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Use of a digital twin to analyze the effect of graphene as a lubricant additive for diesel engines
1
Surface Phenomena Laboratory, Polytechnic School, University of Sao Paulo, Brazil
2
Faculty of Mechanical Engineering, Lublin University of Technology, Poland
3
-, Kinix Consulting Ltda, Brazil
4
-, Gerdau Graphene, Brazil
5
-, Independent Researcher, Brazil
Submission date: 2025-06-11
Final revision date: 2025-10-01
Acceptance date: 2025-10-06
Online publication date: 2025-11-13
Corresponding author
KEYWORDS
TOPICS
ABSTRACT
The effect of two graphene additives to engine oil on diesel engine efficiency was studied. The first additive was a commercially available additive based on graphene oxide (GO). The additive was tested on a small automotive diesel engine. The use of the additive concentration recommended by the manufacturer at 3% in the engine oil resulted in a reduction of the specific fuel consumption from 0.2% to 0.7%, depending on the engine operating conditions. The second additive, currently under development, was based on graphene nanoplatelets (GNP). The additive was tested on a medium-sized diesel engine in a truck. The use of the equivalent GNP concentration of 0.1% resulted in a reduction of fuel consumption in the ESC test by 0.4%. Increasing the concentration of this additive to 0.2% GNP did not result in a further reduction in fuel consumption. Because the engine efficiency benefits resulting from the use of improved oils were close to the measurement uncertainties, the applicability of machine learning using engine on-board diagnostics (OBD) readings to analyze the impact of lubricant additives was investigated. The use of Random Forest, machine learning digital twins, was able to reproduce the OBD instantaneous fuel consumption with excellent accuracy. Further analysis with SHAPLEY values helped to identify the more important engine parameters that affected instantaneous fuel consumption.
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