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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

High-Performance Reaction Wheel Optimization for Fine-Pointing Space Platforms: Minimizing Induced Vibration Effects on Jitter Performance plus Lessons Learned from Hubble Space Telescope for Current and Future Spacecraft Applications

The Hubble Space Telescope (HST) applies large-diameter optics (2.5-m primary mirror) for diffraction-limited resolution spanning an extended wavelength range (approx. 100-2500 nm). Its Pointing Control System (PCS) Reaction Wheel Assemblies (RWAs), in the Support Systems Module (SSM), acquired an unprecedented set of high-sensitivity Induced Vibration (IV) data for 5 flight-certified RWAs: dwelling at set rotation rates. Focused on 4 key ratios, force and moment harmonic values (in 3 local principal directions) are extracted in the RWA operating range (0-3000 RPM). The IV test data, obtained under ambient lab conditions, are investigated in detail, evaluated, compiled, and curve-fitted; variational trends, core causes, and unforeseen anomalies are addressed. In aggregate, these values constitute a statistically-valid basis to quantify ground test-to-test variations and facilitate extrapolations to on-orbit conditions. Accumulated knowledge of bearing-rotor vibrational sources, corresponding harmonic contributions, and salient elements of IV key variability factors are discussed. An evolved methodology is presented for absolute assessments and relative comparisons of macro-level IV signal magnitude due to micro-level construction-assembly geometric details/imperfections stemming from both electrical drive and primary bearing design parameters. Based upon studies of same-size/similar-design momentum wheels' IV changes, upper estimates due to transitions from ground tests to orbital conditions are derived. Recommended HST RWA choices are discussed relative to system optimization/tradeoffs of Line-Of-Sight (LOS) vector-pointing focal-plane error driven by higher IV transmissibilities through low-damped structural dynamics that stimulate optical elements. Unique analytical disturbance results for orbital HST accelerations are described applicable to microgravity efforts. Conclusions, lessons learned, historical context/insights, and perspectives on future applications are given; these previously unpublished data and findings represents a valuable resource for fine-pointing spacecraft or space-based platforms using RWAs, Control Moment Gyros (CMGs), Momentum Wheels, or other ball-bearing-based rotational units.

Hasha, Martin D.↗
NASA NTRS2015

Advanced Bayesian Method for Planetary Surface Navigation

Autonomous Exploration, Inc., has developed an advanced Bayesian statistical inference method that leverages current computing technology to produce a highly accurate surface navigation system. The method combines dense stereo vision and high-speed optical flow to implement visual odometry (VO) to track faster rover movements. The Bayesian VO technique improves performance by using all image information rather than corner features only. The method determines what can be learned from each image pixel and weighs the information accordingly. This capability improves performance in shadowed areas that yield only low-contrast images. The error characteristics of the visual processing are complementary to those of a low-cost inertial measurement unit (IMU), so the combination of the two capabilities provides highly accurate navigation. The method increases NASA mission productivity by enabling faster rover speed and accuracy. On Earth, the technology will permit operation of robots and autonomous vehicles in areas where the Global Positioning System (GPS) is degraded or unavailable.

Center, Julian↗
NASA NTRSDate not supplied

Application of Support Vector Regression to Derive Crater Depth/Diameter From Satellite Images

Through the study of impact crater shapes, one can draw important conclusions about the nature and evolution of planetary surfaces [e.g., 1-4].In particular, studying the depth (d) to diameter (D)ratio (d/D) of a population of impact craters, in combination with crater count statistics, can yield valuable insights regarding rates of erosion and burial[5]. Motivated by the great abundance of available planetary surface image data, the goal of this project is to develop an efficient way to estimate d/D from satellite images of impact craters for which stereo information is not available [6]. We set out to develop and train a machine learning algorithm to extract d/D from a dataset of synthetic impact crater images for which model d/D is known. The applications of machine learning to planetary science are numerous and diverse [7], including automatic planetary surface mapping [8] and the detection of impact craters [9]. Our algorithm makes use of Support Vector Regression (SVR), which is a type of Support Vector Machine (SVM) [10, 11].SVMs are a branch of supervised machine learning valued for their straightforward implementation and versatility in solving both classification and regression problems. In regression analysis, an SVR algorithm produces a hyperplane function to fit the training data points, as well as an ε-tube that surrounds the hyperplane. Tunable hyperparameters include the width of the ε-tube (ε) and the amount an algorithm is penalized for points which fall outside the ε-tube.

L R Chin↗
NASA NTRSDate not supplied

Global SO 2 Data Record from OMPS Instruments on the JPSS Constellation

NASA’s Earth Observing System (EOS) SO 2 climate data record (CDR) started in 2004, with the launch of the Aura/Ozone Monitoring Instrument (OMI) and is now being continued with the SNPP/Ozone Mapping and Profiler Suite (OMPS) launched in 2011. Both OMI and SNPP/OMPS SO 2 CDRs are produced with the Goddard principal component analysis (PCA) spectral fitting algorithm. An advantage of the data-driven PCA retrieval technique is that it enables highly consistent retrievals from different instruments, by inherently accounting for various instrumental factors. To further extend the EOS SO 2 CDR, we are implementing the PCA SO 2 retrieval algorithm with the L1B measurements from OMPS instruments flying on the Joint Polar Satellite System (JPSS) constellation. In this presentation, we will provide an update on our progress in NOAA-20 (launched in 2017) and NOAA-21 (launched in 2022) PCA SO2 retrievals. We will focus on our new NOAA-20/OMPS PCA SO 2 EOS continuity product, to be publicly released in fall of 2023. We will present statistical analyses on the quality of NOAA-20 PCA SO 2 product, including retrieval noise, biases over background areas, and long-term stability. We will compare our PCA SO 2 retrievals from NOAA-20 with those from OMI, SNPP/OMPS, and S5P/TROPOMI (TROPOspheric Monitoring Instrument) for anthropogenic sources as well as large volcanic plumes. We will also discuss the application of a new machine learning technique that helps to further reduce the noise of NOAA-20 SO 2 retrievals. In addition, we will present preliminary PCA SO 2 retrievals from NOAA-21/OMPS, including those from direct readout implementation for aviation disaster avoidance. Finally, we will share some first results applying the PCA algorithm to NASA’s geostationary TEMPO (Tropospheric Emissions: Monitoring of Pollution) instrument to obtain hourly, high resolution SO 2 data over North America.

SO2↗
NASA NTRS1993

NiCd cell reliability in the mission environment

This paper summarizes an effort by Gates Aerospace Batteries (GAB) and the Reliability Analysis Center (RAC) to analyze survivability data for both General Electric and GAB NiCd cells utilized in various spacecraft. For simplicity sake, all mission environments are described as either low Earth orbital (LEO) or geosynchronous Earth orbit (GEO). 'Extreme value statistical methods' are applied to this database because of the longevity of the numerous missions while encountering relatively few failures. Every attempt was made to include all known instances of cell-induced-failures of the battery and to exclude battery-induced-failures of the cell. While this distinction may be somewhat limited due to availability of in-flight data, we have accepted the learned opinion of the specific customer contacts to ensure integrity of the common databases. This paper advances the preliminary analysis reported upon at the 1991 NASA Battery Workshop. That prior analysis was concerned with an estimated 278 million cell-hours of operation encompassing 183 satellites. The paper also cited 'no reported failures to date.' This analysis reports on 428 million cell hours of operation emcompassing 212 satellites. This analysis also reports on seven 'cell-induced-failures.'

Denson, William K.↗
NASA NTRS2016

Exploring and Analyzing Climate Variations Online by Using NASA MERRA-2 Data at GES DISC

NASA Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) (http:giovanni.sci.gsfc.nasa.govgiovanni) is a web-based data visualization and analysis system developed by the Goddard Earth Sciences Data and Information Services Center (GES DISC). Current data analysis functions include Lat-Lon map, time series, scatter plot, correlation map, difference, cross-section, vertical profile, and animation etc. The system enables basic statistical analysis and comparisons of multiple variables. This web-based tool facilitates data discovery, exploration and analysis of large amount of global and regional remote sensing and model data sets from a number of NASA data centers. Long term global assimilated atmospheric, land, and ocean data have been integrated into the system that enables quick exploration and analysis of climate data without downloading, preprocessing, and learning data. Example data include climate reanalysis data from NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) which provides data beginning in 1980 to present; land data from NASA Global Land Data Assimilation System (GLDAS), which assimilates data from 1948 to 2012; as well as ocean biological data from NASA Ocean Biogeochemical Model (NOBM), which provides data from 1998 to 2012. This presentation, using surface air temperature, precipitation, ozone, and aerosol, etc. from MERRA-2, demonstrates climate variation analysis with Giovanni at selected regions.

knowledge base↗
NASA NTRS1985

Mark 4A project training evaluation

A participant evaluation of a Deep Space Network (DSN) is described. The Mark IVA project is an implementation to upgrade the tracking and data acquisition systems of the dSN. Approximately six hundred DSN operations and engineering maintenance personnel were surveyed. The survey obtained a convenience sample including trained people within the population in order to learn what training had taken place and to what effect. The survey questionnaire used modifications of standard rating scales to evaluate over one hundred items in four training dimensions. The scope of the evaluation included Mark IVA vendor training, a systems familiarization training seminar, engineering training classes, a on-the-job training. Measures of central tendency were made from participant rating responses. Chi square tests of statistical significance were performed on the data. The evaluation results indicated that the effects of different Mark INA training methods could be measured according to certain ratings of technical training effectiveness, and that the Mark IVA technical training has exhibited positive effects on the abilities of DSN personnel to operate and maintain new Mark IVA equipment systems.

Stephenson, S. N.↗
NASA NTRS1992

Proceedings of the Workshop on Change of Representation and Problem Reformulation

The proceedings of the third Workshop on Change of representation and Problem Reformulation is presented. In contrast to the first two workshops, this workshop was focused on analytic or knowledge-based approaches, as opposed to statistical or empirical approaches called 'constructive induction'. The organizing committee believes that there is a potential for combining analytic and inductive approaches at a future date. However, it became apparent at the previous two workshops that the communities pursuing these different approaches are currently interested in largely non-overlapping issues. The constructive induction community has been holding its own workshops, principally in conjunction with the machine learning conference. While this workshop is more focused on analytic approaches, the organizing committee has made an effort to include more application domains. We have greatly expanded from the origins in the machine learning community. Participants in this workshop come from the full spectrum of AI application domains including planning, qualitative physics, software engineering, knowledge representation, and machine learning.

Lowry, Michael R.↗
NASA NTRS1989

Bayesian learning

In 1983 and 1984, the Infrared Astronomical Satellite (IRAS) detected 5,425 stellar objects and measured their infrared spectra. In 1987 a program called AUTOCLASS used Bayesian inference methods to discover the classes present in these data and determine the most probable class of each object, revealing unknown phenomena in astronomy. AUTOCLASS has rekindled the old debate on the suitability of Bayesian methods, which are computationally intensive, interpret probabilities as plausibility measures rather than frequencies, and appear to depend on a subjective assessment of the probability of a hypothesis before the data were collected. Modern statistical methods have, however, recently been shown to also depend on subjective elements. These debates bring into question the whole tradition of scientific objectivity and offer scientists a new way to take responsibility for their findings and conclusions.

Denning, Peter J.↗
NASA NTRS2002

The Role of Formal Experiment Design in Hypersonic Flight System Technology Development

Hypersonic airbreathing engine (scramjet) powered vehicles are being considered to replace conventional rocket-powered launch systems. Effective utilization of scramjet engines requires careful integration with the air vehicle. This integration synergistically combines aerodynamic forces with propulsive cycle functions of the engine. Due to the highly integrated nature of the hypersonic vehicle design problem, the large flight envelope, and the large number of design variables, the use of a statistical design approach in design is effective. Modern Design-of-Experiments (MDOE) has been used throughout the Hyper-X program, for both systems analysis and experimental testing. Application of MDOE fall into four categories: (1) experimental testing; (2) studies of unit phenomena; (3) refining engine design; and (4) full vehicle system optimization. The MDOE process also provides analytical models, which are also used to document lessons learned, supplement low-level design tools, and accelerate future studies. This paper will discuss the design considerations for scramjet-powered vehicles, specifics of MDOE utilized for Hyper-X, and present highlights from the use of these MDOE methods within the Hyper-X Program.

McClinton, Charles R.↗
NASA NTRS1990

A study of the portability of an Ada system in the software engineering laboratory (SEL)

A particular porting effort is discussed, and various statistics on analyzing the portability of Ada and the total staff months (overall and by phase) required to accomplish the rehost, are given. This effort is compared to past experiments on the rehosting of FORTRAN systems. The discussion includes an analysis of the types of errors encountered during the rehosting, the changes required to rehost the system, experiences with the Alsys IBM Ada compiler, the impediments encountered, and the lessons learned during this study.

Jun, Linda O.↗
NASA NTRSDate not supplied

Statistical Classification of Biosignature Information: Combining Elemental, Molecular, Reflectance, and Raman Data to Increase Life Detection Confidence

Planetary exploration missions seeking past or present signs of life carry not just a single instrument, but a suite. There is a need to study how these multiple data types can be combined to create “composite” biosignatures [1]. Algorithmic methods using existing data on living and non-living systems, though limited by the n = 1 of Earth, can nonetheless be informative. We assembled a database of 1277 measurements spanning 16 representative systems either indicative or non-indicative of life. Five classification (machine learning) methods were used on each individual data type, then on the entire set. This abstract summarizes the results; the data is described in more detail in [2], and methods in [3].

Biosignatures↗
NASA NTRS2014

Prediction of Acoustic Environments from Horizontal Rocket Firings

In recent years, advances in research and engineering have led to more powerful launch vehicles which can reach areas of space not yet explored. These more powerful vehicles yield acoustic environments potentially destructive to the vehicle or surrounding structures. Therefore, it has become increasingly important to be able to predict the acoustic environments created by these vehicles in order to avoid structural and/or competent failure. The current industry standard technique for predicting launch-induced acoustic environments was developed by Eldred in the early 1970's and is published in NASA SP-80721. Recent work2 has shown Eldred's technique to be inaccurate for current state-of-the-art launch vehicles. Due to the high cost of full-scale and even sub-scale rocket experiments, very little rocket noise data is available. Furthermore, much of the work thought to be applicable to rocket noise has been done with heated jets. Tam3,4 has done an extensive amount of research on jets of different nozzle exit shape, diameter, velocity, and temperature. Though the values of these parameters, especially exit velocity and temperature, are often very low compared to these values in rockets, a lot can be learned about rocket noise from jet noise literature. The turbulent nature of jet and rocket exhausts is quite similar. Both exhausts contain turbulent structures of varying scale-termed the fine and large scale turbulence by Tam. The finescale turbulence is due to small eddies from the jet plume interacting with the ambient atmosphere. According to Tam et al., the noise radiated by this envelope of small-scale turbulence is statistically isotropic. Hence, one would expect the noise from the small scale turbulence of the jet to be nearly omni-directional. The coherent nature of the large-scale turbulence results in interference of the noise radiated from different spatial locations within the jet. This interference-whether it is constructive or destructive-results in highly directional noise radiation. Tam3 has proposed a model to predict the acoustic environment due to jets and while it works extremely well for jets, it was found to be inappropriate for rockets8. A model to predict the acoustic environment due to a launch vehicle in the far-field which incorporates concepts from both Eldred and Tam was created. This was done using five sets of horizontally fired rocket data, obtained between 2008 and 2012. Three of these rockets use solid propellant and two use liquid propellant. Through scaling analysis, it is shown that liquid and solid rocket motors exhibit similar spectra at similar amplitudes. This model is accurate for these five data sets within 5 dB of the measured data for receiver angles of 30deg to 160deg (with respect to the downstream exhaust centerline). The model uses the following vehicle parameters: nozzle exit diameter and velocity, radial distance from source to receiver, receiver angle, mass flow rate, and acoustic efficiency.

Giacomoni, Clothilde↗
NASA NTRS1989

Materials properties numerical database system established and operational at CINDAS/Purdue University

A computerized comprehensive numerical database system on the mechanical, thermophysical, electronic, electrical, magnetic, optical, and other properties of various types of technologically important materials such as metals, alloys, composites, dielectrics, polymers, and ceramics has been established and operational at the Center for Information and Numerical Data Analysis and Synthesis (CINDAS) of Purdue University. This is an on-line, interactive, menu-driven, user-friendly database system. Users can easily search, retrieve, and manipulate the data from the database system without learning special query language, special commands, standardized names of materials, properties, variables, etc. It enables both the direct mode of search/retrieval of data for specified materials, properties, independent variables, etc., and the inverted mode of search/retrieval of candidate materials that meet a set of specified requirements (which is the computer-aided materials selection). It enables also tabular and graphical displays and on-line data manipulations such as units conversion, variables transformation, statistical analysis, etc., of the retrieved data. The development, content, accessibility, etc., of the database system are presented and discussed.

Ho, C. Y.↗
NASA NTRS2022

Deep Interacting Multiple Model Filtering

In this paper, a deep learning-based multiple model estimation framework is presented for the state estimation of hybrid dynamical systems from high dimensional observations such as camera images. A low dimensional vector which represents the measurement of the latent dynamical system and its corresponding variance are learned using a deep encoder neural network. An Interacting Multiple Model (IMM) filter is used to generate the latent state estimates and covariances using multiple dynamical models, which can be learned using backpropagation through time. The state estimates of the dynamical system and the corresponding covariance matrix are generated from the latent state estimates and covariance using a deep decoder neural network. The whole network is trained in an end-to-end manner using a loss function which minimizes the negative log-likelihood of the neural network parameters. Simulation results are presented using a 2D bouncing ball example and estimation error statistics are computed which demonstrates the accuracy and consistency of the estimation.

Ghananeel Rotithor↗
NASA NTRSDate not supplied

Data Science and the Knowledge Discovery Adventure

This talk will cover the important steps involved in the data science and knowledge discovery process: • Initial fact gathering (interview domain experts, review reports, articles, state-of-the-art) • Identify the problem (prediction, classification, statistical analysis, etc.) • Survey supporting data sources • Understand the data (numerical, categorical, text, sampling rate, data quality issues, etc.) • Selecting relevant features and sources • Acquire the data (set up agreements with the data stewards, APIs to download, etc.) • Merge data sources (temporal, spatial, common key, other ontologies...) • Feature Engineering (non linear domain knowledge or physics-based relationships) • Build data processing pipeline (may need to tap into data stream, develop parallel processing algorithm, federated learning etc.) • Build model and test (tune hyper-parameters, cross validation.) • Analyze/Validate results (do the results make sense. Does it answer the original question). • Deploy/Publish (Monitor and assess benefits)

Data science↗
NASA NTRSDate not supplied

End-To-End Uncertainty Quantification with Analytical Derivatives for Design Under Uncertainty

Uncertainty quantification (UQ) is a rapidly growing and evolving discipline, especially within the aerospace community. Performing analysis with UQ can provide decision makers with a wealth of information about a candidate design. However, the value of UQ is fully realized when the information gained during UQ analysis is leveraged in a feedback loop of a design optimization process, often referred to as design under uncertainty. Although design under uncertainty can be a powerful risk mitigation technique, there are a number of roadblocks that prevent its implementation. Two primary factors are computational costs and added complexity of the analysis. High fidelity simulations on the order tens of uncertain variables quickly become computationally infeasible. Also, implementing UQ into an existing multidisciplinary design and optimization (MDO) process often requires extensive knowledge of the UQ methods and careful treatment of the problem formulation. The objective of this work is to address these two primary roadblocks and enable practitioners to efficiently perform design under uncertainty with limited knowledge of the UQ discipline. Methods outlined in this paper demonstrate MDO incorporating UQ into the design process, leveraging an analytic derivative tool chain through the entire optimization. The proposed approach leverages machine learning techniques to generate a differentiable confidence interval output from polynomial chaos models. This technique, coupled with the incorporation of analytical derivatives through the Polynomial Chaos Expansion (PCE) process, eliminates the need to estimate derivatives which are usually obtained from finite difference, complex step, or similar methods. Developing a differentiable confidence interval allows mixed uncertainty problems (both epistemic and aleatory) to be modeled. Without such modeling, these problems cannot accurately predict objective functions containing statistical quantities such as mean and variance. The addition of analytic derivatives to a polynomial chaos-based UQ method decreases the computational costs of performing design under uncertainty by orders of magnitude in comparison with methods such as complex step. The method and codes developed are modular in nature and are a drop-in solution for design under uncertainty within existing MDO problems. A low-fidelity analytical multidisciplinary optimization under uncertainty for a wing design in OpenMDAO is detailed in this paper. This demonstration case will include both objective functions and constraints which are influenced by uncertain parameters.

Ben D Phillips↗