A hybrid global local computational framework for ship hull structural analysis using homogenized model and graph neural network
Yuecheng Cai, Jasmin Jelovica

TL;DR
This paper introduces a hybrid computational framework combining an ESL model and a graph neural network to efficiently and accurately analyze the global and local structural responses of ship hull girders, enabling rapid local detail prediction from global analysis.
Contribution
It develops a novel integrated approach using homogenized ESL modeling and GNNs for detailed ship hull analysis, improving speed and accuracy over traditional methods.
Findings
The framework accurately predicts local stress and displacement fields.
The HGT model outperforms conventional ESL stress estimation methods.
Global prediction error is primarily due to the ESL coarse mesh solution.
Abstract
This study presents a computational framework for global local structural analysis of ship hull girders that integrates an equivalent single layer (ESL) model with a graph neural network (GNN). A coarse mesh homogenized ESL model efficiently predicts the global displacement field, from which degrees of freedom (DOFs) along stiffened panel boundaries are extracted. A global to local DOF mapping and reconstruction procedure is developed to recover detailed boundary kinematics for local analysis. The reconstructed DOFs, together with panel geometry and loading, serve as inputs to a heterogeneous graph transformer (HGT), a subtype of GNN, which rapidly and accurately predicts the detailed stress and displacement fields for any panel within the hull girder. The HGT is trained using high fidelity 3D panel finite element model with reconstructed boundary conditions, enabling it to generalize…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopology Optimization in Engineering · Model Reduction and Neural Networks · Composite Structure Analysis and Optimization
