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At least 1,621 records · Page 90

Identifying and Documenting Expert Knowledge, A Practical Study of Design Patterns

Globalization, powered by digitization, is increasing technology growth and knowledge transfer rates to levels not seen in the previous 3500 years, obscuring absolute truth and accelerating rates of innovation and production. Competing,or remaining competitive, in this global marketplace requires a learning organization adopt a method of capture, retention, and reuse for demonstrated tacit knowledge to accelerate the development of increasingly complex systems of high quality at a reasonable cost. The architectural theory of Patterns and Pattern Language is a validated methodology for mining tacit domain knowledge from a proven system. This work applies architectural theory to a multi-year development experiment and captures exposed knowledgeas a design patternin a model based systems engineering tool, demonstrating applicability ofdigital engineering initiativesin thedescription and reuse of expert design knowledge.By creating and archiving model based expressions of expert knowledge,a learning organization canimprovepractical decision making and avoid uninformed concept phase decision making

systems engineering↗

An Isolated Mass-gap Black Hole or Neutron Star Detected with Astrometric Microlensing

We present the analysis of five black hole candidates identified from gravitational microlensing surveys. Hubble Space Telescope astrometric data and densely sampled light curves from ground-based microlensing surveys are fit with a single-source, single-lens microlensing model in order to measure the mass and luminosity of each lens and determine if it is a black hole. One of the five targets (OGLE-2011-BLG-0462/MOA-2011-BLG-191 or OB110462 for short) shows a significant >1 mas coherent astrometric shift, little to no lens flux, and has an inferred lens mass of 1.6–4.4 M ⨀ . This makes OB110462 the first definitive discovery of a compact object through astrometric microlensing and it is most likely either a neutron star or a low-mass black hole. This compact-object lens is relatively nearby (0.70–1.92 kpc) and has a slow transverse motion of <30 kms -1 . OB110462 shows significant tension between models well fit to photometry versus astrometry, making it currently difficult to distinguish between a neutron star and a black hole. Additional observations and modeling with more complex system geometries, such as binary sources, are needed to resolve the puzzling nature of this object. For the remaining four candidates, the lens masses are <2M ⨀ , and they are unlikely to be black holes; two of the four are likely white dwarfs or neutron stars. We compare the full sample of five candidates to theoretical expectations on the number of black holes in the Milky Way (∼10 8 ) and find reasonable agreement given the small sample size.

Astrophysical black holes↗

Long-Term Health Metric Development Effort

INTRODUCTION Probabilistic Risk Assessment (PRA) is a methodology applied when high-stakes decisions need to be made about complex systems. PRA often uses risk minimization to aid decision making, such as when establishing vehicle requirements or resource allocations. The NASA Human System Risk Board (HSRB) maintains the human spaceflight risk postures, many of which have in-mission medical outcomes, but the full risk set also includes performance and Long-Term Health (LTH) outcomes. An effort is underway to determine the dependencies between LTH risk outcomes and mission characteristics and to identify metrics for quantifying LTH outcomes. The LTH metrics will be incorporated into future PRA tools currently under development to support crew health and performance decision making. LONG-TERM HEALTH (LTH) RISK LTH risk encompasses the timeframe from immediately post-flight, through the rest of an astronaut’s career, through retirement, and until death. LTH outcomes include the time and interventions needed for the astronaut to return to preflight physiological states after experiencing spaceflight hazards and recovery from any in-mission medical events that persist into the post-flight timeframe. It includes chronic complications that may arise due to experiencing in-flight medical conditions or injuries and medical conditions that occur later in life with a higher probability of occurrence or with more severity because of their spaceflight exposure. Finally, LTH outcomes can also include a reduction in life expectancy due to spaceflight exposures. Eighteen of the HSRB risks contain an LTH component, each with a unique incidence rate and consequence severity. ON-GOING LTH METRIC DEVELOPMENT EFFORTS Based on guidance from a stakeholder workshop held on June 1, 2023, only LTH outcomes with causes tied to spaceflight experience that are significantly different from normal healthy aging and which can be affected by changes in in-mission resources are targeted for inclusion within the PRA modeling efforts. Current on-going efforts include a sensitivity analysis of potential in-flight medical conditions to determine which conditions may persist into post-flight and how the consequences may be affected by in-mission medical resources. Also underway is determination of differences in the time for a return to preflight physiological baselines when in-flight countermeasures are available versus when they are not. Analyses performed with publicly available astronaut data and collaborations with the Lifetime Surveillance of Astronaut Health team are being designed to understand which LTH outcomes differ significantly from natural healthy aging. Information exchanges are also occurring with LTH risk custodians to understand how they quantify outcomes. FUTURE PLANS Risk characteristics will be determined for each LTH risk that meets our inclusion criteria and the dependencies of their outcomes to in-mission resources and mission characteristics will be established. The individual risks will be integrated together into one or more high level LTH metrics for inclusion in the PRA tool, so that LTH risks can be considered along with in-mission risks during crew health and performance system decision support trade studies.

B. E. Lewandowski↗

Development of the Miniature Total Organic Carbon Analyzer

Monitoring the Total Organic Carbon (TOC) in spacecraft potable water will be of major importance in long-duration human space exploration. In-flight analysis of potable water produced from a regenerative water processor provides immediate feedback on the quality of reclaimed water for crew health as well as water processing system health monitoring. This paper updates the progress in development of the next generation Total Organic Carbon Analyzer (TOCA) designed for the unique requirements of an exploration-class mission. The current objective is to design, build, certify, deliver, and operate a TOCA technology demonstration on the International Space Station (ISS). The next generation analyzer system technology was previously developed and selected among a feasibility study of other options. The new system provides primary advantages of reduced mass and volume through reduced system complexity and reduced need for consumables; therefore, the flight project is named MiniTOCA. The project has recently completed design of the tech demo instrument and assembled and tested a flight-like engineering development unit. The engineering unit has undergone performance testing and environmental testing which provides confidence for the project to move forward with flight unit production and certification activities. Test results are summarized in this paper. The flight unit is targeted for delivery to ISS in late 2025.

TOC↗

Foundational Concepts in Simulation-Based Resilience Analysis and Design

Resilience is a topic of increasing interest–with ever-present calls from policymakers to increase the resilience of complex systems and infrastructure. However, resilience as a concept can be confusing, because of a lack of a common unified definition and frame of reference. Sometimes it can appear as if resilience analysis is merely duplicating other, more mature fields such as safety, reliability, or risk, while other times it seems as if resilience is providing an “alternative” view with limited rigor. To better understand the resilience concept (and its relation to the broader field of risk management), this paper will present the perspective of simulation-based resilience analysis and design, including some of the foundational precepts and resultant concepts defining the resilience concept. It will further present the motivation for using simulation to understand resilience and highlight some ongoing work and research challenges in this area. From this frame of reference, one can better understand the field of resilience, including how different aspects and definitions of resilience relate to each other, and how resilience relates to broader design considerations and practices.

resilience↗

Efficient and Accurate Terrain Integration & Application to Modeling and Simulation of Planetary Landing Systems

As planetary spacecraft landing system complexity increases, pre-flight performance assessments include correspondingly more detailed and complex models that represent the various environmental elements and vehicle subsystems. Specifically, the relevance and criticality of planetary terrain modeling in the context of precision landing and hazard avoidance (PL&HA) has become evident as both commercial and national agencies return to the Lunar surface and beyond.

Terrain↗

Development of the Miniature Total Organic Carbon Analyzer

Monitoring the Total Organic Carbon (TOC) in spacecraft potable water will be of major importance in long-duration human space exploration. In-flight analysis of potable water produced from a regenerative water processor provides immediate feedback on the quality of reclaimed water for crew health as well as water processing system health monitoring. This paper updates the progress in development of the next generation Total Organic Carbon Analyzer (TOCA) designed for the unique requirements of an exploration-class mission. The current objective is to design, build, certify, deliver, and operate a TOCA technology demonstration on the International Space Station (ISS). The next generation analyzer system technology was previously developed and selected among a feasibility study of other options. The new system provides primary advantages of reduced mass and volume through reduced system complexity and reduced need for consumables; therefore, the flight project is named MiniTOCA. The project has recently completed design of the tech demo instrument and assembled and tested a flight-like engineering development unit. The engineering unit has undergone performance testing and environmental testing which provides confidence for the project to move forward with flight unit production and certification activities. Test results are summarized in this paper. The flight unit is targeted for delivery to ISS in late 2025.

TOC↗

Foundational Concepts in Simulation-Based Resilience Analysis and Design

Resilience is a topic of increasing interest–with ever-present calls from policymakers to increase the resilience of complex systems and infrastructure. However, resilience as a concept can be confusing, because of a lack of a common unified definition and frame of reference. Sometimes it can appear as if resilience analysis is merely duplicating other, more mature fields such as safety, reliability, or risk, while other times it seems as if resilience is providing an “alternative” view with limited rigor. To better understand the resilience concept (and its relation to the broader field of risk management), this paper will present the perspective of simulation-based resilience analysis and design, including some of the foundational precepts and resultant concepts defining the resilience concept. It will further present the motivation for using simulation to understand resilience and highlight some ongoing work and research challenges in this area. From this frame of reference, one can better understand the field of resilience, including how different aspects and definitions of resilience relate to each other, and how resilience relates to broader design considerations and practices.

resilience↗

Contracting Quality Early in the Lifecycle Using AS9145 Data Deliverables

Development schedules and a highly dynamic supply chain are a challenge to developers of complex systems produced at low volume. Flaws in designs, parts and materials availability problems, poor manufacturability, and a lack of knowledge about critical items and key process attributes can be realized well before traditional second-party quality assurance activities begin. Supplier audits and product inspections may have little mitigating effect once these foundational problems have been realized. Their impacts can be significant lifecycle disruption, cost overruns, inability to deliver to plan, and even project cancellation. AS9145, Requirements for Advanced Product Quality Planning and Production Part Approval Process, can be used to drive quality engineering practices into early development lifecycles to significantly reduce this late-cycle risk and to reduce the cost of quality overall. Since its initial publication in 2016, it has had very limited adoption by the DoD, no adoption by NASA, and sparse adoption in the aerospace and defense supply chain. A task group within the Aerospace Industries Association's (AIA) Joint Strategic Quality Council (JSQC) identified that both acquirers and suppliers see as AS9145 as a cost-adder and are hesitant to use it as an alternative to late-stage-heavy quality assurance approaches. A lack of prior use creates large capability gaps in request-for-proposal (RFP) teams, proposal teams, suppliers’ quality management systems (QMS), and in experienced personnel executing the early lifecycle approach. To create a more realizable on-ramp for using AS9145 in the space and defense sectors, the AIA JSQC task team created five deliverable requirements descriptions (DRDs) that can be used in a contract to begin to engage both parties in early lifecycle quality engineering and quality assurance activities, that reduce exposure to late-stage cost and schedule collapse due to unidentified risks in design, supply chain, and manufacturability. These DRDs drive the parties to engage in planning and analysis discussions early on to understand what production risks can be known and how they will focus resources based on safety criticality and the key elements of design and construction. The suppliers and acquirers who will produce the data and information required by the DRD will be able to incrementally evolve their QMS and the acquirer will incrementally be able to track and understand the benefits of cost shifting from late to early development phases. A white paper describing this approach and the five recommended DRDs will be published by the AIA in late 2024 or early 2025.

Jeannette Plante↗

Proof-of-Concept for a Long-Term Health Metric to Quantify End of Mission Health Status in Astronauts

NASA has long used Probabilistic Risk Assessment (PRA) when high-stakes decisions need to be made about complex systems. For spaceflight medical risk, the Human Research Program’s Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is a significant step towards robustly quantifying the risk to crew health during exploration missions. However, there remains a significant gap in the ability to comprehensively characterize and assess risk across the disparate functionalities and capabilities which comprise the entire Crew Health and Performance (CHP) system. To fill this gap, the Crew Health and Performance – Probabilistic Risk Assessment (CHP-PRA) project aims to perform risk characterization for the CHP system by assessing performance risk in addition to medical risk. This effort also includes quantifying Long-Term Health (LTH) risk in addition to in-mission risk outcomes within the CHP-PRA results. LTH risk encompasses the timeframe from immediately post-flight, through the rest of an astronaut’s career, through retirement, and until death. A proof-of-concept LTH risk metric is based on medical condition end-state, as defined by the Evidence Library, capturing the spaceflight specific medical impacts persisting into post-flight[1]. Condition outcomes in the Evidence Library progress through three Clinical Phases (CP): the diagnostic phase (CP1), the treatment/convalescent phase (CP2), and the end-state phase (CP3) which represents the detrimental effects of the condition after the crew member has recovered to the maximal extent. Each CP has an associated Task Impairment (TI), defined as the degree of crew incapacity due to experiencing the condition, and is quantified with a 0-1 range. Conditions with an associated CP3 (e.g. Sepsis, Traumatic Hypovolemic Shock, Sudden Cardiac Arrest, etc.) typically have serious consequences that can cause an astronaut to be fully or partially debilitated throughout the remainder of the mission. Consequently, the Cumulative CP3 TI End-of-Mission Health Status Metric is developed by CHP-PRA to quantify the cumulative effects of all conditions which progressed to the CP3 state throughout the entirety of the mission. Hence, this End-of-Mission Health Status Metric attempts to serve as an indicator of an astronaut’s health state at the time of landing. The severity of the lingering effects of in-mission medical events are dependent on mission activities and the level of available in-mission medical care. This allows the associated cumulative TI metric to be used in comparison with the crew’s end of mission health status for different levels of in-mission resources. This presentation provides the strategy for using CP3 as an LTH metric component, as well as a proof-of-concept demonstration of LTH risk characterization using this component.

long term health↗

Investigating Risks Due to Artemis EVA Tempo Via Probabilistic Risk Assessment

Spaceflight operations pose unique challenges to crew health, safety, and resource management. As space agencies and private companies continue to push the boundaries of human exploration, it is essential to understand the risks associated with Extravehicular Activities (EVAs) and develop strategies to mitigate them. The tempo at which EVAs are conducted – the total number and frequency of these activities – can have a profound impact on medical risks, resource consumption, and overall mission success. Probabilistic risk assessment (PRA) provides a powerful framework for evaluating complex systems and identifying potential hazards. Our work employs the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) [1] to simulate mission events, occurrence and treatment of medical conditions, and track the utilization of resources. Coupled with the Evidence Library [2], a medical evidence base for exploration-class missions developed by the Exploration Medical Capability within NASA’s Human Research Program, we can estimate these risks with increased fidelity and optimize medical kit contents to meet specific mission requirements. This presentation provides a detailed examination of how EVA tempo influences medical risk estimates for a lunar surface design reference mission. A comprehensive analysis is conducted to assess the additional mass and volume burden imposed on medical kits required to maintain adequate levels of risk mitigation. Furthermore, we estimate the distribution of the number of successful EVAs completed based on the level of task impairment imposed by medical events and flight rules related to specific medical events, such as decompression sickness.

Modeling↗

Evaluating Emissions Reductions from DERs in REopt Lite

Creating a clear business justification for an energy & water project is essential for developing projects by transforming data and analysis into a compelling story to secure management buy-in. This presentation provides an overview of how distributed energy resources (DERs) can provide cost savings, resilience benefits, and emissions reduction. Many technical and economic factors affect which distributed energy technologies can best meet site goals, and should be considered concurrently. With increasingly integrated and complex systems, back-of-the envelope calculations are no longer sufficient to determine distributed energy project potential. REopt Lite can be used to model emissions and evaluate the impact of DERs on reducing emissions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Exploring PV Circularity by Modeling Socio-Technical Dynamics of Modules' End-of-Life Management: Preprint

The circular economy (CE) tackles environmental and resource scarcity issues by maximizing value retention in the economy. The concept implies design strategies such as reducing the use of materials or improving products’ durability and end-of-life (EOL) strategies such as reusing products and components and recycling materials. With an estimated 80 million tons of global cumulative EOL photovoltaic (PV) modules, applying CE principles to the PV industry could alleviate resource scarcity issues while also providing economic benefits. However, transitioning to a CE may imply changes in organizations and consumer behaviors. In this context, assessment of CE strategies may require accounting for behavioral change, a requirement that methods from complex system science such as agent-based modeling meet. Thus, this paper uses an agent-based modeling (ABM) approach to study circularity in the photovoltaics supply chain. Four types of agents are represented in the ABM: PV owners, installers, recyclers, and manufacturers. Moreover, five possible EOL options – including three CE strategies – are modeled. Departing from traditional techno-economic analysis, the model includes techno-economic factors as well as social factors to model EOL management decisions. Results show that each dollar decrease in the recycling fees improves the recycling rate by roughly 1.1%. However, excluding social factors underestimates the effect that lower recycling prices have on material circularity.

28 EE - Advanced Manufacturing Office (EE-5A)↗

Smart sensors using artificial intelligence for on-detector electronics and ASICs

Cutting edge detectors push sensing technology by further improving spatial and temporal resolution, increasing detector area and volume, and generally reducing backgrounds and noise. This has led to a explosion of more and more data being generated in next-generation experiments. Therefore, the need for near-sensor, at the data source, processing with more powerful algorithms is becoming increasingly important to more efficiently capture the right experimental data, reduce downstream system complexity, and enable faster and lower-power feedback loops. In this paper, we discuss the motivations and potential applications for on-detector AI. Furthermore, the unique requirements of particle physics can uniquely drive the development of novel AI hardware and design tools. We describe existing modern work for particle physics in this area. Finally, we outline a number of areas of opportunity where we can advance machine learning techniques, codesign workflows, and future microelectronics technologies which will accelerate design, performance, and implementations for next generation experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Simulation-Based Recovery Action Analysis Using the EMRALD Dynamic Risk Assessment Tool

A recovery action is defined as the action that prevents deviant conditions from producing unwanted effects. It generally indicates a kind of countermeasure performed in response to a failure of human action. The recovery actions especially play an important role in complex systems like nuclear power plants (NPPs), which consist of highly sophisticated controllers to ensure that desired performance and safety must be achieved and maintained. This is because a combination of human error and its recovery failure may be able to cause a catastrophic effect on a system. Analyzing recovery actions has been a critical part of HRA, which is a technique to evaluate human errors and provide human error probabilities (HEPs) for application in probabilistic safety assessment (PSA). If recovery actions are not adequately analyzed and applied to PSA models, the PSA results may be under-estimated or be not able to reasonably account for the failure of human actions in the context of PSA. For this reason, some regulatory documents such as ASME/ANS RA-Sb-2013 by the American Society for Mechanical Engineers and the American Nuclear Society and NUREG-1792 by U.S. Nuclear Regulatory Commission have emphasized the importance of recovery analysis within the HRA. A couple of existing HRA methods, such as the Technique for Human Error-Rate Prediction (THERP), the Cause-Based Decision Tree (CBDT), and the Korean Standard HRA (K-HRA), have respectively suggested their own approaches to the HRA recovery analysis. However, there are a couple of limitations to treating recovery actions using only the current HRA methods available. The biggest limitation is that the existing recovery analysis does not explicitly consider a variety of recovery action types and recovery sequences as they occur in actual NPPs. To handle the limitations of existing recovery analysis, this study proposes a simulation-based recovery analysis method using the Event Modeling Risk Assessment Using Linked Diagram (EMRALD) software. The EMRALD software is a dynamic simulation tool for PSA. It supports realistic and dynamic modeling of human actions as they would be performed at NPPs. It is also favorable to simultaneously model the specific moment at which an action is performed, the time it takes to perform the action, and the failure probability of that action. In this paper, a detailed methodology for modeling recovery actions in the simulation platform is proposed with a couple of examples. Then, outputs from the simulation are discussed as reviewing if this novel approach can complement the challenges of existing recovery analyses.

99 GENERAL AND MISCELLANEOUS↗

Operation Optimization Using Reinforcement Learning with Integrated Artificial Reasoning Framework

In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.

Kim, Junyung↗

Understanding Ionizing Radiation-Induced Speciation, Chemistry, and Transport in Nuclear Materials

The research to be presented will cover an overview of three projects supported by the United States Department of Energy Office of Science Solar Photochemistry Program: (i) Radiation-Induced Late Actinide Redox Chemistry (2019–2022); (ii) Understanding Radiation-Induced Iodine Speciation, Chemistry, and Transport in High Temperature Molten Salts (2022–2025); and (iii) Radiation-Induced Chemistry of Nuclear Materials (2023–2026). The overarching aim of these distinct projects is to develop a deep fundamental mechanistic understanding of ionizing-radiation-induced processes occurring in solutions over multiple time and length scales. Investigated solutions range from aqueous to organic to molten salt liquids in the presence and absence of ligands and various redox-active ions and molecules, including the actinides. The ultimate goal of these projects is to utilize the new foundational knowledge gained to construct quantitative multiscale computer models with the capacity to predict radiation-induced behavior in complex systems of relevance to nuclear energy technologies and beyond.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗