eJournals International Colloquium Tribology 23/1

International Colloquium Tribology
ict
expert verlag Tübingen
125
2022
231

Friction control by surface texturing in Internal Combustion Engines

125
2022
Konstantinos Gkagkas
Franz Pirker
András Vernes
ict2310511
23rd International Colloquium Tribology - January 2022 511 Friction control by surface texturing in Internal Combustion Engines Konstantinos Gkagkas Toyota Motor Europe NV/ SA, Zaventem, Belgium Corresponding author: Konstantinos.Gkagkas@toyota-europe.com Franz Pirker AC2T research GmbH, Wiener Neustadt, Austria András Vernes AC2T research GmbH, Wiener Neustadt, Austria also Technische Universität Wien, Vienna, Austria 1. Introduction In light of the European Green Deal, which targets to make Europe climate neutral in 2050 [1], current targets call for reductions of -37.5% for the tailpipe CO2 emissions of newly registered passenger cars for 2030. Sales of electrified and other alternatively powered passenger cars need to pick up strongly to achieve targets [2]. In the same direction, Toyota has announced the “New Vehicle Zero CO2 Emissions Challenge” with the aim to reduce global average CO2 emissions from new vehicles by 90% by 2050, compared to Toyota’s 2010 level [3]. The automotive industry needs, therefore, to keep advancing technologies for internal combustion engines, which will remain widely adopted in the context of hybrid, and plugin hybrid electrified vehicles. Friction accounts approximately for one-third of the fuel energy consumed in passenger cars [4]. Minimization of the environmental impact via the reduction of friction losses is therefore a significant focus point in the automotive sector. Additionally, the demand for low-viscosity oils further increases the importance of the surface topography. Accordingly, this study represents an industrial request where the up-scaling of the frictional performance via numerical simulations is performed with the main goal to optimize the texture of interacting surfaces, while the pairing of materials and the lubricant is kept fixed. 2. Methodology The computational strategy followed here (Fig. 1) will aim at the numerical estimation of the friction coefficient on the macro-scale passing through all length scales below, i.e., from ab-initio calculations, e.g., on additives in the lubricant, molecular dynamics simulation for revealing the ordering of molecules in the lubricant and fluid dynamics to determine the roughness-dependent flow factors. Since this numerical framework is planned to be used for various textures, e.g., either computer-generated or experimental ones, the results are expected to be transferable to larger, say component length scales, by applying proper machine learning (ML) techniques. Experimental data will be obtained using pin-on-disctype as well as properly adapted SRV tribometers by closely following the contact situation of the engine components of interest. The surface topography will be measured before and after each test to use these surface data as input for various numerical simulations. Figure 1: Computational framework for studying frictional losses in internal combustion engines. 3. Results 3.1 Macroscopic scale On the macroscopic scale, the impact of roughness on the lubricant flow will be described by flow factors. Accordingly, the mean lubrication gap for given operating conditions, such as the normal pressure, sliding speed and 512 23rd International Colloquium Tribology - January 2022 Friction control by surface texturing in Internal Combustion Engines temperature, will be calculated on the micro-to-macro scale by using advanced homogenisation techniques [5]. On these scales the numerical findings will be compared with the output of laboratory tests (either pin-on-disc or adapted SRV). Unfortunately, the contact as seen numerically and illustrated in Fig. 2 cannot be directly observed experimentally. Therefore, the relevant tribo-rheological properties of the lubricant molecules or additives, e.g., their layering and viscosity within real operating conditions at high temperatures and pressures, must be numerically determined. 3.2 Microscopic scale At this length scale, where the topography of textures is of crucial importance for the frictional performance, the impact of various geometrical features, such as those of the grooves, on the surface roughness parameters (SRPs) is separately studied by computer generating a large variety of surfaces. This is then completed by similar analyses of the used samples before and after the tribological testing at both laboratory and component level. Finally, an attempt is made in a machine-learning fashion to predict the frictional losses of the computer-generated surfaces based on their calculated SRPs and hence to select the presumably best performing textured surface. For this, those system parameters will be considered which are resulting from the performed laboratory and component-level tests and correlate to the flow factors, recall Sec. 3.1, calculated for each textured surface of interest by taking also into account the numerical tribo-rheological data obtained from the nanoscopic simulations, see Sec. 3.3. Figure 2: Dependence of oil flow on texture and lubrication gap. [6] 3.3 Nanoscopic scale For the simulation of the lubricated contacts at the nano-scale. we employ a coarse-grained model for the description of lubricant molecules that interact with metallic solids, see Fig. 3. Previous studies have shown that strong layering can occur in the case of confined molecules with charged atoms, especially close to the walls [7, 8, 9]. We study the impact of Coulomb interactions on the liquid structure by adjusting the charge of the lubricant molecules, as well as the polarizability of the solid surfaces. In addition, we study the impact of molecule shape on the lubricant layering and the resulting friction forces. Figure 3: Near-wall layering of confined lubricants. [9] 4. Conclusion To speed up materials upscaling and bring new technologies like surface structuring, i-TRIBOMAT develops a wide range of services. The services are standardized tribological model tests, data driven services and various multiscale materials up-scaling tools. In this paper the workflow is presented how to characterize new surface structures and predict their performance on component level using a multi-scale approach which combines simulations, experiments, and ML techniques for the material design of lubricated surfaces in internal combustion engines. We expect that such results will help us to describe the physico-chemical mechanisms that control lubrication and guide us towards the efficient design of novel tribological systems with significantly lower friction losses. This workflow is expected to significantly decrease the costs and time of the development process within Toyota. References [1] COM(2019) 640 final; COMMUNICATION FROM THE COMMISSION TO THE EUROPE- AN PARLIAMENT, THE EUROPEAN COUN- CIL, THE COUNCIL, THE EUROPEAN ECO- NOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS; The European Green Deal, European Commission 23rd International Colloquium Tribology - January 2022 513 Friction control by surface texturing in Internal Combustion Engines [2] European Automotive Manufacturers Association, Making the Transition to Zero-Emission Mobility - 2019 progress report, (2019) [3] Toyota Motor Corporation, Environmental Report 2020, (2020) [4] K. Holmberg, P. Andersson and A. Erdemir, “Global energy consumption due to friction in passenger cars” Trib. Int. 47 (2012) 221-234. [5] A. Almqvist, J. Fabricius, A. Spencer and P. Wall, “Similarities and differences between the flow factos method by Patir and Cheng and homogenization”, ASME. J. of Tribology 133(3) (2011) 031702. [6] S. Sanda, H. Nagakura, N. Katsumi, S. Hotta, K. Kawai and M. Murakami, “Analysis of piston frictional force under engine firing condition”, J. Soc. Auto. Eng. 45(5) (2014) 799-804. [7] R. Capozza, A. Vanossi, A. Benassi and E. Tosatti, “Squeezout phenomena and boundary layer formation of a model ionic liquid under confinement and charging”, J. Chem. Phys. 142 (2015) 064707. [8] A. E. Somers, P. C. Howlett, et al., “A Review of Ionic Liquid Lubricants”, Lubricants 1 (2013), 3-21. [9] M. Dašić, I. Stanković, K. Gkagkas, “Molecular dynamics investigation of the influence of the shape of the cation on the structure and lubrication properties of ionic liquids”, Phys. Chem. Chem. Phys. 21 (2019) 4375-4386 Acknowledgement This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 814494, project iTRIBO- MAT. More details: https: / / www.i-tribomat.eu/ .