This paper discusses the usage of sbpRAY for the optimization of a cavity receiver. New features have been implemented into the program for this purpose. A case study is presented which combines sbpRAY with Rhino and Grasshopper. The example is taken from Next-CSP1, a research project investigating a high temperature solar thermal power plant with a cavity receiver which uses solid particles as heat transfer medium and as a storage material [1]. The influence of several parameters on the output variables is investigated.
REFERENCES
1.
Next-CSP – High temperature concentrated solar thermal power plant with particle receiver and direct thermal storage [Online]
2016
. [Cited: 13.05.2019] http://next-csp.eu/2.
D.
Gebreiter
, G.
Weinrebe
, M.
Wöhrbach
, F.
Arbes
, F.
Gross
, W.
Landman
, “sbpRAY – A Fast and Versatile Tool for the Simulation of Large Scale CSP Plants
,” in Solar PACES Annual Conference 2018
, Casablanca
.3.
4.
F.
von Reeken
, G.
Weinrebe
, T.
Keck
, M.
Balz
, “Heliostat cost optimization study,” in AIP Conference Proceedings
1734
, 160018
, Solar Paces Conference 2016
, Cape Town
.5.
J.
Greenwood
, “The correct and incorrect generation of a cosine distribution of scattered particles for Monte-Carlo modelling of vacuum systems
,” in Vacuum 67
(2002
).6.
A.M.
Clausing
, “Convective Losses From Cavity Solar Receivers – Comparisons Between Analytical Predictions and Experimental Results
,” in Journal of Solar Energy Engineering
105
, pp. 29
–33
(1983
).7.
J.
Samanes
, J.
García-Barberena
, F.
Zaversky
, “Modeling Solar Cavity Receivers: A Review and Comparison of Natural Convection Heat Loss Correlations
,” in Energy Procedia
69
, pp. 543
–552
(2015
).8.
R.E.
Perez
, P.W.
Jansen
, and J.R.R.A.
Martins
, “pyOpt: A Python-Based Object-Oriented Framework for Nonlinear Constrained Optimization
,” in Structures and Multidisciplinary Optimization
45
(1
), pp. 101
–118
(2012
).
This content is only available via PDF.
© 2020 Author(s).
2020
Author(s)
