skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


This content will become publicly available on January 3, 2026

Title: Airfoil Oscillator System: Data-Driven System Identification
With the computational resources becoming available, data-driven methods have emerged as powerful means for equation discovery and model construction. Sparse regression methods such as SINDy (Sparse Identification for Nonlinear Dynamical Systems) can be used for developing reduced-order models of nonlinear systems. In this study, the authors examine how SINDy can be used for developing low-dimensional models for airfoil systems, which experience unsteady aerodynamic loads and flutter instabilities. For a system of multiple closely spaced airfoil oscillators, analytical models are not readily available to determine flutter instabilities, and one has to take recourse to experimental and numerical means. In this work, as a starting point, data collected through simulations of unsteady aerodynamics of a single airfoil oscillator system are considered and a reduced-order model is constructed based on this data.  more » « less
Award ID(s):
2131594
PAR ID:
10604008
Author(s) / Creator(s):
;
Publisher / Repository:
American Institute of Aeronautics and Astronautics
Date Published:
ISBN:
978-1-62410-723-8
Format(s):
Medium: X
Location:
Orlando, FL
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; (, PLOS Computational Biology)
    Alber, Mark (Ed.)
    Biological systems exhibit complex dynamics that differential equations can often adeptly represent. Ordinary differential equation models are widespread; until recently their construction has required extensive prior knowledge of the system. Machine learning methods offer alternative means of model construction: differential equation models can be learnt from data via model discovery using sparse identification of nonlinear dynamics (SINDy). However, SINDy struggles with realistic levels of biological noise and is limited in its ability to incorporate prior knowledge of the system. We propose a data-driven framework for model discovery and model selection using hybrid dynamical systems: partial models containing missing terms. Neural networks are used to approximate the unknown dynamics of a system, enabling the denoising of the data while simultaneously learning the latent dynamics. Simulations from the fitted neural network are then used to infer models using sparse regression. We show, via model selection, that model discovery using hybrid dynamical systems outperforms alternative approaches. We find it possible to infer models correctly up to high levels of biological noise of different types. We demonstrate the potential to learn models from sparse, noisy data in application to a canonical cell state transition using data derived from single-cell transcriptomics. Overall, this approach provides a practical framework for model discovery in biology in cases where data are noisy and sparse, of particular utility when the underlying biological mechanisms are partially but incompletely known. 
    more » « less
  2. ; ; (, Journal of Sound and Vibration)
    Limit-cycle oscillations of bodies with airfoil cross-sections is a subject of keen interest for engineering applications. In systems consisting of multiple such closely spaced bodies, the aerodynamic interactions amongst two or more such bodies can influence the system response. The nature of these interactions is examined with respect to variations in external parameters such as freestream speed and system parameters such as inter-oscillator spacing and the number of airfoil oscillators. By using a co-simulation scheme, which consists of a reduced order three degree-of-freedom piezostructural system and an unsteady vortex lattice method fluid solver, the effects of these parameters on the resulting aerodynamic loads on the system, the overall dynamic response, and the critical flutter speed are studied. In a three-airfoil oscillator system, the effect of the position of the inner airfoil oscillator is extensively studied with a focus on characterizing airfoil interactions and airfoil-wake interactions. For different parallel configurations, studies of bifurcations with respect to different control parameters are conducted. 
    more » « less
  3. ; ; ; (, AIChE Journal)
    Abstract Over the past few decades, several data‐driven methods have been developed for identifying a model that accurately describes the process dynamics. Lately, sparse identification of nonlinear dynamics (SINDy) has delivered promising results for various nonlinear processes. However, at any instance of plant‐model mismatch or process upset, retraining the model using SINDy is computationally expensive and cannot guarantee to catch up with rapidly changing dynamics. Hence, we propose operable adaptive sparse identification of systems (OASIS) framework that extends the capabilities of SINDy for accurate, automatic, and adaptive approximation of process models. First, we use SINDy to obtain multiple models from historical data for varying input settings. Next, using these models and their training data, we build a deep neural network that is incorporated in a model predictive control framework for closed‐loop operation. We demonstrate the OASIS methodology on the identification and control of a continuous stirred tank reactor. 
    more » « less
  4. ; ; ; (, 2022 International Conference on Robotics and Automation (ICRA))
    This paper presents four data-driven system models for a magnetically controlled swimmer. The models were derived directly from experimental data, and the accuracy of the models was experimentally demonstrated. Our previous study successfully implemented two non-model-based control algorithms for 3D path-following using PID and model reference adaptive controller (MRAC). This paper focuses on system identification using only experimental data and a model-based control strategy. Four system models were derived: (1) a physical estimation model, (2, 3) Sparse Identification of Nonlinear Dynamics (SINDY), linear system and nonlinear system, and (4) multilayer perceptron (MLP). All four system models were implemented as an estimator of a multi-step Kalman filter. The maximum required sensing interval was increased from 180 ms to 420 ms and the respective tracking error decreased from 9 mm to 4.6 mm. Finally, a Model Predictive Controller (MPC) implementing the linear SINDY model was tested for 3D path-following and shown to be computationally efficient and offers performances comparable to other control methods. 
    more » « less
  5. ; (, Journal of Physics: Conference Series)
    This paper investigates the energy production of a “meso-scale”, wind-based energy harvester that exploits the torsional aeroelastic instability of a rigid blade-airfoil, elastically supported at equidistant supports. Torsional flutter is a single mode aeroelastic instability phenomenon, in which a diverging dynamic angular rotation of a body occurs. The apparatus relies on a simple mechanism that uses flow-induced pitch motion to extract and convert airflow kinetic energy to electrical energy. The system is composed by a rigid blade-airfoil, connected to a support structure through a non-linear restoring force (torsional spring-like) mechanism that enables the rotation about a reference pivot axis. The proposed technology is designed to be efficient in the range of low and medium wind speeds (10-13 m/s), in which horizontal-axis wind turbines and other harvesters are not efficient. Deterministic pre-flutter, incipient flutter and post-critical vibrations of the apparatus have been already explored in a previous study. This work aims to further investigate the aeroelastic behavior of the “flapping foil” by examining the effect of turbulence, random experimental error and modeling simplifications of the aeroelastic forces. The analysis is conducted at incipient flutter in the frequency domain using classical unsteady force models. Monte Carlo methods are employed to solve for the probability of incipient flutter speed. Several configurations are considered to improve the efficiency of the energy harvester. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email