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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Cleanroom certification model

The Cleanroom software development methodology is designed to take the gamble out of product releases for both suppliers and receivers of the software. The ingredients of this procedure are a life cycle of executable product increments, representative statistical testing, and a standard estimate of the MTTF (Mean Time To Failure) of the product at the time of its release. A statistical approach to software product testing using randomly selected samples of test cases is considered. A statistical model is defined for the certification process which uses the timing data recorded during test. A reasonableness argument for this model is provided that uses previously published data on software product execution. Also included is a derivation of the certification model estimators and a comparison of the proposed least squares technique with the more commonly used maximum likelihood estimators.

Currit, P. A.↗
NASA NTRS1987

Calibration and comparison of the NASA Lewis free-piston Stirling engine model predictions with RE-1000 test data

A free-piston Stirling engine performance code is being upgraded and validated at the NASA Lewis Research Center under an interagency agreement between the Department of Energy's Oak Ridge National Laboratory and NASA Lewis. Many modifications were made to the free-piston code in an attempt to decrease the calibration effort. A procedure was developed that made the code calibration process more systematic. Engine-specific calibration parameters are often used to bring predictions and experimental data into better agreement. The code was calibrated to a matrix of six experimental data points. Predictions of the calibrated free-piston code are compared with RE-1000 free-piston Stirling engine sensitivity test data taken at NASA Lewis. Reasonable agreement was obtained between the code prediction and the experimental data over a wide range of engine operating conditions.

Geng, Steven M.↗
DOE OSTI2020

An Empirical Study on the Use of the Rancor Microworld Simulator to Support Full-scope Data Collection

A lack of data has been identified as a major challenge in human reliability analysis (HRA). Accordingly, several institutes and researchers have tried to collect HRA data from different data sources such as actual historical measurements, expert judgements, or simulator studies. While the most recent studies predominantly focus on collecting data using full-scope simulators with actual operators, Idaho National Laboratory (INL) has begun to collect HRA data using a simplified simulator, i.e., the Rancor Microworld simulator, with student participants. Full-scope studies have been known to have several intrinsic challenges to securing enough quantity of the data due to many reasons like the high cost for performing experiments or requiring actual operators’ cooperation. The ultimate goal of INL’s effort aims to infer actual operators’ data collected from a full-scope simulator on the basis of microworld data with student subjects as well as collect additional data that could be missed in the full-scope research. As a first step to achieve this goal, this paper projects an experimental plan for investigating the differences in human performance between individuals in two groups: 1) an actual operator and 2) a student when using the Rancor Microworld simulator. A randomized factorial experiment design has been developed with two independent variables, i.e., type of scenario and type of subject. Six human performance measures, i.e., 1) time, 2) error, 3) workload, 4) situation awareness, 5) patterns of attention and 6) the number of manipulations were selected. A couple of scenarios and their procedures available to the Rancor Microworld simulator have been developed.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2003

Properties of Backscattered Ultraviolet Reflectances in Cloudy Atmosphere

We use a Plane-Parallel Cloud (PPC) model to illustrate how Mie scattering from cloud particles interacts with Rayleigh scattering in the atmosphere and produces a complex wavelength dependence in the top-of-the-atmosphere (TOA) reflectances measured by satellite instruments that operate in the ultraviolet (UV) part of the spectrum. Comparisons of the PPC model-derived spectral dependence of reflectances with the Total Ozone Mapping Spectrometer (TOMS) measurements show surprisingly good agreement over a wide range of observational conditions. The PPC model results also are compared with the results of two other cloud models: Lambert Equivalent Reflectivity (LER) and Modified Lambert Equivalent Reflectivity (MLER) that have been used to analyze satellite data in the UV. These models assume that clouds are opaque Lambertian reflectors rather than Mie scattering particles. Although one of these models (MLER) agrees reasonably well with the data, the results from this model appear somewhat unphysical and may not be suitable for interpreting satellite data if one desires high accuracy. We also use the PPC model to illustrate how clouds can perturb tropospheric O3 absorption in complex ways that cannot be explained by models that treat them as reflecting surfaces rather than as volume scatterers.

Ahmad, Z.↗
NASA NTRS1982

Spectrum of the microwave background radiation

A review is given of the present status of measurements of the spectrum of the microwave background. Factors that limit experimental accuracy are discussed with particular reference to high-frequency measurements. A selection of the available measurements yields a data set that is reasonably consistent with the black-body spectrum for a temperature of 2.9 K. A simple statistical analysis suggests either that there are errors in the data set, or that deviations from a black-body spectrum exist. The difficulties inherent in properly averaging the results from different observers are described. Prospects for improved measurements are summarized.

Richards, P. L.↗
NASA NTRS1985

Corrected sidereal anisotropy for underground moons

Data from underground muon telescopes in New Mexico and Bolivia are analyzed in sidereal time and anti-sidereal time in the rigidity range 20 GV to a few 100's of GV. Using both vertical and north- and south- pointing telescopes in both hemispheres, a latitude range of 70 N to 50 S is covered. It is shown that there is an anti-sidereal variation of the P 1 over 2 type, having opposite phase in the Northern and Southern Hemispheres, and maximum amplitude at mid latitudes. The anti-sidereal data are used to correct the sidereal data, using the Nagashima method (Nagashima, 1984); the resulting corrected sidereal vectors for northern hemisphere telescopes have their sidereal maxima close to 3h sidereal time, in reasonable agreement with sidereal data at higher energies from small air showers. The Nagashima correction also eliminates effects due to the reversal of the Sun's polar magnetic field which show up in the uncorrected sidereal data.

Swinson, D. B.↗
NASA NTRS1991

Data analysis using scale-space filtering and Bayesian probabilistic reasoning

This paper describes a program for analysis of output curves from Differential Thermal Analyzer (DTA). The program first extracts probabilistic qualitative features from a DTA curve of a soil sample, and then uses Bayesian probabilistic reasoning to infer the mineral in the soil. The qualifier module employs a simple and efficient extension of scale-space filtering suitable for handling DTA data. We have observed that points can vanish from contours in the scale-space image when filtering operations are not highly accurate. To handle the problem of vanishing points, perceptual organizations heuristics are used to group the points into lines. Next, these lines are grouped into contours by using additional heuristics. Probabilities are associated with these contours using domain-specific correlations. A Bayes tree classifier processes probabilistic features to infer the presence of different minerals in the soil. Experiments show that the algorithm that uses domain-specific correlation to infer qualitative features outperforms a domain-independent algorithm that does not.

Kulkarni, Deepak↗
DOE OSTI2025

Deep learning of structural morphology imaged by scanning X-ray diffraction microscopy

Scanning X-ray nanodiffraction microscopy is a powerful technique for spatially resolving nanoscale structural morphologies by diffraction contrast. One of the critical challenges in experimental nanodiffraction data analysis is posed by the convergence angle of nanoscale focusing optics which creates simultaneous dependency of the far-field scattering data on three independent components of the local strain tensor-corresponding to dilation and two potential rigid body rotations of the unit cell. All three components are in principle resolvable through a spatially mapped sample tilt series; however, traditional data analysis is computationally expensive and prone to artifacts. In this study, we implement NanobeamNN, a convolutional neural network specifically tailored to the analysis of scanning probe X-ray microscopy data. NanobeamNN learns lattice strain and rotation angles from simulated diffraction of a focused X-ray nanobeam by an epitaxial thin film and can directly make reasonable predictions on experimental data without the need for additional fine-tuning. We demonstrate that this approach represents a significant advancement in computational speed over conventional methods, as well as a potential improvement in accuracy over the current standard.

Luo, Aileen [Cornell Univ., Ithaca, NY (United Sta↗
NASA NTRS1985

Preliminary study: Moisture-polymer interaction. Stuby objectives

The problems associated with mathematically modeling water-module interaction phenomena, including sorption and desorption, diffusion, and permeation are discussed. With reliable analytical models, an extensive materials data base, and solar radiation surface meteorological observations (SOLMET) weather data, predicting module lifetimes in realistic environments can become a practical reality. The status of the present techniques of simulating the various transport mechanisms was reported. The Dent model (a modified Brunauer-Emmet-Teller) approach represented polyvinyl butyral (PVB) sorption data. A 100-layer material model and Fick's diffusion model gave diffusivity values exhibiting adequate agreement with those measured for PVB. Diffusivity of PVB is concentration dependent, decreasing as the water content in PVB increases. The temperature dependence of diffusion in PVB is well modeled by the Arrhenius rate equation. Equilibrium conductivity and leakage current data are well represented by Hearle's model for bulk ionic conductivity. A nodal network analysis using the Systems Improved Numerical Differencing Analyzer (SINDA) Thermal Analyzer gave reasonable correlation with measurable data. It is concluded that realistic lifetime predictions seem to be feasible.

Wen, L. C.↗
NASA NTRS1994

Object-oriented model-driven control

A monitoring and control subsystem architecture has been developed that capitalizes on the use of modeldriven monitoring and predictive control, knowledge-based data representation, and artificial reasoning in an operator support mode. We have developed an object-oriented model of a Controlled Ecological Life Support System (CELSS). The model based on the NASA Kennedy Space Center CELSS breadboard data, tracks carbon, hydrogen, and oxygen, carbodioxide, and water. It estimates and tracks resorce-related parameters such as mass, energy, and manpower measurements such as growing area required for balance. We are developing an interface with the breadboard systems that is compatible with artificial reasoning. Initial work is being done on use of expert systems and user interface development. This paper presents an approach to defining universally applicable CELSS monitor and control issues, and implementing appropriate monitor and control capability for a particular instance: the KSC CELSS Breadboard Facility.

Drysdale, A.↗
DOE OSTI2020

Hybrid geological modeling: Combining machine learning and multiple-point statistics

Accurately modeling and constructing a geologically realistic subsurface model remains an outstanding problem as the morphology controls the flow behaviors. Particularly, one of the pattern-based methods, namely cross-correlation based simulation, has been proved to be an effective way to reconstruct a realistic model, at both small and large scales. However, conditioning to point data in the large-scale problems is still a crucial issue in these algorithms, since there is always a trade-off between the quality of the realizations and the degree of point data reproduction. Specifically, it is not practical to build a training image (TI) which includes all the possibilities and variabilities. Therefore, finding a pattern that can represent the point data and, at the same time, preserving the connectivities is difficult. This leads to producing highly-connected realizations with a significant mismatch or poor models with a reasonable degree of point data reproduction. To accurately reproduce the densely distributed hard data, pixel-based methods can also produce some unrealistic artifacts around the hard data. In this paper, to overcome this challenge, however, we use pattern-based methods as they often produce more disconnected geobodies when dealing with dense hard data, and proposed a hybrid algorithm using the pattern-based methods and convolutional neural network (CNN). The trained CNN model is utilized to improve the quality of conditioning to point data for the original realizations generated by the pattern-based algorithm. As such, the mismatch locations are identified, and the same regions are used in the training of CNN to mimic the procedure through which a missing region can be filled. To evaluate the performance of the proposed hybrid algorithm, it is tested on cases with different dimensions and different numbers of facies. Then, the newly improved realizations are compared with the initial realizations generated by the pattern-based algorithm. The comparison is also conducted by the flow simulation test. And it indicates that the proposed hybrid algorithm can better reproduce the point data, while the connectivities are better preserved.

58 GEOSCIENCES↗
DOE OSTI2021

Widespread deep seismicity in the Delaware Basin, Texas, is mainly driven by shallow wastewater injection

Significance The Delaware Basin, western Texas, has experienced a surge in the number of earthquakes. We investigate basin-wide earthquake, hydrogeologic, industrial, and geodetic data spanning 1993–2020 to identify the reasons for the increase. We use these data and models to quantify the stress changes from wastewater injection and hydrocarbon extraction. We demonstrate that the basin-wide seismicity is dominated by the stresses caused by shallow injection that are transmitted vertically by deforming the underlying crust. We analyze both injection and production data to understand how subsurface properties control earthquake locations and timings. We suggest that injecting wastewater into porous formations will minimize seismic hazard. We establish a framework explaining why earthquakes sometimes occur and sometimes do not occur within human activity-impacted areas.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

Finite solid representation of robot manipulation in three-dimensional geometric reasoning

Intelligence robotic system design involves sensor allocation and sensor data fusion. In the field of geometric reasoning, a fast and effective representation method is needed to determine the physical dimensions of the manipulator as well as the motion environment in sufficient detail. Due to the capability of current sensor technology, the integrity of the sensor information is location dependent and point-wise, line-wise and plane-wise in nature. In order to maneuver the robot by reasoning the environment, there is a minimum requirement of prior information for integrating the allocated sensor feedback. Some new theoretical developments of how to associate the number and the locations of the sensors with three-dimensional geometry of the robot and the motion environment is presented. The related collision avoidance problem (CAP) will be addressed as well.

Chen, Alexander Y. K.↗
NASA NTRS1992

Modelling atmospheric scatterers using spacecraft observations

Voyager images of Triton indicate considerable spatial variability in the concentration of at least two different scattering components in the atmosphere. Data from high phase angle limb scans were fit to Mie scattering models to derive mean particle sizes, number densities, and vertical extent for both types of scattering material at ten different locations between 15 deg S and 70 deg S. These fits reveal a thin haze at latitudes equatorward of 25-30 deg S. The imaging data can be fit reasonably well by both conservatively scattering and absorbing hazes with particle sizes near 0.18 micron and optical depths of order 0.001-0.01. Rayleigh scattering haze fits the imaging data somewhat less well, and can be totally ruled out by combining the imaging and UVS measurements. At high southern latitudes, Triton displays clouds below an altitude of approximately 8 km, as well as the haze at higher altitudes. The clouds have particle sizes which may range from 0.7-2.0 microns, or may be near 0.25 micron. The atmospheric optical depth poleward of 30 deg S must be generally greater than 0.1, but need not be more than 0.3. Horizontal inhomogeneities are quite noticeable, especially at longitudes east of (i.e., higher than) 180 deg.

Rages, Kathy A.↗
NASA NTRS1993

Two-dimensional computational analysis of a transport high-lift system and a comparison with flight-test results

Two currently available coupled inviscid/viscous multielement computational codes, including a relatively simple panel method and an Euler method, are used to analyze a high-lift system. The results are compared with two-dimensional wind-tunnel test results and then with the three-dimensional flight-test results obtained from the NASA Langley Transport Systems Research Vehicle five-element high-lift wing section. Comparisons were also made between the panel method, the Euler method, and flight data for two high-lift configurations, one representing a take-off configuration and the other an approach configuration. For the take-off configuration, both codes agreed reasonably well with experimental data, but both codes were found to overpredict the flap upper-surface pressures for the approach configuration.

Hardin, Jay D.↗
DOE OSTI2024

An Approach to Dynamic Human Reliability Analysis and Its Data Collection Framework

Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Alerga: Alert Aggregation and Reasoning in GOOSE Simulation Pipeline

IEC 61850 specifies the Generic Object Oriented Substation Event (GOOSE) protocol as one option for low latency communication of substation-related events. Due to its strict timing requirements, GOOSE lacks any form of encryption or authentication and has only minimal integrity guarantees. These absences render the protocol vulnerable to a variety of communication anomalies, including adversarial action. In particular, an adversary with access to the substation network can launch man in the middle (MITM) attacks. We propose Alerga, a set of tools to allow operators to mitigate some of the risks of the protocol while retaining its strengths. To that end, we have developed first a GOOSE simulation pipeline including data generation, anomaly detection, alert handling, causal reasoning and data visualization components. The simulator is designed to be modular, allowing operators to swap components to better fit their network capabilities. The volume of alert traffic on a substation network threatens operators with alert fatigue. In order to combat this, we secondly present a novel form of alert aggregation and processing, offering operators a condensed view of any threats to the system. Thirdly, to facilitate the handling of these threats, our causal reasoning system traces the alerts back to their most likely cause, generating an initial hypothesis for operators to investigate.

alert aggregation↗
DOE OSTI2024

Complete and Correct Transfer of Information (CACTI)

Many distributed systems, file transfer mechanisms, and message passing systems offer reliability mechanisms such as acknowledgements, retries, and durability. While these tools may be “good enough” for their typical use cases, they may not offer sufficient coverage for the wide range of faults that impact data transfers and communication. A gap in the reliability measures may lead to some small amount of data loss. Some high-consequence systems cannot tolerate the loss or corruption of even a single record. We present seven principles that will counter a wide range of faults and protect against data loss and corruption. These principles bring together lessons learned from a wide range of technologies and can inform appropriate system design and application usage. These principles will help readers reason on how prevent data loss in a multi-hop pipeline and how to properly use tools that may have a deficiency in reliability.

97 MATHEMATICS AND COMPUTING↗