Model of rough surfaces with Gaussian processes

Surface roughness plays a critical role and has effects in, e.g. fluid dynamics or contact mechanics. For example, to evaluate fluid behavior at different roughness properties, real-world or numerical experiments are performed. Numerical simulations of rough surfaces can speed up these studies because they can help collect more relevant information. However, it is hard to simulate rough surfaces with deterministic or structured components in current methods. In this work, we present a novel approach to simulate rough surfaces with a Gaussian process (GP) and a noise model because GPs can model structured and periodic elements. GPs generalize traditional methods and are not restricted to stationarity so they can simulate a wider range of rough surfaces. In this paper, we summarize the theoretical similarities of GPs with auto-regressive moving-average processes and introduce a linear process view of GPs. We also show examples of ground and honed surfaces simulated by a predefined model. The proposed method can also be used to fit a model to measurement data of a rough surface. In particular, we demonstrate this to model turned profiles and surfaces that are inherently periodic.

Automated summary

Surface roughness is important in fluid dynamics and contact mechanics, and its evaluation often requires real-world or numerical experiments. This paper proposes a novel approach to simulate rough surfaces using Gaussian processes (GPs) and a noise model, allowing for a wider range of simulations compared to traditional methods. The paper introduces the theoretical similarities between GPs and auto-regressive moving-average processes, and demonstrates the use of the proposed method in modeling turned profiles and inherently periodic surfaces.