## 2 Methodology
### Weight Breakdown Evaluation  
The aircraft weight breakdown is expressed as in Equation (3):  
\[W=\Big{(}m_{st}+m_{w}+m_{sy}+m_{op}+m_{p}+m_{pr}+m_{f}+m_{b}\Big{)}g \tag{3}\]  
where \(g\) is the standard gravity. In particular, the structural mass of fuselage, fin, nacelle, and landing gear (\(m_{st}\)), the onboard systems mass (\(m_{sy}\)), and the operating items mass (\(m_{op}\)) are calculated according to the semi-empirical relations collected in [48], whereas payload mass \(\big{(}m_{p}\big{)}\) is evaluated by assuming 95 kg/passenger (including the baggage). The propulsion system mass \(\big{(}m_{pr}\big{)}\) is evaluated as a function of the installed specific power obtained through the matching chart for both thermal and electrical power sources, and by using the literature data on their specific power density [49,50], while the fuel \((m_{f})\) and the battery mass \((m_{b})\) are two outputs of the numerical simulation. In particular, the term \(m_{st}\) does not include the structural wing mass \((m_{w})\), which is evaluated by means of a specific structural model developed to estimate the mass of lifting system of both cantilever-wing and BW. This is of paramount importance in this comparative work, as the two lifting architectures being compared have quite different structural configurations, and using models that are unable to detect such differences could lead to errors in the comparative evaluation. Therefore, it was decided to use physics-based structural mass prediction models starting from a finite element modeling (FEM) of the wings-fuselage assembly. Specifically, the developed structural model exploits metamodeling technique to estimate the structural mass of the lifting system, as this approach allows for obtaining high level of accuracy and low computational cost [51,52], which is an important feature in conceptual assessments; the developed procedure is widely described in [53,54], whereas a brief overview is proposed in the following. The metamodeling process mainly follows these steps: (_i_) definition of the main design variables and their boundaries, hence identifying an \(n\)-dimensional design space; (_ii_) sampling of the design space by means of a specific technique (e.g., central composite or Latin hypercube) to identify a subset of configurations to be analyzed; (_iii_)  
Figure 2: AVL model of TW (**left**) and BW (**right**) configurations. run of FEM simulations for each configuration belonging to the subset; _(iv)_ definition of an interpolative model (e.g., polynomial function or splines) and calculation of its unknown coefficients through fitting models (e.g., least square regression or best linear predictor); and _(v)_ the interpolative model represents a response surface able to predict the output of the physical problem. This procedure has been used to develop a FEM-based metamodel that allows for predicting the lifting system structural mass, without the need to conduct a FEM analysis for each assessed configuration during the conceptual design, hence providing a huge cut of the computation time. Regarding phase _(i)_, geometric and structural design variables defining the lifting system have been selected for both BW and TW, as sketched in Figure 3; due to the different shapes of BW and TW, geometric design variables are different, whereas structural variables defining the wing box are fixed equally for the two architectures. The FEM simulations to sample the design space have been carried out by means of the software ABAQUS (v 6.14) [55; 56] to obtain relevant outputs, such as wing tip displacement (\(u\)), maximum equivalent stress on forward (\(\sigma^{F}\)) and rear (\(\sigma^{R}\)) wing, and wing structural mass. The structural mesh consists of shell and beam elements; shell elements are used to model skins, ribs, and spar webs, whereas beam elements are utilized to model stringers and spar caps.