Numerical Simulation of Flow and Heat Transfer Characteristics in Non-Closed Ring-Shaped Micro-Pin-Fin Arrays

In this study, flow and heat transfer characteristics in novel non-closed 3/4 ring-shaped micro-pin-fin arrays with in-line and staggered layouts were investigated numerically. The flow distribution, wake structure, vorticity field and pressure drop were examined in detail, and convective heat transfer features were explored. Results show that vortex pairs appeared earlier in the ring-shaped micro-pin-fin array compared with the traditional circular devices. Pressure drop across the microchannel varied with layout of the fins, while little difference in pressure drop was observed between ring-shaped and circular fins of the same layouts, with the maximum difference being 1.43%. The staggered ring-shaped array was found to outperform the in-line array and the circular arrays in convective heat transfer. A maximum increase of 21.34% in heat transfer coefficient was observed in the ring-shaped micro-pin-fin array in comparison with the circular micro-pin-fin array. The overall thermal-hydraulic performance of the microstructure was evaluated, and the staggered ring-shaped array with a fin height of 0.5 mm exhibited the best performance among the configurations studied

Hybrid Quantum Neural Network Image Anti-Noise Classification Model Combined with Error Mitigation

In this study, we present an innovative approach to quantum image classification, specifically designed to mitigate the impact of noise interference. Our proposed method integrates key technologies within a hybrid variational quantum neural network architecture, aiming to enhance image classification performance and bolster robustness in noisy environments. We utilize a convolutional autoencoder (CAE) for feature extraction from classical images, capturing essential characteristics. The image information undergoes transformation into a quantum state through amplitude coding, replacing the coding layer of a traditional quantum neural network (QNN). Within the quantum circuit, a variational quantum neural network optimizes model parameters using parameterized quantum gate operations and classical–quantum hybrid training methods. To enhance the system’s resilience to noise, we introduce a quantum autoencoder for error mitigation. Experiments conducted on FashionMNIST datasets demonstrate the efficacy of our classification model, achieving an accuracy of 92%, and it performs well in noisy environments. Comparative analysis with other quantum algorithms reveals superior performance under noise interference, substantiating the effectiveness of our method in addressing noise challenges in image classification tasks. The results highlight the potential advantages of our proposed quantum image classification model over existing alternatives, particularly in noisy environments