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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 OSTI2012

Lessons Learned from Safety Events

The Hydrogen Incident Reporting and Lessons Learned website (www.h2incidents.org) was launched in 2006 as a database-driven resource for sharing lessons learned from hydrogen-related safety events to raise safety awareness and encourage knowledge-sharing. The development of this database, its first uses and subsequent enhancements have been described at the Second and Third International Conferences on Hydrogen Safety. [1,2] Since 2009, continuing work has not only highlighted the value of safety lessons learned, but enhanced how the database provides access to another safety knowledge tool, Hydrogen Safety Best Practices (http://h2bestpractices.org). Collaborations with the International Energy Agency (IEA) Hydrogen Implementing Agreement (HIA) Task 19 – Hydrogen Safety and others have enabled the database to capture safety event learnings from around the world. This paper updates recent progress, highlights the new “Lessons Learned Corner” as one means for knowledge-sharing and examines the broader potential for collecting, analyzing and using safety event information.

Hydrogen↗
DOE OSTI2019

Exploring microscopic driving volatility in naturalistic driving environment prior to involvement in safety critical events—Concept of event-based driving volatility

The sequence of instantaneous driving decisions and its variations, known as driving volatility, prior to involvement in safety critical events can be a leading indicator of safety. Here, this study focuses on the component of “driving volatility matrix” related to specific normal and safety-critical events, named “event-based volatility.” The research issue is characterizing volatility in instantaneous driving decisions in the longitudinal and lateral directions, and how it varies across drivers involved in normal driving, crash, and/or near-crash events. To explore the issue, a rigorous quasi-experimental study design is adopted to help compare driving behaviors in normal vs unsafe outcomes. Using a unique real-world naturalistic driving database from the 2nd Strategic Highway Research Program (SHRP), a test set of 9593 driving events featuring 2.2 million temporal samples of real-world driving are analyzed. This study features a plethora of kinematic sensors, video, and radar spatiotemporal data about vehicle movement and therefore offers the opportunity to initiate such exploration. By using information related to longitudinal and lateral accelerations and vehicular jerk, 24 different aggregate and segmented measures of driving volatility are proposed that captures variations in extreme instantaneous driving decisions. In doing so, careful attention is given to the issue of intentional vs. unintentional volatility. The volatility indices, as leading indicators of near-crash and crash events, are then linked with safety critical events, crash propensity, and other event specific explanatory variables. Owing to the presence of unobserved heterogeneity and omitted variable bias, fixed- and random-parameter discrete choice models are developed that relate crash propensity to unintentional driving volatility and other factors. Statistically significant evidence is found that driver volatilities in near-crash and crash events are significantly greater than volatility in normal driving events. After controlling for traffic, roadway, and unobserved factors, the results suggest that greater intentional volatility increases the likelihood of both crash and near-crash events. A one-unit increase in intentional volatility is associated with positive vehicular jerk in longitudinal direction increases the chance of crash and near-crash outcome by 15.79 and 12.52 percentage points, respectively. Importantly, intentional volatility in positive vehicular jerk in lateral direction has more negative consequences than intentional volatility in positive vehicular jerk in longitudinal direction. Compared to acceleration/deceleration, vehicular jerk can better characterize the volatility in microscopic instantaneous driving decisions prior to involvement in safety critical events. Finally, the magnitudes of correlations exhibit significant heterogeneity, and that accounting for the heterogeneous effects in the modeling framework can provide more reliable and accurate results. The study demonstrates the value of quasi-experimental study design and big data analytics for understanding extreme driving behaviors in safe vs. unsafe driving outcomes.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

The role of driver head pose dynamics and instantaneous driving in safety critical events: Application of computer vision in naturalistic driving

This paper investigates the role of driver behavior especially head pose dynamics in safety–critical events (SCEs). Using a large dataset collected in a naturalistic driving study, this paper analyzes the head pose dynamics and driving behavior in moments leading up to crashes or near-crashes. The study uses advanced computer vision and mixed logit modeling techniques to identify patterns and relationships between drivers’ head pose dynamics and crash involvement. The results suggest that driver-head pose dynamics, especially poses that indicate distraction and movement volatility, are important factors that can contribute to undesirable safety outcomes. Marginal effects show that angular deviation for head pose dynamics indicated by yaw, pitch and roll increase the likelihood of crash intensity by 4.56%, 4.92% and 8.26% respectively. Furthermore, traffic flow and lane changing also contribute to increase in likelihood of crash intensity. These findings provide new insights into pre-crash factors, especially human factors and safety–critical events. The study highlights the importance of considering human factors in designing driver assistance systems and developing safer vehicles. This research contributes by examining naturalistic driving data at the microscopic level with early detection of behaviors that lead to SCEs and provides a basis for future research on automation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Investigating the relation between instantaneous driving decisions and safety critical events in naturalistic driving environment

The availability of large-scale naturalistic driving data provides enormous opportunities for studying relationships between instantaneous driving decisions prior to involvement in safety critical events (SCEs). This study investigates the role of driving instability prior to involvement in SCEs. While past research has studied crash types and their contributing factors, the role of pre-crash behavior in such events has not been explored as extensively. The research demonstrates how measures and analysis of driving volatility can be leading indicators of crashes and contribute to enhancing safety. Highly detailed microscopic data from naturalistic driving are used to provide the analytic framework to rigorously analyze the behavioral dimensions and driving instability that can lead to different types of SCEs such as roadway departures, rear end collisions, and sideswipes. Modeling results reveal a positive association between volatility and involvement in SCEs. Specifically, increases in both lateral and longitudinal volatilities represented by Bollinger bands and vehicular jerk lead to higher likelihoods of involvement in SCEs. Further, driver behavior related factors such as aggressive driving and lane changing also increases the likelihood of involvement in SCEs. Driver distraction, as represented by the duration of secondary tasks, also increases the risk of SCEs. Likewise, traffic flow parameters play a critical role in safety risk. The risk of involvement in SCEs decreases under free flow traffic conditions and increases under unstable traffic flow. Further, the model shows prediction accuracy of 88.1 % and 85.7 % for training and validation data. These results have implications for proactive safety and providing in-vehicle warnings and alerts to prevent the occurrence of such SCEs.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Inferring safety critical events from vehicle kinematics in naturalistic driving environment: Application of deep learning Algorithms

Advances in sensing technology has enabled the collection of countless terabytes of second-by-second kinematics data. Such data provides opportunities for real-time monitoring of driving behavior and identification of safety critical events (SCEs) including crashes and near crashes. The concept of volatility is relevant in this context, which identifies instability and erratic variations in driving behavior prior to involvement in SCEs. This study utilized vehicle kinematics from a large-scale naturalistic driving data to develop a deep learning approach based on 1D convolutional neural networks (CNN) for inferring SCEs. The data are unique in the sense that such accurate pre-crash data at high fidelity are not available in traditional crash repositories. This study contributes to the literature by providing a first attempt at predicting responses to SCEs by developing deep learning-based CNN architectures using novel driving volatility based kinematic thresholds for a sample of 9553 events. The key contribution lies in developing a volatility-based CNN input layout that is acceptable to CNN schemes and represents the motion kinematics such as speed, acceleration and volatility measures. Several 1D-CNN architectures were developed using layers, numbers of convolutions, layer patterns, and kernels. Shallow and deep architectures were tested, revealing higher accuracy of shallow architectures in detecting SCEs. The optimal number of epochs were identified using an early stopping method while the CNN performance was improved by increasing the number of epochs. The ensemble CNN had the highest predictive accuracy of 95.6% for detection of crashes and near crashes, which was 2.5% higher than the optimal CNN using 20% hold out test data. The ensemble CNN also outperformed classical machine learning models and model performance reported in past studies on detection of SCEs. Finally, these results have implications for identification of safety hotspots and providing real-time alerts and warnings in connected and highly automated vehicle environment including society of automotive engineers levels 3–5.

42 ENGINEERING↗
DOE OSTI2020

Predicting potential adverse events using safety data from marketed drugs

Abstract Background While clinical trials are considered the gold standard for detecting adverse events, often these trials are not sufficiently powered to detect difficult to observe adverse events. We developed a preliminary approach to predict 135 adverse events using post-market safety data from marketed drugs. Adverse event information available from FDA product labels and scientific literature for drugs that have the same activity at one or more of the same targets, structural and target similarities, and the duration of post market experience were used as features for a classifier algorithm. The proposed method was studied using 54 drugs and a probabilistic approach of performance evaluation using bootstrapping with 10,000 iterations. Results Out of 135 adverse events, 53 had high probability of having high positive predictive value. Cross validation showed that 32% of the model-predicted safety label changes occurred within four to nine years of approval (median: six years). Conclusions This approach predicts 53 serious adverse events with high positive predictive values where well-characterized target-event relationships exist. Adverse events with well-defined target-event associations were better predicted compared to adverse events that may be idiosyncratic or related to secondary target effects that were poorly captured. Further enhancement of this model with additional features, such as target prediction and drug binding data, may increase accuracy.

Daluwatte, Chathuri↗
DOE OSTI2017

Computerized System for Safety Verification of External Beam Radiation Therapy Planning

Purpose: To report an assessment of in-house software, Verifier, developed to improve efficacy and efficiency of the radiation therapy (RT) treatment planning process and quality control review (QCR). Methods and Materials: Radiation therapy plan parameters retrieved from our treatment planning database are used by automated tests to give 75 types of warnings, such as prescription and plan discrepancies. The software is continuously updated on the basis of new issues, ideas, and planning policies. Verifier was retrospectively assessed (2007-2015) by examining impact on treatment plan revisions, frequency of quality improvement incident reports of avoidable RT plan-related safety events, unaddressed issues, and staff efficiency. Results: Plan revisions for specific issues declined dramatically in response to implementation of corresponding Verifier tests. Between 2012 and 2015 our institution's total rate of plan revisions dropped from 18.0% to 11.2%. Between 2008 and 2015 specific tests were added to Verifier while the rate of corresponding avoidable safety events was reduced from 0.34% to 0.00% over the same period. Simulations suggest Verifier saves approximately 2 to 5 minutes per QCR. Conclusions: The decrease in quantifiable metrics of plan revisions and incident reports suggests automatic RT plan-checking software enhances patient safety and clinical efficiency. Although only modest time savings may be gained using Verifier for the QCR itself, the greater impact on efficiency is through avoiding late-stage plan modifications and improving documentation via automation. We encourage other institutions to consider working toward adding similar technologies to enhance their RT quality assurance programs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Shear Thickening Electrolytes for High Impact Resistant Batteries

In this paper, we demonstrate a shear thickening electrolyte that stiffens into a solid-like barrier during a high energy event, like a car crash. This barrier prevents the electrodes from shorting during an impact, reducing the risk of fire or catastrophic safety events. In addition, we have demonstrated the ability to cycle NMC/graphite lithium ion cells over 200 cycles with no loss of capacity after formation. Finally, this chemistry introduces multifunctionality to a material previously feared due to its flammability.

25 ENERGY STORAGE↗
DOE OSTI2026

Validating a Dynamic PWR Safety and Security Model?

Nuclear power plants (NPPs) are assessed for safety and security using separate models that cannot capture how an attacker's decisions and a plant's response unfold together in real time, leaving regulators and operators without a complete picture of true plant vulnerability. Traditional probabilistic risk assessment (PRA) methods treat adversarial events as fixed initiators with predetermined outcomes, and are structurally incapable of representing the time-dependent interplay between physical security events, safety system response, and operator mitigative actions. At Idaho National Laboratory (INL), I contributed to the development and validation of Modeling and Analysis for Safety and Security using the Dynamic EMRALD Framework (MASS-DEF). Where static PRA relies on event-tree logic that cannot evolve mid-scenario, MASS-DEF couples a time-dependent dynamic PRA tool EMRALD (Event Modeling Risk Assessment using Linked Diagrams) with attack simulation software, allowing attacker behavior, plant system states, and operator actions to interact across time. My work focused on validating a general Pressurized Water Reactor (PWR) model. I traced model logic against PWR plant to identified errors in logic and confirm accuracy. I then built and tested attack scenarios against a general PWR model to verify that the model produced expected outcomes across all logical pathways. I also contributed a section to a related technical paper applying the same EMRALD platform to radiation dose modeling. Results show that MASS-DEF can quantitatively demonstrate that many plants exceed their regulatory security thresholds. This demonstrated margin provides a technically defensible basis for reducing the number of guards without compromising regulatory compliance. Physical security costs represent roughly 10% of annual operating budgets, making such reductions directly meaningful to INL's mission of sustaining existing commercial NPPs. This internship strengthened my understanding of nuclear systems, probabilistic modeling, and technical writing, and has solidified my pursuit of a career at a national laboratory.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗
DOE OSTI2018

A safety and licensing roadmap to identify the research and development gaps of commercial molten salt reactors

Molten salt reactors (MSRs) both inherently and through good design possess highly desirable safety attributes. Successful commercial deployment of MSRs is heavily dependent on establishing their safety characteristics in a well-coordinated licensing requirements program. The approach outlined is based on adapting the MSR licensing process to the current LWR–based NRC process that exists today, which has been the traditional approach used by new technologies such as the Pebble Bed Reactor licensing application, the Clinch River Breeder Reactor licensing application, and more recently the NuScale licensing application. NRC has recognized some of the unique passively safe aspects of advanced non-LWR reactors and has introduced an advanced reactor strategic plan which will address policy issues that need to be addressed to accommodate advanced reactor concepts within the existing regulatory framework. Examples of some of these policy issues are the use of functional containment, the use of mechanistic source terms, and more reliance on performance-based and risk-informed regulatory processes, such as the Licensing Modernization Project, which emphasizes the use of risk-informed processes to select licensing basis events, safety categorization of SSC, and well-defined defense-in-depth systems. Applying performance-based, risk-informed regulatory processes is, consequently, vital to substantially decreasing the cost, time, and uncertainty of commercial MSR reactor licensing while providing for the health and safety of the public and protecting the environment. This document is intended to provide a roadmap for the MSR licensing and safety activities necessary to provide a success path to commercialization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Lithium Plating Characteristics in High Areal Capacity Li-Ion Battery Electrodes

Li-ion battery degradation and safety events are often attributed to undesirable metallic lithium plating. Since their release, Li-ion battery electrodes have been made progressively thicker to provide a higher energy density. However, the propensity for plating in these thicker pairings is not well understood. Herein, we combine an experimental plating-prone condition with robust mesoscale modeling to examine electrode pairings with capacities ranging from 2.5 to 6 mAh/cm 2 and negative to positive (N/P) electrode areal capacity ratio from 0.9 to 1.8 without the need for extensive aging tests. Using both experimentation and a mesoscale model, we identify a shift from conventional high state-of-charge (SOC) type plating to high overpotential (OP) type plating as electrode thickness increases. Further, these two plating modes have distinct morphologies, identified by optical microscopy and electrochemical signatures. We demonstrate that under operating conditions where these plating modes converge, a high propensity of plating exists, revealing the importance of predicting and avoiding this overlap for a given electrode pairing. Further, we identify that thicker electrodes, beyond a capacity of 3 mAh/cm 2 or thickness >75 μm, are prone to high OP, limiting negative electrode (NE) utilization and preventing cross-sectional oversizing the NE from mitigating plating. Here, it simply contributes to added mass and volume. The experimental thermal gradient and mesoscale model either combined or independently provide techniques capable of probing performance and safety implications of mild changes to electrode design features.

25 ENERGY STORAGE↗
DOE OSTI2023

Preliminary Nuclear Containment Vessel Modeling for Multi-Hazard Probabilistic Risk Assessment under Seismic Hazards and Concrete Degradation

The current practice for natural phenomena hazards (NPH) risk assessment of nuclear facilities is to compute the risk for each hazard independently and then compound the total risk as a combination of single hazard risks. This state of practice does not consider correlations between hazards and the cascading impacts to structures, systems, and components (SSCs), and could thus underestimate the NPH risk or overestimate the nuclear facility safety. Events such as the Fukushima Daiichi accident have highlighted the importance of multi-hazard risk considerations to nuclear power plants (NPPs) that quantify the cascading damage effects to SSCs in the risk models. Moreover, the current fleet of NPPs in the United States is aging; these NPPs are now expected to operate well beyond their initially planned design life. Aging-related deterioration can potentially decrease the capacity of critical structures such as containment vessels to withstand NPH. Such aging considerations may not be adequately accounted for by the current NPH risk assessment guidelines. This paper presents a preliminary modeling and simulation of a representative reinforced concrete containment vessel subjected to seismic mainshock and aftershock considering concrete degradation due to alkali silica reaction. The broader aim is to develop multi-hazard time-dependent fragility functions that could be subsequently used in the probabilistic risk assessment (PRA) model. The multi-hazard component comes into play due to the consideration of damage to the containment vessel under seismic loads and concrete degradation. Consideration of concrete degradation also brings into play the time-dependent nature of the containment vessel response. The response of the containment vessel under varying degrees of concrete degradation to seismic loads is investigated. The results presented are simulated using the Multi-hazard Analysis for STOchastic time-DOmaiN phenomena (MASTODON) software for seismic analysis and the Blackbear software for concrete degradation and damage modeling. Both software are open source and developed within the Multiphysics Object-Oriented Simulation Environment (MOOSE).

42 ENGINEERING↗
DOE OSTI2022

Autonomous System Subversion Tactics: Prototypes and Recommended Countermeasures

One of the fielding requirements for Advanced and Small Modular Reactors (AR/SMR) is the ability to support remote and autonomous operations. Autonomous Control Systems (ACS) are found on platforms such as Autonomous Space Vehicles, Cruise Missiles, and advanced driver-assistance systems. Each of these ACS implementations depends upon a set of decision support subsystems responsible for supporting Autonomous Mission Managers (names vary based upon field and author preferences). These Autonomous Mission Managers receive inputs from system sensors (e.g., LIDAR collection from an automobile travelling down a street; transients from a nuclear reactor), and perform a set of classifications (e.g., Red Traffic Light; Small Pedestrian at 10m; Load Rejection; Single Coolant Pump Trip), and then use these classifications in combination with recommendation algorithms to achieve platform goals (e.g., Stop the Vehicle at the Traffic Light, Avoid the Small Pedestrian; Trip the Reactor to prevent a Safety Event). The design, implementation, and fielding of an ACS capability will alter the cyber-attack surface such that existing risk management plans will need to be updated to include how to protect and defend against data-science and decision-support-system attack classes. These attack classes would include protection of the design and training environments where algorithm selection and testing and training data would be obvious attack vectors. These attack classes would also require an informed set of detection and response procedures to identify anomalous behaviors and document best practices for anomaly assessment and vulnerability mitigation and remediation. Last year we published a Cyber Threat Assessment Methodology for Autonomous and Remote Operations for AR/SMRs along with a companion publication on Cyber Attack and Defense Use Cases. The focus of the methodology was on describing and enumerating ACS processes, components, and functions such that security engineers could: evaluate subversion options against the target; identify threat actor attributes and capabilities derived from each subversion option; and identify security controls and response countermeasures. The Use Cases document offered detailed methodology examples including an assessment of a Military Base SMR, an Autonomous System Decision Loop, and implementation of AR/SMR Machine Learning algorithms. Our proposal at the end of last year was to focus on implementation of subversion prototypes related to the last Use Case area: AR/SMR Machine Learning (ML) Algorithms. We included six attack scenarios in our Use Cases paper: a Poisoning Attack against ML functions implemented using an FPGA; a Trojaning Attack against ML classifiers exploiting the excitability of Nuclear Engineers; a Backdooring Attack against ML Training environments to ensure persistence of an attack vector; a False Positive Evasion Attack against multi-factor Access Control Systems using clever inputs; an Inference Attack against ML models by an Insider with access to the Operational environment; and an Adversarial Reprogramming Attack against a Material Access Control Video Surveillance System. At the beginning of this year these six attack scenarios were provided to our research teams at Georgia Tech and Idaho State University and each team successfully implemented a subversion attack against a ML implementation to include transient misclassifications. While this is a notable outcome from this type of research, this paper offers the reader insight into not only how to structure and execute these types of attacks, but into the thought process behind how the researcher investigated the problem space, performed initial algorithm implementation, and the trial-and-error behind arriving at the successful subversion prototypes. We include in this paper a set of associated Scenarios on how these subversion prototypes could be implemented and an initial set of guidance for AR/SMR architects, Nuclear Regulators, and Cyber Defenders to implement awareness and defense capabilities into their current operational portfolios.

42 ENGINEERING↗
DOE OSTI2013

Building a Strong Culture That Produces Sustainable Performance - 13444

Washington River Protection Solutions LLC (WRPS) has been involved with culture improvement for a number of years which has included co-chairing the industry effort to develop the EFCOG safety culture guidance documents [1, 2], and integration of this guidance into organizational processes and behavior expectations, described in more detail below. As various organizational cultural assessments have been periodically performed, and subsequent actions implemented to address improvement opportunities, organizational performance has shown improvement. Culture improvement is evident in the company's industrial safety statistics, event rates, safety culture survey results, employee morale, productivity, leadership effectiveness, and employee engagement. There does appear to be a relationship between striving to demonstrate behaviors consistent with excellent safety culture and good organizational performance over the past couple of years at WRPS. As performance continues to be evaluated, an improvement opportunity was identified to further enhance performance through field oriented behavioral/cultural improvement activities. WRPS recently conducted a three month effort to improve consistent implementation of management expectations by increasing management field presence with a focus on interacting real-time with workers and first line supervisors, and changing behaviors as appropriate. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Probabilistic Risk Assessment of Spent Fuel Pool Cooling System Failure Considering Safety and Security Initiating Events

In order to quantify the probable risk associated with a safety system, nuclear safety risk evaluation process is applied which is generally performed in three steps. The first step is to calculate the damage frequency of a critical system. This step is followed by the radioactive source term estimation due to the potential system damage. The final step is the assessment of off-site consequences. The safety risk formula is shown by Eq. (1). P{sub F} is the probability of safety system failure (the probability used describes a random, independent event), and C is the consequence value. R{sub SAF} = P{sub F} * C (1) The security risk associated with an attack on critical infrastructure and/or special nuclear materials theft is usually evaluated using three- term risk equation (probability of attack, probability of adversary success, and consequence). A security threat and the related initiating events are not considered in a pure safety risk analysis. The vulnerability of the physical protection system is performed independently even though the consequence resulting from a security initiating event can be similar to a safety related initiating event and thus gets double counted. The security risk can be calculated using Eq. (2) R{sub SEC} = P{sub A} * P{sub S} * C (2) PA is the probability of attack, P{sub S} is the probability of adversary success (P{sub S} = 1 - P{sub I} P{sub N}). P{sub I} being the probability of interruption which accounts for the probabilities of detection of all timely detection elements along an adversary path. PN being the probability of neutralization: the result of engagement of the response force with the adversaries after the interruption has occurred. C is the consequence magnitude from a successful attack. The two security probabilities do not describe random, independent events. As a result, because of the differences in the randomness and independence of the respective safety and security probabilities, the safety and the security risks are currently calculated separately. The consequence term in nuclear safety and security risk evaluation have the same definition. An effort is made here to study the effect on the overall risk by considering security initiating events along with the subsequent failure of the intermediate safety element. The target selected is a spent fuel storage pool for analyzing the public risk arising from the interplay between nuclear security and safety initiating events. EASI (Estimate of Adversary Sequence Interruption) model is used to calculate the probability of interruption related to a security system that leads to the calculation of the security initiating event frequency. Further progression of this initiating security event to a top event is analyzed using a Probabilistic Safety Assessment (PSA) tool, SAPHIRE (version 8) to obtain a joint pool damage frequency. For the progression of events leading to a top event, the failure of the intermediate safety elements is assumed to be external random events such as heavy load drop and fire events. The objectives of this study is to (1) determine the double accounting of frequency of failure, risk and allocated resources in the scenario of a non-random security initiating event leading to a safety random event chain (2) compare the damage frequency from pure security with the frequency of combined security-safety failure event and pure safety failure events. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗