MWCNT and graphene nanoparticles additives for energy efficiency in engine oil with regression modeling
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MWCNT and graphene nanoparticles additives for energy efficiency in engine oil with regression modeling Isha Srivastava1 · Fateh Singh2 · Ankit Kotia3 · Subrata Kumar Ghosh1 Received: 14 April 2020 / Accepted: 20 October 2020 © Akadémiai Kiadó, Budapest, Hungary 2020
Abstract Quality of engine oil plays vital role in reducing frictional energy loss and enhancing durability. Recently, concept of nanolubricants, which is the suspension of nanoparticles in base lubricants, widely attempted. In the present study, experimental and regression approach has been used to evaluate the rheological properties of graphene/engine oil and MWCNT–graphene/ engine oil nanolubricants. Power law index and consistency index values reveal non-Newtonian and shear thinning behavior of the samples. Result shows that dispersion of 1.8% particle volume fraction makes 155.06% and 62.85% increment in viscosity for MWCNT–graphene/engine oil and graphene/engine oil nanolubricant, respectively. A novel regression model for the dynamic viscosity of nanolubricant is proposed with temperature, nanoparticle volume fraction and shear rate as parameters. The proposed regression model provides viscosity prediction with deviation within 2.57% (R2, 0.99887). MWCNT–graphene/engine oil nanolubricants and regression model provide a cost-effective and eco-friendly approach for enhancement in the properties of lubricant.
* Ankit Kotia [email protected] 1
Department of Mechanical Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad, Dhanbad, India
2
Department of Mathematics, DIT University, Dehradun, India
3
School of Mechanical Engineering, Lovely Professional University, Phagwara, Punjab, India
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Graphic Abstract Plain oil 0.3% MWCNT-Graphene 0.6% MWCNT-Graphene 0.9% MWCNT-Graphene 1.2% MWCNT-Graphene 1.5% MWCNT-Graphene 1.8% MWCNT-Graphene 0.3% Graphene 0.6% Graphene 0.9% Graphene 1.2% Graphene 1.5% Graphene 1.8% Graphene
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Dynamic viscosity/mpa-s
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Engine lubrication
Keywords MWCNT/graphene · Graphene · Engine oil · Viscosity · Regression model List of symbols 𝜑 Particle volume fraction (%) W Mass (g) Ρ Density (g cm−3) r Relative µ Viscosity (mPa-s) 𝛾 Shear rate (s−1) 𝜏 Shear stress (mPa) M Consistency index N Power law index Tr Temperature ratio (T/273.15) np Nanoparticles bl Base lubricant nl Nanolubricant
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Introduction In recent design of automobiles, mileage and engine emissions are considered as vital factors. In this perspective, recent technologies including multi point fuel injection, engine control unit, economizer and others are incorporated to minimize the overall fuel consumption in light to heavy vehicles. In one way, these components make frequent change in engine speed to optimize the fuel economy. The frequent variation in load and shear rate demands for refined rheological properties of engine oil
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