Session: 08-02-01 Wave–Structure Interaction, Noise Modeling, and Marine Hydrodynamics

Submission Number: 180933

Multi-Objective Vibroacoustic Optimization of Ship Hull Structures for Reduced Underwater Noise Emission 

The reduction of underwater radiated noise (URN) has become a key driver in modern ship design, as international environmental and classification standards increasingly require attention to acoustic signatures in addition to structural strength and hydrodynamic efficiency. This study presents a computational framework for the multi-objective vibroacoustic optimization of ship hull structures, targeting concurrent reductions in noise emission and structural mass under dynamic excitation from free surface waves. The framework integrates a non-linear finite element model for fluid flow, structural motion resolved through its hydroelastic eigenmodes, acoustic post-processing based on the Ffowcs Williams-Hawkings formulation, and multi-objective optimization algorithms within a unified workflow. The modal displacement, velocity, and generalized force histories are used to evaluate the radiation efficiency of each structural mode and its contribution to the total far-field acoustic pressure. Insights from vibroacoustic analysis are embedded in a multi-objective optimization framework that balances structural and acoustic performance metrics.

The optimization employs evolutionary algorithms for global exploration and gradient-based refinement for convergence acceleration. To mitigate computational cost, Gaussian process and machine learning-based surrogate models are trained on high-fidelity simulation data to predict panel responses across the design space. Stiffener placement and sizing are varied, allowing systematic evaluation of trade-offs between structural weight, modal frequency distribution, and URN level. Results demonstrate that optimized stiffening layouts on a single panel can achieve several decibels of broadband URN reduction without compromising stiffness. Enhanced control of modal coupling suppresses low-order resonances that most efficiently radiate acoustic energy into the surrounding fluid. The framework is further extended to bow-region optimization, where multiple panels interact under spatially varying pressure fields and free-surface proximity effects, enabling assessment under realistic operating conditions. This methodology forms a digital optimization environment to guide early-stage design decisions for full-scale ship structures. The results demonstrate the feasibility of physics-driven design optimization for low-noise ship hulls and outline a path toward integrated hydro-structural-acoustic optimization framework for next-generation environmentally compliant marine vessels.

Presenting Author: Ishan Neogi The University of British Columbia

Presenting Author Biography: Ishan Neogi is a Master of Applied Science candidate in Mechanical Engineering at the University of British Columbia and a Research Assistant in the Computational Multiphysics Laboratory. His current work focuses on fluid-structure interaction and underwater noise prediction, where he develops CFD-FEM simulation frameworks and optimisation pipelines to study noise generation and mitigation from ship hull structures under realistic wave loading. This research brings together high-fidelity flow prediction, structural dynamics, robust optimisation, and machine learning-based surrogate modelling to create a multidisciplinary engineering approach for quieter, more efficient marine operations.

Authors:

Ishan Neogi The University of British Columbia
Rajeev Jaiman The University of British Columbia
Jasmin Jelovica The University of British Columbia