Recent offshore oil and gas loading facilities developed in the Arctic area have led to a considerable awareness of the iceberg draft approximation, where deep keel icebergs may gouge the ocean floor, and these submar...Recent offshore oil and gas loading facilities developed in the Arctic area have led to a considerable awareness of the iceberg draft approximation, where deep keel icebergs may gouge the ocean floor, and these submarine infrastructures would be damaged in the shallower waters. Developing reliable solutions to estimate the iceberg draft requires a profound understanding of the problem’s dominant parameters. As such, the dimensionless groups of the parameters affecting the iceberg draft estimation were determined for the first time in the present study. Using the dimensionless groups recognized and the linear regression(LR) analysis, nine LR models(i.e., LR 1 to LR 9) were developed and then validated using a comprehensive dataset, which has been constructed in this study. A sensitivity analysis distinguished the premium LR models and important dimensionless groups. The best LR model, as a function of all dimensionless parameters, was able to estimate the iceberg draft with the highest level of precision and correlation along with the lowest degree of complexity. The ratio of iceberg length to iceberg height as the “iceberg length ratio” and the ratio of iceberg width to iceberg height as the “iceberg width ratio” was detected as the important dimensionless groups in the estimation of the iceberg draft. An uncertainty analysis demonstrated that the best LR model was biased towards underestimating the iceberg drafts. The premium LR model outperformed the previous empirical models.Ultimately, a set of LR-based relationships were derived for estimating the iceberg drafts for practical engineering applications, e.g., the early stages of the iceberg management projects.展开更多
基金the financial support of “Wood Group”, which established a Research Chair program in the Arctic and Harsh Environment Engineering at the Memorial University of Newfoundlandthe “Natural Science and Engineering Research Council of Canada (NSERC)”the “Newfoundland Research and Development Corporation (RDC) (now TCII)” through “Collaborative Research and Developments Grants (CRD)”。
文摘Recent offshore oil and gas loading facilities developed in the Arctic area have led to a considerable awareness of the iceberg draft approximation, where deep keel icebergs may gouge the ocean floor, and these submarine infrastructures would be damaged in the shallower waters. Developing reliable solutions to estimate the iceberg draft requires a profound understanding of the problem’s dominant parameters. As such, the dimensionless groups of the parameters affecting the iceberg draft estimation were determined for the first time in the present study. Using the dimensionless groups recognized and the linear regression(LR) analysis, nine LR models(i.e., LR 1 to LR 9) were developed and then validated using a comprehensive dataset, which has been constructed in this study. A sensitivity analysis distinguished the premium LR models and important dimensionless groups. The best LR model, as a function of all dimensionless parameters, was able to estimate the iceberg draft with the highest level of precision and correlation along with the lowest degree of complexity. The ratio of iceberg length to iceberg height as the “iceberg length ratio” and the ratio of iceberg width to iceberg height as the “iceberg width ratio” was detected as the important dimensionless groups in the estimation of the iceberg draft. An uncertainty analysis demonstrated that the best LR model was biased towards underestimating the iceberg drafts. The premium LR model outperformed the previous empirical models.Ultimately, a set of LR-based relationships were derived for estimating the iceberg drafts for practical engineering applications, e.g., the early stages of the iceberg management projects.
