## 1 Introduction  
The rapid progress in the electric vertical takeoff and landing (eVTOL) industry warrants the need for fundamental understanding of flight-enabling technology. Aircraft from the scale of unmanned aerial vehicles (UAVs) for drone delivery to urban air mobility (UAM) transport vehicles rely on multiple rotors to attain controlled flight. These rotors will experience significant aerodynamic interactions that have implications on performance and noise. It is necessary to develop experimental and computational methods for the evaluation of these vehicles, whose noise could have implications on their acceptance in our society [1]. However, even the fundamental problem of a single rotor in hover poses an issue to the scientific community because of the difficulty in characterizing the noise sources. Experiments are contaminated by the influence of external factors, such as motor noise, interactions with test equipment, the influence of ground effect, recirculation in confined environments, and turbulence or gusts in open environments [2, 3, 4, 5, 6]. In addition, even when the effects of the environment are mitigated, the sources that contribute to both the tonal and broadband noise cannot be investigated without high resolution surface data or advanced flow visualization techniques, both of which are still in development. Computational approaches fulfill this need to study aeroacoustics in greater detail by providing insight into the flow physics and noise source localization [7; 8]. Yet, it is challenging to incorporate all of the potential external factors from the experiments into computations; this calls into question whether validation is possible. In lieu of validation, one can consider verification. However, even this is a bit complicated, with some in the field using Navier-Stokes (NS) codes that are based on the governing equations for fluid motion and others using Lattice-Boltzmann methods (LBM) based on kinetic equations to describe fluid motion. Each of these approaches is even further subdivided: for example, Navier-Stokes methods range in order of increasing turbulence resolution and cost from unsteady Reynolds-averaged Navier-Stokes (URANS) to large eddy simulation (LES) to direct numerical simulation (DNS) [9]. Due to the intense computational requirements of LES and DES methods, hybrid approaches, such as hybrid URANS/LES or detached eddy simulation (DES), have been popularized in which the turbulence is fully resolved when the turbulence length scales are greater than that of the CFD grid spacing. However, these approaches typically require a turbulence model near the wall, and model selection can also affect the results [10; 11; 12]. Similarly, the LBM modeler has to make choices in terms of their collision models, subgrid-scale turbulence models, and boundary conditions [13]. There is no unified approach for either LBM or NS that can be applied to every situation, due to computational constraints. Finally, for both LBM and NS methods, the user must also generate a mesh that is compatible with the chosen models and chosen geometry. Improper mesh generation is often the largest source of user error for CFD codes. The resolution of turbulence and overall numerical accuracy in the entire domain dictates which computational aeroacoustics (CAA) methodology should be used, which noise sources are automatically captured, and the accuracy of the acoustic prediction. In this work, a DES is used to capture some of the turbulent spectrum; due to the nature of the grid, the pressure is only expected to be resolved well near the rotor surfaces. This scenario is well-suited for usage of the Frowcs-Williams Hawking (FWH) analogy, which uses near-field velocity and pressure results to obtain the acoustics [14]. This methodology introduces additional issues of its own. For example, there is much debate over whether a permeable surface integration is superior to a simple impermeable, or solid, surface integration for small rotors [15].