Aircraft engine controllers are designed and operated to provide desired performance and stability margins. The purpose of life-extending-control (LEC) is to study the relationship between control action and engine component life usage, and to design an intelligent control algorithm to provide proper trade-offs between performance and engine life usage. The benefit of this approach is that it is expected to maintain safety while minimizing the overall operating costs. With the advances of computer technology, engine operation models, and damage physics, it is necessary to reevaluate the control strategy fro overall operating cost consideration. This paper uses the thermo-mechanical fatigue (TMF) of a critical component to demonstrate how an intelligent engine control algorithm can drastically reduce the engine life usage with minimum sacrifice in performance. A Monte Carlo simulation is also performed to evaluate the likely engine damage accumulation under various operating conditions. The simulation results show that an optimized acceleration schedule can provide a significant life saving in selected engine components. Guo, Ten-Huei and Chen, Philip and Jaw, Link Glenn Research Center NASA/TM-2005-213373, AIAA Paper 2004-6468, E-14846

Aircraft engine controllers are designed and operated to provide desired performance and stability margins. The purpose of life-extending-control (LEC) is to study the relationship between control action and engine component life usage, and to design an intelligent control algorithm to provide proper trade-offs between performance and engine life usage. The benefit of this approach is that it is expected to maintain safety while minimizing the overall operating costs. With the advances of computer technology, engine operation models, and damage physics, it is necessary to reevaluate the control strategy fro overall operating cost consideration. This paper uses the thermo-mechanical fatigue (TMF) of a critical component to demonstrate how an intelligent engine control algorithm can drastically reduce the engine life usage with minimum sacrifice in performance. A Monte Carlo simulation is also performed to evaluate the likely engine damage accumulation under various operating conditions. The simulation results show that an optimized acceleration schedule can provide a significant life saving in selected engine components.

Turbine engine controllers are typically designed and operated to meet required or desired performance criterion within stability margins, while maximizing fuel efficiency. The U.S. Air Force turbine engine research program is seeking to incorporate sustainable cost reduction into this approach, by considering a life-cycle design objective if the life of the engine is considered as an objective during the design of the engine controller. Specifically during aircraft takeoff, the turbine engines are subject to high temperature variations that aggravate the stress of the material used in their construction and thus a negative effect in their life spans. Therefore, the control strategy needs to be re-evaluated to include operating cost, and extending the life of the engine is one way to reduce that. Life-Extending Control (LEC) is an area that deals with control action, engine component life usage, and designing an intelligent control algorithm embedded in the FADEC. This paper evaluates the LEC, based on critical components research, to demonstrate how an intelligent engine control algorithm can drastically reduce the engine life usage, with minimum sacrifice in performance. Finally, a generic turbine engine is extensively simulated using a sophisticated non-linear model of the turbine engine. The paper concludes that LEC is worth consideration and further research should include development of the damage models for turbine engines, and experimental research that could correlate the damage models to actual damage for turbine engines. This could lead to implementation of online damage models in real-time that will allow for more robust damage prevention.

This handbook analyzes and develops methods and models to optimize solutions for energy access (for industry and the general world population alike) in terms of reliability and sustainability. With a focus on improving the performance of energy systems, it brings together state-of-the-art research on reliability enhancement, intelligent development, simulation and optimization, as well as sustainable development of energy systems. It helps energy stakeholders and professionals learn the methodologies needed to improve the reliability of energy supply-and-demand systems, achieve more efficient long-term operations, deal with uncertainties in energy systems, and reduce energy emissions. Highlighting novel models and their applications from leading experts in this important area, this book will appeal to researchers, students, and engineers in the various domains of smart energy systems and encourage them to pursue research and development in this exciting and highly relevant field.

Research advances in embedded computational intelligence, communication, control, and new mechanisms for sensing, actuation, and adaptation hold the promise to transform aerospace. The result will be air and space vehicles, propulsion systems, exploration systems, and vehicle management systems that respond more quickly, provide large-scale distributed coordination, work in dangerous or inaccessible environments, and augment human capabilities. Advances in Intelligent and Autonomous Aerospace Systems seeks to provide both the aerospace researcher and the practicing aerospace engineer with an exposition on the latest innovative methods and approaches that focus on intelligent and autonomous aerospace systems. The chapters are written by leading researchers in this field, and include ideas, directions, and recent results on intelligent aerospace research issues with a focus on dynamics and control, systems engineering, and aerospace design. The content on uncertainties, modeling of large and highly non-linear complex systems, robustness, and adaptivity is intended to be useful in both the sub-system and the overall system level design and analysis of various aerospace vehicles.A broad spectrum of methods and approaches are presented, including: * Bio-Inspiration * Fuzzy Logic * Genetic Algorithms * Q-Learning * Markov Decision Processes * Approximate Dynamic Programming * Artificial Neural Networks * Probabilistic Maps * Multi-Agent Systems * Kalman, particle, and confidence filtering