CNN Embedded Topology Optimization framework for Designing Channel Flow Layouts

Topology optimization, as a non-heuristic and highly versatile design tool, has attracted considerable interest and extensive investigation across diverse fields. However, when dealing with multivariate problems such as optimizing channel flow layout, the increased computational cost due to the large-scale finite element solution process restricts its practicality. In this study, we proposed a novel machine learning embedded topology optimization framework to achieve a high-efficient procedure for optimizing channel flow problems. The framework incorporates a convolutional neural network that proficiently learns and replace the conventional finite element derivation process. Moreover, it can autonomously adjust and update its network parameters online, ensuring a continuous improvement in prediction accuracy. In the case study of a 90-degree bending design, conducted without any offline training data, the proposed method outperforms the original topology optimization method: over 100 iterations, the total time required for calculations is reduced by 60%; Simultaneously, the optimized result exhibits a decrease of 4.8% in the pressure drop value. The further improvement of the optimized results is due to the introduction of the prediction error to address the local optimization trend of the original method. The proposed method has the potential to replace existing techniques and facilitate more efficient and superior new topology optimization processes.