## 2 State of the Art
Figure 1: Multidisciplinary Design Optimization (MDO) framework without geometric model in the loop. Figure 2: MDO framework with geometric model in the loop. Automation of the design process in aviation applications and the application to the supporting structures of flying objects are the subject of intensive research. This is not only because of:  
1. The need to organize the design process, increase its efficiency and record, and integrate knowledge and experience resources from other sources and previous projects. 2. Integration of these automation procedures in more extensive structural improvement and optimization procedures considering structural analysis--Computational Structural Mechanics (CSM). In terms of this approach, there are three main areas of application of the above type of analysis. The most characteristic examples include the consideration of topology optimization, the use of composite materials, and the definition of the structural layout. 3. Integration of load-bearing structure automation procedures in more extensive methods for improving and optimizing structures also considers other fields [6]. In terms of this approach, such relationships are most often investigated in aeroelastic analysis, aircraft performance, and costs or market demands. Such integrated approaches are also called MDO (multidisciplinary design optimization) [7; 16; 18], Model-Based Design (MBD) [8; 19], Model-Based Optimization (MBO) [20]. Usually, CSM methods are used as high-fidelity models in the detailed design phase and sometimes also as medium-fidelity models in the preliminary design stage on the simplified geometry. Although, in the conceptual design phase there is often a need to obtain alternative concepts load-bearing structures. On the other hand, relying on simplified geometry encounters a severe problem precisely because of the large amount of work involved in generating alternative concepts of support structures, which often exceed the time required for numerical analyses and their interpretation. Such a bottleneck is usually eliminated using methods of automating the process of generating a geometric model. It is necessary to use a model with a high degree of detail, considering the aspect of manufacturing technology, design dependencies, the engineering correctness of the model, and structural consistency. Generative modeling yields good results [10]. Generative modelling (GM) is one of the most popular knowledge-based engineering techniques used in the design field [8; 15; 21]. The main idea behind GM is to elaborate the CAD model, which will automatically, or at least semiautomatically, generate the model's geometry based on the design requirements and integrated knowledge [9; 15; 22]. The general schema of GM functioning is presented in Figure 3.  
The essential advantage of using the GM, which is one of knowledge-based engineering (KBE) methods of design automation, is the possibility to reduce the time a designer spends on some routine design tasks and put more effort into creative parts of the project, as  
Figure 3: The general schema of the GM functioning [10; 15; 22]. presented in Figure 4[15, 22, 23]. Thanks to this, it is possible to develop a new approach to some problems, which may be better and more efficient than those currently used. On the other side of the coin, development of the GM is time-consuming and usually requires the assistance of an expert in the field [15, 21]. Moreover, the number of knowledge engineers who are essential during knowledge acquisition and structuralizing is limited [15, 19, 23]. However, the effort put into the GM development may bring more benefits in the long-term perspective, especially in the number of projects that can be realized in a shorter time and with less effort, as presented in Figure 4[9, 15, 23].