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The ZephIR300 is a continuous wave LIDAR with a range of 10 m to 300 m. During HilFlowS the ZephIR300 was programmed to measure from 10 m to 150 m. In addition, the ZephIR300 provided a measurement at 1 m height using an onboard meteorological sensor. The ZephIR300 has a 30° scanning cone angle, data sampling rate of approximately 15 s, wind speed accuracy of 0.5 percent, and direction accuracy of 0.5°.
Temperature sensing is one of the most common measurements of a nuclear reactor monitoring system. The coolant fluid flow in a reactor core depends on the reactor power state. We investigated the monitoring and estimation of the thermocouple time series using machine learning for a range of flow regimes. Measurement data were obtained, in two separate experiments, in a flow loop filled with water and with liquid metal Galinstan. We developed long short-term memory (LSTM) recurrent neural networks (RNNs) for sensor predictions by training on the sensor’s own prior history, and transfer learning LSTM (TL-LSTM) by training on a correlated sensor’s prior history. Sensor cross-correlations were identified by calculating the Pearson correlation coefficient of the time series. The accuracy of LSTM and TL-LSTM predictions of temperature was studied as a function of Reynolds number (Re). The root-mean-square error (RMSE) for the test segment of time series of each sensor was shown to linearly increase with Re for both water and Galinstan fluids. Using linear correlations, we estimated the range of values of Re for which RMSE is smaller than the thermocouple measurement uncertainty. For both water and Galinstan fluids, we showed that both LSTM and TL-LSTM provide reliable estimations of temperature for typical flow regimes in a nuclear reactor. The LSTM runtime was shown to be substantially smaller than the data acquisition rate, which allows for performing estimation and validation of sensor measurements in real time.
Artificial neural networks (ANNs) for performing spectroscopic gamma-ray source identification have been previously introduced, primarily for applications in controlled laboratory settings. To understand the utility of these methods in scenarios and environments more relevant to nuclear safety and security, this work examines the use of ANNs for mobile detection, which involves highly variable gamma-ray background, low signal-to-noise ratio measurements, and low false alarm rates. Simulated data from a 2” × 4” × 16” NaI(Tl) detector are used in this work for demonstrating these concepts, and the minimum detectable activity (MDA) is used as a performance metric in assessing model performance.In addition to examining simultaneous detection and identification, binary spectral anomaly detection using autoencoders is introduced in this work, and benchmarked using detection methods based on Non-negative Matrix Factorization (NMF) and Principal Component Analysis (PCA). On average, the autoencoder provides a 12% and 23% improvement over NMF- and PCA-based detection methods, respectively. Additionally, source identification using ANNs is extended to leverage temporal dynamics by means of recurrent neural networks, and these time-dependent models outperform their time-independent counterparts by 17% for the analysis examined here. The paper concludes with a discussion on tradeoffs between the ANN-based approaches and the benchmark methods examined here.
A system discriminating fissile material from nonfissile material wherein a digital data acquisition unit collects data at high rate, and processes large volumes of data directly to count neutrons from the unknown source and detect excess grouped neutrons to identify fission. The system includes a Poisson neutron generator for in-beam interrogation of a possible fissile neutron source and inducing neutron emission therefrom, and a DC power supply that exhibits electrical ripple of less than one part per million. A neutron count histogram and Poisson count distribution are overlaid to provide a visual indication of the difference in correlation of natural and induced emitted neutrons from the radiation source to characterize the neutron source as fissile material or non-fissile material.
A method of discriminating fissile material from non-fissile material with a digital data acquisition system that collects data at high rate, and processes large volumes of data directly to count neutrons from the unknown source and detect excess grouped neutrons to identify fission. The system includes a Poisson neutron generator for in-beam interrogation of a possible fissile neutron source and inducing neutron emission therefrom, and a DC power supply that exhibits electrical ripple of less than one part per million. A neutron count histogram and Poisson count distribution are overlaid to provide a visual indication of the difference in correlation of natural and induced emitted neutrons from the radiation source to characterize the neutron source as fissile material or non-fissile material.
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This paper reports on the development of a self-synchronizing underwater acoustic network developed for remote monitoring of mooring loads in Wave Energy Converters (WECs). This network uses Time Division Multiple Access and operates self-contained with the ability for users to remotely transmit commands to the network as needed. Each node is a self-contained unit, consisting of a protocol adaptor board, an FAU-DPAM underwater acoustic modem and a battery pack. A node can be connected to a load cell, to a topside user or to the WEC. Every node is swapable. The protocol adaptor board, named Protocol Adaptor for Digital LOad Cell (PADLOC) supports a variety of digital load cell message formats (CAN, MODBUS, custom ASCII) and underwater acoustic modem serial formats. PADLOC enables topside users to connect to separate load cells through a user-specific command. This is especially important if the user is monitoring multiple load cells during deployment or maintenance, when the primary data system may be offline. Each PADLOC board handles formatting, buffering and has a one-on-one serial connection with each pair (node) of a digital load cell and acoustic modem. In addition, each PADLOC board handles the timekeeping and power saving features for each node. The only limitation is the data bit rate and delay limitations associated with the underwater acoustic modem. A four node self-synchronizing network has been developed to demonstrate the load cell monitoring capability using the PADLOC technology on the CalWave WEC.
The search beyond the Standard Model explores dark matter and a potential fifth force. DarkLight@ARIEL investigates the dark photon as a mediator between ordinary and dark matter. The experiment aims to measure the process e?Ta ? e?TaX ? e?Ta(e?e+), by detecting a charged lepton pair in the final state. The spectrometers will be instrumented with Gas Electron Multiplier (GEM) detectors with minimal material budget for tracking. A novel GEM construction technique is employed for fabricating 25 cm x 40 cm GEM detectors, where all layers are mechanically stretched and assembled within a double frame. The dissertation outlines physics motivation, methods, experimental setup, and the role of GEM detectors in DarkLight. A comprehensive discussion on GEM detectors, focusing on the "NS2" technique, including design, parts, assembly, testing, and performance evaluation will be discussed. The proton has garnered attention due to discrepancies in measuring its charge radius using muonic hydrogen and electron-based methods. The 2010 muonic hydrogen measurement of the proton charge radius Rp = 0.84184(67) fm, showed a significant 7? discrepancy compared to the previously known value of Rp = 0.8775(51) fm, giving rise to the proton radius puzzle. Through simultaneous measurements of e ? p and ? ? p elastic scattering, MUSE facilitates a precise and direct comparison of the proton radius. The physics background of the proton radius puzzle, measurement techniques, the approach adopted by the MUSE experiment, its significance and the experimental setup will be discussed. Furthermore, the role of GEM detectors, along with an in-depth analysis of their data and efficiency will be discussed and evaluated.
Rotating detonation engines (RDEs) theoretically achieve pressure gain by continuously propagating detonation waves in a cylindrical annulus. Research aims to implement them in gas turbines for improved propulsion and power generation efficiency. Current diagnostic methods, such as point measurements and optical diagnostics, face limitations due to high data acquisition rates required to analyze rapid detonation interactions. In contrast, image classification and time series classification achieve real-time capabilities with rates of 4 and 5 Hz, respectively. Object detection offers the highest time-step resolution at 20 μsec, while real-time methods require longer resolutions of 80 msec, highlighting the advancements in diagnostic capabilities for RDEs.
Online reconstruction is key for monitoring purposes and real time analysis in High Energy and Nuclear Physics experiments. A necessary component of reconstruction algorithms is particle identification that combines information left by a particle passing through several detector components to identify the particle’s type. Of particular interest to electro-production Nuclear Physics experiments such as CLAS12 is electron identification which is used to trigger data recording. A machine learning approach was developed for CLAS12 to reconstruct and identify electrons by combining raw signals at the data acquisition level from several detector components. Here, this approach achieves an electron identification purity above 75% whilst retaining an efficiency close to 100%. The machine learning tools are capable of running at high rates exceeding the data acquisition rates and will allow electron reconstruction in real-time. This work enhances online analyses and monitoring and can contribute to improved triggering at CLAS12. This machine learning driven approach will also be crucial for experiments aiming to transition to streaming readout operations where online reconstruction will be a key component of the data taking paradigm.
There is a paucity of data on the rates of ipsilateral breast tumor recurrence (IBTR) in BRCA1/2–associated breast cancer (BC). Scarcer yet are outcomes data in BRCA1/2 mutation carriers in the setting of newer mastectomy techniques, such as skin-sparing mastectomies (SSM) and nipple-sparing mastectomies (NSM).
The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.
This dataset provides projected grid stress and reliability results (including all model inputs and outputs from an open-source grid operations modeling framework - GO), for the Integrated Multisector, Multiscale Modeling (IM3) project, under varying levels of data center demand growth between 2025 and 2035 in the U.S. Western Interconnection. The scenarios and sensitivity experiments are combinations of different data center demand growth rates and energy, weather, population and economic pathways. Data center demand growth projections were sourced from the Electric Power Research Institute (EPRI). The data center demand growth projection names are: Low (3.71% annual data center demand growth) Moderate (5% annual data center demand growth) High (10% annual data center demand growth) Higher (15% annual data center demand growth) Energy, weather, population and economic pathways are informed by two Shared Socioeconomic Pathways (SSP3 and SSP5) and two Representative Concentration Pathways (RCP4.5 and RCP8.5) following the hotter general circulation model (GCM) forcing group from a set of perturbed thermodynamics simulations. The resulting pathway names are: rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 The main scenarios and sensitivity experiments are detailed below. Reference scenario: The projected grid stress and reliability results for the U.S. Western Interconnection from a previous study. This scenario does not consider data center demand growth explicitly. Data center scenario: Building on the reference scenario, this scenario considers various data center growth rates and how they impact the U.S. Western Interconnection. Data center loads are modeled as flat 8760-hr profiles. This scenario does not consider new generation and transmission capacities specifically designed to meet the new data center demands. The related folder is named "flat". Delayed generator retirements sensitivity experiment: Building on the data center scenario, this experiment explores the impact of different levels of natural gas and nuclear generator retirement delays. The resulting scenario names are: (1) postponing 100% nuclear retirements; (2) postponing 100% nuclear and 25% natural gas retirements; (3) postponing only 50% natural gas retirements; (4) postponing 100% nuclear and 50% natural gas retirements; (5) postponing 100% nuclear and 75% natural gas retirements; and (6) postponing 100% nuclear and 100% natural gas retirements. The related folder names are: no_gen_retire_0_gas, no_gen_retire_25_gas, no_gen_retire_50_gas, no_gen_retire_50_gas_only, no_gen_retire_75_gas, and no_gen_retire_100_gas. Demand response through curtailment sensitivity experiment: Building on the data center scenario, this experiment explores the impact of different participation and compensation levels of data center demand response. The resulting scenario names are: (1) 5% demand available for curtailment with 750 $/MWh compensation; (2) 5% demand available for curtailment with 500 $/MWh compensation; (3) 5% demand available for curtailment with 250 $/MWh compensation; (4) 15% demand available for curtailment with 750 $/MWh compensation; (5) 15% demand available for curtailment with 500 $/MWh compensation; and (6) 15% demand available for curtailment with 250 $/MWh compensation. The related folder names are: dr_cost_250_drup_0_drdown_5, dr_cost_250_drup_0_drdown_15, dr_cost_500_drup_0_drdown_5, dr_cost_500_drup_0_drdown_15, dr_cost_750_drup_0_drdown_5, and dr_cost_750_drup_0_drdown_15. Combination of delayed generator retirements and demand response through curtailment sensitivity experiment: The impact of combining postponing 100% nuclear and 25% natural gas retirements with 5% demand available for curtailment with 750 $/MWh compensation is simulated. The related folder is named "dr_cost_750_drup_0_drdown_5_nuc_100_gas_25". Please refer to the README file for a detailed description of the dataset including individual files and references.
These workbooks contain modeled estimates of long-run marginal emission rates (LRMER) for the contiguous United States. A LRMER is an estimate of the rate of emissions that would be either induced or avoided by a change in electric demand, taking into account how the change could influence both the operation as well as the structure of the grid (i.e., the building and retiring of capital assets, such as generators and transmission lines). It is therefore distinct from the more-commonly-known short-run marginal, which treat grid assets as fixed. Long-run marginal emissions rates are generally appropriate to use when trying to comprehensively estimate the impact of a long-lived (i.e., more than several years) intervention. There are two workbooks that supply the data at two different geographic resolutions: states and GEA regions (20 regions that are similar to, but not exactly the same as, the US EPA's eGRID regions). For more data underlying these emissions factors, see the Cambium 2022 project at https://scenarioviewer.nrel.gov/. For more details on input assumptions and methodology see the associated report (Cambium 2022 Scenario Descriptions and Documentation, https://www.nrel.gov/docs/fy23osti/84916.pdf). This data is planned to be updated annually. Information on the latest versions can be found at https://www.nrel.gov/analysis/cambium.html.
NO and NO 2 (NO x ) play major roles in both tropospheric and stratospheric chemistry. This paper provides a novel method to obtain a global and accurate photolysis rate for NO 2 based on satellite data. The photolysis rate J(NO 2 ) dominates the daytime diurnal variation of NO x photochemistry. Here the spatial variation of J(NO 2 ) at 50–90°S in December from 20–40km is obtained using data from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) experiment. Because NO and NO 2 rapidly exchange with one another in the daytime, J(NO 2 ) can be attained assuming steady state, and the results are shown to be consistent with model results. The J(NO 2 ) value decreases as the solar zenith angle increases and has a weak altitude dependence. A key finding is that satellite-derived J(NO 2 ) increases in the polar regions, in good agreement with model predictions, due to the effects of ice and snow on surface albedo. Thus, the method presented here provides an observation-based check on the role of albedo in driving polar photochemistry.
These workbooks contain modeled estimates of long-run marginal emission rates (LRMER) for the contiguous United States. The LRMER is an estimate of the rate of emissions that would be either induced or avoided by a long-term (i.e., more than several years) change in electrical demand. It incorporates both the projected changes to the electric grid, as well as the potential for an incremental change in electrical demand to influence the structural evolution of the grid (i.e., the building and retiring of capital assets, such as generators and transmission lines). It is therefore distinct from the more-commonly-known short-run marginal, which treats grid assets as fixed. The Levelized LRMER worksheet within each workbook is set up to produce a levelized long-run marginal emission rate based on user-provided inputs. These levelized LRMER values are intended for analysts to use when estimating the emissions induced (or avoided) by a long-term change in end-use electricity demand. There are two workbooks that supply the data at two different geographic resolutions: states and GEA regions (20 regions that are similar to, but not exactly the same as, the US EPA's eGRID regions). For more data underlying these emissions factors, see the Cambium 2021 project at https://cambium.nrel.gov/. For more details on the inputs into the scenarios available in the workbooks, see the Standard Scenarios 2021 Report (https://www.nrel.gov/docs/fy22osti/80641.pdf). This data was produced as part of the Cambium project. For more details about the methodology, see the Cambium Documentation: Version 2021 (https://www.nrel.gov/docs/fy22osti/81611.pdf). This data is planned to be updated annually. Information on the latest versions can be found at https://www.nrel.gov/analysis/cambium.html.
These workbooks contain modeled estimates of long-run marginal emission rates (LRMER) for the contiguous United States' electric sector. A LRMER is an estimate of the rate of emissions that would be either induced or avoided by a change in electric demand, taking into account how the change could influence both the operation as well as the structure of the grid (i.e., the building and retiring of capital assets, such as generators and transmission lines). These workbooks provide data for 18 GEA regions covering the contiguous United States. Mappings of these regions to ZIP codes and counties is given in this workbook in the corresponding tabs. For more data underlying these emissions factors, see the Cambium 2023 project at https://scenarioviewer.nrel.gov/. For more details on input assumptions and methodology see the associated report (Cambium 2023 Scenario Descriptions and Documentation, https://www.nrel.gov/docs/fy24osti/88507.pdf). Users are advised to review section 4 of the report, which discusses limitations and caveats of the data. This data is planned to be updated annually. Information on the latest versions can be found at https://www.nrel.gov/analysis/cambium.html.
Abstract A helium‐filled mylar balloon carrying a smartphone and infrasound sensors ascended to a stratospheric height of 35 km over the surface detonation of a chemical explosive, with a total acoustic propagation distance of 127 km. The smartphone was configured to collect multi‐modal data at high rates from internal sensors. Analysis of the data shows successful collection of the explosion signal by both the smartphone's microphone and its accelerometers, the first from an ascending balloon. Comparison of the acoustic signal with that collected by other infrasound sensors, both airborne and ground‐based, provides insight into the possibilities and limitations of collecting acoustic data from the stratosphere.