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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Cyber Conservative Operations

In an era of increasingly sophisticated and pervasive cyber threats, robust cyber resilience strategies are more critical than ever. This paper introduces the concept of Cyber Conservative Operations, a proactive approach designed to assist critical infrastructure owners and operators in managing risk and maintaining resilience in the face of imminent, yet not occurring, cyber events. By leveraging the principles of Cyber-Informed Engineering (CIE) and the energy sector’s practice of conservative operations, Cyber Conservative Operations offer a framework for planning and executing coordinated active defense and resilience-oriented actions ahead of cyber events. This approach aims to minimize the consequences of digitally-enabled hazards and ensure swift recovery from anticipated cyber threats. Cyber Conservative Operations enhance the ability of owners and operators to execute pre-planned defense and resilience actions, thereby reducing the impact of digitally-enabled hazards. These operations are crucial for addressing impacts that cannot be precisely quantified or forecasted and for preparing organizations for rapid recovery from imminent digital threats. The paper begins with a discussion of conservative operations as applied to the bulk power system (BPS), a current mechanism allowing BPS entities to enact defensive operating plans to mitigate impending grid unreliability. Building on this model, we present a concept for implementing cyber conservative operations at asset owner facilities. The paper concludes with two case studies of cyber conservative operations drawn from high-profile cyber events, illustrating the practical application and benefits of this proactive approach.

42 - ENGINEERING↗
DOE OSTI2024

Advancing spatiotemporal forecasts of CO 2 plume migration using deep learning networks with transfer learning and interpretation analysis

Accurate and timely forecasts of CO 2 plume distribution throughout the injection and post-injection phases are crucial for detecting plume migration, assessing leakage risks, and supporting operational decisions in geologic carbon storage (GCS). Current convolutional neural network-based approaches primarily focus on spatial information and overlook temporal dependencies in plume distributions, thus limiting their ability to capture dynamic movement effects and provide accurate predictions of plume migration. In this work, we propose two deep learning models, Auto-Encoder (AE)-LSTM and Encoder-Decoder (ED)-ConvLSTM, each uniquely designed to capture both spatial and temporal features. We apply the proposed methods to forecast the dynamic distribution of CO 2 plumes based on 108 reservoir simulations over a 30-year injection and a 30-year post-injection period. The results indicate that the ED-ConvLSTM model outperforms the AE-LSTM model in accurately predicting the spatiotemporal dynamics of CO 2 plume migration, achieving R 2 values above 0.99. To provide a deeper understanding of these model predictions, we employ a gradient-based explanation method on the trained models. This approach provides insights into the influence of input variables on plume migration forecasts and uncovers the underlying prediction mechanisms of the proposed models. Furthermore, we introduce a transfer learning technique, enabling fast and accurate plume migration forecasting in the post-injection phase by leveraging the trained model during the injection phase. This reduces the necessity for extensive data collection or re-training. In conclusion, the methods proposed in our work enhances the performance and interpretability of CO 2 plume migration forecasts, thereby facilitating informed decision-making throughout the entire lifecycle of GCS applications.

58 GEOSCIENCES↗
DOE OSTI2025

Decoding substance use disorder severity from clinical notes using a large language model

Substance use disorder (SUD) poses a major concern due to its detrimental effects on health and society. SUD identification and treatment depend on a variety of factors such as severity, co-determinants (e.g., withdrawal symptoms), and social determinants of health. Existing diagnostic coding systems used by insurance providers, like the International Classification of Diseases (ICD-10), lack granularity for certain diagnoses, but American clinicians will add this granularity (as that found within the Diagnostic and Statistical Manual of Mental Disorders classification or DSM-5) as supplemental unstructured text in clinical notes. Traditional natural language processing (NLP) methods face limitations in accurately parsing such diverse clinical language. Large language models (LLMs) offer promise in overcoming these challenges by adapting to diverse language patterns. This study investigates the application of LLMs for extracting severity-related information for various SUD diagnoses from clinical notes. We propose a workflow employing zero-shot learning of LLMs with carefully crafted prompts and post-processing techniques. Through experimentation with Flan-T5, an open-source LLM, we demonstrate its superior recall compared to the rule-based approach. Focusing on 11 categories of SUD diagnoses, we show the effectiveness of LLMs in extracting severity information, contributing to improved risk assessment and treatment planning for SUD patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Risk-Aware Measurement Synchronization and Recovery for DSSE With Heterogeneous Data Sources

Power distribution systems are increasingly integrating heterogeneous sensors with varying data reporting rates and types, which pose challenges to achieving observability at the desired temporal resolution of distribution system state estimation (DSSE). Multisensor failures caused by extreme events exacerbate these issues, introducing substantial uncertainties into DSSE. This article proposes a novel solution to these challenges by ensuring high-resolution system observability despite heterogeneous data sources and multisensor failures. First, a deep learning architecture combining long short-term memory (LSTM) and graph convolutional network (GCN) is employed to synchronize meters with different reporting rates, aiming to achieve system observability. A random-walk-model-based approach is introduced to generate pseudo-measurements while properly characterizing their uncertainties under multisensor failures. Finally, a disaster-risk-informed observability metric (RiOM) is defined to quantify the uncertainty associated with state estimation results. The proposed framework offers deeper insights into the system observability on the fly compared with conventional analysis. The effectiveness of the framework is demonstrated on an IEEE standard test case and a large-scale real-world distribution feeder in mid-Minnesota in the U.S.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

Pre-flight risk assessment in Emergency Medical Service (EMS) helicopters

The Emergency Medical Service (EMS) industry has been the subject of several television and newspaper articles (Harvey and Jensen, 1987) which emphasized the negative aspects, (e.g., fatalities and high accident rates), rather than the life saving services performed. Until recently, the accident rate of the EMS industry has been five times as high as that of other civil helicopters. This high accident rate has been coupled with the dramatic rise in the number of programs. The industry has built from a single service at its inception in 1972, to over 180 in 1987 (Spray, 1987), to the point that 93 percent of the contiguous U.S. is now covered by some type of EMS service. These factors prompted the National Transportation Safety Board (NTSB) to study the accidents that occurred between May 11, 1978 and December 3, 1986 (NTSB, 1988). The NTSB report concluded that 'Sound pilot judgment is central to safe flight operations.' They further stated that '... factors unique to EMS helicopter operations--such as the influence of the mission itself, program competition, and EMS program management perspectives--can drastically influence pilot judgment during the EMS mission.' One of the most difficult decisions that a pilot must make is whether to accept or decline a mission. A pre-flight risk assessment system (SAFE) was developed at NASA-Ames Research Center for civil EMS operations to aid pilots in making this decision objectively. The ability of the SAFE system to predict mission risk profiles was tested at an EMS facility. The results of this field study demonstrated that the usefulness of SAFE was highly dependent on the type of mission flown. SAFE is now being modified so that it can 'learn' with each mission flown. For example, after flying a mission to a particular site, an EMS pilot would input information about this mission into the system, such as new buildings, wires, or approach procedures. Then, the next time a pilot flew a similar mission or one to the same area, this additional information would be taken into account in computing a risk assessment.

Shively, R. J.↗
NASA NTRSDate not supplied

Probability of Obstacle Collision for UAVs in Presence of Wind

For incorporation of unmanned aerial vehicles into the National Airspace, ensuring safety of the airspace including the vehicles, people, and property on the ground is of utmost importance. One of the safety-critical factors for unmanned aviation flights is the risk of deviating from a planned trajectory resulting in a variety of hazards, including potential loss of separation between vehicle and obstacles or unexpected battery energy consumption. Off-nominal conditions introduced by component failures, degraded controllability and environmental disturbances such as wind gusts can lead to unacceptable unexpected deviations from the flight trajectory. It is essential to accurately model such effects on the flight trajectory while computing safety thresholds such as minimum separation from surrounding obstacles, available battery resource to complete the mission or determining delay in the expected time of arrival of flights. In this paper, a tool is presented based on Gaussian Process Regression for wind representation over a pre-defined trajectory for fast, yet approximated, in-time evaluation of possible trajectory deviations caused by wind gusts. The deviation in the planned trajectory caused by wind is further simulated utilizing a 6 degrees-of-freedom (DOF) UAV trajectory simulator comprising of a rotorcraft lumped-mass model with LQRI controller. Both steady-state wind and wind gust effects are investigated. The probability of collision with obstacle is computed and demonstrated on real flight data from experimental flights of an octocopter at NASA Langley Research Center in the presence of simulated obstacles and wind conditions. Effect of varying wind conditions and varying UAV airspeed is further demonstrated on experimental flights in the presence of wind measured by ground based weather service stations. The proposed approach would eventually benefit timely mitigation of current and future safety-critical events in autonomous systems by enabling risk-informed decision making.

Portia Banerjee↗
DOE OSTI2025

Climate Nowcasting

The climate is changing so rapidly that climatologies based on historical statistics cannot reliably capture the current risk of extreme weather events hazardous to society. Decision relevant projections of weather extreme probability over the next 10–15 years are needed to enable adaptation and resilience in the face of this evolving risk. Current weather forecasts/predictions and long-term climate projections for decades into the future are inadequate for providing this information to stakeholders that need it, targeting forecast horizons either too short or too far into the future. We argue that a new approach is needed: climate nowcasting. Climate nowcasting would focus on user-inspired extreme metrics over the next 10–15 year time frame, targeting specific impacts and locations down to a local scale, by engaging with stakeholders to understand their needs and provide information in a format relevant for decision making. Importantly, climate nowcasting will not consist of a single approach or data set, involving rather data fusion from different sources of information: simulations, observations and data driven methods, likely through different weights depending on the metric of interest. Predictions must be accompanied by serious engagement with stakeholders facing climate risks, and clearly present uncertainties and limitations of any prediction. Such a vision is very different from how typical climate or weather forecasts are applied today.

Gettelman, Andrew↗
DOE OSTI2023

Deep Sigma Point Processes-Assisted Chance-Constrained Power System Transient Stability Preventive Control

Here this paper proposes a deep sigma point processes (DSPP)-assisted chance-constrained power system transient stability preventive control method to deal with uncertain renewable energy and loads-induced stability risk. The traditional transient stability-constrained preventive control is reformulated as a chance-constrained optimization problem. To deal with the computational bottleneck of the time-domain simulation-based probabilistic transient stability assessment, the DSPP is developed. DSPP is a parametric Bayesian approach that allows us to predict system transient stability with high computational efficiency while accurately quantifying the confidence intervals of the predictions that can be used to inform system instability risk. To this end, with a given preset confidence probability, we embed DSPP into the primal dual interior point method to help solve the chance-constrained preventive control problem, where the corresponding Jacobian and Hessian matrices are derived. Comparison results with other existing methods show that the proposed method can significantly speed up preventive control while maintaining high accuracy and convergence

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Predicting Biological Cleanliness: An Empirical Bayes Approach for Spacecraft Bioburden Accounting

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Bayesian Framework for Bioburden Density Estimation in Planetary Protection

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2023

An Integrated Framework for Risk Assessment of Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology Refinement and Exploration

This report documents activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2023 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a technical basis to support effective, and secure DI&C technologies for digital upgrades/designs. A risk assessment-informed framework was proposed for this strategy, which aims to (1) provide a best-estimate, risk informed capability to quantitatively estimate the safety margin obtained from plant modernization, especially for safety-related DI&C systems, (2) support and supplement existing risk informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems, (4) assure the long-term safety and reliability of safety-related DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals, the LWRS-developed framework provides a means to address relevant technical issues by: (1) defining a risk informed analysis process for DI&C upgrade that integrates hazard analysis, reliability analysis, and consequence analysis, (2) applying risk informed tools to address common cause failures (CCFs) and quantify corresponding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the component level, system level, and plant level, and (4) providing insights and suggestions on designs to manage the risks, thus to support the development and deployment of advanced DI&C technologies in nuclear power plants (NPPs). Adding diversity within a system or components is the primary means to eliminate and mitigate CCFs, but diversity also increases system complexity and may not address all sources of systematic failures. Optimization of diversity and redundancy applications for the safety-critical DI&C systems remains a challenge. To deal with the technical issues in addressing potential software CCFs in safety-related DI&C systems of NPPs and supporting relevant design optimization, the proposed framework provides: (a) A best-estimate, risk informed capability to address new technical digital issues quantitatively, focusing on software CCFs in safety-related DI&C systems of NPPs; (b) A common and a modularized platform for DI&C designers, software developers, cybersecurity analysts, and plant engineers to predict and prevent risk in the early design stage of DI&C systems; (c) Technical bases and risk informed insights to assist users address the risk informed alternatives for evaluation of CCFs in safety-related DI&C systems of NPPs; and (d) A risk informed tool that offers a capability of design architecture evaluation of various DI&C systems to support system design decisions in diversity and redundancy applications. The research and development efforts of this project in FY 2023 are focused on refining current methods on software CCF modeling and estimation and exploring additional innovative approaches to risk assessment of DI&C systems to enable a more comprehensive and complete assessment of various safety-related DI&C design architectures. The primary audience of this report are DI&C designers, engineers, and probabilistic risk assessment (PRA) practitioners. This includes stakeholders, such as the nuclear utilities and regulators who consider the deployment and upgrade of DI&C systems, DI&C software developers and reviewers, and cybersecurity specialists. It should be noted that all the analyses are performed for the demonstration of the methodology, not for the evaluation of an actual digital control system. Results are obtained based on limited design information and testing data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2014

Exploration Systems Development (ESD) Approach to Enterprise Risk Management

The National Aeronautics and Space Administration (NASA) Exploration Systems Development (ESD) Division has implemented an innovative approach to Enterprise Risk Management under a unique governance structure and streamlined integration model. ESD's mission is to design and build the capability to extend human existence to deep space. The Enterprise consists of three Programs: Space Launch System (SLS), Orion, and Ground Systems Development and Operations (GSDO). The SLS is a rocket and launch system that will be capable of powering humans, habitats, and support systems to deep space. Orion will be the first spacecraft in history capable of taking humans to multiple destinations within deep space. GSDO is modernizing Kennedy's spaceport to launch spacecraft built and designed by both NASA and private industry. ESD's approach to Enterprise Risk Management is commensurate with affordability and a streamlined management philosophy. ESD Enterprise Risk Management leverages off of the primary mechanisms for integration within the Enterprise. The Enterprise integration approach emphasizes delegation of authority to manage and execute the majority of cross-program activities and products to the individual Programs, while maintaining the overall responsibility for all cross-program activities at the Division. The intent of the ESD Enterprise Risk Management approach is to improve risk communication, to avoid replication and/or contradictory strategies, and to minimize overhead process burden. This is accomplished by the facilitation and integration of risk information within ESD. The ESD Division risks, Orion risks, SLS risks, and GSDO risks are owned and managed by the applicable Program. When the Programs have shared risks with multiple consequences, they are jointly owned and managed. When a risk is associated with the integrated system that involves more than one Program in condition, consequence, or mitigation plan, it is considered an Exploration Systems Integration (ESI) Risk. An ESI risk may require visibility and risk handling by multiple organizations. The Integrated Risk Working Group (IRWG) is a small team of Risk experts that are responsible for collaborating and communicating best practices. In addition, the forum facilitates proper integration of risks across the Enterprise. The IRWG uses a Continuous Risk Management approach for facilitating the identification, analysis, planning, tracking, and controlling of ESI Risks. The ESD Division, Programs, and Integrated Task Teams identify ESI Risks. The IRWG maintains a set of metrics for understanding Enterprise Risk process and the overall Risk Posture. The team is also actively involved in the modeling of risk for Enterprise Performance Management. With the Enterprise being constrained in Schedule and Budget, and with significant technical complexity, the appropriate use of Risk Management techniques is crucial to the success of the Enterprise. The IRWG achieves this through the modified approach, providing a forum for collaboration on risks that cross boundaries between the separate entities.

Bauder, Stephen P.↗
DOE OSTI2023

Digital Engineering and Cybersecurity Decision Analysis in Early Phases of SMR-Driven IES Projects

Considerable efforts are underway to ensure cybersecurity is integrated into the systems engineering lifecycle. Cyber-informed engineering and security-by-design frameworks are intended to identify and engineer out cybersecurity risks throughout the lifecycle. While these approaches are valuable for promoting the need to include cybersecurity considerations in early design phases to create more secure systems, they may not consider the entirety of digital risks. Digital risks in a digital instrumentation and control system include adversarial and unintentional risks from internal and external factors, such as human performance errors, design flaws, environmental conditions, and equipment degradation or failure. This report provides a detailed discussion on digital risk prior to describing the background and concept of operations for a small modular reactor-driven integrated energy system connected to industrial applications. The challenges of competing objectives and competing stakeholder requirements are discussed and the impacts on digital engineering, security considerations, and interdependencies are evaluated for mission-level, facility-level, and system-level decisions.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS1988

Radiation protection guidelines for space missions

The current radiation protection guidelines of the National Aeronautics and Space Administration (NASA) were recommended in 1970. The career limit was set at 4.0 Sv (400 rem). Using the same approach as in 1970 but current risk estimates, a considerably lower career limit would obtain today. Also, there is now much more information about the radiation environments that will be experienced in different missions. Furthermore, since 1970 women have joined the ranks of the astronauts. For these and other reasons, it was considered necessary to re-examine the radiation protection guidelines. This task has been undertaken by the National Council on Radiation Protection and Measurements Scientific Committee 75. Within the magnetosphere, the radiation environment varies with altitude and inclination of the orbit. In outer space missions, galactic cosmic rays, with the small but important heavy-ion component, determine the radiation environment. The new recommendations for career dose limits, based on lifetime excess risk of cancer mortality, take into account age at first exposure and sex. The career limits range from 1.0 Sv (100 rem) for a 24-y-old female up to 4.0 Sv (400 rem) for a 55-y-old male, compared with the previous single limit of 4.0 Sv (400 rem). The career limit for the lens of the eye has been reduced from 6.0 Sv (600 rem) to 4.0 Sv (400 rem).

NASA Discipline Radiation Health↗
NASA NTRS2016

Aviation Safety Concerns for the Future

The Future Aviation Safety Team (FAST) is a multidisciplinary international group of aviation professionals that was established to identify possible future aviation safety hazards. The principle was adopted that future hazards are undesirable consequences of changes, and a primary activity of FAST became identification and prioritization of possible future changes affecting aviation. Since 2004, FAST has been maintaining a catalogue of "Areas of Change" (AoC) that could potentially influence aviation safety. The horizon for such changes is between 5 to 20 years. In this context, changes must be understood as broadly as possible. An AoC is a description of the change, not an identification of the hazards that result from the change. An ex-post analysis of the AoCs identified in 2004 demonstrates that changes catalogued many years previous were directly implicated in the majority of fatal aviation accidents over the past ten years. This paper presents an overview of the current content of the AoC catalogue and a subsequent discussion of aviation safety concerns related to these possible changes. Interactions among these future changes may weaken critical functions that must be maintained to ensure safe operations. Safety assessments that do not appreciate or reflect the consequences of significant interaction complexity will not be fully informative and can lead to inappropriate trade-offs and increases in other risks. The FAST strongly encourages a system-wide approach to safety risk assessment across the global aviation system, not just within the domain for which future technologies or operational concepts are being considered. The FAST advocates the use of the "Areas of Change" concept, considering that several possible future phenomena may interact with a technology or operational concept under study producing unanticipated hazards.

emerging risks↗
DOE OSTI2026

Developing a Process for Collaboration-Based Siting of a Federal Consolidated Interim Storage Facility in the United States: Learning from Communities Living with Legacy Waste

In June of 2023, the U.S. Department of Energy (DOE), Office of Nuclear Energy (NE) announced the selection of its Collaboration-Based Siting (CBS) Consortia, a group of 12 awardees, including the Consortium for Risk Evaluation with Stakeholder Participation (CRESP) (led by Vanderbilt University) to assist DOE-NE with the development of its process for siting a federal consolidated interim storage facility (FCISF) for spent nuclear fuel (SNF) storage. At this time, DOE NE is not soliciting interested host communities, rather the CBS Consortia are tasked with in-depth engagement, mutual learning, and capacity building to provide DOE NE with feedback on the CBS process. CRESP’s objective is to engage communities in two regions (the Pacific Northwest and the Southeast) with sites currently storing defense- and research-related SNF to foster learning concerning the best and worst practices in community participation in risk-informed decision making. This includes learning from existing structures for public input in radioactive waste management decision making [e.g. citizen advisory boards (CABs)] and other local parties about how to build and sustain trust among the parties. CRESP has been working to engage stakeholders and Tribes surrounding two DOE sites historically differing in receptiveness to engaging with DOE and trusting in DOE to accomplish its missions. CRESP’s approach to date has consisted of (1) engaging voluntarily members and former members of the DOE Office of Environmental Management’s CABs to develop a mutual understanding of their perspectives, values, and experiences related to risk and participatory decision making; (2) engaging members of those communities that would likely be part of any radioactive waste management discussions and who may provide valuable feedback for the development of the CBS process; and in the longer term, (3) engaging communities to foster knowledge sharing on understanding and definitions of risk and how risk is factored into community decision making. Our emphasis is to identify opportunities for improving risk communication frameworks, strategies, and decision making. The selection of a future FCISF site is likely to result in the creation of a structure similar to a CAB composed of members of the local community. We anticipate that these insights can help DOE NE learn from existing participatory risk communication structures in place at sites storing defenseor research-related SNF to understand best practices for fostering enduring and participatory relationships with future CABs. Within this paper, we (1) describe CRESP’s overall approach to assisting DOE NE with maturing the CBS process; (2) present a summary of preliminary phase 1 project results, including the development of a body of knowledge (describing available resources to support community engagement, risk communica tion, and participatory decision making) and community ecosystem information (leveraging demographic, social, economic, and environmental attribute mapping and advanced sentiment analysis of social media data); and (3) based on these results, provide observations for future research opportunities to support the development of CBS processes for radioactive waste management facilities, generally.

collaboration-based siting↗
DOE OSTI2022

Risk-Informed Operations and Maintenance Decision Making Using Deep Reinforcement Learning

A challenge for operating nuclear power plants is the significant cost of operations and maintenance, at times consuming up to 66% of annual operating costs. This project aims to build a framework for a risk-informed asset-management tool that integrates inspections, repairs, spare-part inventory, supply chain, and business choices to lower overall O&M costs. Our approach uses a combination of data-driven modeling and deep reinforcement learning to create and implement optimal maintenance policies for the existing nuclear fleet, as well as new advanced reactors. The creation of an asset management tool that uses these advanced methods will give operators new capabilities to help reduce the burden of O&M spending in nuclear power plants.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2016

Promoting a Culture of Tailoring for Systems Engineering Policy Expectations

NASA's Marshall Space Flight Center (MSFC) has developed an integrated systems engineering approach to promote a culture of tailoring for program and project policy requirements. MSFC's culture encourages and supports tailoring, with an emphasis on risk-based decision making, for enhanced affordability and efficiency. MSFC's policy structure integrates the various Agency requirements into a single, streamlined implementation approach which serves as a "one-stop-shop" for our programs and projects to follow. The engineers gain an enhanced understanding of policy and technical expectations, as well as lesson's learned from MSFC's history of spaceflight and science missions, to enable them to make appropriate, risk-based tailoring recommendations. The tailoring approach utilizes a standard methodology to classify projects into predefined levels using selected mission and programmatic scaling factors related to risk tolerance. Policy requirements are then selectively applied and tailored, with appropriate rationale, and approved by the governing authorities, to support risk-informed decisions to achieve the desired cost and schedule efficiencies. The policy is further augmented by implementation tools and lifecycle planning aids which help promote and support the cultural shift toward more tailoring. The MSFC Customization Tool is an integrated spreadsheet that ties together everything that projects need to understand, navigate, and tailor the policy. It helps them classify their project, understand the intent of the requirements, determine their tailoring approach, and document the necessary governance approvals. It also helps them plan for and conduct technical reviews throughout the lifecycle. Policy tailoring is thus established as a normal part of project execution, with the tools provided to facilitate and enable the tailoring process. MSFC's approach to changing the culture emphasizes risk-based tailoring of policy to achieve increased flexibility, efficiency, and effectiveness in project execution, while maintaining appropriate rigor to ensure mission success.

Blankenship, Van A.↗