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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

An Experimental Investigation of Students’ Learning Effects When Using a Simplified Nuclear Simulator

Securing enough data has been a main challenge in human reliability analysis (HRA). Many researchers and institutes have made a lot of efforts for collecting HRA data to produce reasonable human error probabilities (HEPs) as well as reduce the uncertainty of HRA quantification. Representatively, U.S. Nuclear Regulatory Commission (U.S. NRC), Korea Atomic Energy Research Institute (KAERI) and Idaho National Laboratory (INL) have led lots of empirical research regarding the HRA data collection. The U.S. NRC and KAERI have mainly carried out full-scope simulator research collecting HRA data through experiments using full-scope simulators with actual operators. In contrast, INL has experimentally collected the data using simplified simulators and student operators. INL has proposed the Simplified Human Error Experimental Program (SHEEP) framework to complement full-scope data collection efforts by suggesting a way to infer full-scope data based on experimental data collected from students operating simplified simulators, specifically the Rancor Microworld Simulator (Rancor) and the Compact Nuclear Simulator (CNS). The aim of the SHEEP framework is to lower the entry point for collecting useful HRA data by securing large sample sizes at a reasonable amount of cost and labor while also guaranteeing a high degree of freedom when designing experiments. The authors’ previous research investigated whether data collected from the SHEEP framework could support a representative full-scope study. Besides, human performance differences between professional and student operators when using Rancor and CNS have been analyzed to understand the lack of fidelity of the simplified simulators and student operators within the SHEEP study. As a follow up research, this study experimentally investigates students’ learning effects and the performance trends over a certain period when using Rancor. This study aims to find out 1) how much training or education is required to collect HRA data from non-experts (i.e., students) when using Rancor and 2) how much differences there are in human performance measures between students and professional operators. In this study, a longitudinal experiment is developed. The four experiment trials with two weeks interval are carried out for sixteen undergraduate students majoring nuclear engineering at Chosun University. Totally four scenarios randomly selected from ten Rancor scenarios are used in each experiment trial. Four human performance measurements (i.e., workload, situation awareness, time and error) are considered in the experiment. Lastly, the trend of students’ performance is compared with operator data having been collected from the previous experiment.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Inference, Prediction, & Entropy-Rate Estimation of Continuous-Time, Discrete-Event Processes

Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground. However, a much broader class of discrete-event processes operates in continuous-time. Here, we provide new methods for inferring, predicting, and estimating them. The methods rely on an extension of Bayesian structural inference that takes advantage of neural network’s universal approximation power. Based on experiments with complex synthetic data, the methods are competitive with the state-of-the-art for prediction and entropy-rate estimation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

An Accurate, Error-Tolerant, and Energy-Efficient Neural Network Inference Engine Based on SONOS Analog Memory

In this work, we demonstrate SONOS (silicon-oxide-nitrideoxide- silicon) analog memory arrays that are optimized for neural network inference. The devices are fabricated in a 40nm process and operated in the subthreshold regime for in-memory matrix multiplication. Subthreshold operation enables low conductances to be implemented with low error, which matches the typical weight distribution of neural networks, which is heavily skewed toward near-zero values. This leads to high accuracy in the presence of programming errors and process variations. We simulate the end-to-end neural network inference accuracy, accounting for the measured programming error, read noise, and retention loss in a fabricated SONOS array. Evaluated on the ImageNet dataset using ResNet50, the accuracy using a SONOS system is within 2.16% of floating-point accuracy without any retraining. The unique error properties and high On/Off ratio of the SONOS device allow scaling to large arrays without bit slicing, and enable an inference architecture that achieves 20 TOPS/W on ResNet50, a >10× gain in energy efficiency over state-of-the-art digital and analog inference accelerators.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

The internet worm

In November 1988 a worm program invaded several thousand UNIX-operated Sun workstations and VAX computers attached to the Research Internet, seriously disrupting service for several days but damaging no files. An analysis of the work's decompiled code revealed a battery of attacks by a knowledgeable insider, and demonstrated a number of security weaknesses. The attack occurred in an open network, and little can be inferred about the vulnerabilities of closed networks used for critical operations. The attack showed that passwork protection procedures need review and strengthening. It showed that sets of mutually trusting computers need to be carefully controlled. Sharp public reaction crystalized into a demand for user awareness and accountability in a networked world.

Denning, Peter J.↗
DOE OSTI2021

New Physics in the Hot Electron Furnace (Final Report)

The primary purpose of this award was experiments and simulations related to the idea of a ‘Hot Electron Furnace’ where nonlocal heat transport would allow unique new experimental opportunities at x-ray free electron lasers. The period of this award included beamtime at the MEC endstation of the LCLS, and the execution of those experiments was a primary goal. Unfortunately, the beamtime had limited success due to (1) inability of the LCLS to deliver the expected two-color operations mode, and (2) failure of the MEC endstation’s own software to save the cavity timing data that would have allowed inference of time delays for the few hours when two-color mode briefly operated. The beamtime was then switched to operate in single-color (one pulse) mode, and studies were performed of x-ray heating and probe, simultaneously. Two interesting manuscripts are finally near submission, having been delayed by student graduations, need for collaborator simulations of x-ray heating processes, and then COVID. I am hopeful that they will soon be submitted (especially the paper on x-ray heating of MgO) because one of the principal students involved has returned from industry to take a postdoc position at SLAC. Below, we first list all publications and other work products (including the two that are nearing submission) and then summarize several outcomes from this effort. During the term of this award three UW physics graduate students were at least partially supported, and all three received their Ph.D.’s with significant components of their supported work in the dissertation.

47 OTHER INSTRUMENTATION↗
NASA NTRS1992

An expert system shell for inferring vegetation characteristics: The learning system (tasks C and D)

This report describes the implementation of a learning system that uses a data base of historical cover type reflectance data taken at different solar zenith angles and wavelengths to learn class descriptions of classes of cover types. It has been integrated with the VEG system and requires that the VEG system be loaded to operate. VEG is the NASA VEGetation workbench - an expert system for inferring vegetation characteristics from reflectance data. The learning system provides three basic options. Using option one, the system learns class descriptions of one or more classes. Using option two, the system learns class descriptions of one or more classes and then uses the learned classes to classify an unknown sample. Using option three, the user can test the system's classification performance. The learning system can also be run in an automatic mode. In this mode, options two and three are executed on each sample from an input file. The system was developed using KEE. It is menu driven and contains a sophisticated window and mouse driven interface which guides the user through various computations. Input and output file management and data formatting facilities are also provided.

Harrison, P. Ann↗
NASA NTRS1977

Inverse solution of the pseudoscalar transfer equation through nonlinear matrix inversion

Nonlinear matrix inversion operators have been developed which, applied to observed radiances, infer maximal information regarding atmospheric scattering parameters and vertical distribution of radiant sources and sinks. The algorithm has the attractive feature of noise discrimination, attributing instrumental errors to extra-atmospheric sources.

King, J. I. F.↗
NASA NTRS1978

Correlation of combustor acoustic power levels inferred from internal fluctuating pressure measurements

Combustion chamber acoustic power levels inferred from internal fluctuating pressure measurements are correlated with operating conditions and chamber geometries over a wide range. The variables include considerations of chamber design (can, annular, and reverse-flow annular) and size, number of fuel nozzles, burner staging and fuel split, airflow and heat release rates, and chamber inlet pressure and temperature levels. The correlated data include those obtained with combustion component development rigs as well as engines.

Vonglahn, U. H.↗
NASA NTRS1978

Correlation of combustor acoustic power levels inferred from internal fluctuating pressure measurements

Combustion chamber acoustic power levels inferred from internal fluctuating pressure measurements are correlated with operating conditions and chamber geometries over a wide range. The variables include considerations of chamber design (can, annular, and reverse-flow annular) and size, number of fuel nozzles, burner staging and fuel split, airflow and heat release rates, and chamber inlet pressure and temperature levels. The correlated data include those obtained with combustion component development rigs as well as engines.

Von Glahn, U. H.↗
NASA NTRS1986

Multi-system report integration using blackboards

Blackboards are an Al problem solving methodology. A blackboard system consists of a structured data base (the blackboard) holding input and derived inferences and a collection of procedures for deriving inferences (knowledge sources). Each knowledge source is specialized to operate on some portion of the blackboard. The knowledge sources are invoked opportunistically as the information on the blackboard increases. The methodology is well suited to integrating multiple streams of fully reduced and qualitatively different data such as active radar track reports, passive electronic intelligence reports, and human intelligence reports about enemy intentions. This paper sketches the nature of the blackboard problem solving methodology with an emphasis on those features suiting it to such applications. The sketch is illustrated with examples from a relatively simple multi-system report integration problem. Relevant applications currently under development at Stanford's Knowledge Systems Laboratory are also described.

Delaney, J. R.↗
NASA NTRS2003

Qualifying the Sunpower M87N Cryocooler for Operation in the AMS-02 Magnetic Field

The Alpha Magnetic Spectrometer-02 (AMs-02) experiment uses a superfluid helium dewar to cool a large superconducting magnet. The outer vapor-cooled shields of the dewar are to be held at 80 K by four Sunpower M87N cryocoolers. These cryocoolers have magnetic components that might interact with the external applied field generated by the superconducting magnet, thereby degrading the cryocoolers' performance. Engineering models of the Sunpower M87 have been qualified for operation in a magnetic environment similar to the AMs-02 magnetic environment. Although there was no noticeable performance degradation at field levels that were comparable to AMs-02 field levels, there appears to be a small performance degradation at higher field levels. It was theorized that there were three possible issues related to these performance losses at high magnetic fields: i) induced piston rubbing on the cylinder wall due to forces and torques on the linear motor due to the applied magnetic fields; ii) Magnetic hysteretic and/or eddy current damping of the balancer due to its motion in the applied magnetic fields; iii) Inductance losses in motor due to the applied magnetic field. The experiments conducted at the Massachusetts Institute of Technology (MIT) cyclotron facility in June 2002 were designed to test these. Tests were performed over a range of field levels that were lower, comparable, and higher than the field levels that the cryocoolers will experience in the AMs-02 operating environment. This paper describes the experiments and the inferences derived from them.

Mustafi, Shuvo↗
NASA NTRS2022

Natural Language Understanding and Extraction of Flight Constraints Recorded in Letters of Agreement

This paper presents an automated information extraction and inference technique using natural language processing for extracting flight operational procedures and constraints embedded in heritage air traffic management documents. The extracted flight constraints can be digitized and fit into existing airspace information exchange models such as the Aeronautical Information Exchange Model (AIXM). This approach offers a digitized solution to disseminate airspace operating conditions to diverse air users and stakeholders in the National Airspace System (NAS). Furthermore, the digitized flight procedures can provide operational flexibility for emerging advanced air mobility providers and reduce traffic controller workload while maintaining current safety standards. To demonstrate this process, 1,972 Letters of Agreement (LOAs) have been selected for processing, named entity extraction, constraint identification and extraction. This dataset is derived from a subset of documents related to Air Route Traffic Control Centers (ARTCC) operations. We experimented with various traditional information extraction techniques, state-of-the-art machine learning and deep learning models to perform named entity recognition and pattern recognition on our dataset. We present the results from our experiments and demonstrate 99.0% F-1 score for named entity recognition, and a 96.6% accuracy for our entire workflow up to named entity recognition. We also discuss constraint definitions using generic patterned templates and extensions to this work in applying entity linking to digitally extracting relevant constraints.

Natural Language Processing↗
NASA NTRSDate not supplied

Natural Language Understanding and Extraction of Flight Constraints Recorded in Letters of Agreement

This paper presents an automated information extraction and inference technique using natural language processing for extracting flight operational procedures and constraints embedded in heritage air traffic management documents. The extracted flight constraints can be digitized and fit into existing airspace information exchange models such as the Aeronautical Information Exchange Model (AIXM). This approach offers a digitized solution to disseminate airspace operating conditions to diverse air users and stakeholders in the National Airspace System (NAS). Furthermore, the digitized flight procedures can provide operational flexibility for emerging advanced air mobility providers and reduce traffic controller workload while maintaining current safety standards. To demonstrate this process, 1,972 Letters of Agreement (LOAs) have been selected for processing, named entity extraction, constraint identification and extraction. This dataset is derived from a subset of documents related to Air Route Traffic Control Centers (ARTCC) operations. We experimented with various traditional information extraction techniques, state-of-the-art machine learning and deep learning models to perform named entity recognition and pattern recognition on our dataset. We present the results from our experiments and demonstrate 99.0% F-1 score for named entity recognition, and a 96.6% accuracy for our entire workflow up to named entity recognition. We also discuss constraint definitions using generic patterned templates and extensions to this work in applying entity linking to digitally extracting relevant constraints.

Natural Language Processing↗
NASA NTRS2000

Constraints on Martian Soil Composition as Inferred from Viking XRFS and Pathfinder APXS and IMP Data

With the successful operation of the Alpha Proton X-Ray Spectrometer (APXS) during 1997's Mars Pathfinder (MPF) mission, geochemistry data are now available from three sites on Mars. APXS raw spectra for six soils and five rocks have been converted to compositional abundances. The Viking Lander X-Ray Fluorescence Spectrometer (XRFS) successfully measured elemental abundances of nine soils at Viking 1 and eight soils at Viking 2. Although the three landing sites are located in different parts of Mars, the soils exhibit broad similarities, with an iron-rich chemistry similar to that of palagonite. However, the Pathfinder sods show some significant differences from Viking soils, notably an enrichment in silica and depletion in sulfur. The XRFS samples consisted of near-surface and deep (up to 22 cm) soils acquired by a collector head at the cod of a retractable boom. It was possible to collect and analyze pebbles as large a 2 cm, but only sod, some in the form of consolidated clods, was sampled. In contrast, the APXS measured materials in situ. This resulted in MPF "rock" analyses that probably had a significant dust component and, as explored here, "soil" analyses that may have contained a rocky component We examine several possibilities to explain these differences and other attributes of the APXS and XRFS data sets: 1) The APXS soil measurements actually sampled a mixture of Viking-like soil and small bits of high-silica, low-sulfur rock, 2) The soils were derived from high-silica rocks mixed with a minor component of globally-homogenized dust; these soils are chemically distinct and have a separate geologic history from the Viking soils. 3) The weathering environment was different at the Pathfinder landing site compared to the Viking sites, and 4) Uncertainties in the XRFS and APXS measurements result in reported elemental abundances different than those that are actually present We show that none of the possibilities can be discounted, but that an MPF soil distinct in composition from Viking sods is best supported by the available data.

Bridges, N. T.↗
NASA NTRS1992

An expert system shell for inferring vegetation characteristics: Implementation of additional techniques (task E)

The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The VEG subgoal PROPORTION.GROUND.COVER has been completed and a number of additional techniques that infer the proportion ground cover of a sample have been implemented. Some techniques operate on sample data at a single wavelength. The techniques previously incorporated in VEG for other subgoals operated on data at a single wavelength so implementing the additional single wavelength techniques required no changes to the structure of VEG. Two techniques which use data at multiple wavelengths to infer proportion ground cover were also implemented. This work involved modifying the structure of VEG so that multiple wavelength techniques could be incorporated. All the new techniques were tested using both the VEG 'Research Mode' and the 'Automatic Mode.'

Harrison, P. Ann↗
DOE OSTI2024

Abstract for CRADA between National Energy Technology Laboratory and Shell International Exploration & Production, Inc

Introducing CO₂ into geothermal systems as a working fluid in reservoirs can enhance geothermal conductivity, production, and pressure maintenance. A cross-disciplinary interaction of geothermal reservoir stimulation and CO₂ utilization satisfies renewable energy demands and operations that support sustainable energy infrastructure. Challenges to implementing CO₂-stimulated geothermal enhancement (CS-GE) include (1) accurately characterizing and imaging CO₂-stimulated geothermal reservoirs; (2) quantitatively inferring CS-GE evolution under current and future engineered conditions for cost effective operations; and (3) monitoring resources by improving observational methods to advance the understanding of complex geothermal systems for sweep efficiency and, ultimately, cost effectiveness. NETL and Shell will collaborate under this CRADA to develop software to image key features, including CO₂-stimulated fracture imaging, CO₂ fluid sweep imaging and heat transfer and exchange imaging by leveraging available datasets and applying advanced Artificial Intelligence/Machine Learning (AI/ML), multi-level data analytics and data/information fusion to better understand the comprehensive mechanisms of CO₂-stimulated geothermal systems.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

UNH TDP - Concurrent Measurements of Inflow, Power Performance, and Loads for a Grid-Synchronized Vertical Axis Cross-Flow Turbine Operating in a Tidal Estuary

This data was collected between October 12 and December 15 of 2021 at the University of New Hampshire (UNH) and Atlantic Marine Energy Center (AMEC) turbine deployment platform (TDP). This data set includes over 29 days of grid connected turbine operation during this 65 day time frame. The priority for this measurement campaign was to collect data while the turbine was electrically connected to the grid by means of a rectifier and inverter. The Fall_2021_UNH_Measurement_Timeline.png highlights when each instrument was functioning and the Fall_2021_UNH_Test_Log.jpg indicates the four main regions for analysis available from this measurement campaign. The TDP is a floating structure moored on the Portsmouth facing side of Memorial Bridge pier #2, which spans the Piscataqua River between Portsmouth, NH and Kittery, ME. The Piscataqua River connects the Great Bay Estuary to the Gulf of Maine and the river currents are dominated by tidal forcing with water velocities exceeding 2.5 m/s during spring ebb tides at this site which were previously characterized by Kaelin Chancey (Assessment Of The Localized Flow And Tidal Energy Conversion System At An Estuarine Bridge - UNH MS Thesis 2019). The turbine under test was a modified New Energy Corporation (Calgary, CA) model EVG-025 4-blade H-Darrius type vertical axis cross flow turbine that rotates in the clockwise direction with a rotor diameter of 3.2m and blade length of 1.7m. The hydro-foil profile was a NACA 0021 with a 10 inch chord length and a blade preset pitch angle of +4deg with a positive angle corresponding with the toe in direction. The standard EVG-025 has a rotor diameter of 3.4m and its rated power output is 25kW at 3 m/s. The rotor diameter was reduced to accommodate the size of the existing TDP moon-pool. This project was pursued to quantify device performance for cross flow turbines operating in a marine environment. Accurate physical models, to characterize cross flow turbine performance, require real operational data sets due to the complexity of blade fluid interactions. This data can help support model development which will help predict turbine performance when analyzing perspective project locations in the future. Instrumentation was deployed to measure; water speed/direction, electrical power output, turbine shaft speed, turbine thrust force, and platform motion. Concurrent measurements of these parameters allow for correlations (cause and affect) to be inferred, allowing for characterization of device performance over a range of operating conditions. Water currents were measured using Acoustic Doppler Current Profilers (ADCP's) and Acoustic Doppler Velocimeters (ADV's) directly upstream and downstream of the turbine for inflow, wake and turbulence measurements. Electrical power output was measured using the Voltsys rectifier and the Shark power meter. Shaft speed was calculated based on the Voltsys measurements of the permanent magnet three phase generator AC generation frequency, coupled directly to the cross flow turbine under test (i.e., no gear box). Platform motions were captured using a Yost IMU (inertial measurement unit). Turbine thrust loading was measured using a reaction arm about the turbine deployment platform spanning beam, where two bi-directional load cells were connected to the system via a pinned connection. This submission includes zipped folders for each instrument containing quality controlled (QC'd) data in daily .csv files for the relevant duration specific to each instrument, along with separate .csv file that contains the units for each variable. Some instrument daily files are quite large and can pose a challenge for a visual spreadsheet editor to open. A processing software like MATLAB or Python is recommended. Note the degree of QC varied between each instrument due to time constraints. Particular time and attention was given to perform quality control tests on the acoustic based instruments that are particularly suscep...

16 TIDAL AND WAVE POWER↗
NASA NTRS2015

Peak Pc Prediction in Conjunction Analysis: Conjunction Assessment Risk Analysis

Satellite conjunction risk typically evaluated through the probability of collision (Pc). Considers both conjunction geometry and uncertainties in both state estimates. Conjunction events initially discovered through Joint Space Operations Center (JSpOC) screenings, usually seven days before Time of Closest Approach (TCA). However, JSpOC continues to track objects and issue conjunction updates. Changes in state estimate and reduced propagation time cause Pc to change as event develops. These changes a combination of potentially predictable development and unpredictable changes in state estimate covariance. Operationally useful datum: the peak Pc. If it can reasonably be inferred that the peak Pc value has passed, then risk assessment can be conducted against this peak value. If this value is below remediation level, then event intensity can be relaxed. Can the peak Pc location be reasonably predicted?

Operations↗