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Optimización de fluidos basados en grafeno MXene para la conversión y almacenamiento de energía solar utilizando un

Mohamed Bechir Ben Hamida¹, Ali Basem², Ala Eldin A Awouda³

¹Engineering Sciences Research Center (ESRC), Deanship of Scientific Research, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

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Resumen

La optimización de los nanofluidos de grafeno/MXeno para la energía solar es ahora más eficiente. Un marco híbrido mejora precisamente la conductividad térmica y la viscosidad dinámica, reduciendo los costos de las aplicaciones solares.

Palabras clave:

Eficiencia energética El grafeno El MXeno Optimización multiobjetivo Metodología de la superficie de respuesta Energía solar

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Área de la Ciencia:

Ciencias de los materiales
Nanotecnología
Energía renovable

Sus antecedentes:

Los fluidos basados en grafeno/MXeno ofrecen potencial para los sistemas de energía solar.
Optimizar sus propiedades termofísicas es complejo.
La mejora de la conductividad térmica (TC) y la viscosidad dinámica (DV) es crucial.

Objetivo del estudio:

Desarrollar un marco híbrido para optimizar las propiedades de los nanofluidos de grafeno/MXeno.
Mejorar la conductividad térmica (TC) y la viscosidad dinámica (DV) para aplicaciones de energía solar.
Proporcionar una metodología precisa y rentable.

Principales métodos:

Metodología de la superficie de respuesta (RSM) para el modelado predictivo.
Algoritmos de optimización heurística y metaheurística (EHC, NSGA-II, MOALO).
Técnicas de toma de decisiones (función de deseabilidad, VIKOR).

Principales resultados:

Los modelos RSM mostraron una alta precisión (R2 > 0,998).
Se identificaron las condiciones óptimas: ~60°C, 1,5-2% en peso de MF, proporción de 0,47-0,5 MXeno.
El análisis de la toma de decisiones reveló compensaciones TC/DV basadas en la distribución del peso.

Conclusiones:

El marco híbrido optimiza efectivamente las propiedades de los nanofluidos.
Las proporciones óptimas de MXene dependen de la fracción de masa y la temperatura.
Este enfoque reduce los costos computacionales y de laboratorio para las aplicaciones de energía solar.