Computational study of the vortex path variation with the VG height

An extensive range of conventional, vane-type, passive vortex generators (VGs) are in use for successful applications of flow separation control. In most cases, the VG height is designed with the same thickness as the local boundary layer at the VG position. However, in some applications, these conventional VGs may produce excess residual drag. The so-called low-profile VGs can reduce the parasitic drag associated to this kind of passive control devices. As suggested by many authors, low-profile VGs can provide enough momentum transfer over a region several times their own height for effective flow-separation control with much lower drag. The main objective of this work is to study the variation of the path and the development of the primary vortex generated by a rectangular VG mounted on a flat plate with five different device heights h = delta, h(1) = 0.8 delta, h(2) = 0.6 delta, h(3) = 0.4 delta and h(4) = 0.2 delta, where delta is the local boundary layer thickness. For this purpose, computational simulations have been carried out at Reynolds number Re = 1350 based on the height of the conventional VG h = 0.25m with the angle of attack of the vane to the oncoming flow beta = 18.5 degrees. The results show that the VG scaling significantly affects the vortex trajectory and the peak vorticity generated by the primary vortex.

Dividing population genetic distance data with the software Partitioning Optimization with Restricted Growth Strings (PORGS): an application for Chinook salmon (Oncorhynchus tshawytscha), Vancouver Island, British Columbia

A new method of finding the optimal group membership and number of groupings to partition population genetic distance data is presented. The software program Partitioning Optimization with Restricted Growth Strings (PORGS), visits all possible set partitions and deems acceptable partitions to be those that reduce mean intracluster distance. The optimal number of groups is determined with the gap statistic which compares PORGS results with a reference distribution. The PORGS method was validated by a simulated data set with a known distribution. For efficiency, where values of n were larger, restricted growth strings (RGS) were used to bipartition populations during a nested search (bi-PORGS). Bi-PORGS was applied to a set of genetic data from 18 Chinook salmon (Oncorhynchus tshawytscha) populations from the west coast of Vancouver Island. The optimal grouping of these populations corresponded to four geographic locations: 1) Quatsino Sound, 2) Nootka Sound, 3) Clayoquot +Barkley sounds, and 4) southwest Vancouver Island. However, assignment of populations to groups did not strictly reflect the geographical divisions; fish of Barkley Sound origin that had strayed into the Gold River and close genetic similarity between transferred and donor populations meant groupings crossed geographic boundaries. Overall, stock structure determined by this partitioning method was similar to that determined by the unweighted pair-group method with arithmetic averages (UPGMA), an agglomerative clustering algorithm.

Biological parameters, body length-weight and length-height relationships for various species in Greek waters

Biological/fisheries parameters (L sub(oo) M, F) are presented for four fish species (Gadiculus argenteus; Gaidropsarus mediterraneous; Symphurus ligulatus; Lepidorhombus boscii) as well as body length-weight and length-height relationships for 11 and 12 fish species, respectively, estimated from trawl samples collected using three different cod-ends (stretched mesh size: 14 mm and 20 mm diamond-shaped and 20 mm square-shaped) during 1993-1994, in the western Aegean and North Euboikos Gulf, Greece. The fisheries paramaters, estimated from length-frequency using the ELEFAN approach and software, are discussed in the light of recent information on the selectivity of the presently used trawl cod-end (14 mm diamond shaped)

Filtragem adaptativa aplicada à redução de desvios em séries temporais de previsão numérica climática.

Esta dissertação apresenta resultados da aplicação de filtros adaptativos, utilizando os algoritmos NLMS (Normalized Least Mean Square) e RLS (Recursive Least Square), para a redução de desvios em previsões climáticas. As discrepâncias existentes entre o estado real da atmosfera e o previsto por um modelo numérico tendem a aumentar ao longo do período de integração. O modelo atmosférico Eta é utilizado operacionalmente para previsão numérica no CPTEC/INPE e como outros modelos atmosféricos, apresenta imprecisão nas previsões climáticas. Existem pesquisas que visam introduzir melhorias no modelo atmosférico Eta e outras que avaliam as previsões e identificam os erros do modelo para que seus produtos sejam utilizados de forma adequada. Dessa forma, neste trabalho pretende-se filtrar os dados provenientes do modelo Eta e ajustá-los, de modo a minimizar os erros entre os resultados fornecidos pelo modelo Eta e as reanálises do NCEP. Assim, empregamos técnicas de processamento digital de sinais e imagens com o intuito de reduzir os erros das previsões climáticas do modelo Eta. Os filtros adaptativos nesta dissertação ajustarão as séries ao longo do tempo de previsão. Para treinar os filtros foram utilizadas técnicas de agrupamento de regiões, como por exemplo o algoritmo de clusterização k-means, de modo a selecionar séries climáticas que apresentem comportamentos semelhantes entre si. As variáveis climáticas estudadas são o vento meridional e a altura geopotencial na região coberta pelo modelo de previsão atmosférica Eta com resolução de 40 km, a um nível de pressão de 250 hPa. Por fim, os resultados obtidos mostram que o filtro com 4 coeficientes, adaptado pelo algoritmo RLS em conjunto com o critério de seleção de regiões por meio do algoritmo k-means apresenta o melhor desempenho ao reduzir o erro médio e a dispersão do erro, tanto para a variável vento meridional quanto para a variável altura geopotencial.