Operating hydro turbines in off-design conditions increases the risk of cavitation occurrence, which in turn, leads to numerous problems such as performance degradations, structural vibrations, and most importantly, mechanical damage due to erosion. It is therefore crucial to develop a monitoring system that detects the occurrence and severity of cavitation in real time. For this purpose, a cavitation detection methodology has been developed that is based on the analysis of acoustic emissions of a turbine with machine learning algorithms. In this method, a conventional microphone is used to record the airborne noise emitted from a turbine under different working conditions, and then, a supervised learning algorithm is trained to classify the recorded noise signals into cavitating and non-cavitating categories. The detection system was developed based on laboratory tests and was validated in Ernen hydropower plant located in Canton of Wallis in southeast of Switzerland. This power plant consists of two identical double-flux Francis turbines each having a maximum power of 16 MW and a net head of 270 mWC. The preliminary results obtained from a two-day experimental campaign in the Ernen powerplant are very promising in terms of cavitation detection with a classification accuracy of more than 90 %. The system could be implemented either for real-time monitoring of cavitation occurrence allowing the operators to avoid such a condition or as a post processing tool to evaluate the number of hours a turbine has worked under severe conditions. Work is still ongoing to deploy more complex learning algorithms for this task to minimize expert intervention and/or interpretation during the setup process.
Based on previous experiences which have proven the efficiency of PCM heat exchangers for air temperature control, we designed and simulated new PCM heat exchanger structures made of multiple PCM layers sandwiched between loading and discharge layers. By circulating air or water in the discharge circuit, these heat exchangers can be used for heating air or water, respectively. For both use cases, a three-dimensional analysis of the phase change and calculations of the charge/discharge powers were performed for the fusion and solidification processes. We obtained heating discharge powers ≥ 2.6 kW/m3 for 8 hours for air and ≥ 65 kW/m3 for 13 minutes for water with a respective total storage capacity of 28 kWh/m3 and 37 kWh/m3. The heat extraction of air and water flows and their time dependence are discussed according to the percentage of liquid PCM and the temperature profile of the discharge flow inside the heat exchanger.
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