Gaps and Opportunities in Aircraft Performance Research
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Abstract
Analysis of aircraft performance underpins the design and certification and safe operation of modern aviation systems. In the past, much of the work in this field relied on simple aerodynamic models, standard atmospheric assumptions, and performance values from primarily controlled flight tests. While these approaches have preserved a degree of inherent safety and compliance with regulatory requirements, the methods used have remained relatively unchanged for decades and are occasionally becoming insufficient for the requirements of modern aviation. This paper reviewed the status quo of methodology, outlined some of the static assumptions on which all these methods rely, and noted some of the limitations born from their failure to adapt in a realistic way. We identified several large gaps in practice, which included a disconnect between high fidelity modelling, real time environmental sensors, in-flight optimizations, and a failure to properly leverage new digital technologies like machine learning and digital twins. We explored how challenges associated with regulatory conservatism, data availability, and computational integration are demonstrable examples of systemic barriers that continue to inhibit innovation. We concluded with a vision for the future where adaptive performance frameworks that incorporates high-fidelity simulation, data-driven methods, and regulation will help to deliver the future of aviation, where innovations on safety, efficiency, and sustainability are common, and performance analysis is more than just a means to safeguard operations from risk but also supports aviation's transformation through technology.
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