A Graph Neural Network Based Surrogate Model for Flapping-Driven Ocean Energy Harvesting
Bibliographic record
Abstract
Abstract Cantilevered elastic foils or panels can undergo self-induced large-amplitude flapping oscillations when submerged in a flowing fluid. This canonical phenomenon of fluid-structure interaction, commonly observed in nature, such as in fluttering leaves or fish fins, offers a promising mechanism for ocean energy harvesting. By leveraging the repetitive flapping motion, elastic foils can harness the abundant kinetic energy present in marine environments, including steady ocean currents and unsteady wave-driven flows. The efficiency of energy harvested by these foils is dictated by the amplitude and frequency of the oscillation, which are influenced by interactions with the surrounding flow. Optimizing these parameters is crucial for maximizing energy conversion efficiency and designing robust systems. However, high-fidelity simulations required for optimization over a wide parameter space are computationally expensive. To address this challenge, we propose a graph neural network-based surrogate model (GNN-ROM) tailored for the inverted foil problem. This model effectively handles mesh-structured data from full-order simulations without modifications, enabling efficient and accurate fluid-structure interaction simulations. The inverted foil is modeled as an elastically mounted rigid foil that undergoes a pitching motion around its trailing edge in uniform flow. The coupled fluid-structure system is simulated using a high-fidelity Petrov-Galerkin finite element approach, leveraging the arbitrary Lagrangian-Eulerian (ALE) formulation for precise interface tracking. The high-fidelity simulations serve as ground truth data for the training and evaluation of neural networks. Our deep learning model utilizes a rotation equivariant, quasi-monolithic GNN architecture based on the ALE formulation, wherein the coupled system dynamics are predicted with two sub-networks. First, essential coefficients describing mesh motion are extracted by proper orthogonal decomposition, which are predicted over time using a single multilayer perceptron. Moreover, the GNN-ROM evolves the flow field according to the state of the system. This approach effectively simulates coupled FSI dynamics, offering a robust surrogate model for rapid parametric optimization and control of energy harvesting devices based on inverted foil configurations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".