## 2 State of the Art  
In the design processes, strictly defined or even standardized activities are used, including the design of repeatable parts of the structure. A common practice is to make extensive use of these opportunities through Design Reuse (DR). Computer Aided Design (CAD) techniques make use of specific model solutions (CAD models) along with the principles of determining their features, which make the design process much easier [14]. The idea of using DR in the CAD systems environment gives rise to new possibilities, from simple parameterization to complex Generative Models (GMs). These methods can be applied to small parts, elements, and entire components, and products [10]. Such automation of constructing a geometric model in the CAD environment is also called Geometry-Based Design Automation (GBDA). It is not the only form of automation of the design process. Still, it stands out due to the widespread use of CAD systems in the engineering, automotive, aviation industries, etc. In individual CAD systems, tool-based forms of geometric modelling are quite different. These range from programming and scripting techniques, through extensive construction of various template complexity levels (high-level CAD templates), allowing for the integration of multiple forms of knowledge into the geometric model. Automation itself accelerates the design process at some stage--e.g., it allows for faster generation of geometry and leads to the designer being able to study a much larger space of possible solutions simultaneously [1]. Such automation opens the way to integrating geometric modelling into optimization processes and, with complex multidisciplinary problems, to Multidisciplinary Design Optimization (MDO). However, it should be remembered that there is no definite methodology for the automation of design processes. It is also impossible to answer which of these works can be automated and with what techniques. This applies to the application of knowledge-based engineering methods. The proposed solutions are good practices rather than principles [15]. In a multidisciplinary approach, the geometric model plays a strategic role. So, it is possible to implement the MDO approach [16]:  
1. without considering the geometric model (Figure 1);
2. with a geometric model implemented for visualization of results (Figure 2);
3. with a simplified geometric model in the form of the parametric grid used for optimization analyses (Figure  
methods. If the model is built based on the geometric data used for the calculations, it is used by experts for visualization and comparative purposes only, which helps to interpret the results. In the second group of the optimization approaches (c, d), we deal with a geometric model in the calculation process. The geometry used for calculations is taken from the model, or the geometry data used in the calculations are visualized through the visualization interface. Typically, these methods are used when some form of geometric structure model is required--e.g., the finite element method (FEM) or Vortex Lattice Method (VLM) or any calculation method using geometry input [11]. Depending on the calculation method and the form of the model used, the form of the model itself may be simplified or integrated into a complex CAD model. In both the first and second forms, generative models can be used to assist in the generation of geometry. Especially for the latter and multidisciplinary problems, the optimization computation models can differ significantly from each other. Two types of Simultaneous Analysis and Design (SAND) and Nested Analysis and Design (NAND) models stand out from the several different possible configurations of multidisciplinary computation. With a SAND configuration, the disciplinary optimizer simultaneously determines the value of both design and state variables at a disciplinary level. In contrast, in the case of a NAND framework, the optimizer only determines the design variables, with state variables needing to be calculated at each iteration [17].