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Solar Performance Optimization Code for the Optical Response of Multilayer Stacks in Python: SolPOC Cover

Solar Performance Optimization Code for the Optical Response of Multilayer Stacks in Python: SolPOC

Open Access
|Dec 2025

Figures & Tables

Table 1

Software for optical thin film coating simulation and optimization.

SOFTWARECOMMERCIAL STATUSOPEN SOURCEGUIREF
SCOUT/CODECommercialNoYes[20]
Essential MacLeodCommercialNoYes[21]
FilmStarCommercialNoYes[22]
OpenFiltersFreeYesYes[18, 23]
OpTaliXCommercialNoYes[18]
OptiLayerCommercialNoYes[24]
PhaseCODECommercialNoYes[20]
PyMooshFreeYesNo[16, 25, 26]
TFCalcCommercialNoYes[24]
TMM-FastFreeYesNo[17, 27]
RP-Coating V4CommercialNoYes[28]
SolcoreFreeYesNo[19]
jors-13-523-g1.png
Figure 1

SolPOC uses refractive indices selected from peer-reviewed studies, complemented, if necessary, with EMA theory (for composites or porous layers), to create a thin layer stack. The optical properties are calculated with Abélès formalism, using NumPy package for reduced calculation time.

jors-13-523-g2.png
Figure 2

Number of optical properties calculation per second on 445 different wavelengths on both polarizations, for different packages, using cpu.

jors-13-523-g3.png
Figure 3

Computed reflectivity of 20 layers Bragg mirror with different optical Python package.

jors-13-523-g4.png
Figure 4

Computed average reflectivity of Bragg mirror with different optical Python package, for 2 to 50 thin layers.

jors-13-523-g5.png
Figure 5

SolPOC offers a wide range of objective functions suitable for various applications including solar energy, buildings, vision and more. These objective functions can be optimized using various global optimization methods. Thanks to multiprocessing, Consistency Curves are easy to manage to ensure the optimization quality.

jors-13-523-g6.png
Figure 6

Example of SolPOC capabilities for optimizing all coatings utilized in a solar collector in CSP plant: i) reflective coating, ii) antireflective coating and iii) spectrally selective coating.

Table 2

Industrial coating performance values from SAM [59] vs. theoretical examples illustrating SolPOC optimization capabilities.

SURFACEINDUSTRIAL SOLAR COMPONENTSOPTIMIZATION EXAMPLES
NUMBER OF THIN LAYERSTYPICAL VALUEOPTIMIZATION VARIABLESSOLPOC PERFORMANCE RESULTS
Solar mirror1 metallic layerRS: 0.93511 layers thicknessesRS: 0.966
Vacuum tube1 porous layerTS: 0.9643 layers thicknesses
+ 3 porosity rates
TS: 0.994
Thermal absorber3 or 4 layers with 1 or 2 cermet(s)AS: 0.963
E(300°C): 0.08
rH(300°C): 0.953
6 layers thicknesses
+ 3 cermet inclusion rates
AS: 0.975
E(300°C): 0.074
rH(300°C): 0.966
jors-13-523-g7.png
Figure 7

Reflectivity or transmissivity spectra for each type of coatings after optimization.

jors-13-523-g8.png
Figure 8

Consistency curves for different optimization processes, using DE optimization method.

DOI: https://doi.org/10.5334/jors.523 | Journal eISSN: 2049-9647
Language: English
Submitted on: Jun 24, 2024
Accepted on: Nov 14, 2025
Published on: Dec 12, 2025
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2025 Antoine Grosjean, Pauline Bennet, Thalita Drumond, Amine Mahammou, Denis Langevin, Antoine Moreau, Audrey Soum-Glaude, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.