## 4 Discussion  
This paper analyzed two numerical models of the MBARI-WEC, one created in _Gazebo_ using ROS 2 and another in _WEC-Sim_ using MATLAB/Simulink, and how they compared to both each other and in representing the logged data of the physical MBARI-WEC. Time-domain analyses with monochromatic waves uncovered similar dynamics between the models even though they employed distinct representations of the air spring in the WEC's PTO system. This suggests that the spring stiffness had minimal impact on the overall system performance. The time signal and calculated mean of electrical power emphasized each model's different computational methods for electrical losses. Figure 7 revealed that _Gazebo_ computed higher power values than _WEC-Sim_ for the same speed, underscoring their distinct approaches to calculating electric power loss. This was easily seen in the speed ranges between +/ -300 RPM. In the damping relationship outlined in (3), these speed values correspond to 0 Nm, or equivalently, 0 Arms, resulting in no mechanical power generation. Thus, the calculated electric power was entirely due to the motor torque friction and electrical power losses. Using the equation in [4] for these speed ranges, the motor torque friction resulted in a roughly 3 W loss for both models. For electrical losses,  
Figure 9: Mean electric power difference between _Gazebo_, _WEC-Sim_, and field data, binned by \(H_{s}\) and \(T_{p}\). Black dots represent observations and their corresponding \(H_{s}\) and \(T_{p}\) value. Both **(a)**_Gazebo_ and **(b)**_WEC-Sim_ have less than 30 W difference except for high energy sea states where the error is between 140 to 240 W for _Gazebo_ and 80 to 115 W for _WEC-Sim_. Gazebo's computation relied on the RMS motor winding current, which, as previously explained, was 0 Arms within these speed ranges, yielding zero electrical losses. Therefore, _Gazebo_'s electrical power was roughly negative 3 W, resulting solely from motor torque friction losses. However, for _WEC-Sim_, the research performed in [4] revealed that these speed ranges, along with their corresponding torque, resulted in an electric power loss of approximately 30 W, a significant difference from _Gazebo_. Furthermore, the figure explained the substantial difference between the mean electrical power of _Gazebo_ and _WEC-Sim_ in the 1 m, 8 s monochromatic wave case, as indicated in Table 1, since _WEC-Sim_ incurred higher losses than _Gazebo_ for the same speeds. A statistical analysis was used to investigate the accuracy of each model's outputs compared to MBARI-WEC field data. While both models overestimated system performance when compared with the field observations, _WEC-Sim_ tended to provide more accurate estimates for observations with high-energy sea states compared to _Gazebo_. In contrast, _Gazebo_ demonstrated better predictions for low-energy sea states. While the _Gazebo_ model's outputs eventually deviated from the field observations at a more pronounced rate than _WEC-Sim_ in higher-amplitude cases, it provided a better estimation for a majority of the data points in the field data set. This was evident from the position of the intersection between the linear fit models in comparison to the cluster of data points when studying Figure 8. This could also be observed in Figure 9, where the sea states (black dots) available for analysis primarily had wave heights ranging from 0.5 to 1.1 m and peak periods ranging from 5 to 11 s, which are generally categorized as low-energy sea states. Therefore, the _Gazebo_ model offered more accurate statistical estimates for the more frequently occurring sea states.