Rotorcraft downwash impact on ship airwake: statistics, modelling, and simulation

Schau, Kyle A.; Gaonkar, G. H.; Polsky, Susan

doi:10.1017/aer.2016.47

Cited by 2 publications

(5 citation statements)

References 9 publications

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“…The Lateral component v(t) is selected for providing details of the autospectral model extraction methodology [5]. The methodology remains the same for the vertical component with minor changes in the parameters used in the constraint equations.…”

Section: Methodology Of Autospectramentioning

confidence: 99%

“…Thus, the autospectral morphing must be due to a nonlinear transformation of ABL. Stated otherwise, wind-farm wake turbulence could be idealized as nonlinearly transformed ABL and in turn, an earlier-developed mathematical framework for autospectral modeling of airwake-downwash turbulence could be adapted to modeling wind-farm wake turbulence as well [5,14]. (Airwake-downwash turbulence refers to the coupled flow-field of ship's airwake shed from the superstructure and the helicopter downwash.…”

Section: Basic Of Modelingmentioning

confidence: 99%

“…(Airwake-downwash turbulence refers to the coupled flow-field of ship's airwake shed from the superstructure and the helicopter downwash. Therein [5,14], the mathematical framework "posits" that airwake-downwash turbulence is nonlinearly transformed ABL. Regarding the coherence, the framework of Ref.…”

Section: Basic Of Modelingmentioning

confidence: 99%

“…The autospectrum model is constrained by Equations (4)- (6). The extracted model as given in Equation 1is substituted to obtain the constraint Equations (8)-(10) for the expansion series co-efficients [5].…”

Section: Constraint Equationsmentioning

confidence: 99%

“…The autospectral model extraction from a database of the present study is based on the framework due to Schau, Gaonkar and Polsky [5]. This framework guarantees that the extracted model and the autospectral data points have the same mean square value (a measure of turbulence energy), time scale and the Kolmogorov −5/3 spectral decay.…”

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confidence: 98%

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On Modelling Wind-Farm Wake Turbulence Autospectra and Coherence from a Database

2018

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This study addresses the feasibility of modeling wind-farm wake-turbulence autospectra and coherences from a database: flow velocity points from experimental and computational fluid dynamics (CFD) investigations. Specifically, it first applies an earlier-exercised framework to construct the autospectral models from a database and then it adopts a recently proposed framework to construct the coherence models from a database. While this proposed framework has not been tested against a database, the methodology has been completely formulated with a theoretical basis. These models of autospectrum and coherence are interpretive, and in closed form. Both frameworks basically involve the perturbation series expansion of the autospectra and coherences. The framework for modeling autospectra is tested against a demanding database of wake turbulence inside a wind farm over a complex terrain from a full-scale test. The suitability of these autospectral models for simulation through white-noise driven filters is also demonstrated. Finally, coherence models are generated for assumed values of the perturbation series constants, and these coherence models are used to demonstrate how the coherence models of homogeneous isotropic turbulence deviate from the coherence models of non-homogeneous non-isotropic turbulence such as wind-farm wake turbulence. This feasibility of extracting both the one-point statistics of autospectral models and the two-point statistics of coherence models from a database represents a research avenue that is new and promising in the treatment of wind-farm wake turbulence. This paper also demonstrates the feasibility of fruitfully exploiting the wake treatment methods developed in other fields.

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Section: Methodology Of Autospectramentioning

confidence: 99%

Section: Basic Of Modelingmentioning

confidence: 99%

Section: Basic Of Modelingmentioning

confidence: 99%

Section: Constraint Equationsmentioning

confidence: 99%

mentioning

confidence: 98%

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On Modelling Wind-Farm Wake Turbulence Autospectra and Coherence from a Database

2018

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Optimizing Ship Airwake Database for Predicting Autospectra Using Deep Learning

Krishnan,

Gaonkar,

Motta

2024

j am helicopter soc

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Airwakes are shed behind a ship's superstructure, and they represent a highly turbulent and rapidly distorting flow field. Because of this complexity, they require a baseline: A database of flow velocity points, preferably through experiments and also through high-fidelity computational fluid dynamics simulations. However the database is prohibitively costly, and in situ measurements, at a constant wind angle, are often affected by the weather. A previously developed perturbation scheme helps to generate the autospectrum in closed form from a given wind database, specifically, from the numerically extracted autospectrum for this database. The primary objective of this paper is to demonstrate how deep neural networks can be fruitfully exploited to reduce the dependence on a database of flow velocity points in generating the autospectra, by reducing the amount of in situ data required to achieve the same accuracy as established methods. Two machine learning approaches are developed to predict the autospectrum for a data set when provided with a subset of the wind data set at the input and are assessed using cross-validation. In the first, a neural network is applied to predict the perturbation scheme. Though this method did not predict the autospectrum better than the previously developed perturbation scheme, it laid the foundation for the second method. In the second, a neural network is applied to the autospectra which can be numerically extracted from a database. An improvement is found in the average error computed over all data sets for the model-predicted autospectrum compared to the average error in the autospectrum predicted using the conventional method. The model provided better results for 51.4% of the data sets compared to the conventional method, with many data sets showing marked improvement. The database measured from the Naval Academy YP676 training vessel is used for training and testing the neural network.

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Rotorcraft downwash impact on ship airwake: statistics, modelling, and simulation

Cited by 2 publications

References 9 publications

On Modelling Wind-Farm Wake Turbulence Autospectra and Coherence from a Database

On Modelling Wind-Farm Wake Turbulence Autospectra and Coherence from a Database

Optimizing Ship Airwake Database for Predicting Autospectra Using Deep Learning

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