Multi-disciplinary Design Analysis and Optimization (MDAO) combines system engineering principles with numerical optimization methods to evaluate and optimize design configurations. However, manufacturing and assembly considerations are often neglected, resulting in theoretically optimal designs that require costly redesigns to ensure producibility. This research integrates assembly requirements for multi-part designs into MDAO workflows, continuing on previous research by the TU Delft which addressed manufacturing considerations for single-part products. The methodology incorporates Joint Assessment Methods (JAM) and Geometry Independent Assembly (GIA) tools within the Design and Engineering Engine (DEE). The DEE is a framework for building MDAO workflows based on design requirements. JAMs assess joint feasibility for specific assembly methods, while GIAs evaluate non-joining constraints such as material incompatibility. A wing rib-skin panel use case demonstrates this approach, with the optimizer selecting an optimal, producible solution. Although effective, the method introduces significant computational overhead, with iteration time ranging from 85 to 205 seconds, depending on the assembly method. Future research may reduce this through early break-off of unfeasible solutions and can include new assembly requirements, to optimize more complex products.

When designing complex systems, choices related to the system architecture, the description of the functions and components of a system, greatly influence to which extent design goals can be achieved. The architecture design space, the set of all possible architectures for a given design problem, can be extremely large due to the combinatorial nature of architectural choices. Additionally, the integration of innovative technologies for future systems requires the application of multidisciplinary, simulation-based evaluation. These two challenges are addressed by System Architecture Optimization (SAO): the combination of numerical optimization algorithms with multidisciplinary, simulation-based evaluation, to explore architecture design spaces without requiring the evaluation of all architectures.

A function-based method is developed for modeling SAO problems. The Architecture Design Space Graph (ADSG) is developed to represent function-to-component allocation, component characterization, and component connection choices. Algorithms are developed to automatically encode an ADSG as a numerical optimization problem, and to decode generated design vectors into architecture instances. The modeling method is made available through a web-based GUI application.

SAO problems are solved using evolutionary and Bayesian Optimization (BO) algorithms. A new hierarchical sampling algorithm is developed to prevent under- or over-sampling regions in the design space when building the initial Design of Experiments (DoE). Investigations are performed into correction algorithms and into exposing information about design space hierarchy. For BO, a strategy is developed to deal with hidden constraints stemming from simulation failures. It is shown that both evolutionary and BO algorithms can solve SAO problems, however BO can do so with 92% less function evaluations as demonstrated for a realistic SAO problem.

Multidisciplinary, simulation-based evaluation in large, cross-organizational systems engineering projects is enabled by leveraging collaborative Multidisciplinary Design Analysis and Optimization (MDAO). Methods are developed for automatically modifying the behaviour of a collaborative MDAO workflow for each evaluated system architecture, for propagating all information about the system architecture to the Central Data Schema (CDS) as used in collaborative MDAO, and for executing the architecture generation process in the computational environment where the workflow is executed.

System integrators across industry adhere to the so called V-model to manage their product development process. The V-model starts with the conceptualization of a system architecture, guided by functional and non-functional requirements and ending with the verification and validation of the designed product. In the conceptual design stage, crucial decisions impacting the performance of the final product are made. However, due to limited knowledge about the behavior of emerging technologies, unknown effects of connecting various components, and the low technology readiness levels (TRL) of components, the architectural design space for innovative design projects becomes large and uncertain. This uncertainty is traditionally managed through engineering judgement and large safety factors, which restrict design space exploration and often lead to sub-optimal solutions.

To address this, an uncertainty-based system architecture design exploration and optimization framework is developed in which system architecture design inputs and models, commonly used in model-based systems engineering, are treated as stochastic. This is integrated into a multidisciplinary multi-objective surrogate based optimization framework aimed at finding robust optimal vehicle system architectures.

The proposed framework has been successfully applied to the design of a heavy duty electric vehicle drive train. This resulted in increased system-specific knowledge of the vehicle’s performance in real-world applications by finding the sensitivity of the optimal design solution to operational- and model-uncertainties, thereby facilitating better informed design decisions, enabling trade-offs and reducing project risks.

Early design decisions have a significant influence in the final success of the project. One of the most important decisions is to determine the system architecture, as it highly impacts the performance of the system. System architecture optimization can be used to determine the best possible architectures through the formulation of an optimization problem, allowing to explore the design space without traditional bias and conservatism.

MDO can be used to evaluate the performance of each architecture, allowing to consider the interactions between the multiple and coupled disciplines involved in the design process. To do this, MDO platforms have to satisfy multiple requirements, including the automatic readjustment of the MDO problem for each system architecture. They also have to be adapted to collaborative MDO, so that they can be used in real industrial cases.

Before this research, there was not MDO platform that satisfied all these requirements, impeding the integration of system architecture optimization in the industry. The MDO platform consisting on MDAx and RCE was adapted to collaborative MDO and satisfied all requirements to be used as architecture evaluator, except the automatic readjustment of the MDO problem. To fill the previous gap, the main objective of this research has been to extend MDAx backend code to allow the formulation of these
dynamic MDO problems, allowing to use it in the system architecture optimization process.

To achieve this, first the possible modifications that the system architectures can cause in the MDO problem, called architectural influences, are determined. Then, some possible implementation strategies MDO platforms can use to deal with these influences are presented. After that, the actual implementation process used to extend MDAx backend code is widely discussed.

Afterwards, a benchmark problem based on Fourier series is used to verify the implementation. A real engineering problem, based on the design of a space multistage rocket, is also used as validation to show the potential tool, and more generally, of the methodology. Finally, some conclusions and possible future steps are drawn.

In conclusion, this research allows to reduce the existing gap between system architecture optimization and MDO by obtaining an MDO platform that can be used as an architecture evaluator. Also, the different requirements identified for the inclusion of architectural influences, as well as the benchmark problems discussed, are aimed to help developers to extend their MDO platforms to be adapted to system architecture optimization, reducing the barriers for its implementation in the industry.

With growing demand for air transportation, it is crucial to improve the efficiency of aircraft systems design. A key area of focus is the implementation of manufacturability considerations into the design process at early stages. This could be done by integrating manufacturability analysis modules into an MDAO workflow, however, each production method places unique requirements on the system. This study investigates the impact of considering manufacturing in the initial design phase, using a novel dynamic MDAO workflow that is capable of changing its analysis disciplines, design variables and constraints based on the current design point within an optimisation. This methodology was tested on a wing rib design case, with two production methods available: machining for metal ribs and stamp-forming for composite ribs. Integrating DFM analyses into the design study improved the trustworthiness of optimum results, removing non-manufacturable rib designs. Dynamic MDAO workflows yielded faster multi-objective optimizations and efficient handling of correlated objectives compared to conventional static workflows while providing similar results.

Due to the high aspect ratio and low induced drag of aircraft with strut-braced wings, they are being extensively studied due to their fuel-saving potential. This thesis aims to expand the fundamental knowledge in the design of aircraft with Strut Braced Wings (SBW) by achieving the following objectives.

The objectives of this thesis are twofold, with their primary focus on evaluating the effect of changing the typical design variables for an SBW, such as the wing span, root chord, spanwise strut attachment location, wing taper ratio, wing sweep and engine location on the aerodynamics, weights and performance of an SBW. The first objective was to evaluate the significance of including the propeller slipstream effects in the preliminary stage design optimisation of a low-speed, short-range SBW. This aligned with the hypothesis that the performance of an SBW could be enhanced by using swirl recovery. The second objective of this thesis was to investigate the sensitivity of various design variables to the aircraft’s fuel burn and other performance metrics at the optimum SBW design.

A Design of Experiments (DOE) approach was used to explore the design space involving the typical influential SBW design parameters. The geometry and mesh files were created using OpenVSP for every DOE point. This was followed by the aerodynamic analysis in a panel method-based software called Flightstream, which could capture the relevant aerodynamic flow phenomena with reasonable accuracy. The aerodynamic analysis was performed twice- first without considering slipstream effects and second by simulating them. Regression-based analytical equations, particularly designed for the weight estimation of the wing and strut, were used from the literature. Empirical equations from FLOPS were used for the rest of the aircraft components. The performance of the SBW was calculated iteratively using the Breguet range equation along with a few modifications. All the SBW designs were constrained by a maximum wing loading criterion.

From two sets of the DOE results (with and without propeller effects), it was observed that the two optimum SBW designs were identical in terms of external geometry and performance. A deeper investigation revealed that the variation in the spanwise engine positioning (to maximise the swirl recovery) resulted in a marginal change of the induced drag (less than two drag counts). It was concluded that the propeller slipstream effects could be excluded from the preliminary stage, design optimisation of a propeller-powered, short-range SBW to reduce computational expense. However, the results should be treated with a pinch of salt due to the limitations of panel methods. Moreover, the SBW was optimised only for cruise, not for other flight phases such as take-off and climb, which may benefit from swirl recovery.

Finally, a sensitivity analysis was performed to evaluate the trends in the performance metrics, such as fuel burn, lift-to-drag ratio, wing loading and maximum take-off weight when subjected to variations in the design variables at the optimum. The impact of the fuel burn was quantified, while the other performance metrics were qualitatively answered. The research findings revealed that certain design variables had a greater influence on fuel burn than others when varied by ±10% w.r.t their optimum value...

System architecting is one of the first stages of the engineering problem-solving process. Pivotal decisions regarding the system's overall configuration are taken in this phase. Consequently, decision support tools like system architecture optimization are needed to effectively assess the architectural design space. However, system architecture optimization problems include several features that make them challenging to tackle with traditional optimization techniques, including multiple objectives and mixed-discrete, hierarchical design spaces.

This work examines three algorithms capable of handling the mixed-discrete and multi-objective nature of system architecture optimization problems: genetic algorithms (such as NSGA-II), Bayesian optimization (BO), and the Deep Deterministic Policy Gradient (DDPG) reinforcement learning framework. These algorithms act as the engines that power three strategies capable of addressing hierarchical design spaces: \textit{global exploration}, \textit{nested optimization}, and \textit{decision chain}. While global exploration optimizes all design variables in a single loop and uses imputation on the inactive variables, the nested optimization method divides the problem into multiple hierarchical optimization loops. In contrast, the decision chain approach reframes the problem into a sequential decision-making process within an environment where an agent's actions alter design instances.

Test problems of varying complexity are used to evaluate the suitability of these algorithms and strategies. First, an airfoil optimization problem demonstrates the basic functionality of the selected algorithms with satisfactory results comparable to those found in the literature for the same problem. Next, a mixed-discrete version of the ZDT1 multi-objective problem is evaluated for different problem sizes and proportions of continuous and discrete variables. In this family of problems, the NSGA-II algorithm emerges as the most reliable and best-performing. At the same time, DDPG struggles with many variables, and Bayesian optimization runs into computational performance issues when dealing with large discrete combinatorial spaces.

Later, the architecture optimization strategies are evaluated on two versions of the Goldstein problem, a hierarchical and multi-objective test case. In the first version, which only considers eight architectures, all three strategies show competency in solving the optimization problem, but the global exploration strategy consistently shows the best performance. The decision chain strategy is discarded from further study at this stage due to relatively low performance and implementation difficulties. The second version of the Goldstein problem can be adjusted to different degrees of hierarchy and numbers of architectures. In this case, global exploration rises again as the best-performing strategy.

Finally, a real-world aileron structural optimization study at GKN Fokker shows that the implemented global exploration and nested strategies can find better designs than a random sampling of the design space. The best design for this test problem is achieved with a two-level nested strategy, combining NSGA-II in the outer loop and Bayesian optimization in the inner loop.

In conclusion, this research proposes three adaptive strategies powered by various algorithms to solve system architecture optimization problems. The findings suggest a preference for global exploration when a complete design vector can be pre-established. Otherwise, the nested optimization strategy, which can combine different algorithms' strengths and does not need a predefined design vector, emerges as the most suitable choice for more complex scenarios.

Knowledge Based Engineering (KBE) is a particularly relevant technology for addressing the increasing complexity of engineering systems, the need for rapid time-to-market, and the need for achieving reductions in the costs of product development. KBE applications can be effective means of automating repetitive engineering design tasks, enabling engineers to enhance their designs through optimization and innovation. However, the current development process of KBE applications can be improved, as it has shortcomings that limit a wider adoption of KBE technology. Currently, two primary approaches are employed in the development of KBE applications. The first approach involves directly coding the engineering knowledge within the application itself, while the second approach entails modeling the engineering knowledge outside the application and subsequently converting it into executable code, manually. Both approaches result in applications that are perceived, to varying degrees, as “black boxes”. This makes it challenging to understand how the application reaches its conclusions, which can hinder the end-user's trust in the application and limit its acceptance. To date, a suitable methodology to effectively support KBE app development is lacking, which has considerable implications on the time required for application development, as well as the quality of the applications in terms of traceability of requirements and domain knowledge within the KBE application code, the applications' maintainability and scalability, and (eventually) the ability to preserve and efficiently reuse engineering knowledge.
To address the outlined shortcomings of the current KBE app development process, this thesis proposes a novel framework for the development of KBE applications, based on Model-Based Systems Engineering (MBSE) concepts, to model domain knowledge and requirements, and to support (semi-)automatic generation of KBE apps through visual editing, as opposed to standard coding. The key objectives of this framework are to improve knowledge capture and formalization, requirements traceability, and knowledge reuse in KBE applications. In the proposed framework, the knowledge required for developing a KBE application is first captured in a formal knowledge model that uses the industry-standard Systems Modeling Language (SysML). Source code is then automatically generated for the targeted KBE system (ParaPy) using a model-to-code tool developed in this research. Traceability of requirements onto the various elements of the KBE app architecture is also provided, thereby reducing the typical black-box effect of KBE applications. Furthermore, the framework allows to reuse knowledge from previously generated knowledge models, enabling effective project-to-project knowledge transfer.
This thesis presents the development of three distinct KBE applications using the proposed framework, with the aim of evaluating it in terms of ease of modeling, development time, and quality of the automatically generated (skeleton) code. Preliminary results show that the learning curve to modeling is intuitive and easy enough to learn; the time required for generating the knowledge models is lower than current modeling processes; the automatically generated code is error-free, well-structured, and complies with existing coding standards, providing a correct starting point for further app development, while resulting in time savings in the development of the app skeleton.

In the conceptual design phase, OEMs perform technology assessment to identify feasible solutions for their new aircraft. When the OEM is unable to assess a certain technology themselves, they consult Tier-1 suppliers to involve
them in the design process. The suppliers have higher fidelity analysis tools that can help the OEM with their assessment, but the characteristics of such tools (e.g. high detail of input, computational time to execute) prevent an in-depth exploration of the design space. This thesis proposes a framework that enables a closer collaboration between the OEM and their supplier. The collaboration is streamlined through a web-based collaborative environment. Front-loading principles are applied to capitalise on existing data, reducing the efforts required by the supplier to service the OEM. The framework was successfully applied to an internally developed test case, and a use case within the ongoing collaborative research project DEFAINE, concerning the design of an aileron.

Aircraft design systems are frequently used to synthesize aircraft designs. However, it is inherently difficult to reconfigure these software systems to facilitate a broader range of design studies (e.g. optimization or sensitivity studies) and to address follow-up questions about the synthesized aircraft designs. This report presents an investigation into the feasibility of developing a reconfigurable aircraft design system.

Firstly, the issues preventing current aircraft design systems from being used in a reconfigurable way are identified. These issues are closely tied to the intricate and tightly integrated nature of the source code that underpins these systems. This is unsurprising, given that these systems have typically been devised by experts in aircraft design (with varying expertise in software design) who are primarily interested in solving concrete design problems rather than creating sophisticated source code. In particular, the extensive design logic, characterized by high cyclomatic complexity, combined with cluttered and ambiguous design data structures, makes it challenging to comprehend and adapt the functioning of these systems.

An iterative development methodology is employed to come up with a software architecture aimed at mitigating the identified issues. A number of distinct architectural elements are devised that leverage a centralized semantic data management approach and a standardized interface for the formulation of modular analysis & sizing methods. Furthermore, a prototype aircraft design system that serves as a reference implementation for this architecture is developed. The prototype incorporates a graph database, a self-explanatory ontology (defining the semantics of the data stored in the database), and several abstract base classes for encapsulating the computation logic contained within typical analysis & sizing methods used during aircraft design studies. Concrete instances of these base classes are supposed to interact with the database through a well-defined endpoint interface, which includes extensive logging capabilities.

The prototype aircraft design system exhibits promising characteristics: The semantic data management approach facilitates the creation of a genuinely unambiguous and flexible data model. Simultaneously, it helps uncover inconsistencies and limitations in the employed analysis & sizing methods. Treating analysis & sizing methods as modular and nested instances of standardized classes appears to be the key to achieving reconfigurability. In addition to the unit-testability of these classes, the self-visualization features incorporated within can significantly enhance the comprehensibility and transparency of the analysis & sizing methods.

The architecture development, repository configuration, ontology formulation, and interface generation took a significant amount of time. Furthermore, some critical challenges surfaced, necessitating further investigation. Specifically, some of the employed analysis & sizing methods feature limitations and implicit assumptions that are required for a synthesis system but may need to be revised before being used in a reconfigurable design system based on the proposed architecture. In the end, there was insufficient time left for implementing a comprehensive set of analysis & sizing methods essential for materializing a thorough aircraft design loop. Therefore, achieving a fully functional aircraft design system prototype proved unattainable. Consequently, it was not possible to demonstrate that adopting the proposed architecture yields an aircraft design system that can be used in a reconfigurable way, despite indications that this could very well be the case...

One of the current trends in the aviation world is to work towards an increasingly more computer-aided approach to flying. Despite the improvements, limitations still inevitably exist in terms of power and storage capabilities in the aircraft avionics. To overcome this problem, different solutions have been proposed. A data-driven approach is implemented in this work on a practical application of aircraft performance function. Within the function, the aerodynamics and propulsion submodels are the target of the reduction activity. Neural networks and other surrogate model structures are tested and evaluated on the use case. Notably, several different network architectures are implemented in order to investigate a set of trade-offs between approximation accuracy and model complexity. An analysis of the error introduced by the model approximation is carried out to evaluate the impact at global functional level.

Much like the oil crisis in the 1980s, the aviation industry is once again considering more environmentally sustainable propulsion options due to concerns around climate change. More fuel efficient propulsion methods form the pillar on which environmentally sustainable aviation can be built, since it is the propulsion system that emits harmful greenhouse gasses. The propulsion system consists of an energy carrier, currently kerosene, and an energy to thrust converter, often a jet engine. Although efficiency gains are being made, using kerosene and jet engines will prove infeasible in the long run due to global warming concerns. Replacing the jet engine with a propeller offers an attractive solution since propeller shaft power can be supplied by batteries or fuel cells. Furthermore, propellers are relatively efficient due to their infinite bypass ratio. Additionally, a surge in demand for Urban Air Mobility [3] incentives propeller optimisation studies. Aircraft are a combination of complex and interacting systems. For this reason it is important to consider interactions between wings and propellers when designing either. Propeller-wing optimisation is therefore an increasingly important topic. Propeller-wing optimisation literature is scarce, likely due to the complexity of a coupled propeller-wing system. Optimising a propeller-wing system is possible with high-fidelity simulations but often takes a substantial amount of time. The aim of this research is to address the lack of coupled propeller-wing aerostructural optimisation. The knowledge gap is addressed by designing a novel coupled propeller wing framework that is suited for computationally efficient optimisation studies. Furthermore, the optimisation framework will be modular such that it can be easily extended. The ability to expand the framework increases the scope and impact of this research...

Novel designs like the Flying-V, Prandtl-Plane and Blended Wing-Body show promise towards sustainable aviation. In the preliminary design phase, Sub-scale Flight Test (SFT) is a reliable method to get insight into the flight behavior of these designs. The effectiveness and value provided by SFT depends on the similitude between the SFT model and the full-scale aircraft. The method of
computational scaling is a state-of-the-art method into designing SFT models, that maximizes the similitude. However, this method is often infeasible because of large computational costs. Of the various analyses in this methodology, the aerodynamic analysis is computationally most expensive. Therefore, this research developed a multi-fidelity approach for the assessment of aerodynamic
characteristics of SFT model. The approach utilized a blend of Reynolds- Averaged Navier-Stokes (RANS) and 3D-Panel Method (3DPM). This provided a good tradeoff between accuracy and computational cost, making the method of computational scaling a feasible method into design of SFT models.

This work introduces the Surrogate Advisory System (SAS), a Python package that aims to reduce the existing complexity when working with surrogate-based MDAO systems. SAS automates most of the steps for a CPACS-based MDAO workflow and drastically reduces required efforts to integrate surrogate models within MDAO systems. Key developments enabling this automation, such as a newly developed Python interface with the Process Integration and Design Optimization tool RCE, a comprehensive surrogate modelling engine and a design database structure are discussed. Furthermore, an advisory framework providing insights into the inherent trade-off between computational efficiency and accuracy when working with surrogate models is presented. It is shown that a-priori estimation of a surrogate model's accuracy for a given number of samples is difficult, but that an estimation of the potential surrogate model candidates in a provided workflow can be made.

The aviation industry continues to contribute to anthropogenic climate change. Recent studies have shown that the total radiative forcing from aviation is around three times higher than that from CO₂ alone. To account for the full effect of aviation, climate metrics are needed, which equate the environmental impact of various emissions and effects. However, there is currently no consensus on which climate metric should be used in aviation policy. This thesis systematically analyses existing climate metrics by: 1) comparing the responses to simple emission profiles; 2) investigating the sensitivity to changes in high-level aircraft design variables; 3) using a Monte Carlo simulation to determine inherent biases in metric calculation methods; and 4) evaluating the ability of climate metrics to estimate CO₂-equivalent emissions. It is concluded that the Average Temperature Response (ATR) is the most appropriate climate metric for aviation climate policy. However, it is found that the time horizon remains a subjective choice that must be chosen carefully depending on the climate objective. The GWP*, a newly proposed climate metric, is concluded not to be suitable as a climate metric because of its high variability and secondary time horizon. Nevertheless, it is recommended to investigate the potential of using the GWP* as a Micro Climate Model to further climatic understanding.

This report covers the investigation of impact of uncertainties on the multidisciplinary design optimization of a medium-range single-aisle turbofan aircraft for minimum global warming impact. The employed workflow for the investigation is a five step process, starting with the implementation of the deterministic climate impact model and carrying out of the design optimization for minimal climate impact. The second step involves the characterisation of uncertainties, where the uncertainties within the climate impact model are identified and quantified. The third step involves the uncertainty analysis, where Monte Carlo simulations are performed to estimate the variability in the average temperature reduction potential of the climate-optimized aircraft with respect to the cost-optimized aircraft. In the fourth step, a robust design optimization is carried out using a non-sorted genetic algorithm to minimize both the average temperature response and variability in average temperature response potential. The sensitivity analysis is carried as the last step using the Morris and variance-based Sobol methods, to identify what the key uncertain parameters are towards the uncertainty in climate impact of the aircraft designs. Scientific uncertainty is identified within the linear climate impact model for the carbon impulse response function parameters, species radiative efficiencies, the NOx and contrail altitude forcing factors, methane lifetime, species efficacies, and are all assigned a probabilistic description. Scenario uncertainty is identified in the future average global CO2 atmospheric concentration projection, for which different realistic future scenarios are characterised. Carrying out the uncertainty analysis has shown that the average temperature response reduction potential of the climate-optimized aircraft is highly uncertain, having a 90% likelihood ranging between 17 and 98 % of the average temperature response of the cost optimized aircraft. This is primarily due to large variability in the estimation of contrail average temperature response. Although the robustness-based optimization did not allow to find any significant improvement in robustness for the climate-optimized aircraft, it did allow to identify an array of robust climate-optimized design solutions. From the sensitivity analysis, it was found that the uncertain parameters showing predominant influence on the output variability are the contrail-related radiaitive efficiency and forcing factors. Additionally, a variability of ±50% in average temperature response apportioned to CO2 emissions was identified due to uncertainty related to future average global CO2 concentration projections.

This work is aimed at quantifying the expected advantages and disadvantages of fuel systems for box-wing aircraft, with respect to fuel systems for conventional aircraft. For this, the Multi Model Generator has been extended with an aircraft type-agnostic fuel system module capable of generating preliminary fuel system geometries, assessing the system weight, and estimating the fuel flow for a mission comprised of a single cruise segment. It is found that the box-wing aircraft struggles to maintain within the flight envelope for ferry and maximum range missions, and that the difference in fuel consumption between fuel system designs is negligible. The most effective fuel system design has a mass of 291.59kg (0.23% of aircraft mass), connects the front and rear wing tanks through the connecting element, and features a forward trim tank with a capacity of 858kg (3.0% of total capacity). With this fuel system, the reference box-wing aircraft is stable for maximum payload missions, and unstable at the start of maximum range missions.

The increasing complexity of aeronautical systems and the prevailing focus to develop sustainable technological innovations for future aircraft configurations and technologies have emphasized a particular demand on advancing the Multidisciplinary Design Analysis and Optimization (MDAO) technique. MDAO addresses the complex synergy within aircraft design where multiple coupled disciplines and domains need to be efficiently analyzed and optimized. Considerable developments within MDAO have already generated a significant time reduction for setup and solving of an MDAO problem and consequently in lead time and costs of aircraft design. For example, through the introduction of the software KADMOS (Knowledge-and graph-based Agile Design for Multidisciplinary Optimization System), that can automatically formulate MDAO workflows based on a repository of tools. The aims of this thesis are to further enhance the current application of MDAO for aircraft design and to alleviate the significant computational expense of MDAO problems through the consideration of sensitivity data during the MDAO formulation phase. Therefore, two approaches where the generation and evaluation of sensitivity information play a key role, were evaluated within this thesis, with the subsequent goal to shorten the MDAO execution time. Through the development and the following implementation of a sensitivity analysis tool into the sequencing and decomposition algorithms of KADMOS, this thesis demonstrated a new contribution to investigate on how to adjust the MDAO problem formulation. Sequencing is defined as the execution order and decomposition is the distribution of disciplines over various partitions. Furthermore, sensitivity information was used to identify the non-influential design variables respective to the objective during the MDAO problem formulation. Thus, during this thesis two independent methodologies were created in which the first methodology used a global sensitivity method to identify non-influential design variables, while the second methodology used a local sensitivity method to enhance the sequencing and decomposition algorithms of KADMOS. The methodologies were tested on an aircraft design problem and efficiently demonstrated the identification and removal of low sensitivity design variables from the problem formulation, with minor impact on accuracy. Overall, this thesis demonstrated that although incorporating sensitivity information in the MDAO problem formulation phase is advantageous, the computational effort to perform the sensitivity analysis may diminish the benefits.

Present Knowledge Based Engineering (KBE) applications do not facilitate collaborative processes due to lack of process formalization and orchestration capabilities, leading to ad-hoc solutions. This increases application development time, reduces re-usability, and increases the cognitive burden of users leading to decreased design efficiency. Generic solutions from Workflow Management (WfM) can capture process knowledge in models outside of the KBE application and enable better collaboration. However, a challenge is to maintain flexibility when using WfM, which is rigid compared to other groupware. The research work of this thesis contributes technologies for saving and accessing the information of a KBE application to enable a methodology that flexibly facilitates collaboration by taking advantage of runtime caching and dependency tracking to dynamically populate tasks based on runtime context. These contributions are highlighted through case studies that demonstrate how present limitations of KBE and WfM can be overcome to open a new frontier for the next-generation of engineering software. Based on a verified software prototype, the key research contributions of this thesis are four-fold: (a) development of an information modeling approach making use of GraphQL to flexibly query KBE models to support collaborative activities, (b) development of a generic persistence capability for ParaPy to support transactional usage and retain full design history, (c) implementation of the worklet concept in KBE to increase flexibility of workflows and provide new form of process automation, and (d) creation of correlated dependency technique which has potential for use on emergent workflows. These contributions are paramount to undertaking multiple opportunities for collaborative KBE applications. It is expected that these research contributions provide a theoretical basis for achieving improved collaborative usage of KBE applications.

Considerations that decide producibility of a design are an important part of the design process, and must be included in the early design stages to ensure that these designs can be realised. Not including these considerations carries the risk of incurring additional costs and delays at later stages of product development because of design changes, or can lead to limiting oneself to conservative design choices to reduce the associated risk.
The current process in the industry accounts for these production considerations in design through a manual process that is iterative and time-consuming, and hence forms a bottleneck in being able to trade-off multiple design concepts. Attempts at accounting for these production considerations in an automated way are associated with the limitations of either only considering the manufacturing cost, being specific solutions that work only in certain scenarios, or being dependent on some commercial software tools, which are not fully suitable for use in context of automation and/or at the conceptual design stage. Additionally, the aspects of manufacturing and assembly are usually not considered at the same time in these studies.
Therefore, this thesis aims at developing a methodology that enables the automated inclusion of production considerations in the conceptual design process of aircraft structures, while overcoming shortcomings of the state-of-the-art....