In a competitive market, airlines continually seek solutions that can reduce their operational costs. Flight path optimisation is a commonly pursued approach to this but requires a large amount of data about the flight environment including the weather information, the aircraft performance and the air traffic control (ATC) requirements. Existing programmes require the user to provide this aircraft performance data in advance and are incapable of generating the information on their own. In this study, using a multidisciplinary approach and numerical optimisations, a novel standalone flight path optimiser (SAFPO) solution is proposed and developed to choose the best flight path for a flight between two points in accordance with the cost objectives. SAFPO uses its own performance calculator, predefined ATC routes, and known weather information to find the optimum flight path which minimises fuel consumption and/or flight time. The aerodynamic characteristics of the aircraft are determined using a validated semi-empirical programme called MAPLA, previously developed for light aircraft analysis. Furthermore, the optimisation process consists of a multidisciplinary-feasible (MDF) framework that employs a genetic algorithm (GA) optimiser. The resulting performance characteristics of the aircraft and the optimisation process are compared with the actual information provided within the flight manual of a Beechcraft Baron G58 aircraft. The optimisation results show that SAFPO can be used to make advances in the daily operations of small and local airlines suffering from a lack of aircraft performance data and help them to choose the scenario that best accomplishes their cost objectives.

There are significant variations in the fuel consumption of aircraft during the descent phase of a flight. This paper uses aircraft flight data measurements to develop an improved understanding of these variations. A model of the aircraft engines is developed that is matched to flight data and shown to reproduce the time history of engine parameters. This model is used to determine the overall engine efficiency at each point during a descent. This enables an energy breakdown to be completed, in terms of mechanical energy from fuel, gravitational potential energy and kinetic energy. During descent, the aircraft engines operate at low overall pressure ratios corresponding to low fuel flow rates and low overall efficiencies. On average, the engine overall efficiency during descent is one-third of cruise efficiency. The airframe aerodynamic performance is deteriorated during descent with an average lift-to-drag ratio that is 87% of the average value at cruise. There are also large variations in air-track efficiency, and for the flights analysed the great circle descent distance was found to be 85% of the average descent air distance. To minimise fuel burn, flights should cruise as far as possible before starting descent and follow a trajectory with the shortest possible air distance. The descent air speed should be set to maximise the aircraft lift-to-drag ratio. Such descents could save up to 0.5% of the total aircraft mass in fuel.

Real-time flight data from the Automatic Dependent Surveillance–Broadcast (ADS-B) has been integrated, through a data interface, with a flight performance computer program to predict aviation emissions at altitude. The ADS-B, along with data from Mode-S, are then used to ‘fly’ selected long-range aircraft models (Airbus A380-841, A330-343 and A350-900) and one turboprop (ATR72). Over 2,500 flight trajectories have been processed to demonstrate the integration between databases and software systems. Emissions are calculated for altitudes greater than 3,000 feet (609m) and exclude landing and take-off cycles. This proof of concept fills a gap in the aviation emissions inventories, since it uses real-time flights and produces estimates at a very granular level. It can be used to analyse emissions of gases such as carbon dioxide ($\mathrm{CO}_2$), carbon monoxide (CO), nitrogen oxides ($\mathrm{NO}_x$) and water vapour on a specific route (city pair), for a specific aircraft, for an entire fleet, or on a seasonal basis. It is shown how $\mathrm{NO}_x$ and water vapour emissions concentrate around tropospheric altitudes only for long-range flights, and that the cruise range is the biggest discriminator in the absolute value of these and other exhaust emissions.

Aircraft performance parameters play a critical role in maintaining economic and environmental sustainability in aviation. Furthermore, the ability to calculate aircraft performance parameters accurately for the cruise range contributes to aviation in areas such as the preliminary design of aircraft and air traffic management. This study is focused on cruise range performance, as this is critical to both the evaluation and understanding of the economic and environmental impacts of commercial aircraft. Quick Access Recorders (QAR) data were used for more accurate analysis of the cruise range. The QAR data used in this study included 6,574 short-distance domestic flights by narrow-body turbofan commercial aircraft between 31 different city pairs. To obtain a more accurate cruise range equation, parameters affecting the cruise range performance were determined and studied. First, the drag polar model was improved to take the cambered profile, compressibility effects and cruise airspeeds of commercial aircraft into consideration using the real flight data. Second, Thrust-Specific Fuel Consumption (TSFC) models were compared and the most suitable one for the cruise phase was selected. After these steps, cruise range values were calculated using the Breguet range equation with these improved parameters. When the results of this enhanced range model were compared with the real flight data, the mean absolute percentage error (MAPE) was found to be 2.5% for all the Aircraft and Engine Type Groups (AETGs) considered in the data. This figure corresponds to a 7.9% smaller error than provided by previous range models based on simple parabolic drag polar and TSFC models. According to these results, the application of a simple parabolic drag polar and TSFC is not appropriate for cruise range calculations.

Aircraft performance models play a key role in airline operations, especially in planning a fuel-efficient flight. In practice, manufacturers provide guidelines which are slightly modified throughout the aircraft life cycle via the tuning of a single factor, enabling better fuel predictions. However, this has limitations, in particular, they do not reflect the evolution of each feature impacting the aircraft performance. Our goal here is to overcome this limitation. The key contribution of the present article is to foster the use of machine learning to leverage the massive amounts of data continuously recorded during flights performed by an aircraft and provide models reflecting its actual and individual performance. We illustrate our approach by focusing on the estimation of the drag and lift coefficients from recorded flight data. As these coefficients are not directly recorded, we resort to aerodynamics approximations. As a safety check, we provide bounds to assess the accuracy of both the aerodynamics approximation and the statistical performance of our approach. We provide numerical results on a collection of machine learning algorithms. We report excellent accuracy on real-life data and exhibit empirical evidence to support our modeling, in coherence with aerodynamics principles.

Supersonic civil aircraft present a unique noise certification challenge. High specific thrust required for supersonic cruise results in high engine exhaust velocity and high levels of jet noise during take-off. Aerodynamics of thin, low-aspect-ratio wings equipped with relatively simple flap systems deepen the challenge. Advanced noise reduction procedures have been proposed for supersonic aircraft. These procedures promise to reduce certification noise levels, but they may require departures from normal reference procedures defined in noise regulations. The subject of this article is a take-off performance and noise assessment of a notional supersonic business jet. Analytical models of an airframe and a supersonic engine derived from a contemporary subsonic turbofan core are developed. These models are used to predict take-off trajectories and certification noise levels. Results indicate advanced take-off procedures are helpful in reducing noise along lateral sidelines.