Wind turbine rotor blade monitoring using digital image correlation: a comparison to aeroelastic simulations of a multi-megawatt wind turbine

Optical full-field measurement methods such as Digital Image Correlation (DIC) provide a new opportunity for measuring deformations and vibrations with high spatial and temporal resolution. However, application to full-scale wind turbines is not trivial. Elaborate preparation of the experiment is vital and sophisticated post processing of the DIC results essential. In the present study, a rotor blade of a 3.2 MW wind turbine is equipped with a random black-and-white dot pattern at four different radial positions. Two cameras are located in front of the wind turbine and the response of the rotor blade is monitored using DIC for different turbine operations. In addition, a Light Detection and Ranging (LiDAR) system is used in order to measure the wind conditions. Wind fields are created based on the LiDAR measurements and used to perform aeroelastic simulations of the wind turbine by means of advanced multibody codes. The results from the optical DIC system appear plausible when checked against common and expected results. In addition, the comparison of relative out-of-plane blade deflections shows good agreement between DIC results and aeroelastic simulations.

Copernicus in situ TAC - CMEMS System Requirements Document

This document is the Copernicus Marine Environment Monitoring Service for In Situ observations System Requirements Document (CMES-INS-SRD). It specifies the capabilities of the CMEMS-INS system as a response to the CMEMS-INS system requirements. The Copernicus In Situ Thematic Assembly Centre (In Situ TAC) provides a research and operational framework to develop and deliver In Situ observations and derived products based on such observations, to address progressively global but also regional needs either for monitoring, modelling or downstream service development.

Preparation of environmental monitoring plan pattern for surface water quality in Iran (Case study: Gharehsou River)

Using water quality management programs is a necessary and inevitable way for preservation and sustainable use of water resources. One of the important issues in determining the quality of water in rivers is designing effective quality control networks, so that the measured quality variables in these stations are, as far as possible, indicative of overall changes in water quality. One of the methods to achieve this goal is increasing the number of quality monitoring stations and sampling instances. Since this will dramatically increase the annual cost of monitoring, deciding on which stations and parameters are the most important ones, along with increasing the instances of sampling, in a way that shows maximum change in the system under study can affect the future decision-making processes for optimizing the efficacy of extant monitoring network, removing or adding new stations or parameters and decreasing or increasing sampling instances. This end, the efficiency of multivariate statistical procedures was studied in this thesis. Multivariate statistical procedure, with regard to its features, can be used as a practical and useful method in recognizing and analyzing rivers’ pollution and consequently in understanding, reasoning, controlling, and correct decision-making in water quality management. This research was carried out using multivariate statistical techniques for analyzing the quality of water and monitoring the variables affecting its quality in Gharasou river, in Ardabil province in northwest of Iran. During a year, 28 physical and chemical parameters were sampled in 11 stations. The results of these measurements were analyzed by multivariate procedures such as: Cluster Analysis (CA), Principal Component Analysis (PCA), Factor Analysis (FA), and Discriminant Analysis (DA). Based on the findings from cluster analysis, principal component analysis, and factor analysis the stations were divided into three groups of highly polluted (HP), moderately polluted (MP), and less polluted (LP) stations Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding spatial variations in water quality for effective river water quality management. This study also shows the effectiveness of these techniques for getting better information about the water quality and design of monitoring network for effective management of water resources. Therefore, based on the results, Gharasou river water quality monitoring program was developed and presented.

Modelling of an efficient dynamic smart solar photovoltaic power grid system

A smart solar photovoltaic grid system is an advent of innovation coherence of information and communications technology (ICT) with power systems control engineering via the internet [1]. This thesis designs and demonstrates a smart solar photovoltaic grid system that is selfhealing, environmental and consumer friendly, but also with the ability to accommodate other renewable sources of energy generation seamlessly, creating a healthy competitive energy industry and optimising energy assets efficiency. This thesis also presents the modelling of an efficient dynamic smart solar photovoltaic power grid system by exploring the maximum power point tracking efficiency, optimisation of the smart solar photovoltaic array through modelling and simulation to improve the quality of design for the solar photovoltaic module. In contrast, over the past decade quite promising results have been published in literature, most of which have not addressed the basis of the research questions in this thesis. The Levenberg-Marquardt and sparse based algorithms have proven to be very effective tools in helping to improve the quality of design for solar photovoltaic modules, minimising the possible relative errors in this thesis. Guided by theoretical and analytical reviews in literature, this research has carefully chosen the MatLab/Simulink software toolbox for modelling and simulation experiments performed on the static smart solar grid system. The auto-correlation coefficient results obtained from the modelling experiments give an accuracy of 99% with negligible mean square error (MSE), root mean square error (RMSE) and standard deviation. This thesis further explores the design and implementation of a robust real-time online solar photovoltaic monitoring system, establishing a comparative study of two solar photovoltaic tracking systems which provide remote access to the harvested energy data. This research made a landmark innovation in designing and implementing a unique approach for online remote access solar photovoltaic monitoring systems providing updated information of the energy produced by the solar photovoltaic module at the site location. In addressing the challenge of online solar photovoltaic monitoring systems, Darfon online data logger device has been systematically integrated into the design for a comparative study of the two solar photovoltaic tracking systems examined in this thesis. The site location for the comparative study of the solar photovoltaic tracking systems is at the National Kaohsiung University of Applied Sciences, Taiwan, R.O.C. The overall comparative energy output efficiency of the azimuthal-altitude dual-axis over the 450 stationary solar photovoltaic monitoring system as observed at the research location site is about 72% based on the total energy produced, estimated money saved and the amount of CO2 reduction achieved. Similarly, in comparing the total amount of energy produced by the two solar photovoltaic tracking systems, the overall daily generated energy for the month of July shows the effectiveness of the azimuthal-altitude tracking systems over the 450 stationary solar photovoltaic system. It was found that the azimuthal-altitude dual-axis tracking systems were about 68.43% efficient compared to the 450 stationary solar photovoltaic systems. Lastly, the overall comparative hourly energy efficiency of the azimuthal-altitude dual-axis over the 450 stationary solar photovoltaic energy system was found to be 74.2% efficient. Results from this research are quite promising and significant in satisfying the purpose of the research objectives and questions posed in the thesis. The new algorithms introduced in this research and the statistical measures applied to the modelling and simulation of a smart static solar photovoltaic grid system performance outperformed other previous works in reviewed literature. Based on this new implementation design of the online data logging systems for solar photovoltaic monitoring, it is possible for the first time to have online on-site information of the energy produced remotely, fault identification and rectification, maintenance and recovery time deployed as fast as possible. The results presented in this research as Internet of things (IoT) on smart solar grid systems are likely to offer real-life experiences especially both to the existing body of knowledge and the future solar photovoltaic energy industry irrespective of the study site location for the comparative solar photovoltaic tracking systems. While the thesis has contributed to the smart solar photovoltaic grid system, it has also highlighted areas of further research and the need to investigate more on improving the choice and quality design for solar photovoltaic modules. Finally, it has also made recommendations for further research in the minimization of the absolute or relative errors in the quality and design of the smart static solar photovoltaic module.

Surface acoustic wave streaming in a PDMS microfluidic system: effect of frequency and fluid geometry & A remote ultrasonic glucose sensor

This thesis describes two separate projects. The first is a theoretical and experimental investigation of surface acoustic wave streaming in microfluidics. The second is the development of a novel acoustic glucose sensor. A separate abstract is given for each here. Optimization of acoustic streaming in microfluidic channels by SAWs Surface Acoustic Waves, (SAWs) actuated on flat piezoelectric substrates constitute a convenient and versatile tool for microfluidic manipulation due to the easy and versatile interfacing with microfluidic droplets and channels. The acoustic streaming effect can be exploited to drive fast streaming and pumping of fluids in microchannels and droplets (Shilton et al. 2014; Schmid et al. 2011), as well as size dependant sorting of particles in centrifugal flows and vortices (Franke et al. 2009; Rogers et al. 2010). Although the theory describing acoustic streaming by SAWs is well understood, very little attention has been paid to the optimisation of SAW streaming by the correct selection of frequency. In this thesis a finite element simulation of the fluid streaming in a microfluidic chamber due to a SAW beam was constructed and verified against micro-PIV measurements of the fluid flow in a fabricated device. It was found that there is an optimum frequency that generates the fastest streaming dependent on the height and width of the chamber. It is hoped this will serve as a design tool for those who want to optimally match SAW frequency with a particular microfluidic design. An acoustic glucose sensor Diabetes mellitus is a disease characterised by an inability to properly regulate blood glucose levels. In order to keep glucose levels under control some diabetics require regular injections of insulin. Continuous monitoring of glucose has been demonstrated to improve the management of diabetes (Zick et al. 2007; Heinemann & DeVries 2014), however there is a low patient uptake of continuous glucose monitoring systems due to the invasive nature of the current technology (Ramchandani et al. 2011). In this thesis a novel way of monitoring glucose levels is proposed which would use ultrasonic waves to ‘read’ a subcutaneous glucose sensitive-implant, which is only minimally invasive. The implant is an acoustic analogy of a Bragg stack with a ‘defect’ layer that acts as the sensing layer. A numerical study was performed on how the physical changes in the sensing layer can be deduced by monitoring the reflection amplitude spectrum of ultrasonic waves reflected from the implant. Coupled modes between the skin and the sensing layer were found to be a potential source of error and drift in the measurement. It was found that by increasing the number of layers in the stack that this could be minimized. A laboratory proof of concept system was developed using a glucose sensitive hydrogel as the sensing layer. It was possible to monitor the changing thickness and speed of sound of the hydrogel due to physiological relevant changes in glucose concentration.

Application of an online-biomass sensor in an optical multisensory platform prototype for growth monitoring of biotechnical relevant microorganism and cell lines in single-use shake flasks

In the context of this work we evaluated a multisensory, noninvasive prototype platform for shake flask cultivations by monitoring three basic parameters (pH, pO2 and biomass). The focus lies on the evaluation of the biomass sensor based on backward light scattering. The application spectrum was expanded to four new organisms in addition to E. coli K12 and S. cerevisiae [1]. It could be shown that the sensor is appropriate for a wide range of standard microorganisms, e.g., L. zeae, K. pastoris, A. niger and CHO-K1. The biomass sensor signal could successfully be correlated and calibrated with well-known measurement methods like OD600, cell dry weight (CDW) and cell concentration. Logarithmic and Bleasdale-Nelder derived functions were adequate for data fitting. Measurements at low cell concentrations proved to be critical in terms of a high signal to noise ratio, but the integration of a custom made light shade in the shake flask improved these measurements significantly. This sensor based measurement method has a high potential to initiate a new generation of online bioprocess monitoring. Metabolic studies will particularly benefit from the multisensory data acquisition. The sensor is already used in labscale experiments for shake flask cultivations.

The viability of a tool for fetal health monitoring

Health monitoring has become widespread these past few years. Such applications include from exercise, food intake and weight watching, to specific scenarios like monitoring people who suffer from chronic diseases. More and more we see the need to also monitor the health of new-born babies and even fetuses. Congenital Heart Defects (CHDs) are the main cause of deaths among babies and doctors do not know most of these defects. Hence, there is a need to study what causes these anomalies, and by monitoring the fetus daily there will be a better chance of identifying the defects in earlier stages. By analyzing the data collected, doctors can find patterns and come up with solutions, thus saving peoples’ lives. In many countries, the most common fetal monitor is the ultrasound and the use of it is regulated. In Sweden for normal pregnancies, there is only one ultrasound scan during the pregnancy period. There is no great evidence that ultrasound can harm the fetus, but many doctors suggest to use it as little as possible. Therefore, there is a demand for a new non-ultrasound device that can be as accurate, or even better, on detecting the FHR and not harming the baby. The problems that are discussed in this thesis include how can accurate fetus health be monitored non-invasively at home and how could a fetus health monitoring system for home use be designed. The first part of the research investigates different technologies that are currently being used on fetal monitoring, and techniques and parameters to monitor the fetus. The second part is a qualitative study held in Sweden between April and May 2016. The data for the qualitative study was collected through interviews with 21 people, 10 mothers/mothers-to-be and 11 obstetricians/gynecologists/midwives. The questions were related to the Swedish pregnancy protocol, the use of technology in medicine and in particular during the pregnancy process, and the use of an ECG based monitoring device. The results show that there is still room for improvements on the algorithms to extract the fetal ECG and the survey was very helpful in understanding the need for a fetal home monitor. Parents are open to new technologies especially if it doesn't affect the baby's growth. Doctors are open to use ECG as a great alternative to ultrasound; on the other hand, midwives are happy with the current system. The remote monitoring feature is very desirable to everyone, if such system will be used in the future.

Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Stochastic methods based on time-series modeling combined with geostatistics can be useful tools to describe the variability of water-table levels in time and space and to account for uncertainty. Monitoring water-level networks can give information about the dynamic of the aquifer domain in both dimensions. Time-series modeling is an elegant way to treat monitoring data without the complexity of physical mechanistic models. Time-series model predictions can be interpolated spatially, with the spatial differences in water-table dynamics determined by the spatial variation in the system properties and the temporal variation driven by the dynamics of the inputs into the system. An integration of stochastic methods is presented, based on time-series modeling and geostatistics as a framework to predict water levels for decision making in groundwater management and land-use planning. The methodology is applied in a case study in a Guarani Aquifer System (GAS) outcrop area located in the southeastern part of Brazil. Communication of results in a clear and understandable form, via simulated scenarios, is discussed as an alternative, when translating scientific knowledge into applications of stochastic hydrogeology in large aquifers with limited monitoring network coverage like the GAS.

A self-inducible heterologous protein expression system in Escherichia coli

International audience

Atmospheric mercury concentrations observed at ground-based monitoring sites globally distributed in the framework of the GMOS network

Long-term monitoring of data of ambient mercury (Hg) on a global scale to assess its emission, transport, atmospheric chemistry, and deposition processes is vital to understanding the impact of Hg pollution on the environment. The Global Mercury Observation System (GMOS) project was funded by the European Commission (http://www.gmos.eu) and started in November 2010 with the overall goal to develop a coordinated global observing system to monitor Hg on a global scale, including a large network of ground-based monitoring stations, ad hoc periodic oceanographic cruises and measurement flights in the lower and upper troposphere as well as in the lower stratosphere. To date, more than 40 ground-based monitoring sites constitute the global network covering many regions where little to no observational data were available before GMOS. This work presents atmospheric Hg concentrations recorded worldwide in the framework of the GMOS project (2010–2015), analyzing Hg measurement results in terms of temporal trends, seasonality and comparability within the network. Major findings highlighted in this paper include a clear gradient of Hg concentrations between the Northern and Southern hemispheres, confirming that the gradient observed is mostly driven by local and regional sources, which can be anthropogenic, natural or a combination of both.

Changes in the storage and sink of carbon dioxide in subsurface atmospheres controlled by climate-driven processes: the case of the Ojo Guareña karst system

A comprehensive environmental monitoring program was conducted in the Ojo Guareña cave system (Spain), one of the longest cave systems in Europe, to assess the magnitude of the spatiotemporal changes in carbon dioxide gas (CO2) in the cave–soil–atmosphere profile. The key climate-driven processes involved in gas exchange, primarily gas diffusion and cave ventilation due to advective forces, were characterized. The spatial distributions of both processes were described through measurements of CO2 and its carbon isotopic signal (δ13C[CO2]) from exterior, soil and cave air samples analyzed by cavity ring-down spectroscopy (CRDS). The trigger mechanisms of air advection (temperature or air density differences or barometric imbalances) were controlled by continuous logging systems. Radon monitoring was also used to characterize the changing airflow that results in a predictable seasonal or daily pattern of CO2 concentrations and its carbon isotopic signal. Large daily oscillations of CO2 levels, ranging from 680 to 1900 ppm day−1 on average, were registered during the daily oscillations of the exterior air temperature around the cave air temperature. These daily variations in CO2 concentration were unobservable once the outside air temperature was continuously below the cave temperature and a prevailing advective-renewal of cave air was established, such that the daily-averaged concentrations of CO2 reached minimum values close to atmospheric background. The daily pulses of CO2 and other tracer gases such as radon (222Rn) were smoothed in the inner cave locations, where fluctuation of both gases was primarily correlated with medium-term changes in air pressure. A pooled analysis of these data provided evidence that atmospheric air that is inhaled into dynamically ventilated caves can then return to the lower troposphere as CO2-rich cave air.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

Silicon Photonic Device for Wavelength Sensing and Monitoring

Over the last decade advances and innovations from Silicon Photonics technology were observed in the telecommunications and computing industries. This technology which employs Silicon as an optical medium, relies on current CMOS micro-electronics fabrication processes to enable medium scale integration of many nano-photonic devices to produce photonic integrated circuitry. However, other fields of research such as optical sensor processing can benefit from silicon photonics technology, specially in sensors where the physical measurement is wavelength encoded. In this research work, we present a design and application of a thermally tuned silicon photonic device as an optical sensor interrogator. The main device is a micro-ring resonator filter of 10 $\mu m$ of diameter. A photonic design toolkit was developed based on open source software from the research community. With those tools it was possible to estimate the resonance and spectral characteristics of the filter. From the obtained design parameters, a 7.8 x 3.8 mm optical chip was fabricated using standard micro-photonics techniques. In order to tune a ring resonance, Nichrome micro-heaters were fabricated on top of the device. Some fabricated devices were systematically characterized and their tuning response were determined. From measurements, a ring resonator with a free-spectral-range of 18.4 nm and with a bandwidth of 0.14 nm was obtained. Using just 5 mA it was possible to tune the device resonance up to 3 nm. In order to apply our device as a sensor interrogator in this research, a model of wavelength estimation using time interval between peaks measurement technique was developed and simulations were carried out to assess its performance. To test the technique, an experiment using a Fiber Bragg grating optical sensor was set, and estimations of the wavelength shift of this sensor due to axial strains yield an error within 22 pm compared to measurements from spectrum analyzer. Results from this study implies that signals from FBG sensors can be processed with good accuracy using a micro-ring device with the advantage of ts compact size, scalability and versatility. Additionally, the system also has additional applications such as processing optical wavelength shifts from integrated photonic sensors and to be able to track resonances from laser sources.

Physics-Based Modeling of Power System Components for the Evaluation of Low-Frequency Radiated Electromagnetic Fields

The low-frequency electromagnetic compatibility (EMC) is an increasingly important aspect in the design of practical systems to ensure the functional safety and reliability of complex products. The opportunities for using numerical techniques to predict and analyze system’s EMC are therefore of considerable interest in many industries. As the first phase of study, a proper model, including all the details of the component, was required. Therefore, the advances in EMC modeling were studied with classifying analytical and numerical models. The selected model was finite element (FE) modeling, coupled with the distributed network method, to generate the model of the converter’s components and obtain the frequency behavioral model of the converter. The method has the ability to reveal the behavior of parasitic elements and higher resonances, which have critical impacts in studying EMI problems. For the EMC and signature studies of the machine drives, the equivalent source modeling was studied. Considering the details of the multi-machine environment, including actual models, some innovation in equivalent source modeling was performed to decrease the simulation time dramatically. Several models were designed in this study and the voltage current cube model and wire model have the best result. The GA-based PSO method is used as the optimization process. Superposition and suppression of the fields in coupling the components were also studied and verified. The simulation time of the equivalent model is 80-100 times lower than the detailed model. All tests were verified experimentally. As the application of EMC and signature study, the fault diagnosis and condition monitoring of an induction motor drive was developed using radiated fields. In addition to experimental tests, the 3DFE analysis was coupled with circuit-based software to implement the incipient fault cases. The identification was implemented using ANN for seventy various faulty cases. The simulation results were verified experimentally. Finally, the identification of the types of power components were implemented. The results show that it is possible to identify the type of components, as well as the faulty components, by comparing the amplitudes of their stray field harmonics. The identification using the stray fields is nondestructive and can be used for the setups that cannot go offline and be dismantled