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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Spatiotemporal features of traffic help reduce automatic accident detection time

Quick and reliable automatic detection of traffic accidents is of paramount importance to save human lives in transportation systems. However, automatically detecting when accidents occur has proven challenging, and minimizing the time to detect accidents (TTDA) by using traditional features in machine learning (ML) classifiers has plateaued. We hypothesize that accidents affect traffic farther from the accident location than previously reported. Therefore, leveraging traffic signatures from neighboring sensors that are adjacent to accidents should help improve their detection. We confirm this hypothesis by using verified ground-truth accident data, traffic data from radar detection system sensors, and light and weather conditions and show that we can minimize the TTDA while maximizing classification performance by considering spatiotemporal features of traffic. Specifically, we compare the performance of different ML classifiers (i.e, logistic regression, random forest, and XGBoost) when controlling for different numbers of neighboring sensors and TTDA horizons. We use data from interstates 75 and 24 in the metropolitan area that surrounds Chattanooga, TN. Our results show that the XGBoost classifier produces the best results by detecting accidents as quickly as 1.0 min after their occurrence with an area under the receiver operating characteristic curve of up to 83% and an average precision of up to 49%. We describe limitations, open challenges, and how the proposed framework can be used for quicker operational accident detection.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Detecting Short Circuits: Post Accident Electric Vehicle Battery Safety Check

Fast and accurate detection of soft short circuits (SCs) in the battery packs of damaged electric vehicles is needed by first responders and mechanics to mitigate the potential risk from battery fires that may occur hours, days, or weeks after an accident. Here, this paper presents an SC-detection algorithm for potentially damaged lithium-ion batteries that works quickly and without a priori knowledge of the battery-pack chemistry, capacity, state of charge, or state of health. The proposed universal SC-detection algorithm is designed to be implemented on an inexpensive handheld device that can connect to and monitor the voltages of all cells in a pack. Transient filtering and linear-quadratic state observation provide estimates of normalized SC current for every cell in the pack. Cells with SC-current estimates outside a sigma-based threshold are detected. Simulations, experiments, and electric vehicle (EV) crash data are used to verify the speed, sensitivity, and accuracy of the method, demonstrating 96% accurate detection of 0.0027 C SCs in under 1 h for 5S cell groups in the lab and no false positives for crashed Volkswagen, Chevrolet, and Tesla vehicles without SCs.

25 - ENERGY STORAGE↗
DOE OSTI2023

Criticality Accident Alarm System Modeling for the Uranium Processing Facility

The Criticality Accident Alarm System (CAAS) for the Uranium Processing Facility (UPF) provides coverage for two primary processing facilities. For UPF, detailed documentation and drawings of the design are readily available. While this design information allows for some CAAS modeling features to match the actual design with a high level of accuracy, assumptions and simplifications are necessary as it is unrealistic and unnecessary to replicate many aspects of the design. A set of modeling guidelines and techniques were used to develop consistency between the MCNP6 models. While ensuring conservatism, overly conservative choices must be avoided to demonstrate the minimum accident of concern can be detected. To ensure proper modeling choices were made for the final models, a group of parametric studies were performed to evaluate the impact on the dose rates detected at the individual detectors. Some of these modeling choices and/or associated parametric studies include: - Modeling the grid steel area above the detectors as homogeneous stainless steel and determining the impact on detectability with an increasing density. - Modeling the presence of equipment in close proximity to the CAAS detectors. - Determining an appropriate material density to represent miscellaneous items not specifically modeled. - Evaluating the impact of sprinkler activation. The results of UPF CAAS analyses show that the modeling assumptions made based on the detailed design and parametric studies are appropriate and demonstrate accurate yet conservative models have been developed for detecting minimum accident of concern within the UPF.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Analysis of AP1000 Small-Break Loss-of-Coolant Accident Using Reactor Transient Simulator

The Westinghouse Electric Company’s Advanced Passive Reactor (AP1000) is characterized by the incorporation of passive safety systems (PSSs) designed to ensure core cooling during transient events. The assessment of PSSs requires evaluation of their performance through a combination of experiments and simulations employing various thermal-hydraulic codes. In addition, detailed evaluation of PSSs for a specific reactor system transient analysis such as loss-of-coolant-accident analysis supports understanding representative integral effects test facility development and the further evolution model development and assessment process. Developing a reactor system code is a complex and time-consuming process that requires significant engineering expertise and effort. It can take several months to even years to complete in the early stages of reactor system design and analysis. However, this process can be expedited through the use of transient simulator models for similar reactor systems, which can be used for lesson learning and training purposes. This study uses the Personal Computer Transient Analyzer (PCTRAN) code. The main advantage of PCTRAN is its ease of use and ability to run faster than real time. This study presents the results obtained for a small-break loss-of-coolant accident (SBLOCA) for two breaks using the full version (licensed) of PCTRAN. The purpose of this investigation is to evaluate the overall system behavior during the postulated SBLOCA event as well as assess the capability of the PCTRAN code to reproduce the system response during transient events. The obtained results were compared with the Westinghouse NOTRUMP system code. The PCTRAN code proved to be reliable in predicting the qualitative behavior of the system in both transient cases. As for the system response, it was found that it is contingent on the activation time of the PSSs. The differences in reactor coolant system pressure between the two codes were attributed to the critical flow model and simplification of mass and energy balance. Despite PCTRAN’s limitations, it can still provide a reasonable prediction of various reactor parameters such as pressure, mass flow rate, and void fraction during a SBLOCA scenario. It is worth noting that PCTRAN currently employs a bulk approach similar to that of the Modular Accident Analysis Program (MAAP) and MELCOR codes. However, the upcoming version of PCTRAN will include an artificial intelligence–based detection and accident prevention system, as well as different models for different reactor components. Consequently, PCTRAN has the potential to be upgraded to match the system thermal-hydraulic codes of the U.S. Nuclear Regulatory Commission and become more widely used in cybersecurity to safeguard nuclear power plants from cyberattacks.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

A traffic accident dataset for Chattanooga, Tennessee

This publication presents an annotated accident dataset which fuses traffic data from radar detection sensors, weather condition data, and light condition data with traffic accident data (as illustrated in Fig. 1) in a format that is easy to process using machine learning tools, databases, or data workflows. The purpose of this data is to analyze, predict, and detect traffic patterns when accidents occur. Each file contains a timeseries of traffic speeds, flows, and occupancies at the sensor nearest to the accident, as well as 5 neighboring sensors upstream and downstream. It also contains information about the accident type, date, and time. In addition to the accident data, we provide baseline data for typical traffic patterns during a given time of day. Overall, the dataset contains 6 months of annotated traffic data from November 2020 to April 2021. During this timeframe, and 361 accidents occurred in the monitored area around Chattanooga, Tennessee. This dataset served as the basis for a study on topology-aware automated accident detection for a companion publication [1].

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Spectral Data Fusion From Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and X-ray Fluorescence (XRF) Analyzers for Improved Detection of Cerium in a Simulated Dispersal Accident

Here, this work implements a mid-level data fusion methodology on spectral data from handheld X-ray fluorescence and laser-induced breakdown spectroscopy analyzers to quantify plutonium surrogate (CeO 2 ) contamination in soil samples for the first time. Spectral data from each analyzer were used independently to train supervised machine learning regressions to predict Ce concentration. Fused features from both data sets were then used to train the same models, comparing prediction performance by evaluating model precision and sensitivity. Fusing principal component scores from the two sensors yielded an order of magnitude improvement in precision and sensitivity of predictions made with an artificial neural network, compared to predictions made by models trained on independent sensor data. As a result, a boosted ensemble trained on the fused spectral features yielded an ideal predictor with root-mean-squared error on the order of 10 –6 and calculated limit of detection order 10 –5 wt %.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Development of advanced vacuum technologies for extending plasma pulse duration on EAST

Advanced vacuum technologies, including pumping, fueling and wall conditioning, have been successfully developed or upgraded to efficiently control the fuel and impurity particles to extend the plasma pulse duration in the experimental advanced superconducting tokamak (EAST). To improve the particle exhaust rate cryopumps with a 60% increase in pumping speed and ∼2 times increase in saturation capacity have been developed, and molecular pumps with a ∼30% increase in pumping speed have been upgraded. In order to monitor the molecular pump status while avoiding bearing faults and overload accidents, a fault detection system has been built which can offer an early warning to avoid more losses within the fusion device. A series of fueling technologies have been developed including gas injection system, supersonic molecular beam injector, pellet injector (PI), massive gas injector and shattered pellet injector, installed at the midplane and divertor positions at different ports to improve fueling uniformity and efficiency. Meanwhile, routine wall conditioning such as electric and hot N 2 baking, ion cyclotron wall conditioning and glow discharge cleaning have been successfully developed to remove the impurity particles from the inner component and materials. The low Z material wall coating and real-time powder injection during plasma discharge are also designed and applied to further improve particle control capability. Finally, by using these advanced vacuum related technologies, good vacuum (<2 × 10 −6 Pa) and wall conditions are realized, and the fuel and impurity particles can be effectively and stably controlled, which promotes the achievement of the record plasma of ∼1056 s pulse duration with the line-averaged electron density of 1.8 × 10 19 m −3 on EAST. They provide a very important reference for vacuum system design and operation for future fusion devices.

EAST↗
DOE OSTI2022

Driver Distraction Behavior Detection using a Vision Transformer Model based on Transfer Learning Strategy

Driver distraction behavior is one of the critical factors in traffic accidents. Thus, advanced driver state detection system has become the focus in the field of intelligent vehicle. However, in practical applications, insufficient samples of driving distraction behaviors bring great challenges to training a personalized behavior distraction detection model for a specific driver. To this end, a novel transformer model based on a transfer learning strategy is proposed in this paper to accurately recognize driver distraction behavior. Inspired by the effect of the transformer network in visual recognition, we firstly present a transformer behavior distraction detection system to identify the behavior categories that cause driver distraction. Then, for the specific driving dataset in practical application scenarios, the transfer learning strategy is introduced into the driver distraction detection model to further train the general transformer network. The effectiveness of the transformer based on the transfer learning strategy is validated compared with other traditional deep learning methods. The results show that the proposed detection method has better generalization ability and higher accuracy.

Fang, Zhenwu↗
DOE OSTI2020

Patchy snapshots of nuclear chain reactions

Stochastic fluctuations of the neutron population within a nuclear reactor are typically prevented by operating the core at a sufficiently high power. This regime, where the evolution of the neutron density is essentially deterministic, is key for automatic protection and safety systems to safely detect unwanted power excursions during an accident, and to rapidly initiate a reactor trip procedure in case it is needed. Recent works, supported by numerical simulations, have however reported that, for large reactors, the branching nature of the fission reactions might induce strongly non-Poissonian patterns in the neutron spatial distribution, and that stochastic fluctuations might still persist at reactor powers close to operating conditions (startup phase). An international program conducted by LANL, IRSN and CEA was therefore setup to experimentally detect and characterize such fluctuations and correlations. An experiment took place in 2017 at the Reactor Critical Facility (RCF) of the Rensselaer Polytechnic Institute (USA). In this paper we will report the main findings of this experimental program, supported by stochastic models and by the development of a dedicated high-fidelity Monte Carlo simulation code. We will in particular describe and explain the strong patchiness in neutron power distributions measured at the RCF, as well as a peculiar ‘blinking’ behavior of the core, and discuss the consequences of these findings on nuclear safety.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

DEVELOPMENT OF TRANSIENT FISSION GAS RELEASE CAPABILITIES TO STUDY HIGH BURNUP COMMERCIAL FUEL PERFORMANCE UNDER LOSS OF COOLANT ACCIDENT CONDITIONS

The contribution of transient fission gas release to the burst behaviour of high burnup fuel during a loss of coolant accident (LOCA) for commercial light water reactors is a complex phenomenon that is not fully understood. During the temperature ramp associated with a LOCA, there is significant, additional transient fission gas release in excess of steady state fission gas release from normal operation. The timing of this release and the various conditions that impact this release warrant further study. To better characterize this phenomenon, a system was developed to capture and characterize fission gas released from irradiated fuel during simulated LOCAs. This system is an upgrade to the existing Oak Ridge National Laboratory Severe Accident Test Station LOCA test apparatus and detects fission gas by gamma counting cryogenically cooled cold traps and by using an online gas mass spectrometer. The examined fuel can be pressurized or open to the furnace atmosphere. The design and operation of this new capability are discussed. Results from the first tests on high burnup fuel are presented. In the initial test, the LOCA relevant transient fission gas release was 5.3% for an unpressurized segment. Post-test microscopy on the tested samples is presented to demonstrate the likely radial source of transient fission gas release.

Harp, Jason↗
DOE OSTI2024

Anomaly Detection in Connected and Autonomous Vehicle Trajectories Using LSTM Autoencoder and Gaussian Mixture Model

Connected and Autonomous Vehicles (CAVs) technology has the potential to transform the transportation system. Although these new technologies have many advantages, the implementation raises significant concerns regarding safety, security, and privacy. Anomalies in sensor data caused by errors or cyberattacks can cause severe accidents. To address the issue, this study proposed an innovative anomaly detection algorithm, namely the LSTM Autoencoder with Gaussian Mixture Model (LAGMM). This model supports anomalous CAV trajectory detection in the real-time leveraging communication capabilities of CAV sensors. The LSTM Autoencoder is applied to generate low-rank representations and reconstruct errors for each input data point, while the Gaussian Mixture Model (GMM) is employed for its strength in density estimation. The proposed model was jointly optimized for the LSTM Autoencoder and GMM simultaneously. The study utilizes realistic CAV data from a platooning experiment conducted for Cooperative Automated Research Mobility Applications (CARMAs). The experiment findings indicate that the proposed LAGMM approach enhances detection accuracy by 3% and precision by 6.4% compared to the existing state-of-the-art methods, suggesting a significant improvement in the field.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

The Fuel Motion Monitoring System at TREAT - Current Status and Future Plans

An important component of the United States Nuclear Fuel Safety Transient Testing Program, the Fuel Motion Monitoring System (FMMS) at Idaho National Laboratory's Transient Reactor Test Facility (TREAT) is fast-neutron hodoscope capable of imaging the location, movement, and relocation of nuclear fuel experiments under simulated transient accident conditions. The FMMS was refurbished in parallel with the TREAT restart program starting in 2014, restoring 96-channels of fast-neutron detection. Since returning to operation in 2017 the FMMS has supported many fuel safety experiments supporting accident tolerant fuel development, light-water reactor safety, space thermal nuclear propulsion fuel development, and advanced reactor research and development. In Phase 2 of the FMMS restoration. work is now under way to expand the FMMS' field-of-view by adding an additional 96 channels of fast neutron detectors to the system's hodoscope, along with an expanded data acquisition system and associated transient timing electronics. An overview of the FMSS system and its fast-neutron detectors will be presented along with examples of current FMMS imaging performance and associated information.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2021

Characterization of Surrogate Molten Salt Reactor Aerosol Streams

Measuring the aerosol evolution from MSRs is important for monitoring the off-gas system of the reactor and is particularly important for detecting off-normal conditions. In a molten salt reactor (MSR) accident scenario, an aerosol release would be a major factor in the source term. This aerosol stream would likely be generated from a breach in the cover gas system, which causes particles produced from fission itself to escape, or from a salt spill that produces aerosols through splashing and secondary reactions. The particle size of the produced aerosols is anticipated to vary greatly and range from 0.01 to 10 µm. The transport of these aerosols would be dependent on the particle size. A better understanding of aerosol generation, size, and monitoring methods are needed to inform estimation and mitigation of potential aerosolized source terms from MSRs. While salt spill experiments are being performed at Argonne National Laboratory, the development of aerosol characterization and monitoring methods are being developed at Oak Ridge National Laboratory. To generate prototypic aerosols for use in testing monitoring instruments and mitigation methods, a surrogate aerosol stream was produced with a Collison nebulizer, and the particle size distributions were measured with a cascade impactor. The results demonstrated that by changing the nebulizer pressure, the aerosol particle size distribution can be adjusted to best match the region of interest for experiments with higher pressures, driving the particle size down. However, nearly all aerosols formed exceeded 1 µm in diameter, providing a lower bound for the surrogate aerosol stream. In addition to verifying the applicability of this surrogate aerosol stream, this work has shown that a laser induced breakdown spectroscopy monitoring system that is under development is resilient to changes in particle sizes, increasing its robustness for off-gas monitoring.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Using artificial intelligence to detect human errors in nuclear power plants: A case in operation and maintenance

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Portable chemical detection platform for on-site monitoring of odorant levels in natural gas

The adequate odorization of natural gas is critical to identify gas leaks and to reduce accidents. To ensure odorization, natural gas utility companies collect samples to be processed at core facilities or a trained human technician smells a diluted natural gas sample. In this work, we report a detection platform that addresses the lack of mobile solutions capable of providing quantitative analysis of mercaptans, a class of compounds used to odorize natural gas. Detailed description of the platform hardware and software components is provided. Designed to be portable, the platform hardware facilitates extraction of mercaptans from natural gas, separation of individual mercaptan species, and quantification of odorant concentration, with results reported at point-of-sampling. The software was developed to accommodate skilled users as well as minimally trained operators. Detection and quantification of six commonly used mercaptan compounds (ethyl mercaptan, dimethyl sulfide, n-propylmercaptan, isopropyl mercaptan, tert–butyl mercaptan, and tetrahydrothiophene) at typical odorizing concentrations of 0.1–5 ppm was performed using the device. We demonstrate the potential of this technology to ensure natural gas odorizing concentrations throughout distribution systems.

03 NATURAL GAS↗
DOE OSTI2020

AMS / BARC Joint Survey (Summary Report)

Report describes a series of joint aerial radiation survey flights at various locations in Southern Nevada and Colorado performed by AMS and BARC (U.S. and India). The flights were conducted over areas of well-characterized natural background in Nevada, at the Large-Area Calibration Pads at Grand Junction, CO, and over real ground contamination at the Nevada National Security Site. The intention of the joint surveys was to compare the responses, processes, and procedures of each country’s aerial radiation detection system. These systems can detect and map ground contamination that may result from a nuclear or radiological accident or incident, and play a significant role in the national radiological emergency response capabilities of both countries.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

SpecFIDLER User Manual: Software Version 2.4.0

The Spectroscopic Field Instrument for Detection of Low Energy Radiation (SpecFIDLER) allows response teams to detect and quantify plutonium contamination on the ground. Notional scenarios include dispersion from a weapon accident, or the launch failure of a space probe containing a radioisotope thermoelectric generator. Unlike other instruments, the thin-window sodium iodide detector is sensitive to the low-energy gamma rays emitted by plutonium isotopes. The system supports both mobile survey as well as stationary sampling. This manual provides information about installing, maintaining, and troubleshooting the SpecFIDLER. The scope of this document includes the physical hardware, software for data acquisition, and algorithms for data analysis. Recent changes to the software and algorithms aim to streamline the operation of the system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗