This work presents a machine-learning (ML) strategy for the identification of the design region that guarantees minimum losses for Low Pressure Turbine (LPT) blades, allowing the definition of the optimal blade shape. The data-driven procedure is twofold. Firstly, an advanced loss-correlation model (M1) that describes the LPT efficiency as a function of the main flow and geometrical parameters, also accounting for unsteady effects, has been trained from a numerical database. Then, a second model (M2) has been tuned to interpret the corresponding blade geometries. Proper Orthogonal Decomposition (POD) has been applied to formally decompose the blade shape into modes and coefficients. The modes provide basis functions, while the coefficients give the weights that, depending on the combination of the design parameters, define the blade shape. Gaussian Process (GP) and Cross-Validation techniques have been used for tuning both M1 and M2. Once properly tuned, the overall procedure provides the loss-correlation model (M1) for the identification of the design region that is expected to minimize losses, and the geometrical model (M2) for a quick definition of the corresponding optimal blade shape. The procedure can be extended to other engineering applications where own efficiency and geometrical data are available.

中文翻译：

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用于识别最佳 LPT 设计区域和相关叶片形状的 ML 策略

这项工作提出了一种机器学习 (ML) 策略，用于识别设计区域，保证低压涡轮 (LPT) 叶片的最小损失，从而定义最佳叶片形状。数据驱动的过程是双重的。首先，我们从数值数据库中训练了一种先进的损失相关模型 (M1)，该模型将 LPT 效率描述为主流和几何参数的函数，同时也考虑了非稳态效应。然后，对第二个模型 (M2) 进行调整以解释相应的叶片几何形状。应用本征正交分解 (POD) 将叶片形状正式分解为模态和系数。模态提供基函数，而系数则给出权重，权重根据设计参数的组合定义叶片形状。高斯过程 (GP) 和交叉验证技术已用于调整 M1 和 M2。一旦正确调整，整个过程就会提供损耗相关模型 (M1)，用于识别预期最小化损耗的设计区域，并提供几何模型 (M2)，用于快速定义相应的最佳叶片形状。该过程可以扩展到其他可以获得自身效率和几何数据的工程应用。