Methods, Apparatus And Systems For Real Time Identification And Control Of Modes Of Oscillation

A system for real time identification of modes of oscillation includes a sensor, an observer, a controller and an actuator. The sensor senses a controlled system such as a combustor, and generates a signal indicative of the modes of oscillation in the controlled system. For example, these modes of oscillation can be combustion instabilities. The observer receives the signal from the sensor, and uses the signal to determine modal functions and frequencies of the modes of interest with a pair of integrals with changing time limits. The controller receives the modal functions and frequency for each mode of interest from the observer, and effects a gain and phase shift for each mode. Based on the modal functions, the frequency, the gain and the phase shift, the controller generates and outputs a control signal, that is supplied to the actuator. The actuator controls the modes of oscillation of the controlled system, based on the control signal. The system of this invention can be used to damp or enhance oscillation modes of the controlled system, depending upon whether the oscillation modes are beneficial or detrimental to system performance.Georgia Tech Research Corporatio

HOMOGENEOUS AND HETEROGENEOUS SENSORS FOR COMBUSTION SYSTEMS

Due to increasingly stringent emission regulations, it is important to develop clean combustors. Combustion behavior is very complex in almost all practical power plant systems. Measurement of temperature, pressure, local flow, and chemical composition inside the flame provides critical information to develop cleaner combustors. This would result in significant improvement in energy efficiency and reduce the environmental impact. A high density sensor network system would assist in understanding the various ongoing processes occurring within the combustors. This dissertation is focused on how much additional information can be gathered from multiple sensors. Four different time delay estimation methods (using cross correlation, phase transform, generalized cross correlation with maximum-likelihood estimation, and average square difference function) were examined using two sensors. Phase transform offered better results to calculate the time delay between a given pair of microphones. This has the potential to determine local noise generation sources from within flows and flames with the additional information on local noise generation source. As a step towards the development of a sensor network, different sensors were examined. A micro-thermocouple, microphone and microphone probes were utilized to enhance understanding of the flame with detailed information on the various ongoing processes in a premixed swirl flame. High frequency temperature and pressure measurements were used to identify the thermal and acoustic characteristics of the flame and combustor. The local distributions of fluctuating pressure and temperature were measured in different regions, in and around the flame. Pressure fluctuation showed significant variation in different directions for the combustive case relative to non-combustive flow. Also a comparison of the pressure and temperature fluctuations revealed that maximum temperature fluctuations occur mostly near to the visible flame boundary while maximum pressure fluctuation occur further away from the flame. Acoustic data from the premixed swirl combustor showed variation in fuel to air ratio changes the spatial distribution of noise as measured by different sensors placed around the combustor. A comparison of different sensors showed that a single sensor does not capture all the information with changes in fuel to air ratio

A new power MEMS component with variable capacitance

Autonomous devices such as wireless sensors and sensor networks need a long battery lifetime in a small volume. Incorporating micro-power generators based on ambient energy increases the lifetime of these systems while reducing the volume. This paper describes a new approach to the conversion of mechanical energy, available in vibrations, to electrical energy. The conversion principle is based on charge transportation between two parallel capacitors. An electret is used to polarize the device. A large-signal model was developed, allowing simulations of the behavior of the generator. A small-signal model was then derived in order to quantify the output power as a function of the design parameters. These models show the possibility of generating up to 40 muW with a device of 10 mm 2. A layout was made based on a standard SOI-technology, available in an MPW. With this design a power of 1 muW at 1020 Hz is expected

The application of neural networks to the SSME startup transient

Feedforward neural networks were used to model three parameters during the Space Shuttle Main Engine startup transient. The three parameters were the main combustion chamber pressure, a controlled parameter, the high pressure oxidizer turbine discharge temperature, a redlined parameter, and the high pressure fuel pump discharge pressure, a failure-indicating performance parameter. Network inputs consisted of time windows of data from engine measurements that correlated highly to the modeled parameter. A standard backpropagation algorithm was used to train the feedforward networks on two nominal firings. Each trained network was validated with four additional nominal firings. For all three parameters, the neural networks were able to accurately predict the data in the validation sets as well as the training set

Propulsion Control Technology Development Needs to Address NASA Aeronautics Research Mission Goals for Thrusts 3a and 4

The Commercial Aero-Propulsion Control Working Group (CAPCWG), consisting of propulsion control technology leads from The Boeing Company, GE Aviation, Honeywell, Pratt & Whitney, Rolls-Royce, and NASA (National Aeronautics and Space Administration) Glenn Research Center, has been working together over the past year to identify propulsion control technology areas of common interest that we believe are critical to achieving the challenging NASA Aeronautics Research goals for Thrust 3a: Ultra-Efficient Commercial Vehicles - Subsonic Transports, and Thrust 4: Transition to Alternative Propulsion and Energy. This paper describes the various propulsion control technology development areas identified by CAPCWG as most critical for NASA to invest in. For Thrust 3a these are: i) Integrated On-Board Model Based Engine Control and Health Management; ii) Flexible and Modular Networked Control Hardware and Software Architecture; iii) Intelligent Air/Fuel Control for Low Emissions Combustion; and iv) Active Clearance Control. For Thrust 4a, the focus is on Hybrid Electric Propulsion (HEP) for single aisle commercial aircraft. The specific technology development areas include: i) Integrated Power and Propulsion System Dynamic Modeling for Control; ii) Control Architectures for HEP; iii) HEP Control Verification and Validation; and iv) Engine/Airplane Control Integration. For each of the technology areas, the discussion includes: problem to be solved and how it relates to NASA goals, and the challenges to be addressed in reducing risk

Funding and Strategic Alignment Guidance for Infusing Small Business Innovation Research Technology Into Aeronautics Research Mission Directorate Projects at NASA Glenn Research Center for 2015

This document is intended to enable the more effective transition of NASA Glenn Research Center (GRC) SBIR technologies funded by the Small Business Innovation Research (SBIR) program as well as its companion, the Small Business Technology Transfer (STTR) program into NASA Aeronautics Research Mission Directorate (ARMD) projects. Primarily, it is intended to help NASA program and project managers find useful technologies that have undergone extensive research and development (RRD), through Phase II of the SBIR program; however, it can also assist non-NASA agencies and commercial companies in this process. aviation safety, unmanned aircraft, ground and flight test technique, low emissions, quiet performance, rotorcraf

Propulsion Controls and Diagnostics Research in Support of NASA Aeronautics and Exploration Mission Programs

The Controls and Dynamics Branch (CDB) at National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) in Cleveland, Ohio, is leading and participating in various projects in partnership with other organizations within GRC and across NASA, the U.S. aerospace industry, and academia to develop advanced propulsion controls and diagnostics technologies that will help meet the challenging goals of NASA programs under the Aeronautics Research and Exploration Systems Missions. This paper provides a brief overview of the various CDB tasks in support of the NASA programs. The programmatic structure of the CDB activities is described along with a brief overview of each of the CDB tasks including research objectives, technical challenges, and recent accomplishments. These tasks include active control of propulsion system components, intelligent propulsion diagnostics and control for reliable fault identification and accommodation, distributed engine control, and investigations into unsteady propulsion systems

Air Breathing Propulsion Controls and Diagnostics Research at NASA Glenn Under NASA Aeronautics Research Mission Programs

This lecture will provide an overview of the aircraft turbine engine control research at NASA (National Aeronautics and Space Administration) Glenn Research Center (GRC). A brief introduction to the engine control problem is first provided with a description of the current state-of-the-art control law structure. A historical aspect of engine control development since the 1940s is then provided with a special emphasis on the contributions of GRC. The traditional engine control problem has been to provide a means to safely transition the engine from one steady-state operating point to another based on the pilot throttle inputs. With the increased emphasis on aircraft safety, enhanced performance and affordability, and the need to reduce the environmental impact of aircraft, there are many new challenges being faced by the designers of aircraft propulsion systems. The Controls and Dynamics Branch (CDB) at GRC is leading and participating in various projects in partnership with other organizations within GRC and across NASA, other government agencies, the U.S. aerospace industry, and academia to develop advanced propulsion controls and diagnostics technologies that will help meet the challenging goals of NASA programs under the Aeronautics Research Mission. The second part of the lecture provides an overview of the various CDB technology development activities in aircraft engine control and diagnostics, both current and some accomplished in the recent past. The motivation for each of the research efforts, the research approach, technical challenges and the key progress to date are summarized. The technologies to be discussed include system level engine control concepts, gas path diagnostics, active component control, and distributed engine control architecture. The lecture will end with a futuristic perspective of how the various current technology developments will lead to an Intelligent and Autonomous Propulsion System requiring none to very minimum pilot interface, interfacing directly with the flight management system to determine its mode of operation, and providing personalized engine control to optimize its performance given the current condition and mission objectives