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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

NSCOR for Evaluating Risk Factors and Biomarkers for Adaptation and Resilience to Spaceflight: Emotional Valence and Social Processes in ICC/ICE Environments

Space exploration class missions, such as a mission to Mars, will require optimization of human performance, adaptability, and resilience. This NASA Specialized Center of Research (NSCOR) utilizes the NIMH Research Domain Criteria (RDoC) framework to identify biological and behavioral markers of individual social adaptation and emotional resilience (as well as vulnerability) to spaceflight-relevant stressors such as living in extended isolation. The overarching goal of this NSCOR is to obtain novel information to help identify biomarkers of individuals who are resilient and/or adaptable to the stressors of isolated, confined, and controlled (ICC) and isolated, confined, and extreme (ICE) environments.A total of N=90 healthy adult astronaut surrogates are being studied in three spaceflight-analog environments: (1) n=40 healthy adults in the Isolation and Confinement Analog Research Unit (ICARUS), an ICC at the University of Pennsylvania, during 7-day missions, for a target total of 280 subject days; (2) n=32 healthy adult astronaut surrogates studied in NASA’s Human Exploration Research Analog (HERA), an ICC at Johnson Space Center during 45-day missions, for a target total of 2,112 subject days; and (3) n=18 healthy adults in the Alfred-Wegener-Institute’s Neumayer Station III, an ICE in Antarctica, during 14-month missions, for a target total of 7,560 subject days. Dr. Nindl’s Laboratory at the University of Pittsburgh is analyzing a priori selected protein biomarkers in blood, saliva, and urine. Complementary rodent models of exposure to early life stressors, confinement, and isolation are being evaluated at Dr. Hensch’s Laboratory to further validate the neurobehavioral and biological findings from the human studies.Given the inconsistency and varied definition of resilience/adaptation in the scientific literature, the NSCOR team developed a composite resilience/adaptation measure that reflects the most relevant outcomes to resilience/adaptation across psychosocial and neurobehavioral functions, as well as neurocognitive and spaceflight-relevant operational performance. To achieve this, group consensus was attained from subject matter experts to produce a rank-order of importance for each input variable. The final resilience/adaptation score included 36 variables that were collected across spaceflight analogs. As of 10/1/2021, the NSCOR project acquired data on n=27 subjects at ICARUS, n=16 at HERA, and n=18 at Neumayer. The COVID-19 pandemic delayed data acquisition at ICARUS and HERA.Among subjects studied to date, 99% of neural and neurobehavioral data (e.g., neuroimaging for structure and function, behavioral measures) as well as blood, saliva, and urine for biochemical assays have been acquired. For rodent models, Dr. Hensch’s laboratory has established biochemical and behavioral parameters reflecting confinement stress in social networks of mice for comparison to stress responses in the human spaceflight analog environments.Group social behaviors were measured with a Social Network Analysis (SNA) approach to define objective parameters associated with sociability and its plasticity by sex. This analytic approach may help identify a network of individuals who are more effective teammates or more likely to generate new social relationships. Data acquisition, biomarker assessment, and data quality control will continue through September 2022.

D F Dinges↗
DOE OSTI2020

Grain boundary structure search by using an evolutionary algorithm with effective mutation methods

Grain boundaries (GBs) accommodate the misorientation between adjacent grains in a polycrystalline material. GBs are geometrically described by the macroscopic and microscopic degrees of freedom. Besides, at the atomistic level, GBs exhibit complicated behaviors under varying thermodynamic conditions. The complexity of atomistic GB structures demands stochastic searching for possible states. The effectiveness of stochastic search methods relies on techniques to recreate and select atomistic structures. In this work, we developed a new mutation operator that can induce direct and collective atomistic structure changes to boost the search efficiency of exploring GB structures with evolutionary algorithms (EA). We implemented the mutation methods along with innovative selection, crossover, boundary condition preprocessing methods to form an EA-based package to explore GB structures in grand canonical ensembles with atomistic simulations. We used this package to study the [001] symmetric tilt grain boundaries (STGBs) in FCC copper (Cu), the [110] STGBs in BCC tungsten (W), and the $[1\bar{2}10]$ STGBs in HCP magnesium (Mg). The results show that our design and implementation based on new mutation procedures, selection, and boundary conditions provide a high-quality search of atomistic GB structures in the grand canonical ensemble for different crystal lattices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Vehicular Re-Identification from Uncontrolled Multiple Views

Vehicle re-identification (re-ID) across disparate sensing modalities remains a fundamental challenge for transportation research. In this work, we introduce a deep multi-view vehicle re-ID framework that leverages Siamese networks to compare pairs of vehicle images and produce matching scores, enabling robust association across drastically different viewpoints such as those from UAVs, surveillance cameras, and ground sensors. The model exploits convolutional neural networks to learn features that remain discriminative under changes in angle, distance, and illumination, supporting more generalizable re-ID performance. As part of this effort, we also developed an automated pipeline to synchronize roadside and UAV video streams, producing a multi-perspective dataset that complements preexisting real collections and a synthetic dataset generated in this study. Together, these contributions advance the capability to re-identify vehicles across wide viewing baselines; establish a foundation for scalable, reproducible research in vehicle re-ID; and open pathways for future applications, such as inferring routine behaviors, movement patterns, and daily habits of the individual associated with the vehicle.

convolutional neural networks↗
NASA NTRS1974

Protonospheric columnar electron content determination. II - Observations

The results of over 300 hours of measurements of the quantity I sub w - the difference between total columnar electron content, derived from the differential phase-path data, and Faraday electron content - is presented. The interpretation of the measurements in terms of protonospheric content is discussed in the light of a previous analysis which demonstrates that good protonospheric content measurements can only be obtained under certain geometries of observations unless information on the shape and height of the ionospheric layer is available. A strong correlation between I sub w and the ionospheric content is observed in most of the Arecibo data, because they were collected under unfavorable conditions. The Stanford data, on the other hand, yield good measurements of the protonospheric content. The May 1969 observations show a strong depletion of protonospheric content during a magnetic storm and its subsequent recovery. This is in general agreement with flux tube content behavior observed by the whistler technique during similar conditions.

Almeida, O. G.↗
NASA NTRS1982

Partial discharge testing under direct voltage conditions

DC partial discharge (PD) (corona) testing is performed using a multichannel analyzer for pulse storing, and data is collected during increase of voltage and at quiescent voltage levels. Thus high voltage ceramic disk capacitors were evaluated by obtaining PD data interspersed during an accelerated life test. Increased PD activity was found early in samples that later failed catastrophically. By this technique, trends of insulation behavior are revealed sensitively and nondestructively in high voltage dc components.

Bever, R. S.↗
NASA NTRS1997

Two Improved Algorithms for Envelope and Wavefront Reduction

Two algorithms for reordering sparse, symmetric matrices or undirected graphs to reduce envelope and wavefront are considered. The first is a combinatorial algorithm introduced by Sloan and further developed by Duff, Reid, and Scott; we describe enhancements to the Sloan algorithm that improve its quality and reduce its run time. Our test problems fall into two classes with differing asymptotic behavior of their envelope parameters as a function of the weights in the Sloan algorithm. We describe an efficient 0(nlogn + m) time implementation of the Sloan algorithm, where n is the number of rows (vertices), and m is the number of nonzeros (edges). On a collection of test problems, the improved Sloan algorithm required, on the average, only twice the time required by the simpler Reverse Cuthill-Mckee algorithm while improving the mean square wavefront by a factor of three. The second algorithm is a hybrid that combines a spectral algorithm for envelope and wavefront reduction with a refinement step that uses a modified Sloan algorithm. The hybrid algorithm reduces the envelope size and mean square wavefront obtained from the Sloan algorithm at the cost of greater running times. We illustrate how these reductions translate into tangible benefits for frontal Cholesky factorization and incomplete factorization preconditioning.

Kumfert, Gary↗
NASA NTRS2002

Alternative Metrics for Evaluating the Resilence of Advanced Life Support Systems

Ensuring the safety of the crew is a key performance requirement of a life support system. However, a number of conceptual and practical difficulties arise when devising metrics to concretely measure the ability of a life support system to maintain critical functions in the presence of anticipated and unanticipated faults. Resilience is a dynamic property of a life support system that depends on the complex interactions between faults, controls and system hardware. We review some of the approaches to understanding the robustness or resilience of complex systems being developed in diverse fields such as ecology, software engineering and cell biology and discuss their applicability to regenerative life support systems. We also consider how approaches to measuring resilience vary depending on system design choices such as the definition and choice of the nominal operating regime. Finally, we explore data collection and implementation issues such as the key differences between the instantaneous or conditional and average or overall measures of resilience. Extensive simulation of a hybrid computational model of a water revitalization subsystem (WRS) with probabilistic, component-level faults provides data about off-nominal behavior of the system. The data are used to consider alternative measures of resilience as predictors of the system's ability to recover from component-level faults.

Bell, Ann Maria↗
NASA NTRS2005

Monitoring a Complex Physical System using a Hybrid Dynamic Bayes Net

The Reverse Water Gas Shift system (RWGS) is a complex physical system designed to produce oxygen from the carbon dioxide atmosphere on Mars. If sent to Mars, it would operate without human supervision, thus requiring a reliable automated system for monitoring and control. The RWGS presents many challenges typical of real-world systems, including: noisy and biased sensors, nonlinear behavior, effects that are manifested over different time granularities, and unobservability of many important quantities. In this paper we model the RWGS using a hybrid (discrete/continuous) Dynamic Bayesian Network (DBN), where the state at each time slice contains 33 discrete and 184 continuous variables. We show how the system state can be tracked using probabilistic inference over the model. We discuss how to deal with the various challenges presented by the RWGS, providing a suite of techniques that are likely to be useful in a wide range of applications. In particular, we describe a general framework for dealing with nonlinear behavior using numerical integration techniques, extending the successful Unscented Filter. We also show how to use a fixed-point computation to deal with effects that develop at different time scales, specifically rapid changes occuring during slowly changing processes. We test our model using real data collected from the RWGS, demonstrating the feasibility of hybrid DBNs for monitoring complex real-world physical systems.

Lerner, Uri↗
NASA NTRS2008

Tools, Techniques, and Applications: Normalizing the VR Paradigm

Oshynee's precision Learning Objective performance factor rubrics with associated behavioral anchors integrates with Thinking Worlds(TradeMark), to provide event data recording and dynamic prescriptive feedback. Thinking Worlds(TradeMark) provides SCORM parametric data for reporting within the game and within overarching curricula or workplace evaluation strategy. - Open-sourced, browser-based digital dashboard reporting tools collect data from TW, LMS, LCMS, HR, and workplace metrics or control systems The games may be delivered across the internet or in a range of networked and stand-alone methods using the delivery model (s) required by the host organization.

Duncan, Gaeme↗
NASA NTRS2023

CFD Modeling of Bi-Directional PMD inside Cryogenic Propellant Tanks Onboard Parabolic Flights

Future cryogenic propulsion systems will require efficient methods with which to transfer cryogenic propellants from a depot storage tank to a customer receiver tank to minimize cost and maximize reusability. The Reduced Gravity Cryogenic Transfer project is currently developing advanced cryogenic fluid management technology and developing and validating new numerical models for three phases of transfer: line chilldown, tank chilldown, and tank fill. Additionally, multiple liquid nitrogen (LN 2 ) parabolic flight transfer rigs are being designed by universities and NASA to investigate the gravitational sensitivities that exist in these three technologies. In order to maximize the collection of low-g data during flights, it is required to extract as much (LN 2 as possible from the supply tank, despite variable gravity levels. The purpose of this paper is to present computational fluid dynamics (CFD) volume of fluid simulations of (LN 2 behavior in the supply tank onboard parabolic flights to validate the optimal design of a bi-directional propellant management device (PMD) using the commercial software FLOW-3D. A parametric study is conducted on the effects of gravity level, fill level, pore size, open area, thickness, and type of baffle on PMD performance. Based on results, the PMD as designed exceeds the targeted expulsion efficiency.

Jason Hartwig↗
DOE OSTI2021

Reproduction of melting behavior for vitrified hillforts based on amphibolite, granite, and basalt lithologies

Abstract European Bronze and Iron Age vitrified hillforts have been known since the 1700s, but archaeological interpretations regarding their function and use are still debated. We carried out a series of experiments to constrain conditions that led to the vitrification of the inner wall rocks in the hillfort at Broborg, Sweden. Potential source rocks were collected locally and heat treated in the laboratory, varying maximum temperature, cooling rate, and starting particle size. Crystalline and amorphous phases were quantified using X-ray diffraction both in situ, during heating and cooling, and ex situ, after heating and quenching. Textures, phases, and glass compositions obtained were compared with those for rock samples from the vitrified part of the wall, as well as with equilibrium crystallization calculations. ‘Dark glass’ and its associated minerals formed from amphibolite or dolerite rocks melted at 1000–1200 °C under reducing atmosphere then slow cooled. ‘Clear glass’ formed from non-equilibrium partial melting of feldspar in granitoid rocks. This study aids archaeological forensic investigation of vitrified hillforts and interpretation of source rock material by mapping mineralogical changes and glass production under various heating conditions.

42 ENGINEERING↗
DOE OSTI2024

A Case Study Investigating the Low Summertime CAPE Behavior in the Global Forecast System

Convective available potential energy (CAPE) is an important index for storm forecasting. Recent versions (v15.2 and v16) of the Global Forecast System (GFS) predict lower values of CAPE during summertime in the continental United States than analysis and observation. We conducted an evaluation of the GFS in simulating summertime CAPE using an example from the Unified Forecast System Case Study collection to investigate the factors that lead to the low CAPE bias in GFS. Specifically, we investigated the surface energy budget, soil properties, and near-surface and upper-level meteorological fields. Results show that the GFS simulates smaller surface latent heat flux and larger surface sensible heat flux than the observations. This can be attributed to the slightly drier-than-observed soil moisture in the GFS that comes from an offline global land data assimilation system. The lower simulated CAPE in GFS v16 is related to the early drop of surface net radiation with excessive boundary layer cloud after midday when compared with GFS v15.2. A moisture-budget analysis indicates that errors in the large-scale advection of water vapor does not contribute to the dry bias in the GFS at low levels. Common Community Physics Package single-column model (SCM) experiments suggest that with realistic initial vertical profiles, SCM simulations generate a larger CAPE than runs with GFS IC. SCM runs with an active LSM tend to produce smaller CAPE than that with prescribed surface fluxes. Note that the findings are only applicable to this case study. Including more warm-season cases would enhance the generalizability of our findings.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An Intelligent Adaptable Monitoring Package. Final Report

The “Intelligent Adaptable Monitoring Package” project was a four-year effort that demonstrated the feasibility of integrated sensing packages at tidal and wave energy sites. Such integration is generally required by the breadth of sensors required to understand environmental effects at marine energy sites and the operational difficulty of deploying, maintaining, and recovering such sensors. Over the course of the project, the Adaptable Monitoring Package (AMP) was deployed in multiple settings, each corresponding to a project budget period: - Budget Period 1: Demonstration of cabled deployment at Pacific Northwest National Laboratory’s Marine Science Laboratory. The deployment highlighted AMP hardware endurance over a 4-month deployment in a tidally-dominated environment and laid the groundwork for machine learning algorithms to detect and classify targets present in active sonar data. - Budget Period 2: Demonstration of an autonomous deployment at PacWave South off the coast of Newport, Oregon. The deployment highlighted the stability of AMP hardware and software, with the autonomous package collecting data on a duty cycle over a 1.5-month deployment. - Budget Period 3: Demonstration of an autonomous deployment powered by a wave energy converter at the U.S. Navy’s Wave Energy Test Site. The deployment highlighted the potential of wave energy to power ocean observatories and led to the development of machine learning algorithms to detect and classify targets in optical camera data. In aggregate, this project’s greatest success was demonstrating the AMP’s flexibility in a range of deployment scenarios. Each budget period represented a “first of a kind” demonstration of integrated instrumentation – cabled AMP, autonomous AMP, wave-energy powered AMP – and each deployment helped to identify and set goals for the next. Further, despite the exploratory nature of these deployments, each one achieved high system up-time and proved that flexible integration of multiple sensors in a single package represents a viable strategy for marine energy environmental monitoring. The key lessons learned from the project are: - Without continuous power, either from a shore cable or in situ source, many of the benefits of integration are lost (If continuous power is not available, the ability to detect rare events is lost, as is the ability to minimize the risk of behavioral changes through adaptive sensing. However, even on a duty cycle, there is still value in being able to acquire synchronous data from multiple sensors.); and - Observations from a moving platform present substantially greater data processing challenges than those from stationary platforms. Finally, these deployments also demonstrate an important truth: successful integration alone does not guarantee that relevant data are collected. To grow the knowledge base about environmental interactions with marine energy converters, integrated systems, like the AMP, need to include the right sensor mix and connect the data pipelines to effective processing algorithms. These deployments establish a strong foundation for future collaborations with the environmental research community: not only to understand the environmental effects of marine energy, but also to improve our general ability to study life in the sea.

16 TIDAL AND WAVE POWER↗
NASA NTRSDate not supplied

Impact of the ISS Environment on CNS in Drosophila Melanogaster

Altered gravity and elevated carbon dioxide (CO 2 ) levels as experienced on the ISS can adversely affect human health across various organ systems, especially the Central Nervous System (CNS). Investigating these changes is essential for understanding the long-term effects of spaceflight on human physiology to ensure crew health. Ground-based analogs provide an efficient method to evaluate alterations induced by chronic spaceflight on a larger scale. This study focuses on CNS changes in response to hypergravity (HG) and elevated CO 2 levels via a ground-based analog using the well-established model organism, Drosophila melanogaster. We hypothesize behavior and physiological changes immediately post exposure to HG+CO 2 , along with chronic effects up to 25 days post-exposure. Adult male and female flies were exposed to varying gravity loads (1g, 1.2g, and 3g) and elevated CO 2 levels (~4000 ppm, mimicking CO 2 levels on the ISS) for 15 days and were assessed immediately and at 5-, 10-, and 25-days post-exposure, mirroring astronauts’ post-return profiling. The flies were assessed for neurobehavioral changes, including longevity and negative geotaxis; brain morphological changes, such as dopaminergic neuron count, apoptosis, and glial cell density; and bioenergetic changes in the brain, including mitochondrial abundance and membrane potential. Longevity remained unchanged under hypergravity, even with the addition of elevated CO 2 as a stressor. However, despite the stable lifespan, quality of life appeared to be affected, as shown by negative geotaxis and neuroanatomical changes. Negative geotaxis testing revealed a reduction in motor ability at R0 across all conditions, which correlated with a decrease in dopaminergic (DA) neuron count. Additional observations suggested further systemic alterations, including a decrease in glial cell count, an increase in apoptosis, and notable bioenergetic changes. Collectively, these findings contribute to our understanding of the long-term effects of spaceflight on the CNS.

spaceflight↗
DOE OSTI2022

Stochastic simulation of occupant-driven energy use in a bottom-up residential building stock model

The residential buildings sector is one of the largest electricity consumers worldwide and contributes disproportionally to peak electricity demand in many regions. Strongly driven by occupant activities, household energy consumption is stochastic and heterogeneous in nature. However, most residential energy models applied by industry use homogeneous, deterministic activity schedules, which work well for predictions of annual energy consumption, but can result in unrealistic hourly or sub-hourly electric load profiles, with exaggerated or muted peaks. The increasing proportion of variable renewable energy generators means that representing the heterogeneity and stochasticity of occupant behavior is now crucial for reliable planning at both bulk-power and distribution-system scales. This work presents a novel and open-source occupancy simulation approach that can simulate a diverse set of individual occupant and household event schedules for all major electricity, fuel, and hot water end uses. To accomplish this, we evaluated three alternative occupant activity simulation approaches before selecting a hybrid combining time-inhomogeneous Markov chains and probability-sampling of event durations and magnitudes. Further, we integrated the stochastic occupancy simulation with an open-source bottom-up physics-simulation building stock model and published a set of 550,000 diverse household end-use activity schedules representing a national housing stock. The simulator was verified against time-use survey data, and simulation results were validated against measured end-use electricity data for accuracy and reliability. While we use data for the United States, our application demonstrates how similar approaches could be applied using the time-use survey data collected in many countries around the world.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Multiphysics analysis of fuel fragmentation, relocation, and dispersal susceptibility–Part 3: Thermal hydraulic evaluation of large break LOCA under high-burnup conditions

Increasing the peak rod average burnup of pressurized water reactor (PWR) fuel beyond 62 GWd/tU may increase fuel fragmentation, relocation, and dispersal (FFRD) susceptibility during a large break loss of coolant accident (LBLOCA). TRACE thermal hydraulic (TH) LBLOCA analyses were performed for a realistic 24-month high-burnup PWR equilibrium cycle, to inform subsequent transient BISON high-burnup FFRD susceptibility evaluations. Realistic LBLOCA systems behavior was first established by configuring to and comparing with the BEMUSE OECD LBLOCA benchmark. Fuel and operating conditions were then applied from high-burnup VERA depletion calculations. LBLOCA simulations were performed for 281 selected high-burnup rods, for which transient TH boundary conditions were collected for later use in BISON. The TRACE results indicated that rod linear heat rate (rather than burnup) is the main predictor of peak cladding temperature (PCT) during the event. PCT typically occurred at a local burnup lower than the rod-average burnup, especially for twice-burned fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Kensor: Coordinated Intelligence from Co-Located Sensors

Internet of Things (IoT) is becoming more pervasive in many installations, including homes, manufacturing plants, and industrial facilities of all kinds. The data that IoT produces is a reflection of usual behavior such as daily routines and scheduled tasks, but also from unexpected behavior due to unintentional or undesirable abnormalities. Here, we focus on achieving coordinated intelligence about normal and abnormal phenomena from multiple sensors that are geographically colocated in close proximity, monitoring and controlling a set of co-located devices. Given a set of co-located sensors, we seek an intelligent approach that would automatically determine the “normal” patterns of behaviors among the correlated sensors. After normal behavior is extracted, later monitoring should detect any deviant variations over time. An example application is an entry monitoring and alert system for facilities such as nuclear reactors, where badge readers, door locks, lights, weight trackers and other co-located sensors at the entry point are collectively tracked. To address this problem, we identify the possible solution approach that can be used to solve its different variants. The implemented model is developed as a combination of rules and Markov Chain methods.

Kotevska, Olivera↗
DOE OSTI2024

CFD modeling of high-flux plate-and-frame membrane modules for industrial carbon capture

In this work, we study the application of membrane-based separation systems for carbon capture, considering plate-and-frame membrane modules. The successful deployment of membrane CO2 capture system relies on high-performing membranes as well as effective membrane modules that can fully exploit the developed membranes. A plate-and-frame membrane module is especially attractive for CO2 capture from industrial flue gas due to its lower pressure drop compared to its counterparts such as spiral wound modules and hollow fiber modules. To design better plate-and-frame modules, we investigate their basic unit - a single membrane stack through a combination of computational modeling and experimental investigations. The modeling approach is based on Computational Fluid Dynamics (CFD) to represent a multiphysics problem, including the fluid flow and diffusion processes within a membrane module. We use experimental data collected under different operating conditions to validate the CFD model. Numerical results suggest a good agreement between experiments and model outputs for the CO2 recovery, CO2 mole fraction in the retentate and permeate, and stage-cut. The CFD model is able to predict accurately the flow behavior, providing valuable insights on the effects of fluid dynamics on mass transfer of CO2. We also carry out a sensitivity analysis to identify the effect of key parameters on the CO2 recovery and the CO2 purity of the outlet streams.

Dosso, Cheick↗