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The Advanced Thermally Actuated Clearance Control System underwent several studies. Improved flow path isolation quantified what can be gained by making the HPT case nearly adiabatic. The best method of heat transfer was established, and finally two different borrowed air cooling circuits were evaluated to be used for the HPT Active Clearance Control System.
The objective is to provide turbine-cooling technologies to meet Propulsion 21 goals related to engine fuel burn, emissions, safety, and reliability. Specifically, the GE Aviation (GEA) Advanced Turbine Cooling and Thermal Management program seeks to develop advanced cooling and flow distribution methods for HP turbines, while achieving a substantial reduction in total cooling flow and assuring acceptable turbine component safety and reliability. Enhanced cooling techniques, such as fluidic devices, controlled-vortex cooling, and directed impingement jets, offer the opportunity to incorporate both active and passive schemes. Coolant heat transfer enhancement also can be achieved from advanced designs that incorporate multi-disciplinary optimization of external film and internal cooling passage geometry.
An extensive study of new fan exhaust nozzle technologies was performed. Three new uniform chevron nozzles were designed, based on extensive CFD analysis. Two new azimuthally varying variants were defined. All five were tested, along with two existing nozzles, on a representative model-scale, medium BPR exhaust nozzle. Substantial acoustic benefits were obtained from the uniform chevron nozzle designs, the best benefit being provided by an existing design. However, one of the azimuthally varying nozzle designs exhibited even better performance than any of the uniform chevron nozzles. In addition to the fan chevron nozzles, a new technology was demonstrated, using devices that enhance mixing when applied to an exhaust nozzle. The acoustic benefits from these devices applied to medium BPR nozzles were similar, and in some cases superior to, those obtained from conventional uniform chevron nozzles. However, none of the low noise technologies provided equivalent acoustic benefits on a model-scale high BPR exhaust nozzle, similar to current large commercial applications. New technologies must be identified to improve the acoustics of state-of-the-art high BPR jet engines.
We have studied the application of the baseline Model Predictive Control (MPC) algorithm to the control of main fuel flow rate (WF36), variable bleed valve (AE24) and variable stator vane (STP25) control of a simulated high-bypass turbofan engine. Using reference trajectories for thrust and turbine inlet temperature (T41) generated by a simulated new engine, we have examined MPC for tracking these two reference outputs while controlling a deteriorated engine. We have examined the results of MPC control for six different transients: two idle-to-takeoff transients at sea level static (SLS) conditions, one takeoff-to-idle transient at SLS, a Bode power command and reverse Bode power command at 20,000 ft/Mach 0.5, and a reverse Bode transient at 35,000 ft/Mach 0.84. For all cases, our primary focus was on the computational effort required by MPC for varying MPC update rates, control horizons, and prediction horizons. We have also considered the effects of these MPC parameters on the performance of the control, with special emphasis on the thrust tracking error, the peak T41, and the sizes of violations of the constraints on the problem, primarily the booster stall margin limit, which for most cases is the lone constraint that is violated with any frequency.
The overall requirements necessary for sensing bearing distress and the related criteria to select a particular rotating sensor were established during the phase I. The current phase II efforts performed studies to evaluate the Robustness and Durability Enhancement of the rotating sensors, and to design, and develop the Built-in Telemetry System concepts for an aircraft engine differential sump. A generic test vehicle that can test the proposed bearing diagnostic system was designed, developed, and built. The Timken Company, who also assisted with testing the GE concept of using rotating sensors for the differential bearing diagnostics during previous phase, was selected as a subcontractor to assist General Electric (GE) for the design, and procurement of the test vehicle. A purchase order was prepared to define the different sub-tasks, and deliverables for this task. The University of Akron was selected to provide the necessary support for installing, and integrating the test vehicle with their newly designed test facility capable of simulating the operating environment for the planned testing. The planned testing with good and damaged bearings will be on hold pending further continuation of this effort during next phase.
The performance and gaseous emissions were measured for a well-stirred reactor operating under lean conditions for two fuels: JP8 and a synthetic Fisher-Tropsch fuel over a range of equivalence ratios from 0.6 down to the lean blowout. The lean blowout characteristics were determined in LBO experiments at loading parameter values from 0.7 to 1.4. The lean blowout characteristics were then explored under higher loading conditions by simulating higher altitude operation with the use of nitrogen as a dilution gas for the air stream. The experiments showed that: (1) The lean blowout characteristics for the two fuels were close under both low loading and high loading conditions. (2) The combustion temperatures and observed combustion efficiencies were similar for the two fuels. (3) The gaseous emissions were similar for the two fuels and the differences in the H2O and CO2 emissions appear to be directly relatable to the C/H ratio for the fuels.
NASA has been commissioned to investigate the feasibility of an orbiting Space Solar Power system that is capable of generating power from space and transmitting the power to earth based rectennas.
The paper presents the hardware implementation and initial tests from a low-power, high-speed reconfigurable sensor fusion processor.
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The NASA F-15 Intelligent Flight Control System project team has developed a series of flight control concepts designed to demonstrate the benefits of a neural network-based adaptive controller. The objective of the team was to develop and flight-test control systems that use neural network technology, to optimize the performance of the aircraft under nominal conditions, and to stabilize the aircraft under failure conditions. Failure conditions include locked or failed control surfaces as well as unforeseen damage that might occur to the aircraft in flight. The Intelligent Flight Control System team is currently in the process of implementing a second generation control scheme, collectively known as Generation 2 or Gen 2, for flight testing on the NASA F-15 aircraft. This report describes the Gen 2 system as implemented by the team for flight test evaluation. Simulation results are shown which describe the experiment to be performed in flight and highlight the ways in which the Gen 2 system meets the defined objectives.
For the past few decades, Rome Air Development Center (RADC) has been conducting research in Artificial Intelligence (AI). When the recent advances in hardware technology made many AI techniques practical, the Intelligence and Reconnaissance Directorate of RADC initiated an applications program entitled Knowledge Based Intelligence Systems (KBIS). The goal of the program is the development of a generic Intelligent Analyst System, an open machine with the framework for intelligence analysis, natural language processing, and man-machine interface techniques, needing only the specific problem domain knowledge to be operationally useful. The development of KBIS is described.
In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.
This presentation provides an overview of the research and engineering in the competency fields of advanced communications and intelligent systems with emphasis on advanced technologies, architecture definition and system development for application in current and future aeronautics and space systems.
The integration of human and machine intelligence in space systems is outlined with respect to the contributions of artificial intelligence. The current state-of-the-art in intelligent assistant systems (IASs) is reviewed, and the requirements of some real-world applications of the technologies are discussed. A concept of integrated human-machine intelligence is examined in the contexts of: (1) interactive systems that tolerate human errors; (2) systems for the relief of workloads; and (3) interactive systems for solving problems in abnormal situations. Key issues in the development of IASs include the compatibility of the systems with astronauts in terms of inputs/outputs, processing, real-time AI, and knowledge-based system validation. Real-world applications are suggested such as the diagnosis, planning, and control of enginnered systems.
Artificial Intelligence and Expert Systems are the important component of RICIS (Research Institute and Information Systems) research program. For space applications, a number of problem areas that should be able to make good use of the above tools include: resource allocation and management, control and monitoring, environmental control and life support, power distribution, communications scheduling, orbit and attitude maintenance, redundancy management, intelligent man-machine interfaces and fault detection, isolation and recovery.