Technology forecasting is an essential starting point for conceptual design of any complex engineering system. In fact, many research projects are focused on developing a small set of promising technologies to a suitable readiness level. However, selecting a set of technologies from a larger pool is a nontrivial task, opposed by uncertainty and subjective tradeoffs. This paper proposes a probabilistic method to represent technologies and quantify their effects, while accounting for uncertainty. Using probabilistic inversion, technologies can be selected from a larger set to meet a certain combination of requirements. Several test cases illustrate the method and how it may be used in conceptual design projects. It is concluded that probabilistic inversion enables answering technology development and selection queries, which would be challenging to answer with traditional deterministic approaches, or purely forward uncertainty propagation approaches.

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Technology selection is ubiquitous in all manners of complex systems engineering, design and everyday business operations. Technologies are often complex entities shrouded in uncertainty, assumptions and interpretation. Therefore, quantifying their effect (i.e. outcome) becomes challenging for several reasons. First, as simulations may not be available for novel technologies, experts have to estimate their impacts. Secondly, as the technologies may not be well-defined, different experts have different interpretations and will assign different outcomes. Thirdly, even if analysis methods are available, there is epistemic and model uncertainty in the outcome. Finally, available analysis frameworks are typically application-specific, non-extensible and inflexible. It is the objective of this thesis to pave the way towards a technology selection methodology that offers a structured, repeatable and traceable way to represent technologies and consecutively quantify their impacts on an engineering system. Such a methodology is implemented as a decision support system, i.e. a computer program that assists a decision maker throughout the decision-making process. Three components of said methodology can be identified: technology representation and portfolio generation, technology (portfolio) evaluation and technology (portfolio) selection...@en

In conceptual design of any engineering system, decisions are made regarding which technologies to include and where. One of the first stages of that process is constructing the technology compatibility matrix (TCM), which indicates the compatibility of each pair in a technology set. Rather than constructing a TCM with expert judgment, this study develops a method based on graph transformation rules, allowing for a formal description of technologies. The TCM is then automatically derived. An ontology based on the Basic Formal Ontology is developed to describe systems and technologies, and provides axioms to derive statements about these descriptions. The method is demonstrated with four inference examples, showing how the inferences are made. An industry case study demonstrates the method's ability to mimic human expert reasoning. Although the approach is labour-intensive during setup, it enables knowledge capturing, automated reasoning and can be extended to provide quantitative analysis, to save time and effort.

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Evaluation and assessment of novel technologies for aerospace applications is essential for business strategy and decision making regarding development efforts. However, technology evaluation and assessment are challenging to perform objectively using a structured approach. As a first step towards a more objective and structured approach a graph-based description of engineering systems is described herein. Analyses can be applied to such a description through pattern matching, after which the quantities of interest can be computed by an automated algorithm using a dependency graph. The approach is applied to a simplified aircraft model, to perform a mission analysis and compute fuel burn. It is shown the method successfully computes the required parameter and is easily adapted to analyze an electric aircraft as well. @en

The need to reduce the pollutant impact of aircraft emission drives the research on aircraft design progress through off-design performance improvement. This report proposes to investigate the effect of maneuver load alleviation technology via wing control surfaces for this purpose. A methodology is presented to model the MLA technology in aircraft conceptual design and to evaluate its impact on both existing and clean-sheet design. In addition, the possibility to consider flexible wings when under the influence of 2.5-g maneuver loads is addressed, to assess the impact of aeroelasticity in on wing weight in the conceptual design phase. The aeroelastic analysis method is validated against a higher-order analysis method with excellent correlation between the results from the two methods. Subsequently, the method is applied to the redesign of medium-range, single-aisle aircraft. It is shown that applying MLA using both the flaps and the ailerons can result in a fuel burn reduction and maximum take-off mass reduction of 2.1% and 2.2%, respectively. @en

One of the first stages during technology evaluation and selection is constructing the technology compatibility matrix, which indicates the compatibility of each pair in a technology portfolio. Construction of the technology compatibility matrix usually involves subjective expert judgment, which inhibits assessing each pair consistently and storing the decision rationale. Therefore, this study develops a method based on graph transformations, allowing for a formal theoretical description of technologies. In order to automate this process, two algorithms use these graph transformations to deduce technology compatibility and dependencies between technologies. The first checks whether changes made by a pair of transformations are compatible with each other. The second defines dependencies for each technology and attempts to resolve these using other technologies. The method is put into practice for a technology portfolio within Saab Aeronautics and the automatically generated compatibility matrix is compared to a pre-existing expert-judgment-based matrix for the same portfolio. Roughly 90% of the entries in the algorithm-based TCM are identical to the expert-based TCM. Therefore, the automated method is capable of capturing much of human reasoning in this context. However, in order to fully agree with human expertise, physics and design reasoning should be included to expand the scope of inference. @en

Evaluation and assessment of novel technologies for aerospace applications is essential for business strategy and decision making regarding development efforts. Since technology is evaluated in the conceptual design phase and little is known about the technology, large uncertainty is present. This uncertainty needs to be accurately assessed and managed. To investigate the research efforts that have been performed to perform technology evaluation under uncertainty, a literature review was conducted, focusing on methods and modeling approaches to assign and quantify these uncertainties. It is found that probability theory is still the most popular theory for representing uncertainty. Polynomial Chaos Expansions and Stochastic Collocation methods are gaining popularity for propagating uncertainty through a modeling environment, but Monte Carlo Simulations are still widely used. Commonly, surrogate models are used to reduce computational effort. Other efforts focus on the use of multifidelity approaches to reduce computational effort when high-fidelity methods are required. Four issues that may need to be addressed in future research were identified.@en

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