The continuous increase in the number of flights in the last decades caused a steepgrowth of aviation-related pollution to the point that the aviation sector is responsible for3% of the global greenhouse gas emissions. Regulators have been slow at catching up withthis problem, and stringent emission targets have been put in place only very recently. Asa consequence, innovative solutions to make airplanes comply with regulations must besought in a short time span. However, the aviation industry is known to be risk-averse andslow at incorporating innovation, especially when it comes to new aerodynamic designs.A decisive acceleration to the design development process has been given by the in-troduction of aerodynamic shape optimization techniques (ASO), using a computationalfluid dynamics (CFD) code to optimize the shape of a part of an aircraft in order to re-duce its aerodynamic drag and, consequently, its overall carbon footprint. The first partof this dissertation focuses on the optimization of the wing-fuselage junction, a regionwhere complex flow phenomena significantly contribute to the total drag of the aircraft.The ASO discovers an innovative shape of the fuselage that reduces drag by activating apropulsive pressure force that would otherwise be null.However, the CFD code used for the ASO is subject to uncertainties and errors and sois the complex experiment carried out to validate the optimized design. As a consequence,the confidence in the results of Reynolds-averaged Navier-Stokes (RANS) simulationsand wind-tunnel experiments of complex flow phenomena is often limited. Hence, thesecond part of the dissertation explores ways to reduce these errors by developing twovariational data assimilation (DA) techniques that inject sparse experimental data into aRANS code in order to correct the Reynolds stress tensor (RST) computed by a lineareddy viscosity turbulence model, one of the largest sources of errors in a CFD simulation.The DA problem is formulated in a way to guarantee the physical realizability of theRST and the results demonstrate an excellent ability to reconstruct complex flow fields.Finally, the DA methodology is extended to incorporate corrections to the experimentalangle of attack and Mach number, thus being able to simultaneously correct turbulencemodeling and wind-tunnel wall interference errors. The methodology is validated on 2Dand 3D test cases, showing that different corrections for the angle of attack and Machnumber than those from conventional correction techniques are needed for an optimalreconstruction of the flow field around the test object.
|Qualification||Doctor of Philosophy|
|Award date||26 Mar 2021|
|Publication status||Published - 2021|
- Data Assimilation
- Turbulent Flow Reconstruction
- Wind-tunnel corrections