The design of a navigation guidance and control system for an unmanned surface vehicle for environmental monitoring

Maintaining the ecosystem is one of the main concerns in this modern age. With the fear of ever-increasing global warming, the UK is one of the key players to participate actively in taking measures to slow down at least its phenomenal rate. As an ingredient to this process, the Springer vehicle was designed and developed for environmental monitoring and pollutant tracking. This special issue paper highlighted the Springer hardware and software architecture including various navigational sensors, a speed controller, and an environmental monitoring unit. In addition, details regarding the modelling of the vessel were outlined based mainly on experimental data. The formulation of a fault tolerant multi-sensor data fusion technique was also presented. Moreover, control strategy based on a linear quadratic Gaussian controller was developed and simulated on the Springer model.
Gaussian controller is developed and simulated on the Springer model.

The cost of water from an autonomous wave-powered desalination plant

A techno-economic model of an autonomous wave-powered desalination plant is developed and indicates that fresh water can be produced for as little as £0.45/m3. The advantages of an autonomous wave-powered desalination plant are also discussed indicating that the real value of the system is enhanced due to its flexibility for deployment and reduced environmental impact. The modelled plant consists of the Oyster wave energy converter, conventional reverse osmosis membranes and a pressure exchanger–intensifier for energy recovery. A time-domain model of the plant is produced using wave-tank experimentation to calibrate the model of Oyster, manufacturer's data for the model of the reverse osmosis membranes and a hydraulic model of the pressure exchanger–intensifier. The economic model of the plant uses best-estimate cost data which are reduced to annualised costs to facilitate the calculation of the cost of water. Finally, the barriers to the deployment of this technology are discussed, but they are not considered insurmountable.

An autonomous wave-powered desalination system

The potential for an autonomous wave-powered desalination system is considered and it is identified that the most promising configuration is a reverse osmosis (RO) plant utilising a pressure exchanger-intensifier for energy recovery. A numerical model of the RO plant with a pressure exchanger-intensifier is developed that shows that a specific energy consumption of less than 2.0 kW h/m3 over a wide range of sea-water feed conditions, making it particularly suitable for use with a variable power source such as wave energy. A numerical model of the combined wave-power and desalination plant is also developed that shows that it is possible to supply the desalination plant with sea-water directly pressurised by the wave energy converter, eliminating the cost and energy losses associated with converting the energy into electricity and back to pressurised water. For a typical sea-state the specific hydraulic energy consumption of the desalination plant is estimated to be 1.85 kW h/m3 whilst maintaining a recovery-ratio of less than 25 to 35% to avoid the need for chemical pre-treatment to eliminate scaling problems. It is suggested that the economic potential for wave-powered desalination depends on these energy and cost savings more than compensating for the reduction in membrane life that occurs with variable feed conditions.

Artificial Intelligence Model for Flowable Concrete Mixtures Used in Underwater Construction and Repair

This study explores using artificial neural networks to predict the rheological and mechanical properties of underwater concrete (UWC) mixtures and to evaluate the sensitivity of such properties to variations in mixture ingredients. Artificial neural networks (ANN) mimic the structure and operation of biological neurons and have the unique ability of self-learning, mapping, and functional approximation. Details of the development of the proposed neural network model, its architecture, training, and validation are presented in this study. A database incorporating 175 UWC mixtures from nine different studies was developed to train and test the ANN model. The data are arranged in a patterned format. Each pattern contains an input vector that includes quantity values of the mixture variables influencing the behavior of UWC mixtures (that is, cement, silica fume, fly ash, slag, water, coarse and fine aggregates, and chemical admixtures) and a corresponding output vector that includes the rheological or mechanical property to be modeled. Results show that the ANN model thus developed is not only capable of accurately predicting the slump, slump-flow, washout resistance, and compressive strength of underwater concrete mixtures used in the training process, but it can also effectively predict the aforementioned properties for new mixtures designed within the practical range of the input parameters used in the training process with an absolute error of 4.6, 10.6, 10.6, and 4.4%, respectively.

Factorial Design of Modelling Mix Proportion Parameters of Underwater Composite Cement Grouts

There is an increasing need to identify the rheological properties of cement grout using a simple test to determine the fluidity, and other properties of underwater applications such as washout resistance and compressive strength. This paper reviews statistical models developed using a factorial design that was carried out to model the influence of key parameters on properties affecting the performance of underwater cement grout. Such responses of fluidity included minislump and flow time measured by Marsh cone, washout resistance, unit weight, and compressive strength. The models are valid for mixes with 0.35–0.55 water-to-binder ratio (W/B), 0.053–0.141% of antiwashout admixture (AWA), by mass of water, and 0.4–1.8% (dry extract) of superplasticizer (SP), by mass of binder. Two types of underwater grout were tested: the first one made with cement and the second one made with 20% of pulverised fuel ash (PFA) replacement, by mass of binder. Also presented are the derived models that enable the identification of underlying primary factors and their interactions that influence the modelled responses of underwater cement grout. Such parameters can be useful to reduce the test protocol needed for proportioning of underwater cement grout. This paper attempts also to demonstrate the usefulness of the models to better understand trade-offs between parameters and compare the responses obtained from the various test methods that are highlighted.

Effect of Mixture Composition on Washout Resistance of Highly Flowable Underwater Concrete

The underwater casting of relatively thin lifts of concrete in water requires the proportioning of highly flowable concrete that can resist water dilution and segregation and spread readily into place. An investigation was carried out to determine the effects of antiwashout admixture concentration, water-cementitious materials ratio, and binder composition on the washout resistance of highly flowable concrete. Two main types of antiwashout admixtures were used: 1) a powdered welan gum at concentrations of 0.07 and 0.15% (by mass of binder); and 2) a liquid-based cellulosic admixture with dosages up to 1.65 L/100 kg of binder. The water-cementitious materials ratios were set at 0.41 and 0.47, corresponding to high-quality underwater concrete. Four binder compositions were used: a standard Canadian Type 10 cement, the same cement with 10% silica fume replacement, the cement with 50% granulated blast-furnace slag replacement, and a ternary cement containing 6% silica fume and 20% Class F fly ash. The concentrations of anti-washout admixture have direct impact on washout resistance. For a given washout loss, greater slump flow consistency can be achieved with the increases in anti-washout admixture concentration and decreases in water-binder ratio. The washout mass loss can be reduced, for a given consistency