Traditionally, analytical equations used in tribo-dynamic modelling, such as those used for predicting central film thickness within elastohydrodynamic lubricated contacts, have led to timely computations, but tend to lack the accuracy of numerical solvers. However, it can be shown that data-driven solutions, such as machine learning, can significantly improve computational efficiency of tribo-dynamic simulations of machine elements without comprising accuracy relative to the numerical solution. During this study, artificial neural networks (ANNs) are trained using data produced via numerical solutions, which are constrained by the regimes of lubrication to ensure the quality of the training data set. Multiple ANNs are then implemented to predict EHL central film thickness, as well as viscous and boundary friction, in multiple commonly used machine elements, such as a rolling element bearing and a spur gear. The viscous and boundary friction ANN prediction are compared directly against ball-on-disc experimental measurements to validate its accuracy.
Fuel economy is a growing concern for both manufacturers within the automotive sector and consumers. Increasing government legislation is driving towards greener vehicles with reduced CO2 and NOx emissions and greater fuel economy, especially within urban environments. Manufacturers use new technologies in their powertrain systems to tackle these problems. This paper simulates and evaluates the performance of using a half toroidal CVT in series with a conventional multi-speed transmission, by analysing different shifting strategies to optimise fuel consumption and NOx emissions over the NEDC using this novel approach. The results show an 8.83% increase in fuel economy and up to an 11.34% reduction in NOx emissions is possible using this arrangement. The introduction of CVT adds a further 1.18% increase in fuel economy and 3.59% decrease in NOx emissions. The paper concludes that this novel arrangement should be considered by automotive manufacturers as a solution for improvements to powertrain technology.
Using an atomic force microscope, a nanoscale wear characterization method has been applied to a commercial steel substrate AISI 52100, a common bearing material. Two wear mechanisms were observed by the presented method: atom attrition and elastoplastic ploughing. It is shown that not only friction can be used to classify the difference between these two mechanisms, but also the ‘degree of wear’. Archard's Law of adhesion shows good conformity to experimental data at the nanoscale for the elastoplastic ploughing mechanism. However, there is a distinct discontinuity between the two identified mechanisms of wear and their relation to the load and the removed volume. The length-scale effect of the material's hardness property plays an integral role in the relationship between the ‘degree of wear’ and load. The transition between wear mechanisms is hardness-dependent, as below a load threshold limited plastic deformation in the form of pile up is exhibited. It is revealed that the presented method can be used as a rapid wear characterization technique, but additional work is necessary to project individual asperity interaction observations to macroscale contacts.
With the increasing stringent emissions legislation on ICEs, alongside requirements for enhanced fuel efficiency as key driving factors for many OEMs, there are many research activities supported by the automotive industry that focus on the development of hybrid and pure EVs. This change in direction from engine downsizing to the use of electric motors presents many new challenges concerning NVH performance, durability and component life. This paper presents the development of experimental methodology into the measurement of NVH characteristics in these new powertrains, thus characterizing the structure borne noise transmissibility through the shaft and the bearing to the housing. A feasibility study and design of a new system level test rig have been conducted to allow for sinusoidal radial loading of the shaft, which is synchronized with the shaft's rotary frequency under high-speed transient conditions in order to evaluate the phenomena in the system. The present work introduces a new component level test rig that can predict the response of new EV and hybrid systems using different types of rolling element bearings such as deep groove ball bearings, angular contact roller bearings, tapered roller bearings and the cylindrical roller bearings. Moreover, it is possible to investigate the influence of factors such as bearing clearance and the amount of axial bearing preload which will be used to further explore the capability of bearing CAE tools. The test rig has multiple novel elements compared to those previously developed. In particular, the rotational speed of the shaft, which significantly exceeds that of previously reported rigs, and the excitation frequency ramp up at the same rate as the frequency of the shaft enabling the phenomena found in Hybrid and EVs. The sinusoidal radial load is supplied using a loading device featuring a single load point to minimize undesired excitation effects. With respect to structure borne noise the system response is captured through the vibrational displacement of the shaft and bearing housing.
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