To simulate today’s complex tribo-contact scenarios, a methodological breakdown of a complex design problem into simpler sub-problems is essential to achieve acceptable simulation outcomes. This also helps to manage iterative, hierarchical systems within given computational power. In this paper, the authors reviewed recent trends of simulation practices in tribology to model tribo-contact scenario and life cycle assessment (LCA) with the help of simulation. With the advancement of modern computers and computing power, increasing effort has been given towards simulation, which not only saves time and resources but also provides meaningful results. Having said that, like every other technique, simulation has some inherent limitations which need to be considered during practice. Keeping this in mind, the pros and cons of both physical experiments and simulation approaches are reviewed together with their interdependency and how one approach can benefit the other. Various simulation techniques are outlined with a focus on machine learning which will dominate simulation approaches in the future. In addition, simulation of tribo-contacts across different length scales and lubrication conditions is discussed in detail. An extension of the simulation approach, together with experimental data, can lead towards LCA of components which will provide us with a better understanding of the efficient usage of limited resources and conservation of both energy and resources.

A two-step idea is presented for efficiently training a neural network (NN) surrogate of a parametric, high-fidelity, high-dimensional computational fluid dynamics model (HDM). It consists first in using a Gaussian process, an acquisition function, the HDM, and a scalar quantity obtained by post-processing the HDM to adaptively sample the parameter space. Next, a NN is trained for a quantity of interest using the HDM solutions computed at the sampled parameter points. This active learning approach is illustrated with numerical experiments for the prediction of the lift-over-drag ratio of a cambered NACA airfoil and the pressure coefficient of a flying wing aircraft mAEWing2 in large parameter spaces of flight conditions and shape design variables. The results demonstrate the superior efficiency and accuracy delivered by the proposed training over alternatives based on uniform and random samplings. The resulting surrogate models are suitable for time-critical applications such as design optimization and uncertainty quantification.