Flight deck interval management and delegated separation for equivalent visual operations
An emerging Next Generation Air Transportation System concept — Equivalent Visual Operations (EVO) — can be achieved using an electronic means to provide sufficient visibility of the external world and other required flight references on flight deck displays that enable the safety, operational tempos, and visual flight rules (VFR)-like procedures for all weather conditions. Synthetic and enhanced flight vision system technologies are critical enabling technologies to EVO. Current research evaluated concepts for flight deck-based interval management (FIM) operations, integrated with Synthetic Vision and Enhanced Vision flight-deck displays and technologies. One concept involves delegated flight deck-based separation, in which the flight crews were paired with another aircraft and responsible for spacing and maintaining separation from the paired aircraft, termed, “equivalent visual separation.” The operation required the flight crews to acquire and maintain an “equivalent visual contact” as well as to conduct manual landings in low-visibility conditions. The paper describes results that evaluated the concept of EVO delegated separation, including an off-nominal scenario in which the lead aircraft was not able to conform to the assigned spacing resulting in a loss of separation.
B-737 autopilot design and implementation for simulated flight management system
One of the major investments of the Federal Aviation Administration's (FAA) Next Generation Air Transportation (NextGen) program is in Four-Dimensional (4D) Trajectory Based Operations (TBO). The heart of 4D TBO is the autopilot capability on any National Airspace System (NAS) operating aircraft. Autopilot is an essential component of the Flight Management System (FMS), a vital piece in the future automation of the aviation industry. In this research, we propose a simulation-based design of an autopilot for Boeing-737 that can be extended to other types of aircraft. The paper will present the control mechanism in autopilot altitude hold, as well as detailed results of the control loop tests. Multiple tests consisting of diverse maneuvers are simulated to verify the performance of the aircraft. Finally, the autopilot is compared against a conventional autopilot in performing similar maneuvers and the results are displayed. This simulation-based framework allows for cost-effective and risk-free prototyping, verifying, and validating future 4D TBO concepts.
A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization
This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.