Aircraft design optimization

GeoMACH: Geometry-centric MDO of aircraft configurations with high-fidelity

References: [Hwang and Martins, AIAA 2012-5605], [Ivaldi et al., AIAA 2015-3436]

GeoMACH is a NASA-funded effort to develop an open-source aircraft geometry modeling capability to support high-fidelity aircraft design optimization. As a geometry modeler, it is unique in that it is designed to model unconventional configurations with a smooth parametrization with efficient derivative computation, all while maintaining compatibility with structured multi-block CFD. It uses a watertight representation of the aircraft outer mold line (OML) using an untrimmed union of tensor-product B-spline surfaces, and uses an interpolation approach to handle intersections between aircraft components without introducing discontinuities or even non-differentiable points.

3-D Aerodynamic shape optimization of a truss-braced wing design eliminates the shock in the region between the fuselage, main wing, and struts, reducing drag by roughly 60%. [Hwang et al., AIAA 2014-2041]

Large-scale MDO of an electric aircraft for on-demand mobility

Reference: [Hwang and Ning, AIAA 2018-1384]

NASA's X-57 Maxwell is an experimental aircraft developed as a demonstrator for distributed electric propulsion (DEP). In this context, DEP refers to the placement of small electrically-driven propellers across the leading edge of the wing, to increase airflow over the wing. These 'high-lift propellers' activate during low-speed phases of flight only (take-off and landing), where the increased dynamic pressure enable the lift requirements to be met with a much smaller wing. This enables a higher-aspect-ratio wing for better aerodynamic efficiency during cruise and reduced susceptibility to turbulence.

Here, we develop a system-level multidisciplinary model of the aircraft that includes the vortex lattice method for aerodynamic analysis, blade element momentum theory for propeller analysis, 1-D structural finite element analysis for the wing, and the equations of motion across the mission profile. We optimize the battery sizing, altitude profile, velocity profile, propeller RPMs, angle of attack, propeller blade chord and twist distributions, and propeller diameters, with range as the objective function. MDO results in a 12% increase in range, obtained through efficiency improvements that enable a span increase and a larger high-lift propeller diameter, and the span increase allows the aircraft to carry a larger battery for the increased range.

Large-scale MDO results in a 12% increase in range through a larger wing span. [Hwang and Ning, AIAA 2018-1384]