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Eingeladener Vortrag
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Following a modular concept an integrated monitoring system has been developed that includes all components of offshore wind turbines (OWEC). Using commercially available measurement equipments of the involved partners this system was tested at the wind turbine Multibrid M5000_2 in Bremerhaven. Additional results of the research project are findings on the use of embedded FBG sensors in rotor blades that allow condition assessments based on local information. Newly developed algorithms for the identification of external loads and damage characteristics of the structural components have been tested in operation. Developed methods for monitoring based structural assessment were used, which will be suitable to optimize structural maintenance works.
To ensure a high operational reliability of offshore wind turbines (OWEC) with economically acceptable repair and maintenance efforts, comprehensive diagnosis and supervision concepts are required. Automatic monitoring Systems will be an essential part of such concepts. Because of the fact, that during Operation there will be static and dynamic interaction between the components ‘structure’, ‘machinery’ and ‘blades’ it is necessary to develop the monitoring techniques in an overall concept. These monitoring Systems are supposed to be applied for the design and testing as well as for the Operation and maintenance phases. The knowledge of the dynamic behavior of wind turbines is important both for the design and for a safe Operation. The available monitoring data from a period of three years, allow first conclusions on the long-term Operation of such Systems in terms of quality requirements to the instrumentation to the structure and the rotor blades
Active Thermography
(2016)
Active thermography is a nondestructive testing method for identification of near surface defects and signs of early deterioration. The presentation explains the potential and Limits of the method and the equipment for application to concrete structures. Advanced data analysis can increase the quality of imaging of the measurement. The method is usually applied once for analysis of the current condition. If applied periodically, the method can be used for observation of a deterioration process.
For reaction monitoring and process control using NMR instruments, in particular, after acquisition of the FID the data needs to be corrected in real-time for common effects using fast interfaces and automated methods. When it comes to NMR data evaluation under industrial process conditions, the shape of signals can change drastically due to nonlinear effects. Additionally, the multiplet structure becomes more dominant because of the comparably low-field strengths which results in overlapping of multiple signals. However, the structural and quantitative information is still present but needs to be extracted by applying predictive models.
We present a range of approaches for the automated spectra analysis moving from statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modeling). By using the benefits of traditional qNMR experiments data analysis models can meet the demands of the PAT community (Process Analytical Technology) regarding low calibration effort/calibration free methods, fast adaptions for new reactants, or derivatives and robust automation schemes.
Currently research in chemical manufacturing moves towards flexible plug-and-play approaches focusing on modular plants, capable of producing small scales on-demand with short down-times between individual campaigns. This approach allows for efficient use of hardware, a faster optimization of the process conditions, and thus, an accelerated introduction of new products to the market [1]. Driven mostly by the search for chemical syntheses under biocompatible conditions, so-called “click” chemistry rapidly became a growing field of research. The resulting simple one-pot reactions are so far only scarcely accompanied by an adequate optimization via comparably straightforward and robust analysis techniques. Here we report on a fast and reliable calibration-free online high field NMR monitoring approach for technical mixtures. It combines a versatile fluidic system, continuous-flow measurement with a time interval of 20 s per spectrum, and a robust, automated algo-rithm to interpret the obtained data. All spectra were acquired using a 500 MHz NMR spectrometer (Varian) with a dual band flow probe having a 1/16 inch polymer tubing working as a flow cell. Single scan 1H spectra were recorded with an acquisition time of 5 s, relaxation delay of 15 s.
Phage display is used to find specific target binding peptides for polypropylene (PP) surfaces. PP is one of the most commonly used plastics in the world. Millions of tons are produced every year. PP binders are of particular interest because so far gluing or printing on PP is challenging due to its low surface energy. A phage display protocol for PP was developed followed by Next Generation DNA Sequencing of the whole phage library. Data analysis of millions of sequences yields promising peptide candidates which were synthesized as PEG conjugates. Fluorescence-based adsorption-elution-experiments show high adsorption on PP for several sequences.
Data analysis of SAS measurements has been dominated by the classical curve fitting approach. This method finds optimal parameters of a scattering model composed of analytical expressions. SASfit represents such a classical curve fitting toolbox: it is one of the mature programs for small-angle scattering data analysis and has been available and used for many years. The latest developments [1] will be extended by improving the interoperability of the extensive data base of models with third-party analysis software. An updated format of model definitions is also presented, which allows model function plug-ins to be used with the Python language.
To complement the classical curve fitting method, the user-friendly opensource Monte Carlo regression package McSAS was developed. Most importantly, the form-free Monte Carlo approach of McSAS means that it is not necessary to provide any further mathematical restrictions to the Parameter distribution. Future developments include separating the core optimization from the GUI (allowing 'headless' integration), as well as parallel computing which reduces the computing time proportional to the number of available computing cores. The headless mode is presented by an example of Operation within interactive programming environments such as a Jupyter notebook.
The promising results of Monte Carlo based data analysis for determining form-free Parameter distributions motivated the evaluation of the method with dynamic light scattering (DLS) data. For this purpose, the method was adapted for analyzing correlation curves such as those from multi-angle dynamic light scattering (DLS) data. The development of McDLS intends to overcome limitations of existing methods at reliably determining the modality of size distributions. An example of Monte Carlo based data analysis of multimodal DLS measurements will be presented.
This poster deals with improvements and characteriztion of small-angle scattering limitations, by looking at the trifecta of Data collection and uncertainty propagation, data analysis methodologies, and real-world tests. It is found that - with appropriate care and instrumentation - accuracies of 1% on mean nanomaterial sizes, and 10% on the size distribution width as well as the volume fraction can be achieved.