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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Performance deterioration due to acceptance testing and flight loads; JT90 jet engine diagnostic program

The results of a flight loads test of the JT9D-7 engine are presented. The goals of this test program were to: measure aerodynamic and inertia loads on the engine during flight, explore the effects of airplane gross weight and typical maneuvers on these flight loads, simultaneously measure the changes in engine running clearances and performance resulting from the maneuvers, make refinements of engine performance deterioration prediction models based on analytical results of the tests, and make recommendations to improve propulsion system performance retention. The test program included a typical production airplane acceptance test plus additional flights and maneuvers to encompass the range of flight loads in revenue service. The test results indicated that aerodynamic loads, primarily at take-off, were the major cause of rub-indicated that aerodynamic loads, primarily at take-off, were the major cause of rub-induced deterioration in the cold sectin of the engine. Differential thermal expansion between rotating and static parts plus aerodynamic loads combined to cause blade-to-seal rubs in the turbine.

Olsson, W. J.↗
NASA NTRS1982

CF6 Jet Engine Diagnostics Program: High pressure compressor clearance investigation

The effects of high pressure compressor clearance changes on engine performance were experimentally determined on a CF6 core engine. The results indicate that a one percent reduction in normalized average clearance, expressed as a fraction of airfoil length, improves compressor efficiency by one percent. Compressor clearances are reduced by the application of rotor bore cooling, insulation of the stator casing, and use of a low coefficient of expansion material in the aft stages. This improvement amounts to a reduction of normalized average clearance of 0.78 percent, relative to CF6-60 compressor, which is equivalent to an improvement in compressor efficiency of 0.78 percent.

Radomski, M. A.↗
NASA NTRS1982

CF6 jet engine diagnostics program. High pressure turbine roundness/clearance investigation

The effects of high pressure turbine clearance changes on engine and module performance was evaluated in addition to the measurement of CF6-50C high pressure turbine Stage 1 tip clearance and stator out-of-roundness during steady-state and transient operation. The results indicated a good correlation of the analytical model of round engine clearance response with measured data. The stator out-of-roundness measurements verified that the analytical technique for predicting the distortion effects of mechanical loads is accurate, whereas the technique for calculating the effects of certain circumferential thermal gradients requires some modifications. A potential for improvement in roundness was established in the order of 0.38 mm (0.015 in.), equivalent to 0.86 percent turbine efficiency which translates to a cruise SFC improvement of 0.36 percent. The HP turbine Stage 1 tip clearance performance derivative was established as 0.44 mm (17 mils) per percent of turbine efficiency at take-off power, somewhat smaller, therefore, more sensitive than predicted from previous investigations.

Howard, W. D.↗
NASA NTRS1981

CF 6 engine diagnostics

A summary of the activities which led to defining deterioration rates of the CF6 family of engines, a description of what was learned, and an identification of means of conserving fuel based upon the program findings are presented. The program to define the deterioration levels and modes for the CF6 family of engines involved four distinct phases: analysis of inbound engine test results, analysis of airline cruise data, analysis of airline test cell data resulting from testing of refurbished engines, and inspection of engine hardware.

Stricklin, R.↗
NASA NTRS1978

Performance deterioration based on existing (historical) data; JT9D jet engine diagnostics program

The results of the collection and analysis of historical data pertaining to the deterioration of JT9D engine performance are presented. The results of analyses of prerepair and postrepair engine test stand performance data from a number of airlines to establish the individual as well as average losses in engine performance with respect to service use are included. Analysis of the changes in mechanical condition of parts, obtained by inspection of used gas-path parts of varying age, allowed preliminary assessments of component performance deterioration levels and identification of the causitive factors. These component performance estimates, refined by data from special engine back-to-back testing related to module performance restoration, permitted the development of preliminary models of engine component/module performance deterioration with respect to usage. The preliminary assessment of the causes of module performance deterioration and the trends with usage are explained, along with the role each module plays in overall engine performance deterioration. Preliminary recommendations with respect to operating and maintenance practices which could be adopted to control the level of performance deterioration are presented. The needs for additional component sensitivity testing as well as outstanding issues are discussed.

Sallee, G. P.↗
DOE OSTI2023

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

Real-time monitoring of combustion behavior is a crucial step toward actively controlled rotating detonation engine (RDE) operation in laboratory and industrial environments. Various machine learning methods have been developed to advance diagnostic efficiencies from conventional postprocessing efforts to real-time methods. Here this work evaluates and compares conventional techniques alongside convolutional neural network (CNN) architectures trained in previous studies, including image classification, object detection, and time series classification, according to metrics affecting diagnostic feasibility, external applicability, and performance. Real-time, capable diagnostics are deployed and evaluated using an altered experimental setup. Image-based CNNs are applied to externally provided images to approximate dataset restrictions. Image classification using high-speed chemiluminescence images and time series classification using high-speed flame ionization and pressure measurements achieve classification speeds enabling real-time diagnostic capabilities, averaging laboratory-deployed diagnostic feedback rates of 4–5 Hz. Object detection achieves the most refined resolution of 20 μs in postprocessing. Image and time series classification require the additional correlation of sensor data, extending their time-step resolutions to 80 ms. Comparisons show that no single diagnostic approach outperforms its competitors across all metrics. This finding justifies the need for a machine learning portfolio containing a host of networks to address specific needs throughout the RDE research community.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2008

Application of the Systematic Sensor Selection Strategy for Turbofan Engine Diagnostics

The data acquired from available system sensors forms the foundation upon which any health management system is based, and the available sensor suite directly impacts the overall diagnostic performance that can be achieved. While additional sensors may provide improved fault diagnostic performance there are other factors that also need to be considered such as instrumentation cost, weight, and reliability. A systematic sensor selection approach is desired to perform sensor selection from a holistic system-level perspective as opposed to performing decisions in an ad hoc or heuristic fashion. The Systematic Sensor Selection Strategy is a methodology that optimally selects a sensor suite from a pool of sensors based on the system fault diagnostic approach, with the ability of taking cost, weight and reliability into consideration. This procedure was applied to a large commercial turbofan engine simulation. In this initial study, sensor suites tailored for improved diagnostic performance are constructed from a prescribed collection of candidate sensors. The diagnostic performance of the best performing sensor suites in terms of fault detection and identification are demonstrated, with a discussion of the results and implications for future research.

Sowers, T. Shane↗
NASA NTRS2008

Application of the Systematic Sensor Selection Strategy for Turbofan Engine Diagnostics

The data acquired from available system sensors forms the foundation upon which any health management system is based, and the available sensor suite directly impacts the overall diagnostic performance that can be achieved. While additional sensors may provide improved fault diagnostic performance, there are other factors that also need to be considered such as instrumentation cost, weight, and reliability. A systematic sensor selection approach is desired to perform sensor selection from a holistic system-level perspective as opposed to performing decisions in an ad hoc or heuristic fashion. The Systematic Sensor Selection Strategy is a methodology that optimally selects a sensor suite from a pool of sensors based on the system fault diagnostic approach, with the ability of taking cost, weight, and reliability into consideration. This procedure was applied to a large commercial turbofan engine simulation. In this initial study, sensor suites tailored for improved diagnostic performance are constructed from a prescribed collection of candidate sensors. The diagnostic performance of the best performing sensor suites in terms of fault detection and identification are demonstrated, with a discussion of the results and implications for future research.

Sowers, T. Shane↗
NASA NTRS1994

Neural network application to comprehensive engine diagnostics

We have previously reported on the use of neural networks for detection and identification of faults in complex microprocessor controlled powertrain systems. The data analyzed in those studies consisted of the full spectrum of signals passing between the engine and the real-time microprocessor controller. The specific task of the classification system was to classify system operation as nominal or abnormal and to identify the fault present. The primary concern in earlier work was the identification of faults, in sensors or actuators in the powertrain system as it was exercised over its full operating range. The use of data from a variety of sources, each contributing some potentially useful information to the classification task, is commonly referred to as sensor fusion and typifies the type of problems successfully addressed using neural networks. In this work we explore the application of neural networks to a different diagnostic problem, the diagnosis of faults in newly manufactured engines and the utility of neural networks for process control.

Marko, Kenneth A.↗
NASA NTRS1982

B747/JT9D flight loads and their effect on engine running clearances and performance deterioration; BCAC NAIL/P and WA JT9D engine diagnostics programs

Flight loads on the 747 propulsion system and resulting JT9D blade to outer airseal running clearances during representative acceptance flight and revenue flight sequences were measured. The resulting rub induced clearance changes, and engine performance changes were then analyzed to validate and refine the JT9D-7A short term performance deterioration model.

Olsson, W. J.↗
NASA NTRS1982

Summary report for CF6 jet engine diagnostics program

Cockpit cruise recordings and test cell data in conjunction with hardware inspection results from airline overhaul shops were analyzed to define the extent and magnitude of performance deterioration of the General Electric CF6 high bypass turbofan engines. The magnitude of Short Term deterioration from the Long Term was isolated and the individual damage mechanisms that were the cause for the majority of the performance deterioration were identified. A potential for reduction in compressor clearance and a potential for improvement in turbine roundness, which corresponds to cruise SFC reductions of 0.38 and 0.36 percent, respectively, were identified.

Fasching, W. A.↗
NASA NTRS1981

JT9D jet engine diagnostics program

Performance deterioration in the JT9D-7 is a flight sensitive phenomenon caused by one short term and two long term wear modes. The short term deterioration occurs primarily during airplane acceptance testing and therefore does not affect airline operation. The long term wear takes place continuously over the engine life so that the performance loss can be minimized by a sound maintenance program. Short term deterioration is primarily due to flight load induced blade and gas path seal wear which result in increased gas path running clearances. The wear occurs in all engine modules but has the most deleterious effect on the low pressure compressor and high pressure turbine performance. The wear occurs during conditions that combine minimum axisymmetric running clearances and maximum engine distortion or asymmetric closure.

Olsson, W. J.↗
NASA NTRS2019

Thermal History Mapping Technology for Turbine Engine Diagnostics

The infusion of ceramic matrix composites (CMCs) in next generation gas turbines represents daunting challenges as the failure of protective coating, known as Environmental Barrier Coating (EBC), means little remnant life of CMC components. Consequently reliable lifing is critical to the success of CMC components. Accurate thermal mapping of EBC in rig and engine test is of paramount importance because temperature is the most critical variable for EBC life. Current temperature measurement technologies include thermocouples, pyrometers, IR cameras, and thermal paints. Thermocouples require a passage to access the components and pyrometers and IR cameras require line-of-sight. Thermal paints are based on color-sensitive pigments that visually indicate the temperature that a component experienced. Thermal paints do not require line-of-sight, however, they have a low resolution and a limited temperature capability (~1400°C). Thermal history coating is based on oxide ceramic which luminesces when excited with a light source. The luminescence changes when the material's structure changes triggered by heat impact. The maximum temperature to which the material was exposed can be determined from the luminescence change. This paper describes the principles of this new technique and demonstrates its capabilities for EBCs exposed to excess of 1500°C.

Lee, Kang N.↗
NASA NTRS1996

Continued Development of Expert System Tools for NPSS Engine Diagnostics

The objectives of this grant were to work with previously developed NPSS (Numerical Propulsion System Simulation) tools and enhance their functionality; explore similar AI systems; and work with the High Performance Computing Communication (HPCC) K-12 program. Activities for this reporting period are briefly summarized and a paper addressing the implementation, monitoring and zooming in a distributed jet engine simulation is included as an attachment.

Lewandowski, Henry↗
NASA NTRS1990

Rocket engine diagnostics using neural networks

Two problems in applying neural networks to fault detection and identification are (1) the complexity of the sensor data to fault mapping and (2) the lack of sufficient training data. Here, methods are derived and tested in an architecture which addresses these two problems. First, the sensor data to fault mapping is decomposed into three simpler mappings which perform sensor data compression, hypothesis generation, and sensor fusion. Efficient training is performed for each mapping separately. Second, the neural network which performs sensor fusion is structured to detect new unknown faults for which training examples were not presented. These methods were tested on a task of fault detection and identification in the Space Shuttle Main Engine (SSME). Results indicate that the decomposed neural network architecture can be trained efficiently, can identify faults for which it has been trained, and can detect the occurrence of faults for which it has not been trained.

Whitehead, Bruce A.↗
NASA NTRS1977

JT9D engine diagnostics. Task 2: Feasibility study of measuring in-service flight loads

The feasibility of measuring JT9D propulsion system flight inertia loads on a 747 airplane is studied. Flight loads background is discussed including the current status of 747/JT9D loads knowledge. An instrumentation and test plan is formulated for an airline-owned in-service airplane and the Boeing-owned RA001 test airplane. Technical and cost comparisons are made between these two options. An overall technical feasibility evaluation is made and a cost summary presented. Conclusions and recommendations are presented in regard to using existing inertia loads data versus conducting a flight test to measure inertia loads.

Kafka, P. G.↗
NASA NTRS1979

NASA CF6 jet engine diagnostics program: Long-term CF6-6D low-pressure turbine deterioration

Back-to-back performance tests were run on seven airline low pressure turbine (LPT) modules and four new CF6-6D modules. Back-to-back test cell runs, in which an airline LPT module was directly compared to a new production module, were included. The resulting change, measured in fuel burn, equaled the level of LPT module deterioration. Three of the LPT modules were analytically inspected followed by a back-to-back test cell run to evaluate current refurbishment techniques.

Smith, J. J.↗
NASA NTRS1990

Signal processing of spectroscopic data for engine diagnostic applications

A description is given of research based on a series of spectra taken from the Diagnostic Test Facility (DTF) plume at NASA-Stennis Space Center. The DTF was designed to obtain spectra scaling approximately to that of the SSME, yet allow full control of dopant and thruster conditions. DTF spectra were used in this research to develop a technique for quantitative identification of an alloy. A mathematically based identification technique was tried, with reasonable sucess for one-element alloys, and only fair success for a two-element alloy. This approach was found to have the simplicity required for DSP implementation, thus allowing real-time diagnosis as a possibility. Spreadsheets were organized to handle the proliferation of development equations required, which number about 50,000. The identification work reinforced the presence of nonlinearity to dopant level.

Nail, Bert↗