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NASA NTRSDate not supplied

Micrometer to Atomic Scale Characterisation of Primitive Astromaterials Using A Novel Method, Metis-Fa: A Coordinated Atom Probe Tomography, Transmission Electron Microscopy and NanoSIMS Approach

Introduction: Presolar grains preserve isotopic, chemical and microstructural records of physical and chemical processing, and formation mechanisms within a vast range of evolved stellar systems, the interstellar medium, solar nebula and their parent bodies. These evolutionary records are preserved at the micrometric to atomic scale, requiring coordinated studies to expand our understanding of evolutionary processes occurringthroughout ours and external stellar systems [1]. NanoSIMS enabled rapid in situ identification and isotopic characterisation of presolar grains and their stellar origins using 17O/16O and 18O/16O, and 13C/12C isotopic ratios [1]. Coordination with transmission electron microscopy (TEM) revealed crystallographic and localised contextual relationships and quantitively constrained their major and minor compositions [1]. However, trace elements cannot be quantified, the most sensitive geochemical tracers of environmental conditions, essential to unravelling the chemical record of their evolutionary pathway and parent stellar systems [2-3] . Furthermore, owing to the combination of technical limitations (only 5 – 7 isotopes can be measured per NanoSIMS run) and their small grain sizes of 100 nm < 3 μm (with rare exceptions in nanodiamonds (2 nm ≤) and SiC (< 40 μm)), the number of measurable isotopes per grain volume is limited [1,3] . Through more comprehensive isotopic studies of presolar grains, NanoSIMS studies have shown the importance of the latter, identifying Fe and Mg as important indicators of nuclear synthetic processing and their stellar origins, respectively [4- 5]. Coordination of NanoSIMS and Atom Probe Tomography (APT) revealed morphological signatures, and isotopic and chemical signatures at major to trace levels without requirements for preselection of elements [6]. However, crystallographic signatures in localized contextual relationships cannot be measured. Consequently, coordination of NanoSIMS, TEM and APT is essential to gain access to almost all contextual, structural and geochemical signatures within each presolar grain.Transmission electron microscopy requires a 100 nm thin lamella which is unstable in APT and would not produce any viable data. Atom probe tomography requires a needle-shaped specimen which when measured in TEM removes the local context, impacts the quality of the TEM diffraction images due to the shank angle of the needle, and can alter the chemistry of beam sensitive materials from the higher degree of surface exposure at the tip. To address these issues, we developed METIS-Fa (Multi-technical measurements of Electron Transparent materials using an Indium Sandwich - a FIB approach). A novel method which enables coordination of NanoSIMS, TEM and APT for generalized and targeted studies of individual grains, including beam sensitive materials, without compromising sample preparation requirements for TEM and APT. This method requires only indium and a Focus Ion Beam (FIB), minimizing the movement of fragile materials while still enabling preparation of TEM lamella into APT needles. Samples: Initial experimental development and testing of the method occurred at Astromaterials Research and Exploration Science (ARES), Johnson Space Centre (JSC), NASA and APT measurements and needle preparation occurred at JdLC, Curtin University. Synthetic silicate samples were used as analogs for presolar silicates when performing a trial run of the method. Samples were extracted from a polished thin section created at JSC, NASA, comprised of 38 wt.% Si, 17 wt.% FeO, 13 wt.% MgO, 12 wt.% Al, 11 wt.% Ca based on electron microprobe analysis (EMPA) [8] . Experimental details, pressure and temperature conditions were presented in [8] and references therein. Testing of the capability to target individual grains in mineral matrices using this method for acquisition in APT, measured matrix regions in meteoritic thin sections of primitive meteorites. These meteorites and their identified presolar grains for future targeted studies are detailed in [9]. Techniques: The TEM-FIB lamella were prepared using a FIB. An e-beam assisted pt deposition was used as a protective coating for the synthetic and meteoritic samples. When targeting individual grains, a secondary e-beam assisted pt deposition button is placed over the desired grain before the protective coating to denote its location. A JEOL 2500SE field-emission TEM was used for high-resolution imaging, energy-dispersive X-ray (EDX) and electron diffraction data.TheMETIS-Fa method was experimentally designed, tested and executed using a FIB at ARES, JSCNASA. Needles for APT were prepared using the Tescan Lyra3 GM Dual Beam Focus Ion Beam (FIB) Field Emission SEM (FE-SEM) at the JdLC, Curtin University. Atom probe tomography measurements were conducted using a CAMECA Local Electrode Atom Probe, LEAP 4000X HR. Two pure indium needles were analyzed initially to constraining acquisition parameters and stability under the beam. Manual acquisition was required to maintain evaporation of specimen’s at the apex and monitor interactions with measurement parameters. Experimental Design: Indium foil is pressed onto an Al stub with a pneumatic press and mounted into the FIB adjacent to the TEM-FIB lamella of interest. Using a FIB, two indium slices (5 μm x ~300 nm x 3 μm) are extracted from indium foil and aligned with the TEM-FIB lamella before touching the TEM-FIB lamella. Each slice is then attached through cold welding to the FIB-TEM lamella. This approach eliminates the need for chemical treatments and proved effective for aligning the Indium within the region of interest for APT, holding it in place for up to 4 days during testing.Once both indium slices are attached within their pre-determined region per grain targeting requirements, they are gradually melted onto the FIB-TEM lamella.When targeting a specific grain, measurements should be taken of the pt button and its distance from edge to edge of the lamella before and after sandwiching. A secondary button should be placed over the same region after the Indium slices have been attached to improve precision when preparing APT needles. Results: Figure 1 shows two indium slices melted onto a FIB-TEM lamella, adding additional bulk for preparation into APT needles as shown in Figure 2 [7] . The latter was essential so samples could be measured in TEM and APT without compromising sample preparation requirements and consequently data quality and acquisition stability. METIS-Fa proved effective forimproving geometry. Figure 3 shows a successful APTrun of the synthetic silicate. EMPA, TEM and APTshowed no chemical alterations. During targetingtesting, a solar silicate grain was successfully identifiedand measured in TEM, and prepared into an APTneedle. However, the indium was melted too long during sample preparation, causing expansion andformation of internal porosity leading to sample loss.Conclusion: METIS-Fa greatly expands the number of isotopic and chemical signatures measured per grain volume, and enables measurements of contextual, structural, crystallographic, isotopic and geochemical signatures within individual grains. Gaining access to such a vast range of evolutionary signatures required for expanding our understanding of external stellar and planetary systems and the evolution of our solar system. This method was designed for application to a vast range of phases including being sensitive materials and thus provides a way for coordination of NanoSIMS, TEM and APT not just for the study of presolar grains and by extension primitive astromaterials, but studies in a vast range of other fields including the geosciences and material sciences.Acknowledgments: Thankyou to ARES, JSC, NASA; JdLC Curtin University and Space Science Technology Centre for the use of laboratory facilities and funding [confirm].

Nicole D Nevill↗

NASA NTRSDate not supplied

Pre-Flight Hazardous Gas Assessment Methodology for the Space Launch System

The complexity of the Artemis I liquid rocket propulsion systems produces many sources of hazards during the operation of the vehicle. Pre-flight hazardous gas leakages pose fire and explosion risks to rocket that could potentially lead to loss of the vehicle, mission, and crew. The National Aeronautics and Space Administration (NASA) uses a probability risk assessment strategy to assess the likelihood of the hazard and the consequence should the hazardous situation occur. For high probability hazards that pose a potentially catastrophic consequence to the vehicle, the most effective strategy for reducing the risk is accomplished by engineering and implementing systems that actively mitigate the hazard. A hazards analysis is completed to determine the likelihood and severity of each hazard. These hazard analyses are informed using relevant empirical data and physics-based analytical models. Time-accurate Computational Fluid Dynamics models used to quantify and understand the risks posed to Artemis I due to expulsions of hazardous gas near the vehicle. Several sources of hazardous gas that posed a risk to Artemis I during nominal pre-flight operations are analyzed in this work. The results of the analyses were used to develop flight rationale and make risk acceptance decisions for the Artemis I launch.

Brian R. Richardson↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

Supportability Concepts for Crewed Deep Space Exploration

Supportability—defined as the set of system characteristics that influence the logistics and support required to enable safe and effective operations—will be a much larger driver of mass, risk, and crew time for future human space exploration due to the more challenging mission context. For Mars, systems must operate in a logistically isolated environment for much longer durations than previous missions, which results in a higher probability of system failure and therefore an increased need for maintenance or contingency options. Mars missions also lack access to quick aborts, which increases the consequences of an unrecoverable system failure. Together, this higher likelihood and consequence of failure results in an increase in supportability-related risk. Supportability analysis is an important part of systems development that helps designers better understand the impacts of system and mission decisions on risk, mass, and crew time. The real-world processes that drive maintenance requirements and other supportability-related characteristics are probabilistic, and therefore they require different conceptual approaches and models than are used for more deterministic aspects of space systems. This paper provides an overview of supportability analysis, addresses key concepts, and provides examples of how supportability analysis can be incorporated into system development. Specifically, system supportability involves stochastic processes, and therefore must be evaluated using probabilistic models. These models can be used to perform sensitivity analysis even if system characteristics are not yet fully defined. Failure rates cannot be measured directly, but tests provide valuable data that can help refine those estimates. Human spaceflight architectures are complex, and exhibit coupled behavior that should be examined with integrated systems analysis that includes an assessment of supportability.

Supportability↗

NASA NTRSDate not supplied

Supportability Concepts for Crewed Deep Space Exploration

Supportability↗

NASA NTRSDate not supplied

Wildfire Segmentation From Remotely Sensed Data Using Quantum-Compatible Conditional Vector Quantized-Variational Autoencoders

Wildfires represent a critical environmental hazard with multifaceted implications for ecosystems, communities, and public health [1]. The escalating frequency and intensity of wildfires globally have intensified the urgency for robust segmentation methodologies to facilitate effective mitigation, response, and recovery strategies [2]. Accurate wildfire segmentation is pivotal for delineating fire boundaries, assessing progression patterns, and prioritizing resource allocation during emergency scenarios. Furthermore, precise segmentation enables stakeholders, including policymakers, environmental scientists, and emergency responders, to formulate evidence-based strategies, thereby minimizing socio-economic disruptions and ecological degradation. Consequently, advancing wildfire segmentation techniques through innovative technological interventions remains a paramount research imperative. Although foundational in wildfire segmentation, traditional deterministic models exhibit inherent limitations that compromise their efficacy in dynamic and uncertain environments. These models often operate on rigid algorithms prioritizing deterministic classifications, thereby overlooking the inherent complexities and uncertainties associated with wildfire behavior and satellite data variability. Such deterministic frameworks tend to produce oversimplified representations that fail to capture the intricate nuances of evolving fire dynamics, spatial heterogeneity, and environmental interactions [1]. Consequently, the deterministic approach’s propensity for uncertainty collapsing [1, 3] hampers the accuracy, reliability, and applicability of segmentation outcomes in real-world scenarios. Contrastingly, stochastic models offer a more nuanced and adaptable framework for wildfire segmentation. By integrating probabilistic elements into the modeling paradigm, stochastic approaches, particularly probabilistic approaches such as variational auto encoders (VAEs) [4], facilitate comprehensive uncertainty assessment, enabling researchers to quantify and incorporate uncertainties into segmentation outcomes effectively. This probabilistic nature empowers stochastic models to encapsulate variability, account for data inconsistencies, and adapt to evolving environmental conditions, enhancing segmentation accuracy, reliability, and robustness. Embracing stochastic methodologies thus catalyzes advancements in wildfire science by fostering a more holistic, adaptive, and resilient segmentation framework. Despite VAEs demonstrating significant promise in various applications, they come with inherent limitations that have garnered attention within the machine learning community. One of the primary drawbacks lies in their reliance on static priors, which essentially assume a fixed distribution for latent variables, thereby limiting the model’s flexibility to capture complex data structures effectively [5]. This static nature leads to suboptimal representations, especially when dealing with complex and high-dimensional data. Additionally, VAEs often struggle with generating sharp and realistic samples, a phenomenon commonly referred to as mode collapse [5, 7, 6]. Furthermore, the optimization process in VAEs, which involves balancing the reconstruction loss and the regularization term, can sometimes be challenging to fine-tune [7]. In recent efforts to address these shortcomings, alternative approaches like Vector Quantized Variational Auto encoders(VQ-VAEs) [7], address the challenges by incorporating discrete latent variables and leveraging techniques that enhance the quality and diversity of generated samples while maintaining efficient training dynamics. VQ-VAEs propose a dynamic prior distribution generation mechanism that diverges from the static priors commonly associated with traditional VAEs. This dynamic approach allows for more adaptive and context-aware latent variable representations, thereby potentially capturing complex data structures more effectively. Unlike autoregressive prior models such as PixelCNN, which, despite their ability to model dependencies across data dimensions, suffer from significant computational inefficiencies and lack flexibility in handling diverse datasets. In our work, we propose to use a generative quantum-compatible approach to help alleviate the shortcomings of autoregressive prior model in VQ-VAEs. Restricted Boltzmann Machines (RBMs) are a viable alternative prior model that can learn prior distributions in a faster and more flexible manner. In this research endeavor, we meticulously curate a state-of-the-art dataset leveraging satellite MODIS data in conjunction with VIIRS fire masks, derived from Fire Radiative Power (FRP), thereby encapsulating diverse wildfire scenarios and environmental contexts. We developed a conditional VQ-VAE architecture with the RBM prior model that is trained in a supervised manner for segmenting wildfire masks. This innovative approach synergistically harnesses deep learning capabilities, enabling the generation of segmentation maps characterized by heightened precision, granularity, and contextual relevance. Furthermore, replacing the autoregressive prior learning method proposed by the original VQ-VAE with a prior density approximation via quantum-compatible RBM facilitates expedited inference processes, augments flexibility in prior sampling, optimizes computational efficiency and establishes a groundbreaking benchmark in wildfire segmentation methodologies.

quantum machine learning↗

NASA NTRSDate not supplied

Risk Characterization Research for Artemis II: Human Factors and Behavioral Performance

BACKGROUND Artemis II will be the first time NASA astronauts go beyond low-Earth orbit (LEO) since the Apollo era, and the first astronauts heading into space in the Orion vehicle. As such, it provides a critical opportunity to refine our understanding of the likelihood and consequences associated with the Behavioral Medicine (BMed), Team, Human System Integration Architecture (HSIA), and Sleep Risks, and prepare for future Moon and Mars missions. However, Artemis II research efforts are uniquely shaped by in-mission data collection constraints. There is currently no in-mission crew time available to complete measures. In-mission data will need to be collected unobtrusively from available data streams (e.g., audiovisual, existing records such as schedules, and actigraphy). Accordingly, the overarching goal of our research is to utilize Artemis II data to further define the likelihood and consequences of these risks, and to create an unobtrusive research infrastructure that can be expanded to include future Artemis missions. This goal spans four aims across three research phases: (1) identify and operationally define key performances metrics and constructs across the four aforementioned risks, (2) develop an unobtrusive methodology and coding scheme for in-mission data collection, (3) characterize performance decrements due to Bmed, Team, HSIA, and Sleep Risks, and (4) develop a data infrastructure for future Artemis missions. The following details results of Phase I efforts in which we address Aims 1 and 2 to develop an unobtrusive measurement plan and coding scheme to capture key constructs, contributing factors, and performance decrements across each risk area. METHOD As part of Phase I, we conducted an interdisciplinary literature review and consulted with SMEs to identify unobtrusive methodologies that leverage text, audio, and/or video data as well as conceptualize key performance metrics, contributing factors, and BMed, Team, HSIA, and Sleep risk constructs related to performance decrements. The Phase I effort resulted in a finalized pre- and post-mission protocol for Artemis II, along with a measurement and coding scheme for in-mission Artemis II data. Phase II will involve data collection from the upcoming Artemis II mission. Phase III will include data processing, coding, depiction, analysis, and report writing of the Artemis II data. RESULTS & DISCUSSION To date, we have completed Phase I efforts. Specifically, we identified BMed, Team, HSIA, and Sleep risk constructs related to performance metrics, summarized how these constructs can be measured using audiovisual data collected during the mission, and worked with NASA’s HFBP Element to finalize a data collection protocol that leverages audiovisual input from the Orion spacecraft system. Our protocol includes novel unobtrusive methodologies that adhere to in-mission data streams and subsequent constraints (e.g., limited storage space on GoPro cameras, ambient noise impeding audio files) to best capture in-mission phenomena across each risk area. We will present our results from Phase I efforts, namely best practices for unobtrusive measurement as identified through literature reviews and SME consultation as well as codebook excerpts for use in Artemis II. We will include a description of planned work as we prepare for Phase II and Phase III of this research plan and the Artemis II mission itself. SUMMARY We describe progress on our Human Factors and Behavioral Performance Research for Artemis II study.

behavioral health↗

NASA NTRSDate not supplied

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↗

NASA NTRS2025

Potential Health Impacts, Treatments, and Countermeasures of Martian Dust on Future Human Space Exploration

The challenges of human space exploration produce some of humanity’s greatest technological and scientific advances, not excluding innovations in medicine. The microgravity environment causes a whole host of physiological changes, and exposure to dust on the Moon caused considerable pulmonary distress to astronauts during the Apollo missions. As the National Aeronautics and Space Administration (NASA)and other space organizations prepare for long duration exploration missions to Mars, the hazards and consequences of the Martian surface need to be accounted for. This review investigates how substances analogous to the hazardous components of Martian dust have caused disease in people on Earth. Because of the small grain size of Martian dust, dust on Mars is more likely to cause lung irritation, absorb into the bloodstream, and lead to diseases in astronauts. Toxic components of Martian dust to astronaut health include perchlorates, silica, nanophase iron oxides, and gypsum in addition to trace amounts of toxic metals whose abundances are debated: chromium, beryllium, arsenic, and cadmium. Predicted effects of dust exposure ranges from asymptomatic to life threatening, with many substances being carcinogenic and most damage impacting the pulmonary system. The longer transit time for astronauts to return home makes the operations of performing emergency medical treatment more difficult and increases both the likelihood and consequences of developing chronic disease. Exposure mitigation needs to be prioritized; however, supplements may be taken to prevent disease from breakthrough exposures and treatment regimens could lessen morbidity and mortality. Treatments and equipment need to be carefully thought out and transported with the astronauts to be prepared for all possible scenarios.

Justin L Wang↗

DOE OSTI2020

A PROVEN APPROACH FOR EFFECTIVE COMPUTER SECURITY SELF-ASSESSMENTS AT NUCLEAR FACILITIES

A proven method for conducting cybersecurity self-assessments at nuclear power plants is now available for international use. This method was originally developed by Pacific Northwest National Laboratory, under the sponsorship of the U.S. Nuclear Regulatory Commission (NRC), for use at U.S. nuclear power plants. The “Method,” described in NUREG/CR-6847 “Cyber Security Self-Assessment Method for U.S. Nuclear Power Plants,” was originally a limited release document that was withheld from public disclosure but is now publicly available. The Method provides a systematic, phased, and risk-informed approach to help decision makers and security specialists understand their relative cybersecurity posture. Completed Method assessments may be used to support or validate selection of computer security controls to mitigate cyber threats as well as demonstrate compliance with regulations or statutes enacted by competent authorities. The Method assesses the cybersecurity posture of key systems at a nuclear facility with a focus on protection of design base functions. It considers both physical and digital elements of system vulnerabilities and the resulting potential consequences from exploitation. It is well-suited for addressing blended cyberattacks. A semi-quantitative analytical approach is used in the evaluation of potential vulnerabilities, consequences, and risks and provides a technical basis for the selection of security controls to mitigate cyberattacks. The Method’s application at U.S. nuclear power plants has been very encouraging. The only nuclear plant in the United States that did not have adverse findings during its initial NRC computer security inspection prepared for inspection through the diligent application of this self-assessment method and the implementation of recommendations that came out of that self-assessment. Nuclear facilities around the world might find application of the Method extremely helpful for making cost-effective, risk-based decisions regarding computer security and for preparing to pass computer security inspections by their competent authorities.

Cyber security, Nuclear security, Self-assessment↗

DOE OSTI2020

Excitons in two-dimensional metal-halide perovskites

Georgia Tech School of Chemistry and Biochemistry Special Summer Seminar Series, Prof Carlos Silva; While polarons --- charges bound to a lattice deformation induced by electron-phonon coupling --- are primary photoexcitations at room temperature in bulk metal-halide hybrid organic-inorganic perovskites (HOIP), excitons --- Coulomb-bound electron-hole pairs --- are the stable quasi-particles in their two-dimensional (2D) analogues. Here we address the fundamental question: are polaronic effects consequential for excitons in 2D-HIOPs? Based on our recent work, we argue that polaronic effects are manifested intrinsically in the exciton spectral structure, which is comprised of multiple non-degenerate resonances with constant inter-peak energy spacing. We highlight measurements of population and dephasing dynamics that point to the apparently deterministic role of polaronic effects in excitonic properties. We contend that an interplay of long-range and short-range exciton-lattice couplings give rise to exciton polarons, a character that fundamentally establishes their effective mass and radius, and consequently, their quantum dynamics. Given this complexity, a fundamentally far-reaching issue is how Coulomb-mediated many-body interactions---elastic scattering such as excitation-induced dephasing, inelastic exciton bimolecular scattering, and multi-exciton binding---depend upon the specific exciton-lattice coupling within the structured excitation lineshape. We measure the intrinsic and density-dependent exciton dephasing rates of the multiple excitons and their dependence on temperature by means of two-dimensional coherent excitation spectroscopy. We find that diverse excitons display distinct intrinsic dephasing rates mediated by phonon scattering involving different effective phonons, and contrasting rates of exciton-exciton elastic scattering. These findings establish specifically the consequence of distinct lattice dressing on exciton many-body quantum dynamics, which critically define fundamental optical properties that underpin photonics and quantum optoelectronics.

solar cells, metal-halide perovskites, excitons, p↗

DOE OSTI2020

Geothermal Sector Cybersecurity Vulnerability Assessment

A review of geothermal sector-specific cybersecurity vulnerabilities and risks (consequences) was conducted at the request of the Geothermal Technologies Office (GTO). The vulnerabilities and risks reviewed in this study have relevance to achieving the 2019 GeoVision Report (DOE GTO 2019) technological advancements and expected sector growth. The study offers areas for consideration but does not quantify the likelihood (frequency) of the consequences. This cybersecurity analysis project represents a proactive effort to identify areas to enhance cybersecurity in geothermal development and operations. It was not initiated to address any immediate threat or specific known risk. Of the eight identified vulnerabilities analyzed, the review identified reservoir data system monitoring as one that is unique to geothermal systems and may warrant further investigation to better understand risk and mitigation. A detailed analysis of the other vulnerabilities may highlight additional uniqueness relative to other industries. Further research actions are recommended to better quantify risk and enhance cybersecurity preparedness of the sector. As the geothermal industry grows, the cybersecurity strategies to be deployed will be of increasing importance to ensure resilient, reliable, and secure clean energy for years to come.

cyber-physical security↗

DOE OSTI2021

Assessing the Potential for Inadvertent Human Intrusion at the Area 3 and Area 5 Radioactive Waste Management Sites on the Nevada National Security Site, Nye County, Nevada

This paper recommends an approach to inadvertent human intrusion (IHI) at the Area 3 and Area 5 Radioactive Waste Management Sites (RWMSs) on the Nevada National Security Site (NNSS). IHI analysis uses the consequences of an individual inadvertently contacting buried waste to set waste concentration limits for near-surface disposal of low-level radioactive waste (LLW). Regulatory agencies are increasingly applying risk-informed decision-making to LLW waste management (NRC 2006). Risk-informed decision-making combines scientific risk assessment with stakeholder values and perceptions to determine a level of acceptable risk. Risk considers not only the consequences of an event, but also its probability of occurring.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Adapting Existing Energy Models for Resilience Analysis

Resilience is a topic receiving much attention in relation to energy systems, with particular attention being paid to the supply of electricity. As a result of the growing interest in energy sector resilience, research communities have proposed a plethora of candidate resilience indicators and metrics, most of which remain immature at different scales and segments within the energy system. A necessary focus of the research community lies in implementing, testing, and validating resilience metrics and analysis approaches in energy sector models, which will be invaluable for informing resilience planning and investment decisions. Recognizing these challenges that need to be addressed, we explore how to effectively integrate resilience considerations into energy sector models and tools. The overarching goal of the effort was to evaluate the data needs, methodologies, and outcomes—including consequences and/or changes in investment or operational decisions due to avoided consequences—based on resilience analysis in a range of existing tools. In particular, we selected five models originally built at NREL to explore non-resilience energy research questions to implement and exercise resilience metrics and analysis approaches. To demonstrate the importance of perspective, we selected models that represent different segments of the energy sector, geographic scales, and modeling approaches. A second important aspect of our effort was the development of generalized power interruption scenarios. These scenarios were intended to help establish a framework for simulating the effects of real-world threats in terms of their impacts on system components and, in turn, power interruption.

ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Quantitative Risk Analysis of High Safety Significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants using IRADIC Technology

This report documents the activities performed by Idaho National Laboratory (INL) during fiscal year (FY) 2021 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 strong technical basis to support effective, licensable, and secure DI&C technologies for digital upgrades/designs. An integrated risk assessment technology for the DI&C systems (IRADIC technology) was proposed for this strategy, which aims to (1) provide a best-estimate risk-informed capability to quantitatively and accurately estimate the safety margin obtained from plant modernization, especially for the High Safety Significant Safety-related (HSSSR) DI&C systems, (2) develop an advanced risk assessment technology to support transition from analog to DI&C technologies for nuclear industry, (3) assure the long-term safety and reliability of vital HSSSR DI&C systems, (4) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals and deal with the expensive licensing justifications from regulatory insights, the IRADIC technology is instructive for nuclear vendors and utilities to effectively lower the costs associated with digital compliance and speed industry advances by: (1) defining an integrated risk-informed analysis process for DI&C upgrade, including hazard analysis, reliability analysis, and consequence analysis, (2) applying systematic and risk-informed tools to address common cause failures (CCFs) and quantify responding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the individual level, system level, and plant level, (4) providing insights and suggestions on designs to manage the risks; thus, to support the development, licensing, and deployment of advanced DI&C technologies on nuclear power plant (NPPs). In this report, an approach for performing software CCF analysis, given limited data, is developed and demonstrated using a case study of a highly redundant digital reactor trip system. Consequence analysis is also performed based on different accident scenarios. Results indicate that plant modernization including the improvement of HSSSR DI&C systems will make great benefits to plant safety by providing more safety margins to accident management. In addition, a novel approach is proposed in this report for the quantification of software hazards when sufficient operational and testing data available. The method incorporates software development quality as well as strong analysis techniques to identify and link software defects to potential failure modes. The approach includes both semantic and test-based analysis to detect failures that can exist in different stages of the software development life cycle. This method is applied to an advanced human system interface relevant to reactor trip safety developed from the APR 1400 design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Failure Mechanism Traceability and Application in Human System Interface of Nuclear Power Plants using RESHA

In recent years, there has been considerable effort to modernize existing and new nuclear power plants with digital instrumentation and control systems (DI&C). However, there has also been considerable concern both by industry and regulatory bodies for the risk and consequence analysis of these systems. Of particular concern are digital common cause failures (CCFs) specifically related to software defects. These “misbehaviors” by the software can occur in both the control and monitoring of a system. While many new methods have been proposed to identify potential software failure modes, such as Systems-theoretic Process Analysis (STPA), Hazard and Consequence Analysis for Digital Systems (HAZCADS), etc., these methods are focused primarily on the control action pathway of a system. In contrast, the information feedback pathway lacks unsafe control actions (UCAs), which are typically related to software basic events; thus, assessment of software basic events in such systems is unclear. In this work, we present the idea of intermediate processors and unsafe information flow (UIF) to help safety analysts trace failure mechanisms in the feedback pathway and how they can be integrated into a fault tree for improved assessment capability. The concepts presented are demonstrated in two comprehensive case studies, a smart sensor integrated platform for unmanned autonomous vehicles and another on a representative advanced human system interface (HSI) for safety critical plant monitoring. The qualitative software basic events are identified, and a fault tree analysis is conducted based on a modified Redundancy-guided Systems-theoretic Hazard Analysis (RESHA) methodology. The case studies demonstrate the use of UIF and intermediate processors in the fault tree to improve traceability of software failures in highly complex digital instrumentation feedback. The improved method can also clarify fault tree construction when multiple component dependencies are present in the system.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Failure Mechanism Traceability and Application in Human System Interface of Nuclear Power Plants using RESHA

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Modeling the Fundamental Radiation Chemistry of the Organic Diluent, and the Effect of Metal Ion Complexation on the Radiochemical Behavior of Active Compounds

All used nuclear fuel (UNF) reprocessing technologies must operate efficiently in the presence of an intense, multi-component (predominantly alpha, beta, and gamma) radiation field. Consequently, radiation-induced degradation of reprocessing systems is of concern, as it negatively impacts process performance over time due to the destruction of both active compounds (ligands, phase modifiers, holdback agents, etc.) and the formation of degradation products. Reprocessing solvent system radiolysis has been linked to changes in separation efficiency and physical properties of solvent mixtures, solvent-recycle longevity, crud formation, and other unexpected outcomes that impact the efficient recovery of valuable materials (e.g., the actinides) and the volume of hazardous radioactive waste for final disposal, i.e., in a geological repository. Consequently, a fundamental understanding of radiolytic processes and their effects on reprocessing solvent system performance is critical for: (i) the cost-effective development and innovation of separation technologies; (ii) the design and implementation of predictive radiation chemical models for process monitoring and lifetimes; and (iii) potentially the ability to exploit radiolytic phenomenon to our benefit, e.g., strategic radiolysis of active molecules to liberate specific degradation products that aid subsequent process stages. Despite extensive investigation into the radiolytic behavior of active solvent system compounds, little attention has been given to understanding (i) the radiation chemical behavior and modification of the organic diluent and (ii) the effect of metal ion complexation on the radiochemical behavior of active compounds.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗