Thermohydrodynamic analysis of a CuO/SAE 30 nanofluid-lubricated journal bearing: Pressure, thermal, and tribological performance

Hydrodynamic journal bearings operating under elevated loads and speeds are susceptible to significant thermal degradation of lubricant viscosity through viscous dissipation, a phenomenon inadequately captured by conventional isothermal analyses. This study presents a coupled thermohydrodynamic ( TEHD ) numerical investigation of a plain journal bearing lubricated with copper oxide ( CuO ) nanoparticles dispersed in SAE 30 base oil at volume fractions of φ = 0, 0.5, 1.0, 1.5, 2.0, and 3.0 vol%. The effective thermophysical properties of the CuO / SAE 30 nanofluid were evaluated using the Brinkman and Maxwell models for dynamic viscosity and thermal conductivity, respectively. The governing equations comprising the modified Reynolds equation with coupled Vogel temperature–viscosity and Barus pressure–viscosity dependencies, and the depth-averaged one-dimensional energy equation incorporating viscous dissipation were solved simultaneously using a cell-centred finite-difference iterative scheme subject to Gumbel cavitation and periodic boundary conditions. Parametric investigations were conducted over bearing diameter (30–60 mm), eccentricity ratio (0.3–0.7), radial clearance (30–100 μm), and oil inlet temperature (40–100°C), yielding fifteen computed performance metrics per operating point. Results indicate that incorporating 2.0 vol .% CuO enhances maximum hydrodynamic pressure by 14.3%, reduces peak film temperature by 8.2°C, decreases friction coefficient by 11.9%, and improves the film-thickness-based index by 28.3% relative to the neat base oil. A non-monotonic performance optimum is identified in the range 1.5–2.0 vol .%, beyond which agglomeration-induced viscosity increases generate excess viscous heating that progressively offsets the thermal conductivity benefit. These findings establish quantitative design guidelines for nanofluid-lubricated journal bearings in industrial rotating machinery and provide a validated computational framework extensible to other nanoparticle–lubricant systems.