||Wright-Patterson AFB, OH 454337103
The next generation of gas turbine engines for aircraft and power generation systems are facing new requirements for operational and maintenance capabilities in addition to ever-increasing requirements for performance, reliability, and reduction of cost of ownership. Future engine systems must develop integrated thermal management, eliminate or limit the operational impact of fuel cooling, and provide system-wide Prognostics Health Management (PHM) without affecting reliability, performance, or size limits. In order to meet these goals, engine control systems must have the ability to respond to the operational environment real-time. A modern FADEC is responsible for many subsystems, for optimizing performance, and detecting failures. Classical control systems are inadequate for performing all these tasks. To manage these responsibilities and provide reliable system operation, use of new control algorithms/architectures, along with distributed, localized active control strategies are needed. Implementation of these strategies requires distributed sensors, actuators, and processors that can withstand harsh environments.
Thoughtful analysis of performance of loosely coupled distributed systems under dynamic conditions is essential for gaining a fundamental understanding of their behavior. The analysis and validation of these strategies on a real FADEC system in a real environment is time consuming and expensive. A simulation-based research technique is a viable alternative.
In light of these facts, proposals are solicited in any of the following areas:.
1. Engine Controls: New control techniques including model-based control and model-predictive control are prime candidates for large-scale multivariable plants. Application of nonlinear constrained control strategies may result in new control algorithms and/or requirements that influence the design of distributed architectures. Other areas of research include, but are not limited to, control algorithms for distributed sensors, actuators, and other propulsion subsystems and failure management strategies based on artificial intelligence (AI) or machine learning (ML) techniques.
2. Integrated Control of Power, Propulsion and Thermal Management Systems: Future aircraft will have more subsystems with conflicting cooling, thermal management, and power needs. Military planes may also have unique high power payloads. The propulsion systems in these aircraft, in addition to incorporating variable engine features, will require high power generation/extraction, and advanced thermal management systems. Traditional methods for controls are not very effective at optimizing energy/power flow in these systems.
Next generation aircraft will include technologies that require Integrated Power, Propulsion and Thermal Management Systems (IPPTMS). In IPPTMS, megawatt-scale propulsion and power is merged with thermal management to form a complex system of systems in which energy is dynamically routed across the aircraft in multiple forms. Interaction between subsystems gives rise to constraints that cannot be handled properly by the control of individual systems. Additionally, propriety information and intellectual property is unique to each subsystem, and generally may not be shared with other subsystems. These factors and others make the control and optimization of an interconnected system beyond the capability of traditional control methodology.
3. Simulation, Integration, Software Verification and Validation (V&V): In the distributed control framework, the stability and performance of a closed loop control system is impacted by various communication constraints such as: latency, bandwidth limitations, non-deterministic operation, asynchronous operation, and/or unreliable communications. The best vehicle for analyzing these effects and developing new solutions is a simulation. The result of these simulations should be the development of requirements for the communication networks and validated algorithms for managing events and disturbances. A distributed architecture also presents an opportunity to reduce FADEC software complexity and V&V cost via partitioning and modularization.
4. Diagnostics and PHM: In the past, most FADEC systems utilized a combination of Built-In Tests (BIT) and Fault Detection and Isolation (FDI) algorithms to detect failures. The transition to distributed architecture presents an opportunity to partition the PHM functions and functionally integrate them with the control algorithms for improving the overall system availability despite degradations. In a distributed FADEC control system with distributed PHM the amount and types of health/degradation information is likely to be higher quality and greater quantity. The integration of this information in real-time leads to development and validation of a diagnostics reasoner - an area of research interest because of its potential for extracting the full benefits PHM in a distributed architecture.
5. Cybersecurity of Aircraft System: Considering cyber security, there is a need to study the advantages and disadvantages of implementing backup control strategies at the federated level vs. integrated supervisory VMS (vehicle management system) level. This task is not to assess the vulnerabilities and detection/prevention of cyber-attack, but to investigate the best approach for vehicle-propulsion-power/thermal control in the case of primary digital control compromise. Requirements for advanced engine technology maturation programs have included the capability for backup engine control to ensure flight safety and “get home” capability in the case of cyber-attack.
Distributed Propulsion Control; Model-Based Control; PHM; Algorithm Development; Real-Time Simulation; FADEC; Sensor; Software V&V; Aircraft System; Cyber Security