Search NASA⌕ Search

SEARCH · Search NASA

Results for “sampling size”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Statistics of associations among IR galaxies

In the course of expanding the search of Kleinmann et. al. (1988) for distant, infrared-luminous objects, the authors noticed (as is often remarked) that a large number of infrared-selected galaxies have close neighbors or show merger characteristics (e.g., tidal tails, distorted disks). Because the sample size is large (567 infrared galaxies and 2182 field galaxies), this sample is ideal for statistically examining the importance of interactions among infrared galaxies. In particular, the authors compare the nearest-neighbor distribution and the two-point correlation function of their sample with that of a control sample of field galaxies.

Gallimore, Jack F.↗
DOE OSTI2022

An Easy‐to‐Use Custom‐Built Cell for Neutron Powder Diffraction Studies of Rechargeable Batteries

Abstract In operando powder diffraction remains one of the most powerful tools for non‐destructive investigation of battery electrode materials. While in operando X‐ray, especially synchrotron radiation, powder diffraction is by now a routine experimental technique, in operando neutron powder diffraction is still less established. We present a new electrochemical cell for in operando neutron powder diffraction, which is, first and foremost, easy to use, but can also cycle electrode materials under electrochemical conditions close to those achieved using standard laboratory cells. The cell has been designed in multiple sizes, and high‐quality electrochemical and neutron powder diffraction data is presented for sample sizes as low as 48 mg total active material. The cell handles lithium‐ion and sodium‐ion materials equally well, with no difference in how the cell is prepared and assembled. The cell is intended to be used as sample environment at powder diffractometers at the neutron facilities MLZ, ORNL and ACNS.

25 ENERGY STORAGE↗
DOE OSTI2020

Additional Analysis of the 2H-Evaporator Wall Scale Sample HTF-17-57

Savannah River National Laboratory previously analyzed scale samples from both the wall and cone sections of the 242-16H Evaporator prior to chemical cleaning. The samples were analyzed for uranium and plutonium isotopes required for a Nuclear Criticality Safety Assessment of the scale removal process. The analysis of the scale samples found the material to contain crystalline nitrated cancrinite and clarkeite and significant amounts of mercury. Savannah River Remediation subsequently requested additional analyses on the samples. Inspection of the sample bottles revealed that no sample remained of the cone scale (HTF-17-56) while approximately 4 g of the wall scale sample (HTF-17-57) remained. The wall sample was subsequently ground with a mortar and pestle, at which time it was discovered that the sample contained a significant amount of liquid, metallic mercury. The portion of the sample that was not mercury was approximately 3 g. This portion of the sample was analyzed to determine the concentrations of selected radionuclides. The following radionuclide concentrations were measured: <9.6E+02 dpm/g H-3, 2.9E+02 dpm/g C-14, 5.6E+03 dpm/g Tc-99, and 1.6E+02 dpm/g I-129. Blank samples were also analyzed to evaluate sample contamination from the Shielded Cells environment. All blank sample results were below detectable limits. Due to the limited sample size and the anticipated low levels of the target radionuclides, no replicate sample analysis was conducted.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R 2 ) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2014

Temporal Wind Pairs for Space Launch Vehicle Capability Assessment and Risk Mitigation

Space launch vehicles incorporate upper-level wind assessments to determine wind effects on the vehicle and for a commit to launch decision. These assessments make use of wind profiles measured hours prior to launch and may not represent the actual wind the vehicle will fly through. Uncertainty in the winds over the time period between the assessment and launch introduces uncertainty in assessment of vehicle controllability and structural integrity that must be accounted for to ensure launch safety. Temporal wind pairs are used in engineering development of allowances to mitigate uncertainty. Five sets of temporal wind pairs at various times (0.75, 1.5, 2, 3 and 4-hrs) at the United States Air Force Eastern Range and Western Range, as well as the National Aeronautics and Space Administration's Wallops Flight Facility are developed for use in upper-level wind assessments on vehicle performance. Historical databases are compiled from balloon-based and vertically pointing Doppler radar wind profiler systems. Various automated and manual quality control procedures are used to remove unacceptable profiles. Statistical analyses on the resultant wind pairs from each site are performed to determine if the observed extreme wind changes in the sample pairs are representative of extreme temporal wind change. Wind change samples in the Eastern Range and Western Range databases characterize extreme wind change. However, the small sample sizes in the Wallops Flight Facility databases yield low confidence that the sample population characterizes extreme wind change that could occur.

Decker, Ryan K.↗
NASA NTRS2015

Temporal Wind Pairs for Space Launch Vehicle Capability Assessment and Risk Mitigation

Space launch vehicles incorporate upper-level wind assessments to determine wind effects on the vehicle and for a commit to launch decision. These assessments make use of wind profiles measured hours prior to launch and may not represent the actual wind the vehicle will fly through. Uncertainty in the winds over the time period between the assessment and launch introduces uncertainty in assessment of vehicle controllability and structural integrity that must be accounted for to ensure launch safety. Temporal wind pairs are used in engineering development of allowances to mitigate uncertainty. Five sets of temporal wind pairs at various times (0.75, 1.5, 2, 3 and 4-hrs) at the United States Air Force Eastern Range and Western Range, as well as the National Aeronautics and Space Administration's Wallops Flight Facility are developed for use in upper-level wind assessments on vehicle performance. Historical databases are compiled from balloon-based and vertically pointing Doppler radar wind profiler systems. Various automated and manual quality control procedures are used to remove unacceptable profiles. Statistical analyses on the resultant wind pairs from each site are performed to determine if the observed extreme wind changes in the sample pairs are representative of extreme temporal wind change. Wind change samples in the Eastern Range and Western Range databases characterize extreme wind change. However, the small sample sizes in the Wallops Flight Facility databases yield low confidence that the sample population characterizes extreme wind change that could occur.

Decker, Ryan K.↗
DOE OSTI2021

CIS Project 22359, Final Technical Report. Discretized Posterior Approximation in High Dimensions

Our primary aim in this work is to understand how to efficiently obtain reliable uncertainty quantification in automatic learning algorithms with limited training datasets. Standard approaches rely on cross-validation to tune hyper parameters. Unfortunately, when our datasets are too small, holdout datasets become unreliable—albeit unbiased—measures of prediction quality due to the lack of adequate sample size. We should not place confidence in holdout estimators under conditions wherein the sample variance is both large and unknown. More poigniantly, our training experiments on limited data (Duersch and Catanach, 2021) show that even if we could improve estimator quality under these conditions, the typical training trajectory may never even encounter generalizable models.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2002

STARDUST and Interplanetary Dust Particles - Big Science from Small Samples

Comets are primitive bodies that are widely believed to be a reservoir of preserved interstellar and circumstellar grains, and molecular cloud materials (organics). Direct samples of cometary dust along with interstellar grains will be returned by the STARDUST Mission in 2006. Analyses of interplanetary dust particles (IDPs) and analogue materials in the laboratory provide constraints and serve as "ground truth" for evaluating various hypotheses on the nature of comets and interstellar grains. Anhydrous IDPs are the most primitive remnants of the primordial Solar System, and are our only known samples of comets. These cometary lDPs are rich in preserved interstellar organic compounds II]. In addition, abundant interstellar silicates have recently been discovered in cluster lDPs [2]. In some of these IDPs, the presolar silicate abundance reaches 1 wt %, exceeding the total presolar grain abundance in meteorites by three orders of magnitude, where presolar silicates are still notably absent. The results to date support the idea that comets are rich in presolar materials, but are at odds with the common perception that they are 'pristine aggregates of interstellar grains'. These results underscore the scientific importance of sample return missions to comets. The technology for the analysis of micrometer-sized samples is well advanced. The newest generation of ion probe instruments allow for isotopic analyses at the submicrometer level. The nature of the organic matter is analyzed using Infrared and soft X-ray spectroscopy techniques on synchrotron-based instruments, also at the micrometer-scale and smaller. Electron microscopy and spectroscopy provide details on the mineralogy and chemistry of constituent grains in !DPs at nearly the atomic scale. Novel sample preparation techniques have been developed such that all of these measurements can now be made on the same 10 micrometer diameter particle. Returned comet samples captured in aerogel will pose new challenges in sample analysis, but should provide a major leap in our understanding of the fundamental building blocks of our Solar System.

Keller, L. P.↗
DOE OSTI2025

Efficient Monte Carlo event generation for neutrino-nucleus exclusive cross sections

Modern neutrino-nucleus cross section computations need to incorporate sophisticated nuclear models to achieve greater predictive precision. However, the computational complexity of these advanced models often limits their practicality for experimental analyses. To address this challenge, we introduce a new Monte Carlo method utilizing normalizing flows to generate surrogate cross sections that closely approximate those of the original model while significantly reducing computational overhead. As a case study, we built a Monte Carlo event generator for the neutrino-nucleus cross section model developed by the Ghent group. This model employs a Hartree-Fock procedure to establish a quantum mechanical framework in which both the bound and scattering nucleon states are solutions to the mean-field nuclear potential. The surrogate cross sections generated by our method demonstrate excellent accuracy with a relative effective sample size of more than 98.4%, providing a computationally efficient alternative to traditional Monte Carlo sampling methods for differential cross sections.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1986

Inorganic and protein crystal growth - Similarities and differences

Transport and interface kinetic concepts for the design and control of inorganic crystal growth experiments are reviewed, and their applications and limitations in protein crystal growth are considered. It is suggested that the interfacial concentration gradients are steeper for faster crystallization, and that the interfacial concentration distributions for the protein and the precipitant can differ significantly. Results show that uniformity in crystal composition and steady-state conditions in growth kinetics are favored by larger sample size, since surface-tension gradients drive strong in microgravity experiments and in small samples on earth.

Rosenberger, F.↗
NASA NTRSDate not supplied

X-Ray Diffraction Based Phase Identification in NiTiHf Shape Memory Alloys with Variable Hf Content

NiTiHf type shape memory alloys have been studied for use in high temperature actuator applications, the most studied of which is the NiTi-20Hf atomic % composition. In this study, six samples of NiTi-Hf including 20Hf, 30Hf, 35Hf, 40Hf, 45Hf, and 50Hf were evaluated by both 1D and 2D X-ray diffraction (XRD) to elucidate what phases and crystal structures were present at room temperature. Given small sample sizes and relatively large grains, multiple cross-sections were measured to increase the sampling statistics, and 2D XRD frames were collected to corroborate the 1D XRD data. Rietveld and structureless refinements were both performed, allowing for adjustments in orientation lattice constant, and structure factor. At room temperature, the primary martensitic phase in the NiTiHf alloys with 20 at% and lower Hf is a monoclinic B19’ phase (P1121/m space group). This phase was identifiable, even in small amounts, in the higher Hf samples. The primary phase in the 30 -50 at% Hf samples is an orthorhombic B33 (Cmcm space group) phase. This work supported thermal analyses indicating the phase transition paths from the two different room temperature martensitic phases to the B2 austenitic phase.

Laura Wilson↗
DOE OSTI2025

Optical 14 C Tracing for Biological and Pharmaceutical Applications Using Two-Color Cavity Ringdown Spectroscopy

Laser-based 14 C quantitation has been proposed as a more affordable, higher-throughput, table-top alternative to accelerator mass spectrometry (AMS). Here, we demonstrate the feasibility of a mid-IR 14 C detector based on two-color cavity ringdown spectroscopy (2C-CRDS) for low-level 14 C isotope tracing in biological studies. The 2C-CRDS technique quantifies the sample 14 C content by measuring the 14 CO 2 absorption signals from the combusted samples with mid-IR lasers. With 2C-CRDS, we previously demonstrated the most sensitive and accurate optical measurements of 14 CO 2 . The current detection sensitivity and quantitation accuracy of the instrument, at a few parts per quadrillion (where a quadrillion = 10 15 ) 14 C/C mole fraction, is competitive against AMS. Here, by applying the 2C-CRDS 14 C sensor to two applications relevant to 14 C-labeled biochemical analysis and pharmaceutical studies, we demonstrate sub-fCi level (where 1 fCi = 10 –15 Ci) quantitation of sample 14 C activity, with a minimum sample-size requirement of 3 mg of carbon. The current measurement throughput, ~25 min/sample, is largely limited by the sampling efficiency of the online combustion and CO 2 processing interface to the 2C-CRDS instrument. The possibility of a significantly improved measurement throughput of a few minutes per sample is suggested by the results of a flow-through 14 CO 2 sampling scheme. In conclusion, this improved measurement efficiency, combined with the relatively low cost and compact size of a 2C-CRDS sensor, could potentially revolutionize high-sensitivity 14 C tracing in biological, pharmaceutical, and clinical studies.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1995

Gravity Sensors and the Role of 3-D Visualization and Simulation in Biomedical Research

Ross 3.0 software was developed in the Biocomputation Center for semi-automated reconstruction of objects from serial thin sections. Data are captured directly from a transmission electron microscope via a video camera to a graphics workstation where the sections are mosaicked and contours are traced, registered and displayed by semi-automated methods. For the first time, macular type II cells are described completely for their innervation patterns. The purposes are to learn more about the fundamental circuitry of the macula and to demonstrate whether the terminals are altered morphologically by space flight. Current examples, from the medial part of the macula, are from maculas collected in-flight on the Space Life Sciences-2 mission, 4.5 hrs post-flight, and from a ground control. Results show that the typical type 11 cell receives processes from up to six nearby calyces or afferents. Nearly all the processes are elongated; some have bouton-like swellings and numerous vesicles. Multiple (2 to 4) processes from a single calyx to a type II cell are common, and approx. 1/3 of the processes innervate 2 type II cells of a neighboring cluster of 3 cells. About 2% of type II cells resemble type I cells morphologically and are surrounded by demicalyces. Differences in size or shape of the terminals under flight conditions could not be determined because the sample size is still too small; but it is clear that reconstruction methods provide insights into macular circuitry not obtainable by other techniques. The results demonstrate a morphological basis for interactions between adjacent receptive fields, through feedback-feedforward connections, during preprocessing of linear acceleratory information by the maculas. While the methods are currently being tested using vestibular maculas as the model system, it is clear that the technology is applicable to any tissue that can be physically or optically sectioned. ROSS software has already been implemented for reconstructing objects from tissues studied by confocal and by transmitted light microscopy, and research into magnetic resonance imaging-computational tomography combined visualization are underway.

Ross, Muriel D.↗
NASA NTRS1987

Non-equilibrium freezing of water-ice in sandy basaltic regoliths and implications for fluidized debris flows on Mars

Many geomorphic features on Mars were attributed to Earth-analogous, cold-climate processes involving movement of water or ice lubricated debris. Clearly, knowledge of the behavior of water in regolith materials under Martian conditions is essential to understanding the postulated geomorphic processes. Experiments were performed with sand-sized samples of natural basaltic regoliths in order to further elucidate how water/regolith interactions depend upon grain size and mineralogy. The data reveal important contrasts with data for clay-mineral substrates and suggest that the microphysics of water/mineral interactions might affect Martian geomorphic processes in ways that are not fully appreciated. Sand and silt sized fractions of two soils from the summit of Mauna Kea were used as Mars-analogous regolith materials. Temperatures were measured for water/ice phase transitions as wet slurries of individual soil fractions which were cooled or heated at controlled rates under a carbon dioxide atmosphere. Freezing and melting of ice was studied as a function of water/soil mass ratio, soil particle size, and thermal-cycle rate. Comparison tests were done under the same conditions with U.S. Geological Survey standard rock powders.

Gooding, J. L.↗
NASA NTRS2009

Sm-Nd, Rb-Sr, and Mn-Cr Ages of Yamato 74013

Yamato 74013 is one of 29 paired diogenites having granoblastic textures. The Ar-39 - Ar-40 age of Y-74097 is approximately 1100 Ma. Rb-Sr and Sm-Nd analyses of Y-74013, -74037, -74097, and -74136 suggested that multiple young metamorphic events disturbed their isotopic systems. Masuda et al. reported that REE abundances were heterogeneous even within the same sample (Y-74010) for sample sizes less than approximately 2 g. Both they and Nyquist et al. reported data for some samples showing significant LREE enrichment. In addition to its granoblastic texture, Y-74013 is characterized by large, isolated clots of chromite up to 5 mm in diameter. Takeda et al. suggested that these diogenites originally represented a single or very small number of coarse orthopyroxene crystals that were recrystallized by shock processes. They further suggested that initial crystallization may have occurred very early within the deep crust of the HED parent body. Here we report the chronology of Y-74013 as recorded in chronometers based on long-lived Rb-87 and Sm-147, intermediate- lived Sm-146, and short-lived Mn-53.

Nyquist, L. E.↗
NASA NTRS1990

Correlations between environmental parameters and nuclear activity

The occurrence and quantitative properties of Seyfert 1 and Seyfert 2 galaxies as a function of the galaxy environment were studied in a systematic way. For this investigation the authors selected from the Catalogue of Quasars and Active Nuclei (Veron & Veron, 1989) all Seyfert galaxies with the following properties: (1) m sub V less than or equal to 15; (2) listed classification as Sey 1, Sey 2, or Sey 3; (3) v sub rad less than or equal to 20,000 km s(sup -1). This results in a sample size of 242 Seyfert galaxies. For all these objects their galaxy environment was inspected on plates. The vicinity of each sample Seyfert galaxy was searched for companion galaxies out to at least 0.5 Mpc. Lacking redshift information, the authors adopted as a companion galaxy any galaxy in this area having a size between 20 and 200 percent of the Seyfert galaxy size. As an important environmental parameter for Seyfert activity, the authors consider the galaxy density within an environment of 500 kpc radius. They distinguish four density classes: D1: no companion (isolated Seyfert); D2: 1 companion; D3: 2 to 5 companions (group); and D4: less than or equal to 6 companions (dense group). The distribution of the various Seyfert classes among these density classes is listed in tabular form, both in absolute numbers and in percentages (given in brackets). From these statistics the environments of Sey 1, Sey 2, and Sey 3 galaxies do not differ in general with the exception that among the isolated galaxies the Seyfert 1 galaxies dominate conspicuously. No significant dependence of morphological type (de Vaucouleurs T-type) on density class is observed both for Sey 1 and Sey 2 galaxies. On the basis of an extensive search of the Seyfert literature the authors also investigated the dependence of the intrinsic parameters of the sample Seyfert galaxies on density class.

Kollatschny, W.↗
DOE OSTI2021

Deep Learning Estimation of Daily Ground–Level NO 2 Concentrations from Remote Sensing Data

The limited number of nitrogen dioxide (NO 2 ) surface measurements calls for the development of highly accurate approaches to estimating surface NO 2 concentrations. In this study, we leverage a new satellite instrument, the TROPOspheric Monitoring Instrument (TROPOMI), along with other predictor variables, to estimate daily surface NO 2 concentrations over Texas in 2019. We use the deep convolutional neural network (Deep-CNN), an advanced deep learning algorithm, to obtain estimates and achieve a correlation coefficient (R) of 0.91, an index of agreement (IOA) of 0.95, and a mean absolute bias (MAB) of 1.75 ppb in surface NO 2 estimation. Additionally, we leverage a novel approach, SHapley Additive exPlanations (SHAP), to describe how Deep-CNN understands each predictor variable. The SHAP results show that the Deep-CNN model has an advanced understanding of the dataset, revealing that TROPOMI closely captures levels of NO 2 . In addition, we show the superiority of our Deep-CNN model at estimating surface NO 2 over other well-known machine learning and regression models in the field, including the support vector machines (SVM), random forest (RF), and multiple linear regression (MLR). Although SVM and RF show strong capabilities at estimating surface NO 2 concentrations, their accuracy is inferior to that of the Deep-CNN model, ranking second and third in model accuracy in this study. The MLR, however, shows a poor ability at NO 2 estimation and ranks last among all models. Furthermore, testing the impact of sample size on model performance, we also show that, compared to other models, Deep-CNN needs more samples to trigger its strength at surface NO 2 estimation.

54 ENVIRONMENTAL SCIENCES↗