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The Mediterranean Sea comprises just 0.8 % of the global oceanic surface, yet considering its size, it is regarded as a disproportionately large sink for anthropogenic carbon due to its physical and biogeochemical characteristics. An underwater glider mission was carried out in March–April 2016 close to the BOUSSOLE and DyFAMed time series moorings in the northwestern Mediterranean Sea. The glider deployment served as a test of a prototype ion-sensitive field-effect transistor pH sensor. Dissolved oxygen (O2) concentrations and optical backscatter were also observed by the glider and increased between 19 March and 1 April, along with pH. These changes indicated the start of a phytoplankton spring bloom, following a period of intense mixing. Concurrent measurements of CO2 fugacity and O2 concentrations at the BOUSSOLE mooring buoy showed fluctuations, in qualitative agreement with the pattern of glider measurements. Mean net community production rates (N) were estimated from glider and buoy measurements of dissolved O2 and inorganic carbon (DIC) concentrations. Glider and buoy DIC concentrations were derived from a salinity-based total alkalinity parameterisation, glider pH, and buoy CO2 fugacity. The spatial coverage of glider data allowed calculating advective O2 and DIC fluxes. Mean N estimates for the euphotic zone between 10 March and 3 April were (−17 ± 35) for glider O2, (44 ± 94) for glider DIC, (17 ± 35) for buoy O2 and (49 ± 86) mmol m–2 d–1 for buoy DIC, all indicating net metabolic balance over these 25 days. However, these 25 days were actually split into a period of net DIC increase and O2 decrease between 10 and 19 March and a period of net DIC decrease and O2 increase between 19 March and 3 April. The latter period is interpreted as the onset of the spring bloom. The regression coefficients between O2 and DIC-based N estimates were 0.25 ± 0.08 for the glider data and 0.54 ± 0.06 for the buoy, significantly lower than the canonical metabolic quotient o

Several trigger mechanisms have been proposed for the onset of the phytoplankton spring bloom. Among these is that phytoplankton cells begin to bloom when they experience higher average light levels in shallower mixed layers, a result of the surface net heat fluxes becoming positive and wind strength decreasing. We study the impact of these two forcings in the northwestern Mediterranean Sea. We take advantage of hourly measurements of oceanic and atmospheric parameters collected at two neighbouring moorings during the months of March and April in the years 2016 to 2019, combined with glider data in 2016. We identify the onset of the surface phytoplankton growth as concomitant with the start of significant biological activity detected by a sudden decrease in dissolved inorganic carbon derived from measurements in the upper 10 m of the water column. A rapid reduction in wind stress following high-wind events is observed at the same time. A resulting shallow mixing layer favours carbon uptake by phytoplankton lasting a few days. Simultaneously, the air–sea net heat flux switches from negative to positive, linked to changes in the latent air–sea heat flux, which is proportional to the wind speed. This results in an increased thermal stratification of the ocean's surface layers. In 2016, glider data show that the mixing layer is significantly shallower than the mixed layer at the onset of the surface phytoplankton bloom. We conclude that decreases in the mixing- and mixed-layer depths lead to the onset of the phytoplankton growth due to the relaxation of wind speed following storms. We estimate net daily community production in the mixing layer over periods of 3 d between 2016 and 2019 as between 38 and 191 mmol C m−2. These results have important implications, as biological processes play a major role in the seasonal evolution of surface pCO2 and thereby the rate of reduction in atmospheric CO2 by exchange at the air–sea interface.

One of the significant challenges facing industrial plant design is ensuring the integrity of massive design datasets generated during the project execution. This work is motivated by personal experience of data integrity issues during projects caused by insufficient automation affecting the quality of deliverables. Therefore, this project sought automated solutions for detecting anomalies in industrial design data in the form of outliers. Several novel methods are proposed, based on the Hidden Markov Model (HMM) and a modified General Regression Neural Network called the Margin-Based GRNN (MB-GRNN) along with optimisation techniques that minimise computation time. An HMM was used for validating process plant tag numbers using a self-learning approach. Results from experimental data show that HMM performance is equivalent to that of a custom-made design rule checking algorithm. The choice of components in industrial design involves setting specific design parameters that typically must reside inside permissible ranges called “design margins”. The MB-GRNN has the ability to estimate these permissible margins directly from design data and indicate potential design errors resulting from the invalid choice of design parameters by identifying data points outside of the estimated margins as outliers. The extremal permissible margin boundaries are determined by “stretching out” the upper and lower GRNN surfaces using an iterative application of stretch factors (a second kernel weighting factor). The method creates a variable insensitive band surrounding the data cloud, interlinked with the normal regression function, providing upper and lower margin boundaries. These boundaries can then be used to determine outliers and to predict a range of permissible values of design parameters during design. This method was compared to Parzen-Windows and another proximity-based method. The MB-GRNN also benefits from a modified algorithm for estimating the smoothing parameter using a co

Increasing atmospheric CO2 concentrations are resulting in a reduction in seawater pH, with potential detrimental consequences for marine organisms. Improved efforts are required to monitor the anthropogenically driven pH decrease in the context of natural pH variations. We present here a high resolution surface water pH data set obtained in summer 2011 in North West European Shelf Seas. The aim of our paper is to demonstrate the successful deployment of the pH sensor, and discuss the carbonate chemistry dynamics of surface waters of Northwest European Shelf Seas using pH and ancillary data. The pH measurements were undertaken using spectrophotometry with a Lab-on-Chip pH sensor connected to the underway seawater supply of the ship. The main processes controlling the pH distribution along the ship’s transect, and their relative importance, were determined using a statistical approach. The pH sensor allowed 10 measurements h−1 with a precision of 0.001 pH units and a good agreement with pH calculated from a pair of discretely sampled carbonate variables dissolved inorganic carbon (DIC), total alkalinity (TA) and partial pressure of CO2 (pCO2) (e.g., pHDICpCO2). For this summer cruise, the biological activity formed the main control on the pH distribution along the cruise transect. This study highlights the importance of high quality and high resolution pH measurements for the assessment of carbonate chemistry dynamics in marine waters.

This document will provide information about the approach that used to get the reading information from the wind blade in laboratory. This part is mainly focusing on getting a signal reading from a strain gauge to Arduino. The development of this project was based upon the requirement to study the effect of the turbulence that might affect the wind turbine blade. The investigation of the load that the blade experienced will then be analysed to ensure the wind turbine is able to maximise the production and also take into account the safety issues that arise according to the standard after project design constructing and implementation occurred in the real system. This document will show the details of the instruments and the design approach that enabled capturing of the load data in a controlled environment. In field testing, strain gauges on the root of turbine blades capture the blade flapwise and edgewise loading. This project focuses on development of a monitoring system to capture strain gauge data. To simplify the experiment, strain gauge are loaded in the controlled environment of a laboratory. A programme and a setup to implement this project has been developed at this. A results gathered in Chapter 4 Result and Analysis documented throughout this stage will be more reliable, proven and realistic if the content of this document is demonstrated on the real wind blade that located in Pilot Plant area. However, to develop the full functionality of this project, a person also needs to understand the transmitter and receiver aspects of the project. The establishment of the basic data logging for the main project was achieved based on the result collected.

The aim of this project is to generate a circuit which could control LEDs flashing at short time intervals around 10 nanoseconds...

This report presents methods and technologies used to investigate the use of commercially available Ultra-Wide Band (UWB) radiolocation technology in the positioning of autonomous vehicles. UWB radio has unique transmission characteristics which make pico-second resolution timing possible, a requirement for centimetre accuracy in positioning applications. Firstly, an investigation into the performance capabilities of UWB positioning infrastructure was undertaken, followed by the development of relative positioning algorithms utilising a relative and decentralised control approach. These algorithms are based on feedback provided by radiolocation data in combination with sensor fusion elements, including inertial (accelerometers), optical (optical flow sensors) and absolute ranging sensors such as Light Detection and Ranging (LIDAR). Performance of the UWB infrastructure was determined utilising static and dynamic measurement techniques to verify overall positioning accuracy and statistical characteristics, with respect to two different ranging algorithms, Time Difference of Arrival (TDOA) and Two Way Ranging (TWR). Data collected from an array of static collection points has shown, in the recommended infrastructure configuration, that commercially available UWB technology has a positional accuracy with an average offset from the real coordinate system of 7.30-15.28%. Logic-based tests using statistical inputs indicate the TDOA algorithm succeeds in more accurate positioning in 79.63%, 58.33% and 85.19% of test cases across twenty-seven points in three vertical planes respectively. Data collected from autonomous flight-path algorithms has provided practical insight into applications of the technology and its dynamic capabilities. Results demonstrate that in a high radio interference environment, performance of the UWB positioning system and drone achieve a 0.11% and 1.23% error on average from the programmed flight path gradient for TDOA and TWR respectively. The resu

Power transformers play a very important role in electrical power networks and are frequently operated longer than their expected design life. Therefore, to ensure their best operating performance in a transmission network, the fault condition of each transformer must be assessed regularly. For an accurate fault diagnosis, it is important to have maximum information about an individual transformer based on unbiased measurements. This can best be achieved using artificial intelligence (AI) that can systematically analyse the complex features of diagnostic measurements. Clustering techniques are a form of AI that is particularly well suited to fault diagnosis. To provide an assessment of transformers, a hybrid k-means algorithm, and probabilistic Parzen window estimation are used in this research. The clusters they form are representative of a single or multiple fault categories. The proposed technique computes the maximum probability of transformers in each cluster to determine their fault categories. The main focus of this research is to determine a quantitative health index (HI) to characterize the operating condition of transformers. Condition assessment tries to detect incipient faults before they become too serious, which requires a sensitive and quantified approach. Therefore, the HI needs to come from a proportionate system that can estimate health condition of transformers over time. To quantify this condition, the General Regression Neural Network (GRNN), a type of AI, has been chosen in this research. The GRNN works well with small sets of training data and avoids the needs to estimate large sets of model parameters, following a largely non-parametric approach. The methodology used here regards transformers as a collection of subsystems and summarizes their individual condition into a quantified HI based on the existing agreed benchmarks drawn from IEEE and CIGRE standards. To better calibrate the HI, it may be mapped to a failure probability estimate for e

The purpose of this project is further development of a pre-existing unit to produce fluid for testing liquid heating solar collector according to the Australian and New Zealand Standard AS/NZS 2535.1.2007. The standard recommends that the inlet temperature and flow rate to the solar collectors must be constrained to an accuracy of ±0.1oC for the temperature and ±1L/min percent of the flow rate at the set point. Achieving these recommendations as an objective of this project will enable the unit to be utilised in solar collector testing. Various modifications were implemented to enhance the performance of the unit and to comply with the standard’s recommendations. A new sophisticated data acquisition system was added for controlling the unit, and a new software application was developed. Operating the unit in open loop, to understand the behaviour of the unit as a multiple-inputs and multiple-outputs system (MIMO), identified the models for each expected loop. A rule of thumb tuning method was used to design a PI controller for the heating system. Moreover, the previous control strategy with a PI controller was modified for controlling of the MIMO. This method was useful to reduce the interaction between the final process variables in the system. Obtained steady state responses were analysed for the flow rate and temperature to compare with the limited boundaries. The final results showed that the system was able to supply water to the solar collector within the required accuracy. Achievement of the final temperature and flow rate of the fluid within the required constraints of the standard has proved that the developed unit can be utilised to perform solar collector testing. However, some more steps for further work were suggested to enable the unit to be adopted in professional tests.

Earthing Transformers are an integral part of power and distribution systems around the world, although, little consideration is given to their ongoing monitoring and maintenance. The failure of an earthing transformer can cause a multitude of issues including compromised stability and safety of the electrical network. The necessity to maintain both safety and stability of electrical networks highlights valuable real world applications for an SFRA earthing transformer testing toolkit. As a starting point, the project adopted a review of existing research along with an analysis of earthing transformer design principles. Research found that because of an inherent design strategy, many ZN wound earthing transformers have a unique failure type in common; axial displacement of the inner and outer windings. The second project stage involved physical simulation of an earthing transformer’s axial windings displacement using SFRA as a diagnosis tool. Simulation results provided evidence that (for the given test subject) defect detection is possible using SFRA benchmarked comparisons. Analysis of benchmarked comparisons found deviation only at select resonances with general spectral shape retention for all other points along the SFRA trace. Spectral consistency of benchmarked comparisons allowed the implementation of a speech processing technique known as Mel-Frequency Cepstral Coefficients (MFCC). An adaption of the MFCC process introduced a way of encoding and distilling the SFRA trace data while exaggerating critical points of deviation. The third major project stage involved the development of code using Mathworks MATLAB as a platform to the fulfil data management and computational requirements of the adapted MFCC. Select variables were isolated throughout the code to ensure that the process was tune-able on multiple levels for future optimisation. By selecting and mapping the appropriate resultant cepstral coefficients against each other, it was found that a meaningful r

A recent downturn in the mining industry has meant that lowering costs has become an essential practice for companies to remain profitable [1]. Current stockpile height sensors on the market come at a big cost to companies. This report details the research and development of a low-cost alternative for real-time stock height measurement. To develop a competitive option, the sensor would need to be able to withstand industrial environmental elements, such as heavy moving product and high dust levels. Substantial research into sensor technologies resulted in light detection and ranging (LiDAR) being the front runner for this devices measurement tool. Development of the sensing device was centralised around having rotational capabilities to increase the measurement area of the stockpile using a single sensor. This measurement information attained from the LiDAR sensor was then processed using an Arduino Uno. Additional capabilities of the developed device allowed for three modes in which the stockpile data can be interpreted; an LCD screen on the sensor, Microsoft Excel featuring a real-time plot and output to a programmable logic controller (PLC). Extensive testing was then undertaken to ensure the sensor was capable of meeting the initial requirements set. The sensor performed well when tested on products with varying reflectivity, incident angles, surface roughness and on replica stockpile scenarios. Atmospheric properties were tested, which included varying amounts of ambient light and dust levels. Ambient light tests gave reliable results while theoretical research into LiDAR and obscurants, such as dust, showed that there are technologies that can be incorporated to overcome dust, rain, and fog. Upon completion of this thesis, a viable prototype has been developed, costing only 1.2% of the price of the nearest competitor currently on the market, while offering similar performance capabilities.

A sky camera network was installed at Murdoch University capturing high resolution whole sky images every ten seconds for a study, to be completed by the University of Oldenburg, investigating short term solar irradiance forecasting[1]. The study investigates the effectiveness of sky camera imaging and processing techniques used to determine forecasted cloud induced variations on photo-voltaic (PV) power systems based on images and validation data acquired at Murdoch University in Perth, Western Australia. This project demonstrates the effectiveness of two 180-degree fish-eye Vivotek network security cameras recording 1920x1920 pixel RGB images simultaneously at 10 second intervals to provide sufficient information for accurate solar irradiance and PV power output modelling. Results from the collaboration between Murdoch University and the University of Oldenburg were presented at the World Renewable Energy Congress which provide a detailed analysis of solar forecasting accuracy and the associated fuel savings in comparison to other forms of generation and PV management systems. A persistence method of forecasting was determined to properly utilize sky camera forecasting’s ability to predict future cloud events and reduce the spinning reserve (SR) that is required in PV and diesel power systems. The results showed that a total of 61% of the time between 10th November and 28th December 2016 the model identified clear sky irradiance. A total of 84 cloud events occurred during this period and 76 of these were correctly identified giving a total miss rate of ~8% [2]. Based on these findings, a further study was conducted by the University of Oldenburg which determined estimated fuel savings achieved with a sky camera based forecasting control method. The findings showed up to 5.5% reduction of fuel consumption in a PV diesel power network with 60% of total capacity being installed PV[3]. The forecasting method used was outlined in a report (West et al 2014) and variatio

The current approach to implementing safety measures for the purposes of preserving human-life and property as either hardware or software has a number of inherent limitations. This report explores and evaluates the use of the Field Programmable Gate Array (FPGA) available on the National Instruments myRIO-1900 platform as means of combining the strengths of both approaches, with the classical ball and beam control problem used as a basis of testing a number of common features of safety-systems. Two different ball and beam configurations were used. During the prototyping phase, the beam was directly driven by a stepper motor while the final project apparatus utilised a standard servo and push-rod configuration. The change occurred due to a failure of the operational failure of the stepper motor. As a result, limited useful test data was gathered. However, feedback and analysis addressed a number of larger issues encountered during prototyping prior to the development of the final project build. Results gathered during testing of the final prototype provided arguments for and against the use of an FPGA for the purpose of implementing safety systems. It was demonstrated that FPGA-based redundancy provides a means of reliably executing a multitude of time-dependent safety and monitoring processes at once. However, it was also demonstrated that FPGA-based systems lack some important benefits associated with hardware and software systems. FPGA function complexity and size is quite limited and time consuming to implement, and the chip itself lacks the robustness commonly associated with hardware.

Reliable single directional frequency data transfer is a method of electronic communication that is a potential alternative to a bi-directional or wired method. It is intended to determine whether or not single directional data transfer can be designed to perform at the same reliability level as other methods of data transmission. The reason for researching this is to see whether, two-way communication is necessary. Upon finding results to this question it will be determined if single directional frequency data transfer can be as power efficient as bi directional data transfer. It will also look into the overall performance of the method and how it can deal with inhibiting factors that will be introduced to simulate real world external variations in the signal. How reliable single directional frequency transfer is, will be determined through experiments that are tasked at finding the maximum transmission rate of the devices made and the distance that data transmission can be conducted over. The investigation will require the design of an experimental apparatus that will allow results to be found in the maximum possible transmission rate and distance transmission can cover. The experimental apparatus will consist of two possessors, one for encoding a message, the other for decoding a message. The apparatus will also require an integrated radio frequency transmitter circuit as well as a receiver radio frequency integrated circuit. The processors will need to be coded with a new protocol that will allow the incorporation of three forward error correction techniques. This is so that a basic, intermediate and advanced method of forward error correction can be compared when gathering results. Throughout this research consideration into all aspects that can possibly improve the energy efficiency of the electrical apparatus will be addressed an implemented. By creating an experimental apparatus that is competitive against other data transmission methods in terms of energy e