*1.1. Advanced Morphological Approach*

The AMA is based on the classical Morphological Analysis (MA) by Fritz Zwicky [5] dating to the middle of the twentieth century. The initial MA introduced product decomposition into functional and/or characteristic attributes, which can be each fulfilled by corresponding sets of given implementation alternatives (denoted as "options"), systematized in a Morphological matrix (MM). Such problem structuring allows to obtain an exhaustive solution space, containing all possible option combinations (denoted as "solutions").

The AMA by Rakov and Bardenhagen [3] is based on the MA and its application in the search for promising technical systems by Rakov [6] from the late twentieth century. The main steps of the AMA combined with a brief presentation of the workshop data flow are schematically shown in Figure 2. The method extends the MA by assigning evaluations to each option according to pre-defined criteria on a qualitative scale from 0 to 9 [3]. The generated solutions combine the scores of the selected options by adding their separated criteria evaluations, which can also be weighted. Subsequently, one can visualize the resulting solutions based on their summed criteria scores, allowing to compare their criterion-specific and multi-criteria performance [3]. The lower left part of Figure 2 exhibits the main data processing steps after obtaining individual pairwise technology comparisons during an expert workshop and their integration into the main AMA methodology.

**Figure 2.** Main steps of the enhanced AMA including the workshop. Attr.—attribute; Opt.—option; Eval.—evaluation.

One of the main problems of morphological analysis is to reduce the dimensionality of the problem and to reduce the number of solutions to be solved [7]. Often, the reduction of the morphological set of solutions by the analysis of incompatible options is used. In some cases, graph theory and genetic algorithms are used [8,9]. In the case of AMA, clustering is used to aid the identification of similar solutions in vast solution spaces.

The MA has been applied in conceptual aircraft design in different forms and contexts, discussed and compared with the AMA in detail in Reference [2]. For example, the Technology Identification, Evaluation and Selection method by Mavris and Kirby [10] uses the MM for impact estimation of technologies and the search for their optimal combination for improved resource allocation. Although direct system simulation is also avoided, the method uses "physics-based analytical models" [10] which are applicable for a more extensive configuration modeling/representation up to a preliminary configuration design. In another work, Ölvander et al. [11] implement conceptual design of sub-systems by describing the MM options with quantitative data and mathematical models. In contrast with these MA applications, the AMA uses a qualitative approach to compare non-existent technologies lacking experimental data or/and are hard to model.

As shown in Figure 1, the AMA has underwent further development by implementing uncertainty modeling with fuzzy numbers, problem-specific hierarchical problem definition by means of the Fuzzy Analytical Hierarchy Process (FAHP) as well as multi-criteria decision-making (MCDM) methods (see Reference [4]). Based on this approach, a methodology for organized expert panels shall be studied, which would evaluate the MM options

and serve as a scientific ground for the generated solution space. A first iteration of the methodology testing has already been conducted within a first workshop on the design use case of a search and rescue aircraft (SAR), described in detail in Reference [12]. As a result, a solution space containing 54 configurations was generated which yielded a multi-criteria optimum implementing hybrid aerodynamic/aerostatic lift generation, fully hydrogenbased non-distributed propulsion and wing morphing. The first workshop implemented a set of initial features such as solely individual evaluations, mathematical aggregation and the use of fuzzy numbers for uncertainty modeling. It served as a starting point for the development of a full scale concept, aimed in the second workshop iteration.

In this context, the second workshop extended the functionality of the workshop and its post-processing to a next level and was applied on the use case of wing morphing architectures. The added major improvements include the behavioral aggregation of evaluations in the form of group discussions focused on aircraft design aspects, the possibility for the experts to edit their evaluations, and the weighting of the participants' assessments based on their expertise. The current article presents the methodology and results of the second workshop, as well as the aspects needed for the integration of SEJE methods into aircraft conceptual design with the AMA.
