Reduced Order Modelling for the Optimization of CSP Tower Receivers and Their Cavities for High Temperature Applications

Authors

DOI:

https://doi.org/10.52825/solarpaces.v1i.682

Keywords:

Reduced Order Model, Tower-Cavity Heat Loses Optimization

Abstract

We present a Reduced Order Method approach to the heat exchange and loses in a simulated 3D cavity of CSP tower receivers. We validate the method in a 2D Boussinesq model problem for natural convection monitoring temperature, pressure and velocity for different values of the Rayleigh number. For the 3D problem of heat loses estimation we compute the snapshots with Ansys Fluent in a realistic model of a cavity with wind velocity and wall temperatures as varying parameters. The reduction in computational time can be up to four orders of magnitude with relative errors of 10^-5.

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References

P. Brenner, S. Grivet-Talocia, A. Quarteroni, G. Rozza, W. Schilders, L. M. Silveila. Model Order Reduction Vols. 1, 2 & 3. De Gruyter (2021) (https://doi.org/10.1515/9783110499001).

J.C. Herruzo, J. Valverde, M.A. Herrada and J. Galan-Vioque, Cavity losses estimations in CSP applications, AIP Proceedings, 1, 210007 (2018) (https://doi.org/10.1063/1.5067209).

Ballarin, F., Rebollo, T. C., Ávila, E. D., Mármol, M. G., Rozza, G. Certified Reduced Basis VMS-Smagorinsky model for natural convection flow in a cavity with variable height. Computers & Mathematics with Applications, 80(5), 973-989 (2020) (https://doi.org/10.1016/j.camwa.2020.05.013).

Rubino, S. Numerical analysis of a projection-based stabilized POD-ROM for incompressible flows. SIAM Journal on Numerical Analysis, 58(4), 2019-2058 (2020) (https://doi.org/10.1137/19M1276686).

Chaturantabut, S., & Sorensen, D. C. Discrete empirical interpolation for nonlinear model reduction. In Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference (pp. 4316-4321). IEEE (2009, December) (DOI: https://doi.org/10.1109/CDC.2009.5400045).

Hijazi, S., Stabile, G., Mola, A., & Rozza, G. Data-driven POD-Galerkin reduced order model for turbulent flows. Journal of Computational Physics, 416, 109513 (2020) (https://doi.org/10.1016/j.jcp.2020.109513).

Kunisch, K., & Volkwein, S. Galerkin proper orthogonal decomposition methods for parabolic problems. Numerische mathematik, 90(1), 117-148 (2001) (https://doi.org/10.1007/s002110100282).

Chacón Rebollo, T., Gómez Mármol, M., Hecht, F., Rubino, S., & Sánchez Muñoz, I. A high-order local projection stabilization method for natural convection problems. Journal of Scientific Computing, 74(2), 667-692 (2018) (doi.org/10.1007/s10915-017-0469-9).

Demo et al., (2018). EZyRB: Easy Reduced Basis method. Journal of Open-Source Software, 3(24), 661, https://doi.org/10.21105/joss.00661.

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Published

2023-12-19

How to Cite

Valverde, J., Galan-Vioque, J., Herruzo, J. C., Rubino, S., Chacón, T., & Nuñez Fernandez, C. (2023). Reduced Order Modelling for the Optimization of CSP Tower Receivers and Their Cavities for High Temperature Applications. SolarPACES Conference Proceedings, 1. https://doi.org/10.52825/solarpaces.v1i.682

Conference Proceedings Volume

Vol. 1 (2022): SolarPACES 2022, 28th International Conference on Concentrating Solar Power and Chemical Energy Systems

Section

Analysis and Simulation of CSP and Hybridized Systems

License

Copyright (c) 2023 Juan Valverde, Jorge Galan-Vioque, Juan Carlos Herruzo, Samuele Rubino, Tomás Chacón, Carlos Nuñez Fernandez

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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