Modelling flexible thrust performance for trajectory prediction applications in ATM

Reduced thrust operations are of widespread use nowadays due to their inherit benefits for engine conservation. Therefore, in order to enable realistic simulation of air traffic management (ATM) scenarios for purposes such as noise and emissions assessment, a model for reduced thrust is required. This paper proposes a methodology for modelling flexible thrust by combining an assumed temperature (AT) polynomial model identified from manufacturer take-off performance data and public thrust models taken from typical ATM performance databases. The advantage of the proposed AT model is that it only depends on the take-off conditions —runway length, airport altitude, temperature, wind, etc. The results derived from this methodology were compared to simulation data obtained from manufacturer’s take-off performance tools and databases. This comparison revealed that the polynomial model provides AT estimations with sufficient accuracy for their use in ATM simulation. The Base of Aircraft Data (BADA) and the Aircraft Noise and Performance (ANP) database were chosen as representative of aircraft performance models commonly used in ATM simulation. It was observed that there is no significant degradation of the overall accuracy of their thrust models when using AT, while there is a correct capture of the corresponding thrust reduction.Peer ReviewedPostprint (published version

Investigation on soft computing techniques for airport environment evaluation systems

Spatial and temporal information exist widely in engineering fields, especially in airport environmental management systems. Airport environment is influenced by many different factors and uncertainty is a significant part of the system. Decision support considering this kind of spatial and temporal information and uncertainty is crucial for airport environment related engineering planning and operation. Geographical information systems and computer aided design are two powerful tools in supporting spatial and temporal information systems. However, the present geographical information systems and computer aided design software are still too general in considering the special features in airport environment, especially for uncertainty. In this thesis, a series of parameters and methods for neural network-based knowledge discovery and training improvement are put forward, such as the relative strength of effect, dynamic state space search strategy and compound architecture. [Continues.

Wind Turbine Noise and Wind Speed Prediction

In order to meet the US Department of Energy projected target of 35% of US energy coming from wind by 2050, there is a strong need to study the management and development of wind turbine technology and its impact on human health, wildlife and environment. The prediction of wind turbine noise and its propagation is very critical to study the impacts of wind turbine noise for long term adoption and acceptance by neighboring communities. The prediction of wind speed is critical in the assessment of feasibility of a potential wind turbine site. This work presents a study on prediction of wind turbine noise and wind speed using a noise propagation model and artificial neural network (ANN) methods respectively. The noise propagation model utilized Openwind, a software package used for wind project design and optimization, to predict a noise map based on inputs acquired from a potential wind energy demonstration site in Georgia. The resultant noise of the wind turbines and the ambient surroundings were predicted in the neighborhood for different scenarios. The nonlinear autoregressive (NAR) neural network and nonlinear autoregressive neural network with exogenous inputs (NARX) were used to predict wind speed utilizing one year of hourly weather data from four locations around the US to train, validate, and test these networks. This study optimized both neural network configurations and it was demonstrated that both models were suitable for wind speed prediction. Both models were implemented for single-step and multi-step ahead prediction of wind speed for all four locations and results were compared. NARX model gave better prediction performance than NAR model and the difference was statistically significant

Imitating Driver Behavior with Generative Adversarial Networks

The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems. Traditional modeling methods have employed simple parametric models and behavioral cloning. This paper adopts a method for overcoming the problem of cascading errors inherent in prior approaches, resulting in realistic behavior that is robust to trajectory perturbations. We extend Generative Adversarial Imitation Learning to the training of recurrent policies, and we demonstrate that our model outperforms rule-based controllers and maximum likelihood models in realistic highway simulations. Our model both reproduces emergent behavior of human drivers, such as lane change rate, while maintaining realistic control over long time horizons.Comment: 8 pages, 6 figure

A Route Selection Problem Applied to Auto-Piloted Aircraft Tugs

The antithetical needs of increasing the air traffic, reducing the air pollutant and noise emissions, jointly with the prominent problem of airport congestion spur to radically innovate the entire ground operation system and airport management. In this scenario, an alternative autonomous system for engine-off taxiing (dispatch towing) attracts the interest of the civil aviation world. Even though structural and regulatory limitations undermine the employment of the already existing push-back tractors to this purpose, they remain the main candidates to accomplish the mission. New technologies are already under investigation to optimize towbarless tractor joints, so as to respond to the structure safety requirements. However, regulation limitations will soon be an issue. In this paper, a software solution for a route selection problem in a discretized airport environment is presented, in the believe that a full-authority control system, including tractors’ selection logic, path planning and mission event sequencing algorithms will possibly meet the regulation requirements. Four different algorithms are implemented and compared: two Hopfield-type neural networks and two algorithms based on graph theory. They compute the shortest path, accounting for restricted airport areas, preferential directions and dynamic obstacles. The computed route checkpoints serve as a reference for the tractor autopilot. Two different missions are analyzed, concerning the towing of departing and arriving aircraft respectively. A single mission consists of three different events, called phases: Phase 1 goes from the actual tractor position (eventually the parking zone) to the selected aircraft (parked or just landed); Phase 2 is the actual engine-off taxi towing; Phase 3 is the tractor return to its own parking zone. Both missions are simulated and results are reported and compared