Nonlinear Dynamics of Friction and Wear Processes

Friction and wear are governed not merely by contact mechanics but by rich nonlinear dynamics in which small variations in loading, surface topology or material properties can induce abrupt transitions between steady sliding, stick–slip oscillations and chaotic behaviour. Under cyclic or variable loading, interfacial microstructural evolution gives rise to emergent phenomena such as bifurcations, strange attractors and scale-invariant fluctuations. Modern experimental platforms combine high-resolution vibration sensing, acoustic emission monitoring and advanced signal-processing techniques to capture these effects in real time. Theoretical approaches draw on phase-space reconstruction, Lyapunov exponents and fractal measures to quantify instability thresholds and the progression of wear regimes. Practical applications span from mitigating railway wheel–rail damage and optimising boundary lubrication in high-precision bearings to predictive maintenance of rotating machinery. By elucidating the interplay between nonlinear contact forces, surface roughness evolution and lubricant film dynamics, researchers aim to extend component lifetimes, reduce energy losses and forecast failure before catastrophic breakdown.

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Nonlinear Dynamics of Friction and Wear Processes publication trend

The graph below shows the total number of articles in nonlinear dynamics of friction and wear processes across all publications each year (not limited to Nature Index journals).

Technical terms

Nonlinear dynamics: The branch of dynamics studying systems whose output is not directly proportional to the input, often leading to complex phenomena such as chaos and bifurcations.

Lyapunov exponent: A quantitative measure of how fast neighbouring trajectories in phase space diverge or converge, with positive values indicating sensitivity to initial conditions and potential chaos.

Correlation dimension: A fractal measure of an attractor’s geometric complexity in reconstructed phase space, reflecting the minimal number of degrees of freedom needed to describe the system’s dynamics.

Strange attractor: A non-periodic attractor in phase space upon which a system’s trajectories evolve in a bounded but aperiodic manner, characteristic of chaotic regimes.

Multifractal analysis: A technique for characterising signals exhibiting multiple scaling exponents, capturing heterogeneity in fluctuation amplitudes across time or space scales.

Variational mode decomposition (VMD): A signal-processing method that decomposes a time series into a predefined number of band-limited intrinsic mode functions optimised via variational principles.

References

1. Nonlinear Vibration Feature Recognition Method for Reciprocating Compressor Cylinder Based on VMD‐Multifractal Spectrum. Shock and Vibration (2023).
2. Analysis of Chaotic Features in Dry Gas Seal Friction State Using Acoustic Emission. Lubricants (2025).
3. Features of dynamic processes in the “indentor – coating” system during tests on a tribometer. VESTNIK of Samara University Aerospace and Mechanical Engineering (2023).