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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Survey of Machine Learning-Based Rotating Detonation Engine Diagnostics: Evaluation for Broad Application in Experimental Facilities

This work evaluates and compares various convolutional neural networks (CNNs) trained in previous NETL studies according to metrics effecting diagnostic feasibility, external applicability, and performance. Each CNN surveyed, including image classification, object detection, and time series classification, is used to develop total RDE diagnostics and evaluated alongside conventional techniques with respect to real-time capabilities. Real-time capable diagnostics are deployed and evaluated in the laboratory environment using an altered experimental setup, which is outlined herein for possible adaptations in external experimental facilities.

Johnson, Kristyn↗
DOE OSTI2023

Survey of Machine Learning-Based Rotating Detonation Engine Diagnostics: Evaluation for Broad Application in Experimental Facilities

This work evaluates and compares various convolutional neural networks (CNNs) trained in previous NETL studies according to metrics effecting diagnostic feasibility, external applicability, and performance. Each CNN surveyed, including image classification, object detection, and time series classification, is used to develop total RDE diagnostics and evaluated alongside conventional techniques with respect to real-time capabilities. Real-time capable diagnostics are deployed and evaluated in the laboratory environment using an altered experimental setup, which is outlined herein for possible adaptations in external experimental facilities.

Johnson, Kristyn↗
NASA NTRS2002

Rocket Engine Oscillation Diagnostics

Rocket engine oscillating data can reveal many physical phenomena ranging from unsteady flow and acoustics to rotordynamics and structural dynamics. Because of this, engine diagnostics based on oscillation data should employ both signal analysis and physical modeling. This paper describes an approach to rocket engine oscillation diagnostics, types of problems encountered, and example problems solved. Determination of design guidelines and environments (or loads) from oscillating phenomena is required during initial stages of rocket engine design, while the additional tasks of health monitoring, incipient failure detection, and anomaly diagnostics occur during engine development and operation. Oscillations in rocket engines are typically related to flow driven acoustics, flow excited structures, or rotational forces. Additional sources of oscillatory energy are combustion and cavitation. Included in the example problems is a sampling of signal analysis tools employed in diagnostics. The rocket engine hardware includes combustion devices, valves, turbopumps, and ducts. Simple models of an oscillating fluid system or structure can be constructed to estimate pertinent dynamic parameters governing the unsteady behavior of engine systems or components. In the example problems it is shown that simple physical modeling when combined with signal analysis can be successfully employed to diagnose complex rocket engine oscillatory phenomena.

Nesman, Tom↗
NASA NTRS2005

Propulsion Control Technology Development in the United States A Historical Perspective

This paper presents a historical perspective of the advancement of control technologies for aircraft gas turbine engines. The paper primarily covers technology advances in the United States in the last 60 years (1940 to approximately 2002). The paper emphasizes the pioneering technologies that have been tested or implemented during this period, assimilating knowledge and experience from industry experts, including personal interviews with both current and retired experts. Since the first United States-built aircraft gas turbine engine was flown in 1942, engine control technology has evolved from a simple hydro-mechanical fuel metering valve to a full-authority digital electronic control system (FADEC) that is common to all modern aircraft propulsion systems. At the same time, control systems have provided engine diagnostic functions. Engine diagnostic capabilities have also evolved from pilot observation of engine gauges to the automated on-board diagnostic system that uses mathematical models to assess engine health and assist in post-flight troubleshooting and maintenance. Using system complexity and capability as a measure, we can break the historical development of control systems down to four phases: (1) the start-up phase (1942 to 1949), (2) the growth phase (1950 to 1969), (3) the electronic phase (1970 to 1989), and (4) the integration phase (1990 to 2002). In each phase, the state-of-the-art control technology is described and the engines that have become historical landmarks, from the control and diagnostic standpoint, are identified. Finally, a historical perspective of engine controls in the last 60 years is presented in terms of control system complexity, number of sensors, number of lines of software (or embedded code), and other factors.

Jaw, Link C.a↗
NASA NTRS2006

Hydrocarbon-Fueled Rocket Engine Plume Diagnostics: Analytical Developments and Experimental Results

A viewgraph presentation describing experimental results and analytical developments about plume diagnostics for hydrocarbon-fueled rocket engines is shown. The topics include: 1) SSC Plume Diagnostics Background; 2) Engine Health Monitoring Approach; 3) Rocket Plume Spectroscopy Simulation Code; 4) Spectral Simulation for 10 Atomic Species and for 11 Diatomic Molecular Electronic Bands; 5) "Best" Lines for Plume Diagnostics for Hydrocarbon-Fueled Rocket Engines; 6) Experimental Set Up for the Methane Thruster Test Program and Experimental Results; and 7) Summary and Recommendations.

Tejwani, Gopal D.↗
NASA NTRS2002

Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

Kobayashi, Takahisa↗
NASA NTRS1997

Develop Advanced Nonlinear Signal Analysis Topographical Mapping System

During the development of the SSME, a hierarchy of advanced signal analysis techniques for mechanical signature analysis has been developed by NASA and AI Signal Research Inc. (ASRI) to improve the safety and reliability for Space Shuttle operations. These techniques can process and identify intelligent information hidden in a measured signal which is often unidentifiable using conventional signal analysis methods. Currently, due to the highly interactive processing requirements and the volume of dynamic data involved, detailed diagnostic analysis is being performed manually which requires immense man-hours with extensive human interface. To overcome this manual process, NASA implemented this program to develop an Advanced nonlinear signal Analysis Topographical Mapping System (ATMS) to provide automatic/unsupervised engine diagnostic capabilities. The ATMS will utilize a rule-based Clips expert system to supervise a hierarchy of diagnostic signature analysis techniques in the Advanced Signal Analysis Library (ASAL). ASAL will perform automatic signal processing, archiving, and anomaly detection/identification tasks in order to provide an intelligent and fully automated engine diagnostic capability. The ATMS has been successfully developed under this contract. In summary, the program objectives to design, develop, test and conduct performance evaluation for an automated engine diagnostic system have been successfully achieved. Software implementation of the entire ATMS system on MSFC's OISPS computer has been completed. The significance of the ATMS developed under this program is attributed to the fully automated coherence analysis capability for anomaly detection and identification which can greatly enhance the power and reliability of engine diagnostic evaluation. The results have demonstrated that ATMS can significantly save time and man-hours in performing engine test/flight data analysis and performance evaluation of large volumes of dynamic test data.

Jong, Jen-Yi↗
NASA NTRS2003

Rocket Engine Plume Diagnostics at Stennis Space Center

The Stennis Space Center has been at the forefront of development and application of exhaust plume spectroscopy to rocket engine health monitoring since 1989. Various spectroscopic techniques, such as emission, absorption, FTIR, LIF, and CARS, have been considered for application at the engine test stands. By far the most successful technology h a been exhaust plume emission spectroscopy. In particular, its application to the Space Shuttle Main Engine (SSME) ground test health monitoring has been invaluable in various engine testing and development activities at SSC since 1989. On several occasions, plume diagnostic methods have successfully detected a problem with one or more components of an engine long before any other sensor indicated a problem. More often, they provide corroboration for a failure mode, if any occurred during an engine test. This paper gives a brief overview of our instrumentation and computational systems for rocket engine plume diagnostics at SSC. Some examples of successful application of exhaust plume spectroscopy (emission as well as absorption) to the SSME testing are presented. Our on-going plume diagnostics technology development projects and future requirements are discussed.

Tejwani, Gopal D.↗
NASA NTRS1993

Space Shuttle Main Engine plume diagnostics: OPAD approach to vehicle health monitoring

The process of applying spectroscopy to the Space Shuttle Main Engine (SSME) for plume diagnostics, as it exists today, originated at Marshall Space Flight Center in Huntsville, Alabama, and its implementation was assured largely through the efforts of Sverdrup, AEDC, in Tullahoma, Tennessee. This process, Optical Plume Anomaly Detection (OPAD), has formed the basis for various efforts in the development of in-flight plume spectroscopy and in addition produced a viable test stand vehicle health monitor. The purpose of this paper will be to provide an introduction to the OPAD system by discussing the process of obtaining data as well as the methods of examining and interpreting the data.

Powers, W. T.↗
NASA NTRS2001

A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

Kobayashi, Takahisa↗
NASA NTRS2003

Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics

In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated.

Kobayashi, Takahisa↗
NASA NTRS1993

Specialized data analysis for the Space Shuttle Main Engine and diagnostic evaluation of advanced propulsion system components

The Marshall Space Flight Center is responsible for the development and management of advanced launch vehicle propulsion systems, including the Space Shuttle Main Engine (SSME), which is presently operational, and the Space Transportation Main Engine (STME) under development. The SSME's provide high performance within stringent constraints on size, weight, and reliability. Based on operational experience, continuous design improvement is in progress to enhance system durability and reliability. Specialized data analysis and interpretation is required in support of SSME and advanced propulsion system diagnostic evaluations. Comprehensive evaluation of the dynamic measurements obtained from test and flight operations is necessary to provide timely assessment of the vibrational characteristics indicating the operational status of turbomachinery and other critical engine components. Efficient performance of this effort is critical due to the significant impact of dynamic evaluation results on ground test and launch schedules, and requires direct familiarity with SSME and derivative systems, test data acquisition, and diagnostic software. Detailed analysis and evaluation of dynamic measurements obtained during SSME and advanced system ground test and flight operations was performed including analytical/statistical assessment of component dynamic behavior, and the development and implementation of analytical/statistical models to efficiently define nominal component dynamic characteristics, detect anomalous behavior, and assess machinery operational condition. In addition, the SSME and J-2 data will be applied to develop vibroacoustic environments for advanced propulsion system components, as required. This study will provide timely assessment of engine component operational status, identify probable causes of malfunction, and indicate feasible engineering solutions. This contract will be performed through accomplishment of negotiated task orders.

Source record↗
NASA NTRS2018

Assurance of Model-Based Fault Diagnosis

Autonomy is an increasingly important technology for robotic scientific and commercial spacecraft. An important motivation for developing onboard autonomy is to enable quick response to dynamic environment and situations, including fault conditions that a spacecraft may encounter. The reliability of such autonomous capabilities depends on the quality of their knowledge of a spacecraft’s health state. Model-based approaches to fault management, i.e. model-based fault diagnosis (MBFD), is one approach to continuously verify correct behavior in addition to diagnosing symptoms to estimate the spacecraft’s health state. The proper functioning of MBFD is dependent on 1) the quality of the model that is analyzed and compared to the outputs of onboard sensors to estimate the system’s health state, and 2) the correct functioning of the diagnosis engine that interrogates the model and compares its analysis to observed system behavior. We are currently developing Verification and Validation (V&V) approaches to provide the necessary confidence that MBFD systems are correctly estimating the health of on-board spacecraft components and systems. Our work is intended to narrow the gap between the rapidly maturing field of ModelBased System Engineering (including MBFD) and the less-well understood area of identifying and applying appropriate V&V techniques to MBFD. Our effort is investigating three areas: 1) developing V&V techniques for the diagnostic model, 2) developing V&V techniques for the diagnostic engine in isolation, and 3) developing V&V techniques for the diagnostic engine and model in combination. This paper describes the work we have completed in the first area. We describe our approach to selecting a system to be represented by a model, the approach to modeling the system, the verification approach we developed for the model, and the results of the verification activity. We conclude with a description of the work remaining in the last two areas, which will be addressed over the next two years.

Chung, Seung↗
NASA NTRS1991

Rocket engine plume diagnostics using video digitization and image processing - Analysis of start-up

Video digitization techniques have been developed to analyze the exhaust plume of the Space Shuttle Main Engine. Temporal averaging and a frame-by-frame analysis provide data used to evaluate the capabilities of image processing techniques for use as measurement tools. Capabilities include the determination of the necessary time requirement for the Mach disk to obtain a fully-developed state. Other results show the Mach disk tracks the nozzle for short time intervals, and that dominate frequencies exist for the nozzle and Mach disk movement.

Disimile, P. J.↗
NASA NTRS1990

Annual Health Monitoring Conference for Space Propulsion Systems, 2nd, Cincinnati, OH, Nov. 14, 15, 1990, Proceedings

This conference discusses health management systems for liquid rocket engines, signal processing of spectroscopic data for engine diagnostic applications, real-time diagnostics of the reusable rocket engine using on-line system identification, and nonlinear parameter estimation for rocket engine health monitoring. Plume diagnostics are considered including discussion on preliminary plume diagnostics on the SSME using video digitization techniques and baseline plume emmissions. The development of a Fabry-Perot Interferometer for rocket engine plumes is also considered. The topic of computational modeling is also discussed, addressing such topics as probabalistic dynamics of material loss in the SSME turbomachinery, state-variable modelling of fluid-dynamics processes, and a model for the SSME high pressure oxidizer turbopump shaft seal system. A discussion of sensor development includes the topics of sensor development for the rocket engine condition monitoring system and the heat flux measurements in SSME turbine blade tester.

Source record↗
NASA NTRS1991

Infusion of instrumentation technology (engine plume diagnostics) into operational test programs

The following subject areas are covered: hierarchy of civil space technology program; evolution of technology development; technology validation process; potential impacts; impacts of technology infusion into flight certifications; potential solutions; examples of current technology infusion into flight certification; OAET-CSTI health monitoring and controls; and challenges for future space vehicle propulsion systems.

Woods, E. G.↗
NASA NTRS1980

CF6-6D engine short-term performance deterioration

Studies conducted as part of the NASA-Lewis CF6 jet engine diagnostics program are summarized. An 82-engine sample of DC-10-10 aircraft engine checkout data that were gathered to define the extent and magnitude of CF6-6D short term performance deterioration were analyzed. These data are substantiated by the performance testing and analytical teardown of CF6-6D short term deterioration engine serial number (ESN) 451507.

Kramer, W. H.↗