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Recently, plasmonic nanofluids (i.e., a suspension of plasmonic nanoparticlesin a base fluid) have been widely employed in direct-absorption solarcollectors because the localized surface plasmon supported by plasmonicnanoparticles can greatly improve the direct solar thermal conversionperformance. Considering that the surface plasmon resonance frequency ofmetallic nanoparticles, such as gold, silver, and aluminum, is usually locatedin the ultraviolet to visible range, the absorption coefficient of a plasmonicnanofluid must be spectrally tuned for full utilization of the solar radiationin a broad spectrum. In the present study, a modern design process in the formof a genetic algorithm (GA) is applied to the tailoring of the spectralabsorption coefficient of a plasmonic nanofluid. To do this, the majorcomponents of a conventional GA, such as the gene description, fitness functionfor the evaluation, crossover, and mutation function, are modified to besuitable for the inverse problem of tailoring the spectral absorptioncoefficient of a plasmonic nanofluid. By applying the customized GA, weobtained an optimal combination for a blended nanofluid with the desiredspectral distribution of the absorption coefficient, specifically a uniformdistribution, solar-spectrum-like distribution, and a step-function-likedistribution. The resulting absorption coefficient of the designed plasmonicnanofluid is in good agreement with the prescribed spectral distribution withinabout 10\% to 20\% of error when six types of nanoparticles are blended.Finally, we also investigate how the inhomogeneous broadening effect caused bythe fabrication uncertainty of the nanoparticles changes their optimalcombination.

Tailoring the Spectral Absorption Coefficient of a Blended Plasmonic Nanofluid Using a Customized Genetic Algorithm