Measurement of Collision Conditions in Magnetic Pulse Welding Processes

MPW （magnetic pulse welding） is a solid state joining technology that allows for the generation of strong metallic bonds, even between dissimilar metals. Due to the absence of external heat, critical intermetallic phases can largely be avoided. In this process, Lorentz forces are utilized for the rapid acceleration of at least one of the two metallic joining partners leading to the controlled high velocity impact between them. The measurement of the collision conditions and their targeted manipulation are the key factors of a successful process development. Optical measuring techniques are preferred, since they are not influenced by the prevalent strong magnetic field in the vicinity of the working coil. In this paper, the characteristic high velocity impact flash during MPW was monitored and evaluated using phototransistors in order to measure the time of the impact. The results are in good accordance with the established PDV （photon Doppler velocimetry） and show a good repeatability. Furthermore, the collision front velocity was investigated using adapted part geometries within a series of tests. This velocity component is one of the key parameters in MPW; its value decreases along the weld zone. With the help of this newly introduced measurement tool, the magnetic pressure distribution or the joining geometry can be adjusted more effectively.

Robust current control design of a three phase voltage source converter

In this paper,a robust design method for current control is proposed to improve the performance of a three phase voltage source converter(VSC)with an inductorcapacitor-inductor(LCL)filter.The presence of the LCL filter complicates the dynamics of the control system and limits the achievable control bandwidth(and the overall performance),particularly when the uncertainty of the parameters is considered.To solve this problem,the advanced H?control theory is employed to design a robust current controller in stationary coordinates.Both control of the fundamental frequency current and suppression of the potential LC resonance are considered.The design procedure and the selection of the weight functions are presented in detail.A conventional proportional-resonant PR controller is also designed for comparison.Analysis showed that the proposed H∞ current controller achieved a good frequency response with explicit robustness.The conclusion was verified on a 5 kW VSC that had a LCL filter.

On the calibration of rotational augmentation models for wind turbine load estimation by means of CFD simulations

In this work the improved version of an engineering model which accounts for rotational augmentation effects by means of computational fluid dynamics(CFD)calibration is explored and discussed.Based on an analysis of the NREL Phase VI wind turbine,the novel modeling is presented,which uses as base line the formulation proposed by Chaviaropoulos and Hansen.The model is calibrated based on CFD simulations using OpenFOAM.The corresponding correction of the two dimensional polars is straightforward implemented within MoWiT,an in-house software for load calculation.The novel formulation results in improved lift and drag coefficients prediction in all considered cases,reducing the deviation with respect to the rotating CFD cases down to few percent.The optimal configuration including the correction for tip effects of Shen shows better agreements at the very tip of the blade.Furthermore the range of applicability for large wind turbine rotor blades based on a virtual 10MW rotor model is discussed.

Enabling Virtual Met Masts for wind energy applications through machinelearning-methods

As wind is the basis of all wind energy projects, a precise knowledge about its availability is needed. For ananalysis of the site-specific wind conditions, Virtual Meteorological Masts (VMMs) are frequently used. VMMsmake use of site calibrated numerical data to provide precise wind estimates during all phases of a wind energyproject. Typically, numerical data are used for the long-term correlation that is required for estimating theyield of new wind farm projects. However, VMMs can also be used to fill data gaps or during the operationalphase as an additional reference data set to detect degrading sensors. The value of a VMM directly dependson its ability and precision to reproduce site-specific environmental conditions. Commonly, linear regressionis used as state of the art to correct reference data to the site-specific conditions. In this study, a frameworkof 10 different machine-learning methods is tested to investigated the benefit of more advanced methods ontwo offshore and one onshore site. We find significantly improving correlations between the VMMs and the reference data when using more advanced methods and present the most promising ones. The K-NearestNeighbors and AdaBoost regressors show the best results in our study, but Multi-Output Mixture of GaussianProcesses is also very promising. The use of more advanced regression models lead to decreased uncertainties;hence those methods should find its way into industrial applications. The recommended regression models canserve as a starting point for the development of end-user applications and services.