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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 739 records · Page 41

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NASA NTRS1988

Data dependent systems methodology for lumped mass modeling of structures

Limitations of the frequency domain methods in analyzing structura1 vibrations has created an awareness of the comparative merits of the time domain methods. Although time domain methods would be ideal for modeling large precisions space systems, the popular methods based on fitting theoretical response to actual data by least squares are too sensitive to noise and require too much data to be suitable for orbiting space crafts. This paper briefly reviews the theory and illustrative applications of a time domain methodology called Data Dependent Systems (DDS) that eliminates these limitations. Simulation results are presented to demonstrate a better than 4-place accuracy in the identifications of all system parameters, both modal (frequencies, damping ratios, and mode shapes) and physical (mass, stiffness, and damping matrices).

Pandit, Sudhakar M.↗

NASA NTRS1994

In-flight estimation of gyro noise on the Upper Atmosphere Research Satellite (UARS) and Extreme Ultraviolet Explorer (EUVE) missions

This paper characterizes the low-frequency noise response of the Teledyne dry rotor inertial reference unit (DRIRU) gyroscopes on the Upper Atmosphere Research Satellite (UARS) and the Extreme Ultraviolet Explorer (EUVE). The accuracy of spacecraft attitude estimation algorithms that use gyro data for propagating the spacecraft attitude is sensitive to gyro noise. EUVE gyro data were processed to validate a single-axis gyro noise model, which is used onboard various spacecraft. The paper addresses the potential impact of temperature effects on the gyro noise model and the overall impact on attitude determination accuracy. The power spectral density (PSD) of the gyro noise is estimated from UARS in-flight data by Fast Fourier Transform (FFT). The role of actuator dynamics on the PSD function is also discussed.

Lee, M.↗

DOE OSTI2022

EUCLID Sensitivity Database

This report documents the EUCLID sensitivity database along with its several use-cases. EUCLID computed sensitivities for the following integral responses: Criticality of ICSBEP critical assemblies, LLNL pulsed-sphere neutron-leakage spectra, reaction rates in selected ICBSEP critical assemblies, delayed neutron factions of selected ICSBEP critical assemblies, reactivity coefficients in two ICSBEP critical assemblies, sub-critical assembly responses and Rossi-alpha of selected critical assemblies. It is described for each response what the reported observable constitutes, the method we used to obtain the sensitivities, and which integral experiments were studied. It is also documented briefly in what format these sensitivities are stored. These sensitivities were used for many aspects of the EUCLID project, like ML-supported large-scale nuclear-data validation, or optimization of integral experiments. But these sensitivities can also be applied for more established processes in the nuclear-data application field such as adjustment or assessing the upper sub-critical limit.

Delayed Neutron Fraction↗

NASA NTRS1978

Non-thermal radio emission from Saturn

Direct, strong evidence for non-thermal radio emission from Saturn exists in the hectometric data observed by Imp 6. The planet has been tentatively identified as a decametric source, but the most sensitive and most recent data fail to confirm this. At metric or decimetric wavelengths Saturn has no non-thermal emission like Jupiter's synchrotron sources. Finally, a comparative study of Earth and Jupiter radio emissions suggests lightning discharges.

Warwick, J. W.↗

NASA NTRS1985

Implementation of design sensitivity analysis with existing finite element codes

A numerical method is presented to implement structural design sensitivity analysis theory, using the versatility and convenience of existing finite element structural analysis programs. Design variables such as thickness and cross-sectional areas of components of individual members and built-up structures are considered. Structural performance functionals considered include displacement and stress. The method is also applicable for eigenvalue problem design sensitivity analysis. It is shown that calculations can be carried out outside existing finite element codes, using postprocessing data only. Thus, design sensitivity analysis software does not have to be embedded in an existing finite element code. Feasability of the method is shown through analysis of several problems, including a built-up structure. Accurate design sensitivity results are obtained without the uncertainty of numerical accuracy associated with selection of finite difference perturbations.

Choi, Kyung K.↗

NASA NTRS1988

Intermolecular potential for thermal H2O-He collisions

Theoretical potentials for rotational excitation of H2O by He were constructed via several methods, all of which start with a large basis set SCF interaction. The semiempirical Hartree-Fock with damped dispersion model adds a damped long-range attraction with parameters adjusted to fit experimental total differential cross sections. Purely ab initio potentials add correlation energies obtained via perturbation theory (MP2 and MP4) or a variational method (ICF1). Scattering calculations were performed on all surfaces to compare wih available beam scattering and pressure broadening data and to assess sensitivity of state-to-state rates to uncertainties in the potential. From comparison with the limited experimental data, the ICF1 surface appears to be marginally better than the MP4 surface. Thermal rates calculated from this surface should be accurate to better than 50 percent, at least for the larger, more important rates.

Palma, Amedeo↗

NASA NTRS1995

Application of non-coherent Doppler data types for deep space navigation

Recent improvements in computational capability and Deep Space Network technology have renewed interest in examining the possibility of using one-way Doppler data alone to navigate interplanetary spacecraft. The one-way data can be formulated as the standard differenced-count Doppler or as phase measurements, and the data can be received at a single station or differenced if obtained simultaneously at two stations. A covariance analysis is performed which analyzes the accuracy obtainable by combinations of one-way Doppler data and compared with similar results using standard two-way Doppler and range. The sample interplanetary trajectory used was that of the Mars Pathfinder mission to Mars. It is shown that differenced one-way data is capable of determining the angular position of the spacecraft to fairly high accuracy, but has relatively poor sensitivity to the range. When combined with single station data, the position dispersions are roughly an order of magnitude larger in range and comparable in angular position as compared to dispersions obtained with standard data two-way types. It was also found that the phase formulation is less sensitive to data weight variations and data coverage than the differenced-count Doppler formulation.

Bhaskaran, Shyam↗

NASA NTRS1995

Application of Non-Coherent Doppler Data Types for Deep Space Navigation

improvements in computational capability and DSN technology have renewed interest in examining the possibility of using one-way Doppler data alone to navigate interplanetary spacecraft. The one-way data can be formulated as the standard difference-count Doppler or as phase measurements, and the data can be received at a single station or differenced if obtained simultaneously at two stations. A covariance analysis is performed which analyzes the accuracy obtainable by combinations of one-way Doppler data and compared with similar results using standard tow-way Doppler and range. The sample interplanetary trajectory used was that of the Mars Pathfinder mission to Mars. It is shown that differenced one-way data is capable of determining the angular position of the spacecraft to fairly high accuracy, but has relatively poor sensitivity to the range. When combined with single station data, the position dispersions are roughly an order of magnitude larger in range and comparable in angular position as compared to dispersions obtained with standard data two-way types. It was also found that the phase formulation is less sensitive to data weight variations and data coverage than the differenced-count Doppler formulation.

Deep↗

DOE OSTI2023

Peatland hydrology across scale: a probabilistic framework for confronting variability, heterogeneity, and uncertainty (Final Report)

Our project aimed to guide the representations of peatland hydrology and water-carbon feedbacks within Earth System Models, by improving process understanding and developing parsimonious models that elucidate the effects of hydroclimatic variability and spatial heterogeneity. Our research questions centered on the three key drivers of peatland hydrology: (1) seasonal and interannual hydroclimatic fluctuations, (2) spatial heterogeneity of peatland microtopography, and (3) hydrological connectivity across landscape units within a peatland watershed. Our analyses and model testing used existing long-term datasets within the Marcell Experimental Forest (MEF), with new data collected whenever necessary. To date, we have demonstrated the strong influence of water table elevations on the temperature sensitivity of CH 4 emissions, using a newly developed eddy covariance dataset spanning eleven years at MEF. Specifically, higher water tables dampen the increase in CH 4 emissions in the spring as well as their decrease in the fall, resulting in hysteresis. These results imply that any hydroclimatological changes in peatlands that shift seasonal water availability from winter to summer will increase annual CH 4 emissions, even if soil temperature remains unchanged. To further investigate water table elevation change within the peatland microtopography, we installed automated water table gauges across four bog-forest transects (the “lagg”) in two peatland watersheds at MEF, which are hotspots of intense biogeochemical activity. These measurements will give us new information about the extent of lagg expansion and contraction during snowmelt and high intensity rainfall events, as well as the directionality of water and nutrient flow into the bog from the surrounding forests. Finally, we investigated the hydrological connectivity across the peatland watershed complex during early spring – a critical period where water table is elevated from snowmelt – using extended hydrological records from MEF (e.g., snow and frost depths, water table elevations, streamflow). The importance of thermal and wintertime processes within the Energy Exascale Earth System Model (E3SM) was determined through sensitivity analyses. Results from data analyses show that (i) streamflow has decreased over decades likely due to increased evapotranspiration rates (despite no detectable trends in precipitation), (ii) spring streamflow generation is controlled by a sequence of fill-and-spill pathways from the snowpack to the stream through peatland water stores, and (iii) frost depth is a key explanatory variable for the timing and magnitude of streamflow. These results suggest that frost plays an important role in connecting surface water storage in peatlands to stream outlets, and that the hydrological connectivity across the peatland watershed complex can mediate the sensitivity of hydrological responses to climate variations.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Testing of Two Mars Powered Descent Vehicle Concepts in the Langley Unitary Plan Wind Tunnel

Testing was conducted in the NASA Langley Unitary Plan Wind Tunnel in order to investigate the aerodynamic interference of sub-scale versions of two Mars powered descent vehicle concepts at supersonic Mach numbers (2.4 and 3.5): a model based on a blunt hypersonic inflatable aerodynamic decelerator (HIAD) and the second representing a more slender rigid vehicle with body flaps (CobraMRV). Each model was designed to accommodate up to eight nozzles, with the HIAD model having five different nozzle configurations to investigate the effects of nozzle location, cant angle, and area ratio. The models were tested with high pressure air as the nozzle plume gas, and included the following instrumentation: high-speed video, discrete steady state and high-frequency pressure, pressure sensitive paint, and a new flow-through force and moment balance for the HIAD model. The high-speed imagery showed the overall expected growth of the shock layer to increasing thrust levels. The discrete stagnation pressure data on the HIAD model with four and eight blowing nozzles was sensitive to thrust coefficient with the nozzles canted at 0 degrees, whereas having nozzles canted outward 20 degrees and/or being located closer to the heatshield shoulder largely removed that sensitivity. The CobraMRV model stagnation pressure was more sensitive to tunnel Mach number and sideslip angle, due to the nozzle arrangement and plume interference. Pressure sensitive paint data quality was compromised by paint damage from frequent model changes, especially for the HIAD model. However, pressure coefficient data on the CobraMRV model showed the same trends as the discrete pressure measurements, as well as a sensitivity to non-zero sideslip angles. The aerodynamic force coefficients were derived from the pressure sensitive paint data. On the HIAD models, the primary force coefficient decreased with increasing thrust due to the nozzle plumes blocking flow to the heatshield area surrounding the nozzle exist. On the CobraMRV model, the force coefficient was relatively insensitive to thrust coefficient at the lower Mach number when sideslip angle was 0 degrees The force coefficient decreases with increasing thrust coefficient when the sideslip angle is 10 degrees. Balance data quality was negatively impacted by thermal drift issues that were not apparent in pre-test calibration measurements, thus preventing usable test data. Results and lessons learned will be used to take further technology development steps, including more advanced ground test techniques and flight testing.

Karl T Edquist↗

NASA NTRSDate not supplied

Testing of Two Mars Powered Descent Vehicle Concepts in the Langley Unitary Plan Wind Tunnel

Supersonic Retropropulsion↗

DOE OSTI2021

Resonant Extraction and Extinction Measurement for the Mu2e Experiment

The Mu2e experiment, currently under construction at Fermilab, will search for coherent neutrinoless muon to electron conversion, extending the sensitivity of searches for charged lepton flavour violation by four orders of magnitude in 3-5 years of data-taking. This improved sensitivity is made possible by using a pulsed beam structure that is optimized for reducing prompt backgrounds when muons are stopped on an aluminium target. Producing a high-rate pulsed beam is achieved using resonant extraction of a circulating proton beam, an “AC dipole” with a time-varying field to deflect out-of-time protons, and a system to measure the extinction of out-of-time beam particles incident on the muon production target. These proceedings summarize the systems that have been designed to achieve the required level of extinction and to continuously place limits on the presence of out-of-time beam hitting the production target with a sensitivity of <1e-10.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1978

The UCSD/MIT hard X-ray and low energy gamma-ray experiment for HEAO-1 - Design and early results

Primary scientific objectives of the experiment are the measurement of the spectra and time variations of discrete sources and the spectrum and isotropy of the diffuse background, all in the 10 keV to 10 MeV energy range. The instrument consists of an array of 7 NaI(Tl)/CsI(Na) phoswich-type detectors and 8 large CsI(Na) shields. In order to achieve the maximum sensitivity allowed by weight, data rate and funding constraints of the mission, three detector and aperture geometries were selected. The instrument's total sensitive area is 494 sq cm. The fields of view vary from 1.7 deg x 20 deg FWHM at low energies to 43 deg FWHM at high energies. The basic instrument concepts, details of the mechanical and electrical design, and pre-launch calibrations are presented. In-orbit performance is described and background measurements are analyzed and used to estimate the experiment's ultimate sensitivity.

Matteson, J. L.↗

DOE OSTI2021

Analysis of Automated Fault Detection and Diagnosis Records as an Indicator of HVAC Fault Prevalence: Methodology and Preliminary Results

Faults in commercial buildings can cause energy waste and other performance problems such as reduced occupant comfort, reduced equipment longevity, and increased noise. However, it is currently unknown how commonly faults occur in different equipment types. A method has been developed to estimate the prevalence of faults in air handling units, air terminal units, and rooftop units. This method includes two types of data. The first is data from several automated fault detection and diagnostics (AFDD) software technologies. This type of data provides a large sample that represents a wide range of building types, geographical locations, and equipment types. It includes fault diagnoses from thousands of buildings around the United States, as well as anonymized metadata describing the building and equipment characteristics. The number of fault records is in the order of 107. However, despite the size and richness of the data sample, this data contains some degree of inaccuracy, i.e., false positive and false negative findings. Therefore, the study includes a second type of data, coming from manual inspection of buildings that have had the same AFDD methods applied to them (from the commercial AFDD offerings). Since the field tests are conducted in buildings with AFDD-generated fault prevalence data, they can be combined with the larger sample size to provide insight into the potential biases or lower sensitivity of the AFDD data. Once a library of fault prevalence data is built, it will be studied to provide further insight into the drivers of fault prevalence, for example, whether prevalence is correlated with building type, geographical location (which is tied to climate and to utility rates), building size, etc. This paper describes the methods developed for this study and illustrates them with preliminary data. It discusses some of the challenges of harmonizing disparate outputs from multiple AFDD vendors, application of a unifying fault taxonomy, and fault prevalence metrics.

Ebrahimi Fakhar, Amir↗

DOE OSTI2023

Lithium-Ion Battery Diagnostics Using Electrochemical Impedance via Machine-Learning

Diagnosing battery states such as health, state-of-charge, or temperature is crucial for ensuring the safety and reliability of electrochemical energy storage systems. While some states, such as temperature, may be measured using cheap sensors, accurate diagnosis of battery health metrics usually requires time-consuming performance measurements, making them infeasible for use in real-world operation. These health metrics can be measured during lab-testing and then estimated on-line using predictive life models or via state observer algorithms such as Kalman filters, but these predictive methods should be supplemented by actual measurement of battery health whenever possible to ensure reliability. Rapid measurement of battery health may be done by various types of fast diagnostic techniques such as electrochemical impedance spectroscopy (EIS), which can be performed in only a few minutes and require only a fraction of the energy and power needed for a full charge and discharge measurement. But there is a substantial challenge for estimating battery health using EIS data, as EIS is sensitive to cell temperature, state-of-charge, current, and resting time in addition to health. Thus, utilizing EIS data to predict battery capacity requires correcting for all these additional variables, a task that is extremely difficult to handle analytically. This talk utilizes machine-learning methods to estimate the effectiveness of battery capacity prediction from EIS data, leveraging a data set of hundreds of EIS measurements recorded at varying temperature and state-of-charge throughout a 500-day aging study of 32 commercial, large-format NMC-Graphite lithium-ion batteries. Using EIS as input to machine-learning models is complicated by the nonlinear response of impedance to battery health, temperature, and state-of-charge, as well as the collinearity between the impedance response at neighboring frequencies, which can easily lead to overfit models. To train robust models, features from EIS data need to be extracted from the data or some subset of critical frequencies selected. Many approaches for extracting and selecting features from EIS data from electrochemical analysis and machine-learning fields were identified for analysis: using the entire raw spectra; selection of one, two, or many frequencies from the entire spectra; selecting interesting points from the EIS measurement using domain knowledge; fitting EIS with an equivalent-circuit model; calculating statistics on the raw impedance values; and reducing the dimensionality of the data using unsupervised linear (principal component analysis) and non-linear (uniform manifold approximation and projection) methods. These approaches were rigorously compared using a machine-learning pipeline approach, training linear, Gaussian process, and random forest regression models and quantifying performance using cross-validation as well as a held-out test set. An artificial neural network model trained on the raw spectra was also tested. Promising pipelines were fine-tuned via Bayesian hyperparameter optimization using cross-validation loss and training with class-specific weights to counter data set imbalance. The most reliable method for utilizing impedance in this work was the selection of two optimal frequencies through an exhaustive search, resulting in about 2% mean absolute error on test data for both Gaussian process and random forest model architectures. Interrogation of a variety of models reveals critical frequencies of 100 Hz and 103 Hz for this data set, though the optimal set of frequencies is not necessarily intuitive, i.e., the best performing models are not simply those that use impedance at frequencies that have the highest correlation to the relative discharge capacity. The best performing model is an ensemble model, which is able to predict battery capacity with 1.9% mean absolute error for unseen cells using impedance recorded at a variety of temperatures and states-of-charge.

battery↗

DOE OSTI2022

Utilizing Sensitivity and Correlation Coefficients from MCNP and Whisper to Guide Microreactor Experiment Design

When designing experiments for full-scale reactor systems, MCNP®* and Whisper can be used to create neutronic models and compare the similarity of two nuclear systems via correlation coefficients for κ eff , effective multiplication factor. This thesis applies this framework to a conceptual heat-pipe, yttrium-hydride moderated microreactor system and experiments. The framework is intended as a supplement to other neutronics/thermal/multiphysics analyses and provides a concrete method to measure the neutronic similarity of two systems. By analyzing the shared nuclear data uncertainty, as well as sensitivity to nuclear data over all neutron energies, highly informative experiments can be designed to aid in the development of microreactor and other advanced reactor technologies and systems.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2019

Wood Decomposition: Understanding Processes Regulating Carbon Transfer to Soil Carbon Pools Using FACE Wood at Multiple Scales

Dead wood comprises approximately 20% of forest biomass. While it is widely recognized for its role in ecosystem functions relative to habitat and nutrient cycling, there are considerable uncertainties associated with its role in the forest carbon (C) cycle. The principal uncertainty is whether dead wood contributes to the soil C pool. With little supporting evidence, the presumption is that C in wood is respired back to the atmosphere through decomposition processes. The inability to attribute forest soil C to its source components has been a barrier to addressing this important forest carbon cycle question. The U.S. Dept. of Energy’s Free Air CO 2 Enrichment (FACE) experiment produced wood that has a distinct δ 13 C signature that can be used to trace its fate through organic matter decomposition processes. Using two hardwood species (aspen, birch) from the Rhinelander FACE site and pine from the Duke FACE site, the FACE Wood Decomposition Experiment (FWDE) was established in 2011 to provide the foundation for addressing wood decomposition and its associated processes at the continental scale. Plots were installed on nine experimental forests representing a range in bioclimatic forest conditions within the continental U.S. Each identical plot has logs of the three species incubating on the soil surface and pine logs suspended in the air, representing standing dead wood. Abiotic conditions are monitored continuously, and soil and forest conditions characterized. Logs on the sites have been measured every two years to assess decay. This project was initiated in 2016 with specific objectives to: (a) determine whether dead wood C contributes to the soil C pool, (b) assess the fungal community composition mediating the decay, (c) assess the interactions of termites with microbial decay, and (d) develop a mechanistic model to simulate wood decomposition. The principal field sampling was conducted in 2017; correspondingly, the results reported here represent six years of decomposition. Wood decomposition varied from 13 to 75 % loss across the bioclimatic gradient. Sites in the southeastern U.S. coastal plain and piedmont had fastest decomposition rates for each of the species. In contrast sites in the Rocky Mountains had the slowest decay rates, which were associated with cold temperatures. Standing deadwood decomposes at a much slower rate than logs on the soil surface; a response due to differences in wood moisture content. Wood carbon was measured in the mineral soil across each of the sites; accordingly, this is the first study to affirm that a portion of the wood C does become part of the soil C pool. Averaged across the FWDE sites, approximately 1.4% of the soil C in the upper 10 cm of the mineral soil directly beneath the log was derived from the deadwood; within 50 cm of the log, wood C accounted for an average of 2.1%. These results demonstrate that the contribution of deadwood to the soil C pool is a soil volume surrounding the log; and it’s likely a larger volume than what we sampled. We also hypothesized that there would be more wood C in the mineral soil when termites were present. However, that hypothesis wasn’t supported by measurements on the Santee Experimental Forest termite exclusion (SEF-TX) plots under FACE logs that incubated for 6 years. Across all the sites, the majority of the wood C was transferred to the atmosphere. Approximately 7,000 fungal species were sequenced. The fungal communities were different among each of the sites suggesting local colonization; interestingly, within sites the three species also exhibited differences in fungal community composition. White rot was the dominant fungal form in pine and hardwood, which yields less lignin in the wood residue. Brown rot occurred in approximately 20% of the pine and aspen logs, and it did not occur in birch logs. The occurrence of brown rot was only weakly correlated to site factors. We conducted two additional experiments to assess the effects of termites on wood decomposition and the associated interactions with the fungal community. Pine and birch logs were incubated on the Santee Experimental Forest termite exclusion plots in upland and riparian settings. There was no difference in decay between areas where termites were able colonize and plots where they had been excluded. Highly variable foraging by termites was a primary contributor the result. Correspondingly, fungal communities did not vary on logs incubated among the two treatments, but they did reflect site and species differences. The second experiment was conducted on each of the nine FWDE sites where pine and birch wood blocks were incubated for two years with and without a wrapping designed to exclude termites and other arthropods. The effects of termites on wood decomposition could be confirmed on two of the four sites known to have subterranean termites. On the two southeastern sites, termites contributed approximately 8% to mass loss in pine blocks over two years. A major factor clouding the role of termites in wood decomposition is the inability to assess the time, duration, and intensity of the foraging with the logs. Another uncertainty is related to whether a piece of wood will be foraged. A process-based wood decomposition model was developed representing biological processes mediated by fungi, bacteria, termites, beetles, as well as physical and chemical processes. The Coarse Wood Decomposition Model (CWDM) was parameterized based on a thorough review of the literature and it was validated using data from the FWDE. In the initial setup, the model considers softwoods and hardwoods groups, as opposed to individual species. Sensitivity analyses incorporating climate data from 89 locations in North America demonstrated that CWDM was stable from the tropics to the boreal zone. Model calibration and validation showed it to perform very well. The CWDM is poised for additional testing with subsequent FWDE data as well as other data sets, and it is available for incorporation into forest biogeochemical models or DOEs Earth System Modeling framework.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Enabling Model Organism and Commercial Astronaut Data Access Through the NASA Open Science Data Repository

NASA’s Open Science Data Repository (OSDR) brings together omics data from NASA’s GeneLab project and non-omics data, including physiological, phenotypic, imaging, and behavioral data from NASA’s Ames Life Sciences Data Archive (ALSDA) collected from decades of space biology research, providing open and FAIR (findable, accessible, interoperable, and reusable) access of these precious data to scientists world-wide. This rich source of meticulously curated metadata and data from spaceflight and analog studies has been mined by the scientific community resulting in dozens of high impact scientific publications that reveals a complex network of molecular and physiological effects of spaceflight across living systems, from microbes to plants, to mammals. Understanding how these effects translate to the human condition is critical as we move deeper into the era of commercial space travel. However, the integration of data, specifically omics data, from astronauts is particularly challenging due to their sensitive nature. OSDR has risen to this challenge by developing a mechanism to control access to identifiable levels of omics data, such as raw sequence data, while enabling public access to processed, unidentifiable, data and associated metadata that will allow the scientific community to interrogate human astronaut data alongside data from model organisms to begin answering these critical questions. The 2021 SpaceX Inspiration4 (I4) mission collected a comprehensive atlas of biological measurements from four civilian astronauts, providing a wealth of data to characterize the effects of spaceflight on the human body. These data include both non-omics and omics assays such as direct RNA sequencing (RNA-seq), single nuclei ATAC-seq and RNA-seq, metagenomics, proteomics, and comprehensive metabolic and cytokine panels, all of which have been integrated into the OSDR system across no less than 9 studies. Each study has been carefully curated using community-backed OSDR standards for sample and assay level metadata ensuring these data are findable and accessible. In addition to hosting both raw and processed data from the principal investigator team for each assay type, the GeneLab team plans to re-process the I4 omics data using GeneLab’s standard processing pipelines. The GeneLab processed data outputs will allow for comparisons across studies on OSDR and enable visualization of these data through the OSDR data visualization platform thereby enabling data reusability and interoperability. Here we describe the robust privacy and security protocols implemented by OSDR to safeguard sensitive health data from astronauts while facilitating metadata and processed data sharing for research purposes. We further provide a road map for navigating the vast amount of data provided for each I4 study on the OSDR, including experimental design, associated experiments, payloads, and missions, data generation and analysis protocols, and associated scientific articles. Additionally, we illustrate how to interrogate the standardized metadata provided in the sample and assay tables as well as instructions for how to download and access the data. The I4 datasets described here re present the first ever comprehensive collection of commercial astronaut data.

Amanda M Saravia-Butler↗