Four ensembles of TAMOC integral oil-gas plume simulations for comparison of uncertainty quantification techniques

Model data used for a comparison of uncertainty quantification techniques, including polynomial chaos and gaussian process regression. This data set consists of oil plume ensemble simulations generated using Texas A&M oil calculator (TAMOC, version 0.1.5, https://github.com/socolofs). We quantify the uncertainty of the model outputs caused by the uncertainty of 5 model inputs parameters in this experiment, including entrainment coefficient, entrainment ratio, gas to oil ratio, 95 percentile of droplet size (d95) and droplet size distribution spreading ratio. We only select the plume trap height and peel height as our model outputs. This dataset supports the following publication: Iskandarani, M., Wang, S., Srinivasan, A., Carlisle Thacker, W., Winokur, J., & Knio, O. M. (2016). An overview of uncertainty quantification techniques with application to oceanic and oil-spill simulations. Journal of Geophysical Research: Oceans, 121(4), 2789–2808. doi:10.1002/2015jc011366本数据集用于不确定性量化技术的对比研究，涵盖多项式混沌（polynomial chaos）与高斯过程回归（gaussian process regression）两类方法。本数据集包含使用得克萨斯农工大学石油计算器（Texas A&M Oil Calculator, TAMOC，版本0.1.5，https://github.com/socolofs）生成的油羽集合模拟数据。本实验针对5个模型输入参数的不确定性所引发的模型输出不确定性开展量化分析，这5个参数分别为卷吸系数、卷吸比、气油比、液滴尺寸95百分位数（d95）以及液滴尺寸分布扩展比。本实验仅选取羽流捕获高度与剥离高度作为模型输出变量。本数据集支持下述学术出版物：Iskandarani M、Wang S、Srinivasan A、Carlisle Thacker W、Winokur J与Knio O M于2016年发表于《地球物理研究杂志：海洋》（Journal of Geophysical Research: Oceans）第121卷第4期，页码范围2789–2808的论文《不确定性量化技术综述及其在海洋与溢油模拟中的应用》，其DOI为10.1002/2015jc011366。