INVESTIGATING THE MALFUNCTION OF A HYDRO- GENERATOR'S COOLING-SYSTEM
Electro-magnetic losses within a hydro-generator are sources of heat which heat the generator, and then have to be conducted away. This may, however, cause a temperature rise of the stator bars thus reducing the life-span of the insulation materials. Therefore, any temperature rise of the stator windings is limited. In order to inspect the operation of a cooling-system, several temperature sensors have to be integrated into the stator core in order to measure its temperature, which should not exceed 80 0 C. Temperature measurements performed on a generator showed some deviations from the design temperatures, especially at full-power operation. Thus, an attempt was made to analyse the operation of a cooling-system, and to eventually increase its cooling capacity. It was impossible to perform any complex measurements during the generator’s operation, due to safety reasons. Therefore, our work was limited to 3D CFD simulations using ANSYS CFX 11 solver. The simulations were partly supported by the measurements of some main integral parameters, such as mass-flow rate and pressure drop across the cooling ducts and radiators, as performed during generator idling. The results are presented and discussed in this paper.
Computational determination of convective heat transfer and pressure drop coefficients of hydrogenerators ventilation system.
The objective of the present work is to determinate the pressure drop and the heat transfer coefficients, normally applied to analytical calculations of hydrogenerators thermal design, obtained by applying numerical calculation (Computational Fluid Dynamics – CFD) and validated by experimental results and field measurements. The object of study is limited to the most important region of the ventilation system (the cooling air ducts of stator core) to get numerical results of heat transfer and pressure drop coefficients, which are impacted mostly by the entrance of air ducts. The numerical calculations considered three-dimensional, steady-state, incompressible and turbulent flow; and were based on the Finite Volume methodology. The turbulent flow computations were carried out with procedures based on RANS equations by selecting k-omega SST (Shear-Stress Transport) as turbulence model. Grid quality metrics were monitored and the uncertainties due to discretization errors were evaluated by means of a grid independence study and application of an uncertainty estimation procedure based on Richardson extrapolation. The validation of numerical method developed by the present work (specifically to simulate the flow dynamics behavior and to obtain numerically the pressure drop coefficient of the airflow to enter and pass through the Stator Core Air Duct in a hydrogenerator) is performed by comparing the numerical results to experimental data published by Wustmann (2005). The reference experimental data were obtained by a model test. The comparison between numerical and experimental results shows that the difference of pressure drop for Reynolds numbers higher than 5000 is 2% at maximum, while for lower Reynolds numbers, the difference increases significantly and reaches 10%. It is presented that the most reasonable hypothesis for higher discrepancy at lower Reynolds numbers can be assigned to the experiment’s non-steady-state condition. It is to conclude that the proposed numerical method is validated for the upper region of the analyzed range. Additionally to the model test validation, field measurements were executed in order to confirm numerical results. Measurements of pressure drop in the stator core of a real hydrogenerator were a challenge. Nevertheless, despite all the difficulties and considerable high field measuring uncertainties, trend curves behavior are similar to numerical results. Finally, series of numerical calculation, varying geometrical parameters of the air-duct inlet design and operational data, were done in order to obtain pressure drop coefficients trend curves to be directly applied to analytical calculation routines of whole hydrogenerator ventilation systems. Parallel to it, thermal numerical calculation was executed in the prototype simulation in order to define the convective heat transfer coefficient.
Towards infield, live plant phenotyping using a reduced-parameter CNN
There is an increase in consumption of agricultural produce as a result of the rapidly growing human population, particularly in developing nations. This has triggered high-quality plant phenotyping research to help with the breeding of high-yielding plants that can adapt to our continuously changing climate. Novel, low-cost, fully automated plant phenotyping systems, capable of infield deployment, are required to help identify quantitative plant phenotypes. The identification of quantitative plant phenotypes is a key challenge which relies heavily on the precise segmentation of plant images. Recently, the plant phenotyping community has started to use very deep convolutional neural networks (CNNs) to help tackle this fundamental problem. However, these very deep CNNs rely on some millions of model parameters and generate very large weight matrices, thus making them difficult to deploy infield on low-cost, resource-limited devices. We explore how to compress existing very deep CNNs for plant image segmentation, thus making them easily deployable infield and on mobile devices. In particular, we focus on applying these models to the pixel-wise segmentation of plants into multiple classes including background, a challenging problem in the plant phenotyping community. We combined two approaches (separable convolutions and SVD) to reduce model parameter numbers and weight matrices of these very deep CNN-based models. Using our combined method (separable convolution and SVD) reduced the weight matrix by up to 95% without affecting pixel-wise accuracy. These methods have been evaluated on two public plant datasets and one non-plant dataset to illustrate generality. We have successfully tested our models on a mobile device.