With the focus on the reduction of fossil fuel emissions, aircraft are continuously growing towards higher fuel efficiency. The traditional limits of aircraft performance can be surpassed through the use of composite materials which offer a reduction in aircraft weight. Due to the multidisciplinary nature of aircraft design, integration of different disciplinary analyses is required to arrive at a feasible design. The inclusion of composite design in the preliminary design process, however has become a challenge. This is due to the high computational cost associated with the composite aeroelastic tailoring tools used in the design process. A possible solution is available in the form of surrogate models which can reduce the computational costs. The current work focuses on the development of a methodology that allows the inclusion of surrogate-based model in the optimization for a computationally expensive aeroelastic tool (PROTEUS) developed at TU Delft. The resulting methodology can be expanded to a generic, computationally expensive tool in a multidisciplinary optimization setting. Wing design optimization is carried out based on surrogate modeling methodology and metal based design method. Comparison is made of the final optimized designs based on structural and performance parameters.

With Multidisciplinary Design Analysis and Optimization (MDAO) a fully automated aircraft design analysis is set up and optimization algorithms are used to obtain better designs by balancing the synergy between components. In the EU project AGILE, a new methodology and framework were developed to make the MDAO approach more accessible to industry. A key component of this framework is the KADMOS package. KADMOS is used to formulate large, heterogeneous MDAO problems and their execution process before they are implemented as executable workflows. This thesis focuses on the automation of a key step in the problem formulation for MDAO systems: the execution process definition, i.e. the order and grouping of the disciplines. Several sequencing and decomposition algorithms are developed to optimize the execution order of the disciplines and their division over multiple processors for parallel execution. The algorithms are verified and validated on thousands of MDAO systems using a scalable mathematical test case. Furthermore, the conceptual design of a conventional aircraft is performed using a novel implementation of the Initiator toolbox in KADMOS to test the algorithms in a realistic aircraft design problem. This showed that the algorithms resulted in a setup time reduction due to the automation of the execution process formulation and a reduced convergence time thanks to the improved usage of computational resources.

This research presents the development and implementation of a methodology for rudder design optimization using front-loading. The project has been performed in cooperation with Fokker Aerostructure, which tries to keep up with the increasing competitiveness in the aviation industry. To support this goal, the Knowledge based Rudder and Interface Optimization System (KRIOS) was developed. KRIOS automates the design process of a detailed composite rudder structure, using the existing (in-house developed) Rudder-Generator (RG) and Hinge Design and Optimization Tool (HDOT), which was then wrapped in an optimization framework. KRIOS was verified with and existing rudder, taking into account the model’s current limitations. The developed methodology proposes different optimization strategies that were each studied to assess their benefits and limitations. Thereby, showing how a complex structure like the rudder can be optimized and what strategy achieves a more optimal solution, within a limited amount of time. This helps engineers to increase the product performance of a rudder while reducing the design lead time.

Multidisciplinary Design Analysis and Optimization (MDAO) promises major advances in aircraft design. However, judging from todays aircraft, MDAO is clearly not fully embraced by the industry yet. This is due to technical difficulties (coupling incompatible tools) and non-technical barriers (intellectual property).

Recent research, such as the AGILE project, aims at enabling 3rd generation MDO, where collaboration of distributed teams is key. Maturing MDAO is important to eventually make Overall Aircraft Design possible. However, the industry is still in the early days of 1st generation MDO application.

This thesis proposes a new 1st generation MDO 'pipeline' benefiting from the latest tools developed for 3rd generation MDO. This open-source tool-chain makes it easy to connect analyses and include gradient information. It is shown that gradient inclusion yields dramatically reduced computational costs. Rapid (re)configuration and inclusion of gradient information using this pipeline is demonstrated by considering the Sellar problem and a wing optimization.