more detailed component test rigs for gears, bearings and seals. This allows proper model parametrisation. With all the input data in place, one can proceed with simulations of the actual transmissions.
additives reveal similar behaviour – e.g. detergents, corrosion inhibitors and metal deactivators.
Figure 4: Effect of an organic friction modifier on friction loss measured in an FZG back-to-back gear test rig at two different load stages.
Figure 3: Dynamic simulations of lubricant flow and heat removal efficiency in a gearbox using the Particleworks software. Two oil viscosity grades are compared.
This strategy can be further enhanced by application of artificial intelligence / machine learning (AI/ML) tools. Unfortunately, availability of quality data in a unified format that would allow direct comparisons is often a problem. This hinders development of robust lubricant models. At the moment, there are no simulation tools that take into account effects of individual additives in lubricant formulations, not to mention possible interactions between different additives. It is not uncommon that a tribological test indicates a remarkable effect achievable with a certain additive, while real-life experience suggesting otherwise. For example, some organic friction modifiers show spectacular performance when evaluated in a reciprocating friction tester or a mini-traction machine. However, when put to the test in a finished product, the outcome is uncertain. For instance, in transmission fluids, the use of certain classes of friction modifiers was associated with problems such as excessive oil aeration, clutch slipping and gear scuffing. One instructive example is shown in Figure 4, where an organic friction modifier (OFM) was used together with a borate-aminophosphate (BAP) EP/AW additive. At low loading stages, we see a reduction in friction when OFM is used in the formulation. However, the same product causes an increase in friction at higher loading stages since it compromises scuffing protection. Many surface-active
Hence, close cooperation is needed between manufacturers of specialty chemicals who carry out extensive testing of various additive packages, EV transmission hardware manufacturers, and data scientists working in the field of simulation- based engineering. By combining micro-level TEHD simulations and macro-level CFD simulations, one can gain a much better understanding of the effects of lubrication and surface finish characteristics on the transmission performance (Figure 5).
Figure 5: The path from basic tribotests to simulation-based engineering. References:
B. Zhmud, M. Najjari, B. Brodmann, The Effects of the Lubricant Properties and Surface Finish Characteristics on the Tribology of High-Speed Gears for EV Transmissions, Lubricants 12 (2024) 112. M. Merelli, e-Motor cooling with oil jets: impact of flowrates and oil properties, Proc. AEIT Automotive 2023, Modena, Italy, Jul 17-19, 2023. S. Pocinki, P. Scinto, R. Wilkinson, The
Q.LIFE®
Engine:
A Work of Statistical Engineering, Quality Engineering, 24 (2012) 215.
tribonex.com
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LUBE MAGAZINE NO.181 JUNE 2024
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