Extracting Stochastic Airwake Models from a Database for Engineering Analysis and Simulation
01 Apr 2012-Journal of The American Helicopter Society (American Helicopter Society)-Vol. 57, Iss: 2, pp 1-15
About: This article is published in Journal of The American Helicopter Society.The article was published on 2012-04-01. It has received 5 citations till now. The article focuses on the topics: Engineering analysis.
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TL;DR: In this paper, a mathematical framework for extracting interpretive turbulence models in closed form from these autospectral statistics was proposed to provide a foothold on airwake-downwash phenomenon for engineering analysis.
Abstract: Helicopter downwash impact on ship airwake is addressed from a three-pronged approach: (1) Analysis of one-point statistics of autospectrum and two-point statistics of cross-spectrum and coherence from a Computational Fluid Dynamics database of flow velocities effected by helicopter downwash and shipboard airwake; (2) Development of a mathematical framework for extracting interpretive turbulence models in closed form from these autospectral statistics; and (3) Simulation through white-noise-driven filters for the extracted models. The framework begins with an earlier-exercised perturbation-type series expansion of the autocorrelation for all three velocity components, where the first term of the series has a form of the von Karman longitudinal or lateral correlation function. After transformation into equivalent series of autospectrum, the coefficients in the series are evaluated by satisfying theoretical constraints and fitting a curve on a set of selected autospectral data points generated from the database. The framework represents a sensible combination of series expansion, exploitation of a database, and theoretical constraints to provide a foothold on airwake-downwash phenomenon for engineering analysis. It ensures that the extracted model and the autospectral data points have the same time scale, mean square value, and asymptotic decay according to the Kolmogorov –5/3 Law. The framework's strengths and weaknesses, and its major advancement over the earlier series-expansion schemes are also addressed. Finally it is shown that downwash increases airwake energy (mean square value) by one order of magnitude, and almost all of this airwake-downwash energy is concentrated within the bandwidth (0.16 < f(Hz) < 1.6) that affects flight mechanics.
3 citations
25 Jun 2012
TL;DR: In this article, the authors extracted autospectral models of airwake-downwash turbulence from a database and used them to design white-noise-driven shaping filters.
Abstract: 1 Autospectrum models of airwake-downwash turbulence are extracted from a database. These extracted models are interpretive models with a simple analyt ical structure that aids delineation, routine simul ation, and application as a predictive tool. Airwake refer s to turbulence shed from the ship superstructure, and the database, to a set of autospectral points of flow v elocity data from experimental and CFD-based investigations. The model extraction is based on an earlier-proposed framework developed from first principles, which addresses all three velocity comp onents. For each component, the autocorrelation is represented by a separate perturbation series in wh ich the first term has a form of von Karman longitu dinal or lateral correlation function. These series are t hen transformed into equivalent perturbation series of autospectra. The perturbation coefficients are eval uated by satisfying the algorithmic constraints and fitting a curve on a set of selected spectral data points in the low frequency bandwidth (0 < fHz < 1.6). In this paper, the focus is on extracting vertical turbulence auto spectrum models and designing white-noise-driven shaping filters for this bandwidth, as required in flight d ynamics applications. Generally, a second-order perturbation correction (a three-term perturbation series) is ad equate, and the extracted models lend themselves well to construction of shaping filters. The strengths and weaknesses of the database and the extracted models are discussed as well.
2 citations
TL;DR: In this paper , an air-wake model applicable for flight mechanics analyses and real-time simulations is presented, which is based on system identification methodologies applied to wind-tunnel experimental data.
Abstract: Ship air-wake modeling is a critical task needed to support the design and validation of algorithms that can assist a helicopter’s pilot during shipboard launch and recovery operations. In fact, these operations are often carried out in challenging conditions and impose a significant workload on the pilot. In this framework, the paper presents an air-wake model applicable for flight mechanics analyses and real-time simulations. The definition of the model is based on system identification methodologies applied to wind-tunnel experimental data. The main innovation of the approach consists of the definition of a modular structure of the model that allows setting up a multistep identification strategy and exploiting the most suitable technique for the estimation of each set of the model’s parameters. The validation of the obtained model highlighted a good capability to reproduce the air-wake flowfield in several flow conditions. The model was applied to test trajectory generation and tracking algorithms for helicopter automatic takeoff and landing on a ship deck.