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NASA NTRS1980

Aerodynamic heating for gaps in laminar and transitional boundary layers

The presence of gaps, slots, and/or steps in a surface may significantly perturb a supersonic boundary layer. The paper discusses heat-transfer distributions for a variety of gap configurations which were obtained by placing instrumented inserts in a flat-plate structural carrier. The data were obtained in a 3.50 foot hypersonic wind tunnel at a freestream Mach number of 5.10 over a range of Reynolds number from 2,570,000 to 8,110,000. The variables of the test program included the freestream Reynolds number and the gap configuration, e.g., width, depth, step height, number, and orientation.

Bertin, J. J.↗

DOE OSTI2024

Monsoonal MCS Initiation, Rainfall, and Diurnal Gravity Waves over the Bay of Bengal: Observation and a Linear Model

Abstract Previous observational studies have indicated that mesoscale convective systems (MCSs) contribute the majority of precipitation over the Bay of Bengal (BoB) during the summer monsoon season, yet their initiation and propagation remain incompletely understood. To fill this knowledge gap, we conducted a comprehensive study using a combination of 20-yr satellite observations, MCS tracking, reanalysis data, and a theoretical linear model. Satellite observations reveal clear diurnal propagation signals of MCS initiation frequency and rainfall from the west coast of the BoB toward the central BoB, with the MCS rainfall propagating slightly slower than the MCS initiation frequency. Global reanalysis data indicate a strong association between the offshore-propagating MCS initiation frequency/rainfall and diurnal low-level wind perturbations, implying the potential role of gravity waves. To verify the hypothesis, we developed a 2D linear model that can be driven by realistic meteorological fields from reanalysis. The linear model realistically reproduces the characteristics of offshore-propagating diurnal wind perturbations. The wind perturbations, as well as the offshore propagation signals of MCS initiation frequency and rainfall, are associated with diurnal gravity waves emitted from the coastal regions, which in turn are caused by the diurnal land–sea thermal contrast. The ambient wind speed and vertical wind shear play crucial roles in modulating the timing, propagation, and amplitude of diurnal gravity waves. Using the linear model and satellite observations, we further show that the stronger monsoonal flows lead to faster offshore propagation of diurnal gravity waves, which subsequently control the offshore propagation signals of MCS initiation and rainfall. Significance Statement Rainfall over the Bay of Bengal (BoB) is primarily contributed by large and organized rainfall systems in the summer monsoon season. During this season, these systems are commonly observed over the east coast of India around midnight, the western BoB in the morning, and the central BoB in the afternoon. This eastward rainfall propagation is confirmed by observations, reanalysis data, and a theoretical model to have a strong association with atmospheric diurnal gravity waves. These waves are caused by land–sea thermal contrast and can trigger rainfall systems over the offshore regions. We also found that the diurnal gravity waves, as well as their triggered rainfall systems, can be greatly modulated by the large-scale monsoonal flows. All the above findings improve our understanding of diurnal rainfall cycle over the BoB and may contribute to the future improvement of rainfall forecast over the region.

Meteorology & Atmospheric Sciences↗

DOE OSTI2026

Variable rate neural compression for sparse detector data

Particle colliders produce data at extraordinary rates, posing major challenges for transmission and storage. High-throughput compression algorithms are therefore essential. In the sPHENIX experiment taking data at the Relativistic Heavy Ion Collider, a time projection chamber records three-dimensional (3D) particle trajectories that are highly sparse, making conventional learning-free lossy compression ineffective. Convolutional neural networks have surpassed traditional methods in compression ratio and accuracy. However, they fail to exploit sparsity for efficiency. To address these gaps, we present BCAE-VS, a bicephalous convolutional autoencoder with variable compression ratio for sparse data, which adapts compression to input complexity through key-point identification and sparse convolution. BCAE-VS achieves higher accuracy and compression ratios than prior neural approaches while being orders of magnitude smaller. Moreover, its throughput increases with sparsity—a property not observed in other methods. Although it was developed for collider experiments, BCAE-VS readily extends to other sparse data domains, such as light detection and ranging (LiDAR) sensing and 3D microscopy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

The ABCflux database: Arctic–boreal CO 2 flux observations and ancillary information aggregated to monthly time steps across terrestrial ecosystems

Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO 2 ) fluxes in terrestrial ecosystems across the rapidly warming Arctic–boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic–boreal CO 2 fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO 2 exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19 % of the monthly observations), snow diffusion (3 %) and eddy covariance (78 %) techniques. The largest number of observations were collected during the climatological summer (June–August; 32 %), and fewer observations were available for autumn (September–October; 25 %), winter (December–February; 18 %), and spring (March–May; 25 %). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO 2 fluxes and to better estimate the terrestrial ABZ CO 2 budget.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Operation and Maintenance of PV Systems: Data Science, Analysis, and Standards

This effort improves the effectiveness and reduce uncertainty in O&M cost through four primary objectives/tasks: 1) institutionalize standards for reliability and availability reporting for large PV power plants; 2) bridge systemic O&M knowledge gaps around important topics affecting O&M; 3) characterize systemic failure modes and patterns and accelerate O&M experiential learning cycles using field data; and 4) establish a baseline understanding of UPVS O&M cost drivers. Key results of this effort include publication of IEC standards, published topical papers on O&M topics, training, and characterize field data for climate- and service-related patterns (additional details below). Integrating these results serves to reduce performance risk and facilitate improvement in the way solar projects are operated and maintained. Results are well received and two publications are among the most successful SETO publications at NREL ("Model of Operation and Maintenance Costs for Photovoltaic Systems with over 40,000 downloads and "Best Practices in Operation and Maintenance of PV Systems, 3rd Ed." with over 90,000 downloads).

14 SOLAR ENERGY↗

NASA NTRS2008

Issues in Data Fusion for Satellite Aerosol Measurements for Applications with GIOVANNI System at NASA GES DISC

We look at issues, barriers and approaches for Data Fusion of satellite aerosol data as available from the GES DISC GIOVANNI Web Service. Daily Global Maps of AOT from a single satellite sensor alone contain gaps that arise due to various sources (sun glint regions, clouds, orbital swath gaps at low latitudes, bright underlying surfaces etc.). The goal is to develop a fast, accurate and efficient method to improve the spatial coverage of the Daily AOT data to facilitate comparisons with Global Models. Data Fusion may be supplemented by Optimal Interpolation (OI) as needed.

Gopalan, Arun↗

DOE OSTI2025

Enhancing fire emissions inventories for acute health effects studies: integrating high spatial and temporal resolution data

Daily fire progression information is crucial for public health studies that examine the relationship between population-level smoke exposures and subsequent health events. Issues with remote sensing used in fire emissions inventories (FEI) lead to the possibility of missed exposures that impact the results of acute health effects studies. This paper provides a method for improving an FEI dataset with readily available information to create a more robust dataset with daily fire progression. High temporal and spatial resolution burned area information from two FEI products are combined into a single dataset, and a linear regression model fills gaps in daily fire progression. The combined dataset provides up to 71% more PM 2.5 emissions, 69% more burned area, and 367% more fire days per year than using a single source of burned area information. The FEI combination method results in improved FEI information with no gaps in daily fire emissions estimates. The combined dataset provides a functional improvement to FEI data that can be achieved with currently available data.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

NASA NTRS2013

A Full Snow Season in Yellowstone: A Database of Restored Aqua Band 6

The algorithms for estimating snow extent for the Moderate Resolution Imaging Spectroradiometer (MODIS) optimally use the 1.6- m channel which is unavailable for MODIS on Aqua due to detector damage. As a test bed to demonstrate that Aqua band 6 can be restored, we chose the area surrounding Yellowstone and Grand Teton national parks. In such rugged and difficult-to-access terrain, satellite images are particularly important for providing an estimation of snow-cover extent. For the full 2010-2011 snow season covering the Yellowstone region, we have used quantitative image restoration to create a database of restored Aqua band 6. The database includes restored radiances, normalized vegetation index, normalized snow index, thermal data, and band-6-based snow-map products. The restored Aqua-band-6 data have also been regridded and combined with Terra data to produce a snow-cover map that utilizes both Terra and Aqua snow maps. Using this database, we show that the restored Aqua-band-6-based snow-cover extent has a comparable performance with respect to ground stations to the one based on Terra. The result of a restored band 6 from Aqua is that we have an additional band-6 image of the Yellowstone region each day. This image can be used to mitigate cloud occlusion, using the same algorithms used for band 6 on Terra. We show an application of this database of restored band-6 images to illustrate the value of creating a cloud gap filling using the National Aeronautics and Space Administration s operational cloud masks and data from both Aqua and Terra.

Gladkova, Irina↗

DOE OSTI2023

Machine-Learning-aided Approach for Predicting the Thermal Expansion Behaviors in Advanced Test Reactor Capsules (NURETH-20 full paper)

Instrumented experiments at test reactors are essential to deploying new advanced reactor systems. Designing new experiments and generating data on specific conditions require both time and cost investment. A high-fidelity model of the experiment environment can be created using finite element analysis software to support the actual experiments, but computation time is still a concern in applying outcomes to real-time usage (e.g., a digital twin). This research proposes a machine-learning-aided approach to temperature and displacement predictions, based on the thickness of the outer gas gap on the experimental capsule used for the in-pile demonstration of a novel thermal conductivity probe in the Advanced Test Reactor. The capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. There were gas gaps between the fuel and rodlet and between the inner and outer capsule. The learning data consisted of an experimental capsule’s radial distributions of temperature and displacement, as obtained from Abaqus and the physical features. For the first step, temperature was predicted using three positional parameters. Then the displacement was predicted using six different positional parameters. Each physical feature was normalized to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement in all cases involving interpolation and extrapolation. Also, data similarity enhancement increased the similarity between training and target data increasing the predictive accuracy of machine-learning models. In some cases of extrapolation, the accuracy of the machine-learning model showed limited performance, but still data similarity enhancement improved the accuracy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRSDate not supplied

High School Citizen Scientists Use AI/ML to Predict Intra-Ocular Pressure From Gene Expression Data for Spaceflown Mice

Artificial Intelligence (AI) and Machine Learning (ML) have increasingly become pivotal in biological and biomedical research, largely due to the culture of open data sharing and its associated benefits. The methodologies inherent in AI/ML are particularly adept at identifying and forecasting biological phenotypes from the vast amounts of data generated by next-generation sequencing technologies. These techniques offer substantial promise for advancing research in space biosciences and for the development of automated systems for monitoring space health. Nevertheless, there are crucial aspects to consider when training, validating, and testing machine learning models in both biological research and clinical contexts. It is essential that Open Science principles, including data sharing and the availability of open-source code, are complemented by high-quality, publicly accessible training resources. These resources should focus on best practices and include modules based on real-world scientific cases and data to ensure that future AI/ML practitioners gain practical experience with genuine problems. Addressing this knowledge gap, we have designed, developed, and delivered both interactive and self-paced training programs for citizen scientists worldwide, enabling them to utilize AI/ML for space biology research. This initiative was made possible through generous funding from a Transformation to Open Science Training grant. The interactive training sessions, conducted this summer, utilized AI/ML techniques to analyze data from the Open Science Data Repository, specifically targeting the effects of spaceflight on ocular structure and function. The dataset OSD-583, from the Rodent Research 9 mission, provides experimental data detailing the ocular responses of mice subjected to a 35-day spaceflight, compared with ground control counterparts. Using OSD-583 as observational data, our summer training participants applied AI/ML methods to predict intraocular pressure from RNA-seq data and identify the genes most predictive of the observed responses. Further analysis through pathway enrichment and gene set enrichment revealed that these genes are involved in molecular and cellular processes contributing to retinal degeneration.

James Casaletto↗

NASA NTRS2009

Development of a Methodology to Gather Seated Anthropometry in a Microgravity Environment

The Constellation Program's Crew Exploration Vehicle (CEV) is required to accommodate the full population range of crewmembers according to the anthropometry requirements stated in the Human-Systems Integration Requirement (HSIR) document (CxP70024). Seated height is one of many critical dimensions of importance to the CEV designers in determining the optimum seat configuration in the vehicle. Changes in seated height may have a large impact to the design, accommodation, and safety of the crewmembers. Seated height can change due to elongation of the spine when crewmembers are exposed to microgravity. Spinal elongation is the straightening of the natural curvature of the spine and the expansion of inter-vertebral disks. This straightening occurs due to fluid shifts in the body and the lack of compressive forces on the spinal vertebrae. Previous studies have shown that as the natural curvature of the spine straightens, an increase in overall height of 3% of stature occurs which has been the basis of the current HSIR requirements. However due to variations in the torso/leg ratio and impact of soft tissue, data is nonexistent as to how spinal elongation specifically affects the measurement of seated height. In order to obtain this data, an experiment was designed to collect spinal elongation data while in a seated posture in microgravity. The purpose of this study was to provide quantitative data that represents the amount of change that occurs in seated height due to spinal elongation in microgravity environments. Given the schedule and budget constraints of ISS and Shuttle missions and the uniqueness of the problem, a methodology had to be developed to ensure that the seated height measurements were accurately collected. Therefore, simulated microgravity evaluations were conducted to test the methodology and procedures of the experiment. This evaluation obtained seat pan pressure and seated height data to a) ensure that the lap restraint provided sufficient restraint to eliminate any gap between the subject s gluteal surface and the seat pan and b) to document any necessary design and procedural changes needed due to the microgravity environment. The methodology and setup used during the simulated microgravity evaluations was replicable to the proposed methodology and setup for in-space missions. A flight-like Shuttle seat, pressure sensors, anthropometer, and existing hardware was used to measure seated height and contact area while experiencing microgravity. The outlying buttock and thigh surface contact areas were collected to determine if the subjects were in contact with the seat pan, while a measurer recorded their seated height with an anthropometer. The Anthropometry and Biomechanics Facility (ABF) completed data collection from three microgravity flights to assess the restraint methods and techniques to be used for the in-flight procedures performed by the crewmembers in orbit. The first flight demonstrated that the restraint system on the seat, used in a nominal configuration, did not sufficiently restrain a person in the seat. The results showed the subjects were not in full contact with the seat pan, resulting in inaccurate sitting height data. Thus, a second flight was conducted to test different restraint system options. The results showed that by 1) changing the restraint system from the nominal 3-points of the 5-point harness, which is used for crewmembers when fully suited with emergency equipment, and 2) rerouting the lap straps around the joint of the backrest, where the backrest and seat pan are joined, resulted in the optimal method to restrain a subject. This rerouting method allowed for the anchor location to change and pull the subjects back into the seat instead of being anchored at the side of the subjects thighs. The results from the third flight validated the final restraint system, which resulted in a verified methodology for collecting seated anthropometry to ultimately determine the amount of spil elongation in a microgravity environment.

Rajulu, Sudhakar↗

DOE OSTI2021

Interpreting the Lipidome: Bioinformatic Approaches to Embrace the Complexity

Background Improvements in mass spectrometry (MS) technologies coupled with bioinformatics developments have allowed considerable advancement in the measurement and interpretation of lipidomics data in recent years. Since research areas employing lipidomics are rapidly increasing, there is a great need for bioinformatic tools that capture and utilize the complexity of the data. Currently, the diversity and complexity within the lipidome is often concealed by summing over or averaging individual lipids up to (sub)class-based descriptors, losing valuable information about biological function and interactions with other distinct lipids molecules, proteins and/or metabolites. Aim of review To address this gap in knowledge, novel bioinformatics methods are needed to improve identification, quantification, integration and interpretation of lipidomics data. The purpose of this mini-review is to summarize exemplary methods to explore the complexity of the lipidome. Key scientific concepts of review Here we describe six approaches that capture three core focus areas for lipidomics: (1) lipidome annotation including a resolvable database identifier, (2) interpretation via pathway- and enrichment-based methods, and (3) understanding complex interactions to emphasize specific steps in the analytical process and highlight challenges in analyses associated with the complexity of lipidome data.

Kyle, Jennifer E.↗

DOE OSTI2022

A computational pipeline to generate a synthetic dataset of metal ion sorption to oxides for AI/ML exploration

The charged mineral/electrolyte interfaces are ubiquitous in the surface and subsurface–including the surroundings of the geological disposal sites for radioactive waste. Therefore, understanding how ions interact with charged surfaces is critically important for predicting radionuclide mobility in the case of waste leakage. At present, the Surface Complexation Models (SCMs) are the most successful thermodynamic frameworks to describe ion retention by mineral surfaces. SCMs are interfacial speciation models that account for the effect of the electric field generated by charged surfaces on sorption equilibria. These models have been successfully used to analyze and interpret a broad range of experimental observations including potentiometric and electrokinetic titrations or spectroscopy. Unfortunately, many of the current procedures to solve and fit SCM to experimental data are not optimal, which leads to a non-transferable or non-unique description of interfacial electrostatics and consequently of the strength and extent of ion retention by mineral surfaces. Recent developments in Artificial Intelligence (AI) offer a new avenue to replace SCM solvers and fitting algorithms with trained AI surrogates. Unfortunately, there is a lack of a standardized dataset covering a wide range of SCM parameter values available for AI exploration and training–a gap filled by this study. Here, we described the computational pipeline to generate synthetic SCM data and discussed approaches to transform this dataset into AI-learnable input. First, we used this pipeline to generate a synthetic dataset of electrostatic properties for a broad range of the prototypical oxide/electrolyte interfaces. The next step is to extend this dataset to include complex radionuclide sorption and complexation, and finally, to provide trained AI architectures able to infer SCMs parameter values rapidly from experimental data. Here, we illustrated the AI-surrogate development using the ensemble learning algorithms, such as Random Forest and Gradient Boosting. These surrogate models allow a rapid prediction of the SCM model parameters, do not rely on an initial guess, and guarantee convergence in all cases.

Li, Chunhui↗

NASA NTRS2012

Performance Data Report: Space Medicine Division, Human Research Program, Behavioural Health and Performance Research Element

This report is the result of a collaborative effort between NASA?s Behavioral Health & Performance (BHP) Research and Operations Group to investigate and determine the availability of data pertaining to behavioral performance (and other pertinent variables) that have been collected by the laboratories at NASA?s Johnson Space Center. BHP?s Operations and Research groups collaborated to systematically identify what types of performance data are needed in relevant BHP performance domains and also to conduct structured interviews with NASA personnel to identify which data do or do not exist currently (and for instances where such data exist, to evaluate the type, quality, accessibility, and confidentiality of those data). The authors defined outcome categories of performance that encapsulate BHP performance domains, mapped BHP Research Risks and Gaps onto those performance outcome categories, and identified and prioritized indicators for each outcome category. The team identified key points of contact (subject matter experts [SMEs]) as potential interviewees, created a template for structured interview questions about sources and accessibility of performance data, and coordinated and conducted structured interviews with the SMEs. The methodology, results, and implications of this effort, as well as forward work needed, are discussed in this report.

Shea, Camille↗

NASA NTRS2019

Ground System PRA Data Availability Case Study

Presentation to support a Summit/TIM with various Stakeholders (Ground and Flight) to perform a GAP analysis associated with ground and spacecraft maintenance, maintainability, and availability activities in order to identify missing Agency planning and guidance materials.

Physics of Failure↗

DOE OSTI2021

ECAR-2322 As-Run Thermal Analysis Of The AGC-2 Experiment

The second Advanced Graphite Creep (AGC-2) experiment was designed to irradiate various types of graphite specimens at a temperature of 600°C. The specimens were irradiated in an instrumented leadout capsule experiment in the south flux trap of the ATR during cycles 149A, 1498, 1508, 151A, and 1518. Temperature was monitored using twelve thermocouples located at various elevations in the reactor core, and a helium-argon gas mixture was used for gas gap temperature control of the specimens. The purpose of this analysis is to calculate specimen temperature using measured data on reactor power and helium-argon gas flows, and as-run calculations of heating rates and displacement per atom (DPA) in graphite. The accuracy of the model is assessed by comparing measured and calculated thermocouple temperatures. Uncertainty in gas gaps may preclude an accurate temperature calculation. In these cases, adjustments are made to the thermal model in order to reconcile the measured and calculated thermocouple temperature and to ensure the accuracy of the calculated specimen temperature.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

ECAR-3386 AS-RUN THERMAL ANALYSIS OF THE AGC-3 EXPERIMENT

The third Advanced Graphite Creep (AGC-3) experiment was designed to irradiate various types of graphite specimens at a temperature of 900°C. The specimens were irradiated in an instrumented leadout capsule experiment in the east flux trap of the ATR during cycles 152B, 154B, 155A, and 155B. Temperature was monitored using twelve thermocouples located at various elevations in the reactor core, and a helium-argon gas mixture was used for gas gap temperature control of the specimens. The purpose of this analysis is to calculate specimen temperature using measured data on reactor power and helium-argon gas flows, and as-run calculations of heating rates and displacement per atom (DPA) in graphite. The accuracy of the model is assessed by comparing measured and calculated thermocouple temperatures. Uncertainty in gas gaps may preclude an accurate temperature calculation. In these cases, adjustments are made to the thermal model in order to reconcile the measured and calculated thermocouple temperature and to ensure the accuracy of the calculated specimen temperature.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2013

Robotic Technology Development at Ames: The Intelligent Robotics Group and Surface Telerobotics

Future human missions to the Moon, Mars, and other destinations offer many new opportunities for exploration. But, astronaut time will always be limited and some work will not be feasible for humans to do manually. Robots, however, can complement human explorers, performing work autonomously or under remote supervision from Earth. Since 2004, the Intelligent Robotics Group has been working to make human-robot interaction efficient and effective for space exploration. A central focus of our research has been to develop and field test robots that benefit human exploration. Our approach is inspired by lessons learned from the Mars Exploration Rovers, as well as human spaceflight programs, including Apollo, the Space Shuttle, and the International Space Station. We conduct applied research in computer vision, geospatial data systems, human-robot interaction, planetary mapping and robot software. In planning for future exploration missions, architecture and study teams have made numerous assumptions about how crew can be telepresent on a planetary surface by remotely operating surface robots from space (i.e. from a flight vehicle or deep space habitat). These assumptions include estimates of technology maturity, existing technology gaps, and likely operational and functional risks. These assumptions, however, are not grounded by actual experimental data. Moreover, no crew-controlled surface telerobotic system has yet been fully tested, or rigorously validated, through flight testing. During Summer 2013, we conducted a series of tests to examine how astronauts in the International Space Station (ISS) can remotely operate a planetary rover across short time delays. The tests simulated portions of a proposed human-robotic Lunar Waypoint mission, in which astronauts in lunar orbit remotely operate a planetary rover on the lunar Farside to deploy a radio telescope array. We used these tests to obtain baseline-engineering data.

robotics↗