Data-driven physical fields reconstruction of supercritical-pressure flow in regenerative cooling channel using POD-AE reduced-order model

In order to effectively estimate the physical fields of active cooling channel with supercritical pressure hydrocarbon fuel, a novel data-driven reduced-order model framework is firstly proposed in the present work, which is established via combining reduced-order modeling method based on proper orthogonal decomposition and autoencoder neural network (POD-AE) with Kriging surrogate model. This framework mainly contains offline stage and online stage. Preliminary dimension reduction and feature capture of high-dimensional physical fields data is conducted using POD in the offline stage, and low-dimensional reduced-order models (ROMs) are constructed by the linear combination of orthogonal bases and feature coefficients. Specially, feature coefficients of ROMs are used to trained an AE, and then lower-dimensional secondary reduced-order models (2nd-ROMs) are established by secondary compression for ROMs using the trained AE. Furthermore, the Kriging model is trained for implicitly mapping boundary conditions or temperature data from the sensors on the bottom surface to the established 2nd-ROMs. In the online stage, physical fields estimation under new boundary conditions or wall temperature monitoring based on temperature data of sensors can be quickly fulfilled using this framework. Fifty groups of test conditions are considered to demonstrate the accuracy and efficiency of the POD-AE ROM framework. The results prove that it shows good efficiency, accuracy in estimating and monitoring the physical fields of the cooling channel with supercritical pressure n-dodecane within sample space. Compared with the traditional POD-based ROM framework, the proposed framework still achieved acceptable accuracy under all testing conditions when the original data was compressed to extremely low dimensions. And the calculation time only slightly increased.