Browse Topic: Data acquisition and handling
This study aims to develop Control Equivalent Gust Input (CEGI) and Rotor Control Equivalent Gust Input (RCEGI) profiles that accurately reproduce vehicle response to deterministic gusts. This involves creating an inverse model using adaptive neural networks in order to map vehicle response to pilot and rotor control inputs. The accuracy of the CEGI and RCEGI models are then quantified using the Time Domain Integrated Cost Function (Ref. 1) to determine trends within the CEGI and RCEGI models for gusts of varying shape, magnitude, and duration as well as at varying flight conditions. Analysis using the cost function shows that the CEGI and RCEGI models follow similar trends. Both models are more accurate for gusts of short duration and small amplitude, and both models are more accurate for sinusoidal gusts than top hat gusts.
The paper discusses the design and high-fidelity flight dynamics modeling of a 13-lb lift-plus-cruise unmanned aerial vehicle (UAV) using Rotorcraft Comprehensive Analysis System (RCAS) in order to (1) better understand its physics of flight during a wide range of maneuvers, and (2) provide insight into the fidelity needed to achieve quantitative accuracy when compared to flight test data. Wind tunnel tests of the full aircraft were performed at a 65% scale to provide lookup tables for the flight dynamics model. Flight test data was collected while providing high control inputs to excite a variety of dynamic states in hovering and cruising modes to systematically validate the physics model. Near quantitative agreement was observed between the model predictions and test data during hover; however, the predictions began to disagree at higher forward cruising speeds. To address the discrepancy between the prediction and experiment, the flight dynamics model was improved by learning a
This paper presents updates to The Rotorcraft Optimization Tools (RCOTools) package to streamline iterative rotorcraft comprehensive design. The work is presented in three parts. Part I. a brief introduction to our simplified API is shown, in addition to a new mission profile dashboard. Part II. demonstrates high-throughput using the embarrassingly parallel paradigm to produce large-scale datasets structured by simple design of experiments (DOE) as shown by our discussion on urban air mobility (UAM) emission minimization. Such datasets provide a necessary component for rapid database generation and supervised machine learning. Part III. the API is used to couple rotor performance and sizing optimization. A simple technique for ultra-fast hover calibration is given, as well as possible applications for neural network modeling in comprehensive design. These enhancements accelerate design workflows and enable data-driven approaches for next-generation urban air mobility and planetary
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