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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

High Ice Water Content at Low Radar Reflectivity near Deep Convection: Part II. Evaluation of Microphysical Pathways in Updraft Parcel Simulations

The aeronautics industry has established that a threat to aircraft is posed by atmospheric conditions of substantial ice water content (IWC) where equivalent radar reflectivity (Ze) does not exceed 20-30 dBZ and supercooled water is not present; these conditions are encountered almost exclusively in the vicinity of deep convection. Part 1 (Fridlind et al., 2015) of this two-part study presents in situ measurements of such conditions sampled by Airbus in three tropical regions, commonly near 11 km and -43 C, and concludes that the measured ice particle size distributions are broadly consistent with past literature with profiling radar measurements of Z(sub e) and mean Doppler velocity obtained within monsoonal deep convection in one of the regions sampled. In all three regions, the Airbus measurements generally indicate variable IWC that often exceeds 2 gm (exp -3) with relatively uniform mass median area-equivalent diameter (MMD(sub eq) of 200-300 micrometers. Here we use a parcel model with size-resolved microphysics to investigate microphysical pathways that could lead to such conditions. Our simulations indicate that homogeneous freezing of water drops produces a much smaller ice MMD(sub eq) than observed, and occurs only in the absence of hydrometeor gravitational collection for the conditions considered. Development of a mass mode of ice aloft that overlaps with the measurements requires a substantial source of small ice particles at temperatures of about -10 C or warmer, which subsequently grow from water vapor. One conceivable source in our simulation framework is Hallett-Mossop ice production; another is abundant concentrations of heterogeneous ice freezing nuclei acting together with copious shattering of water drops upon freezing. Regardless of the production mechanism, the dominant mass modal diameter of vapor-grown ice is reduced as the ice-multiplication source strength increases and as competition for water vapor increases. Both mass and modal diameter are reduced by entrainment and by increasing aerosol concentrations. Weaker updrafts lead to greater mass and larger modal diameters of vapor-grown ice, the opposite of expectations regarding lofting of larger ice particles in stronger updrafts. While stronger updrafts do loft more dense ice particles produced primarily by raindrop freezing, we find that weaker updrafts allow the warm rain process to reduce competition for diffusional growth of the less dense ice expected to persist in convective outflow.

Reflectivity↗
NASA NTRS2016

Probabilistic Modeling of Aircraft Trajectories for Dynamic Separation Volumes

With a proliferation of new and unconventional vehicles and operations expected in the future, the ab initio airspace design will require new approaches to trajectory prediction for separation assurance and other air traffic management functions. This paper presents an approach to probabilistic modeling of the trajectory of an aircraft when its intent is unknown. The approach uses a set of feature functions to constrain a maximum entropy probability distribution based on a set of observed aircraft trajectories. This model can be used to sample new aircraft trajectories to form an ensemble reflecting the variability in an aircraft's intent. The model learning process ensures that the variability in this ensemble reflects the behavior observed in the original data set. Computational examples are presented.

Lewis, Timothy A.↗
NASA NTRS2016

Infrared Studies of the Reflective Properties of Solar Cells and the HS376 Spacecraft

In 2015, a selection of HS-376 buses were observed photometrically with the United Kingdom Infrared Telescope (UKIRT) to explore relationships between time-on-orbit and Near Infrared (NIR) color. These buses were chosen because of their relatively simple shape, for the abundance of similar observable targets, and their surface material being primarily covered by solar cells. While the HS-376 spacecraft were all very similar in design, differences in the specific solar cells used in the construction of each model proved to be an unconstrained variable that could affect the observed reflective properties. In 2016, samples of the solar cells used on various models of HS-376 spacecraft were obtained from Boeing and were analyzed in the Optical Measurements Center at the Johnson Space Center using a visible-near infrared field spectrometer. The laboratory-based spectra are convolved to match the photometric bands previously obtained using UKIRT and compared with the on-orbit photometry. The results and future work are discussed here.

Frith, James↗
NASA NTRS2019

Decadal-Scale Vegetation Change Driven by Salinity at Leading Edge of Rising Sea Level

As sea levels rise, low-lying coastal forests increasingly are subject to stressors such as inundation and saltwater exposure. At long timescales (for example, centuries), the extent of inundation and saltwater exposure will increase; however, on a decadal timescale, the role of these drivers may differ in both magnitude and direction. To investigate the drivers of decadal-scale vegetation change, we measured the changes in five metrics of vegetation composition and structure between 2003/2004 and 2016/ 2017 at 98 plots distributed across a vegetation gradient from coastal forest to brackish marshes (< 0.5–18 ppt). We used elevation as a proxy of inundation vulnerability and soil sodium concentration as a proxy of saltwater exposure, and we investigated relationships between these two variables and the change in vegetation conditions between the two sampling periods. Soil sodium concentration was a significant predictor of vegetation change for all five vegetation metrics, whereas the effect of elevation was not significant for any of the metrics. The one site that was affected by wildfire twice during the duration of the study shifted almost completely from forest to marsh with limited regeneration ofwoody vegetation observed in 2016/ 2017. Our results show that salinization in our system is a more important driver of vegetation change than inundation potential. Furthermore, the effects of drought-induced salinization could be amplified by the elevated risk of wildfire during droughts. Forecasting the response of coastal wetlands to rising sea levels will require a better understanding of the individual and combined effects of salinity, droughts, and wildfires on vegetation.

Taillie, Paul J.↗
NASA NTRS2019

Numerical Investigation and Optimization of a Flushwall Injector for Scramjet Applications at Hypervelocity Flow Conditions

An investigation utilizing Reynolds-averaged simulations (RAS) was performed in order to demonstrate the use of design and analysis of computer experiments (DACE) methods in Sandia’s DAKOTA software package for surrogate modeling and optimization. These methods were applied to a flow- path fueled with an interdigitated flushwall injector suitable for scramjet applications at hyper- velocity conditions and ascending along a constant dynamic pressure flight trajectory. The flight Mach number, duct height, spanwise width, and injection angle were the design variables selected to maximize two objective functions: the thrust potential and combustion efficiency. Because the RAS of this case are computationally expensive, surrogate models are used for optimization. To build a surrogate model a RAS database is created. The sequence of the design variables comprising the database were generated using a Latin hypercube sampling (LHS) method. A methodology was also developed to automatically build geometries and generate structured grids for each design point. The ensuing RAS analysis generated the simulation database from which the two objective functions were computed using a one-dimensionalization (1D) of the three-dimensional simulation data. The data were fitted using four surrogate models: an artificial neural network (ANN), a cubic polynomial, a quadratic polynomial, and a Kriging model. Variance-based decomposition showed that both objective functions were primarily driven by changes in the duct height. Multiobjective design optimization was performed for all four surrogate models via a genetic algorithm method. Optimal solutions were obtained at the upper and lower bounds of the flight Mach number range. The Kriging model predicted an optimal solution set that exhibited high values for both objective functions. Additionally, three challenge points were selected to assess the designs on the Pareto fronts. Further sampling among the designs of the Pareto fronts may be required to lower the surrogate model errors and perform more accurate surrogate-model-based optimization.

Shenoy, Rajiv R.↗
NASA NTRSDate not supplied

Artificial Neural Networks to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
NASA NTRSDate not supplied

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
NASA NTRS2023

Pervasive aqueous alteration in the early Solar System revealed by potassium isotopic variations in Ryugu samples and carbonaceous chondrites

C-type asteroids are the presumed home to carbonaceous chondrites, some of which contain abundant life-forming volatiles and organics. For the first time, samples from a C-type asteroid (162173 Ryugu) were successfully returned to Earth by JAXA’s Hayabusa2 mission. These pristine samples, uncontaminated by the terrestrial environment, allow a direct comparison with carbonaceous chondrites. This study reports the stable K isotopic compositions (expressed as δ 41 K) of Ryugu samples and seven carbonaceous chondrites to constrain the origin of K isotopic variations in the early Solar System. Three aliquots of Ryugu particles collected at two touchdown sites have identical δ 41 K values, averaged at -0.194 ± 0.038‰ (2SD). The K isotopic composition of Ryugu falls within the range of δ 41 K values measured on representative CI chondrites, and together, they define an average δ 41 K value of -0.185 ± 0.078‰ (2SE), which provides the current best estimate of the K isotopic composition of the bulk Solar System. Samples of CI chondrites with δ 41 K values that deviate from this range likely reflect terrestrial contaminations or compositional heterogeneities at sampled sizes. In addition to CI chondrites, substantial K isotopic variability is observed in other carbonaceous chondrites and within individual chondritic groups, with δ 41 K values inversely correlated with K abundances in many cases. These observations indicate widespread fluid activity occurred in chondrite parent bodies, which significantly altered the original K abundances and isotopic compositions of chondrules and matrices established at their accretion.

Asteroid Ryugu↗
DOE OSTI2021

Spatial and temporal variability of tritium in precipitation within South Africa and it's bearing on hydrological studies

Tritium, the radioactive isotope of hydrogen, has been used to understand groundwater recharge processes for decades. The current variation of tritium in the atmosphere is largely attributed to stratospheric production and fall out rates as well as global circulation phenomena controlling the hydrological cycle. Global controls on the variability in atmospheric tritium activity are poorly suited to explain local variation and tritium activities in precipitation are often assumed to be uniform over both local and regional catchments and watersheds. This assumption can result in both over and under estimation of modern recharge within an aquifer when using tritium as the recharge proxy. In order to minimize the inherent prediction residuals associated with tritium based recharge investigations, the variability of tritium in precipitation was modelled from 127 spatial precipitation samples taken over a two year period, combined with a 76 precipitation sample group-set taken over a one year period in a single location. Precipitation events were traced backward in time, from the point of collection, using HYSPLIT modelling to ascertain the origins of moisture content as well as the altitudes of moisture origin reached along the particle track. Tritium activities, collected over a one year period in Paarl, range from 0.45 to 4.16 TU and have a mean of 1.59 TU. Spatial storm events in the Western Cape in 2017 and 2018 had a range from 0 to 2.2 and 0.37 to 3.27 TU, respectively, with mean activities of 1.18 (n = 34) and 1.25 TU (n = 32). Both storm events had similar tritium variability (σ = 0.5 n = 35 and 0.48 n = 32). Regional precipitation events had the largest range of tritium activities (0.55–12.2 TU). Although not all tritium activities can be explained by interrogating the water mass origin, this study suggests that approximately 90% of events can be completely or partially attributed to the origin of the water mass. The variability of tritium, both spatially and temporally, was higher than expected, confirming that when uniform tritium inputs are used, the groundwater system would provide inaccurate modern recharge estimates. Higher spatial resolution of tritium variation in precipitation for a particular region will improve our ability to relate tritium activities in groundwater to local precipitation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1982

Pilot opinions of sampling effects in lateral-directional control

Flight experiments with a microprocessor control system were conducted to determine the effects of variations in sampling parameters on several pilots' opinions of lateral-directional flying qualities. Princeton's variable-response research aircraft (VRA), which is equipped with a microprocessor based digital flight control system (Micro-DFCS), was the test vehicle. Two U.S. Navy pilots evaluated the effects of sampling rate, quantization, and pure time delay during tracking, approach, and landing. Aircraft carrier approach tasks were conducted using a Navy approach mirror. Acquisition and tracking of fixed objects on the ground provided additional information related to the Navy misson. The longitudinal controls were implemented with analog electronics, while the lateral-directional pilot inputs (stick and rudder) were fed to the Micro-DFCS, which commanded the ailerons and rudder. The conceptual relationship between the evaluation pilot's lateral-directional inputs, the flight computer, and the aircraft are illustrated.

Stengel, R. F.↗
DOE OSTI2023

Classification Analysis of Southwest Pacific Tropical Cyclone Intensity Changes Prior to Landfall

This study evaluates the ability of a random forest classifier to identify tropical cyclone (TC) intensification or weakening prior to landfall over the western region of the Southwest Pacific Ocean (SWPO) basin. For both Australia mainland and SWPO island cases, when a TC first crosses land after spending ≥24 h over the ocean, the closest hour prior to the intersection is considered as the landfall hour. If the maximum wind speed (V max ) at the landfall hour increased or remained the same from the 24-h mark prior to landfall, the TC is labeled as intensifying and if the V max at the landfall hour decreases, the TC is labeled as weakening. Geophysical and aerosol variables closest to the 24 h before landfall hour were collected for each sample. The random forest model with leave-one-out cross validation and the random oversampling example technique was identified as the best-performing classifier for both mainland and island cases. The model identified longitude, initial intensity, and sea skin temperature as the most important variables for the mainland and island landfall classification decisions. Incorrectly classified cases from the test data were analyzed by sorting the cases by their initial intensity hour, landfall hour, monthly distribution, and 24-h intensity changes. TC intensity changes near land strongly impact coastal preparations such as wind damage and flood damage mitigations; hence, this study will contribute to improve identifying and prioritizing prediction of important variables contributing to TC intensity change before landfall.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

The materials irradiation experiment for testing plasma facing materials at fusion relevant conditions

The Materials Irradiation Experiment (MITE-E) was constructed at the University of Wisconsin-Madison Inertial Electrostatic Confinement Laboratory to test materials for potential use as plasma-facing materials (PFMs) in fusion reactors. PFMs in fusion reactors will be bombarded with x-rays, neutrons, and ions of hydrogen and helium. More needs to be understood about the interactions between the plasma and the materials to validate their use for fusion reactors. The MITE-E simulates some of the fusion reactor conditions by holding samples at temperatures up to 1000 °C while irradiating them with helium or deuterium ions with energies from 10 to 150 keV. The ion gun can irradiate the samples with ion currents of 20 μA–500 μA; the typical current used is 72 μA, which is an average flux of 9 × 10{sup 14} ions/(cm{sup 2} s). The ion gun uses electrostatic lenses to extract and shape the ion beam. A variable power (1-20 W), steady-state, Nd:YAG laser provides additional heating to maintain a constant sample temperature during irradiations. The ion beam current reaching the sample is directly measured and monitored in real-time during irradiations. The ion beam profile has been investigated using a copper sample sputtering experiment. The MITE-E has successfully been used to irradiate polycrystalline and single crystal tungsten samples with helium ions and will continue to be a source of important data for plasma interactions with materials.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1981

The HEAO-1 soft X-ray survey of cataclysmic variable stars

A survey of the soft X-ray emission from 206 cataclysmic variable stars, including dwarf novae in optically quiescent states, classical novae, recurrent novae, nova-like objects and polars, is reported. This is an extension of a previous survey of dwarf novae in outbursts by Cordova et al. (1980). The number of surveyed stars that correspond to features greater than 2 sigma is not in excess of the number of chance coincidences expected on the basis of a random sample of positions distributed over the entire sky. While a few cataclysmic variables have been previously identified as X-ray sources, the results of this survey imply that cataclysmic variables as a class must be, at best, low-luminosity X-ray sources with fluxes below the HEAO-1 A2 detector's sensitivity limit of a few times 10 to the -11th erg/sq cm-s in the energy range 0.1-3 keV. The X-ray luminosities of the cataclysmic variables are consistent with predictions from disk accretion models, provided that a wide range of accretion rates apply.

Cordova, F. A.↗
DOE OSTI2021

Genetic and behavioral adaptation of Candida parapsilosis to the microbiome of hospitalized infants revealed by in situ genomics, transcriptomics, and proteomics

Background Candida parapsilosis is a common cause of invasive candidiasis, especially in newborn infants, and infections have been increasing over the past two decades. C. parapsilosis has been primarily studied in pure culture, leaving gaps in understanding of its function in a microbiome context. Results. Here, we compare five unique C. parapsilosis genomes assembled from premature infant fecal samples, three of which are newly reconstructed, and analyze their genome structure, population diversity, and in situ activity relative to reference strains in pure culture. All five genomes contain hotspots of single nucleotide variants, some of which are shared by strains from multiple hospitals. A subset of environmental and hospital-derived genomes share variants within these hotspots suggesting derivation of that region from a common ancestor. Four of the newly reconstructed C. parapsilosis genomes have 4 to 16 copies of the gene RTA3, which encodes a lipid translocase and is implicated in antifungal resistance, potentially indicating adaptation to hospital antifungal use. Time course metatranscriptomics and metaproteomics on fecal samples from a premature infant with a C. parapsilosis blood infection revealed highly variable in situ expression patterns that are distinct from those of similar strains in pure cultures. For example, biofilm formation genes were relatively less expressed in situ, whereas genes linked to oxygen utilization were more highly expressed, indicative of growth in a relatively aerobic environment. In gut microbiome samples, C. parapsilosis co-existed with Enterococcus faecalis that shifted in relative abundance over time, accompanied by changes in bacterial and fungal gene expression and proteome composition. Conclusions The results reveal potentially medically relevant differences in Candida function in gut vs. laboratory environments, and constrain evolutionary processes that could contribute to hospital strain persistence and transfer into premature infant microbiomes.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Microstructural Characterization of Felsite Fragments From the Apollo Next Generation Sample Analysis (ANGSA) Double Drive Tube 73001/73002

While the Moon’s surface is dominated by mafic igneous products formed through the crystallization of the lunar magma ocean, and the subsequent eruption of mare basalts – felsic magmatic fragments (variably referred to as felsites, granites, rhyolites or granophyres) have been identified from a number of Apollo samples (e.g. 12013[1], 14321[2], 15405[3] and 73215[4]). Magmatic edifices like the Gruithuisen Domes – silicic constructs sometimes found proximal to the basaltic mare provinces filling nearside basins, may represent a petrogenetic origin. However, this is, is complicated by the fact that they apparently predate mare volcanism (e.g. [5]). Additionally, crater-counting indicates that these silicic surface features also postdate radiogenic ages of Apollo felsites [6]. Various modes of felsic magmatism have been invoked to explain the presence of felsic lithologies on the Moon, including: 1) fractional crystallization and silicate liquid immiscibility; 2) partial melting of lunar crust through basaltic underplating; and 3) fractional crystallization of the mare parent magmas. Although these models are plausible, based on known Apollo samples and lunar meteorites, they are complicated by the lack of sample lithologies with intermediate composition between basaltic magmas and the felsic components, and the fact that partial melting of many crustal rock types on the Moon (e.g., anorthosite, troctolites) are unlikely to form granites. Along with other unique magmatic lithologies, new felsites have been identified within the < 1 mm size fractions of the ANGSA double drive core tube (73001/73002) from Apollo 17 station 3, sampling the light mantle landslide deposit from the South Massif [7]. These additional examples of lunar felsite will help us to better constrain the processes that lead to the formation of these evolved lithologies. To do this, we have employed a gambit of high-resolution scanning electron microscopy (SEM) and scanning transmission electron microscopy (TEM) analytical techniques, including electron backscatter diffraction (EBSD), cathodoluminescence and high-angle annular dark field (HAADF) imaging. These data provide unique insights into the felsite mineralogy and microstructures including the coexistence of quartz and tridymite in many of the fragments. In addition, these analyses provide petrological context and assist targeted in situ secondary ion mass spectrometry (SIMS) measurements of the U-Pb systematics of accessory minerals, and the volatile abundances and D/H ratios of apatite grains identified within the clasts.

lunar↗
NASA NTRSDate not supplied

Small-Sample Estimator Decisions – Certainly Uncertain in Human Spaceflight at NASA

Within statistics when estimating means, we rely heavily on the Central Limit Theorem (CLT) to aid in our inference, however in cases where the CLT does not apply we begin to be strongly limited in our choices. If a violation to the CLT comes in the form of small samples and unknown population variance, yet we maintain the assumption of normality, the use of t-distributions is perfectly valid. We explore cases where population normality is not assumed, where sample sizes are small, and where some actionable estimate of the central tendency of a distribution is needed such as for spaceflight operational decisions. Here we identify different scenarios based on measures taken from spaceflight, characterizing performance of estimators of central tendency within our set of simulations. We examine bias and variability of standard estimators of central tendency as they apply to varying small sample sizes and varying population distributions. Further work in this area is needed to develop a framework or set of guidelines for individuals set in these situations. Even with limited information, decisions need to be made.

Central Tendency↗
DOE OSTI2025

Machine Learning Prediction of Tritium‐Helium Groundwater Ages in the Central Valley, California, USA

Abstract Groundwater ages provides insight into recharge rates, flow velocities, and vulnerability to contaminants. The ability to predict groundwater ages based on more accessible parameters via Machine Learning (ML) would advance our ability to guide sustainable management of groundwater resources. In this study, ML models were trained and tested on a large data set of tritium concentrations and tritium‐helium groundwater ages from the California Central Valley, a large groundwater basin with complex land use, irrigation, and water management practices. The ML models were trained on 63 features, including location, well construction information, landscape characteristics, and climate variables, water chemistry, and stable isotopes. The Bagging regressor method can accurately classify (F1‐score = 0.91) groundwater samples as either modern or pre‐modern whereas the accuracy of the ML prediction of continuous tritium‐helium groundwater ages is limited and explains only of the variability in this data set. In general, ML groundwater age prediction relies mostly on features related to (a) the source of groundwater recharge, (b) contaminant history, (c) aquifer materials, (d) well construction, and (e) geochemical reactions along flow paths.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1976

An algorithm for the weighting matrices in the sampled-data optimal linear regulator problem

The sampled-data optimal linear regulator problem provides a means whereby a control designer can use an understanding of continuous optimal regulator design to produce a digital state variable feedback control law which satisfies continuous system performance specifications. A basic difficulty in applying the sampled-data regulator theory is the requirement that certain digital performance index weighting matrices, expressed as complicated functions of system matrices, be computed. Infinite series representations are presented for the weighting matrices of the time-invariant version of the optimal linear sampled-data regulator problem. Error bounds are given for estimating the effect of truncating the series expressions after a finite number of terms, and a method is described for their computer implementation. A numerical example is given to illustrate the results.

Armstrong, E. S.↗