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<p>In this study, we present and validate an ensemble-based Hankel Dynamic Mode Decomposition with control (HDMDc) for uncertainty-aware seakeeping predictions of a high-speed catamaran. Experimental measurements (time histories) of wave elevation at the longitudinal center of gravity, heave, pitch, notional flight-deck velocity, notional bridge acceleration, and total resistance were collected from irregular wave basin tests on a replica of the Delft 372, a 1:33.3 scale replica of a notional 100 m catamaran under sea state 5 conditions at <i>Fr</i> = 0.425, and organized into training, validation, and test sets. The HDMDc algorithm constructs an equation-free linear reduced-order model of the seakeeping vessel by augmenting states and inputs with their time-lagged copies to capture nonlinear and memory effects. Two ensembling strategies, namely Bayesian HDMDc (BHDMDc) and Frequentist HDMDc (FHDMDc), are compared in providing seakeeping prediction and uncertainty quantification. In BHDMDc, hyperparameters are considered stochastic variables with prior distribution, while the FHDMDc aggregates multiple models obtained over different data subsets, both ensemble methods produce posterior mean forecasts with confidence intervals. The FHDMDc approach is found to improve the accuracy of predictions compared to the deterministic counterpart, also providing robust uncertainty estimation, whereas the application of BHDMDc to the present test case is not found beneficial in comparison to the deterministic model. FHDMDc-derived probability density functions for the motions closely match both experimental data and unsteady Reynolds-averaged Navier-Stokes results, demonstrating reliable and computationally efficient seakeeping prediction for design and operational support.</p>

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Data-driven uncertainty-aware seakeeping prediction of the Delft 372 catamaran using ensemble Hankel dynamic mode decomposition

  • 期刊论文

  • 发表日期：

    2026年2月1日

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  • Giorgio Palma,
  • Andrea Serani,
  • Matteo Diez

摘要

In this study, we present and validate an ensemble-based Hankel Dynamic Mode Decomposition with control (HDMDc) for uncertainty-aware seakeeping predictions of a high-speed catamaran. Experimental measurements (time histories) of wave elevation at the longitudinal center of gravity, heave, pitch, notional flight-deck velocity, notional bridge acceleration, and total resistance were collected from irregular wave basin tests on a replica of the Delft 372, a 1:33.3 scale replica of a notional 100 m catamaran under sea state 5 conditions at Fr = 0.425, and organized into training, validation, and test sets. The HDMDc algorithm constructs an equation-free linear reduced-order model of the seakeeping vessel by augmenting states and inputs with their time-lagged copies to capture nonlinear and memory effects. Two ensembling strategies, namely Bayesian HDMDc (BHDMDc) and Frequentist HDMDc (FHDMDc), are compared in providing seakeeping prediction and uncertainty quantification. In BHDMDc, hyperparameters are considered stochastic variables with prior distribution, while the FHDMDc aggregates multiple models obtained over different data subsets, both ensemble methods produce posterior mean forecasts with confidence intervals. The FHDMDc approach is found to improve the accuracy of predictions compared to the deterministic counterpart, also providing robust uncertainty estimation, whereas the application of BHDMDc to the present test case is not found beneficial in comparison to the deterministic model. FHDMDc-derived probability density functions for the motions closely match both experimental data and unsteady Reynolds-averaged Navier-Stokes results, demonstrating reliable and computationally efficient seakeeping prediction for design and operational support.