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A multirotor trim algorithm is developed and validated using the HPCMP CREATETM-AV Helios rotorcraft analysis and simulation framework. Validation data were sourced from experiments in which a quadrotor with fixed-pitch rotors was trimmed for steady hover and forward flight in the NASA Langley Low-Speed Aeroacoustic Wind Tunnel. For each flight condition, the aircraft attitudes were fixed so that the only control variables were the rotation rates of the four rotors, which were varied until the residual loads were minimal. The resultant trim states were first replicated in the computational framework to verify the aerodynamic solver. Then, the trim algorithm was validated in Helios by constraining the aircraft attitudes and searching for the rotor speeds that minimized the residual loads. The analysis demonstrated excellent agreement between the predicted and measured trim states. Finally, free-flight trim cases were simulated to quantify the effect of the trim constraints and verify the experimentally trimmed flight conditions. The predicted free-flight trim state showed reasonable agreement to the constrained case, with negligible change in the residual loads, indicating trim was achieved in both the experiments and the simulations. [ABSTRACT FROM AUTHOR]

Research shows that an aeroacoustic inversion model enables inputting data accurately. Data further indicate that the method is robust as determined by the estimated errors on the accuracy of inverse solutions.

Algorithms that enable acoustic sensing vehicles to autonomously map acoustic fields are being developed, but some require an analytical representation of the data collected from the acoustic field with specific continuity properties. One optimal control technique that has these requirements and has been used in autonomous acoustic sensing algorithms is Pontryagin's Maximum Principle (PMP). Given that most real-world fields are modeled by numerical methods, the need to create continuous analytical representations from data is a hurdle in realistic applications of these autonomous algorithms. In this work, finite series of basis functions are used to solve this problem. The basis functions are selected to meet the criteria of the PMP, and are used to approximate the field from the data collected. While this approach meets the criteria of the algorithm, it can be a computationally expensive approach for complex fields and large datasets. This paper looks at the trade off between accuracy and computational time, for different frequencies and levels of field complexity. A previously published ocean profile developed from data taken off the island of Elba is used in the normal mode software, Kraken, to generate the acoustic fields used to test this method. The candidate basis functions considered are trigonometric functions analogous to finite Fourier series, and Legendre polynomials. The Legendre polynomials are shown to have higher accuracy when used with a dynamically selected exclusion range at low frequencies and a cubic spline interpolation method for generating intermediate data points at higher frequencies. Run times are calculated and while both candidate basis functions are shown to meet the requirements of the PMP algorithm, the Legendre polynomials require the least amount of run time. [ABSTRACT FROM AUTHOR]