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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 415 records · Page 23

All sources NASA NTRS DOE OSTI

NASA NTRS2014

Microbiology and Crew Medical Events on the International Space Station

The closed environment of the International Space Station (ISS) creates an ideal environment for microbial growth. Previous studies have identified the ubiquitous nature of microorganisms throughout the space station environment. To ensure safety of the crew, microbial monitoring of air and surface within ISS began in December 2000 and continues to be monitored on a quarterly basis. Water monitoring began in 2009 when the potable water dispenser was installed on ISS. However, it is unknown if high microbial counts are associated with inflight medical events. The microbial counts are determined for the air, surface, and water samples collected during flight operations and samples are returned to the Microbiology laboratory at the Johnson Space Center for identification. Instances of microbial counts above the established microbial limit requirements were noted and compared inflight medical events (any non-injury event such as illness, rashes, etc.) that were reported during the same calendar-quarter. Data were analyzed using repeated measures logistic regression for the forty-one US astronauts flew on ISS between 2000 and 2012. In that time frame, instances of microbial counts being above established limits were found for 10 times for air samples, 22 times for surface samples and twice for water. Seventy-eight inflight medical events were reported among the astronauts. A three times greater risk of a medical event was found when microbial samples were found to be high (OR = 3.01; p =.007). Engineering controls, crew training, and strict microbial limits have been established to mitigate the crew medical events and environmental risks. Due to the timing issues of sampling and the samples return to earth, identification of particular microorganisms causing a particular inflight medical event is difficult. Further analyses are underway.

Oubre, Cherie M.↗

NASA NTRS2017

Potential Basin-Scale Estimates of Arctic Snow Depth with Sea Ice Freeboards from CryoSat-2 and ICESat-2: An Exploratory Analysis

The potential of deriving snow depth estimates using differences in freeboard heights from CryoSat-2 (CS-2) and ICESat-2 (IS-2) is examined. In our analysis, we use lidar freeboard from the Airborne Topographic Mapper (ATM) on Operation IceBridge (OIB) as proxy of IS-2 total (snow plus ice) freeboard. Snow depths are estimates from the OIB snow radar. Differences in height between the total (ATM) and ice (CS-2) freeboards are related to snow depth by the refractive index of the snow layer, which is dependent on snow density. For two years (2014 and 2015), regression of the ATM and CS-2 freeboard differences against OIB snow depth gives correlations of approximately 0.80, estimated eta (sub s) of 1.21, and standard errors of 8 centimeters. The resulting refractive index, eta (sub s), can be compared to that expected of the Arctic snow cover in early spring (1.25 plus or minus 0.05). The expected biases and variability in the regression analysis are discussed. Results suggest that snow depth can be estimated from the freeboard differences. The benefits of adjusting the orbit of CS-2 for providing more optimized overlaps between IS-2 and CS-2 are considered.

Sea ice↗

DOE OSTI2023

Spatiotemporal features of traffic help reduce automatic accident detection time

Quick and reliable automatic detection of traffic accidents is of paramount importance to save human lives in transportation systems. However, automatically detecting when accidents occur has proven challenging, and minimizing the time to detect accidents (TTDA) by using traditional features in machine learning (ML) classifiers has plateaued. We hypothesize that accidents affect traffic farther from the accident location than previously reported. Therefore, leveraging traffic signatures from neighboring sensors that are adjacent to accidents should help improve their detection. We confirm this hypothesis by using verified ground-truth accident data, traffic data from radar detection system sensors, and light and weather conditions and show that we can minimize the TTDA while maximizing classification performance by considering spatiotemporal features of traffic. Specifically, we compare the performance of different ML classifiers (i.e, logistic regression, random forest, and XGBoost) when controlling for different numbers of neighboring sensors and TTDA horizons. We use data from interstates 75 and 24 in the metropolitan area that surrounds Chattanooga, TN. Our results show that the XGBoost classifier produces the best results by detecting accidents as quickly as 1.0 min after their occurrence with an area under the receiver operating characteristic curve of up to 83% and an average precision of up to 49%. We describe limitations, open challenges, and how the proposed framework can be used for quicker operational accident detection.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS1993

Performance characteristics of LOX-H2, tangential-entry, swirl-coaxial, rocket injectors

Development of a high performing swirl-coaxial injector requires an understanding of fundamental performance characteristics. This paper addresses the findings of studies on cold flow atomic characterizations which provided information on the influence of fluid properties and element operating conditions on the produced droplet sprays. These findings are applied to actual rocket conditions. The performance characteristics of swirl-coaxial injection elements under multi-element hot-fire conditions were obtained by analysis of combustion performance data from three separate test series. The injection elements are described and test results are analyzed using multi-variable linear regression. A direct comparison of test results indicated that reduced fuel injection velocity improved injection element performance through improved propellant mixing.

Howell, Doug↗

DOE OSTI2024

Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model

The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: μ ECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1985

Ozone-temperature relationships in the stratosphere

Utilizing independent estimates of ozone and temperature fields from the SBUV (Nimbus 7) and NOAA operational satellites, respectively, for the period 1978-1981, the coefficient of variation between the two parameters is determined. This coefficient is defined as A = Delta-O3 x (T)/Delta T x (O3) wehre Delta is an incremental change in either temperature or ozone and the bracket is a mean state. In practice, A is determined on a daily basis by regression of ozone mixing ratio versus temperature around a latitude circle during the winter season and the bracket value is the daily zonal average. This has the advantage of keeping the solar zenith angle fixed for a daily value while allowing it to change during the season. This is done at 30, 10, 5, 2, and 1 mb from 20 deg to 60 deg latitude in both hemispheres. The results are summarized and compared with those determined from a one-dimensional photochemical model applied to different latitudes.

Miller, A. J.↗

NASA NTRS1991

Radar estimates of soil moisture over the Konza Prairie

Radar-backscatter measurements were made to estimate soil moisture. The helicopter-mounted radar was flown along selected transects that coincided with soil-moisture measurements. The radar operated at microwave frequencies of 5.3 and 9.6 GHz and at selected incidence angles between 0 and 60 degrees. Vertical polarization was used for two days and horizontal polarization was used for three days. The scattering-coefficient data from different days were grouped by frequency and antenna angles and then related to soil moisture along the flight paths using linear regression. A measure of linearity for the regression ranged between 0.9 and 0.5. The larger coefficients were for X-band measurements made at large antenna-incidence angles, and the smaller coefficients were for C-band measurements made at incidences angles near vertical.

Gogineni, S.↗

NASA NTRSDate not supplied

Advancements in Blowing Dust Detection at Night via Machine Learning

This presentation introduces operational users to a machine-learning based Dust Probability product developed by the NASA SPoRT program for the application of detecting and monitoring blowing dust plumes at night. Advances in earth observing satellites has improved monitoring and detection of dust both day and night through derived imagery such as the Dust RGB. However, limitations of the RGB at night result in less contrast between dust and land surface features, as seen by the user. A Machine Learning (ML) model has been developed and applied to GOES-16 ABI to overcome this limitation and improve nighttime dust detection. The ML capability is a subset of Artificial Intelligence methods. In this case the Dust ML model was developed using a simple Random Forest (RF) model, typically used to solve classification challenges (or to provide regression type output). The goal was to leverage the strengths of the RF model to learn how to identify blowing dust, and hence, overcome the limitation of a user trying to detect blowing dust within the satellite imagery by eye alone. A brief description of the ML model development will be provided. However, the focus of the presentation will be on the initial user feedback from the assessment of this tool for the 2022 blowing dust events of March through April. During this time several users across the U.S. Southwest collaborated to apply this Dust ML product at night as a complement to the existing Dust RGB in order to determine if it provided greater operational efficiency and value.

Machine Learning↗

NASA NTRS1995

Application of impact dampers in vibration control of flexible structures

Impact dampers belong to the category of passive vibration devices used to attenuate the vibration of discrete and continuous systems. An impact damper generally consists of a mass which is allowed to travel freely between two defined stops. Under the right conditions, the vibration of the structure to which the impact damper is attached will cause the mass of the impact damper to strike the structure. Previous analytical and experimental research work on the effect of impact dampers in attenuating the vibration of discrete and continuous systems have demonstrated their effectiveness. It has been shown in this study that impact dampers can increase the intrinsic damping of a lightly-damped flexible structure. The test structure consists of a slender flexible beam supported by a pin-type support at one end and supported by a linear helical flexible spring at another location. Sinusoidal excitation spanning the first three natural frequencies was applied in the horizontal plane. The orientation of the excitation and the test structure in the horizontal plane minimizes the effect of gravity on the behavior of the test structure. The excitation was applied using a linear sine sweep technique. The span of the test structure, the mass of the impact damper, the distance of travel, and the location of the impact damper along the span of the test structure were varied. The damping ratio are estimated for sixty test configurations. The results show that the impact damper significantly increases the damping ratio of the test structure. Statistical analysis of the results using the method of multiple linear regression indicates that a reasonable fit has been accomplished. It is concluded that additional experimental analysis of flexible structures in microgravity environment is needed in order to achieve a better understanding of the behavior of impact damper under conditions of microgravity. Numerical solution of the behavior of flexible structures equipped with impact dampers is also needed to predict stresses and deformations under operating conditions of microgravity in space applications.

Akl, Fred A.↗

NASA NTRS2004

High Lapse Rates in AIRS Retrieved Temperatures in Cold Air Outbreaks

The Atmospheric Infrared Sounder (AIRS) experiment, on NASA's Aqua spacecraft, uses a combination of infrared and microwave observations to retrieve cloud and surface properties, plus temperature and water vapor profiles comparable to radiosondes throughout the troposphere, for cloud cover up to 70%. The high spectral resolution of AIRS provides sensitivity to important information about the near-surface atmosphere and underlying surface. A preliminary analysis of AIRS temperature retrievals taken during January 2003 reveals extensive areas of superadiabatic lapse rates in the lowest kilometer of the atmosphere. These areas are found predominantly east of North America over the Gulf Stream, and, off East Asia over the Kuroshio Current. Accompanying the high lapse rates are low air temperatures, large sea-air temperature differences, and low relative humidities. Imagery from a Visible / Near Infrared instrument on the AIRS experiment shows accompanying clouds. These lines of evidence all point to shallow convection in the bottom layer of a cold air mass overlying warm water, with overturning driven by heat flow from ocean to atmosphere. An examination of operational radiosondes at six coastal stations in Japan shows AIRS to be oversensitive to lower tropospheric lapse rates due to systematically warm near-surface air temperatures. The bias in near-surface air temperature is seen to be independent of sea surface temperature, however. AIRS is therefore sensitive to air-sea temperature difference, but with a warm atmospheric bias. A regression fit to radiosondes is used to correct AIRS near-surface retrieved temperatures, and thereby obtain an estimate of the true atmosphere-ocean thermal contrast in five subtropical regions across the north Pacific. Moving eastward, we show a systematic shift in this air-sea temperature differences toward more isothermal conditions. These results, while preliminary, have implications for our understanding of heat flow from ocean to atmosphere. We anticipate future improvements in the AIRS retrieval algorithm will lead to improved understanding of the exchange of sensible and latent heat from ocean to atmosphere, and more realistic near-surface lapse rates.

cold air outbreak↗

NASA NTRS2016

Exploration Analysis of Carbon Dioxide Levels and Ultrasound Measures of the Eye During ISS Missions

Enhanced screening for the Visual Impairment/Intracranial Pressure (VIIP) Syndrome, including in-flight ultrasound, was implemented in 2010 to better characterize the changes in vision observed in some long-duration crewmembers. Suggested possible risk factors for VIIP include cardiovascular changes, diet, anatomical and genetic factors, and environmental conditions. As a potent vasodilator, carbon dioxide (CO (sub 2)), which is chronically elevated on the International Space Station (ISS) relative to typical indoor and outdoor ambient levels on Earth, seems a plausible contributor to VIIP. In an effort to understand the possible associations between CO (sub 2) and VIIP, this study analyzes the relationship between ambient CO (sub 2) levels on ISS and ultrasound measures of the eye obtained from ISS fliers. CO (sub 2) measurements will be pulled directly from Operational Data Reduction Complex for the Lab and Node 3 major constituent analyzers (MCAs) on ISS or from sensors located in the European Columbus module, as available. CO (sub 2) measures between ultrasound sessions will be summarized using standard time series class metrics in MATLAB including time-weighted means and variances. Cumulative CO (sub 2) exposure metrics will also be developed. Regression analyses will be used to quantify the relationships between the CO (sub 2) metrics and specific ultrasound measures. Generalized estimating equations will adjust for the repeated measures within individuals. Multiple imputation techniques will be used to adjust for any possible biases in missing data for either CO (sub 2) or ultrasound measures. These analyses will elucidate the possible relationship between CO (sub 2) and changes in vision and also inform future analysis of inflight VIIP data.

Young, M.↗

DOE OSTI2024

GP-BayesOpInf

SAND2025-01851O GP-BayesOpInf is a software tool that uses algorithms to combine Gaussian process regression, principal component analysis, and linear Bayesian inference to produce a probabilistic reduced-order model for time-dependent systems. Numerical examples include the compressible Euler equations for an ideal gas, a heat diffusion process with a nonlinear reaction term, and a set of ordinary differential equations describing a compartmental model in epidemiology. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗

DOE OSTI2025

Next-Level Energy Management in Manufacturing: Facility-Level Energy Digital Twin Framework Based on Machine Learning and Automated Data Collection

This research introduces an energy prediction framework at the facility level supported by automated data collection and machine learning models. It investigates whether reducing the prediction time scale allows for applying more complex machine learning techniques and if those techniques improve the prediction accuracy. The primary advantages of this framework lie in its automation of the energy prediction process and its provision of real-time energy data suitable for use in energy dashboards or digital twins. A sitewide dataset was created by combining 15 min energy and daily production data of five shops—assembly, battery, body (electric), body (gas), and paint—from a globally recognized electric vehicle manufacturer. Various machine learning models were evaluated on daily, weekly, and monthly datasets, including, in increasingly complex order: naïve, simple linear regression, net regularized generalized linear regression, principal component regression, k-nearest neighbor, random forest, and Bayesian regularized neural network. Compared to the current state-of-the-art energy consumption prediction for the industrial facility level, this research investigates more complex models and smaller time intervals for higher accuracy. The findings revealed that the more complex monthly models require a minimum of a year and a half of data to operate, while weekly models demand a year of data to achieve improved accuracy. Daily models can operate with only six months of data but exhibit poor performance due to reduced prediction accuracy of production. Key challenges identified include access to reliable, high-quality energy and production data and the initial demand for human labor.

digital twin↗

NASA NTRS1974

Reflectance of vegetation, soil, and water

The author has identified the following significant results. The majority of the rangelands of Hidalgo County, Texas are used in cow-calf operations. Continuous year-long grazing is practiced on about 60% of the acreage and some type of deferred system on the rest. Mechanical brush control is used more than chemical control. Ground surveys gave representative estimates for 15 vegetable crops produced in Hidalgo County. ERTS-1 data were used to estimate the acreage of citrus in the county. Combined Kubleka Munk and regression models, that included a term for shadow areas, gave a higher correlation of composite canopy reflectance with ground truth than either model alone.

Wiegand, C. L.↗

NASA NTRSDate not supplied

Probabilistic Modeling of a Three-Stage Human Landing System Architecture

Unmitigated uncertainties are known to have previously led to failed development programs; in order to combat these uncertainties, risks and their impacts must be understood and handled to ensure program success. In this paper, a probabilistic methodology to handle uncertainties is demonstrated on a three-element Human Landing System (HLS) concept, which allows tracking of current best estimates of the vehicle’s performance and assessment of its robustness against uncertainties. This methodology has two key parts: ﬁrst, the creation of a dynamic architecture model of a three-element HLS concept; and second, its use with surrogate modeling and range estimating techniques to capture and propagate uncertainties. The DYnamic Rocket EQuation Tool (DYREQT), a space systems synthesis and sizing framework used by NASA, was used as to model the HLS architecture. For the probabilistic analysis, uncertainties of interest within the HLS concept were enumerated and represented as parameters within the DYREQT model as inputs for vehicle stages or mission proﬁle events. Range estimating — a probabilistic method that combines Monte Carlo sampling, focus on critical parameters, and heuristics to assess risk and opportunities — is then adapted with operational parameters as well as vehicle parameters in the DYREQT model to capture mission uncertainty alongside vehicle uncertainty. To perform the range estimation portion of this methodology, the DYREQT model was sampled using a Design of Experiments (DoE) to eﬃciently explore the architecture design space with respect to the set of uncertainty parameters. Then, the results were used to create surrogate models, multivariate regressions that can visualize hypercube trends in the design space, of the architecture with respect to the uncertainty parameters. Using a correlation matrix constructed for the uncertainty parameters, previously independent samples were transformed to perform a Correlated Monte Carlo on the surrogate models. This probabilistic methodology was proved to provide insight into the underlying uncertainties of the three-element HLS architecture.

Stephanie Y Zhu↗

NASA NTRSDate not supplied

Probabilistic Modeling of a Three-Stage Human Landing System Architecture

Stephanie Y. Zhu↗

DOE OSTI2024

A Machine Learning Initializer for Newton-Raphson AC Power Flow Convergence

Power flow computations are fundamental to many power system studies. Obtaining a converged power flow case is not a trivial task especially in large power grids due to the non-linear nature of the power flow equations. One key challenge is that the widely used Newton based power flow methods are sensitive to the initial voltage magnitude and angle estimates, and a bad initial estimate would lead to non-convergence. This paper addresses this challenge by developing a random-forest (RF) machine learning model to provide better initial voltage magnitude and angle estimates towards achieving power flow convergence. This method was implemented on a real ERCOT 6102 bus system under various operating conditions. By providing better Newton-Raphson initialization, the RF model precipitated the solution of 2,106 cases out of 3,899 non-converging dispatches. These cases could not be solved from flat start or by initialization with the voltage solution of a reference case. Finally, results obtained from the RF initializer performed better when compared with DC power flow initialization, Linear regression, and Decision Trees.

random forest↗

NASA NTRS2007

Aeroelastic Model Structure Computation for Envelope Expansion

Structure detection is a procedure for selecting a subset of candidate terms, from a full model description, that best describes the observed output. This is a necessary procedure to compute an efficient system description which may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modeling may be of critical importance in the development of robust, parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion that may save significant development time and costs. In this study, a least absolute shrinkage and selection operator (LASSO) technique is investigated for computing efficient model descriptions of non-linear aeroelastic systems. The LASSO minimises the residual sum of squares with the addition of an l(Sub 1) penalty term on the parameter vector of the traditional l(sub 2) minimisation problem. Its use for structure detection is a natural extension of this constrained minimisation approach to pseudo-linear regression problems which produces some model parameters that are exactly zero and, therefore, yields a parsimonious system description. Applicability of this technique for model structure computation for the F/A-18 (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) Active Aeroelastic Wing project using flight test data is shown for several flight conditions (Mach numbers) by identifying a parsimonious system description with a high percent fit for cross-validated data.

Kukreja, Sunil L.↗