Propagation of uncertainty and sensitivity analysis in an integral oil‐gas plume model. Issue 5 (27th May 2016)

• Record Type: Journal Article
• Authors: Wang, Shitao; Iskandarani, Mohamed; Srinivasan, Ashwanth; Thacker, W. Carlisle; Winokur, Justin; Knio, Omar M.
• Is Part Of: Journal of geophysical research. Volume 121:Issue 5(2016:May)
• Page Start: 3488
• Page End: 3501
• Publication Date: 2016-05-27
• Subjects: uncertainty quantification -- polynomial chaos -- integral plume model -- sensitivity analysis -- deep water horizon; Oceanography -- Periodicals; 551.4605
• DOI: 10.1002/2015JC011365
• Languages: English
• ISSNs: 2169-9275

Abstract

Abstract: Polynomial Chaos expansions are used to analyze uncertainties in an integral oil‐gas plume model simulating the Deepwater Horizon oil spill. The study focuses on six uncertain input parameters—two entrainment parameters, the gas to oil ratio, two parameters associated with the droplet‐size distribution, and the flow rate—that impact the model's estimates of the plume's trap and peel heights, and of its various gas fluxes. The ranges of the uncertain inputs were determined by experimental data. Ensemble calculations were performed to construct polynomial chaos‐based surrogates that describe the variations in the outputs due to variations in the uncertain inputs. The surrogates were then used to estimate reliably the statistics of the model outputs, and to perform an analysis of variance. Two experiments were performed to study the impacts of high and low flow rate uncertainties. The analysis shows that in the former case the flow rate is the largest contributor to output uncertainties, whereas in the latter case, with the uncertainty range constrained by aposteriori analyses, the flow rate's contribution becomes negligible. The trap and peel heights uncertainties are then mainly due to uncertainties in the 95% percentile of the droplet size and in the entrainment parameters. Key Points: Uncertainties in trap and peel height are quantified via their probability density functions The dominant contributors to output uncertainties are identified via analysis of variance When the flow rate uncertainty is constrained, the droplet size dominates the output uncertainties