An algorithm utilizing four basic processes was described for chemical oil spill dispersion. Initial dispersion was calculated using a modified Delvigne equation adjusted to chemical dispersion, then the dispersion was distributed over the mixing depth, as predicted by the wave height. Then the droplets rise to the surface according to Stokes’ law. Oil on the surface, from the rising oil and that undispersed, is re-dispersed. The droplets in the water column are subject to coalescence as governed by the Smoluchowski equation. A loss is invoked to account for the production of small droplets that rise slowly and are not re-integrated with the main surface slick. The droplets become less dispersible as time proceeds because of increased viscosity through weathering, and by increased droplet size by coalescence. These droplets rise faster as time progresses because of the increased size. Closed form solutions were provided to allow practical limits of dispersibility given inputs of oil viscosity and wind speed. Discrete solutions were given to calculate the amount of oil in the water column at specified points of time. Regression equations were provided to estimate oil in the water column at a given time with the wind speed and oil viscosity. The models indicated that the most important factor related to the amount of dispersion, was the mixing depth of the sea as predicted from wind speed. The second most important factor was the viscosity of the starting oil. The algorithm predicted the maximum viscosity that would be dispersed given wind conditions. Simplified prediction equations were created using regression.

中文翻译：

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化学分散溢油算法的开发

描述了一种利用四个基本过程的算法用于化学溢油分散。初始分散是使用调整为化学分散的修正 Delvigne 方程计算的，然后分散在混合深度上分布，如波浪高度所预测的那样。然后液滴根据斯托克斯定律上升到表面。表面上的油，来自上升的油和未分散的油，被重新分散。根据 Smoluchowski 方程，水柱中的液滴会发生聚结。调用损失来解释缓慢上升且未与主表面浮油重新结合的小液滴的产生。随着时间的推移，由于风化导致的粘度增加，以及聚结导致液滴尺寸增加，液滴变得更难分散。由于尺寸增加，这些液滴随着时间的推移上升得更快。提供了封闭形式的解决方案，以在给定油粘度和风速输入的情况下允许分散性的实际限制。给出离散解来计算指定时间点水柱中的油量。提供了回归方程以在给定时间用风速和油粘度估计水柱中的油。模型表明，与分散量相关的最重要因素是根据风速预测的海洋混合深度。第二个最重要的因素是起始油的粘度。该算法预测了在给定风力条件下将被分散的最大粘度。使用回归创建了简化的预测方程。提供了封闭形式的解决方案，以在给定油粘度和风速输入的情况下允许分散性的实际限制。给出离散解来计算指定时间点水柱中的油量。提供了回归方程以在给定时间用风速和油粘度估计水柱中的油。模型表明，与分散量相关的最重要因素是根据风速预测的海洋混合深度。第二个最重要的因素是起始油的粘度。该算法预测了在给定风力条件下将被分散的最大粘度。使用回归创建了简化的预测方程。提供了封闭形式的解决方案，以在给定油粘度和风速输入的情况下允许分散性的实际限制。给出离散解来计算指定时间点水柱中的油量。提供了回归方程以在给定时间用风速和油粘度估计水柱中的油。模型表明，与分散量相关的最重要因素是根据风速预测的海洋混合深度。第二个最重要的因素是起始油的粘度。该算法预测了在给定风力条件下将被分散的最大粘度。使用回归创建了简化的预测方程。