This study presents a procedure to reduce the uncertainty of wind power density estimations, which is useful to improve the energy production predictions of wind farms. Power density is usually determined from the wind speed measured by a cup anemometer and the air density value (conventional procedure). An alternative procedure based on wind speed and dynamic pressure estimations provided by a cup anemometer is proposed. The dynamic pressure is obtained by means of a calibration curve that relates the anemometer rotation frequency and the dynamic pressure measured by a Pitot tube. The quadratic regression, used to define the calibration curve, and its uncertainty are both detailed. A comparison between the alternative procedure and the conventional one points out the advantage of the proposed alternative since results show a high reduction of the indirect measurement uncertainty of wind power density.

Cutting force and cutter-workpiece engagement (CWE) evolve similarly during 2.5D milling operations. Therefore, CWE is related with the efficiency of a pocket machining. We propose an algorithm to approximate CWE by means of the engagement arc length. This arc is determined by the intersection between a disk (the end mill), and the machined area obtained by Boolean operations. Our procedure is easy to implement, and the computed CWE behaves as the cutting force measured in real tests.