Nowadays Pumps working as Turbines (PaT) are devices widely used to perform energy recovery in hydraulic grids, thus improving their overall efficiency, and to build small hydropower plants. In this work, a centrifugal pump has been numerically investigated in turbine operating mode by means of the open-source CFD code OpenFOAM with emphasis on the flow field at the runner outlet. Due to the reduced number of blades in a PaT, the mean outlet relative velocity angle differs from the blade angle. In order to account for this phenomenon, the slip factor is introduced. The slip factor is investigated and its application to a 1D model is shown in order to highlight the improvement in predicting the characteristic curve of a centrifugal pump used in reverse mode as a turbine (PaT) especially at its part-load.

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District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, prolonging the investment return period. The main scope of this paper is to assess the feasibility of using the heat demand -outdoor temperature function for heat demand forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations.

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