Data-informed lifting line theory
We present a data-driven framework that extends the predictive capability of classical lifting-line theory (LLT) to a wider aerodynamic regime by incorporating higher-fidelity aerodynamic data from panel method simulations. A neural network architecture with a convolutional layer followed by fully connected layers is developed, comprising two parallel subnetworks to separately process spanwise collocation points and global geometric/aerodynamic inputs such as angle of attack, chord, twist, airfoil distribution, and sweep. Among several configurations tested, this architecture is most effective in learning corrections to LLT outputs. The trained model captures higher-order three-dimensiona...
| Authors | Arjun Sharma, Jonas A. Actor, Peter A. Bosler |
|---|---|
| Primary category | physics.flu-dyn |
| Published | 30 March 2026 |
| Last updated at source | 30 March 2026 |
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