Existing numerical electromagnetic (EM) solvers are usually computationally expensive, time consuming, and memory demanding. Recent advances in deep learning (DL) techniques have demonstrated superior efficiency and provide an alternative pathway for speeding up simulations by serving as effective computational tools. In this paper, we propose a DL framework for real-time predictions of the scattering from an isolated nano-structure in the near-field regime. We find that, to achieve precise approximation of the optical response obtained from numerical simulations, the proposed DL framework only requires a small training data set. The fully trained framework can be three orders of magnitude faster than a conventional EM solver based on the finite difference frequency domain method (FDFD). Furthermore, the proposed DL framework has demonstrated robustness to changes in design variables which govern the nano-structure geometry and material selection as well as properties of the incident wave, shedding light on universal scattering predictions at the nano scale via deep learning techniques. This framework increases the viability of the design and analysis of complex nanostructures, offering great potential for applications pertaining to complex light-matter interaction between electromagnetic fields and nanomaterials.
In this paper, a new coupled membrane-hydrodynamics-heat conduction model is proposed, which is verified by both experiments and linear thermoacoustic theory. The temperature and normalized volume flow rate generated by the coupled model are consistent with both experimental data and the results of the DeltaEC ® simulation. The coupled model can also deal with large amplitude and multidimensional flows in thermoacoustic refrigeration systems. The flow loss of the thermoacoustic refrigerator is analysed quantitatively based on the coupled model. The effects of the stack thickness, the roughness of the resonance tube and the expansion angle of the contraction tube on the flow loss are studied. These analyses are of great significance in avoiding flow loss and improving the efficiency of thermoacoustic refrigeration systems.
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