Predicting the contact mechanical response for various types of surfaces is and has long been a subject, where many researchers have made valuable contributions. This is because the surface topography has a tremendous impact on the tribological performance of many applications. The contact mechanics problem can be solved in many ways, with less accurate but fast asperity-based models on one end to highly accurate but not as fast rigorous numerical methods on the other. A mathematical model as fast as an asperity-based, yet as accurate as a rigorous numerical method is, of course, preferred. Artificial neural network (ANN)–based models are fast and can be trained to interpret how in- and output of processes are correlated. Herein, 1,536 surface topographies are generated with different properties, corresponding to three height probability density and two power spectrum functions, for which, the areal roughness parameters are calculated. A numerical contact mechanics approach was employed to obtain the response for each of the 1,536 surface topographies, and this was done using four different values of the hardness per surface and for a range of loads. From the results, 14 in situ areal roughness parameters and six contact mechanical parameters were calculated. The load, the hardness, and the areal roughness parameters for the original surfaces were assembled as input to a training set, and the in situ areal roughness parameters and the contact mechanical parameters were used as output. A suitable architecture for the ANN was developed and the training set was used to optimize its parameters. The prediction accuracy of the ANN was validated on a test set containing specimens not seen during training. The result is a quickly executing ANN, that given a surface topography represented by areal roughness parameters, can predict the contact mechanical response with reasonable accuracy. The most important contact mechanical parameters, that is, the real area of contact, the average interfacial separation, and the contact stiffness can in fact be predicted with high accuracy. As the model is only trained on six different combinations of height probability density and power spectrum functions, one can say that an output should only be trusted if the input surface can be represented with one of these.

There are many moving machine assemblies with conformal tribological contacts at very high contact pressures, e.g., sliding bearings, propeller shaft bearings and machine guideways. Furthermore, applications such as trunnion and guide vane bearing in Kaplan turbines have very low sliding speeds and oscillatory types of motion. Although there is a vast selection of tribology test rigs available, there is still a lack of test equipment to perform friction and wear tests under high contact pressure, reciprocatory sliding and large area contact. The aim of this work is thus to develop a novel reciprocating tribometer and test method that enables friction and wear tests under low-speed reciprocatory sliding with contact pressures up to 90 MPa in a flat-on-flat contact configuration. First, a thorough description of the test rig design is given. Secondly, the influence of contact pressure and stroke length on the tribological properties of a stainless steel and polymer composite material combination is studied. The significance of considering creep, friction during the stroke and contact temperature is specifically highlighted. The novel tribometer can be used to screen different bearing and shaft material combinations and to evaluate the friction and wear performance of self-lubricating bearings for the specific operating conditions found in Kaplan turbines.

In cross-country skiing races, the difference between the fastest and the second fastest time can be minuscule. As in all endurance sports, cross-country skiing requires the use of energy to overcome resistive forces, in this case primarily aerodynamic drag and friction between the skis and snow. Even a slight reduction in either of these can determine the outcome of a race. The geometry of the ski exerts a profound influence on the friction between the skis and snow. As a result of the flexible modern cross-country skis, the camber profile and gliding properties to be influenced by the skiers’ position. Here, based on the location of the normal force corresponding to the plantar pressure, we characterize the ski camber while performing three variations of the downhill tucking position. We found that when gliding on a classic ski, the risk of contact between the kick wax and snow can be reduced by tucking in a leaning backwards position (i.e. by moving the skier’s center of mass backwards). With the tucking position, the percentage of the skier’s body weight that is distributed onto the friction interface at the rear of the skis varies between 63.5% in Gear 7 (leaning forward) on a skating ski and 93.0% in Gear 7 (leaning backwards) on a classic ski.

In a cross-country skiing competition, the time difference between the winner and the skier coming in at second place is typically very small. Since the skier spends much of the energy on overcoming resistive forces, a relatively small reduction in these forces can have a significant impact on the results. The resistive forces come partly from the friction, at the tribological interface between the ski and the snow, and as with many tribological processes, the characterisation of its origin plays an important role in determining the frictional properties. Furthermore, in cross-country ski friction, there are several scales impacting the frictional performance, with the major contributors being the ski-camber profile and ski-base structure. Macro-scale measurements of the ski-camber profile under loading are often used to determine how adequate the ski is for use under specific conditions. The characteristic properties usually assessed are the force required to collapse the ski in order to obtain a certain camber height, the topography of the kick-wax zone, and the length (determined by simple means) of the frictional interfaces associated with the rear- and front glide zones, i.e., the apparent contact length. These measurements are, however, commonly performed by loading the ski against a much stiffer counter surface than snow and this affects the quantification of the characteristic properties. To date, some mathematical models have been proposed, but there is no reliable approach for determining the macro-scale properties of the contact between a cross-country ski and a counter surface using simulations. In the present paper, an Artificial Neural Network (ANN) has been trained to predict the ski-camber profile for various loads applied at different positions. A well-established deterministic approach has been employed to simulate the contact between the ANN-predicted ski-camber profile and a linearly elastic body with a flat upper surface, representing the snow. Our findings indicate that this method is feasible for the determination of relevant macro-scale contact characteristics of different skis with snow. Moreover, we show that the apparent contact area does not linearly depend on the load and that the material properties of the counter surface also exert a large impact when quantifying the apparent contact area and the average apparent contact pressure.

In winter sports, the equipment often comes into contact with snow or ice, and this contact generates a force that resists motion. In some sports, such as cross-country skiing, this resistive force can significantly affect the outcome of a race, as a small reduction in this force can give an athlete an advantage. Researchers have examined the contact between skis and snow in detail, and to fully understand this friction, the entire ski must be studied at various scales. At the macro scale, the entire geometry of the ski is considered and the apparent contact between the ski and the snow is considered and at the micro-scale the contact between the snow and the ski-base textures. In the present work, a method for characterising the contact between the ski-base texture and virtual snow will be presented. Six different ski-base textures will be considered. Five of them are stone-ground ski bases, and three of them have longitudinal linear textures with a varying number of lines and peak-to-valley heights, and the other two are factory-ground “universal” ski bases. The sixth ski base has been fabricated by a steel-scraping procedure. In general, the results show that a ski base texture with a higher $S_{pk}$ value has less real contact area, and that the mutual differences can be large for surfaces with similar $S_a$ values. The average interfacial separation is, in general, correlated with the $S_a$ value, where a “rougher” surface exhibits a larger average interfacial separation. The results for the reciprocal average interfacial separation, which is related to the Couette type of viscous friction, were in line with the general consensus that a “rougher” texture performs better at high speed than a “smoother” one, and it was found that a texture with high $S_a$ and $S_{pk}$ values resulted in a low reciprocal average interfacial separation and consequently low viscous friction. The reciprocal average interfacial separation was found to increase with increasing real contact area, indicating a correlation between the real area of contact and the Couette part of the viscous friction.

It has been over a century since the interest in inventing the optimal topology for bearings arose. A significant achievement was published by Lord Rayleigh, who found the step-bearing geometry which maximise the load-carrying capacity when the classical Reynolds equation is used to model thin film flow of an iso-viscous and incompressible fluid. Since then, new optimisation methods considering some variants of governing equations for finding the best possible bearings have surfaced, one of which will be presented in this paper. Here, two different formulations for compressible flow, i.e. ideal gas and constant bulk modulus compressibility, as well as the classical Reynolds formulation will be used in combination with the method of moving asymptotes for topological optimisation. All three of these problem formulations provide us with unique geometries, which either maximise the load-carrying capacity or minimise friction, for fluids with a wide variety of compressibility.