What are Tail-Centric performance databases and why is it important?

Let’s start by a definition, the aircraft performance is the combination of its engines and airframe capacities and characterizes what the aircraft is capable of flying in terms of routes and associated payloads.

To represent this aircraft performance, Original Equipment Manufacturers (OEM’s) create a digital twin of the aircraft embedded in a database. These databases are dispatched and used by the airlines in operation in various tools (flight planning software, ground based performance tools and in-flight calculations such as those from the Flight Management System (FMS).

The process of generating these databases is complexe, requires skills, experience ,actual flight tests data and has been refined by manufacturers for decades. In-house group of specialized people in aerodynamics, engines, flight tests, aircraft performance engineers assess, validate and generate the most accurate representation of what the aircraft can achieve at an entry into service level. 

As explained in the previous article (What’s more to say about aircraft performance monitoring?), the in-service aircraft performance deviates from entry into service and is accounted for by the Performance Factor. This factor is widely used and adjusts the fuel consumption, however, studies show that the levels of performance do not change uniformly. Consequently, to go beyond the adjustment of the mean level of the performance deviation, the complete database should be adjusted per aircraft and during its lifecycle.

Thanks to the emergence of new technology, higher computation power and statistical analysis (neural network, machine learning, etc..), the process of generating a performance database has been greatly improved. Coupled with the means of treating massive operational data coming from the black-box (FDR flight data recorder), it allows now to go this step forward by generating tail-centric performance databases in a more continuous and streamlined way. 

But the results provided by algorithms and machine learning need to be interpreted and extrapolated beyond data, to cover specific operations that are not often encountered in operations (one engine inoperative for example). Therefore, to ensure the most precise computations and more accurately represent the aircraft: the technical skills, physics understanding, and aircraft manufacturer knowledge needs to be integrated to achieve the full benefits of such a solution.

There is only one optimized way to fly an aircraft and tail-centric performance databases will help reduce emissions and meet the sustainability targets that the whole aviation industry set, the benefits are large and will be detailed in another article. To achieve this vision, at NAVBLUE we took an iterative approach to contribute along the way by optimizing each phase (climb, cruise, descent), my next article will focus on accounting for the aircraft degradation in order to optimize descent trajectory for Airbus aircraft.

Kevin Ward

Fuel & Flight Efficiency Expert at NAVBLUE

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