Recent technological advances have lead to the development and application of multirotors for commercial package delivery and air taxis. They differ from helicopters because they operate at lower Reynolds numbers, induce more rotor-rotor interactions, and are controlled using variable-speed rather than variable-pitch rotors. The dynamic flow field of a multirotor leads to complex aerodynamics and aeroacoustics that can only be fully captured computationally using high-fidelity methods. A very popular high-fidelity approach used in the traditional rotorcraft simulation field is unsteady Reynolds-averaged Navier-Stokes (URANS). The URANS equations are implemented in this research using the CREATE-AV Helios computational code suite; however, when applying this method to smaller multirotors, three major challenges arise.
The first challenge is to understand how the computational methods perform at the lower operational Reynolds numbers relevant to smaller multirotors. Implementation of URANS requires turbulence modeling, and models should account for laminar-turbulent transition at lower Reynolds numbers. This challenge is addressed by evaluating the effect of laminar-turbulent transition models in the Helios suite. This thesis demonstrates that current laminar-turbulent transition models in Helios do not improve either performance or noise predictions for small rotors. Fully-turbulent models provide reasonable accuracy at a lower cost.
The second challenge to the high-fidelity multirotor simulation framework is the lack of an appropriate trim algorithm.
For a computational prediction to be useful, it is necessary to simulate the rotorcraft in realistic flight conditions. This is accomplished via computation of trim, the set of controls and vehicle state that achieves a desired flight condition. Trim algorithms exist for helicopters that utilize blade pitch control but there has been no validation of high-fidelity computations that include trim for multirotors that control using individual rotor-speeds. This thesis presents the development and validation of a high-fidelity approach for multirotor trim using loose aerodynamic coupling. The newly developed trim algorithm currently solves unconstrained multirotor systems, which are underdetermined because the number of controls is greater than the number of targets, by adding an additional optimization for minimum power. The importance of including trim and performing the aerodynamic calculations with high-fidelity is demonstrated.
The third challenge arises when applying high-fidelity methods to predict multirotor aeroacoustics. URANS alone cannot capture the full acoustic spectrum directly because the turbulent fluctuations are modeled and the grid cannot extend to the far field reliably. Therefore, it is necessary to develop models for portions of the spectrum and to use an acoustic analogy that converts sources in the CFD as noise to the far-field. However, there are complex interactional effects, a multitude of types of noise sources, and different methods for utilizing an acoustic analogy. It is also difficult to develop computations and experiments in which the noise sources match. The PSU-WOPWOP code is used to implement the Ffowcs-Williams and Hawkings equation with both impermeable and permeable surface approaches. Additionally, UCD-Quietfly is used to model some broadband noise sources. Through adaptation of existing tools and development of novel computational methods, the ability to predict multirotor noise is explored via computational investigation with comparison to experimental measurements to the extent possible.
This thesis offers a new high-fidelity capability for predicting multirotor aerodynamics and aeroacoustics in trimmed flight. The approach enables final design analysis for developers of emerging multirotor systems who need reliable performance and noise predictions before full prototype development and testing. Capabilities developed in this thesis provide a platform upon which new trim optimization approaches can be created and tested, such as one that minimizes noise. / 2023-05-23T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/44775 |
| Date | 24 May 2022 |
| Creators | Thai, Austin David |
| Contributors | Grace, Sheryl M. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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