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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Preparation and analysis of standardized waste samples for Controlled Ecological Life Support Systems (CELSS)

The preparation and analysis of standardized waste samples for controlled ecological life support systems (CELSS) are considered. Analysis of samples from wet oxidation experiments, the development of ion chromatographic techniques utilizing conventional high pressure liquid chromatography (HPLC) equipment, and an investigation of techniques for interfacing an ion chromatograph (IC) with an inductively coupled plasma optical emission spectrometer (ICPOES) are discussed.

Carden, J. L.↗
NASA NTRS2010

Robotic Arm Manipulator Using Active Control for Sample Acquisition and Transfer, and Passive Mode for Surface Compliance

A robotic arm that consists of three joints with four degrees of freedom (DOF) has been developed. It can carry an end-effector to acquire and transfer samples by using active control and comply with surface topology in a passive mode during a brief surface contact. The three joints are arranged in such a way that one joint of two DOFs is located at the shoulder, one joint of one DOF is located at the elbow, and one joint of one DOF is located at the wrist. Operationally, three DOFs are moved in the same plane, and the remaining one on the shoulder is moved perpendicular to the other three for better compliance with ground surface and more flexibility of sample handling. Three out of four joints are backdriveable, making the mechanism less complex and more cost effective

Liu, Jun↗
NASA NTRS1971

Apollo 14 composite casting demonstration

The purpose of the demonstration was to show that mixtures of materials of different specific gravities would remain stable in the liquid state and during freezing in low g and not segregate as they do on earth. An inflight demonstration was performed on the Apollo 14 mission during the translunar and and transearth coast periods. The apparatus consisted of an electrical heater, a heat sink device for cooling, and sealed metal capsules containing matrix materials having a low-melting point and dispersants. The evaluation of the demonstration samples was accomplished by comparing space processed (flight) samples with (control) samples processed on the ground under otherwise similar conditions. In the low q environment of space flight the dispersions of particles, fibers, and gases in a liquid metal matrix were maintained during solidification. Dispersions of normally immiscible liquids were also maintained during solidification.

Yates, I. C., Jr.↗
NASA NTRS1993

Effects of orbital exposure on RTV during the LDEF mission

Thermomechanical analysis (TMA), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA) were performed on samples of RTV 511 exposed on the Long Duration Exposure Facility (LDEF) mission for 6 years in orbit and unexposed RTV 511 control samples. Slices 20- to 400-microns thick were removed from the exposed surface down to a depth of 1,500 microns through the 3-mm thick samples. The TMA and DSC results, which arise from the entire slice and not just its surface, showed no significant differences between the LDEF exposed and the control samples. TMA scans were run from ambient to 500 C; results were compared by a tabulation of the onset temperatures for flow. DSC scans were run from ambient to 600 C; no endotherms or exotherms occurred over the range observed. What appear to be glass transition temperatures were compared for the samples as a function of section depth within the sample and between the exposed and control samples. The TGA scans from 25 to 900 C, which arise from the surface of the sample initially, showed a slight increase in the top most 105-micron slice (the exposed, discolored side) in the weight loss at 600 C in oxygen. This weight loss dropped to bulk values at the next slice below the top section, a mean depth of 258 microns. The control sample also showed an increase in weight loss as the top surface was approached, but the 600 C weight losses were very inconsistent. The LDEF RTV sample appears to be mechanically undamaged, with a surface layer which oxidizes slightly faster as a result of orbital exposure.

Brower, William E., Jr.↗
NASA NTRS2016

Mars Sample Thermal Control During Mars Ascent and Orbit

Although NASA has no official plans at this time for a mission to return samples from Mars, the Program Formulation Office of the Mars Exploration Program sponsors ongoing mission concept studies, systems analyses, and technology investments which explore different strategies for the potential return of samples from Mars, consistent with the charter of the program and stated priorities of the science community. Maintaining the thermal integrity of collected samples would be very important. In general, samples would be collected, sealed inside tubes, and left on the surface for later retrieval. They would then be inserted into an OS (Orbiting Sample), and carried to a Mars or Solar orbit via a MAV (Mars Ascent Vehicle). Subsequently, an Earth return vehicle would rendezvous with the OS and bring it back to Earth. During ascent from Mars, the OS could serve as the nose cone of the MAV and would be subjected to significant aerodynamic heating from the Martian atmosphere. Once the OS is released from the MAV, its external surface would be exposed to potentially several years of sunlight, eclipse, planetary IR, albedo, and space. The challenge is to ensure that these samples are kept at thermally moderate conditions to preserve their integrity in these widely different environments. Various thermal techniques have been investigated to achieve sample thermal control: use of thermal protection shields and surfaces (ablative and non-ablative) to protect them from adverse exposure to ascent heating, as well combinations of thermo-optical coatings during the orbital phase. The work described herein is part of this ongoing effort & will describe the key challenges related to the thermal control of the potential Mars samples during these phases and the corresponding schemes to overcome them.

Bhandari, Pradeep↗
NASA NTRS1971

Apollo 14 composite casting demonstration

This program assisted in the design and implementation of the composite casting demonstration for the Apollo 14 mission. Both flight and control samples were evaluated. Some conclusions resulting from a comparison of the flight and control samples were: (1) Solidification in neither the flight nor control samples was truly directional. (2) Apparent intermittent contact of the melt with the container in the flight samples led to unusual nucleation and growth structures. (3) There was greater uniformity, on a macro scale, of both pores and structural features in the flight sample; presumably the result of the reduced gravity conditions. (4) It seems quite feasible to produce enhanced dispersions of gases and dense phases in a melt which is solidified in reduced gravity. (5) A two-stage heating/cooling cycle may help directional solidification. (6) Sample materials should be selected from materials in which the dispersant fully wets the matrix material. (7) Experiments should be conducted in two modes: (1) where the melt is in good thermal contact with the container, and (2) where the melt is in a free-float condition.

Source record↗
NASA NTRS2008

Systems and methods for self-synchronized digital sampling

Systems and methods for self-synchronized data sampling are provided. In one embodiment, a system for capturing synchronous data samples is provided. The system includes an analog to digital converter adapted to capture signals from one or more sensors and convert the signals into a stream of digital data samples at a sampling frequency determined by a sampling control signal; and a synchronizer coupled to the analog to digital converter and adapted to receive a rotational frequency signal from a rotating machine, wherein the synchronizer is further adapted to generate the sampling control signal, and wherein the sampling control signal is based on the rotational frequency signal.

Samson, Jr., John R.↗
NASA NTRS1981

Time and frequency domain analysis of sampled data controllers via mixed operation equations

Specification of the mathematical equations required to define the dynamic response of a linear continuous plant, subject to sampled data control, is complicated by the fact that the digital components of the control system cannot be modeled via linear ordinary differential equations. This complication can be overcome by introducing two new mathematical operations; namely, the operation of zero order hold and digial delay. It is shown that by direct utilization of these operations, a set of linear mixed operation equations can be written and used to define the dynamic response characteristics of the controlled system. It also is shown how these linear mixed operation equations lead, in an automatable manner, directly to a set of finite difference equations which are in a format compatible with follow on time and frequency domain analysis methods.

Frisch, H. P.↗
NASA NTRS1992

Effects of orbital exposure on Halar during the LDEF mission

Thermomechanical Analysis (TMA), Differential Scanning Calorimetry (DSC), and Thermogravimetric Analysis (TGA) were performed on samples of Halar exposed on the LDEF Mission for 6 years in orbit and unexposed Halar control samples. Sections 10-100 microns thick were removed from the exposed surface down to a depth of 1,000 microns through the 3 mm thick samples. The TMA and DSC results, which arise from the entire slice and not just its surface, showed no differences between the LDEF and the control samples. TMA scans were run from ambient to 300 C; results were compared by a tabulation of the glass transition temperatures. DSC scans were run from ambient to 700 C; the enthalpy of melting was compared for the samples as a function of section depth with the sample. The TGA results, which arise from the surface of the sample initially, showed a sharp increase in the topmost 50 micron section (the exposed, discolored side) in the weight loss of 170 C in oxygen. This weight loss dropped to bulk values in the range of depth of 50-200 microns. The control sample showed only a slight increase in weight loss as the top surface was approached. The LDEF Halar sample appears to be mechanically undamaged, with a surface layer which oxidizes faster as a result of orbital exposure.

Brower, William E., Jr.↗
NASA NTRS1972

Supervisory sampling and control: Sources of suboptimality in a prediction task

A process supervisor is defined as a person who decides when to sample the process input and what values of a control variable to specify in order to maximize (minimize) a given value function of input sampling period, control setting, and process state. Presented experimental data in such a process where the value function is a time-averaged sampling cost plus mean squared difference between input and control variable. The task was unpaced prediction of the output of a second order filter driven by white noise. Experimental results, when compared to the optical strategy, reveal several consistently suboptimal behaviors. One is a tendency not to choose a long prediction interval even though the optimal strategy dictates that one should. Some results are also interpreted in terms of those input parameters according to which each subjects' behavior would have been nearest optimal. Differences of those parameters from actual input parameters served to quantify how subjects' prediction behavior differed from optimal.

Sheridan, T. B.↗
NASA NTRS1983

Electrostatic Levitator With Feedback Control

Sample position automatically maintained. Object levitated by electrostatic field between two electrodes. Because of particular curved electrode shape, levitation field has stable horizontal position on vertical axis of symmetry. Vertical position of object sensed and compared with preset value. When position error is detected, amplitude of levitating field is increased or decreased to restore zero error. System offers options well to containerless processing.

Rhim, W. K.↗
NASA NTRS1974

Modern sampled-data control theory: Design of the Large Space Telescope

This investigation was undertaken to determine the effect of varying the sampling period on the dynamic response of the sampled-data Large Space Telescope (LST) system. A range of sampling periods was recommended based on the criterion that self-sustained oscillations are to be avoided in the LST system. The step responses of the LST system were then investigated when various sampling periods are used. For small sampling periods, the dynamic behavior of the sampled-data system is very similar to that of the continuous-data system. When T is large (but less than 0.25 sec) the overshoot of the step response of the sampled-data becomes greater. However, the dynamic behavior of the sampled-data system may be improved by redesigning the controller. It appears that a sampling period as high as 0.1 second is feasible for the LST system. However, it should be noted that the conclusions are obtained with the existing system model. Other practical considerations such as noise, coupling effects and quantization errors, may restrict the sampling period to a lower value.

Kuo, B. C.↗
NASA NTRS1994

Effect of microgravity on collagenase deprotoeinization and EDTA decalcification of bone fragments

Undecalcified (n = 140) and decalcified (n = 11) bone fragments were treated with either collagenase (to remove collagen portion; undecalcified n = 64, decalcified n = 11) or EDTA (to remove mineral portion; n= 76) under the reduced gravity environment on US Space Shuttle mission STS-57. The fragments were initially stored in Dulbecco's phosphate buffer solution. After orbit had been established, fragments were exposed to either a neutral buffered collagenase or EDTA solution. Reactions were terminated (neutral buffered formalin for collagenase, 21% CuSO4-5H2O for EDTA) before reentry to earth's atmosphere. Differences in bone samples mass from before flight to after flight were measured. EDTA-treated sample mass was corrected for CuSO4 content. Flight and matched ground (gravitational control) sample showed similar EDTA-induced loss of mineral mass. Collagenase treatments, however, appeared to be more effective in flight samples compared to ground control samples. The flight-exposed, collagenase-treated samples showed significantly more loss than did ground samples. The microgravity environment appeared to promote proteolytic reactions in bone more than the EDTA decalcification reaction.

Simske, S. J.↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗