as a radiation monitor, we estimate that the prototype was exposed to a fluence of about 10 10 1-MeV n eq /cm 2 . Channel-by-channel calibration was performed in a data-driven way with the signature from minimum-ionizing particles during beam-on conditions. AGeant4 detector simulation, with inputs from thePythia8 event generator, describes measurements of energy spectra and hit multiplicities reasonably well. These results mark the first deployment, commissioning, calibration, and long-term operation of a SiPM-on-tile calorimeter in a collider environment. This experimental campaign will guide detector designs and operational strategies for the ePIC detector at the future EIC, as well as other applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Evaluation of SNOLAB background mitigation procedures through the use of an ICP-MS based dust monitoring methodology

Dust particulate fallout on materials in use for rare-event searches is a concerning source of radioactive backgrounds due to the presence of the naturally occurring radionuclides 40 K, 232 Th, 238 U, and their progeny in dust. Much effort is dedicated to inform radioactive backgrounds from dust and evaluate the efficacy of mitigation procedures. A great portion of such effort relies on fallout models and assumed dust composition. Here, in this work, an ICP-MS based methodology was employed for a direct determination of fallout rates of radionuclides and stable isotopes of interest from dust particulate at the SNOLAB facility. Hosted in an active mine, the SNOLAB underground laboratory strives to maintain experimental areas at class 2000 cleanroom level. This work validates the mitigation procedures in place at SNOLAB and informs dust backgrounds during laboratory activities. Fallout rates of major constituents of the local rock were measured two to three orders of magnitude lower in the clean experimental areas compared to non-clean transition areas from the mine to the laboratory. An increase of approximately two orders of magnitude in the fallout rate of stable Pb isotopes was determined in an experimental area during activities involving handling of Pb bricks. Increased 40 K, 232 Th, and 238 U fallout rates were measured in clean experimental areas during activities generating particulate.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Assessing parallel path cooling tower performance via artificial neural networks

Real-time monitoring of a research nuclear reactor, a system in which all generated power is dissipated to the environment, can be performed via analysis of the heat rejection from the cooling system. Given an inlet water temperature and flow rate, the reactor power can be well-approximated from the outlet water temperature; however, the instrumentation to measure outlet conditions may not be robust or accurate. If we know how a cooling tower performs from historical data, but cannot measure the outlet temperature, a mathematical representation of the system can be inverted to obtain the outlet water temperature that describes the cooling capacity. Unfortunately, model inversion processes are computationally expensive. To address this, an artificial neural network (ANN) is implemented to assess the performance of a multi-cell cooling tower for a nuclear reactor. This approach leverages the Merkel model to obtain an extensive data set describing performance of the cooling tower cells throughout a wide array of potential operating conditions. The Merkel model is expressed as a function of four parameters: the inlet and outlet water temperatures, inlet air wet bulb temperature, and ratio of liquid-to-gas mass flow rates (L/G), which together provide a non-dimensional number indicative of cooling tower performance, called the Merkel integral. Computing a 4-dimensional data structure that describes finite combinations of the Merkel integral, an inverse model is then generated using an ANN to determine the cell outlet water temperature from the other three model parameters along with the computed Merkel integral. Compared to traditional model inversion methods, the ANN reduces the computational time by approximately 4 orders of magnitude, with effectively no sacrifice to solution accuracy, and could be applied for different cooling towers in the event the performance curve is known. Finally, three use cases of the ANN are then reviewed: (1) determining the cell outlet water temperatures when gas flow at rated conditions (GFRC) is known, (2) performing the prior case without knowledge of the GRFC, and (3) assessing performance differences between the individual tower cells.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

HDG-1 Experiment Irradiation Monitoring Data Qualification Final Report

SUMMARY The U.S. Department of Energy (DOE) Advanced Reactor Technologies (ART) Graphite Research and Development (GRD) Program is conducting a series of six experiments to quantify the effects of irradiation on nuclear-grade graphite. This report documents the qualification of irradiation monitoring data for the fifth experiment, High Dose Graphite-1 (HDG-1). Qualified monitoring data are required by the ART program to support the design and licensing of the first high-temperature reactor (HTR) nuclear plant. Data are classified as Qualified if they meet the usage requirements described in the experiment planning and quality assurance (QA) documents, Failed if they do not meet those requirements and provide no usable information, or Trend if they do not fully meet all requirements but still provide useful information subject to an assessment of how any deficiencies may affect a particular use of the data. HDG-1 irradiation began with Advanced Test Reactor (ATR) Cycle 168B on August 24, 2020, and concluded after Cycle 173C on January 27, 2025. The HDG-1 capsule was removed from the reactor core twice—during core internal change (CIC) Cycle 170A and powered axial locator mechanism (PALM) Cycle 172A—to prevent overheating of the graphite specimens during high-power PALM cycles. The capsule was therefore irradiated during a total of seven normal ATR cycles: 168B, 169A, 171A, 171B, 173A, 173B, and 173C. Irradiation monitoring data evaluated in this report include thermocouple (TC) temperature, gas flow rate, gas moisture, gas pressure, specimen load, and graphite stack displacement. Temperature. A total of 14,508,065 TC temperature records were captured. Of these, 13,901,785 (95.8%) are Qualified and 606,280 (4.2%) are Failed. The principal source of failed temperature data was the instrument failure of TC-9 (Zone 2) on June 24, 2024, and TC-10 (Zone 1) on July 5, 2024, near the end of Cycle 173A, which resulted in 595,554 Failed readings. An additional 379 missing values and 10,347 slightly negative values from TC-13 during ATR outages are also Failed. Neither TC-9 nor TC-10 was used as a temperature-control TC, and their failures did not compromise capsule condition monitoring. Correlation analysis of all 13 TCs found no evidence of virtual junction formation. Control chart analysis revealed clear downward drift of approximately 80°C for TC-6 (Zone 3) relative to other stable TCs, and possible downward drift of approximately 60°C for TC-13 relative to the Zone 5 control TC (TC-1), though TC-13 remained consistent with the Zone 2 control TC (TC-12). Gas flow. A total of 20,088,090 gas flow rate records were captured. Of these, 19,941,463 (99.3%) are Qualified and 146,627 (0.7%) are Failed due to missing values. All argon, helium, and total gas flow data were within expected ranges throughout the irradiation. Gas moisture. A total of 1,116,005 outlet gas moisture values were captured. Of these, 1,101,421 (98.7%) are Qualified and 14,584 (1.3%) are Failed, comprising 14,556 out-of-range values and 28 missing values. The out-of-range moisture values exceeded 22,000 ppmv for approximately 1 week at the beginning of Cycle 173A, when accumulated moisture evaporated after the capsule was retrieved from water storage during PALM Cycle 172A and reinserted into the east flux trap. Moisture levels returned to below 25 ppmv for the remaining three cycles, and the transient high-moisture event did not affect the integrity of specimen irradiation. Gas pressure. A total of 7,812,035 gas pressure values were captured. Of these, 6,642,048 (85.0%) are Qualified and 1,169,987 (15.0%) outlet pressure values are Failed, comprising 718,537 zero outlet pressure values due to sensor failure from Cycle 168B through Cycle 171B, 54,550 missing values, and 396,900 too-low outlet pressure values, ranging from 1.1 to 1.6 psia after sensor replacement during Cycle 173A. Load. A total of 6,696,030 load values were captured. Of these, 6,694,580 (99.98%) are Qualified and 1,450 (0.02%) are Failed due to missing values. Applied loads to the six specimen stacks were stable throughout the irradiation. Stack displacement. A total of 6,696,030 displacement values were captured. Of these, 5,713,297 (85.32%) are Qualified and 3,781 (0.06%) are Failed due to missing values. Stack displacement increased consistently throughout the irradiation, reaching approximately 3.08 in. for Channels 5 and 6 by the end of irradiation. 978,952 (14.62%) substantially elevated displacements observed for Channel 6 beginning in Cycle 171A and for Channel 5 beginning in Cycle 173A are assigned Trend status. Raising pressure. A total of 1,115,999 raising pressure values were captured. Of these, 1,115,430 (99.95%) are Qualified and 569 (0.05%) are Failed due to missing values. Ram pressure. A total of 6,696,030 ram pressure values were captured. Of these, 6,692,249 (99.95%) are Qualified and 3,484 (0.05%) are Failed due to missing values. Stack raising was perf

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Beam-Based Target Alignment for Mu2e

The Mu2e Experiment is a precision experiment at Fermi National Accelerator Laboratory, searching for charged lepton flavor violation (CLFV) in the conversion of a muon to an electron in the presence of an atomic nucleus. In order to achieve the expected single-event sensitivity of $3\times 10^{-17}$ , Mu2e will require an intense muon beam, generated via pion decay. These pions are the product of a proton beam striking a radiatively-cooled tungsten target. In order to maximize pion production and prevent target failure, the beam will have to be aligned with the target center to within 0.5 mm. The production target monitor (PTM) will ensure this alignment. The PTM consists of a series of proportional wire chambers (PWC’s) upstream and downstream of the production target. In this dissertation, I explain the requirements of this detector system and derive the decisions about which detector model to use and how they should be arranged from these requirements. After this, I describe my e xperience building the detectors and show an early use for them in the Mu2e proton beam. I also performed a study of the sensitivity and time resolution of this model of PWC and its electronics, and give the results here. I then describe a series of simulation campaigns which shed light on the interactions between the beam and the target, and how these interactions manifest on the downstream detectors. Finally, I give the target scanning procedure to be used to align the beam and target during experiment start up.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Xenon doping of liquid argon in ProtoDUNE single phase

The Deep Underground Neutrino Experiment (DUNE) will be the next generation long-baseline neutrino experiment. The far detector is designed as a complex of four LAr-TPC (Liquid Argon Time Projection Chamber) modules with 17 kt of liquid argon each. The development and validation of the first far detector technology is pursued through ProtoDUNE Single Phase (ProtoDUNE-SP), a 770 t LAr-TPC at CERN Neutrino Platform. Crucial in DUNE is the photon detection system that will ensure the trigger of non-beam events — proton decay, supernova neutrino burst and BSM searches — and will improve the timing and calorimetry for neutrino beam events. Doping liquid argon with xenon is a known technique to shift the light emitted by argon (128 nm) to a longer wavelength (178 nm) to ease its detection. The largest xenon doping test ever performed in a LAr-TPC was carried out in ProtoDUNE-SP. From February to May 2020, a gradually increasing amount of xenon was injected to also compensate for the light loss due to air contamination. The response of such a large TPC has been studied using the ProtoDUNE-SP Photon Detection System (PDS) and a dedicated setup installed before the run. With the first it was possible to study the light collection efficiency with respect to the track position, while with the second it was possible to distinguish the xenon light (178 nm) from the LAr light (128 nm). The light shifting mechanism proved to be highly efficient even at small xenon concentrations (<20 ppm in mass) furthermore it allowed recovering the light quenched by pollutants. The light collection improved far from the detection plane, enhancing the photon detector response uniformity along the drift direction and confirming a longer Rayleigh scattering length for 178 nm photons, with respect to 128 nm ones. The charge collection by the TPC was monitored proving that xenon up to 20 ppm does not impact its performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Explosion Detection Using Smartphones: Ensemble Learning with the Smartphone High-Explosive Audio Recordings Dataset and the ESC-50 Dataset

Explosion monitoring is performed by infrasound and seismoacoustic sensor networks that are distributed globally, regionally, and locally. However, these networks are unevenly and sparsely distributed, especially at the local scale, as maintaining and deploying networks is costly. With increasing interest in smaller-yield explosions, the need for more dense networks has increased. To address this issue, we propose using smartphone sensors for explosion detection as they are cost-effective and easy to deploy. Although there are studies using smartphone sensors for explosion detection, the field is still in its infancy and new technologies need to be developed. We applied a machine learning model for explosion detection using smartphone microphones. The data used were from the Smartphone High-explosive Audio Recordings Dataset (SHAReD), a collection of 326 waveforms from 70 high-explosive (HE) events recorded on smartphones, and the ESC-50 dataset, a benchmarking dataset commonly used for environmental sound classification. Two machine learning models were trained and combined into an ensemble model for explosion detection. The resulting ensemble model classified audio signals as either “explosion”, “ambient”, or “other” with true positive rates (recall) greater than 96% for all three categories.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Comparison of Supervised and Un-Supervised Machine Learning Algorithms for Threat Detection and Scintillator Performance for Radiation Portal Monitoring

Following the events of September 11, 2001, international border crossing have been equipped with radiation portal monitors (RPMs) to identify illicit radioactive material. Polyvinyl toluene (PVT) scintillators are commonly used due to their low cost and reasonable maintainability, however they offer low spectral resolution. Despite the fact that over twenty years has transpired since this event, radioisotopes are still typically identified by hand-crafted classification algorithms, e.g., total counts or energy windowing, and exhibit relatively poor performance in detecting threats at the low false alarm rates required to support the stream of commerce. While some improvement to performance has been realized via the use of supervised machine learning, these classification algorithms typically utilize simulations in lieu of real data due to the sparsity of data for one or more classes. Accordingly, the performance of these algorithms is somewhat less than optimal when examining experiments or simulations with model mismatch. Consequently, in this work, we examine the application of a number of unsupervised machine learning, anomaly detection based algorithms, to circumvent the inverse crime when analyzing spectroscopy data for RPMs. We also compare anomaly detection results with those obtained via the use of supervised classification detection ML algorithms when model mismatch is introduced between the simulated threat items utilized for training/testing. Finally, we compared the performance of the PVT scintillators to those obtained with higher resolution detectors using both anomaly detection and supervised classification algorithms.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Influence of background sources and topographic resolution in the Weather Research and Forecasting Model on xenon plume characteristics at monitoring stations

For many atmospheric monitoring applications, networks of measurement sites—such as the radionuclide stations of the International Monitoring System—can be sparse. With measurement locations potentially hundreds to thousands of kilometers from a release it is important to quantify the effects of physical processes on transport and dispersion of plumes between source and measurement locations. This study addresses the effects of background sources and topography resolution near the release location of radionuclides. We use the Weather Research and Forecasting (WRF) model with inline chemistry to investigate (1) how an additional, time-varying source of 133 Xe, such as an operational medical isotope production facility, contributes to activity concentration measurements at monitoring sites, and (2) how complex topography influences on atmospheric conditions near emission sources impact plume concentrations at varying distances from the source. Two 133 Xe emission sources, including (1) a high flux rate of short duration representing an explosive event, and (2) a variable and continuous background source, are simulated. The continuous background source contributes significantly to total 133 Xe concentrations at several monitoring stations. Further, a WRF simulation at 9 km horizontal resolution is compared with a nested grid simulation, where the innermost domain has a resolution of 1 km. Increased topographic resolution leads to an improved representation of plume responses to local winds, with topographic influences greatest at locations closest to the sources. Differences between the two domain resolutions decrease at greater distances from the sources, as plumes have time to spread and mix and are influenced by synoptic scale circulation patterns that are represented similarly in both simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Performance Analysis and Radiation Damage Estimation of the Stopping Target Monitor Detectors in Mu2e

The Mu2e experiment aims to search for the charged lepton flavour violating (CLFV) process of a coherent, neutrinoless, conversion of a muon into an electron within the proximity of an aluminium nucleus. Mu2e seeks to measure the ratio (R μe ) of the rate of this conversion process, relative to that of ordinary muon capture. Mu2e will achieve world-leading sensitivity, improving the current limit of Rμe < 7 ×10 -13 (90% C.L.),set by the SINDRUM-II experiment, by an order of 104. This corresponds to a single-event sensitivity of Rμe < 2.87 × 10 -17 (90% C.L.). Many beyond Standard Model (BSM) theories require CLFV to occur at a rate accessible by Mu2e. Any observation of CLFV at Mu2e would have profound implications on particle physics. The Stopping Target Monitor (STM) will be comprised of both a HPGe and a LaBr3 detector which will monitor the signals of photons produced in stopped-muon processes to a required accuracy of 10%. To achieve the desired sensitivity, it is imperative that the detectors perform at their optimal level. The STM detectors are placed within a harsh radiation environment in the form of a high energy ‘flash’ of gamma radiation as well as a flux of fast neutrons. Estimations of radiation damage to HPGe in literature is limited. This thesis reports an approach which utilizes the Kinetic-Energy Released in Matter (KERMA) parameter that considers the energy dependence of the incident flux and the cross-section information for the incident particle. Resampling schemes have been adopted to give a realistic rate over the experiment run time. The results have then been normalized to the radiation damage caused by a 252 Cf source. The estimated time before annealing of the detector is necessary is found to be 75 and 78 months in the case of neutron damage and τ ~ 24 years, 11 months in the case of electron/positron induced damage. This shows significant improvement on the previous estimations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Optical spectroscopy and modeling of uranium gas-phase oxidation: Progress and perspectives

We report that studies related to U gas-phase oxidation through plasma- and thermo-chemistry are important for many fields, including environmental monitoring, forensic analysis, debris analysis in a weapon detonation event, and nucleation physics. Recently, significant efforts have been made to understand the chemical pathways involved in the progression from U atoms to diatoms (UO) and polyatomic molecules (UxOy), employing optical spectroscopy tools and computational modeling. In many studies, laser ablation of U or a U-containing flow reactor are used as a highly resource-efficient, repeatable, tunable, and lab-scale testbed for studying gas-phase oxidation in U plasmas. The spectroscopic analysis of high-temperature gas-phase oxidation of U is challenging due to the congested U spectra, resolution limitations of instrumentation, and the numerous chemical reaction pathways possible. This article focuses on the current understanding and challenges related to studying U plasma chemistry, specifically U gas-phase oxidation and molecular formation, via optical spectroscopy of plasmas and associated computational and spectral modeling. The physical and chemical processes involved in the evolution from U atoms to U oxide molecules to nanoparticles and agglomerates (i.e., debris) are discussed in the context of optical spectroscopic studies. The article concludes by highlighting opportunities for future research efforts based on existing knowledge published in the literature.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗