What the study found
The study found that a reduced-order data assimilation framework could reconstruct dynamic temperature fields in both the airflow and phase change material domains of a solar chimney. The authors also report that it improved estimation of local outlet velocity when the reconstructed fields were used in the forward solver.
Why the authors say this matters
The authors conclude that this framework may improve performance estimation from scarce measurements in a coupled solar chimney with phase change material integration. They also say this is the first application of a reduced-order data assimilation framework to this kind of multiphysics system.
What the researchers tested
The researchers tested a variational data assimilation framework based on a regularized least-squares formulation. It combined a reduced-order model built from high-fidelity finite-volume simulations of unsteady conjugate heat transfer, liquid-solid phase change, and surface radiation 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
Using synthetic measurements, the framework reconstructed 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 the airflow and phase change material domains. Increasing the number of sensors did not significantly improve local temperature accuracy, but it reduced the root-mean-square error of local outlet velocity by 20%.
What to keep in mind
The abstract does not describe broader limitations beyond the measurement conditions tested. The reported error values are tied to the specific solar chimney configuration, the synthetic and real data sets used, and the data-filling strategy applied in this study.
Key points
- A reduced-order data assimilation framework reconstructed temperature fields in both airflow and phase change material domains.
- The study used three measurement sets with 22, 135, and 203 spatial points, expanded by boundary-layer and bi-cubic interpolation.
- Synthetic-measurement tests produced relative errors below 10% for the initial sensor set and below 3% for the expanded sets.
- With real measurements, the method improved local temperature evolution in both domains.
- More sensors did not significantly improve local temperature accuracy, but they reduced outlet-velocity root-mean-square error by 20%.
Disclosure
- Research title:
- Reduced-order data assimilation improved temperature estimation in a PCM solar chimney
- Authors:
- Diego R. Rivera, Ernesto Castillo, Douglas R. Q. Pacheco, Felipe Galarce
- Institutions:
- Pontificial Catholic University of Valparaiso, RWTH Aachen University, Universidad de Santiago de Chile, Universidad de Santiago de Chile
- Publication date:
- 2026-04-23
- OpenAlex record:
- View
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