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Abstract

We present a multi-objective genetic algorithm we developed for the optimization of a flat-plate solar thermal collector. This collector consists of a waffle-shaped Al substrate with NiCrOx cermet and SnO2 anti-reflection conformal coatings. Optimal geometrical parameters are determined in order to (i) maximize the solar absorptance a and (ii) minimize the thermal emittance ε. The multi-objective genetic algorithm eventually provides a whole set of Pareto-optimal solutions for the optimization of α and ε, which turn out to be competitive with record values found in the literature. In particular, a solution that enables α= 97.8% and ε= 4.8% was found.

Original languageEnglish
Pages (from-to)A1641-A1649
JournalOptics Express
Volume22
Issue number21
DOIs
Publication statusPublished - 2014

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flat plates
genetic algorithms
accumulators
absorptance
optimization
emittance
coatings

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abstract = "We present a multi-objective genetic algorithm we developed for the optimization of a flat-plate solar thermal collector. This collector consists of a waffle-shaped Al substrate with NiCrOx cermet and SnO2 anti-reflection conformal coatings. Optimal geometrical parameters are determined in order to (i) maximize the solar absorptance a and (ii) minimize the thermal emittance ε. The multi-objective genetic algorithm eventually provides a whole set of Pareto-optimal solutions for the optimization of α and ε, which turn out to be competitive with record values found in the literature. In particular, a solution that enables α= 97.8{\%} and ε= 4.8{\%} was found.",
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AU - Gaouyat, Lucie

AU - Nicolay, Delphine

AU - Carletti, Timoteo

AU - Deparis, Olivier

PY - 2014

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AB - We present a multi-objective genetic algorithm we developed for the optimization of a flat-plate solar thermal collector. This collector consists of a waffle-shaped Al substrate with NiCrOx cermet and SnO2 anti-reflection conformal coatings. Optimal geometrical parameters are determined in order to (i) maximize the solar absorptance a and (ii) minimize the thermal emittance ε. The multi-objective genetic algorithm eventually provides a whole set of Pareto-optimal solutions for the optimization of α and ε, which turn out to be competitive with record values found in the literature. In particular, a solution that enables α= 97.8% and ε= 4.8% was found.

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