TY - JOUR
T1 - Predicting the impact of graphene oxide and its shapes on the thermal efficiency of Polyalphaolefin-40 using a mathematical model for wind turbine gearboxes
AU - Ahsan, Aamina
AU - Zanj, Amir
AU - Zafar, Ahsan
AU - Iqbal, Najam
AU - Fatima, Adeena
AU - Khan, Wajid
AU - Yousaf, Muhammad Zain
AU - Khan, Baseem
AU - Guerrero, Josep M.
PY - 2025/8
Y1 - 2025/8
N2 - Introduction Gearbox reliability is a significant concern in wind turbines, as it ensures optimal rotational speed to meet generator requirements. However, gearbox failures, often resulting from inadequate lubrication and thermal inefficiencies, can severely affect system performance. Design/methodology/approach To tackle this issue, the current study explores the use and potential of nanolubricants, concentrating on the impact of nanoparticle type and their geometric shapes on heat transfer and lubrication properties. A Brinkman-type steady nanofluid flow is modeled within a channel of length d, influenced by a magnetic field. Polyalphaolefins-40 (PAO-40) oil serves as the base fluid, to which three nanoparticles - Titanium Dioxide (TiO2), Graphene Oxide (GO), and Aluminum Oxide (Al2O3) - are added. The model was generalized using fractionalized Fick's and Fourier's laws; the exact solution of the model is derived through Laplace and Fourier sine transforms, and the results are expressed using the Mittag-Leffler function. Findings GO emerged as the most effective additive, with four of its geometric shapes (blade, platelet, brick, and cylinder) further investigated to assess their thermal and rheological behavior. Among the shapes tested, blade-shaped GO nanoparticles provided the highest improvement in heat transfer (31.91 %), followed by platelet (27.96 %), brick (26.56 %), and cylinder (25 %). Notably, indicates that platelet-shaped GO showed superior viscosity enhancement, which is critical for effective lubrication. Discussion These findings imply that shape-optimized GO nanoparticles can act as highly effective additives in nanolubricants, enhancing both heat transfer and lubrication within wind turbine gearboxes. This advancement could lead to more durable, efficient, and reliable renewable energy systems.
AB - Introduction Gearbox reliability is a significant concern in wind turbines, as it ensures optimal rotational speed to meet generator requirements. However, gearbox failures, often resulting from inadequate lubrication and thermal inefficiencies, can severely affect system performance. Design/methodology/approach To tackle this issue, the current study explores the use and potential of nanolubricants, concentrating on the impact of nanoparticle type and their geometric shapes on heat transfer and lubrication properties. A Brinkman-type steady nanofluid flow is modeled within a channel of length d, influenced by a magnetic field. Polyalphaolefins-40 (PAO-40) oil serves as the base fluid, to which three nanoparticles - Titanium Dioxide (TiO2), Graphene Oxide (GO), and Aluminum Oxide (Al2O3) - are added. The model was generalized using fractionalized Fick's and Fourier's laws; the exact solution of the model is derived through Laplace and Fourier sine transforms, and the results are expressed using the Mittag-Leffler function. Findings GO emerged as the most effective additive, with four of its geometric shapes (blade, platelet, brick, and cylinder) further investigated to assess their thermal and rheological behavior. Among the shapes tested, blade-shaped GO nanoparticles provided the highest improvement in heat transfer (31.91 %), followed by platelet (27.96 %), brick (26.56 %), and cylinder (25 %). Notably, indicates that platelet-shaped GO showed superior viscosity enhancement, which is critical for effective lubrication. Discussion These findings imply that shape-optimized GO nanoparticles can act as highly effective additives in nanolubricants, enhancing both heat transfer and lubrication within wind turbine gearboxes. This advancement could lead to more durable, efficient, and reliable renewable energy systems.
KW - Brinkman-type nanofluid (BTF)
KW - Caputo fractional derivative (CFD)
KW - Generalized/fractionalized Fourier's law & Fick's law (FFL)
KW - Integral transform methods
KW - Mittag-leffler function (MLF)
KW - Nanoparticle shapes variation
UR - http://www.scopus.com/inward/record.url?scp=105008420796&partnerID=8YFLogxK
U2 - 10.1016/j.csite.2025.106307
DO - 10.1016/j.csite.2025.106307
M3 - Article
AN - SCOPUS:105008420796
SN - 2214-157X
VL - 72
JO - Case Studies in Thermal Engineering
JF - Case Studies in Thermal Engineering
M1 - 106307
ER -