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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Ares I-X Ground Diagnostic Prototype

Automating prelaunch diagnostics for launch vehicles offers three potential benefits. First, it potentially improves safety by detecting faults that might otherwise have been missed so that they can be corrected before launch. Second, it potentially reduces launch delays by more quickly diagnosing the cause of anomalies that occur during prelaunch processing. Reducing launch delays will be critical to the success of NASA's planned future missions that require in-orbit rendezvous. Third, it potentially reduces costs by reducing both launch delays and the number of people needed to monitor the prelaunch process. NASA is currently developing the Ares I launch vehicle to bring the Orion capsule and its crew of four astronauts to low-earth orbit on their way to the moon. Ares I-X will be the first unmanned test flight of Ares I. It is scheduled to launch on October 27, 2009. The Ares I-X Ground Diagnostic Prototype is a prototype ground diagnostic system that will provide anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage thrust vector control (TVC) and for the associated ground hydraulics while it is in the Vehicle Assembly Building (VAB) at John F. Kennedy Space Center (KSC) and on the launch pad. It will serve as a prototype for a future operational ground diagnostic system for Ares I. The prototype combines three existing diagnostic tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool that is commercially produced by Qualtech Systems, Inc. It uses a qualitative model of failure propagation to perform fault isolation and diagnostics. We adapted an existing TEAMS model of the TVC to use for diagnostics and developed a TEAMS model of the ground hydraulics. The second tool, Spacecraft Health Inference Engine (SHINE), is a rule-based expert system developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification. The prototype uses the outputs of SHINE as inputs to TEAMS. The third tool, the Inductive Monitoring System (IMS), is an anomaly detection tool developed at NASA Ames Research Center and is currently used to monitor the International Space Station Control Moment Gyroscopes. IMS automatically "learns" a model of historical nominal data in the form of a set of clusters and signals an alarm when new data fails to match this model. IMS offers the potential to detect faults that have not been modeled. The three tools have been integrated and deployed to Hangar AE at KSC where they interface with live data from the Ares I-X vehicle and from the ground hydraulics. The outputs of the tools are displayed on a console in Hangar AE, one of the locations from which the Ares I-X launch will be monitored. The full paper will describe how the prototype performed before the launch. It will include an analysis of the prototype's accuracy, including false-positive rates, false-negative rates, and receiver operating characteristics (ROC) curves. It will also include a description of the prototype's computational requirements, including CPU usage, main memory usage, and disk usage. If the prototype detects any faults during the prelaunch period then the paper will include a description of those faults. Similarly, if the prototype has any false alarms then the paper will describe them and will attempt to explain their causes.

Schwabacher, Mark↗
NASA NTRS1993

Development of methods for inferring cloud thickness and cloud-base height from satellite radiance data

Cloud-top height is a major factor determining the outgoing longwave flux at the top of the atmosphere. The downwelling radiation from the cloud strongly affects the cooling rate within the atmosphere and the longwave radiation incident at the surface. Thus, determination of cloud-base temperature is important for proper calculation of fluxes below the cloud. Cloud-base altitude is also an important factor in aircraft operations. Cloud-top height or temperature can be derived in a straightforward manner using satellite-based infrared data. Cloud-base temperature, however, is not observable from the satellite, but is related to the height, phase, and optical depth of the cloud in addition to other variables. This study uses surface and satellite data taken during the First ISCCP Regional Experiment (FIRE) Phase-2 Intensive Field Observation (IFO) period (13 Nov. - 7 Dec. 1991, to improve techniques for deriving cloud-base height from conventional satellite data.

Smith, William L., Jr.↗
NASA NTRS1985

Analysis of estimation algorithms for CDTI and CAS applications

Estimation algorithms for Cockpit Display of Traffic Information (CDTI) and Collision Avoidance System (CAS) applications were analyzed and/or developed. The algorithms are based on actual or projected operational and performance characteristics of an Enhanced TCAS II traffic sensor developed by Bendix and the Federal Aviation Administration. Three algorithm areas are examined and discussed. These are horizontal x and y, range and altitude estimation algorithms. Raw estimation errors are quantified using Monte Carlo simulations developed for each application; the raw errors are then used to infer impacts on the CDTI and CAS applications. Applications of smoothing algorithms to CDTI problems are also discussed briefly. Technical conclusions are summarized based on the analysis of simulation results.

Goka, T.↗
NASA NTRS1993

Mongoose: Creation of a Rad-Hard MIPS R3000

This paper describes the development of a 32 Bit, full MIPS R3000 code-compatible Rad-Hard CPU, code named Mongoose. Mongoose progressed from contract award, through the design cycle, to operational silicon in 12 months to meet a space mission for NASA. The goal was the creation of a fully static device capable of operation to the maximum Mil-883 derated speed, worst-case post-rad exposure with full operational integrity. This included consideration of features for functional enhancements relating to mission compatibility and removal of commercial practices not supported by Rad-Hard technology. 'Mongoose' developed from an evolution of LSI Logic's MIPS-I embedded processor, LR33000, code named Cobra, to its Rad-Hard 'equivalent', Mongoose. The term 'equivalent' is used to infer that the core of the processor is functionally identical, allowing the same use and optimizations of the MIPS-I Instruction Set software tool suite for compilation, software program trace, etc. This activity was started in September of 1991 under a contract from NASA-Goddard Space Flight Center (GSFC)-Flight Data Systems. The approach affected a teaming of NASA-GSFC for program development, LSI Logic for system and ASIC design coupled with the Rad-Hard process technology, and Harris (GASD) for Rad-Hard microprocessor design expertise. The program culminated with the generation of Rad-Hard Mongoose prototypes one year later.

Lincoln, Dan↗
NASA NTRS1990

Energy balance components from six sites in a native prairie

The surface-flux stations investigated are of similar design and operation, but are located on different slopes and aspects and provided data with significant variations. The largest difference between sites is noticed in the data for soil heat-flux density, and the general variability is theorized to be related to treatment and locations. The number of surface-flux stations required for the prairie topography can be inferred from the results of the data analysis.

Fritschen, Leo J.↗
NASA NTRS1992

An evidential reasoning extension to quantitative model-based failure diagnosis

The detection and diagnosis of failures in physical systems characterized by continuous-time operation are studied. A quantitative diagnostic methodology has been developed that utilizes the mathematical model of the physical system. On the basis of the latter, diagnostic models are derived each of which comprises a set of orthogonal parity equations. To improve the robustness of the algorithm, several models may be used in parallel, providing potentially incomplete and/or conflicting inferences. Dempster's rule of combination is used to integrate evidence from the different models. The basic probability measures are assigned utilizing quantitative information extracted from the mathematical model and from online computation performed therewith.

Gertler, Janos J.↗
NASA NTRS1996

Dual-Frequency Interferometric SAR Observations of a Tropical Rain-Forest

Repeat-pass, interferometric, radar observations of tropical rain-forest collected by the Shuttle Imaging Radar C (SIR-C) in the state of Rondonia, Brazil, reveal signal coherence is destroyed at C-band (5.6-cm) in the forest, whereas L-band (24-cm) radar signals remain strongly coherent over the entire landscape. At L-band, the rms difference in inferred topographic height between the forest and adjacent clearings is 5 m, equivalent to the height noise. Atmospheric delays are large, however, forming kilometer-sized anomalies with a 1.2-cm rms one way. Radar interferometric studies of the humid tropics must therefore be conducted at long radar wavelengths, with kilometric base-lines or with two antennas operating simultaneously.

Rigot, E.↗
NASA NTRS2009

Head Pose Estimation Using Multilinear Subspace Analysis for Robot Human Awareness

Mobile robots, operating in unconstrained indoor and outdoor environments, would benefit in many ways from perception of the human awareness around them. Knowledge of people's head pose and gaze directions would enable the robot to deduce which people are aware of the its presence, and to predict future motions of the people for better path planning. To make such inferences, requires estimating head pose on facial images that are combination of multiple varying factors, such as identity, appearance, head pose, and illumination. By applying multilinear algebra, the algebra of higher-order tensors, we can separate these factors and estimate head pose regardless of subject's identity or image conditions. Furthermore, we can automatically handle uncertainty in the size of the face and its location. We demonstrate a pipeline of on-the-move detection of pedestrians with a robot stereo vision system, segmentation of the head, and head pose estimation in cluttered urban street scenes.

Ivanov, Tonislav↗
NASA NTRS1986

Program for Experimentation With Expert Systems

CERBERUS is forward-chaining, knowledge-based system program useful for experimentation with expert systems. Inference-engine mechanism performs deductions according to user-supplied rule set. Information stored in intermediate area, and user interrogated only when no applicable data found in storage. Each assertion posed by CERBERUS answered with certainty ranging from 0 to 100 percent. Rule processor stops investigating applicable rules when goal reaches certainty of 95 percent or higher. Capable of operating for wide variety of domains. Sample rule files included for animal identification, pixel classification in image processing, and rudimentary car repair for novice mechanic. User supplies set of end goals or actions. System complexity decided by user's rule file. CERBERUS written in FORTRAN 77.

Engle, S. W.↗
NASA NTRS2011

Large Eddy Simulation Study for Fluid Disintegration and Mixing

A new modeling approach is based on the concept of large eddy simulation (LES) within which the large scales are computed and the small scales are modeled. The new approach is expected to retain the fidelity of the physics while also being computationally efficient. Typically, only models for the small-scale fluxes of momentum, species, and enthalpy are used to reintroduce in the simulation the physics lost because the computation only resolves the large scales. These models are called subgrid (SGS) models because they operate at a scale smaller than the LES grid. In a previous study of thermodynamically supercritical fluid disintegration and mixing, additional small-scale terms, one in the momentum and one in the energy conservation equations, were identified as requiring modeling. These additional terms were due to the tight coupling between dynamics and real-gas thermodynamics. It was inferred that if these terms would not be modeled, the high density-gradient magnitude regions, experimentally identified as a characteristic feature of these flows, would not be accurately predicted without the additional term in the momentum equation; these high density-gradient magnitude regions were experimentally shown to redistribute turbulence in the flow. And it was also inferred that without the additional term in the energy equation, the heat flux magnitude could not be accurately predicted; the heat flux to the wall of combustion devices is a crucial quantity that determined necessary wall material properties. The present work involves situations where only the term in the momentum equation is important. Without this additional term in the momentum equation, neither the SGS-flux constant-coefficient Smagorinsky model nor the SGS-flux constant-coefficient Gradient model could reproduce in LES the pressure field or the high density-gradient magnitude regions; the SGS-flux constant- coefficient Scale-Similarity model was the most successful in this endeavor although not totally satisfactory. With a model for the additional term in the momentum equation, the predictions of the constant-coefficient Smagorinsky and constant-coefficient Scale-Similarity models were improved to a certain extent; however, most of the improvement was obtained for the Gradient model. The previously derived model and a newly developed model for the additional term in the momentum equation were both tested, with the new model proving even more successful than the previous model at reproducing the high density-gradient magnitude regions. Several dynamic SGS-flux models, in which the SGS-flux model coefficient is computed as part of the simulation, were tested in conjunction with the new model for this additional term in the momentum equation. The most successful dynamic model was a "mixed" model combining the Smagorinsky and Gradient models. This work is directly applicable to simulations of gas turbine engines (aeronautics) and rocket engines (astronautics).

Bellan, Josette↗
NASA NTRS2021

Quantifying the Hypervolatile Abundances in Jupiter-family Comet 46P/Wirtanen

We present analysis of IR and optical spectroscopy of Jupiter-family comet (JFC) 46P/Wirtanen obtained in 2019 January, when the comet had sufficient geocentric velocity to enable studies of the hypervolatiles CO and CH4, as well as [OI] emission. These species could not be studied near closest approach in mid-December because there was insufficient Doppler shift to separate the cometary emission from their corresponding telluric absorption lines. We employed the [OI] observations as a proxy for the CO2/H2O ratio, as CO2 cannot be observed directly from the ground, and space-based assets sensitive to CO2 were not able to observe 46P during this apparition. We focused our analysis on H2O, CO, CH4, C2H6, CH3OH, and CO2 (via [OI] emission). We detected strong emissions from H2O, C2H6, and CH3OH. Over the 3 nights, we found evidence for changing mixing ratios, mostly due to a variable H2O production rate. In 46P, C2H6 and CH3OH are enriched compared to cometary averages, with mixing ratios relative to H2O of ∼1% and ∼3%, respectively. Measurements of CH4 and CO have been especially rare in JFCs. We report significant 3σ upper limits on CH4/H2O<0.97% and CO/H2O<0.54%. They place CH4 being near-average or depleted, and CO being strongly depleted in 46P compared with Oort cloud comets. 46P has comparable CO/H2O to the few other measurements in JFCs, but enriched in C2H6 and CH3OH. Our inferred CO2/H2O mixing ratio is∼15%, though accounting for systematic uncertainties from the lack of knowledge of [OI] photochemistry means a value between 10% and 20% is likely. The compositional profile of46P is similar to another small, hyperactive comet: 103P/Hartley 2. The mechanism of CO2-driven water-rich ice grain production proposed for 103P/Hartley 2 may be operating on 46P as well.

Comets↗
NASA NTRS2021

3D Cloud Tomography and Droplet Size Retrieval from Multi-Angle Polarimetric Imaging of Scattered Sunlight from Above

Tomography aims to recover a three-dimensional (3D) density map of a medium or an object. In medical imaging,it is extensively used for diagnostics via X-ray computed tomography (CT). We define and derive a tomographyof cloud droplet distributions via passive remote sensing. We use multi-view polarimetric images to fit a 3Dpolarized radiative transfer (RT) forward model. Our motivation is 3D volumetric probing of vertically-developedconvectively-driven clouds that are ill-served by current methods in operational passive remote sensing. Currenttechniques are indeed based on strictly 1D RT modeling and applied to a single cloudy pixel, where cloud geometrydefaults to that of a plane-parallel slab. Incident unpolarized sunlight, once scattered by cloud droplets, changesits polarization state according to droplet size. Therefore, polarimetric measurements in the rainbow and gloryangular regions can be used to infer the droplet size distribution. This work defines and derives a framework for afull 3D tomography of cloud droplets for both their mass concentration in space and their distribution across arange of sizes. This gridded 3D retrieval of key microphysical properties is made tractable by our novel approachthat involves a restructuring and partial linearization of an open-source polarized 3D RT code to accommodate aspecial two-step iterative optimization technique. Physically-realistic synthetic clouds are used to demonstrate themethodology with rigorous uncertainty quantification, while a real-world cloud imaged by AirMSPI is processedto illustrate the new remote sensing capability

Schechner, Yoav Y.↗
NASA NTRS1985

FINDS: A fault inferring nonlinear detection system programmers manual, version 3.0

Detailed software documentation of the digital computer program FINDS (Fault Inferring Nonlinear Detection System) Version 3.0 is provided. FINDS is a highly modular and extensible computer program designed to monitor and detect sensor failures, while at the same time providing reliable state estimates. In this version of the program the FINDS methodology is used to detect, isolate, and compensate for failures in simulated avionics sensors used by the Advanced Transport Operating Systems (ATOPS) Transport System Research Vehicle (TSRV) in a Microwave Landing System (MLS) environment. It is intended that this report serve as a programmers guide to aid in the maintenance, modification, and revision of the FINDS software.

Lancraft, R. E.↗
NASA NTRS2016

Anthropomorphic Robot Design and User Interaction Associated with Motion

Though in its original concept a robot was conceived to have some human-like shape, most robots now in use have specific industrial purposes and do not closely resemble humans. Nevertheless, robots that resemble human form in some way have continued to be introduced. They are called anthropomorphic robots. The fact that the user interface to all robots is now highly mediated means that the form of the user interface is not necessarily connected to the robots form, human or otherwise. Consequently, the unique way the design of anthropomorphic robots affects their user interaction is through their general appearance and the way they move. These robots human-like appearance acts as a kind of generalized predictor that gives its operators, and those with whom they may directly work, the expectation that they will behave to some extent like a human. This expectation is especially prominent for interactions with social robots, which are built to enhance it. Often interaction with them may be mainly cognitive because they are not necessarily kinematically intricate enough for complex physical interaction. Their body movement, for example, may be limited to simple wheeled locomotion. An anthropomorphic robot with human form, however, can be kinematically complex and designed, for example, to reproduce the details of human limb, torso, and head movement. Because of the mediated nature of robot control, there remains in general no necessary connection between the specific form of user interface and the anthropomorphic form of the robot. But their anthropomorphic kinematics and dynamics imply that the impact of their design shows up in the way the robot moves. The central finding of this report is that the control of this motion is a basic design element through which the anthropomorphic form can affect user interaction. In particular, designers of anthropomorphic robots can take advantage of the inherent human-like movement to 1) improve the users direct manual control over robot limbs and body positions, 2) improve users ability to detect anomalous robot behavior which could signal malfunction, and 3) enable users to be better able to infer the intent of robot movement. These three benefits of anthropomorphic design are inherent implications of the anthropomorphic form but they need to be recognized by designers as part of anthropomorphic design and explicitly enhanced to maximize their beneficial impact. Examples of such enhancements are provided in this report. If implemented, these benefits of anthropomorphic design can help reduce the risk of Inadequate Design of Human and Automation Robotic Integration (HARI) associated with the HARI-01 gap by providing efficient and dexterous operator control over robots and by improving operator ability to detect malfunctions and understand the intention of robot movement.

automation/robotic integration↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.

space biology↗
NASA NTRS1984

Modeling the directional reflectance from complete homogeneous vegetation canopies with various leaf-orientation distributions

The directional-reflectance distributions of radiant flux from homogeneous vegetation canopies with greater than 90 percent ground cover are analyzed with a radiative-transfer model. The model assumes that the leaves consist of small finite planes with Lambertian properties. Four theoretical canopies with different leaf-orientation distributions were studied: erectophile, spherical, planophile, and heliotropic canopies. The directional-reflectance distributions from the model closely resemble reflectance distributions measured in the field. The physical scattering mechanisms operating in the model explain the variations observed in the reflectance distributions as a function of leaf-orientation distribution, solar zenith angle, and leaf transmittance and reflectance. The simulated reflectance distribution show unique characteristics for each canopy. The basic understanding of the physical scattering properties of the different canopy geometries gained in this study provide a basis for developing techniques to infer leaf-orientation distributions of vegetation canopies from directional remote-sensing measurements.

Kimes, D. S.↗
NASA NTRS2005

Faults Discovery By Using Mined Data

Fault discovery in the complex systems consist of model based reasoning, fault tree analysis, rule based inference methods, and other approaches. Model based reasoning builds models for the systems either by mathematic formulations or by experiment model. Fault Tree Analysis shows the possible causes of a system malfunction by enumerating the suspect components and their respective failure modes that may have induced the problem. The rule based inference build the model based on the expert knowledge. Those models and methods have one thing in common; they have presumed some prior-conditions. Complex systems often use fault trees to analyze the faults. Fault diagnosis, when error occurs, is performed by engineers and analysts performing extensive examination of all data gathered during the mission. International Space Station (ISS) control center operates on the data feedback from the system and decisions are made based on threshold values by using fault trees. Since those decision-making tasks are safety critical and must be done promptly, the engineers who manually analyze the data are facing time challenge. To automate this process, this paper present an approach that uses decision trees to discover fault from data in real-time and capture the contents of fault trees as the initial state of the trees.

Lee, Charles↗
NASA NTRS2007

Current sheet Formation in a Conical Theta Pinch Faraday Accelerator with Radio-Frequency Assisted Discharge

The inductive formation of current sheets in a conical theta pinch FARAD (Faraday Accelerator with Radio-frequency Assisted Discharge) thruster is investigated experimentally with time-integrated photography. The goal is to help in understanding the mechanisms and conditions controlling the strength and extent of the current sheet, which are two indices important for FARAD as a propulsion concept. The profiles of these two indices along the inside walls of the conical acceleration coil are assumed to be related to the profiles of the strength and extent of the luminosity pattern derived from photographs of the discharge. The variations of these profiles as a function of uniform back-fill neutral pressure (with no background magnetic field and all parameters held constant) provided the first clues on the nature and qualitative dependencies of current sheet formation. It was found that there is an optimal pressure for which both indices reach a maximum and that the rate of change in these indices with pressure differs on either side of this optimal pressure. This allowed the inference that current sheet formation follows a Townsend-like breakdown mechanism modified by the existence of a finite pressure-dependent radio-frequency-generated electron density background. The observation that the effective location of the luminosity pattern favors the exit-half of the conical coil is explained as the result of the tendency of the inductive discharge circuit to operate near its minimal self-inductance. Movement of the peak in the luminosity pattern towards the upstream side of the cone with increasing pressure is believed to result from the need of the circuit to compensate for the increase in background plasma resistivity due to increasing pressure.

Hallock, Ashley K.↗