Validation Of A Low-Fidelity Tool For Sizing Loads Prediction Across Multiple Aircraft Architectures

Abstract

An efficient conceptual design phase must be characterized by fast and reliable procedures that can produce high-level sensitivity analysis and assess the impact of design variations. The increasing complexity of aircraft architecture requires multipurpose solutions that go beyond simple approaches such as regression models, demanding more flexible techniques for route design exploration. This paper presents the specifications of a Python-based airframe loads analysis tool, TONALE, which aims to predict the sizing conditions and associated loads that act on an aircraft. Starting from inputs readily available from the very first iterations, this tool can perform low-fidelity aerodynamic analysis and structural evaluations on a reduced-order model of both traditional and unconventional configurations. The objective is to discuss the results in terms of sizing envelopes by highlighting both the capabilities and the criticalities that emerged during the validation process, for different components and architectures.