# Multi-Disciplinary Optimization of UV-C Filter for Air Disinfection
## 3 Process Automation and Optimization  
The CAD, CFD, and ray tracing simulation models were integrated in a single MDO (multi-disciplinary optimization) process automation workflow, within the optimization platform modeFRONTIER (Figure 8). The process automation workflow is defined by a series of application nodes linked together with data and file nodes, with each one representing a different step of the process. The logic of the process flow is divided into two paths, which are executed in parallel: from one side the application nodes for the CFD analysis, above, and from the other side, below, the application nodes for the ray tracing analysis. In the first of the parallel paths, the first application node is the API-based connector interface with CATIA v5, which allows the automatic updating of the CAD model as a function of the values of the design parameters as described in Section 2.3, for each candidate design proposed by the optimization algorithm. This application is followed by another interface for Ansys, which rebuilds the mesh on the updated CAD model and executes the Fluent CFD simulation. The interface just replaces the original mesh model with the updated one, keeping the CFD boundaries and analysis settings unchanged. In the second parallel path, the first application is a modified CAD node, which, as a function of the same design parameters, updates the geometry of the filter (note that the  
Figure 8: Process automation workflow for the MDO. first CAD model defines the CFD domain around the filter for the CFD analysis, while for the ray tracing analysis the filter geometry itself is needed). The updated geometry file is then transferred to the application, which executes the ray tracing analysis by the TracePro software. When both simulation paths are executed, the results of the CFD analysis (the particle trajectories) and the results of the ray tracing analysis (the distribution of radiation intensity) are transferred to the last application of the workflow. This executes a MATLAB 2024a script, which computes the following objectives and constraints:  
\[\overline{UV_{i}}=\sum_{i=1}^{N}\frac{UV_{i}}{N}\ [\text{Ws/m}^{2}] \tag{5}\]  
\[massflow=\int_{outlet}\rho VdS\bigg{[}\frac{kg}{s}\bigg{]} \tag{6}\]  
\[\text{Min}\ UV_{i}\geq 25\ \left[\text{Ws/m}^{2}\right] \tag{7}\]  
\[\sigma_{UV_{i}}=\sum_{i=1}^{N}\frac{\left(UV_{i}-\overline{UV_{i}}\right)^{2} }{\left(N-1\right)^{2}}<\ 0.667\ \ \overline{UV_{i}} \tag{8}\]  
Equation (5) is the absorption index defined as the first objective function to be maximized. It is computed as the average value of 200 different particle tracks of the ray absorption \(UV_{i}\) as expressed in Equation (4) for a single particle track (i.e., the product of the radiation intensity for exposure time). The value of this objective needs to be higher than 40 \(\text{Ws/m}^{2}\), as stated in [24], to achieve the necessary dose of inactivation. Equation (6) indicates the second objective of the optimization, i.e., the maximization of the mass flow inside the UV-C filter, computed at the outlet section. This is necessary to maximize the air quantity that can be disinfected by the device. In addition, two other constraints are defined by Equations (7) and (8): the minimum absorption index (\(\text{min}\ UV_{i}\)) among the different particle tracks must be higher than 25 \(\text{Ws/m}^{2}\) (to avoid any single particle having a too-low absorption index), and the standard deviation of the different particles index (\(\sigma\ UV_{i}\)) should be lower than 2/3 of the average value (to avoid a too-high dispersion of the particle efficiency distribution). Once the objectives and constraints are defined and the process automation workflow is completed, the simulation and evaluation of the performances of each design proposed can be undertaken by the optimization proprietary algorithm (Pilot) of modeFRONTIER.