Browse Topic: Crankcase lubricants
In lubricating and specialty oil industries, blending is routinely used to convert a finite number of distillation cuts produced by a refinery into a large number of final products matching given specifications regarding viscosity, flash point, pour point or other properties of interest. To find the right component ratio for a blend, empirical or semi-empirical equations linking blend characteristics to those of the individual components are used. Mathematically, the problem of finding the right blend composition boils down to solving a system of equations, often non-linear ones, linking the desired properties of the blend with the properties and percentage of the blend components. This approach can easily be extended to crankcase lubricants, in which case major blend constituents are base oils, additive packages, and viscosity index improvers. Artificial intelligence (AI) tools allow accurate predictions of the basic physicochemical properties of such blends. This allows one to speed up formulation development as the number of test blends and the amount of testing can be significantly reduced. Furthermore, formulation price optimization is possible, taking into account available raw material inventories, shared use of certain raw materials across a number of finished products, etc. There also are tools for price capping that allow blenders to counter risks associated with supply disruptions and price volatility. After completing the “virtual” formulation development, the candidate lubricant properties are fed into the engine tribology simulation block that allows predictions of advanced properties, such as performance in mandatory API and/or ACEA engine test sequences. With the current state-of-the-art, only fuel economy and wear protection can be predicted with sufficient accuracy, for instance the top ring wear (TRW) in Cummins ISM test or Sequence VI FEI. The effect of friction modifiers is factored in using empirical quantifiers for the friction modifier efficacy, depending on which the asperity-asperity friction contribution obtained using the EHD tribology simulations is adjusted. Other difficult to predict properties - cleanliness, cam wear, tappet wear, soot, carbon deposits, etc - require co-processing of large amounts of experimental data and application cases. This is where machine-learning algorithms come handy. In the present communication, the application of AI tools is demonstrated with a focus on ACEA 2021 engine oil development. The AI Formulator Assistant software developed by SBDA using the industry standard CRISP-DM (CRoss Industry Standard Process for Data Mining) platform keeps record of all tests - including failed ones - and uses this information to continuously improve its predictive power.
Rising fuel prices and global concern over climate change have resulted in the need to deliver vehicles with improved fuel efficiency. The aim is to achieve this without compromising vehicle performance, durability or cost. Passenger car manufacturers worldwide are looking at various ways to optimize fuel economy performance. One option is for a vehicle OEM to re-design engine componentry in an effort to reduce engine friction and thereby reduce tailpipe emissions. There is also an increased focus on the crankcase lubricant as a potential tool to improve engine efficiency. This has led to a close collaborative working model between equipment manufacturers and engine oil marketers to create state of the art fluids capable of delivering higher fuel economy benefits without compromising engine durability. This paper describes a structured approach to the design of an advanced engine oil for a diesel passenger car. The aim of this formulation was to deliver a tangible improvement in fuel efficiency whilst maintaining a high level of engine durability. A carefully designed matrix of crankcase fluids was developed with the intention of investigating the relative effect of key lubricant parameters on fuel efficiency. The fuel economy impact of these formulations was assessed using a vehicle running an industry standard emissions drive cycle on a chassis dynamometer. Selected oils were then taken forward for durability evaluation using an engine test bed. The results of the testing showed that this approach to the design and development of an advanced crankcase lubricant can offer a significant improvement in fuel efficiency without compromising durability when compared to conventional oils.
The most important property of the engine oil is its ability to reach all engine parts. Once there, it can build an oil film which protects these parts from wear and ultimately from destruction. No other lubricant property is relevant if the oil cannot be delivered to the critical engine parts. Thus engine oil pumpability, especially pumpability at low temperatures when the viscosity of the lubricant is the highest, is crucially important. The crankcase lubricant industry has recognized this, in requiring good low temperature pumpability for the last three decades. While good low temperature properties of the fresh oils are a necessary requirement for a lubricant, they are not sufficient to ensure the lifetime performance of the oil in the engine. The oil gradually ages in the engine and its properties, including low temperature pumpability, change. A number of bench and engine tests have been developed to predict low temperature pumpability of the aged oils, such as Sequence IIIGA, Romaszewski Oil Bench Oxidation (ROBO) and a new low temperature pumpability test under development by CEC TDG-L-105 group. In this paper we examine the low temperature pumpability of several oils in a modern 2010 emission complaint Heavy Duty Diesel (HDD) engine. We show that good fresh oil low temperature properties such as MRV TP-1 apparent viscosity or gelation index do not guarantee good field performance. We also evaluate a number of bench tests as predictors of field ageing and low temperature performance of the used oils, and we show that while some bench tests exhibit reasonable correlation with the field, not all bench tests can predict failing performance in the field.
Crankcase emissions are a complex mixture of combustion products and, specifically Particulate Matter (PM) from lubricant oil. Crankcase emissions contribute substantially to the particle mass and particle number (PN) emitted from an internal combustion engine. Environmental legislation demands that the combustion and crankcase emissions are either combined to give a total measurement or the crankcase gases are re-circulated back into the engine, both strategies require particle filtration. There is a lack of understanding regarding the physical processes that generate crankcase emissions of lubricant oil, specifically how the bulk lubricant oil is atomised into droplets. In this paper the crankcase of a motored compression ignition engine, has been optically accessed to visualise the lubricant oil distribution. The oil distribution was analysed in detail using high speed laser diagnostics, at engine speeds up to 2000 rpm and oil temperatures of 90°C. High resolution calibrated images show the passive behavior of lubricant oil once it has been supplied to critical engine components. The major mechanisms of oil atomisation have been identified and quantified from high speed images, the generation of oil droplets dp = 10 μm - 3 mm has been captured. The most significant generation mechanism was atomisation of oil films present on the surface of rotating components. The isolated contribution of the crank and camshafts to the atomised oil droplets present in the top of the engine has been recorded. Further breakup, evaporation and condensation from the surface of the atomised oil droplets will generate coarse and fine PM. Results from imaging data show good correlation with sub-micron PN sampling measurements captured in a previous study [1]; namely an increase in particle number concentration with increasing engine speed.
Items per page:
50
1 – 50 of 62