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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Pinacate-gran Desierto Region, Mexico: SIR-A Data Analysis

Radar images (SIR-A) from the Columbia space shuttle were used to assess the radar returns of terrain shaped by volcanic, aeolian, and fluvial processes in northwest Sonora. Field studies and photointerpretation show that sand dunes are poorly imaged by SIR-A, in contrast to SEASAT, evidently a consequence of the greater SIR-A incidence angle; star dunes are visible only as small bright spots representing merging arms at dune apices which may act as corner reflectors. Desert grasses and bushes (approx. 2 m high) have little effect on radar brightness. Only larger trees with woody trunks approx. 0.5 m across are effective radar reflectors; their presence contributes to radar bright zones along some arroyos. The radar brightness of lava flows decreases with surface roughness and presence of mantling windblown sediments and weathering products; however, old uplifted (faulted) flows are of equal brightness to fresh, unmantled aa flows. Maar craters display circular patterns of varying radar brightness which represent a combination of geometry, slope, and distribution of surface materials. Some radar bright rings in the Pinacates resemble craters on radar but are observed to be playas encircled by trees.

Christensen, P.↗
NASA NTRS2012

Modeling Off-Nominal Behavior in SysML

Specification and development of fault management functionality in systems is performed in an ad hoc way - more of an art than a science. Improvements to system reliability, availability, safety and resilience will be limited without infusion of additional formality into the practice of fault management. Key to the formalization of fault management is a precise representation of off-nominal behavior. Using the upcoming Soil Moisture Active-Passive (SMAP) mission for source material, we have modeled the off-nominal behavior of the SMAP system during its initial spin-up activity, using the System Modeling Language (SysML). In the course of developing these models, we have developed generic patterns for capturing off-nominal behavior in SysML. We show how these patterns provide useful ways of reasoning about the system (e.g., checking for completeness and effectiveness) and allow the automatic generation of typical artifacts (e.g., success trees and FMECAs) used in system analyses.

fault protection↗
NASA NTRS1993

Experimental evaluation of the certification-trail method

Certification trails are a recently introduced and promising approach to fault-detection and fault-tolerance. A comprehensive attempt to assess experimentally the performance and overall value of the method is reported. The method is applied to algorithms for the following problems: huffman tree, shortest path, minimum spanning tree, sorting, and convex hull. Our results reveal many cases in which an approach using certification-trails allows for significantly faster overall program execution time than a basic time redundancy-approach. Algorithms for the answer-validation problem for abstract data types were also examined. This kind of problem provides a basis for applying the certification-trail method to wide classes of algorithms. Answer-validation solutions for two types of priority queues were implemented and analyzed. In both cases, the algorithm which performs answer-validation is substantially faster than the original algorithm for computing the answer. Next, a probabilistic model and analysis which enables comparison between the certification-trail method and the time-redundancy approach were presented. The analysis reveals some substantial and sometimes surprising advantages for ther certification-trail method. Finally, the work our group performed on the design and implementation of fault injection testbeds for experimental analysis of the certification trail technique is discussed. This work employs two distinct methodologies, software fault injection (modification of instruction, data, and stack segments of programs on a Sun Sparcstation ELC and on an IBM 386 PC) and hardware fault injection (control, address, and data lines of a Motorola MC68000-based target system pulsed at logical zero/one values). Our results indicate the viability of the certification trail technique. It is also believed that the tools developed provide a solid base for additional exploration.

Sullivan, Gregory F.↗
DOE OSTI2021

Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems

Quantum computing (QC) and deep learning have shown promise of supporting transformative advances and have recently gained popularity in a wide range of areas. Here, this paper proposes a hybrid QC-based deep learning framework for fault diagnosis of electrical power systems that combine the feature extraction capabilities of conditional restricted Boltzmann machine with an efficient classification of deep networks. Computational challenges stemming from the complexities of such deep learning models are overcome by QC-based training methodologies that effectively leverage the complementary strengths of quantum assisted learning and classical training techniques. The proposed hybrid QC-based deep learning framework is tested on a simulated electrical power system with 30 buses and wide variations of substation and transmission line faults, to demonstrate the framework’s applicability, efficiency, and generalization capabilities. High computational efficiency is enjoyed by the proposed hybrid approach in terms of computational effort required and quality of diagnosis performance over classical training methods. In addition, superior and reliable fault diagnosis performance with faster response time is achieved over state-of-the-art pattern recognition methods based on artificial neural networks (ANN) and decision trees (DT).

42 ENGINEERING↗
DOE OSTI2021

Learning the Low Frequency Earthquake Activity on the Central San Andreas Fault

Abstract Low frequency earthquakes (LFEs) originating below the central San Andreas Fault are associated with slow‐slip beneath the seismogenic zone within the more ductile portion of the crust. Monitoring efforts over 15 years detected >1 million LFEs. We train a gradient boosted tree model using statistical features describing the seismic waveforms to estimate the hourly LFE event count. The burst‐like LFE behavior is reproduced, while lower amplitudes are predicted during the most active periods. The hourly event counts are up to 18% greater than the catalog. The ability to continuously monitor LFE activity provides insight to when geodetic measurements of slow slip are possible, without the need for developing a computational‐intensive template‐matching catalog. Similar waveform statistical features are found between detecting LFEs and tremors, which provides additional evidence tremors are composed of LFEs. The approach extracts information contained in continuous seismic waveforms that might benefit detecting precursory signals.

58 GEOSCIENCES↗
NASA NTRS1995

Pipeline synthetic aperture radar data compression utilizing systolic binary tree-searched architecture for vector quantization

A system for data compression utilizing systolic array architecture for Vector Quantization (VQ) is disclosed for both full-searched and tree-searched. For a tree-searched VQ, the special case of a Binary Tree-Search VQ (BTSVQ) is disclosed with identical Processing Elements (PE) in the array for both a Raw-Codebook VQ (RCVQ) and a Difference-Codebook VQ (DCVQ) algorithm. A fault tolerant system is disclosed which allows a PE that has developed a fault to be bypassed in the array and replaced by a spare at the end of the array, with codebook memory assignment shifted one PE past the faulty PE of the array.

Chang, Chi-Yung↗
DOE OSTI2020

Desktop Audits: Overview, Case Studies, Guidance

The Energy Independence and Security Act (EISA) of 2007 requires that federal agencies audit 25% of their 3.1 billion square feet of covered facilities each year. Several newer, advanced options such as monitoring-based commissioning, fault detection and diagnostics, and energy management systems can help support data collection and analysis to be used for EISA compliance. The Federal Energy Management Program's (FEMP’s) Audit Decision Tree, Audit Definitions, and FEMP Guidance can be used by Agencies to determine the best approaches to evaluate their facilities. FEMP continues to develop additional resources to support agencies in meeting EISA compliance. This presentation gives an overview of desktop audits, including case studies and guidance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Machine Learning Classification of Molten Salt Heat Exchanger Channel Plugging using Synthetic Data

This report addresses the requirements of Milestone M3.4 AI capability to identify and predict maintenance events. Development of digital twins (DT) for molten salt reactor (MSR) components is crucial for reducing operating and maintenance costs (O&M) and ensuring commercial viability of these reactors. Our focus is on development of DT for MSR primary system heat exchanger (HX), a critical component, the fault in which can reduce operating efficiency and force reactor shutdown. We are investigating the feasibility of a conceptual DT of HX consisting of internal distributed temperature sensing with fiber optics and machine learning (ML) algorithms to detect and localize faults. To determine the optimal approach to detection and localization of channel plugging, we benchmark seven different ML models: Logistic Regression, K-Nearest Neighbors (KNN), Gaussian Naïve Bayes, Support Vector Machines (SVM), Decision Tree Classifier, Random Forest Tree Classifier, and Feed-Forward Neural Network. ML algorithms are benchmarked using synthetic HX plugging data generated with computational fluid dynamics COMSOL software, with added brown noise to represent experimental noise. We show that the best performance is obtained with the Decision Tree classifier.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

A spatial-statistical investigation of surface expressions associated with cyclic steaming in the Midway-Sunset Oil Field, California

In the Midway Sunset Oil Field in Central California, operators inject steam into the shallow diatomite formation to enhance heavy oil recovery through imbibition, wettability alteration, and viscosity reduction, among other mechanisms. The injected steam, however, does not always remain in the reservoir or return through the wells. In two zones in the study area, the steam comes out at the surface, creating sinkholes, seeps, and steam outlets. These phenomena, called “surface expressions,” pose safety and environmental hazards. Even though these surface expressions are a widespread problem in Central California, they are not well documented and understood. Possible causes of the surface expressions include: high injection pressure, structurally controlled flow patterns, leakage of steam through old improperly abandoned wells, high injection volumes, or flow along naturally occurring faults, among other possible factors. This work examines attributes of the zones with surface expressions in order to determine factors that may contribute to their occurrence. Spatial statistical analysis using logistic regression, random forests, and classification trees is used to explore the relationship between the surface expressions and geological and production-related attributes. The results point to a significant spatial correlation between the surface expressions and two predictors: concentration of plugged wells and geologic seal thickness. The results guide follow-up studies to further investigate the role of well abandonment and seal thickness in the occurrence of surface expressions.

02 PETROLEUM↗
NASA NTRS1989

A design philosophy for multi-layer neural networks with applications to robot control

A system is proposed which receives input information from many sensors that may have diverse scaling, dimension, and data representations. The proposed system tolerates sensory information with faults. The proposed self-adaptive processing technique has great promise in integrating the techniques of artificial intelligence and neural networks in an attempt to build a more intelligent computing environment. The proposed architecture can provide a detailed decision tree based on the input information, information stored in a long-term memory, and the adapted rule-based knowledge. A mathematical model for analysis will be obtained to validate the cited hypotheses. An extensive software program will be developed to simulate a typical example of pattern recognition problem. It is shown that the proposed model displays attention, expectation, spatio-temporal, and predictory behavior which are specific to the human brain. The anticipated results of this research project are: (1) creation of a new dynamic neural network structure, and (2) applications to and comparison with conventional multi-layer neural network structures. The anticipated benefits from this research are vast. The model can be used in a neuro-computer architecture as a building block which can perform complicated, nonlinear, time-varying mapping from a multitude of input excitory classes to an output or decision environment. It can be used for coordinating different sensory inputs and past experience of a dynamic system and actuating signals. The commercial applications of this project can be the creation of a special-purpose neuro-computer hardware which can be used in spatio-temporal pattern recognitions in such areas as air defense systems, e.g., target tracking, and recognition. Potential robotics-related applications are trajectory planning, inverse dynamics computations, hierarchical control, task-oriented control, and collision avoidance.

Vadiee, Nader↗
NASA NTRS2009

Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft

This CD contains files that support the talk (see CASI ID 20100021404). There are 24 models that relate to the ADAPT system and 1 Excel worksheet. In the paper an investigation into the use of Bayesian networks to construct large-scale diagnostic systems is described. The high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems are described in the talk. The data in the CD are the models of the 24 different power systems.

Mengshoel, Ole Jakob↗
NASA NTRS2009

Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft

In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability of the compositional approach, we briefly report on experimental results from the diagnostic competition DXC, where the ProADAPT team, using techniques discussed here, obtained the highest scores in both Tier 1 (among 9 international competitors) and Tier 2 (among 6 international competitors) of the industrial track. While we consider diagnosis of power systems specifically, we believe this work is relevant to other system health management problems, in particular in dependable systems such as aircraft and spacecraft. (See CASI ID 20100021910 for supplemental data disk.)

Mengshoel, Ole Jakob↗
NASA NTRS2008

Efficient Probabilistic Diagnostics for Electrical Power Systems

We consider in this work the probabilistic approach to model-based diagnosis when applied to electrical power systems (EPSs). Our probabilistic approach is formally well-founded, as it based on Bayesian networks and arithmetic circuits. We investigate the diagnostic task known as fault isolation, and pay special attention to meeting two of the main challenges . model development and real-time reasoning . often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-to-use speci.cation language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In essence, we introduce a high-level EPS speci.cation language from which Bayesian networks that can diagnose multiple simultaneous failures are auto-generated, and we illustrate the feasibility of using arithmetic circuits, compiled from Bayesian networks, for real-time diagnosis on real-world EPSs of interest to NASA. The experimental system is a real-world EPS, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. In experiments with the ADAPT Bayesian network, which currently contains 503 discrete nodes and 579 edges, we .nd high diagnostic accuracy in scenarios where one to three faults, both in components and sensors, were inserted. The time taken to compute the most probable explanation using arithmetic circuits has a small mean of 0.2625 milliseconds and standard deviation of 0.2028 milliseconds. In experiments with data from ADAPT we also show that arithmetic circuit evaluation substantially outperforms joint tree propagation and variable elimination, two alternative algorithms for diagnosis using Bayesian network inference.

Mengshoel, Ole J.↗
NASA NTRS1985

The use of automatic programming techniques for fault tolerant computing systems

It is conjectured that the production of software for ultra-reliable computing systems such as required by Space Station, aircraft, nuclear power plants and the like will require a high degree of automation as well as fault tolerance. In this paper, the relationship between automatic programming techniques and fault tolerant computing systems is explored. Initial efforts in the automatic synthesis of code from assertions to be used for error detection as well as the automatic generation of assertions and test cases from abstract data type specifications is outlined. Speculation on the ability to generate truly diverse designs capable of recovery from errors by exploring alternate paths in the program synthesis tree is discussed. Some initial thoughts on the use of knowledge based systems for the global detection of abnormal behavior using expectations and the goal-directed reconfiguration of resources to meet critical mission objectives are given. One of the sources of information for these systems would be the knowledge captured during the automatic programming process.

Wild, C.↗
DOE OSTI2021

AI-Guided Reasoning-Based Operator Support System for the Nuclear Power Plant Management

The Decision-making process in the Nuclear Power Plant (NPP) control room faces some challenges: operator incomplete knowledge, insufficient time for responding to the highly dynamic events, and a large number of indicators to monitor. Because of the complexity of the NPP system, it is hard to pre-plan all the failures/mitigative actions. An intelligent operator support system is vital to mitigate these shortcomings. In this paper, an AI declarative approach (Answer Set Programming (ASP)) is employed to represent our knowledge of the nuclear power plant in the form of logic rules. This represented knowledge is structured to form a reasoning-based operator support system. When an incident occurs, this ASP-based reasoning support system is demonstrated to be capable of fault identification (diagnosis), informing the operator of different scenarios and consequences, and generating the control options (decision making).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Failure prediction and estimation of failure parameters

Machine-learning methods and apparatus are disclosed to determine frictional state or other parameters in an earthquake zone or other failing medium, using acoustic emission, seismic waves, or other detectable indicators of microscopic processes. Predictions of future failures are demonstrated in different regimes. A classifier is trained using time series of acoustic emission data along with historic data of frictional state or failure events. In disclosed examples, random forests and gradient boost trees are used, and grid-search or EGO procedures are used for hyperparameter tuning. Once trained, the classifier can be applied to testing or live data in order to assess a frictional state, assess seismic hazard, or make predictions regarding a future failure event. The technology has been developed in a double direct shear apparatus, but can be widely applied to seismic faults, other terrestrial failures, or failures in man-made structures. Variations are disclosed.

Johnson, Paul Allan↗
NASA NTRS2001

Analysis of the Westland Data Set

The "Westland" set of empirical accelerometer helicopter data with seeded and labeled faults is analyzed with the aim of condition monitoring. The autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; and it has also been found that augmentation of these by harmonic and other parameters call improve classification significantly. Several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior oil training data and is thus able to quantify probability of error in all exact manner, such that features may be discarded or coarsened appropriately.

Wen, Fang↗
NASA NTRS1986

Barberton greenstone belt volcanism: Succession, style and petrogenesis

The Barberton Mountain Land is an early Archean greenstone belt along the eastern margin of the Kaapvaal Craton of southern Africa. Detailed mapping in the southern portion of the belt leads to the conclusion that a substantial thickness is due to original deposition of volcanics and sediments. In the area mapped, a minimum thickness of 12km of predominantly mafic and ultramafic volcanics comprise the Komati, Hooggenoeg, and Kromberg Formations of the Onverwacht Group, and at least one km of predominantly pyroclastic and epiclastic sediments derived from dacitic volcanics comprise the Fig Tree Group. The Barberton greenstone belt formed primarily by ultramafic to mafic volcanism on a shallow marine platform which underwent little or no concurrent extension. Vents for this igneous activity were probably of the non-constructional fissure type. Dacitic volcanism occurred throughout the sequence in minor amounts. Large, constructional vent complexes were formed, and explosive eruptions widely dispersed pyroclastic debris. Only in the final stages of evolution of the belt did significant thrust-faulting occur, generally after, though perhaps overlapping with, the final stage of dacitic igneous activity. A discussion follows.

Byerly, G. R.↗