Landing Gear is one of the most complex and critical sub-systems of an aerial vehicle. Statistics indicate that a considerable number of aircraft accidents occur during take-off and landing, underscoring the significance of stringent processes for designing landing gear.
Wheel-based oleo-pneumatic landing gear systems have emerged as a preferred choice among various energy absorption systems for a wide array of aircraft, including lightweight UAVs, utility, military, passenger, and commercial airplanes. These systems offer superior efficiency compared to alternative designs and play a pivotal role in on-ground operations such as steering, braking, ground manoeuvring, and towing.
In the realm of contemporary engineering, where there is a growing demand for more innovative and intricate products within tighter time and cost constraints, Computer-Aided Design (CAD)/ Computer-Aided Engineering (CAE) has become an indispensable medium. It enables engineers to carry out essential engineering processes, including creation, design, simulation, analysis, optimization, and modifications. With these challenging requirements in mind, a design of landing gear was undertaken, utilizing advanced state-of-the-art simulation tools for system design, multibody dynamics, fluid dynamics, and structural analysis.
The objective was to design, via Multiphysics CAE simulations, a landing gear system with particular specifications such as tail-down landing and high horizontal landing speed. The design of the landing gear system adheres to the standards set by FAR 23 . In this article, we explain the significance and utilization of CAE simulations in the design process of the landing gear with special focus on the use of directly coupled multibody (MBD) and computational fluid dynamics (CFD) simulations. The focus is on two essential functions: energy absorption performance and interaction of structural members with both the airframe and the pavement. By employing CAE simulations, these critical aspects can be designed and thoroughly optimized for optimal performance.
This article appeared in the January 2024 issue of BENCHMARK.
|Swarnkar. R Vasudeva. A Sharma. G Mudgal. G
|10th January 2024