One attractive application of Physics-Informed Neural Network (PINN) is deriving fluid flow information such as velocity and pressure from temperature field. However, this requires developing a suitable loss function for a given flow condition. In this paper we implemented a general loss function in PINN to directly obtain fluid velocity and pressure from temperature information. We first validated this method through cases of natural convection in a square cavity, flow around a cylinder in a square cavity, and two-dimensional channel flow. Additionally, we examined forced convection heat transfer around a cylinder in depth. Results show that different Reynolds numbers (2, 10, 40, and 140) can all be effectively resolved using temperature data. Furthermore, super-resolution predictions of temperature, velocity and pressure are achievable even far outside the training area, suggesting that the proposed PINN method could be used to measure fluid flow within confined spaces via finite visualization windows without tracers.

中文翻译：

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一种通用的物理信息神经网络方法，用于从温度分布推导流体流场


物理信息神经网络 （PINN） 的一个有吸引力的应用是从温度场获取流体流动信息，例如速度和压力。然而，这需要为给定的流动条件开发合适的损失函数。在本文中，我们在 PINN 中实现了一个通用损失函数，以直接从温度信息中获取流体速度和压力。我们首先通过方腔中的自然对流、方腔中圆柱体周围的流动以及二维通道流的情况验证了这种方法。此外，我们还深入研究了圆柱体周围的强制对流传热。结果表明，不同的雷诺数（2、10、40 和 140）都可以使用温度数据有效解析。此外，即使在训练区域之外很远的地方也可以实现温度、速度和压力的超分辨率预测，这表明所提出的 PINN 方法可用于通过没有示踪剂的有限可视化窗口来测量狭窄空间内的流体流动。