An experimental and numerical study of a three-lobe pump for pumped hydro storage applications

Pumped hydro storage (PHS) plays an important role as a matured technology that accounts for the vast majority of global energy storage capacity, and its expansion is therefore desirable. The expansion of PHS in mid- and high-water heads is limited to topographic features, but there is an untapped potential in low-head applications. For most of the PHS applications, a Francis reversible pump-turbine (RPT) is regarded as the most common and cost-effective machine, but it is not a suitable option for water heads of less than 30m. In its place, positive displacement machines like lobe pumps could potentially work as RPT machines and unleash new possibilities for low-head pumped hydroelectric storage. In addition, unlike bladed pumpturbines, lobe pumps-turbines present a fish friendliness design, an important attribute to preserve the aquatic wildlife. This work will therefore present a three-lobe pump that could potentially be used in low-head PHS. An experimental model for a lobe machine will be presented, and its results will be used to validate the computational fluid dynamic simulations. Numerical investigations will address the characteristic curves regarding water-head, rotation speed and flow rate. © 2023 Institute of Physics Publishing. All rights reserved.An experimental and numerical study of a three-lobe pump for pumped hydro storage applicationspublishedVersio

Analysis of coffee leaf rust epidemics with decision tree

A decision tree was developed to aid the understanding of coffee rust epidemics caused by Hemileia vastatrix. Infection rates calculated from monthly assessments of rust incidence were grouped into three classes: reduction or stagnation - TX1; moderate growth (up to 5pp) - TX2; and accelerated growth (above 5pp) - TX3. Meteorological data, expected yield and space between plants were used as explanatory variables for the infection rate classes. The decision tree was trained using 364 examples prepared from data collected in coffee-growing areas between October 1998 and October 2006. The model correctly classified 78% of the training data set and its accuracy was estimated at 73% for the classification of new examples. The success rates of the model were 88%, 57% and 79%, respectively, for the infection rate classes TX1, TX2 and TX3. The most important explanatory variables were mean temperature during leaf wetness periods, expected yield, mean of maximum temperatures during the incubation period and relative air humidity. The decision tree demonstrated its potential as a symbolic and interpretable model. Its model representation identified the existing decision boundaries in the data and the logic underlying them, helping to understand which variables, and interactions between these variables, led to coffee rust epidemics in the field.Uma árvore de decisão foi desenvolvida com o objetivo de auxiliar na compreensão de manifestações epidêmicas da ferrugem do cafeeiro causada por Hemileia vastatrix. Taxas de infecção calculadas a partir de avaliações mensais de incidência da ferrugem foram agrupadas em três classes: redução ou estagnação - TX1; crescimento moderado (até 5p.p.) - TX2; e crescimento acelerado (acima de 5p.p.)- TX3. Dados meteorológicos, carga pendente de frutos do cafeeiro (Coffea arabica) e espaçamento entre plantas foram usados como variáveis explicativas das classes de taxa de infecção. A árvore de decisão foi treinada com 364 exemplos preparados a partir de dados coletados em lavouras de café em produção, de outubro de 1998 a outubro de 2006. Ela classificou corretamente 78% do conjunto de treinamento e a sua acurácia foi estimada em 73% para a classificação de novos exemplos. O acerto do modelo foi de 88%, 57% e 79% dos exemplos, respectivamente, para as classes de taxa de infecção TX1, TX2 e TX3. As variáveis explicativas mais importantes foram a temperatura média nos períodos de molhamento foliar, a carga pendente de frutos, a média das temperaturas máximas diárias no período de incubação e a umidade relativa do ar. A árvore de decisão demonstrou seu potencial como modelo de representação simbólica e interpretável, permitindo a identificação das fronteiras de decisão existentes nos dados e da lógica contida neles, auxiliando na compreensão de quais variáveis e como as interações dessas variáveis conduziram as epidemias da ferrugem do cafeeiro no campo.11412