Abstract A reconstruction technique of kHz time-resolved two-dimensional (2D) surface temperature field was achieved with the discrete point measurements and low sampling rate 2D thermographic phosphor (TP) thermometry measurements using a long short-term memory (LSTM) based artificial intelligence framework. The 2D surface temperature field of a 350 °C plate with a 2.5 Hz swing cooling jet was measured using TP thermometry at a sampling rate of 20 Hz. At the same time, high-frequency thermocouples with a sampling rate of 1 kHz were recorded for the construction of LSTM neural networks training and for validation. The 20 Hz 2D surface temperature field was analyzed with proper orthogonal decomposition to acquire the energy modes and model coefficients. The mode coefficients are then trained with the discrete but high-frequency time-resolved temperature information from the thermocouple by LSTM to acquire the time-resolved mode coefficients. Finally, the high-frequency time-resolved 2D surface temperature field is obtained by reconstructing modes and the time-resolved coefficients. The reconstructed result shows that the current technique can obtain high time-resolved and spatially resolved 2D surface temperature fields very well.

中文翻译：

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使用磷光体温度测量法和适当的正交分解辅助长短期记忆神经网络获取 kHz 频率二维表面温度场

摘要 使用基于长短期记忆 (LSTM) 的人工智能，通过离散点测量和低采样率二维热成像磷光体 (TP) 温度测量，实现了 kHz 时间分辨二维 (2D) 表面温度场的重建技术。框架。使用 TP 测温仪以 20 Hz 的采样率测量具有 2.5 Hz 摆动冷却射流的 350 °C 板的二维表面温度场。同时记录采样率为1 kHz的高频热电偶，用于构建LSTM神经网络训练和验证。使用适当的正交分解分析 20 Hz 2D 表面温度场以获得能量模式和模型系数。然后通过 LSTM 使用来自热电偶的离散但高频的时间分辨温度信息训练模式系数，以获得时间分辨模式系数。最后，通过重构模态和时间分辨系数得到高频时间分辨二维表面温度场。重建结果表明，目前的技术可以很好地获得高时间分辨和空间分辨的二维表面温度场。