Evaluation of the p-y Method in the Design of Monopiles for Offshore Wind Turbines
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Bibliographic record
Abstract
Abstract Offshore wind has enormous potential worldwide to generate a vast quantity of renewable energy but cost efficiencies are required to enable it to be competitive with other energy sources. Monopile foundations, currently designed using the p-y method, are technically viable in supporting larger offshore wind turbines in waters to a depth of 30 m. In this paper, the lateral load-deformation behaviour of two monopiles, 5.0 m and 7.5 m in diameter, installed in soft clays of varying undrained shear strength and stiffness, is investigated using three-dimensional finite element analysis. A combination of axial and lateral loads expected at an offshore wind farm location with a water depth of 30 m has been used in the analysis. Monopile mode of deformation will be assessed principally in terms of its lateral displacement, rotation and bending moments, and the lateral soil resistance mobilised in the clay around it. In addition, comparisons will be made between p-y curves back-calculated from the finite element analyses and those derived using the conventional p-y formulation for soft clay. Through this appraisal, it will be be shown that the p-y method for soft clays, as prescribed in current offshore design codes, over-estimates monopile lateral displacement and rotation at mudline thus under-estimating its lateral capacity and yielding an overly conservative design. Introduction As a proven source of clean, renewable and affordable energy, wind, both onshore and offshore, is vital in achieving a future energy supply that is secure, environmentally friendly and less dependent on fossil fuels. Of the two, offshore wind is considered to be more attractive not only because of reduced noise and visual impact but also due to the availability offshore of large continuous areas, consistent and higher wind speeds, lower wind turbulence and less wind shear. Offshore wind energy, which is a ‘blue’ energy by virtue of it being harnessed in the ocean, is key to the EU realising its target of generating 20% of its energy from renewable sources by 2020. Installation of offshore wind capacity is progressing at an exponential rate, with the EU capacity predicted to grow from the current 3.8 GW to 150 GW in 2030 which would reduce carbon dioxide emissions by 315 million tonnes and meet 14% of the EU electricity demand (EWEA 2011) (EWEA 2012a). In the UK, with planned investment to the tune of £75 billion over the next ten years, offshore wind is the biggest technological opportunity since the pioneering days of offshore oil and gas. Although the EU is expected to lead the way with over three-quarters of the forecast global offshore wind capacity, other countries such as the United States, Canada, China, India and South Korea are also planning to invest in offshore wind farms (GWEC 2012). However, to best capitalise on offshore wind energy, it is vital that the whole life cycle cost of offshore wind projects reduces to enable it to be competitive with alternative sources of energy such as coal, gas, onshore wind and nuclear. In the UK, as illustrated in Figure 1, the Government has set a target of reducing the lifetime cost of an offshore wind farm per unit of energy by 28.6%, from the current £140/MWh to £100/MWh, by the year 2020 to make offshore wind cost-comparable with these other energy sources (The Crown Estate 2012). A breakdown of the £140/MWh levelised cost, given in Figure 2, indicates that the cost of the support structure and foundation accounts for approximately 13% of the total cost of a typical offshore wind farm. Hence, to achieve significant cost savings, the design of foundations for offshore wind turbines need be cost efficient without being excessively conservative.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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 it