The Gaussian weighted trajectory method （GWTM） is a practical implementation of classical S matrix theory （CSMT） in the random phase approximation, CSMT being the first and simplest semi-classical approach of mole...The Gaussian weighted trajectory method （GWTM） is a practical implementation of classical S matrix theory （CSMT） in the random phase approximation, CSMT being the first and simplest semi-classical approach of molecular collisions, developped in the early seventies. Though very close in spirit to the purely classical description, GWTM accounts to some extent for the quantization of the different degrees-of-freedom involved in the processes. While CSMT may give diverging final state distributions, in relation to the rainbow effect of elastic scattering theory, GWTM has never led to such a mathematical catastrophe. The goal of the present note is to explain this finding.展开更多
文摘The Gaussian weighted trajectory method （GWTM） is a practical implementation of classical S matrix theory （CSMT） in the random phase approximation, CSMT being the first and simplest semi-classical approach of molecular collisions, developped in the early seventies. Though very close in spirit to the purely classical description, GWTM accounts to some extent for the quantization of the different degrees-of-freedom involved in the processes. While CSMT may give diverging final state distributions, in relation to the rainbow effect of elastic scattering theory, GWTM has never led to such a mathematical catastrophe. The goal of the present note is to explain this finding.
文摘多环芳烃(polycyclic aromatic hydrocarbons,PAHs)是地下水中的主要有机污染物之一,地下水中多环芳烃运移数值模拟在开展地下水污染高效修复中起重要作用。在实际地下水污染条件下,由于难以准确刻画含水介质中的胶体类型及其分布,通常忽略污染物-胶体共运移机制,建立的模型存在结构误差,导致模型预测具有显著偏差。本研究以荧蒽和菲为研究对象,针对忽略的PAHs-胶体的共运移机制,使用高斯过程回归(Gaussian process regression,GPR)修正模型结构误差,建立耦合数据驱动和物理机制的多环芳烃运移模型。通过饱和砂柱PAHs运移室内试验,对比分析了未耦合和耦合数据驱动方法的模型预测结果。结果表明,忽略PAHs-胶体的共运移机制的地下水多环芳烃运移模型具有显著的模型结构误差,直接进行参数识别不能弥补忽略的共运移机制,预测结果存在显著偏差。使用GPR模型可以有效补偿PAHs-胶体的共运移机制,修正地下水模型的结构误差。验证期荧蒽、菲预测结果的95%置信区间对观测数据的覆盖率分别提升了56.84%和19.04%,纳什系数分别提升了40.09%和21.73%,均方根误差分别降低了33.10%和55.38%,平均绝对误差分别降低了32.00%和46.34%,地下水多环芳烃运移模型的预测性能显著提高。本研究提出的耦合数据驱动和物理机制方法为场地地下水多环芳烃运移精准模拟提供了可行思路,有助于实现地下水污染的精准高效修复。
