A data-driven nonlinear state-space model of the unsteady lift force on a pitching wing

Siddiqui, Muhammad Faheem; Troyer, Tim De; Decuyper, Jan; Csurcsia, Péter Zoltán; Schoukens, Joannes; Runacres, Mark

doi:10.1016/j.jfluidstructs.2022.103706

Cited by 12 publications

(9 citation statements)

References 38 publications

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“…In recent times, the abundance of numerical and experimental datasets has stimulated the use of data-driven methods. Many different data-driven modelling strategies have been investigated for the identification of unsteady aerodynamics systems, such as linear/linearised state-space models (Brunton, Rowley & Williams 2013;Dawson et al 2015), polynomial nonlinear state-space models (Decuyper et al 2018;Siddiqui et al 2022), block-oriented models (Kou, Zhang & Yin 2016) and the sparse identification of nonlinear dynamics method (Brunton, Proctor & Kutz 2016;Loiseau, Noack & Brunton 2018;Sun et al 2021). A promising data-driven technique is the state-space neural network (SS-NN) methodology (Schoukens 2021), which exploits the flexibility of artificial neural networks within a classical state-space representation.…”

Section: Introductionmentioning

confidence: 99%

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Modelling the unsteady lift of a pitching NACA 0018 aerofoil using state-space neural networks

Damiola,

Decuyper,

Runacres

et al. 2024

J. Fluid Mech.

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The development of simple, low-order and accurate unsteady aerodynamic models represents a crucial challenge for the design optimisation and control of fluid dynamical systems. In this work, wind tunnel experiments of a pitching NACA 0018 aerofoil conducted at a Reynolds number $Re = 2.8 \times 10^5$ and at different free-stream turbulence intensities are used to identify data-driven nonlinear state-space models relating the time-varying angle of attack of the aerofoil to the lift coefficient. The proposed state-space neural network (SS-NN) modelling technique explores an innovative methodology, which brings the flexibility of artificial neural networks into a classical state-space representation and offers new insights into the construction of reduced-order unsteady aerodynamic models. The work demonstrates that this technique provides accurate predictions of the nonlinear unsteady aerodynamic loads of a pitching aerofoil for a wide variety of angle-of-attack ranges and frequencies of oscillation. Results are compared with a modified version of the Goman–Khrabrov dynamic stall model. It is shown that the SS-NN methodology outperforms the classical semi-empirical dynamic stall models in terms of accuracy, while retaining a fast evaluation time. Additionally, the proposed models are robust to noisy measurements and do not require any pre-processing of the data, thus involving only a limited user interaction. Overall, these features make the SS-NN technique an excellent candidate for the construction of accurate data-driven models from experimental fluid dynamics data, and pave the way for their adoption in applications entailing design optimisation and real-time control of systems involving lift.

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Section: Introductionmentioning

confidence: 99%

“…2018; Siddiqui et al. 2022), block-oriented models (Kou, Zhang & Yin 2016) and the sparse identification of nonlinear dynamics method (Brunton, Proctor & Kutz 2016; Loiseau, Noack & Brunton 2018; Sun et al. 2021).…”

Section: Introductionmentioning

confidence: 99%

Modelling the unsteady lift of a pitching NACA 0018 aerofoil using state-space neural networks

Damiola,

Decuyper,

Runacres

et al. 2024

J. Fluid Mech.

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“…The PNLSS model, proposed by Paduart et al in 2010, extends the original linear state space model by incorporating nonlinear polynomials, thus enhancing the flexibility and degrees of freedom of the SSM. The process of establishing a PNLSS model is as follows: firstly, define the model structure, avoiding overly high-order polynomials that increase the complexity of parameter estimation and affect accuracy; secondly, design input signals, with common types including swept-sine signals [19] and multisine signals [20]; and lastly, estimate model parameters: initially use the Local Polynomial Method (LPM) [21] to determine the system's non-parametric Best Linear Approximation (BLA), extract LSS model parameters from the BLA using the frequency domain subspace method [22], and finally optimize the corresponding polynomial coefficients and LSS model coefficients using the Levenberg-Marquardt [23] method.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear System identification of Air turbine rocket engine based on Polynomial nonlinear state space model

Liang,

Zhao,

Zhao

et al. 2024

Preprint

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The Air Turbine Rocket (ATR) engine is a promising combined cycle propulsion engine. This paper uses the polynomial nonlinear state-space (PNLSS) model to model and identify the nonlinear system of the ATR engine. A method of multiple uncorrelated step signals is proposed as the excitation signals for the nonlinear system. The adaptive Nelder-Mead simplex (ANMS) algorithm is used as the nonlinear least squares optimization algorithm to solve the PNLSS model parameters. The identification results show that the multiple uncorrelated step signals have good excitation effects on the steady-state operating points and the large-scale dynamic processes of the nonlinear system. Under the same initial values, the ANMS algorithm has obvious advantages over the Levenberg-Marquardt (L-M) algorithm and the standard Nelder-Mead simplex (SNMS) algorithm in terms of optimization effect and convergence speed. The PNLSS model shows higher fitting accuracy and prediction ability than the linear state space (LSS) model for the operating points and the wide-range dynamic processes of the ATR engine. This study provides a new method for excitation signal design and parameter identification for nonlinear systems and lays a foundation for the design of nonlinear controllers.

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“…With regard to the unsteady aerodynamics of pitching airfoils, several techniques have been explored for the construction of data-driven models. The most relevant examples include linear state-space models [10], polynomial nonlinear state-space models (PNLSS) [11] and block-oriented models [12].…”

Section: Introductionmentioning

confidence: 99%

Modeling airfoil dynamic stall using State-Space Neural Networks

Damiola

Decuyper

Runacres

et al. 2023

AIAA SCITECH 2023 Forum

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A data-driven nonlinear state-space model of the unsteady lift force on a pitching wing

Cited by 12 publications

References 38 publications

Modelling the unsteady lift of a pitching NACA 0018 aerofoil using state-space neural networks

Modelling the unsteady lift of a pitching NACA 0018 aerofoil using state-space neural networks

Nonlinear System identification of Air turbine rocket engine based on Polynomial nonlinear state space model

Modeling airfoil dynamic stall using State-Space Neural Networks

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