Keywords

Offshore platform; Frequency-dependent hydrodynamic coefficients; Filter; Morison equation; Drag; Particle Swarm Optimization

Abstract

Accurate prediction of hydrodynamic loads on offshore platforms requires drag coefficients that adequately capture their dependence on wave frequency. However, conventional implementations of Morison’s equation generally assume constant drag coefficients across the entire frequency spectrum, which can lead to inaccuracies in predicting platform responses under complex sea-state conditions. To address this limitation, this study proposes a frequency-dependent hydrodynamic modeling framework in which the platform response is decomposed into distinct frequency regions, specifically low-frequency and high-frequency components. Separate drag coefficients are assigned to each region to better represent the underlying flow physics. The decomposition is achieved using filtering techniques in the frequency domain, where a fifth-order low-pass Butterworth filter is employed to isolate the low-frequency response, while a first-order high-pass filter is used to extract the wave-frequency dynamics. To enable systematic and objective calibration of the model, an automated parameter identification strategy based on Particle Swarm Optimization (PSO) is introduced. This approach effectively handles the nonlinear, non-differentiable, and computationally expensive nature of the model without relying on gradient information. The proposed methodology is validated using experimental data from the INO WINDMOOR 12 MW semi-submersible floating platform. The results demonstrate improved agreement between simulated and measured surge responses across both resonance and wave-frequency regions, compared to conventional constant-coefficient formulations. Overall, the integration of frequency-dependent hydrodynamic modeling with PSO-based parameter optimization provides a robust and efficient framework for enhancing the accuracy of offshore platform response prediction. The proposed approach contributes toward the development of high-fidelity simulation tools for offshore platform design and analysis.

Completion Date

2026

Semester

Spring

Committee Chair

Tuhin Das

Degree

Master of Science in Mechanical Engineering (M.S.M.E.)

College

College of Engineering and Computer Science

Department

Mechanical and Aerospace Engineering

Document Type

Dissertation/Thesis

Identifier

DP0053254

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