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Validation of Multisystem Countermeasures Protocol for Spaceflight during Antarctica Winter-over at Palmer Station (Palmer Countermeasures)

Stressors associated with spaceflight induce persistent immune compromise in astronauts which increase subclinical latent virus reactivation. In select crews, adverse clinical events have been documented. Antarctica winter-over (AWO) mission most closely reproduces these mission stressors: prolonged deployment, extreme environment, circadian misalignment, isolation, station lifestyle, and personal risk. The US maintains three primary stations in Antarctica: South Pole Station, McMurdo, and Palmer. Previous studies suggest that stations located near the interior of Antarctica (South Pole, McMurdo) have confounding effects on the immune system due to persistent hypobaric hypoxia. We hypothesized that winter-over at a coastal station (Palmer) would be more akin to spaceflight due to its normoxic but still extreme environment. Therefore, AWO at Palmer Station was selected, and validated in a pilot study, as the platform for testing and validating the effectiveness of an immune-restorative countermeasure protocol designed for deep space missions. Specifics include diet modifications, nutritional supplementation (vitamin D, probiotic, etc.), prescribed aerobic and resistive exercise, and a protocol of stress relieving virtual reality exercises. A multitude of biological sample types, including blood, saliva, and hair will be collected in tandem with the countermeasures in order to examine the combined effectiveness of the countermeasures. Samples and logs from subjects will be transported from Palmer Station to Johnson Space Center for further processing and distribution to co-investigators at the end of each winter-over. Extracted samples will be analyzed by appropriate testing platforms (Multiplex, qPCR, ELISA, etc.) to monitor alterations in leukocyte distribution, T cell and NK function, cytokine profiles, reactivation of latent herpesviruses, and nutritional factors. The data collected will be compared to a control year in which no countermeasures were deployed to evaluate the overall effectiveness of the analog and to validate the candidate immune countermeasure strategy. AWO 2023 concluded with the 4th in-mission timepoint conducted in September 2023. Samples for 16 subjects, including blood, saliva, hair, surveys, and PCR data, were all successfully returned from Antarctica to NASA/JSC mid-November 2023. Samples have since been distributed to co-investigators for further processing and analysis. With the completion of the first countermeasure year, preliminary data on the effectiveness of the deep-space protocol is being evaluated, however, no conclusions can be drawn yet until the completion of the second AWO countermeasure year, AWO 2024. AWO 2024 commenced in late-March 2024, with 13 subjects consenting and performing their baseline data collections (BDCs). Unique to the 2024 deployment, NSF lifted certain COVID restrictions and rallied the crewmembers in Punta Arenas, Chile. All NSF activities were transferred to this location and NASA was allowed, for the first time, to perform consent briefings, baseline samplings and training in person. This augment greatly increased the likelihood of success for the overwinter activities.

Cody L Gutierrez↗
NASA NTRS1990

A method of classification for multisource data in remote sensing based on interval-valued probabilities

An axiomatic approach to intervalued (IV) probabilities is presented, where the IV probability is defined by a pair of set-theoretic functions which satisfy some pre-specified axioms. On the basis of this approach representation of statistical evidence and combination of multiple bodies of evidence are emphasized. Although IV probabilities provide an innovative means for the representation and combination of evidential information, they make the decision process rather complicated. It entails more intelligent strategies for making decisions. The development of decision rules over IV probabilities is discussed from the viewpoint of statistical pattern recognition. The proposed method, so called evidential reasoning method, is applied to the ground-cover classification of a multisource data set consisting of Multispectral Scanner (MSS) data, Synthetic Aperture Radar (SAR) data, and digital terrain data such as elevation, slope, and aspect. By treating the data sources separately, the method is able to capture both parametric and nonparametric information and to combine them. Then the method is applied to two separate cases of classifying multiband data obtained by a single sensor. In each case a set of multiple sources is obtained by dividing the dimensionally huge data into smaller and more manageable pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than the conventional maximum likelihood method.

Kim, Hakil↗
NASA NTRS1987

Nutrients affecting brain composition and behavior

This review examines the changes in brain composition and in various brain functions, including behavior, that can follow the ingestion of particular foods or nutrients. It details those that are best understood: the increases in serotonin, catecholamine, or acetylcholine synthesis that can occur subsequent to food-induced increases in brain levels of tryptophan, tyrosine, or choline; it also discusses the various processes that must intervene between the mouth and the synapse, so to speak, in order for a nutrient to affect neurotransmission, and it speculates as to additional brain chemicals that may ultimately be found to be affected by changes in the availability of their nutrient precursors. Because the brain chemicals best known to be nutrient dependent overlap with those thought to underlie the actions of most of the drugs used to treat psychiatric diseases, knowledge of this dependence may help the psychiatrist to understand some of the pathologic processes occurring in his/her patients, particularly those with appetitive symptoms. At the very least, such knowledge should provide the psychiatrist with objective criteria for judging when to take seriously assertions that particular foods or nutrients do indeed affect behavior (e.g., in hyperactive children). If the food can be shown to alter neurotransmitter release, it may be behaviorally-active; however, if it lacks a discernible neurochemical effect, the likelihood that it really alters behavior is small.

NASA Program Space Medicine↗
NASA NTRS2022

Assurance Equations: A Cost and Criticality Model for Optimizing Quality Assurance Surveillance

The cost of quality vs cost of failure correction has been a long-running topic of discussion within the Aerospace community. It leads directly to concepts of “risk tolerance”, and risk-based decision-making. It would be valuable if there was a way to compute the optimal investment in customer-executed quality assurance activities using defect significance with respect to performance objectives, the activities’ defect detection effectiveness, and the cost-penalty for late discovery of impactful defects. This optimization is particularly of interest to projects whose budget constraints significantly limit their risk management options.The cost to fix defects (i.e., failure correction) escalates as the project matures. There have been studies attempting to determine the relative cost of fixing defects discovered during various phases of a project life cycle with important implications, all of which suggest growth factors are large. The commonly referred to 1:10:100 rule represents a cost multiplier for repair/rework across the Design to Fab to Test hardware development phases. Cost premiums for QA activities also accumulate when they are treated as mandatory (due to schedule drag) or are performed later than their assigned phase.This paper describes the modeling of development phase -dependencies in the conduct of typical customer-executed quality assurance activities. Our initial modeling encompasses:• Distinct phases of the production lifecycle• Multiple kinds of Defects, each with some a-priori likelihood of being present• Each defect’s impact on performance Objectives for a type of hardware• The cost and efficacy of assurance techniques at detecting such Defects• The costs of fixing those Defects detected in a given phase of the production lifecycleThe model captures assurance activities’ abilities to Detect defects. Upon detection it is assumed that the Defect is immediately fixed. Defects that “escape” detection by some activity may thereafter be detected by a later activity, but by then the cost of fixing the Defect may have escalated. Defects are related to the performance Objectives they would detract from, were those Defects to remain present in the operating system.We have constructed and are exploring, a model that relates the importance of hardware system elements to mission objectives, the impact of types of Defects on those hardware types, the cost of customer-executed assurance activities (i.e., supplier controls) and their effectiveness towards reducing an impactful quality escape, and the cost of Defect correction across production phase. We describe the approach taken to select the key model aspects, why they are relevant to our NASA mission, and our efforts to populate it with relevant and contemporary data. We use a notional example to illustrate model design and function.

Plante, Jeannette↗
DOE OSTI2022

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted 13 >C and 1 H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted 13 C and 1 H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of 1 H and 13 C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

PISCES two-detector covariance matrix fit for the NOvA Experiment

NOvA is a long-baseline neutrino oscillation experiment with two functionally identical detectors: a Near Detector (ND) at Fermilab, placed 1 km from the neutrino source, and a Far Detector (FD) located 810 km away from the ND in Minnesota. NOvA's primary physics goals are the precise measurements of neutrino oscillation parameters $\theta_{23}$ and $\Delta m^2_{32}$ , determine the neutrino mass ordering, and constrain the value of $\delta_{CP}$, via the study of muon neutrino to electron neutrino oscillation. In the standard NOvA three-flavor analysis, oscillation parameters are extracted using an extrapolation technique in which the ND data constrain the FD prediction through a ratio method. While this allows for systematic uncertainties sharing the same effects in both detectors to cancel, it remains an FD-only fit and does not fully leverage the constraining power of the high-statistics ND. This analysis proposes a simultaneous ND+FD fit using the PISCES method. PISCES (Parameter Inference with Systematic Covariance and Exact Statistics) is a framework designed to support complex configurations such as a joint ND+FD fit. This allows PISCES to take full advantage of the ND data to directly constrain systematic uncertainties across all samples. In PISCES, systematic uncertainties are encoded in a fractional covariance matrix, and statistical uncertainties are handled with a Poisson likelihood, making the approach well suited for low-statistics samples. For interpretability, we further use a Newton–Raphson + PCA method to recover per-systematic pulls from the covariance formulation. This poster presents the full PISCES joint ND+FD fit for the NOvA three-flavor analysis, describes its implementation and evaluates its performance through extensive robustness tests and fake data studies. It also provides a comparison between the PISCES joint ND+FD results and the standard NOvA extrapolation method.

Rajaoalisoa, Miriama [Cincinnati U.] (ORCID:000000↗
NASA NTRS2017

Programmatic Considerations to Reduce the Risk of Adverse Renal Stone Events in Spaceflight

Introduction: Microgravity exposure may alter the likelihood that astronauts will experience renal stones. The potential risk includes both acute and chronic health issues, with the potential for significant impact on mission objectives. Methods: To understand the role of the NASA's Human Research Program (HRP) research agenda in both preventing and addressing renal stones in spaceflight, current astronaut epidemiologic data and a summary of programmatic considerations are reviewed. Results: Although there has never been a symptomatic renal stone event in a U.S. crewmember during spaceflight, urine chemistry has been altered - likely due to induced changes in renal physiology as a result of exposure to microgravity. This may predispose astronauts to stone formation, leading the HRP to conduct and sponsor research to: 1) understand the risk of stone formation in space; 2) prevent stones from forming; and 3) address stones that may form by providing novel diagnostic and therapeutic approaches. Discussion: The development of a renal stone during spaceflight is a significant medical concern that requires the HRP to minimize this risk by providing the ability to prevent, diagnose, monitor and treat the condition during spaceflight. A discussion of the risk as NASA understands it is followed by an overview of the multiple mitigations currently under study, including novel ultrasound techniques for stone detection and manipulation, and how they may function as part of a larger exploration medical system.

Antonsen, Erik↗
NASA NTRS2000

Formation of Large Regular Satellites of Giant Planets in an Extended Gaseous Nebula: Satellite Migration And Survival - 2

Using an optically thick inner disk and an extended, optically thin outer disk as described in Mosqueira and Estrada, we compute the torque as a function of position in the subnebula, and show that although the torque exerted on the satellite is generally negative, which leads to inward migration as expected, there are regions of the disk where the torque is positive. For our model these regions of positive torque correspond roughly to the locations of Callisto and Iapetus. Though the outer location of zero torque depends on the (unknown) size of the transition region between the inner and outer disks, the result that Saturn's is found much farther out (at approximately 3r(sub c, sup S) where r(sub c, sup S) is Saturn's centrifugal radius) than Jupiter's (at approximately 2r(sub c, sup J), where r(sub c, sup J) is Jupiter's centrifugal radius) is mostly due to Saturn's less massive outer disk, and larger Hill radius. For a satellite to survive in the disk the timescale of satellite migration must be longer than the timescale for gas dissipation. For large satellites (approximately 1000 km) migration is dominated by the gas torque. We consider the possibility that the feedback reaction of the gas disk caused by the redistribution of gas surface density around satellites with masses larger than the inertial mass causes a large drop in the drift velocity of such objects, thus improving the likelihood that they will be left stranded following gas dissipation. We adapt the inviscid inertial mass criterion to include gas drag, and m-dependent non-local deposition of angular momentum.

Mosqueira, I.↗
NASA NTRS2017

Behavioral Health and Performance Laboratory Standard Measures (BHP-SM)

The Spaceflight Standard Measures is a NASA Johnson Space Center Human Research Project (HRP) project that proposes to collect a set of core measurements, representative of many of the human spaceflight risks, from astronauts before, during and after long-duration International Space Station (ISS) missions. The term "standard measures" is defined as a set of core measurements, including physiological, biochemical, psychosocial, cognitive, and functional, that are reliable, valid, and accepted in terrestrial science, are associated with a specific and measurable outcome known to occur as a consequence of spaceflight, that will be collected in a standardized fashion from all (or most) crewmembers. While such measures might be used to define standards of health and performance or readiness for flight, the prime intent in their collection is to allow longitudinal analysis of multiple parameters in order to answer a variety of operational, occupational, and research-based questions. These questions are generally at a high level, and the approach for this project is to populate the standard measures database with the smallest set of data necessary to indicate further detailed research is required. Also included as standard measures are parameters that are not outcome-based in and of-themselves, but provide ancillary information that supports interpretation of the outcome measures, e.g., nutritional assessment, vehicle environmental parameters, crew debriefs, etc. The project's main aim is to ensure that an optimized minimal set of measures is consistently captured from all ISS crewmembers until the end of Station in order to characterize the human in space. -This allows the HRP to identify, establish, and evaluate a common set of measures for use in spaceflight and analog research to: develop baselines, systematically characterize risk likelihood and consequences, and assess effectiveness of countermeasures that work for behavioral health and performance risk factors. -By standardizing the battery of measures on all crewmembers, it will allow the HRP to evaluate countermeasures that work for one physiological system and ensure another system is not negatively affected. -These measures, named "Standard Measures," will serve as a data repository and be available to other studies under data sharing agreements.

Williams, Thomas J.↗
DOE OSTI2024

Seismic moment tensor classification using elliptical distribution functions on the hypersphere

Discrimination of underground explosions from naturally occurring earthquakes and other anthropogenic sources is one of the fundamental challenges of nuclear explosion monitoring. In an operational setting, the number of events that can be thoroughly investigated by analysts is limited by available resources. The capability to rapidly screen out events that can be robustly identified as not being explosions is, therefore, of great potential benefit. Nevertheless, possible mis-classification of explosions as earthquakes currently limits the use of screening methods for verification of test-ban treaties. Moment tensors provide a physics-based classification tool for the characterization of different seismic sources and have enabled the advent of new techniques for discriminating between earthquakes and explosions. Following normalization and projection of their six-degree vectors onto the hypersphere, existing screening approaches use spherically symmetric metrics to determine whether any new moment tensor may have been an explosion. Here, we show that populations of moment tensors for both earthquakes and explosions are anisotropically distributed on the hypersphere. Distributions possessing elliptical symmetry, such as the scaled von Mises–Fisher distribution, therefore provide a better description of these populations than the existing spherically symmetric models. We describe a method that uses these elliptical distributions in combination with a Bayesian classifier to achieve successful classification rates of 99 per cent for explosions and 98 per cent for earthquakes using existing catalogues of events from the western United States. The 1983 May 5 Crowdie underground nuclear test and 2018 July 20 DAG-1 deep-borehole chemical explosion are the only two explosions out of 140 that are incorrectly classified. Application of the method to the 2006–2017 nuclear tests in the Democratic People’s Republic of Korea yields 100 per cent identification rates and we provide a simple routine MTid for general usage. The approach provides a means to rapidly assess the likelihood of an event being an explosion and can be built into monitoring workflows that rely on simultaneously assessing multiple different discrimination metrics.

58 GEOSCIENCES↗
NASA NTRS2023

Flight Surgeon Survey Results Review: Long-Duration Artemis Medical System ConOps

Background: The Artemis Mission Functional Medical Concept of Operation (ConOp) is an operational document that describes the functional medical capabilities and concept of operations for early Artemis missions of around 30 days duration. It is currently under revision for longer missions (180+ days), aiming to build a foundational diagnostic and treatment capabilities list considering mission parameters, crew selection and training, and mass/volume constraints. An interview of NASA operational flight surgeons was completed to start the revision of the medical ConOp. Methods: In the survey, 20 experienced NASA flight surgeons responded to 57 standardized questions covering 17 categories, 22 questions of which were covered in this analysis including 4 categories: Cardiovascular, Catastrophic Situations, Pulmonary, and Respiratory/O2 Support. These results were categorized into recommendations and observations. The recommendations were used to objectively quantify the responses to look for consensus, differences and uncertainty among responses. The observations remain important but were not included in this review. Analysis: Regarding questions related to changes in medical conditions treated for long-duration missions, percentage of “No Change” responses varied from 20% to 75%. In the “Change” treatment plan responses, a variety of recommendations were made, which were further made into graphics for better illustration. Additionally, recommendations on capability questions in multiple systems were visualized altogether in one product. Discussion: This work demonstrates the importance of collaboration and thorough analysis amongst experienced NASA flight surgeons for helping establish medical systems and contributing to the likelihood of mission success during Artemis long-duration missions. Through this review, we were able to identify some positive attributes as well as areas for improvement, which will facilitate further discussions.

Andrew Bushong↗
DOE OSTI2023

Electrochemical Control of Magnetism on the Breathing Kagome Network of Li x ScMo 3 O 8

Controlling properties within a given functional inorganic material structure type is often accomplished through tuning the electronic occupation, which is in turn dictated by the elemental composition determined at the time of material preparation. We employ electrochemical control of the lithium content, with associated electronic occupancy control, to vary the magnetic properties of a material where a kagome-derived network of Mo 3 triangles carry the spin. In this case, Li is electrochemically inserted into LiScMo 3 O 8 , a layered compound containing a breathing Mo kagome network. Up to two additional Li can be inserted into LiScMo 3 O 8 , transforming it into Li 3 ScMo 3 O 8 . Li 2 ScMo 3 O 8 prepared by electrochemical lithiation is compared to the quantum spin liquid candidate compound Li 2 ScMo 3 O 8 prepared through high-temperature solid-state methods, which has a slightly different structural stacking sequence but a similar kagome-derived network. Magnetic measurements are supported by first-principles calculations, showing that electrons remain localized on the Mo clusters throughout the doping series. As x is varied in Li x ScMo 3 O 8 , the measurements and calculations reveal the evolution from a diamagnetic band insulator at x = 1 to a geometrically frustrated magnet at x = 2, back to a diamagnetic insulator at x = 3. These results indicate a likelihood of strong coupling between the degree of Li disorder and charge/magnetic ordering over the Mo 3 clusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Younger facial looks are associate with a lower likelihood of several age-related morbidities in the middle-aged to elderly

Abstract Background Looking older for one’s chronological age is associated with a higher mortality rate. Yet it remains unclear how perceived facial age relates to morbidity and the degree to which facial ageing reflects systemic ageing of the human body. Objectives To investigate the association between ΔPA and age-related morbidities of different organ systems, where ΔPA represents the difference between perceived age (PA) and chronological age. Methods We performed a cross-sectional analysis on data from the Rotterdam Study, a population-based cohort study in the Netherlands. High-resolution facial photographs of 2679 men and women aged 51.5–87.8 years of European descent were used to assess PA. PA was estimated and scored in 5-year categories using these photographs by a panel of men and women who were blinded for chronological age and medical history. A linear mixed model was used to generate the mean PAs. The difference between the mean PA and chronological age was calculated (ΔPA), where a higher (positive) ΔPA means that the person looks younger for their age and a lower (negative) ΔPA that the person looks older. ΔPA was tested as a continuous variable for association with ageing-related morbidities including cardiovascular, pulmonary, ophthalmological, neurocognitive, renal, skeletal and auditory morbidities in separate regression analyses, adjusted for age and sex (model 1) and additionally for body mass index, smoking and sun exposure (model 2). Results We observed 5-year higher ΔPA (i.e. looking younger by 5 years for one’s age) to be associated with less osteoporosis [odds ratio (OR) 0.76, 95% confidence interval (CI) 0.62–0.93], less chronic obstructive pulmonary disease (OR 0.85, 95% CI 0.77–0.95), less age-related hearing loss (model 2; B = −0.76, 95% CI −1.35 to −0.17) and fewer cataracts (OR 0.84, 95% CI 0.73–0.97), but with better global cognitive functioning (g-factor; model 2; B = 0.07, 95% CI 0.04–0.10). Conclusions PA is associated with multiple morbidities and better cognitive function, suggesting that systemic ageing and cognitive ageing are, to an extent, externally visible in the human face.

Mekić, Selma↗
DOE OSTI2025

A joint analysis of 3D clustering and galaxy × CMB-lensing cross-correlations with DESI DR1 galaxies

The spectroscopic data from DESI Data Release 1 (DR1) galaxies enables the analysis of 3D clustering by fitting galaxy power spectra and reconstructed correlation functions in redshift space. Given low measurements of the amplitude of structure from cosmic shear at z ∼ 1, redshift space distortions (RSD) + Baryon Acoustic Oscillation (BAO) signals from DESI galaxies combined with weak lensing can break degeneracies and provide a tight alternative constraint on the z ∼ 1 amplitude of structure. In this paper we perform joint analyses that combine full-shape + post-reconstruction information from the DESI DR1 BGS and LRG samples along with angular cross-correlations with Planck PR4 and ACT DR6 CMB lensing maps. We show that adding galaxy-lensing cross-correlations tightens clustering amplitude constraints, improving σ 8 uncertainties by 30% over RSD+BAO alone. We also include angular galaxy-galaxy and galaxy-lensing spectra using photometric samples from the DESI Legacy Survey to further improve constraints. Our headline results are σ 8 = 0.803 ± 0.017, Ω m = 0.3037 ± 0.0069, and S 8 = 0.808 ± 0.017. Given DESI's preference for higher σ 8 compared to lower values from BOSS, we perform a catalog-level comparison of LRG samples from both surveys. We test sensitivity to dark energy assumptions by relaxing our ΛCDM prior and allowing for evolving dark energy via the w 0 - w a parameterization. We find our S 8 constraints to be relatively unchanged despite a 3.5σ tension with the cosmological constant model when combining with the Union3 supernova likelihood. Finally we test general relativity (GR) by allowing the gravitational slip parameter (γ) to vary, and find γ = 1.17 ± 0.11 in mild (∼ 1.5σ) tension with the GR value of 1.0.

cosmological parameters from LSS↗
NASA NTRS2006

Autonomic Management of Space Missions

With NASA s renewed commitment to outer space exploration, greater emphasis is being placed on both human and robotic exploration. Even when humans are involved in the exploration, human tending of assets becomes cost-prohibitive or in many cases is simply not feasible. In addition, certain exploration missions will require spacecraft that will be capable of venturing where humans cannot be sent. Early space missions were operated manually from ground control centers with little or no automated operations. In the mid-l980s, the high costs of satellite operations prompted NASA, and others, to begin automating as many functions as possible. In our context, a system is autonomous if it can achieve its goals without human intervention. A number of more-or-less automated ground systems exist today, but work continues with the goal being to reduce operations costs to even lower levels. Cost reductions can be achieved in a number of areas. Ground control and spacecraft operations are two such areas where greater autonomy can reduce costs. As a consequence, autonomy is increasingly seen as a critical approach for robotic missions and for some aspects of manned missions. Although autonomy will be critical for the success of future missions (and indeed will enable certain kinds of science data gathering approaches), missions imbued with autonomy must also exhibit autonomic properties. Exploitation of autonomy alone, without emphasis on autonomic properties, will leave spacecraft vulnerable to the dangerous environments in which they must operate. Without autonomic properties, a spacecraft may be unable to recognize negative environmental effects on its components and subsystems, or may be unable to take any action to ameliorate the effects. The spacecraft, though operating autonomously, may then sustain a degradation of performance of components or subsystems, and consequently may have a reduced potential for achieving mission objectives. In extreme cases, lack of autonomic properties could leave the spacecraft unable to recover from faults. Ensuring that exploration spacecraft have autonomic properties will increase the survivability and therefore the likelihood of success of these missions. In fact, over time, as mission requirements increased demands on spacecraft capabilities and longevity, designers have gradually built more autonomicity into spacecraft. For example, a spacecraft in low-earth orbit may experience an out-of-bounds perturbation of its attitude (orientation) due to increased drag caused by increased atmospheric density at its altitude as a result of a sufficiently large solar flare. If the spacecraft was designed to recognize the excessive attitude perturbation, it could decide to protect itself by going into a safe-hold mode where its internal configuration and operation are altered to conserve power and its coarse attitude is adjusted to point its solar panels toward the Sun to maximize power generation. This is an example of a simple type of autonomic behavior that has actually occurred. Future mission concepts will be increasingly dependent on space system survivability enabled by more advanced types of autonomic behaviors

Hinchey, Michael G.↗
DOE OSTI2022

The Microbial Community and Functional Potential in the Midland Basin Reveal a Community Dominated by Both Thiosulfate and Sulfate-Reducing Microorganisms

The Permian Basin is the highest producing oil and gas reservoir in the United States. Hydrocarbon resources in this region are often accessed by unconventional extraction methods, including horizontal drilling and hydraulic fracturing. Despite the importance of the Permian Basin, there is no publicly available microbiological data from this region. We completed an analysis of Permian produced water samples to understand the dynamics present in hydraulically fractured wells in this region. We analyzed produced water samples taken from 10 wells in the Permian region of the Midland Basin using geochemical measurements, 16S rRNA gene sequencing, and metagenomic sequencing. Compared to other regions, we found that Permian Basin produced water was characterized by higher sulfate and lower total dissolved solids (TDS) concentrations, with a median of 1,110 mg/L and 107,000 mg/L. Additionally, geochemical measurements revealed the presence of frac hits, or interwell communication events where an established well is affected by the pumping of fracturing fluid into a new well. The occurrence of frac hits was supported by correlations between the microbiome and the geochemical parameters. Our 16S rRNA gene sequencing identified a produced water microbiome characterized by anaerobic, halophilic, and sulfur reducing taxa. Interestingly, sulfate and thiosulfate reducing taxa including Halanaerobium, Orenia, Marinobacter, and Desulfohalobium were the most prevalent microbiota in most wells. We further investigated the metabolic potential of microorganisms in the Permian Basin with metagenomic sequencing. We recovered 15 metagenome assembled genomes (MAGs) from seven different samples representing 6 unique well sites. These MAGs corroborated the high presence of sulfate and thiosulfate reducing genes across all wells, especially from key taxa including Halanaerobium and Orenia. The observed microbiome composition and metabolic capabilities in conjunction with the high sulfate concentrations demonstrate a high potential for hydrogen sulfide production in the Permian Basin. Additionally, evidence of frac hits suggests the possibility for the exchange of microbial cells and/or genetic information between wells. This exchange would increase the likelihood of hydrogen sulfide production and has implications for the oil and gas industry.

16S RNA↗
DOE OSTI2023

Autonomous System Inference, Trojan, and Adversarial Reprogramming Attack and Defense (Final)

In the world of ever-advancing technology, Autonomous Systems (AS) find extensive application, bolstering functionalities of critical infrastructures such as nuclear power plants. These systems, however, are increasingly becoming a target for nefarious activities, namely through inference attacks, trojan attacks, and adversarial reprogramming. This paper delves into a comprehensive exploration of machine learning (ML)-driven autonomous control systems within advanced nuclear reactor designs, revealing the vulnerabilities and proposing strategies for defense against potential cyber-attacks. Advanced cyber-attacks against critical infrastructure and the energy sector are becoming more common. With the invention of autonomous control systems (ACS) within advanced nuclear reactor designs, system designers, reactor operators, and regulators must consider cybersecurity during the design and operational phases. This article provides a cyber threat assessment of machine learning (ML)- based digital twinning (DT) technologies in the context of advanced reactor ACS. A cyber-physical testbed was created to emulate nuclear reactor digital instrumentation and controls (I&C) and act as a basis for the ACS. The ACS was designed as two plant-level DTs predicting reactor malfunctions and determining control actions and two component-level DTs responsible for classifying component states and forecasting component inputs and outputs (I/O). Two duplicate ACS designs– one using a traditional ML framework and one using an automated ML (AutoML) framework– were created and tested against cyber-attacks on training data, real-time process data, and ML model architectures to determine their respective qualitative cyber-risk in terms of likelihood and impact. Both frameworks showed similar cyber-resilience against training, real-time, and ML architecture attacks, proving that neither is inherently more secure. Recommended safeguard and security measures are posed to system designers, reactor operators, and regulators to maintain the cybersecurity of ML-based DT technologies such as ACS, prompting a holistic view of shared responsibility for maintaining cyber-secure ML-based systems. As global reliance on generation III reactors begins to be critically assessed, the evolution towards advanced reactor systems utilizing digital instrumentation and controls (I&C) becomes not merely preferable, but essential. The integration of semi and fully autonomous control systems (ACS), powered by digital I&C and machine learning (ML)-based digital twinning (DT) technologies, emerges as a potent strategy to mitigate operations and maintenance costs, thereby enhancing the economic feasibility of novel reactor designs. However, with a staggering 500% and 380% increase in cyber-attacks reported against the energy sector by the United States Department of Energy (DoE) and the European Union respectively, a surge in cyber vulnerabilities specifically targeting the nuclear industry has been 2 markedly observed. Notable incidents, such as the W32.Ramnit spyware infiltration at the Gundremmingen nuclear power plant in Germany and the Dtrack spyware intrusion at the Kudankulam nuclear power plant in India, while not directly compromising core industrial control systems (ICS), underscore a compelling necessity to fortify cybersecurity protocols in safeguarding reactor systems against increasingly adept digital adversaries. In light of this, our investigation extends beyond conventional cybersecurity parameters, diving into the intricate web of potential vulnerabilities woven into ML-based DTs and ACS in advanced reactor systems. A crafted cyber-physical testbed and preliminary ACS were devised to act as a mirror, reflecting potential configurations of advanced reactor control designs. Moreover, this study is intertwined with a scrutinization of ML models, developed either through conventional, manually tuned methodologies or via automated means through AutoML, probing into their cyber-risk profiles within operational technology (OT) environments. Expanding on this, two distinct ACS blueprints were forged – one navigating through the corridors of traditional ML and the other traversing the path of AutoML – in an effort to holistically encapsulate the considerations pivotal to ML-based DT control system design. Employing the SANS Institute Industrial Control System (ICS) Kill Chain and the MITRE ATT&CK Tactics, Techniques, and Procedures (TTP) framework, a structured analysis was conducted, launching three targeted attacks against the training dataset, real-time dataset, and ML models, therein dissecting the potential cyber-attack implications against both ML frameworks within an ACS milieu. It is essential to note that three distinct categories of attacks were conducted against both ACS configurations, each encompassing three distinct ML-based DTs, cumulating in a total of 18 varied attacks. This exploration extends into the realms of Autonomous System Inference, Trojan, and Adversarial Reprogramming Attack and Defense, unraveling vulnerabilities, and opportunities for fortified defenses against such intrusions, particularly where ML-driven technologies, and by extension, ACS, are deployed. Final recommendations, articulated through a lens of security, safeguard, and implementation considerations, are presented for both traditional and AutoML models, anchoring upon the existing knowledge landscape and ML-based DT modeling for ACS, and are offered as a beacon to guide the nuclear industry through the intricate cybersecurity challenges that lie ahead.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Rapid Bayesian High Entropy Alloy Designs Fabricated via Wire Arc Additive Manufacturing

Purpose: This project seeks to demonstrate a new high-throughput (rapid) alloy design technique applied to creating new high entropy alloys (HEAs) for extreme environments. High entropy alloys shift the design paradigm from being focused on a single principal element (e.g. nickel-based alloys) to target alloys that include high atomic fractions (X >10%) of multiple elements. These HEA materials can exhibit sluggish diffusion and enhanced corrosion resistance, ideal for potential applications in advanced ultra supercritical (A-USC) steam cycles for power generation. Scope: The addition of multiple elements in high atomic fractions creates an enormous design space that cannot easily be investigated by traditional material design strategies such as designed of experiments (DOE). This project utilizes a Bayesian machine learning algorithm that has been modified to work with calculation of phase diagrams (CALPHAD) software. This Bayesian algorithm reduces manual inputs and increase the likelihood of achieving an optimal solution. Compositional inputs to this algorithm will be assessed using existing material property models for high temperature strength and corrosion resistance. The target for alloy performance will be a 15% (~100 ⁰C) increase in allowable service temperature beyond heat-resistant stainless steels while maintaining or improving alloy cost and corrosion resistance. Haynes 230 was selected as a baseline, which is 57 wt% Ni with 22 wt% Cr 14 wt% W, and 2 wt% Mo as solid solution strengtheners. In addition to rapid design via Bayesian machine learning, the alloys were rapidly fabricated using a multi-wire arc additive manufacturing (mWAAM) technique which allows for precise control of alloy composition and assessing of alloy design “windows” to study composition effects. Build speeds for wire-arc additive processes are among the highest for additive technologies enabling rapid and reliable sample fabrication when compared to conventional methods such as arc button melting. The mWAAM samples will be rapidly characterized via instrumented indentation for room temperature modulus and strength and for elevated temperature strength via hot hardness tests. After being screened with hardness testing, potential alloys will be further evaluated with conventional microscopy techniques including scanning electron microscopy (SEM) and transmission electron microscopy (TEM) to assess agreement with modeling results. The most promising compositions will also be evaluated by printing full sized tensile specimens for mechanical behavior tests at elevated temperatures. Results: Bayesian machine learning of a single performance function was initially used to optimize five performance metrics: 1) single phase stability, 2) yield strength, 3) creep resistance (low diffusion coefficient), 4) freezing range (weldability), and 5) material cost. The single performance function was suboptimal as assumptions had to be made about the results while formulating the optimization. A goal-oriented Bayesian optimization strategy (Hanaoka, 2021) was implemented with CALPHAD for use with the five metrics above. This multi-objective Bayesian optimization (MOBO) enabled the design of NiCrCoFe alloys with V and W additions. A base composition of NiCoCr was selected as Ni provides a stable FCC matrix, Cr aids corrosion/oxidation resistance, and Co is a solid-solutions strengthener that also improves creep by increasing the activation energy. Fe helps reduce diffusion coefficients and cost. Finally, V and W were selected for their reasonable solubility and high atomic misfit to aid in solid solution strengthening. Cracking of the mWAAM specimens was an early issue, and the Easton solidification cracking model (Easton et al., 2014a) was selected for addition to the MOBO function. High performing alloys fabricated by mWAAM included Ni 28 Cr 25 Co 26 Fe 15 V 8 and Ni 62 Cr 18 Co 1 Fe 3 W 15 . It was observed that even after adapting the mWAAM process for W, the W did not fully dissolve. To fully evaluate the Ni 62 Cr 18 Co 1 Fe 3 W 15 composition, a cored wire (80-20 NiCr sheath/powder core) was manufactured and printed via WAAM, and HIP’ing was utilized to homogenize and densify the printed alloy. The V and W alloys produced met metrics 1 (solid solution), 4 (solidification cracking), and 5 (cost). However, an unmodeled mechanism of thermal stress cracking was identified in the WAAM produced materials, perhaps exacerbated by the lack of grain boundary strengthening elements (B, C). Conclusions & Recommendations: A high-throughput (rapid) alloy design technique was applied to designing and manufacturing new high entropy alloys (HEAs) for extreme environments utilizing MOBO and mWAAM. The developed process was rapid and effective in addressing the mechanisms included in the model. The lack of grain boundary strengthening element additions (e.g., B, C) was a simplification that likely produced thermal stress cracking that turned into a large part of the investigation. Additions on the order of 0.005 wt% B and 0.05 wt% C likely would have minimized thermal stress grain boundary cracking. Overall, the high throughput design strategy is promising for rapid design of metrics-driven alloys for advanced ultra supercritical (A-USC) steam cycles for power generation. The MOBO and mWAAM process could be commercialized to accelerate metrics-driven alloy design. In addition, the cored-wire process utilized for scale-up is a promising high-volume process for WAAM alloy development and scale-up.

36 MATERIALS SCIENCE↗