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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 73 records · Page 4

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NASA NTRS1976

Macroscopic study of time unsteady noise of an aircraft engine during static tests

Static tests of aircraft engines can exhibit greater than 10 db random unsteadiness of tone noise levels because flow disturbances that prevail near test site facilities are ingested. Presumably such changes are related to installation and test site features. Some properties of unsteady noise observed during tests of a Lycoming YF-102 turbofan engine are presented. Time and spatial variations in tone noise obtained from closely spaced far field and inlet duct microphones are displayed. Long to extremely short intermittent tone bursts are observed. Unsteadiness of the tone, its harmonics, and the broadband noise show little similarity. In the far field, identity of tone bursts is retained over a directivity angle of less than 10 deg. In the inlet duct, tone bursts appear to propagate axially but exhibit little circumferential similarity. They show only slight relationship to tone bursts observed in the far field. The results imply an intermittent generation of random mixtures of propagating duct modes.

Clark, B. J.↗

NASA NTRS1976

Macroscopic study of time unsteady noise of an aircraft engine during static tests

Static tests of aircraft engines can exhibit greater than 10 dB random unsteadiness of tone noise levels because flow disturbances that prevail near test site facilities are ingested. Presumably such changes are related to installation and test site features. This paper presents some properties of unsteady noise observed at a NASA-Lewis facility during tests of a Lycoming YF-102 turbofan engine. Time and spatial variations in tone noise obtained from closely spaced far-field and inlet duct microphones are displayed. Long (0.5 sec) to extremely short (0.001 sec) intermittent tone bursts are observed. Unsteadiness of the tone, its harmonics, and the broadband noise show little similarity. In the far-field, identity of tone bursts is retained over a directivity angle of less than 10 deg. In the inlet duct, tone bursts appear to propagate axially but exhibit little circumferential similarity. They show only slight relationship to tone bursts observed in the far field. The results imply an intermittent generation of random mixtures of propagating duct modes.

Clark, B. J.↗

NASA NTRS1921

Preliminary calculation of cylinder dimensions for aircraft engines

It is extremely important in building aircraft engines to determine the requisite cylinder dimensions as accurately as possible, in order that the weight required for a given power shall not be excessive. This report presents a calculation method that depends on the air requirement of the fuel.

Schwager, Otto↗

NASA NTRS2005

Enhanced Bank of Kalman Filters Developed and Demonstrated for In-Flight Aircraft Engine Sensor Fault Diagnostics

In-flight sensor fault detection and isolation (FDI) is critical to maintaining reliable engine operation during flight. The aircraft engine control system, which computes control commands on the basis of sensor measurements, operates the propulsion systems at the demanded conditions. Any undetected sensor faults, therefore, may cause the control system to drive the engine into an undesirable operating condition. It is critical to detect and isolate failed sensors as soon as possible so that such scenarios can be avoided. A challenging issue in developing reliable sensor FDI systems is to make them robust to changes in engine operating characteristics due to degradation with usage and other faults that can occur during flight. A sensor FDI system that cannot appropriately account for such scenarios may result in false alarms, missed detections, or misclassifications when such faults do occur. To address this issue, an enhanced bank of Kalman filters was developed, and its performance and robustness were demonstrated in a simulation environment. The bank of filters is composed of m + 1 Kalman filters, where m is the number of sensors being used by the control system and, thus, in need of monitoring. Each Kalman filter is designed on the basis of a unique fault hypothesis so that it will be able to maintain its performance if a particular fault scenario, hypothesized by that particular filter, takes place.

Kobayashi, Takahisa↗

NASA NTRSDate not supplied

Machine Learning Application in Aircraft Engine Conceptual Design

In the current competitive environment, the successful creation and application of machine learning (ML) technologies have become crucial across multiple industries. This study outlines the process of creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models use supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. Key focus areas include crucial engine parameters such as thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While developing ML models is fundamental, ensuring their seamless deployment is equally important. To address this, a conversational AI chatbot is constructed using natural language processing (NLP) techniques to facilitate the deployment of these ML models. The comprehensive workflow includes several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine conceptual design.

Aircraft Engine↗

NASA NTRS2024

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become crucial across diverse economic sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot that specifically focuses on propulsion has been developed. Leveraging natural language processing (NLP) techniques, this chatbot simplifies the deployment of machine learning (ML) models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

AI Chatbot↗

NASA NTRSDate not supplied

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become imperative across various sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot is constructed, utilizing natural language processing (NLP) techniques, to facilitate the deployment of these ML models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

Development↗

NASA NTRSDate not supplied

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

Development↗

NASA NTRS2004

Evaluation of an Enhanced Bank of Kalman Filters for In-Flight Aircraft Engine Sensor Fault Diagnostics

In this paper, an approach for in-flight fault detection and isolation (FDI) of aircraft engine sensors based on a bank of Kalman filters is developed. This approach utilizes multiple Kalman filters, each of which is designed based on a specific fault hypothesis. When the propulsion system experiences a fault, only one Kalman filter with the correct hypothesis is able to maintain the nominal estimation performance. Based on this knowledge, the isolation of faults is achieved. Since the propulsion system may experience component and actuator faults as well, a sensor FDI system must be robust in terms of avoiding misclassifications of any anomalies. The proposed approach utilizes a bank of (m+1) Kalman filters where m is the number of sensors being monitored. One Kalman filter is used for the detection of component and actuator faults while each of the other m filters detects a fault in a specific sensor. With this setup, the overall robustness of the sensor FDI system to anomalies is enhanced. Moreover, numerous component fault events can be accounted for by the FDI system. The sensor FDI system is applied to a commercial aircraft engine simulation, and its performance is evaluated at multiple power settings at a cruise operating point using various fault scenarios.

Kobayashi, Takahisa↗

NASA NTRS2013

Self Diagnostic Accelerometer Ground Testing on a C-17 Aircraft Engine

The self diagnostic accelerometer (SDA) developed by the NASA Glenn Research Center was tested for the first time in an aircraft engine environment as part of the Vehicle Integrated Propulsion Research (VIPR) program. The VIPR program includes testing multiple critical flight sensor technologies. One such sensor, the accelerometer, measures vibrations to detect faults in the engine. In order to rely upon the accelerometer, the health of the accelerometer must be ensured. Sensor system malfunction is a significant contributor to propulsion in flight shutdowns (IFSD) which can lead to aircraft accidents when the issue is compounded with an inappropriate crew response. The development of the SDA is important for both reducing the IFSD rate, and hence reducing the rate at which this component failure type can put an aircraft in jeopardy, and also as a critical enabling technology for future automated malfunction diagnostic systems. The SDA is a sensor system designed to actively determine the accelerometer structural health and attachment condition, in addition to making vibration measurements. The SDA uses a signal conditioning unit that sends an electrical chirp to the accelerometer and recognizes changes in the response due to changes in the accelerometer health and attachment condition. In an effort toward demonstrating the SDAs flight worthiness and robustness, multiple SDAs were mounted and tested on a C-17 aircraft engine. The engine test conditions varied from engine off, to idle, to maximum power. The two SDA attachment conditions used were fully tight and loose. The newly developed SDA health algorithm described herein uses cross correlation pattern recognition to discriminate a healthy from a faulty SDA. The VIPR test results demonstrate for the first time the robustness of the SDA in an engine environment characterized by high vibration levels.

Tokars, Roger P.↗

NASA NTRS2010

Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy

Simon, Donald L.↗

NASA NTRS2000

Advanced Methods for Aircraft Engine Thrust and Noise Benefits: Nozzle-Inlet Flow Analysis

In this project, we continue to develop the previous joint research between the Fluid Mechanics and Acoustics Laboratory (FM&AL) at Hampton University (HU) and the Jet Noise Team (JNT) at the NASA Langley Research Center (NASA LaRC). The FM&AL was established at Hampton University in June of 1996 and has conducted research under two NASA grants: NAG-1-1835 (1996-99), and NAG-1-1936 (1997-00). In addition, the FM&AL has jointly conducted research with the Central AeroHydrodynamics Institute (TsAGI, Moscow) in Russia under a Civilian Research and Development Foundation (CRDF) grant #RE2-136 (1996-99). The goals of the FM&AL programs are twofold: (1) to improve the working efficiency of the FM&AUs team in generating new innovative ideas and in conducting research in the field of fluid dynamics and acoustics, basically for improvement of supersonic and subsonic aircraft engines, and (2) to attract promising minority students to this research and training and, in cooperation with other HU departments, to teach them basic knowledge in Aerodynamics, Gas Dynamics, and Theoretical and Experimental Methods in Aeroacoustics and Computational Fluid Dynamics (CFD). The research at the HU FM&AL supports reduction schemes associated with the emission of engine pollutants for commercial aircraft and concepts for reduction of observables for military aircraft. These research endeavors relate to the goals of the NASA Strategic Enterprise in Aeronautics concerning the development of environmentally acceptable aircraft. It is in this precise area, where the US aircraft industry, academia, and Government are in great need of trained professionals and which is a high priority goal of the Minority University Research and Education (MUREP) Program, that the HU FM&AL can make its most important contribution. The main achievements for the reporting period in the development of concepts for noise reduction and improvement in efficiency for jet exhaust nozzles and inlets for aircraft engines are as follows: (1) Publications- The AIAA Paper #99-1924 has been presented at the 5th AIAA/CEAS Aeroacoustics Conference, May 10-12, 1999, Seattle, WA; the AIAA Paper #00-3315 has been accepted for the 36th AIAA/ASME/ SAE/ASEE Joint Propulsion Conference, 17-19 July, 2000, Huntsville, AL; and another paper has been accepted for the International Environmental Congress, 14-16 June, 2000, St.-Petersburg, Russia. (2) Two patents were granted on July 20, 1999, and January 12, 2000. (3) Three reports/presentations at the NASA LaRC and GRC (06/22199, 09/26/ 99, and 06/25/00). (4) Grants and Proposals: Four proposals were submitted to the NASA and CRDF; a NASA Faculty Award was granted on January, 2000. A CRDF Young Investigator Program Award was granted for a 3 months visit of the Russian scientist to the HU FM&AL (03/99-05/99). (5) Theory and Numerical Simulations- Analytical theory, numerical simulation, comparison of theoretical with experimental results, and modification of theoretical approaches, models, grids etc. have been conducted for several complicated 2D and 3D nozzle and inlet designs using NASA codes based on full Euler and Navier-Stokes solvers: CFL3D, CRAFT, GODUNOV, and others. New approach for environmental monitoring via infrasound. (6) Experimental Tests: Experimental acoustic tests at the TsAGI, Moscow, with nozzles having Screwdriver or Axisymmetric Plug and Permeable Shells. A small scale working model of the NASA Low Speed Wind Tunnel (LSWT) has been installed in the Experimental Hall of the HU FM&AL (June, 1999). Preliminary preparations for experimental tests were made. (7) Students Research Activity: Involvement of the two graduate students as research assistants in the current research project.

Gilinsky, Mikhail↗

NASA NTRS2020

Machine Learning-Based Predictive Analytics for Aircraft Engine Conceptual Design

Big data and artificial intelligence/machine learning are transforming the global business environment. Data is now the most valuable asset for enterprises in every industry. Companies are using data-driven insights for competitive advantage. With that, the adoption of machine learning-based data analytics is rapidly taking hold across various industries, producing autonomous systems that support human decision-making. This work explored the application of machine learning to aircraft engine conceptual design. Supervised machine-learning algorithms for regression and classification were employed to study patterns in an existing, open-source database of production and research turbofan engines, and resulting in predictive analytics for use in predicting performance of new turbofan designs. Specifically, the author developed machine learning-based analytics to predict cruise thrust specific fuel consumption (TSFC) and core sizes of high-efficiency turbofan engines, using engine design parameters as the input. The predictive analytics were trained and deployed in Keras, an open-source neural networks application program interface (API) written in Python, with Google’s TensorFlow (an open source library for numerical computation) serving as the backend engine. The promising results of the predictive analytics show that machine-learning techniques merit further exploration for application in aircraft engine conceptual design.

deep-learning↗

NASA NTRS1981

New technique for the direct measurement of core noise from aircraft engines

The core noise levels from gas turbine aircraft engines were measured using a technique which requires that fluctuating pressures be measured in the far field and at two locations within the engine core. The cross spectra of these measurements are used to determine the levels of the far-field noise that propagated from the engine vore. The technique makes it possible to measure core noise levels even when other noise sources dominate. The technique was applied to signals measured from an Avco Lycoming YF102 turbofan engine. Core noise levels as a function of frequency and radiation angle were measured and are presented over a range of power settings.

Krejsa, E. A.↗

NASA NTRS1993

Fuel injector design for high temperature aircraft engine

The objective of the Innovative High Temperature Aircraft Engine Fuel Nozzle Program was to design and evaluate a nozzle capable of operating at a combustor inlet air temperature of 1600 F (1144 K) and a fuel temperature of 350 F (450 K). The nozzle was designed to meet the same performance requirements and fit within the size envelope of a current production dual orifice fuel nozzle. The design approach was to use improved thermal protection and fuel passage geometry in combination with fuel passage surface treatment to minimize coking at these extreme fuel and air temperatures. Heat transfer models of several fuel injector concepts were used to optimize the thermal protection, while sample tube coking tests were run to evaluate the effect of surface finish, coatings and tube material on the coking rate. Based on heat transfer analysis, additional air gaps, reduced fuel passage flow area and use of ceramic tip components reduced local fuel wetted wall temperatures by more than 200 F (100 K) when compared to a current production fuel nozzle. Sample tube coking test results showed the importance of surface finish on the fuel coking rate. Therefore, a one micro-inch (0.025 micron) roughness was specified for all fuel passage surfaces. A novel flow divider valve in the tip was also employed to reduce weight, allow room for additional thermal protection, and provide back pressure to reduce the risk of fuel vaporization. The fuel nozzle was fabricated and evaluated in a series of high temperature coking tests. Initial results of these tests indicate that thermal protection and surface treatments were partially successful in preventing nozzle fouling, but additional refinement of the internal flowpath is needed to prevent buildup of coke particles that partially blocked the spin slots which meter the nozzle fuel flow.

R. W. Stickles↗

NASA NTRS2009

An Object-Oriented Computer Code for Aircraft Engine Weight Estimation

Reliable engine-weight estimation at the conceptual design stage is critical to the development of new aircraft engines. It helps to identify the best engine concept amongst several candidates. At NASA Glenn Research Center (GRC), the Weight Analysis of Turbine Engines (WATE) computer code, originally developed by Boeing Aircraft, has been used to estimate the engine weight of various conceptual engine designs. The code, written in FORTRAN, was originally developed for NASA in 1979. Since then, substantial improvements have been made to the code to improve the weight calculations for most of the engine components. Most recently, to improve the maintainability and extensibility of WATE, the FORTRAN code has been converted into an object-oriented version. The conversion was done within the NASA's NPSS (Numerical Propulsion System Simulation) framework. This enables WATE to interact seamlessly with the thermodynamic cycle model which provides component flow data such as airflows, temperatures, and pressures, etc., that are required for sizing the components and weight calculations. The tighter integration between the NPSS and WATE would greatly enhance system-level analysis and optimization capabilities. It also would facilitate the enhancement of the WATE code for next-generation aircraft and space propulsion systems. In this paper, the architecture of the object-oriented WATE code (or WATE++) is described. Both the FORTRAN and object-oriented versions of the code are employed to compute the dimensions and weight of a 300-passenger aircraft engine (GE90 class). Both versions of the code produce essentially identical results as should be the case.

Tong, Michael T.↗

NASA NTRS1980

Multifuel rotary aircraft engine

The broad objectives of this paper are the following: (1) to summarize the Curtiss-Wright design, development and field testing background in the area of rotary aircraft engines; (2) to briefly summarize past activity and update development work in the area of stratified charge rotary combustion engines; and (3) to discuss the development of a high-performance direct injected unthrottled stratified charge rotary combustion aircraft engine. Efficiency improvements through turbocharging are also discussed.

Jones, C.↗

NASA NTRS2011

A Sensitivity Study of Commercial Aircraft Engine Response for Emergency Situations

This paper contains the details of a sensitivity study in which the variation in a commercial aircraft engine's outputs is observed for perturbations in its operating condition inputs or control parameters. This study seeks to determine the extent to which various controller limits can be modified to improve engine performance, while capturing the increased risk that results from the changes. In an emergency, the engine may be required to produce additional thrust, respond faster, or both, to improve the survivability of the aircraft. The objective of this paper is to propose changes to the engine controller and determine the costs and benefits of the additional capabilities produced by the engine. This study indicates that the aircraft engine is capable of producing additional thrust, but at the cost of an increased risk of an engine failure due to higher turbine temperatures and rotor speeds. The engine can also respond more quickly to transient commands, but this action reduces the remaining stall margin to possibly dangerous levels. To improve transient response in landing scenarios, a control mode known as High Speed Idle is proposed that increases the responsiveness of the engine and conserves stall margin

Csank, Jeffrey T.↗