Finite Element Modeling of Iceberg Interaction with Subsea Protection Structures
Bibliographic record
Abstract
Abstract Ice feature interaction with subsea infrastructure or the seabed is acomplex nonlinear event, for which many analytical and advanced computationaltools have been developed with demonstrated application. Although subsea fieldshave been developed in ice gouge environments, such as the Grand Banks, consideration of alternative methods for protecting subsea infrastructure is ofgreat importance. A more in-depth understanding of ice feature mechanicalbehavior and interaction with subsea infrastructure is required. For various iceberg shapes and loading conditions, the finite element modelspresented in this paper examine the interaction of free-floating ice featureswith protective structures located above or partially above the mudline. Apreliminary assessment of an interaction scenario involving a gouging icebergkeel with a buried protection structure is also presented. The outcome of thisstudy enhances understanding of the primary factors to be considered for thedesign of protection structures in ice environments and highlights some of thetechnical issues associated with the development and calibration of advancedsimulation tools. Introduction For conventional design scenarios, protection schemes have been developedfor pipelines and subsea wellheads that include rock placement, mattresses, andstructural frames comprised of steel or concrete (e.g. Alexander, 2008; Berntand Smedsrud, 2007; Copsey and Johnson, 1993; Faden et al., 1980; Figenschouand Wikdal, 1992; Kirkbride and Bloomer, 1994). Ice keel/seabed reactionforces, however, can be an order of magnitude greater than other pipelineloading events such as anchor dragging and pullover (Kenny et al., 2007a, b;Palmer et al., 1990). From this perspective, for subsea infrastructure such aspipelines and wellheads, positioned above or buried beneath the mudline, freelyfloating ice keel contact and seabed ice gouging events have the potential tocause damage or failure of these systems. For large field developments, where technical, risk and economic factorsdemonstrate project viability, subsea facilities can be housed withinprotective offshore structures (e.g. Hibernia or Sakhalin gravity baseplatforms), or excavated drill centers (EDC), such as those used on the TerraNova and White Rose fields on the Canadian Grand Banks (e.g. Allen, 2011;Finch, 1998; Lever and Dunsmore, 2011; Norman et al., 2008). The primaryconcept for subsea protection was to place the infrastructure within an openexcavation within the seabed or within a drilled, cylindrical counter boredepression below the mudline. These ideas evolved in response to the drillingoperations and potential development of hydrocarbon resources in the BeaufortSea (e.g. Logsdon and Field, 1983; McKay et al., 1995; Meadows and Gilbert,1989; Shields, 1994; Stewart and Goldby, 1984; Todd, 1978).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".