Andrés Bellei-Pardo and Balakumar Balachandran
J. Comput. Nonlinear Dynam. Mar 2026, 21(3): 031003
https://doi.org/10.1115/1.4070543

The growing demand for self-powered remote sensors and small electronics has spurred interest in energy harvesters that can operate in low-speed wind environments for which conventional turbines are not best suited. Energy harvesters based on vortex-induced vibrations offer a promising, sustainable alternative. However, modeling and optimization of these systems is challenging as high-fidelity simulations are often prohibitively expensive, while simplified lumped-parameter models fail to capture complex geometries or boundary conditions. Here, the authors address this challenge by presenting a reduced-order modeling framework in which a nonlinear wake oscillator is integrated with a finite-element structural formulation. With this approach, the fidelity required to capture complex mode shapes of the structure is retained while maintaining the computational efficiency of lower-order models. Validation is conducted by using data from three independent experiments. The integrated framework is found to capture the critical lock-in region, wherein vortex shedding and structural frequencies synchronize, which is essential for maximizing power extraction. By accurately predicting key metrics such as voltage output and vibration amplitude without the cost of full fluid dynamics simulations, this model can serve as a useful tool for efficient design and optimization of sustainable energy harvesters.

Erhan Ferhatoglu and Johann Gross
J. Comput. Nonlinear Dynam. Feb 2026, 21(2): 021003
https://doi.org/10.1115/1.4070197

This study advances the prediction of vibration response variability arising from the nonuniqueness of static friction forces (residual traction uncertainty) in turbine blades coupled by frictional interfaces. Utilizing a nonlinear mode-based method, uncertainty is first quantified on amplitude-dependent modal parameters and then forward propagated to the vibration response to obtain frequency response bounds via interval analysis. For the first time, the uncertainty quantification is systematically demonstrated on a state-of-the-art model with a newly developed optimization-based framework. To address the computational demands of the optimization problem, two variants are proposed: (1) performing three optimizations that are independent from the forcing pattern and response location, or (2) conducting six optimizations that enable a full characterization but are valid only for a specific forcing pattern and response location. The former yields a slightly more conservative upper bound of frequency responses, but is limited to the backbone curve computation, significantly reducing the overall computational effort. The effectiveness of the proposed approach is demonstrated using a high-fidelity model of turbine blades coupled by an asymmetric underplatform damper. During the uncertainty quantification phase, the bounds of amplitude-dependent modal parameters, systematically determined through optimization, are validated by comparison with results from multiple Harmonic Balance simulations using manually assigned residual tractions. In the uncertainty propagation phase, the frequency response bounds are shown to successfully capture the full range of vibration response variability, up to the onset of 1:1 internal resonance between the first two modes at higher amplitudes.