What question did this study set out to answer?

To develop a hybrid Galerkin-machine learning framework capable of rapidly predicting the vibration behavior and stability boundaries of immersed, moving carbon nanotube-reinforced composite microplates under coupled multiphysics loads.

September 17, 2026International Journal of Numerical Methods for Heat &amp Fluid Flow

Hybrid Galerkin-machine learning framework for fluid-structure interaction and stability of immersed moving FG-CNTRC microplates under multiphysics effects

View Full Paper

Ask AI

Bookmark

Authors

CQCheng Qiong TTTixian Tian

Discussion

Member takes

Overview

Computational study demonstrates rapid stability modeling of fluid-immersed moving microplates, highlighting the superior predictive accuracy of least-squares boosting.

Key Points

To develop a hybrid Galerkin-machine learning framework capable of rapidly predicting the vibration behavior and stability boundaries of immersed, moving carbon nanotube-reinforced composite microplates under coupled multiphysics loads.
Formulated governing equations through strain-gradient elasticity and incompressible potential-flow theory, solving the resulting complex eigenvalue problems with the Galerkin method.
Generated a high-fidelity numerical database to train and evaluate decision tree, random forest, and least-squares boosting surrogate models.
Least-squares boosting demonstrated the best predictive performance, cutting prediction errors by approximately 45% compared to random forest and by over 60% compared to fine decision trees.
Microplates with an FG-X carbon nanotube distribution showed the widest stability margins, whereas higher fluid density, thermal loads, hygroscopic effects, and bidirectional motion accelerated instability.
Trained surrogate algorithms enabled near-instantaneous stability assessments, eliminating the substantial computational cost of iterative numerical eigenvalue calculations.

Cite This Study

Qiong et al. (2026) studied this question.

synapsesocial.com/papers/6aabb7a65f706d05830e6ec9 https://doi.org/10.1108/hff-06-2026-0747

View Full Paper

Ask AI

Bookmark

September 17, 2026International Journal of Numerical Methods for Heat &amp Fluid Flow

Hybrid Galerkin-machine learning framework for fluid-structure interaction and stability of immersed moving FG-CNTRC microplates under multiphysics effects

View Full Paper

Ask AI

Bookmark

Authors

CQCheng Qiong TTTixian Tian

Discussion

Member takes

Overview

Computational study demonstrates rapid stability modeling of fluid-immersed moving microplates, highlighting the superior predictive accuracy of least-squares boosting.

Key Points

To develop a hybrid Galerkin-machine learning framework capable of rapidly predicting the vibration behavior and stability boundaries of immersed, moving carbon nanotube-reinforced composite microplates under coupled multiphysics loads.
Formulated governing equations through strain-gradient elasticity and incompressible potential-flow theory, solving the resulting complex eigenvalue problems with the Galerkin method.
Generated a high-fidelity numerical database to train and evaluate decision tree, random forest, and least-squares boosting surrogate models.
Least-squares boosting demonstrated the best predictive performance, cutting prediction errors by approximately 45% compared to random forest and by over 60% compared to fine decision trees.
Microplates with an FG-X carbon nanotube distribution showed the widest stability margins, whereas higher fluid density, thermal loads, hygroscopic effects, and bidirectional motion accelerated instability.
Trained surrogate algorithms enabled near-instantaneous stability assessments, eliminating the substantial computational cost of iterative numerical eigenvalue calculations.

Cite This Study

Qiong et al. (2026) studied this question.

synapsesocial.com/papers/6aabb7a65f706d05830e6ec9 https://doi.org/10.1108/hff-06-2026-0747

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark