The Aeronautical Journal 1 Page 1 of 21. © Royal Aeronautical Society 2022 Data-driven mission profile parametrization impact on the aircraft and network optimization Alejandro A. Rios Cruz, Mayara Cond´ e Rocha Murc ¸a, Bento S. de Mattos, and Jos ´ e Alexandre T.G. Fregnani aarios@ita.br Department of Aircraft Design Aeronautics Institute of Technology S˜ ao Jos ´ e dos Campos, S˜ ao Paulo Brazil ABSTRACT Operational factors such as airspace constraints prevent aircraft from flying the shortest dis- tances between the origin-destination airports, impacting fuel consumption during flights. The extra distance flown above a benchmark distance representing the shortest route (often the great circle distance) is referred to as horizontal en route inefficiency (HIE). Recent opera- tional data have shown that HIE may typically vary between 2 to 6 percent (in Europe) and can reach higher values depending on how the airspace constraints influence the flight tra- jectory. The consequences of these inefficiencies concern the air transportation system and how it sets the appropriate fuel economy improvement goals for future aircraft and airline operations to lower economic and environmental impacts. This work explores the impact of the increased distance flown from the aircraft design perspective by incorporating a more realistic scenario by including data-driven (DD) mission profiles in a framework that opti- mizes the aircraft and the airline transport network using the system-of-systems approach. For this purpose, machine learning methods were applied to extract information from Auto- matic Dependent Surveillance-Broadcast (ADS-B) data relative to 10 of the busiest Europe airports over six months. The processed data defined the principal flight patterns used to feed an aircraft performance module whose output is the operating cost. The final framework is incorporated into a genetic algorithm optimizer which includes an aircraft sizing module, a DD mission performance evaluator, and an embedded airline network flow optimizer. The optimization objective is set to maximize the network profit. The results reveal that more conservative estimations regarding profit and direct operating cost are achieved when realistic mission profiles are considered in the aircraft design. Keywords: System of systems; Network optimization; Multidisciplinary design optimiza- tion; Data-drive; ADS-B; Machine learning; Clustering Received DD MM YYYY; revised DD MM YYYY; accepted DD MM YYYY.