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At least 1,063 records · Page 59

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NASA NTRSDate not supplied

Space Launch System Base Aerodynamics Post-Flight Reconstruction for Artemis I

Artemis I was the first uncrewed integrated test flight of the NASA heavy-lift, human-rated, exploration-class launch vehicle, Space Launch System (SLS), and Orion spacecraft. Artemis I successfully launched from Pad39B at NASA Kennedy Space Center on November 16th, 2022. The integrated test flight was composed of launch and ascent of SLS vehicle from lift-off to RS-25 main engine cut-off (MECO), interim cryogenic propulsion stage (ICPS) in-space flight and Orion’s trajectory around the moon and landing in the Pacific Ocean which occurred on December 11th, 2022. The SLS total thrust of 8,800,000 lbf was powered by four LOX/LH2 RS-25 engines and two 5-segment solid rocket boosters. As a result, the base flow field had highly complex phenomena and regimes. SLS base aerodynamics covers vehicle base pressure and integrated axial force during powered flight from lift-off to MECO. This work discusses the SLS base flow physics observed during Artemis I and comparisons of post-flight reconstruction with pre-flight models and Space Shuttle data. This was the first time in-depth base aerodynamics flight reconstruction has been investigated for an exploration-class launch vehicle since the Saturn V Program.

aerothermodynamics↗

NASA NTRSDate not supplied

Space Launch System Base Aerothermodynamics Post-Flight Reconstruction for Artemis I

Artemis I was the first uncrewed integrated test flight of the NASA heavy-lift, human-rated, exploration-class launch vehicle, Space Launch System (SLS), and Orion spacecraft. Artemis I successfully launched from Pad39B at NASA Kennedy Space Center on November 16th, 2023. The integrated test flight is composed of launch and ascent of the SLS vehicle from lift-off to RS-25 main engine cut-off (MECO), interim cryogenic propulsion stage (ICPS) in-space flight and Orion’s trajectory around the moon and landing in the Pacific Ocean which occurred on December 11th, 2023. The SLS total thrust of 8,800,000 lbf is powered by four LOX/LH2 RS-25 engines and two 5-segment solid rocket boosters. As a result, base flow environments for this vehicle are highly complex and extreme. SLS base aerothermodynamics covers rocket plume-induced convection and radiation of the vehicle’s aft region during powered flight from lift-off to MECO. This work discusses the SLS base flow physics observed during Artemis I and comparisons of post-flight reconstruction with pre-flight models and Space Shuttle data. This is the first time in-depth base heating flight reconstruction has been investigated for an exploration-class launch vehicle since the Saturn V Program.

aerothermodynamics↗

NASA NTRSDate not supplied

Base Aerodynamics Post-Flight Reconstruction for Artemis I

aerothermodynamics↗

DOE OSTI2026

Quantum block encoding for one-pair semiseparable matrices

Quantum block encoding (QBE) is a crucial step in the development of most quantum algorithms, as it provides an embedding of a given matrix into a suitable larger unitary matrix. Historically, the development of efficient techniques for QBE has mostly focused on sparse matrices; less effort has been devoted to data-sparse (e.g., rank-structured) matrices. In this work we examine a particular case of rank structure, namely, one-pair semiseparable matrices. We present a new block encoding approach that relies on a suitable factorization of the given matrix as the product of triangular and diagonal factors. To encode the matrix, the algorithm needs $2\log(N)+7$ ancillary qubits. Assuming that the data input oracles can be implemented with polylogarithmic depth, or that a QRAM input model is available, our proposed method requires $\mathcal{O}({\rm polylog} (N))$ time and has an error of $\mathcal{O}(N^2)$, where $N$ is the matrix size.

Antonioli, Giacomo [Pisa U.; CERN] (ORCID:00090000↗

DOE OSTI2024

Street-level temperature estimation using graph neural networks: Performance, feature embedding and interpretability

Estimating street-level air temperature is a challenging task due to the highly heterogeneous urban surfaces, canyon-like street morphology, and the diverse physical processes in the built environment. Though pioneering studies have embarked on investigations via data-driven approaches, many questions remain to be answered. Here, in this study, we leveraged an innovative framework and redefined the street-level temperature estimation problem using Graph Neural Networks (GNN) with spatial embedding techniques. The results showed that GNN models are more capable and consistent of estimating street-level temperature among tested locations, benefiting from its unique strength in handling extensive data over unstructured graph topology. In addition, we conducted in-depth analysis of feature importance to enhance the model interpretability. Among the urban features analyzed in this study, the time-variant canopy density and meter-level land use data emerge as crucial factors. Our findings highlight GNN 's high potential in capturing the complex dynamics between urban elements and their impacts on microclimate, thus offering valuable insights for comprehensive urban data collection and urban climate modeling in general. Collectively, this study also contributes to urban planning and policy by providing avenues to enhance city resilience against climate change, thereby advancing the agenda for environmental stewardship and urban sustainability.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Cosmogenic nuclide and solute flux data from central Cuban rivers emphasize the importance of both physical and chemical mass loss from tropical landscapes

We use 25 new measurements of in situ produced cosmogenic 26 Al and 10 Be in river sand, paired with estimates of dissolved load flux in river water, to characterize the processes and pace of landscape change in central Cuba. Long-term erosion rates inferred from 10 Be concentrations in quartz extracted from central Cuban river sand range from 3.4–189 Mg km –2 yr –1 (mean 59, median 45). Dissolved loads (10–176 Mg km –2 yr –1 ; mean 92, median 97), calculated from stream solute concentrations and modeled runoff, exceed measured cosmogenic- 10 Be-derived erosion rates in 18 of 23 basins. This disparity mandates that in this environment landscape-scale mass loss is not fully represented by the cosmogenic nuclide measurements. The 26 Al / 10 Be ratios are lower than expected for steadystate exposure or erosion in 16 of 24 samples. Depressed 26 Al/ 10 Be ratios occur in many of the basins that have the greatest disparity between dissolved loads (high) and erosion rates inferred from cosmogenic nuclide concentrations (low). Depressed 26 Al/ 10 Be ratios are consistent with the presence of a deep, mixed, regolith layer providing extended storage times on slopes and/or burial and extended storage during fluvial transport. River water chemical analyses indicate that many basins with lower 26 Al/ 10 Be ratios and high 10 Be concentrations are underlain at least in part by evaporitic rocks that rapidly dissolve. Our data show that when assessing mass loss in humid tropical landscapes, accounting for the contribution of rock dissolution at depth is particularly important. In such warm, wet climates, mineral dissolution can occur many meters below the surface, beyond the penetration depth of most cosmic rays and thus the production of most cosmogenic nuclides. Our data suggest the importance of estimating solute fluxes and measuring paired cosmogenic nuclides to better understand the processes and rates of mass transfer at a basin scale.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Space Launch System Liftoff and Separation Dynamics Analysis Tool Chain

A flexible, hierarchical tool chain that is being applied to NASA’s Space Launch System (SLS) for critical dynamics phenomena is described. This tool chain, called CLVTOPS, is used to investigate lateral liftoff movement of the vehicle as it departs and clears the mobile launch tower and separation of the two solid rocket boosters without collision with the core stage and payload. The toolset’s architecture was configured to take advantage of a modern software-engineering approach for maximum flexibility and utilization of open-source simulations and associated tools. As opposed to a “monolithic” approach, scripting languages were used to “bind” together a tool chain to configure and organize input data, execute and produce analysis results, and post-process these results to facilitate a rapid iterative analysis process to quickly address issues and pursue alternatives with emphasis on analysis automation. Key capabilities in the tool chain include processing and mining of very large data sets, a wide range of graphical depictions, and high-fidelity, physics-based simulations. The paper begins with a problem description and the motivation for liftoff and separation dynamics analysis followed by a historical survey of dynamics analyses for previous NASA human-rated launch vehicles. Details of the tool chain and its components are then introduced divided, first, into description of the scripting language architecture used to “bind” the simulation tools, programs, and scripts together and, second, the physics models and simulations. Representative analyses and data products are shown for liftoff and booster separation dynamics that provide in-depth insight to the tool chain’s capabilities. Supporting activities such as simulation tool chain verification, version archiving and data management, and training are addressed. The paper concludes with case examples on how the tool chain can be tailored to related aerospace dynamics analyses, both large and small. These patterns and techniques for SLS dynamics tool construction can be applied for other aerospace simulations.

6DOF↗

NASA NTRS1987

Thematic mapper research in the earth sciences: Small scale patches of suspended matter and phytoplankton in the Elbe River Estuary, German Bight and Tidal Flats

A Thematic Mapper (TM) field experiment was followed by a data analysis to determine TM capabilities for analysis of suspended matter and phytoplankton. Factor analysis showed that suspended matter concentration, atmospheric scattering, and sea surface temperature can be retrieved as independent factors which determine the variation in the TM data over water areas. Spectral channels in the near infrared open the possibility of determining the Angstrom exponent better than for the coastal zone color scanner. The suspended matter distribution may then be calculated by the absolute radiance of channel 2 or 3 or the ratio of both. There is no indication of whether separation of chlorophyll is possible. The distribution of suspended matter and sea surface temperature can be observed with the expected fine structure. A good correlation between water depth and suspended matter distribution as found from ship data can now be analyzed for an entire area by the synoptic view of the TM scenes.

Grassl, H.↗

NASA NTRS1986

Voyager 2 photopolarimeter experiment - Evidence for tenuous outer ring material at Saturn

Voyager 2 photopolarimeter data are employed to characterize two regions of optically thin material outside the Saturn F ring. The study consists of a statistical analysis of optical depth value fluctuations caused by occultations of starlight from Delta Sco as viewed through the material. One the optically thin regions surrounds the orbital semimajor axis of the outer F ring shepherd satellite Pandora and the other thin region lies 144,090 km farther outward. The first region, with an optical depth of 0.008-0.016, is about 200 km in radius and the second, with an optical depth of 0.005-0.013, is about 1000 km in radial width. The data had a resolution of 0.5 km.

Graps, A. L.↗

DOE OSTI2024

Accelerated Depth Computation for Surface Boxplots with Deep Learning

Functional depth is a well-known technique used to derive descriptive statistics (e.g., median, quartiles, and outliers) for 1D data. Surface boxplots extend this concept to ensembles of images, helping scientists and users identify representative and outlier images. However, the computational time for surface boxplots increases cubically with the number of ensemble members, making it impractical for integration into visualization tools. In this paper, we propose a deep-learning solution for efficient depth prediction and computation of surface boxplots for time-varying ensemble data. Our deep learning framework accurately predicts member depths in a surface boxplot, achieving average speedups of 6X on a CPU and 15X on a GPU for the 2D Red Sea dataset with 50 ensemble members compared to the traditional depth computation algorithm. Our approach achieves at least a 99% level of rank preservation, with order flipping occurring only at pairs with extremely similar depth values that pose no statistical differences. This local flipping does not significantly impact the overall depth order of the ensemble members.

Han, Mengjiao↗

NASA NTRS1975

NiCd battery reliability for shuttle serviced spacecraft

Data from life-cycle testing of 22-cell NiCd batteries designed for shuttle-service spacecrafts are analyzed, first by averaging battery cycle lives as a function of temperature and depth of discharge, and then by calculating single cell failure and survival probabilities as a function of temperature, depth of discharge, and number of operating cycles. The single cell data are used to calculate battery survival probabilities. A mathematical failure model which is a function of the quantity of charge through the battery and the temperature is characterized, suggesting a predictive capability for accurate estimation of energy storage subsystem life and performance of accelerated tests. The failure mechanism described is apparently independent of most charge-discharge parameters and common to most cell sizes.

Eliason, J.↗

NASA NTRS1979

Mercurian crater rim heights and some interplanetary comparisons

Rim height (Re) data have been gathered through shadow measurements for 30 fresh mercurian craters with diameters (D) between 2.6 and 39 km. Small mercurian craters (i.e., those less than about 16 km in diameter) have higher rims than do lunar craters of the same diameter; on the basis of Re/D measurements alone, however, mercurian craters larger than about 16 km appear to be indistinguishable from their lunar counterparts (those greater than about 21 km in diameter). Apparent crater depths (Ra, the crater depth measured from the initial target surface) derived from empirical data allude to a decrease in Ra/D with calculated modal impact velocity. This is consistent with the hypothesis that higher impact velocities place the effective depth of burst of the event closer to the surface, thus causing a shallower crater.

Cintala, M. J.↗

NASA NTRS2021

Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations

The ocean mixed layer plays an important role in the coupling between the upper ocean and atmosphere across a wide range of time scales. Estimation of the variability of the ocean mixed layer is therefore important for atmosphere-ocean prediction and analysis. The increasing coverage of in situ Argo profile data allows for an increasingly accurate analysis of the mixed layer depth (MLD) variability associated with deviations from the seasonal climatology. However, sampling rates are not sufficient to fully resolve subseasonal (<90 day) MLD variability. Yet, many multivariate observations-based analyses include implicit modeled subseasonal MLD variability. One analysis method is optimal interpolation of in situ data, but the interior analysis can be improved by leveraging surface data with regression or variational approaches. Here, we demonstrate how machine learning methods and satellite sea surface temperature, salinity, and height facilitate MLD estimation in a pilot study of two regions: the mid-latitude southern Indian and the eastern equatorial Pacific Oceans. We construct multiple machine learning architectures to produce weekly 1/2° gridded MLD anomaly fields (relative to a monthly climatology) with uncertainty estimates. We test multiple traditional and probabilistic machine learning techniques to compare both accuracy and probabilistic calibration. We validate our methodology by applying it to ocean model simulations. We find that incorporating sea surface data through a machine learning model improves the performance of spatiotemporal MLD variability estimation compared to optimal interpolation of Argo observations alone. These preliminary results are a promising first step for the application of machine learning to MLD prediction.

Machine Learning↗

NASA NTRS2000

Vertical Distributions of PH3 in Saturn from Observations of Its 1-0 and 3-2 Rotational Lines

Far-infrared Fourier-transform spectrometer measurements of the 1-0 and 3-2 PH3 transitions in Saturn's disk near 267 and 800 GHz (8.9 and 26.7/cm), respectively, were analyzed simultaneously to derive a global mean profile for the PH3 vertical mixing ratio between 100 and 600 mbar total pressure. The far-infrared spectrum is relatively free from spectral interlopers, suffers minimal absorption or scattering by atmospheric particulates, and contains intrinsically weak PH3 lines that are sensitive to a range of atmospheric depths. The combined spectra are inconsistent with a uniform tropospheric mixing ratio, even with a stratospheric cut-off. They are consistent with a volume mixing ratio of PH3 that drops from 1.2 x 10(exp -5) at 645 mbar pressure to a value of 4.1 x 10(exp -7) at 150 mbar pressure, a decrease that is linear is log abundance vs log pressure. The mixing ratio could drop even more quickly at atmospheric pressures below 150 mbar and still be consistent with the data. The mixing ratio may well remain constant with depth for pressures above 630 mbar. The maximum PH3 mixing ratio in this model is consistent with a [P]/[H] ratio in the deep atmosphere that is about a factor of 10 higher than solar composition. Such a model is consistent with rapid mixing up to the radiative-convective boundary and transport by, for example, vertical waves just above this boundary. In the best fitting model, the eddy diffusion coefficient is approximately 10(exp 4) sq cm near 630 mbar, and it must increase with altitude. The predominant PH3 loss mechanisms are direct photolysis by UV radiation and scavenging by H atoms produced by the photolysis.

Orton, G. S.↗

NASA NTRS2024

Enabling Advanced Snow Physics within Land Surface Models Through an Interoperable Model-Physics Coupling Framework

Accurate estimation of snow accumulation and melt is a critical part of decision-making in snow-dominated watersheds. In this study, we demonstrate a flexible methodology to couple a detailed snow model, Crocus, separately to two different land surface models (LSMs), Noah-MP and Noah. The original LSMs and the coupled models (Noah-MP-Crocus and Noah-Crocus) are used to simulate snow depth, snow water equivalent, and other water and energy states and fluxes. The results of simulations are compared against a wide range of independent gridded and point scale reference datasets. Our results show that coupling the detailed snow model, Crocus, with the LSMs improves the snow depth and snow water equivalent relative to independent observations. Overall, larger improvements are obtained with coupling Crocus to the Noah LSM, with the coupled Noah-Crocus configuration reducing the RMSE and bias of snow depth from 2-12% and 57-75%, respectively, relative to Snow Data Assimilation System (SNODAS) and snow product from the University of Arizona. On the other hand, smaller improvements are obtained by coupling Crocus with Noah-MP. The Coupled Noah-MP-Crocus reduces the snow depth bias but slightly degrades the RMSE of snow depth and snow water equivalent. The corresponding impacts in other water budget terms such as evapotranspiration, soil moisture, and streamflow, however, are mixed, pointing to the significant need to improve the coupling assumptions of these processes within land models. Overall, the interoperable coupling framework demonstrated here offers the opportunity to include more detailed snow physics and processes, and to advance data assimilation systems through improved exploitation of information from snow remote sensing instruments.

Snow model↗

DOE OSTI2025

Recent evolution of risk analyses in atomic bomb survivor studies: new methods and applications

Abstract Several decades ago a dramatic leap forward occurred in the development and application of statistical methods for modeling radiation risk at the Radiation Effects Research Foundation (RERF). Poisson regression analysis for grouped person-year cohort data and the linear excess relative risk model were introduced, and subsequently a devoted software system, Epicure® (https://www.hirosoft.com), was developed by researchers at RERF and at the U.S. National Cancer Institute. Numerous advancements in understanding radiation effects on humans were made possible with these methods, which are still the state-of-the-art for risk assessment at RERF and have remained part of the standard toolbox for radiation—and other environmental—epidemiological studies worldwide. Nevertheless, as our understanding of radiation risk has increased, so have the breadth and depth of questions that require answers based on emerging data that are not amenable to these conventional methods. This overview briefly recounts the conventional methods and then describes our recent diversification into the use or development of new statistical approaches to meet the challenges of burgeoning biological data and emerging mechanistic information. We briefly discuss the development and application of new methods, current and planned, that are part of the RERF Statistics Department’s role in supporting institution-wide research, especially in our collaborations involving the Life Span Study, Adult Health Study, and First-generation Offspring Clinical Study. Some approaches to modeling and assessing radiation risk with newer methods mentioned herein have already been published, while some are still in development or are only beginning at the proposal stage.

Oncology↗

NASA NTRS1975

Nearshore coastal mapping

Two test sites of different water quality and bottom topography were used to test for maximum water depth penetration using the Skylab S-192 MSS for measurement of nearshore coastal bathymetry. Sites under investigation lie along the Lake Michigan coastline where littoral transport acts to erode sand bluffs and endangers developments along 1,200 miles of shore, and on the west coast of Puerto Rico where unreliable shoal location and depth information constitutes a safety hazard to navigation. The S-192 and S-190A and B provide data on underwater features because of water transparency in the blue/green portion of the spectrum. Depth of 20 meters were measured with the S-192 in the Puerto Rico test site. The S-190B photography with its improved spatial resolution clearly delineates the triple sand bar topography in the Lake Michigan test site. Several processing techniques were employed to test for maximum depth measurement with least error. The results are useful for helping to determine an optimum spectral bandwidth for future space sensors that will increase depth measurements for different water attenuation conditions where a bottom reflection is detectable.

Polcyn, F. C.↗

NASA NTRS2011

Adaptive System Modeling for Spacecraft Simulation

This invention introduces a methodology and associated software tools for automatically learning spacecraft system models without any assumptions regarding system behavior. Data stream mining techniques were used to learn models for critical portions of the International Space Station (ISS) Electrical Power System (EPS). Evaluation on historical ISS telemetry data shows that adaptive system modeling reduces simulation error anywhere from 50 to 90 percent over existing approaches. The purpose of the methodology is to outline how someone can create accurate system models from sensor (telemetry) data. The purpose of the software is to support the methodology. The software provides analysis tools to design the adaptive models. The software also provides the algorithms to initially build system models and continuously update them from the latest streaming sensor data. The main strengths are as follows: Creates accurate spacecraft system models without in-depth system knowledge or any assumptions about system behavior. Automatically updates/calibrates system models using the latest streaming sensor data. Creates device specific models that capture the exact behavior of devices of the same type. Adapts to evolving systems. Can reduce computational complexity (faster simulations).

Thomas, Justin↗