AI 中文总结
本文提出基于高斯过程回归的非参数多尺度框架，建模高压金粗糙峰接触中受限十六烷的边界润滑，重现基准数据，为边界润滑的预测性连续介质建模提供灵活透明的途径。
AI 中文摘要
边界润滑由滑动界面处的分子过程控制，经典连续介质描述无法触及这些过程。分子动力学（MD）模拟可解析这些过程的原子级细节，但当受限尺度接近分子尺度时，通过半经验本构定律将其输出纳入工程尺度模型变得愈发困难。本文应用基于高斯过程（GP）回归的非参数多尺度框架，对高压达1 GPa、间隙高度低至1.4 nm的金表面间受限十六烷的边界润滑进行建模。GP替代模型在受限流体的非平衡MD模拟上训练，直接向连续介质薄膜求解器提供应力预测，规避了固定形式本构定律的需求。该框架可自然捕捉密度分层、黏度变化等分子现象，以及在高压和小间隙高度下主导摩擦响应的强非线性壁面滑移。本文结果重现了Codrignani等人的原子级基准数据，证明非参数替代模型为边界润滑的预测性连续介质建模提供了灵活且物理透明的途径。
英文摘要
Boundary lubrication is governed by molecular processes at the sliding interface that are inaccessible to classical continuum descriptions. While molecular dynamics (MD) simulations can resolve these processes in atomistic detail, incorporating their output into engineering-scale models through semi-empirical constitutive laws becomes increasingly difficult as confinement approaches the molecular scale. Here, we apply a nonparametric multiscale framework based on Gaussian process (GP) regression to model boundary lubrication of hexadecane confined between gold surfaces under pressures up to 1 GPa and gap heights down to 1.4 nm. The GP surrogates are trained on nonequilibrium MD simulations of the confined fluid and directly provide stress predictions to a continuum thin-film solver, circumventing the need for fixed-form constitutive laws. The framework naturally captures molecular phenomena such as density layering, viscosity changes, and the strongly nonlinear wall slip that dominates the frictional response at high pressures and small gap heights. Our results reproduce the atomistic benchmark data of Codrignani et al., demonstrating that nonparametric surrogate models offer a flexible and physically transparent route toward predictive continuum modeling of boundary lubrication.
