Particle-resolved direct numerical simulations (PR-DNS) play an increasing role in investigating aerosol-cloud-turbulence interactions at the most fundamental level of processes. However, the high computational cost associated with high resolution simulations poses considerable challenges for large domain or long duration simulation using PR-DNS. To address these issues, here we present an emulator of the complex physics-based PR-DNS developed by use of the data-driven Fourier Neural Operator (FNO) method. The effectiveness of the method is showcased by presenting turbulence and temperature fields in a two-dimensional space. The results demonstrate high accuracy at various resolutions and the emulator is two orders of magnitude cheaper in terms of computational demand compared to the physics-based PR-DNS model. Furthermore, the FNO emulator exhibits strong generalization capabilities for different initial conditions and ultra-high-resolution without the need to retrain models. These findings highlight the potential of the FNO method as a promising tool to simulate complex fluid dynamics problems with high accuracy, computational efficiency, and generalization capabilities, enhancing our understanding of the aerosol-cloud-precipitation system.

中文翻译：

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PR-DNS 模拟器：在粒子解析直接数值模拟中使用神经算子加速动态场

粒子解析直接数值模拟 (PR-DNS) 在研究最基本过程层面的气溶胶-云-湍流相互作用方面发挥着越来越重要的作用。然而，与高分辨率模拟相关的高计算成本给使用 PR-DNS 的大域或长时间模拟带来了相当大的挑战。为了解决这些问题，我们在这里提出了一个基于复杂物理的 PR-DNS 模拟器，该模拟器是使用数据驱动的傅里叶神经算子 (FNO) 方法开发的。通过在二维空间中呈现湍流和温度场来展示该方法的有效性。结果表明，与基于物理的 PR-DNS 模型相比，在各种分辨率下都具有高精度，并且仿真器在计算需求方面便宜两个数量级。此外，FNO模拟器对不同的初始条件和超高分辨率表现出强大的泛化能力，而无需重新训练模型。这些发现凸显了 FNO 方法作为模拟复杂流体动力学问题的有前途的工具的潜力，具有高精度、计算效率和泛化能力，增强了我们对气溶胶-云-降水系统的理解。