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Vasanth Karegowdar Rajendrappa
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RESEARCH ARTICLE

Neural network surrogate model analysis of couple stress squeeze film flow with slip velocity and piezo-viscous effects over rough circular plates

Mallesh Narayanareddy1,2 ,  Vasanth Karegowdar Rajendrappa1*
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1 Department of Mathematics, NMAM Institute of Technology (NMAMIT), Nitte (deemed to be University), Udupi, Karnataka , India
2 Department of Mathematics, Sahyadri College of Engineering and Management, Mangalore, Karnataka , India
IJOCTA, 026280152 https://doi.org/10.36922/IJOCTA026280152
Received: 8 July 2026 | Revised: 24 August 2026 | Accepted: 24 August 2026 | Published online: 14 September 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Squeeze-film lubrication plays a crucial role in enhancing the performance, reliability, and service life of tribological components operating under high loads and varying lubrication conditions. This study investigates the squeeze-film lubrication characteristics of rough circular plates lubricated with a pressure-dependent viscous couple-stress fluid under slip velocity. A modified Reynolds equation is developed by incorporating Stokes’ couple stress fluid theory, the Barus pressure–viscosity relationship, slip boundary conditions, and Christensen’s stochastic surface roughness model. The governing equation is analyzed using a Neural network surrogate model (NNSM) to efficiently predict lubrication performance. The effects of the couple stress parameter, pressure–viscosity parameter, slip velocity, and surface roughness on dimensionless pressure, loadcarrying capacity, and squeeze-film time are examined through analytical and numerical analyses. Results indicate that increasing the couple stress parameter enhanced pressure generation, load-carrying capacity, and squeeze-film duration, whereas increasing slip velocity generally reduced pressure and load-carrying capacity. Pressure-dependent viscosity significantly affects pressure distribution and load-supporting capability, while surface roughness produced distinct effects depending on its statistical orientation. The novelty of this work lies in integrating the NNSM with a lubrication model that simultaneously accounts for couple-stress fluid behavior, pressure-dependent viscosity, slip velocity, and surface roughness. The proposed approach provides an efficient method for predicting lubrication characteristics and offers valuable insights for the design and optimization of bearings, gears, seals, dampers, biomedical devices, and other tribological systems.

Graphical abstract
Keywords
Squeeze film
Viscosity variation
Couple stress fluid
Slip velocity
Roughness
Neural network surrogate model
Funding
None.
Conflict of interest
The authors declare no potential competing interests.
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
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