## 2 Methods
### Considerations and Challenges in Practical Implementation  
In this investigation, aeroelastic simulations covering a variety of DLCs in accordance with the IEC 61400-1 standard are implemented and the resulting data analyzed to reach realistic rotor loads and rotational speed combinations. The factor combinations are intended to be applied to a purposely instrumented Vestas V52 WT drivetrain using the 4 MWWT system test bench, illustrated in Figure 2, with the goal of generating measurement data for the development of a virtual WT 6-DOF transmission loads sensor. The primary motivation for the planned measurement campaign being the absence of field data due to the fact that a practical and cost-effective sensing system has not yet been field deployed. The challenge of validating a WT model in the absence of field data is a common challenge in the wind energy industry. Therefore, the IEC 61400-1 standard [43] provides guidelines for the calculations or simulations of a given WT model subject to the variety of wind conditions that are to be expected during operation, i.e., DLCs. Such guidelines are generally followed by the simulation approach in this investigation. While most experts contest unconditional validity of a simulation model [54; 55; 56; 57], the definition of simulation model validity is possible within the bounds of the project and its intended application [12; 58]. Therefore, investigators are encouraged to critically assess the validation requirements of their intended applications of the proposed method, and tailor the fidelity and the comprehensiveness of the model validation approach accordingly. For more considerations and suggestions on the topic of simulation model validity for future applications of the proposed method, see [12] where [PERSON] et al. introduce and discuss the validity of the initial, higher-fidelity Vestas V52 WT model used in this investigation. An assessment by the investigators on the validity of the model based on the resulting simulated loads in multiple known simulated wind conditions was deemed sufficient for the purposes of this investigation. Additionally, measures were implemented to mitigate the effects of potential outliers in the simulated data when identifying the bounds of the experimental design space. Domain knowledge was employed to assess the plausibility of the extremes of each factor as well as the ability of the test bench to reach such extremes without exceeding them. As a result, it was decided to separate the testing boundaries from the simulated extremes with a buffer per factor as previously discussed in Section 2.3.1. The magnitude of the buffer is test bench and drivetrain dependent as well as investigation dependent. For example, the rate of change of a given load in order to reach a desired load state typically impacts the degree of overshoot of the system on the way to that state. Therefore, it is recommended to perform initial tests in order to identify the optimal buffer for the investigation at hand. Figure 5 illustrates a simplified example of such set of tests aiming to incrementally reach a definition of a buffer to avoid exceeding the simulated maximum of a single factor due to system overshoot. Figure 5 is also illustrating the often iterative nature of identifying problematic features of the designed experiments and coming up with solutions to mitigate such issues when they arise. For instance, it is often possible to reduce overshoot by reducing the rate of change that would influence the magnitude of the required buffer. However, this will also have an impact on the duration of time needed to cover all designed experiments. Therefore, a tradeoff between the buffer size and the rate of change may exist in this example. Since such a tradeoff would also depend on the available resources to the investigation at hand, identifying the right compromise between these competing testing scenarios may be critical for the realization of the project goals given the available means.