Turbulence intensity within large offshore wind farms

The so-called Frandsen model forms the basis for the assessment of wind farm level turbulence intensity (TI) in the IEC standard 61400-1 edition 3. It is used in the choice of turbine suitable for a particular wind farm site. The Frandsen model was developed several years ago using field data when turbines and wind farms were of smaller scale than today. There is now an interest in the accuracy of models such as that of Frandsen when applied to the scale of the largest offshore wind farms. In this paper, we present the results of an analysis of the accuracy of the Frandsen model in predicting TI within the Greater Gabbard offshore wind farm. A comparison is made between measured data and predictions from: 1) the original Frandsen model; 2) a simplified version of the Frandsen model and 3) output from the ANSYS WindModeller CFD model. In general, the Frandsen model was found to perform well in the prediction of mean levels of TI but less well than a simplified model using either a freestream ambient TI or a turbine wake TI regardless of distance. Representative or 90% percentile TI levels are less well predicted under direct wake conditions due to the lack of consideration of turbine generated variance in turbulence and the manner in which the 90% percentile freestream TI is incorporated. ANSYS WindModeller was found to perform well in the prediction of mean TI and has the benefit of not requiring upstream TI data. The CFD model can be used to predict representative TI, when complemented with a model for the variance of turbulence. Predictions from the Frandsen model are more sensitive to the choice of freestream data than those from the CFD model

Sequence_Data

All sequence data collected for this project, named based on the gene

ESM4_scripts

R and python script

alnusBarcodes

Barcodes to process ddRAD dat

Alnus_R2

Alnus rubra paired-end ddRadseq; R2 reads. fastq.gz file ~2.95G

Alnus_R1

Alnus rubra paired-end ddRadseq; R1 reads. fastq.gz file ~2.95G

GeneTree_DataFiles.tar

GeneTree_DataFiles.ta

Additional file 1: of Trends in genital warts by socioeconomic status after the introduction of the national HPV vaccination program in Australia: analysis of national hospital data

Supplementary data. Table S1. Summary of procedure and diagnosis codes used to define subcategories. Table S2 Admission rates & estimated post-vaccination reductions, by sex, age, and sociodemographic features – sensitivity analyses. Figure S1. Admission rate ratio (admission rate in July 2010–June 2011 relative to three-year prevaccination mean (July 2004–June 2007)) estimated from the full dataset, SES subset, and remoteness area subset, by age and sex. (DOCX 34 kb

Comparison of the number of cervical cancer cases with the number of women who received treatment for precancer predicted for each strategy.

<p>Numbers shown on the chart represent the number of additional treatments required per cancer case prevented compared to current practice. The number of additional treatments required per cancer case prevented ratio was calculated for each strategy with a higher number precancer treatments and lower number of cervical cancer cases than CP. The calculated ratio is display in the figure on side of the marker that represents the strategy.</p

Modelled screening pathways of (a) CP, (b) S1a, (c) S2a, (d) S3a and (e) S4a. Coloured boxes indicate variations in other sub-strategies assessed.

<p>HG- High-grade (including ASC-H and HSIL); HR HPV– high risk HPV; LG –low-grade (including ASC-US and LSIL); Neg—Negative; OHR HPV- non-16/18 high -risk HPV</p