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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Quantifying and Qualifying Alloys Based on Level of Homogenization: A U-10Mo Alloy Case Study

Homogenization heat treatment is performed to attain uniformity in microstructure which is helpful to achieve the desired workability and microstructure in final products and, eventually, to gain predictive and consistent performance. Fabrication of low-enriched uranium alloys with 10 wt% molybdenum (U-10Mo) fuel plates involves multiple thermomechanical processing steps. It is well known that the molybdenum homogeneity in the final formed product affects the performance in the nuclear reactor. Here, to ensure uniform homogenization, a statistical method is proposed to quantify and characterize the molybdenum concentration variation in U-10Mo fuel plates by analyzing the molybdenum concentration measurement data from scanning electron microscopy energy dispersive spectroscopy line-scan. Statistical tolerance intervals (TI) are employed to determine the qualification of the U-10Mo fuel plate. We formulate an argument for the minimum number of independent samples to define fuel plate qualification if no molybdenum measurement data are available in advance and demonstrate that the given TI requirements can be equivalently reduced to a sample variance criterion in this application. The outcome of the statistical analysis can be used to optimize casting design and eventually increase productivity and reduce fabrication costs. The statistical strategy developed in this paper can be implemented for other applications especially in the field of material manufacturing to assess qualification requirements and monitor and improve the process design.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Background based Gaussian mixture model lesion segmentation in PET

Purpose: Quantitative {sup 18}F-fluorodeoxyglucose positron emission tomography is limited by the uncertainty in lesion delineation due to poor SNR, low resolution, and partial volume effects, subsequently impacting oncological assessment, treatment planning, and follow-up. The present work develops and validates a segmentation algorithm based on statistical clustering. The introduction of constraints based on background features and contiguity priors is expected to improve robustness vs clinical image characteristics such as lesion dimension, noise, and contrast level. Methods: An eight-class Gaussian mixture model (GMM) clustering algorithm was modified by constraining the mean and variance parameters of four background classes according to the previous analysis of a lesion-free background volume of interest (background modeling). Hence, expectation maximization operated only on the four classes dedicated to lesion detection. To favor the segmentation of connected objects, a further variant was introduced by inserting priors relevant to the classification of neighbors. The algorithm was applied to simulated datasets and acquired phantom data. Feasibility and robustness toward initialization were assessed on a clinical dataset manually contoured by two expert clinicians. Comparisons were performed with respect to a standard eight-class GMM algorithm and to four different state-of-the-art methods in terms of volume error (VE), Dice index, classification error (CE), and Hausdorff distance (HD). Results: The proposed GMM segmentation with background modeling outperformed standard GMM and all the other tested methods. Medians of accuracy indexes were VE <3%, Dice >0.88, CE <0.25, and HD <1.2 in simulations; VE <23%, Dice >0.74, CE <0.43, and HD <1.77 in phantom data. Robustness toward image statistic changes (±15%) was shown by the low index changes: <26% for VE, <17% for Dice, and <15% for CE. Finally, robustness toward the user-dependent volume initialization was demonstrated. The inclusion of the spatial prior improved segmentation accuracy only for lesions surrounded by heterogeneous background: in the relevant simulation subset, the median VE significantly decreased from 13% to 7%. Results on clinical data were found in accordance with simulations, with absolute VE <7%, Dice >0.85, CE <0.30, and HD <0.81. Conclusions: The sole introduction of constraints based on background modeling outperformed standard GMM and the other tested algorithms. Insertion of a spatial prior improved the accuracy for realistic cases of objects in heterogeneous backgrounds. Moreover, robustness against initialization supports the applicability in a clinical setting. In conclusion, application-driven constraints can generally improve the capabilities of GMM and statistical clustering algorithms.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Quantifying and Qualifying Alloys Based on Level of Homogenization: A U–10 wt% Mo Alloy Case Study

Homogenization heat treatment is performed to attain uniformity in microstructure of metals and metal alloys, which is helpful to achieve the desired workability and microstructure in final products, and eventually to gain predictable and consistent performance. Fabrication of fuel plates made from a low-enriched uranium alloy with 10 wt% Mo (U-10Mo) involves multiple thermomechanical processing steps. It is well known that the molybdenum homogeneity in the final formed product affects the fuel performance in the nuclear reactor. To make sure these materials are uniformly homogenized, a statistical method was proposed to quantify and characterize the Mo concentration variation in U-10Mo fuel plates by analyzing the Mo concentration measurement data from scanning electron microscopy–energy dispersive spectroscopy line scans. Statistical tolerance intervals were employed to determine the qualification of U-10Mo fuel plates. We formulate an argument for the minimum number of independent samples required to define fuel plate qualification if no Mo measurement data are available in advance, and demonstrate that the given tolerance interval requirements can be equivalently reduced to a sample variance criterion in this application. The outcome of the statistical analysis can be used to optimize casting design and eventually increase productivity and reduce fabrication cost. The statistical strategy developed in this report can be implemented for other applications, especially in the field of material manufacturing to assess qualification requirements and monitor and improve process design.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Conservation of Endophyte Bacterial Community Structure Across Two Panicum Grass Species

Panicum represents a large genus of many North American prairie grass species. These include switchgrass ( Panicum virgatum ), a biofuel crop candidate with wide geographic range, as well as Panicum hallii , a close relative to switchgrass, which serves as a model system for the study of Panicum genetics due to its diploid genome and short growth cycles. For the advancement of switchgrass as a biofuel crop, it is essential to understand host microbiome interactions, which can be impacted by plant genetics and environmental factors inducing ecotype-specific phenotypic traits. We here compared rhizosphere and root endosphere bacterial communities of upland and lowland P. virgatum and P. hallii genotypes planted at two sites in Texas. Our analysis shows that sampling site predominantly contributed to bacterial community variance in the rhizosphere, however, impacted root endosphere bacterial communities much less. Instead we observed a relatively large core endophytic microbiome dominated by ubiquitously root-colonizing bacterial genera Streptomyces , Pseudomonas , and Bradyrhizobium . Endosphere communities displayed comparable diversity and conserved community structures across genotypes of both Panicum species. Functional insights into interactions between P. hallii and its root endophyte microbiome could hence inform testable hypotheses that are relevant for the improvement of switchgrass as a biofuel crop.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1973

A study of the utilization of ERTS-1 data from the Wabash River Basin

The author has identified the following significant results. Nine projects are defined, five ERTS data applications experiments and four supporting technology tasks. The most significant applications results were achieved in the soil association mapping, earth surface feature identification, and urban land use mapping efforts. Four soil association boundaries were accurately delineated from ERTS-1 imagery. A data bank has been developed to test surface feature classifications obtained from ERTS-1 data. Preliminary forest cover classifications indicated that the number of acres estimated tended to be greater than actually existed by 25%. Urban land use analysis of ERTS-1 data indicated highly accurate classification could be obtained for many urban catagories. The wooded residential category tended to be misclassified as woods or agricultural land. Further statistical analysis revealed that these classes could be separated using sample variance.

Landgrebe, D. A.↗
NASA NTRS1993

Wave-turbulence dynamics in the stably stratified boundary layer

New data obtained at the Boulder Atmospheric Observatory are analyzed to obtain separation of wave, turbulence, and mean field necessary for a complete treatment of wave-turbulence interaction. The data were compared with a linear stability analysis of the background atmospheric state, showing good agreement between measured wave parameters (such as wavelength, period, and vector phase velocity) and the eigenvalues of the linear solution. The analysis of the budgets of wave heat flux and temperature variance revealed the essential role of wave-turbulence interaction in maintaining a large amplitude temperature wave and countergradient heat flux. A mechanism for the maintenance of turbulence by waves in strongly stratified boundary layers is described, which emphasizes that the time-mean Richardson number is an irrelevant parameter at such times.

Einaudi, F.↗
NASA NTRS2009

Assessing the Uncertainty of Land Surface Fluxes Derived from Operational Analyses

Operational numerical weather prediction systems assimilate tremendous amounts of observations to produce global analyses of the atmospheric states. Since the systems include significant contributions from the model components, physical fields, such as the turbulent heat fluxes, can also be derived. However, the physical fields are indirectly constrained by the data assimilation, and so subject to uncertainty in the model parameterizations. Recently, 10 international operational analyses have been collected and reprocessed through the Coordinated Enhanced. Observing Period (CEOP). Comparisons against independent observations (global precipitation and outgoing longwave radiation) show that while there is significant range of the individual analyses, the ensemble of analyses provide data closest the observations. Here, we evaluate the collection of land surface turbulent heat fluxes. The data are available for Oct 2002 - Dec 2004, at 6 hourly, daily and monthly intervals. An ensemble mean and its variance have been computed. we will analyze the range of analysis results at daily and seasonal time scales, and where possible compare with existing observations.

Bosilovich, Michael G.↗
NASA NTRS2017

PM2.5 Variation Related to AOD and Meteorological Conditions on Diurnal Scale in Beijing, China

PM2.5 (particulate matter with diameter less than 2.5um) is one of the major air pollutants in many regions around the globe. Column integrated aerosol optical depth (AOD) measurement provides a potential way to estimate surface PM2.5 concentration from the remote sensing technics. In this analysis, we compiled hourly measurements of surface PM2.5 and AOD made in Beijing, China and meteorological conditions provided by NASA Global Modeling and Assimilation Office's Modern-Era Retrospective Analysis for Research and Applications (MERRA) product in recent years. On daily basis, hourly PM2.5 and AOD show positive correlation, i.e. correlation coefficient (R) is higher than 0.5 on about 50% of days. We apply multi-variant analysis to this dataset and estimate the magnitude of AOD variance that can be explained by or related to PM2.5 and relevant meteorological conditions changes.

PM2.5↗
NASA NTRSDate not supplied

Effects of Mild Hypobaric Hypoxia on Visual Field Impairment

INTRODUCTION: The primary objective of the Exploration Atmosphere (EA) study is to validate a new prebreathe protocol necessary before exposure to a suited hypobaric environment during extravehicular activity (EVA) from a habitat ‘exploration atmosphere’ of 56.5kPa (8.2 psia), 34% O2, 66% N2. Prebreathe protocols must be time and resource efficient while also controlling Decompression Sickness (DCS) risk to within acceptable limits. The habitat hypobaric exploration atmosphere also results in a mildly hypoxic environment (piO2 = 128mmHg). As a secondary objective of the EA study, we characterized the effects of 11-day exposure to a mild hypobaric hypoxic environment on visual performance. METHODS: Two, 11-day hypobaric chamber tests were performed (EA-1and EA-2, n=8 each) in NASA’s 20-foot chamber at Johnson Space Center where subjects lived in the exploration atmosphere. Subjects also underwent simulated 6-hour EVAs at 85% O2 and 29.6 kPa during EVA on days 3, 5, 7, 9, and 11. Visual acuity (VA), a measure of spatial resolution, and contrast sensitivity (CS), a measure of ability to distinguish ever finer increments of brightness, were assessed on non-EVA days by using gapped Landolt C testing. The luminance was controlled by using a booth, a light monitor, and a lighting rheostat. EA-1 data had revealed problems in lighting control impacting consistency of the data. Procedures were subsequently updated for the EA-2 test. RESULTS: EA-1 data revealed large variance and data recording errors and was removed from the analysis. One participant left the study at Test Day 3 during EA-2. EA-2 ANOVA results showed CS (mean change=0.010 logCSWeber) and VA (mean change, -0.016 logMAR) between pre-test and 11-day test phases; however, neither VA nor CS changes were statistically significant. There were non-statistically significant declines in VA across test phases. DISCUSSION: Overall, visual field performance did not exhibit clinically significant changes (3 lines or greater change in LogMar chart) during exposure to the mild hypoxic exploration atmosphere environment compared to pre-test baseline. The consistency and stability of VA data during EA-2 suggests that the mild hypobaric hypoxic environment did not cause clinically significant negative impacts to participants’ visual field performance.

Visual Acuity↗
NASA NTRSDate not supplied

Effects of Mild Hypobaric Hypoxia on Visual Field Impairment

INTRODUCTION: The primary objective of the Exploration Atmosphere (EA) study is to validate a new prebreathe protocol necessary before exposure to a suited hypobaric environment during extravehicular activity (EVA) from a habitat ‘exploration atmosphere’ of 56.5kPa (8.2 psia), 34% O2, 66% N2. Prebreathe protocols must be time and resource efficient while also controlling Decompression Sickness (DCS) risk to within acceptable limits. The habitat hypobaric exploration atmosphere also results in a mildly hypoxic environment (piO2 = 128mmHg). As a secondary objective of the EA study, we characterized the effects of 11-day exposure to a mild hypobaric hypoxic environment on visual performance. METHODS: Two, 11-day hypobaric chamber tests were performed (EA-1and EA-2, n=8 each) in NASA’s 20-foot chamber at Johnson Space Center where subjects lived in the exploration atmosphere. Subjects also underwent simulated 6-hour EVAs at 85% O2 and 29.6 kPa during EVA on days 3, 5, 7, 9, and 11. Visual acuity (VA), a measure of spatial resolution, and contrast sensitivity (CS), a measure of ability to distinguish ever finer increments of brightness, were assessed on non-EVA days by using gapped Landolt C testing. The luminance was controlled by using a booth, a light monitor, and a lighting rheostat. EA-1 data had revealed problems in lighting control impacting consistency of the data. Procedures were subsequently updated for the EA-2 test. RESULTS: EA-1 data revealed large variance and data recording errors and was removed from the analysis. One participant left the study at Test Day 3 during EA-2. EA-2 ANOVA results showed CS (mean change=0.010 logCSWeber) and VA (mean change, -0.016 logMAR) between pre-test and 11-day test phases; however, neither VA nor CS changes were statistically significant. There were non-statistically significant declines in VA across test phases. DISCUSSION: Overall, visual field performance did not exhibit clinically significant changes (3 lines or greater change in LogMar chart) during exposure to the mild hypoxic exploration atmosphere environment compared to pre-test baseline. The consistency and stability of VA data during EA-2 suggests that the mild hypobaric hypoxic environment did not cause clinically significant negative impacts to participants’ visual field performance.

Visual Acuity↗
NASA NTRS1972

Evaluation of errors in prior mean and variance in the estimation of integrated circuit failure rates using Bayesian methods

The critical point of any Bayesian analysis concerns the choice and quantification of the prior information. The effects of prior data on a Bayesian analysis are studied. Comparisons of the maximum likelihood estimator, the Bayesian estimator, and the known failure rate are presented. The results of the many simulated trails are then analyzed to show the region of criticality for prior information being supplied to the Bayesian estimator. In particular, effects of prior mean and variance are determined as a function of the amount of test data available.

Fletcher, B. C.↗
NASA NTRS1972

A double-loop tracking system.

A nonlinear analysis which can be used to assess certain statistical characteristics of double-loop tracking systems is presented. It takes into account the mutual coupling effects of the loops in the system. Two approaches are taken to obtain steady-state probability density functions (pdf's) of the system phase errors. From these pdf's, important system performance statistics, e.g., the phase-error variances, can be calculated, thus illustrating the application and usefulness of the analysis. The analysis is applied to a satellite transponder as an example.

Yuen, J. H.↗
NASA NTRSDate not supplied

Assessing Risk Due to Small Sample Size in Probability of Detection Analysis Using Tolerance Intervals

Small sample size (e.g.6-30) poses risk in results of probability of detection (POD) analysis using tolerance intervals. This method is also called as the limited sample or LS POD. The analysis is performed either during NDE procedure qualification or for assessment of reliability of an NDE procedure. The risk is primarily due to sampling error. Smaller samples are not likely to be random to the population or representative of the population. The small samples are likely to be biased. Biased samples have smaller standard deviation compared to the population. POD analysis with small biased sample can lead to overestimation of POD. Many sampling schemes are available in statistics to mitigate sampling risk. Primary objective of POD analysis is to determine a decision threshold from signal response measurements of a sample such that it is less than or equal to population decision threshold for 90% POD. Sampling error implies that this NDE reliability condition is violated. One of sampling types is called a representative sample. Representative samples reduce variance in POD estimates but also reduce magnitude of the error. Sampling sensitivity analysis for some sampling types is performed here using repetitive random sampling or Monte Carlo method. Six sampling types are considered for comparison. Some of the sampling types are similar to drawing a representative sample. LS POD model assumes random sampling. Therefore, random sampling is used as a basis for comparison with each sampling type. The sampling types used in the analysis are, A. Nominal and worst-case sampling, B. Worst-case sampling, C. Nominal case sampling, D. Random sampling, E. Random target, and sub-target sampling. F. Nominal target and sub-target sampling. Results of Monte Carlo simulation indicate that type F sampling can mitigate sampling risk and is also more practical to implement. Type A sampling may also mitigate the sampling risk, but it may be less practical to implement.

Ajay M Koshti↗
DOE OSTI2015

SU-E-J-83: CBCT Based Rectum and Bladder Dose Tracking in the Prostate Radiotherapy

Purpose: The aim of this study is to monitor the volume changes of bladder and rectum and evaluate the dosimetric changes of bladder and rectum using daily cone-beam CT for prostate radiotherapy. Methods: The data of this study were obtained from 12 patients, totally 222 CBCTs. All the volume of the bladder and the rectum on the CBCT were normalized to the bladder and the rectum on their own original CT to monitory the volume changes. To evaluate dose delivered to the OARs, volumes that receive 70Gy (V70Gy), 60Gy, 50Gy, 40Gy and 30Gy are calculated for the bladder and the rectum, V20Gy and V10Gy for rectum additionally. And the deviation of the mean dose to the bladder and the rectum are also chosen as the evaluation parameter. Linear regression analysis was performed to identify the mean dose change of the volume change using SPSS 19. Results: The results show that the variances of the normalize volume of the bladder and the rectum are 0.15–0.58 and 0.13–0.50. The variances of V70Gy, V60Gy, V50Gy, V40Gy and V30Gy of bladder are bigger than rectum for 11 patients. The linear regression analysis indicated a negative correlation between the volume and the mean dose of the bladder (p < 0.05). A 10% increase in bladder volume will cause 5.1% (±4.3%) reduction in mean dose. Conclusion: The bladder volume change is more significant than that for rectum for the prostate cancer patient. The volume changes of rectum are not significant except air gap in the rectum. Bladder volume varies will cause significant dose change. The bladder volume monitoring before fractional treatment delivery would be crucial for accuracy dose delivery.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2021

Accuracy Assessment of Global Internal-Tide Models Using Satellite Altimetry Models Using Satellite Altimetry

Altimeter measurements are corrected for several geophysical parameters in order to access ocean signals of interest, like mesoscale or sub-mesoscale variability. The ocean tide is one of the most critical corrections due to the amplitude of the tidal elevations and to the aliasing phenomena of high-frequency signals into the lower-frequency band, but the internal-tide signatures at the ocean surface are not yet corrected globally. Internal tides can have a signature of several centimeters at the surface with wavelengths of about 50–250 km for the first mode and even smaller scales for higher-order modes. The goals of the upcoming Surface Water Ocean Topography (SWOT) mission and other high-resolution ocean measurements make the correction of these small-scale signals a challenge, as the correction of all tidal variability becomes mandatory to access accurate measurements of other oceanic signals. In this context, several scientific teams are working on the development of new internal-tide models, taking advantage of the very long altimeter time series now available, which represent an unprecedented and valuable global ocean database. The internal-tide models presented here focus on the coherent internal-tide signal and they are of three types: empirical models based upon analysis of existing altimeter missions, an assimilative model and a three-dimensional hydrodynamic model. A detailed comparison and validation of these internal-tide models is proposed using existing satellite altimeter databases. The analysis focuses on the four main tidal constituents: M2, K1, O1 and S2. The validation process is based on a statistical analysis of multi-mission altimetry including Jason-2 and Cryosphere Satellite-2 data. The results show a significant altimeter variance reduction when using internal-tide corrections in all ocean regions where internal tides are generating or propagating. A complementary spectral analysis also gives some estimation of the performance of each model as a function of wavelength and some insight into the residual non-stationary part of internal tides in the different regions of interest. This work led to the implementation of a new internal-tide correction (ZARON'one) in the next geophysical data records version-F (GDR-F) standards.

Loren Carrere↗
NASA NTRSDate not supplied

Variance Decomposition of MEDLI2 Reconstructed Heating Using Neural Networks

The Mars Entry, Descent, and Landing Instrumentation (MEDLI2) sensor suite collected data during entry of the Mars 2020 Perseverance rover into Mars’ atmosphere. This suite included a network of MEDLI2 Instrumented Sensor Plugs (MISPs). Each MISP was comprised of a cylinder made of Thermal Protection System (TPS) material with 1-3 embedded thermocouples (TCs), and it was flush mounted into the heatshield or backshell. Data from these in-depth TCs were used to reconstruct the aeroheating environment of the vehicle throughout entry. Surface heating was posed as an inverse problem, with the goal of estimating the surface heating by minimizing an objective function of the difference between MISP temperature measurements during flight and the temperature predictions derived from the Fully Implicit Ablation and Thermal response (FIAT) program. Given an aerothermal environment, FIAT calculates the material response and provides in-depth temperatures throughout the TPS material. To achieve the reverse, an internal tool called FIAT_Opt runs through multiple different environments until the output temperature at the TC depth closely matches the flight data. 95% confidence intervals on the reconstructed surface heating were obtained using Monte Carlo analysis, in which uncertainties in the thermocouple depth and the TPS material properties (e.g., density, thermal conductivity, heat capacity, emissivity) based on flight-lot material testing were included. A variance decomposition method using Sobol indices was employed to assess the sensitivity of the reconstructed peak heating to the TC placement and material property uncertainties. Variance decomposition was found to require tens of thousands of FIAT_Opt runs in order for the Sobol indices to converge. With a single FIAT_Opt run taking on the order of 40 minutes, the required number of computations would take months to complete, even if using multiple CPUs. To mitigate this problem, three machine learning models (ridge regression with cross-validation, random forest regression, and a deep neural network) were trained and tested using the 2000 Monte Carlo runs that were already completed. A subset of 1600 runs were used to train the model (i.e., training set), while the remaining 400 runs were used as the test set. The predictions from the deep neural network (DNN) on the test set showed nearly perfect agreement to the actual values computed with FIAT_Opt (R2 > 0.99). Using the DNN as a surrogate model, the variance decomposition using 50,000 runs was completed within minutes. The resulting Sobol indices showed that the reconstructed peak surface heating was most sensitive to the uncertainties in the thermal conductivity (ST = 0.37) and heat capacity (ST = 0.26). This method can be leveraged to provide requirements for material property measurements needed to improve the accuracy of surface heating prediction and ultimately lead to the reduction of design margins in the future. This presentation will include background on the MEDLI2 suite; the method used for inverse heating estimation; the way that material property uncertainties were accounted for using Monte Carlo analysis; a brief background on variance decomposition; the motivation for using machine learning in this context; how a neural network was trained on the data to enable variance decomposition in a fraction of the time; and the variance decomposition results for one of the MISPs.

Hannah Alpert↗