This study aims to assess a data assimilation framework based on reduced-order modeling (ROM–DA) to reconstruct the dynamic temperature fields of an inclined phase change material (PCM)-integrated solar chimney configuration. By complementing this framework with a data-filling strategy, our goal is to improve the performance estimation accuracy from scarce measurements.
A variational DA framework based on a regularized least-squares formulation is used to estimate dynamic temperatures in both the airflow and PCM domains of the solar chimney configuration. The method combines: (i) a reduced-order model derived from high-fidelity finite-volume simulations of unsteady conjugate heat transfer with liquid–solid phase change and surface radiation and (ii) three measurement data sets of 22, 135 and 203 spatial points. The data sets are expanded using a hybrid data-filling strategy (boundary-layer and bi-cubic interpolations). The reconstructed temperature fields are subsequently assimilated into the thermally coupled forward solver to enhance the airflow velocity prediction.
The ROM–DA framework accurately reconstructed dynamic temperature fields in both the air and PCM domains using synthetic measurements with relative errors below 10% and 3% for the initial and expanded sensor sets, respectively. When applied to real measurements, the framework improved the fidelity of the local temperature evolution in both domains. Increasing the number of sensors did not significantly improve local temperature accuracy, but it enhanced the dynamic estimation of the local outlet velocity by reducing the root-mean-square error by 20%.
This is the first application of a ROM–DA framework to a coupled multiphysics solar chimney with PCM integration. The study also assesses hybrid data enhancement strategies that improve measurement quality.
