Wind-Tunnel Study of the ARMA Flutter Prediction Method

Raveh, Daniella E.; Iovnovich, M.L.; Nahom, Tzlil

doi:10.2514/6.2018-0702

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Where 𝑢 𝑓 and 𝑦 𝑓 are now the input and output vectors, the 𝐵 𝑓 and 𝐶 𝑓 (𝑖𝜔) matrices are now able to be of any rank, up to the smaller number of variables in 𝑦 𝑓 or 𝑢 𝑓 , leading to the ability to rewrite equation (7) to…”

Section: 𝐴(𝑖𝜔)mentioning

confidence: 99%

“…Therefore, numerous numerical analyses, wind tunnel and ground tests are performed to make sure that the test aircraft is not accidentally brought too close to the flutter boundary. To be able to predict the flutter boundary during flight tests, several different data-analysis methods exist, such as the Zimmerman-Weissenburger flutter margin [4], damping extrapolation [5], the envelope function [6] and the Autoregressive Moving Average (ARMA) method [7]. All of these methods use the data obtained at velocities below the flutter speed to extrapolate the flutter boundary, which in turn leads to long, risky and expensive tests [8].…”

Section: Introductionmentioning

confidence: 99%

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Safe Flutter Determination for Wings Undergoing Large Deflections

Boer

Karpel

Sodja

2023

AIAA SCITECH 2023 Forum

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A new flutter test version of the Parametric Flutter Margin (PFM) method, specifically applied to wings undergoing large deflections is presented. The PFM method adds a stabilising parameter, such as a stabilising mass, to the model such that the flutter velocity is increased. By exciting the stabilising mass in one of the primary (i.e., x, y and z) directions while simultaneously measuring the response in these directions and repeating the excitation in other directions, the flutter margins that are associated with the original model can be determined. To demonstrate the method a wind tunnel test campaign was performed at TU Delft using the Delft Pazy Wing which can exhibit large nonlinear deflection, onto which a flutter pod consisting of a shaker and stabilising mass was placed at the mid-span position at the leading edge of the wing. During the test campaign, three test series were performed. The first identified the flutter boundary through direct flutter tests, with the flutter onset and offset velocities being determined by actually hitting flutter that turned into an LCO. SISO and MIMO PFM tests were then performed to obtain the nominal flutter boundaries without actually hitting flutter, showing a maximum difference of 4.4 % between each other at an angle of attack of 6°. The difference between the MIMO and SISO PFM was found to be increasing with increasing angle of attack, which was as expected. Compared to the directly measured flutter tests, the MIMO PFM results identified a flutter velocity of 4.8 % lower than the directly measured flutter offset velocity at an angle of attack of 4°, and the SISO PFM results identified the flutter velocity to be 8.2 % lower than the offset velocity at 4°angle of attack. The PFM-identified flutter frequencies showed a difference of less than 2 % compared to the direct flutter test, with the difference in identified flutter frequency between the SISO and MIMO PFM reaching a maximum of 2 %. The acquired data shows the potential of the PFM method for performing safer, shorter and consequently cheaper flight tests for the certification procedure of new aircraft configurations. Nomenclature

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Section: 𝐴(𝑖𝜔)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Safe Flutter Determination for Wings Undergoing Large Deflections

Boer

Karpel

Sodja

2023

AIAA SCITECH 2023 Forum

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“…The empirical table shows that those model parameters can be a time function, and the capabilities of the ARMA predictive model will be different when applied to diverse time domains [106]. Authors [107] used the ARMA approach to calculate that the average speed wind per hour increased by 1~10 h. Taking into account the features of seasonal winds, the authors adopted different approaches for each calendar month [108,109]. Moreover, they emphasized that the utilization of power in one hour (MWh) from generation power (MW) for a power predictive parameter.…”

Section: Evaluation Of Wind Speed and Power Forecastsmentioning

confidence: 99%

Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend

et al. 2020

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To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method.

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“…The training, test, and validation datasets used in this study were derived from aeroelastic model wind tunnel flutter test data. The sensor layout of the aeroelastic model is as shown in Figure 2: seven strain measurements [19] were taken from locations marked by blue rectangles (each including bending and torsional data), and 26 acceleration sensors were placed in locations marked by red dots. Three strain measuring points were placed at the root of Here, the models constituted by the CNN architecture and its parameters were saved for reliability verification and the accuracy rate was calculated as follows:…”

Section: Dataset Productionmentioning

confidence: 99%

A Novel Classification Method for Flutter Signals Based on the CNN and STFT

Duan

Zheng

Liu

2019

International Journal of Aerospace Engineering

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Necessary model calculation simplifications, uncertainty in actual wind tunnel test, and data acquisition system error altogether lead to error between a set of actual experimental results and a set of theoretical design results; wind tunnel test flutter data can be utilized to feedback this error. In this study, a signal processing method was established to use the structural response signals from an aeroelastic model to classify flutter signals via deep learning algorithm. This novel flutter signal processing and classification method works by combining a convolutional neural network (CNN) with time-frequency analysis. Flutter characteristics are revealed in both time and frequency domains, which are harmonic or divergent in the time series; the flutter model energy is singular and significantly increases in the frequency view, so the features of the time-frequency diagram can be extracted from the dataset-trained CNN model. As the foundation of the subsequent deep learning algorithm, the datasets are placed into a collection of time-frequency diagrams calculated by short-time Fourier transform (STFT) and labeled with two artificial states, flutter or no flutter, depending on the source of the signal measured from a wind tunnel test on the aeroelastic model. After preprocessing, a cross-validation schedule is implemented to update (and optimize) CNN parameters though the trained dataset. The trained models were compared against test datasets to validate their reliability and robustness. Our results indicate that the accuracy rate of test datasets reaches 90%. The trained models can effectively and automatically distinguish whether or not there is flutter in the measured signals.

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Wind-Tunnel Study of the ARMA Flutter Prediction Method

Cited by 6 publications

References 17 publications

Safe Flutter Determination for Wings Undergoing Large Deflections

Safe Flutter Determination for Wings Undergoing Large Deflections

Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend

A Novel Classification Method for Flutter Signals Based on the CNN and STFT

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