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Without proper post-processing (often using flame, furnace, laser remelting, and induction) or reinforcements’ addition, Ni-based flame-sprayed coatings generally manifest moderate adhesion to the substrate, high porosity, unmelted particles, undesirable oxides, or weak wear resistance and mechanical properties. The current research aimed to investigate the addition of ZrO2 as reinforcement to the self-fluxing alloy coatings. Mechanically mixed NiCrBSi-ZrO2 powders were thermally sprayed onto an industrially relevant high-grade steel. After thermal spraying, the samples were differently post-processed with a flame gun and with a vacuum furnace, respectively. Scanning electron microscopy showed a porosity reduction for the vacuum-heat-treated samples compared to that of the flame-post-processed ones. X-ray diffraction measurements showed differences in the main peaks of the patterns for the thermal processed samples compared to the as-sprayed ones, these having a direct influence on the mechanical behavior of the coatings. Although a slight microhardness decrease was observed in the case of vacuum-remelted samples, the overall low porosity and the phase differences helped the coating to perform better during wear-resistance testing, realized using a ball-on-disk arrangement, compared to the as-sprayed reference samples.
This paper presents a pragmatic approach for stepwise introduction of peer assessment elements in undergraduate programming classes, discusses some lessons learned so far and directions for further work. Students are invited to challenge their peers with their own programming exercises to be submitted through Moodle and evaluated by other students according to a predefined rubric and supervised by teaching assistants. Preliminary results show an increased activation and motivation of students leading to a better performance in the final programming exams.
Article 135 TFEU
(2023)
Article 134 TFEU
(2023)
Advanced Determination of Temperature Coefficients of Photovoltaic Modules by Field Measurements
(2023)
In this work data from outdoor measurements, acquired over the course of up to three years on commercially available solar panels, is used to determine the temperature coefficients and compare these to the information as stated by the producer in the data sheets. A program developed in MatLab App Designer allows to import the electrical and ambient measurement data. Filter algorithms for solar irradiance narrow the irradiance level down to ~1000 W/m2 before linear regression methods are applied to obtain the temperature coefficients. A repeatability investigation proves the accuracy of the determined temperature coefficients which are in good agreement to the supplier specification if the specified values for power are not larger than -0.3%/K. Further optimization is achieved by applying wind filter techniques and days with clear sky condition. With the big (measurement) data on hand it was possible to determine the change of the temperature coefficients for varying irradiance. As stated in literature we see an increase of the temperature coefficient of voltage and a decline for the temperature coefficient of power with increasing irradiance.
In this work a mathematical approach to calculate solar panel temperature based on measured irradiance, temperature and wind speed is applied. With the calculated module temperature, the electrical solar module characteristics is determined. A program developed in MatLab App Designer allows to import measurement data from a weather station and calculates the module temperature based on the mathematical NOCT and stationary approach with a time step between the measurements of 5 minutes. Three commercially available solar panels with different cell and interconnection technologies are used for the verification of the established models. The results show a strong correlation between the measured and by the stationary model predicted module temperature with a coefficient of determination R2 close to 1 and a root mean square deviation (RMSE) of ≤ 2.5 K for a time period of three months. Based on the predicted temperature, measured irradiance in module plane and specific module information the program models the electrical data as time series in 5-minute steps. Predicted to measured power for a time period of three months shows a linear correlation with an R2 of 0.99 and a mean absolute error (MAE) of 3.5, 2.7 and 4.8 for module ID 1, 2 and 3. The calculated energy (exemplarily for module ID 2) based on the measured, calculated by the NOCT and stationary model for this time period is 118.4 kWh, resp. 116.7 kWh and 117.8 kWh. This is equivalent to an uncertainty of 1.4% for the NOCT and 0.5% for the stationary model.