Neutron Calorimeter Design for Pulsed Neutron Experiment Diagnostics
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46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
SEARCH · Engineering Papers
Results for “diagnostics engineering”
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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Collaborative investigation of the internal flow and near-nozzle flow of an eight-hole gasoline injector (Engine Combustion Network Spray G)
The internal details of fuel injectors have a profound impact on the emissions from gasoline direct injection engines. However, the impact of injector design features is not currently understood, due to the difficulty in observing and modeling internal injector flows. Gasoline direct injection flows involve moving geometry, flash boiling, and high levels of turbulent two-phase mixing. In order to better simulate these injectors, five different modeling approaches have been employed to study the engine combustion network Spray G injector. Here these simulation results have been compared to experimental measurements obtained, among other techniques, with X-ray diagnostics, allowing the predictions to be evaluated and critiqued. The ability of the models to predict mass flow rate through the injector is confirmed, but other features of the predictions vary in their accuracy. The prediction of plume width and fuel mass distribution varies widely, with volume-of-fluid tending to overly concentrate the fuel. All the simulations, however, seem to struggle with predicting fuel dispersion and by inference, jet velocity. This shortcoming of the predictions suggests a need to improve Eulerian modeling of dense fuel jets.
ECN↗
Spatially and Temporally Resolved Velocimetry for Hypersonic Flows
The development of new hypersonic flight vehicles is limited by the physical understanding that may be obtained from ground test facilities. This has motivated the present development of a temporally and spatially resolved velocimetry measurement for Sandia National Laboratories (SNL) Hypersonic Wind Tunnel (HWT) using Femtosecond Laser Electronic Excitation Tagging (FLEET). First, a multi-line FLEET technique has been created for the first time and tested in a supersonic jet, allowing simultaneous measurements of velocities along multiple profiles in a flow. Secondly, two different approaches have been demonstrated for generating dotted FLEET lines. One employs a slit mask pattern focused into points to yield a dotted line, allowing for two- or three-component velocity measurements free of contamination between components. The other dotted-line approach is based upon an optical wedge array and yields a grid of points rather than a dotted line. Two successful FLEET measurement campaigns have been conducted in SNL’s HWT. The first effort established optimal diagnostic configurations in the hypersonic environment based on earlier benchtop reproductions, including validation of the use of a 267 nm beam to boost the measurement signal-to-noise ratio (SNR) with minimal risk of perturbing the flow and greater simplicity than a comparable resonant technique at 202 nm. The same FLEET system subsequently was reconstituted to demonstrate the ability to make velocimetry measurements of hypersonic turbulence in a realistic flow field. Mean velocity profiles and turbulence intensity profiles of the shear layer in the wake of a hypersonic cone model were measured at several different downstream stations, proving the viability of FLEET as a hypersonic diagnostic.
33 ADVANCED PROPULSION SYSTEMS↗
Deep Analysis Net with Causal Embedding for Coal-fired Power Plant Fault Detection and Diagnosis (DANCE4CFDD)
Fault detection and diagnosis is critical to power plant operation to ensure attaining high reliability while reducing operation cost. As more renewable power is introduced to the power grid, traditional fossil power plants take on the extra burden of excessive load cycling to compensate the generation variability from renewable power. Such load cycling will pose more reliability challenges to power plant operation. There are a number of challenges faced by today’s asset health management system in coal- fired (or gas) power plants: 1) high-dimensional nonlinear interaction among multiple time series measurements; 2) high measurement variance induced by operational conditions/modes; 3) variation among asset types and plant configurations; and 4) a small number of faulty events to learn from. To cope with these challenges, today’s fielded asset health management systems rely heavily on manual efforts from domain experts and hand-crafted features or rules based on domain knowledge. Despite its role in plant reliability, such a practice is costly and hinders its scalability and sustainability, particularly when a plant undergoes modifications. The objective of this project is to develop a novel end-to-end AI learning system that is trainable (i.e., the AI representation of a complex system behavior can be directly learned from properly labeled data) for accurate fault detection and root cause analysis. The ability to create a fault detection model directly from time series could alleviate the efforts associated with today’s asset management solution development. In the course of this project, we have achieved the following: Created an AI model development environment incorporating state-of-the-art neural network architectures for rapid model development and evaluation; Developed novel learning strategies for training of fault detection model; Developed special-purpose neural network architecture embedded with variable association graph aiming for better interpretability; Developed a learning strategy to leverage a small number of faulty events for enhanced fault detection capability; Conducted detailed experimental study based on public benchmark datasets and demonstrated the effectiveness of the proposed solution; and Validated the developed system with data from both a coal-fired plant boiler dynamic simulation model and real-world coal-fired power plant covering multiple asset and fault types. Overall, the project attained a technology readiness level of TRL 5 from TRL 2 at the beginning of the project.
20 FOSSIL-FUELED POWER PLANTS↗
Reusable Rocket Engine Maintenance Study
Approximately 85,000 liquid rocket engine failure reports, obtained from 30 years of developing and delivering major pump feed engines, were reviewed and screened and reduced to 1771. These were categorized into 16 different failure modes. Failure propagation diagrams were established. The state of the art of engine condition monitoring for in-flight sensors and between flight inspection technology was determined. For the 16 failure modes, the potential measurands and diagnostic requirements were identified, assessed and ranked. Eight areas are identified requiring advanced technology development.
Macgregor, C. A.↗
Pattern-based fault diagnosis using neural networks
An architecture for a real-time pattern-based diagnostic expert system capable of accommodating noisy, incomplete, and possibly erroneous input data is outlined. Results from prototype systems applied to jet and rocket engine fault diagnosis are presented. The ability of a neural network-based system to be trained via the presentation of behavioral patterns associated with fault conditions is demonstrated.
Dietz, W. E.↗
Model-based reasoning for power system management using KATE and the SSM/PMAD
The overall goal of this research effort has been the development of a software system which automates tasks related to monitoring and controlling electrical power distribution in spacecraft electrical power systems. The resulting software system is called the Intelligent Power Controller (IPC). The specific tasks performed by the IPC include continuous monitoring of the flow of power from a source to a set of loads, fast detection of anomalous behavior indicating a fault to one of the components of the distribution systems, generation of diagnosis (explanation) of anomalous behavior, isolation of faulty object from remainder of system, and maintenance of flow of power to critical loads and systems (e.g. life-support) despite fault conditions being present (recovery). The IPC system has evolved out of KATE (Knowledge-based Autonomous Test Engineer), developed at NASA-KSC. KATE consists of a set of software tools for developing and applying structure and behavior models to monitoring, diagnostic, and control applications.
Morris, Robert A.↗
Integrated System Health Management: Foundational Concepts, Approach, and Implementation
Implementation of integrated system health management (ISHM) capability is fundamentally linked to the management of data, information, and knowledge (DIaK) with the purposeful objective of determining the health of a system. It is akin to having a team of experts who are all individually and collectively observing and analyzing a complex system, and communicating effectively with each other in order to arrive to an accurate and reliable assessment of its health. This paper presents concepts, procedures, and a specific approach as a foundation for implementing a credible ISHM capability. The capability stresses integration of DIaK from all elements of a subsystem. The intent is also to make possible implementation of on-board ISHM capability, in contrast to a remote capability. The information presented is the result of many years of research, development, and maturation of technologies, and of prototype implementations in operational systems (rocket engine test facilities). The paper will address the following topics: ISHM Model of a system; detection of anomaly indicators; determination and confirmation of anomalies; diagnostic of causes and determination of effects; consistency checking cycle; sharing of health information; sharing of display information; storage and retrieval of health information; and example implementation.
Figueroa, Fernando↗
Base-Bleed Effect on X-33 Aerospike Plume Induced Base-Heating Environment During Power-Pack Out
A computational heat transfer methodology was developed to study the dual-engine linear aerospike plume induced base-heating environment during one power-pack out, in ascent flight. One power-pack out results in reduction of power levels for both engines. That, in turn, reduces the amount of base-bleed and changes the distribution of base-bleed on the two pillows. Hence, the concern of increased base-heating during power-pack out. The thermo-flowfield of the entire vehicle was computed. The computational methodology for the convective heating is based on a three-dimensional, finite-volume, viscous, chemically reacting, and pressure-based computational fluid dynamics formulation. The computational methodology for the radiative heating is based on a three-dimensional, finite-volume, and spectral-line-based weighted-sum-of-gray-gases absorption computational radiation heat transfer formulation. A separate radiation model was used for diagnostic purposes. The computational methodology was systematically benchmarked. In this study, near-base radiative heat fluxes were computed and they compared well with those measured from an installed linear aerospike engine tests. The base-heating environment of 18 trajectory points selected from three power-pack out ascent scenarios was computed and is presented here. The power-pack out condition has the most impact on convective base-heating when it happens early in flight. The some of its impact comes from the asymmetric and reduced base-bleed.
Wang, Tee-See↗
NO x Control and Measurement Technology for Heavy-Duty Diesel Engines (CRADA Final Report)
The Catalyst CRADA focused on lean-NO x trap (LNT) and selective catalytic reduction (SCR) automotive emission control catalyst technology. It particularly had the goal of developing and applying advanced diagnostics capable of elucidating the network and sequence of catalyst reactions, as well as how they are affected by real-world operation and aging. Another overarching goal and action of this CRADA section was to develop methods to quantify and monitor catalyst states and support the development of full and reduced models for design and in-use control applications. William Partridge was the ORNL principal investigator (PI) throughout the Catalyst CRADA; Neal Currier was the Cummins PI through circa 2017, and Saurabh Joshi was the subsequent Cummins PI.
33 ADVANCED PROPULSION SYSTEMS↗
Use of Convolutional Neural Network Image Classification and High-Speed Ion Probe Data Toward Real-Time Detonation Characterization in a Water-Cooled Rotating Detonation Engine
As rotating detonation engines (RDEs) progress in maturity, the importance of monitoring advancements toward development of active control becomes more critical. Experimental RDE data processing at time scales which satisfy real-time diagnostics will likely require the use of machine learning. This study aims to develop and deploy a novel real-time monitoring technique capable of determining detonation wave number, direction, frequency, and individual wave speeds throughout experimental RDE operational windows. To do so, the diagnostic integrates image classification by a convolutional neural network (CNN) and ionization current signal analysis. Wave mode identification through single-image CNN classification bypasses the need to evaluate sequential images and offers instantaneous identification of the wave mode present in the RDE annulus. Here, real-time processing speeds are achieved due to low data volumes required by the methodology, namely one short-exposure image and a short window of sensor data to generate each diagnostic output. The diagnostic acquires live data using a modified experimental setup alongside Pylon and PyDAQmx libraries within a python data acquisition environment. Lab-deployed diagnostic results are presented across varying wave modes, operating conditions, and data quality, currently executed at 3–4 Hz with a variety of iteration speed optimization options to be considered as future work. These speeds exceed that of conventional techniques and offer a proven structure for real-time RDE monitoring. The demonstrated ability to analyze detonation wave presence and behavior during RDE operation will certainly play a vital role in the development of RDE active control, necessary for RDE technology maturation toward industrial integration.
42 ENGINEERING↗
Ability of Particulate Matter Index to describe sooting tendency of various gasoline formulations in a stratified-charge spark-ignition engine
This study investigates the ability of Particulate Matter Index (PMI) to describe the sooting behavior of various gasoline formulations in a stratified-charge (SC) spark-ignition engine. Specifically, the engine was operated at 2000 rpm with an intake pressure of 130 kPa where soot formation is known to primarily occur in the bulk gases. Exhaust soot emissions were measured for nine test fuels at various exhaust gas recirculation levels. A comparison between measured soot levels and PMI shows that PMI is a relatively poor predictor of the sooting tendency of the tested fuels under lean SC combustion. Among the fuels, three fuels, namely the diisobutylene blend, High Olefin, and E30 fuels exhibit measured soot behavior opposite of that predicted by PMI. Optical diagnostics were utilized to further investigate the in-cylinder phenomena for these three fuels. Analysis of natural luminosity and diffused back-illumination extinction imaging suggests that fuel-induced differences in the amount of soot formed are responsible for a majority of the discrepancy in measured versus predicted sooting tendency. Fuel-induced differences in soot oxidation and spray development seem to play minor roles. Because the combustion and air-fuel mixing processes for lean SC combustion are different from conventional stoichiometric operation it is hypothesized that the PMI correlation needs to be modified to account for differences in stoichiometric air-fuel ratio and level of oxygenation between fuels. Furthermore, the role of fuel volatility in PMI possibly needs to be de-emphasized for SC operation with fuel injection into compression-heated gases.
42 ENGINEERING↗
A Foreign Object Damage Event Detector Data Fusion System for Turbofan Engines
A Data Fusion System designed to provide a reliable assessment of the occurrence of Foreign Object Damage (FOD) in a turbofan engine is presented. The FOD-event feature level fusion scheme combines knowledge of shifts in engine gas path performance obtained using a Kalman filter, with bearing accelerometer signal features extracted via wavelet analysis, to positively identify a FOD event. A fuzzy inference system provides basic probability assignments (bpa) based on features extracted from the gas path analysis and bearing accelerometers to a fusion algorithm based on the Dempster-Shafer-Yager Theory of Evidence. Details are provided on the wavelet transforms used to extract the foreign object strike features from the noisy data and on the Kalman filter-based gas path analysis. The system is demonstrated using a turbofan engine combined-effects model (CEM), providing both gas path and rotor dynamic structural response, and is suitable for rapid-prototyping of control and diagnostic systems. The fusion of the disparate data can provide significantly more reliable detection of a FOD event than the use of either method alone. The use of fuzzy inference techniques combined with Dempster-Shafer-Yager Theory of Evidence provides a theoretical justification for drawing conclusions based on imprecise or incomplete data.
Turso, James A.↗
Inductive System Monitors Tasks
The Inductive Monitoring System (IMS) software developed at Ames Research Center uses artificial intelligence and data mining techniques to build system-monitoring knowledge bases from archived or simulated sensor data. This information is then used to detect unusual or anomalous behavior that may indicate an impending system failure. Currently helping analyze data from systems that help fly and maintain the space shuttle and the International Space Station (ISS), the IMS has also been employed by data classes are then used to build a monitoring knowledge base. In real time, IMS performs monitoring functions: determining and displaying the degree of deviation from nominal performance. IMS trend analyses can detect conditions that may indicate a failure or required system maintenance. The development of IMS was motivated by the difficulty of producing detailed diagnostic models of some system components due to complexity or unavailability of design information. Successful applications have ranged from real-time monitoring of aircraft engine and control systems to anomaly detection in space shuttle and ISS data. IMS was used on shuttle missions STS-121, STS-115, and STS-116 to search the Wing Leading Edge Impact Detection System (WLEIDS) data for signs of possible damaging impacts during launch. It independently verified findings of the WLEIDS Mission Evaluation Room (MER) analysts and indicated additional points of interest that were subsequently investigated by the MER team. In support of the Exploration Systems Mission Directorate, IMS is being deployed as an anomaly detection tool on ISS mission control consoles in the Johnson Space Center Mission Operations Directorate. IMS has been trained to detect faults in the ISS Control Moment Gyroscope (CMG) systems. In laboratory tests, it has already detected several minor anomalies in real-time CMG data. When tested on archived data, IMS was able to detect precursors of the CMG1 failure nearly 15 hours in advance of the actual failure event. In the Aeronautics Research Mission Directorate, IMS successfully performed real-time engine health analysis. IMS was able to detect simulated failures and actual engine anomalies in an F/A-18 aircraft during the course of 25 test flights. IMS is also being used in colla
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Validation & Verification of CFD Models for Cryogenic Fluid Management of Propellant Tanks in Space
This article describes the need and the strategy for CFD model development, validation, and verification for Cryogenic Fluid Management (CFM) of Propellant Tanks in Space. It describes the type of CFD models that must be developed to address the future needs of Space CFM. It also discusses the two classes of experiments currently used to validate the fidelity of the CFD models. These two experiment classes are: (a) the small-scale simulant fluid science experiments that are equipped with scientific diagnostics to elucidate the underlying two-phase fluid physics of the CFM processes; and (b) the large-scale cryogenic experiments that assess the engineering performance of the propellant tank for storage and transfer. The current status of the CFD model development and validation is briefly assessed by presenting examples of segregated two-phase flow problems that have been successfully modeled. The future model development directions for CFM situations involving more complex interpenetrating phases are also defined.
Cryogenic Fluid Management↗
Ion Velocity Characterization of the 12.5-kW Advanced Electric Propulsion System Engineering Hall Thruster
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Electric propulsion↗