Jan Decuyper

• PhD Engineering Sciences
• Professor (Assistant) at Vrije Universiteit Brussel

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 intensit...

A known challenge when building nonlinear models from data is to limit the size of the model in terms of the number of parameters. Especially for complex nonlinear systems, which require a substantial number of state variables, the classical formulation of the nonlinear part (e.g. through a basis expansion) tends to lead to a rapid increase in the...

Multivariate functions emerge naturally in a wide variety of data-driven models. Popular choices are expressions in the form of basis expansions or neural networks. While highly effective, the resulting functions tend to be hard to interpret, in part because of the large number of required parameters. Decoupling techniques aim at providing an alter...

Accurate unsteady aerodynamic models are essential to estimate the forces on rapidly pitching wings and to develop model-based controllers. As system identification is arguably the most successful framework for model predictive control in general, in this paper we investigate whether system identification can be used to build data-driven models of...

Engineers and scientists want mathematical models of the observed system for understanding, design, and control. Many mechanical and civil structures are nonlinear. This paper illustrates a combined nonparametric and parametric system identification framework for modelling a nonlinear vibrating structure. First step of the process is the analysis:...

Multivariate functions emerge naturally in a wide variety of data-driven models. Popular choices are expressions in the form of basis expansions or neural networks. While highly effective, the resulting functions tend to be hard to interpret, in part because of the large number of required parameters. Decoupling techniques aim at providing an alter...

In this paper, we present an analytical solution to the nonlinear optimization problem encountered in the context of the filtered canonical polyadic decomposition. It is shown that the originally proposed alternating least squares (ALS) approach can be significantly accelerated through a thorough analysis of one of the matrix factors, that is, the...

In this paper, we present an analytical solution to the nonlinear optimization problem encountered in the context of the filtered canonical polyadic decomposition. It is shown that the originally proposed alternating least squares (ALS) approach can be significantly accelerated through a thorough analysis of one of the matrix factors, that is, the...

Engineers and scientists want mathematical models of the observed system for understanding, design and control. Many mechanical and civil structures are nonlinear. This paper illustrates a combined nonparametric and parametric system identification framework for modeling a nonlinear vibrating structure. First step of the process is the analysis: me...

The integration of renewable and residual energy sources requires a detailed level of knowledge of district heating components such as thermal pipes, pipes, pumps, and heat exchanger of a substation. The thermal energy stored in these components can be utilized to match the heat demand and heat generation in time for next generation district heatin...

Black-box model structures are dominated by large multivariate functions. Usually a generic basis function expansion is used, e.g. a polynomial basis, and the parameters of the function are tuned given the data. This is a pragmatic and often necessary step considering the black-box nature of the problem. However, having identified a suitable functi...

Nonlinear Auto-Regressive eXogenous input (NARX) models are a popular class of nonlinear dynamical models. Often a polynomial basis expansion is used to describe the internal multivariate nonlinear mapping (P-NARX). Resorting to fixed basis functions is convenient since it results in a closed form solution of the estimation problem. The drawback, h...

This is a demonstration of the PNLSS Toolbox 1.0. The toolbox is designed to identify polynomial nonlinear state-space models from data. Nonlinear state-space models can describe a wide range of nonlinear systems. An illustration is provided on experimental data of an electrical system mimicking the forced Duffing oscillator, and on numerical data...

Nonlinear Auto-Regressive eXogenous input (NARX) models are a popular class of nonlinear dynamical models. Often a polynomial basis expansion is used to describe the internal multivariate nonlinear mapping (P-NARX). Resorting to fixed basis functions is convenient since it results in a closed form solution of the estimation problem. The drawback, h...

Black-box model structures are dominated by large multivariate functions. Usually a generic basis function expansion is used, e.g. a polynomial basis, and the parameters of the function are tuned given the data. This is a pragmatic and often necessary step considering the black-box nature of the problem. However, having identified a suitable functi...

This is a demonstration of the PNLSS Toolbox 1.0. The toolbox is designed to identify polynomial nonlinear state-space models from data. Nonlinear state-space models can describe a wide range of nonlinear systems. An illustration is provided on experimental data of an electrical system mimicking the forced Duffing oscillator, and on numerical data...

Nonlinear state-space modelling is a very powerful black-box modelling approach. However powerful, the resulting models tend to be complex, described by a large number of parameters. In many cases interpretability is preferred over complexity, making too complex models unfit or undesired. In this work, the complexity of such models is reduced by re...

This is a demonstration of the PNLSS Toolbox 1.0. The toolbox is designed to identify polynomial nonlinear state-space models from data. Nonlinear state-space models can describe a wide range of nonlinear systems. An illustration is provided on experimental data of an electrical system mimicking the forced Duffing oscillator, and on numerical data...

Nonlinear Auto-Regressive eXogenous input (NARX) models are a popular class of nonlinear dynamical models. Often a polynomial basis expansion is used to describe the internal multivariate nonlinear mapping (P-NARX). Resorting to fixed basis functions is convenient since it results in a closed form solution of the estimation problem. The drawback, h...

Black-box model structures are dominated by large multivariate functions. Usually a generic basis function expansion is used, e.g. a polynomial basis, and the parameters of the function are tuned given the data. This is a pragmatic and often necessary step considering the black-box nature of the problem. However, having identified a suitable functi...

The decoupling algorithm proposed by Dreesen et al. (SIAM J. Matrix Anal. Appl., 2015) was developed for MIMO polynomials. Polynomial based models are commonly used in nonlinear system identification despite their poor extrapolation behaviour and the often poor numerical conditioning of the resulting estimation problems. Several alternatives have b...

In this work recurrent neural networks of the LSTM type are used to describe unsteady fluid loading. Indirect load measurements are especially useful for structural health monitoring applications. Relying on discrete spatiotemporal measurements of the velocity field, both the forces and the corresponding displacement of a cylinder subjected to vort...

Dynamic fluid loading is of major concern to the off shore industry. Impacted structures may exhibit accelerated fatigue and even failure. Load monitoring has therefore become an integral part of structural health monitoring. Obtaining direct load measurements can be cumbersome, e.g. obtaining strain gauge or pressure taps measurements. In this wor...

In aerodynamics, as in many engineering applications, a parametrised mathematical model is used for design and control. Often, such models are directly estimated from experimental data. However, in some cases, it is better to first identify a so-called nonparametric model, before moving to a parametric model. Especially when nonlinear effects are p...

A persisting challenge in nonlinear dynamical modelling is parameter inference from data. Provided that an appropriate model structure was selected, the identification problem is profoundly affected by a choice of initialisation. A particular challenge that may arise is initialisation within a region of the parameter space where the model is not co...

A nonlinear model relating the imposed motion of a circular cylinder, submerged in a fluid flow, to the transverse force coefficient is presented. The nonlinear fluid system, featuring vortex shedding patterns, limit cycle oscillations and synchronisation, is studied both for swept sine and multisine excitation. A nonparametric nonlinear distortion...

A nonlinear model relating the imposed motion of a circular cylinder, submerged in a fluid flow, to the transverse force coefficient is presented. The nonlinear fluid system, featuring vortex shedding patterns, limit cycle oscillations and synchronisation, is studied both for swept sine and multisine excitation. A nonparametric nonlinear distortion...

Nonlinear state-space modelling is a very powerful black-box modelling approach. However powerful, the resulting models tend to be complex, described by a large number of parameters. In many cases interpretability is preferred over complexity, making too complex models unfit or undesired. In this work, the complexity of such models is reduced by re...

A persisting challenge in nonlinear dynamical modelling is parameter inference from data. Provided that an appropriate model structure was selected, the identification problem is profoundly affected by a choice of initialisation. A particular challenge that may arise is initialisation within a region of the parameter space where the model is not co...

Multivariate polynomials are omnipresent in black-box modelling. They are praised for their flexibility and ease of manipulation yet typically fall short in terms of insight and interpretability. Hence often an alternative representation is desired. Translating the coupled polynomials into a decoupled form, containing only univariate polynomials ha...

In this work we study the effect of using different types of excitation signals as training data when constructing black box nonlinear models. We focus in particular on the class of nonlinear systems which exhibit autonomous oscillations. Three type of excitation signals are considered: random-phase multisines, filtered white noise and swept sines....

In this article, we develop a nonlinear data-driven model of the aerodynamic force on an aerofoil made to oscillate in pitch in a wind tunnel under constant flow conditions. The proposed model structure is a polynomial nonlinear state-space model (PNLSS) which is an extension of the classical linear state-space model with nonlinear functions. In th...

The flow-induced vibration of bluff bodies is an important problem of many marine, civil, or mechanical engineers. In the design phase of such structures, it is vital to obtain good predictions of the fluid forces acting on the structure. Current methods rely on computational fluid dynamic simulations (CFD), with a too high computational cost to be...

The flow-induced vibration of bluff bodies is an important problem of many marine, civil, or mechanical engineers. In the design phase of such structures, it is vital to obtain good predictions of the fluid forces acting on the structure. Current methods rely on computational fluid dynamic simulations (CFD), with a too high computational cost to be...

Polynomial nonlinear state-space (PNLSS) models have proven to be very useful in modelling highly nonlinear systems, encountered over a variety of engineering applications. In this work, we focus on modelling the kinematics of an oscillating circular cylinder, submerged in a low Reynolds number flow. Such a set up is typically used to study vortex...

In this work a black-box identification approach is used to identify a nonlinear model for nonlinear systems exhibiting autonomous oscillations. We focus on the proces of vortex-induced vibrations where fluid-structure interactions cause vibrations. A polynomial nonlinear state space model structure is proposed and fitted to the data acquired from...

In this work we explain the basic ideas of a novel modelling methodology for unsteady aerodynamics based on system identification techniques. We aim to construct transfer functions (as they are often used in noise and vibration engineering) to model periodic unsteadiness. This is of direct use for the study of limit-cycle oscillations (LCO) in flut...

In this paper we address the use of system identification techniques as a means for constructing a model relating fluid variables (pressure, velocity, vorticity) measured in the wake to the oscillatory behaviour of a cylinder in a flow. We describe how the model can be decomposed in three separate transfer functions, linked in a loop configuration....