What the study found
A reduced-order data assimilation framework was able to reconstruct dynamic temperature fields in an inclined phase change material (PCM)-integrated solar chimney. The authors report that this approach also improved the estimation of airflow outlet velocity.
Why the authors say this matters
The authors state that the method is intended to improve performance estimation accuracy when measurements are scarce. They also conclude that this is the first application of a reduced-order data assimilation framework to a coupled multiphysics solar chimney with PCM integration.
What the researchers tested
The researchers used a variational data assimilation framework based on a regularized least-squares formulation to estimate dynamic temperatures in both the airflow and PCM domains. The framework combined a reduced-order model derived from high-fidelity finite-volume simulations with three measurement data sets of 22, 135, and 203 spatial points, expanded using boundary-layer and bi-cubic interpolation.
What worked and what didn't
With synthetic measurements, the framework reconstructed dynamic temperature fields with relative errors below 10% for the initial sensor set and below 3% for the expanded sensor sets. With real measurements, it improved the fidelity of local temperature evolution in both domains, and increasing the number of sensors did not significantly improve local temperature accuracy but did reduce the root-mean-square error of local outlet velocity by 20%.
What to keep in mind
The abstract does not describe detailed experimental limitations beyond the measurement scarcity addressed by the method. The reported performance is based on the specific solar chimney configuration, the selected sensor sets, and the data-filling strategy used in the study.
Key points
- The study reconstructed temperature fields in a PCM-integrated solar chimney using reduced-order data assimilation.
- The authors report that the framework improved local outlet velocity estimation, cutting root-mean-square error by 20%.
- Synthetic-measurement errors were below 10% for the initial sensor set and below 3% for the expanded sensor sets.
- With real measurements, the method improved the fidelity of local temperature evolution in both airflow and PCM domains.
- The authors describe this as the first ROM–DA application to a coupled multiphysics solar chimney with PCM integration.
Disclosure
- Research title:
- ROM-based data assimilation improved solar chimney temperature estimation
- Authors:
- Diego R. Rivera, Ernesto Castillo, Felipe Galarce, Douglas R. Q. Pacheco
- Institutions:
- Universidad de Santiago de Chile, RWTH Aachen University, Pontificial Catholic University of Valparaiso
- Publication date:
- 2026-04-23
- OpenAlex record:
- View
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