A transfer learning metamodel using artificial neural networks applied to natural convection flows in enclosures
This work addresses computational fluid dynamics problems for researchers and engineers, offering an incremental improvement in efficiency for specific benchmark cases.
The paper tackled predicting Nusselt numbers for natural convection flows in enclosures using transfer learning with artificial neural networks, achieving reduced computational and training costs while handling systems with higher physical complexity.
In this paper, we employed a transfer learning technique to predict the Nusselt number for natural convection flows in enclosures. Specifically, we considered the benchmark problem of a two-dimensional square enclosure with isolated horizontal walls and vertical walls at constant temperatures. The Rayleigh and Prandtl numbers are sufficient parameters to simulate this problem numerically. We adopted two approaches to this problem: Firstly, we made use of a multi-grid dataset in order to train our artificial neural network in a cost-effective manner. By monitoring the training losses for this dataset, we detected any significant anomalies that stemmed from an insufficient grid size, which we further corrected by altering the grid size or adding more data. Secondly, we sought to endow our metamodel with the ability to account for additional input features by performing transfer learning using deep neural networks. We trained a neural network with a single input feature (Rayleigh) and extended it to incorporate the effects of a second feature (Prandtl). We also considered the case of hollow enclosures, demonstrating that our learning framework can be applied to systems with higher physical complexity, while bringing the computational and training costs down.