Investigation of wave-driven hydroelastic interactions using numerical and physical modelling approaches

Wave-driven hydroelasticity is of great importance to a wide range of applications within offshore and coastal engineering. Harnessing the benefits of hydroelasticity or minimising its impacts, depending on the application, has recently led to substantial investment in research effort in this field. However, the complex and strongly-coupled nature of the problem generally make the impacts very case specific, highlighting the importance of accurate numerical tools for assessing the impact on a case-by-case basis. Therefore, this study aims to provide novel experimental data to assist with the development of a coupled numerical methodology for simulating fully nonlinear hydroelastic interactions with highly-flexible floating structures. Novel physical data from a laboratory campaign conducted at the University of Plymouth is presented, and used as a reference for assessing the capabilities of an existing coupled numerical approach. The numerical model is a partitioned approach based within the open-source computational fluid dynamics software OpenFOAM and consisting of a two-phase fluid solver; a linear solid model for small deformations solved via the block-coupled method; and strongly-coupled through the Dirichlet–Neumann method with dynamic Aitken under-relaxation. The numerical model is shown to capture well the wave-induced deformation, and the qualitative differences between structures of varying dimensions. However, the high computational cost limits the scope of this work to 2-D, and future work should focus on optimising the approach to allow for application in 3-D problems

Experimental and Numerical Collaborative Latching Control of Wave Energy Converter Arrays

A challenge while applying latching control on a wave energy converter (WEC) is to find a reliable and robust control strategy working in irregular waves and handling the non-ideal behavior of real WECs. In this paper, a robust and model-free collaborative learning approach for latchable WECs in an array is presented. A machine learning algorithm with a shallow artificial neural network (ANN) is used to find optimal latching times. The applied strategy is compared to a latching time that is linearly correlated with the mean wave period: It is remarkable that the ANN-based WEC achieved a similar power absorption as the WEC applying a linear latching time, by applying only two different latching times. The strategy was tested in a numerical simulation, where for some sea states it absorbed more than twice the power compared to the uncontrolled WEC and over 30% more power than a WEC with constant latching. In wave tank tests with a 1:10 physical scale model the advantage decreased to +3% compared to the best tested constant latching WEC, which is explained by the lower advantage of the latching strategy caused by the non-ideal latching of the physical power take-off model.SUPERFARM

Performance of a Direct-Driven Wave Energy Point Absorber with High Inertia Rotatory Power Take-off

An alternating rotatory generator using an eddy current break is developed as a physicalscale model of a direct-driven floating point absorber power take-off (PTO) for wave tank tests. It isshown that this design is a simple and cost-effective way to get an accurate linear damping PTO. Thedevice shows some beneficial characteristics, making it an interesting option for full scale devices:For similar weights the inertia can be significantly higher than for linear generators, allowing it tooperate with natural frequencies close to typical wave frequencies. The influence of the higher inertiaon the power absorption is tested using both a numerical simulation and physical wave tank tests.With the increased inertia the PTO is able to absorb more than double the energy of a comparabledirect-driven linear generator in some sea states. Moreover, the alternating rotatory generator allowsthe absorption characteristic to be tuned by changing the inertia and the generator damping

A Model Free Control Based on Machine Learning for Energy Converters in an Array

This paper introduces a machine learning based control strategy for energy converter arrays designed to work under realistic conditions where the optimal control parameter can not be obtained analytically. The control strategy neither relies on a mathematical model, nor does it need a priori information about the energy medium. Therefore several identical energy converters are arranged so that they are affected simultaneously by the energy medium. Each device uses a different control strategy, of which at least one has to be the machine learning approach presented in this paper. During operation all energy converters record the absorbed power and control output; the machine learning device gets the data from the converter with the highest power absorption and so learns the best performing control strategy for each situation. Consequently, the overall network has a better overall performance than each individual strategy. This concept is evaluated for wave energy converters (WECs) with numerical simulations and experiments with physical scale models in a wave tank. In the first of two numerical simulations, the learnable WEC works in an array with four WECs applying a constant damping factor. In the second simulation, two learnable WECs were learning with each other. It showed that in the first test the WEC was able to absorb as much as the best constant damping WEC, while in the second run it could absorb even slightly more. During the physical model test, the ANN showed its ability to select the better of two possible damping coefficients based on real world input data

Lobar and segmental liver atrophy associated with hilar cholangiocarcinoma and the impact of hilar biliary anatomical variants: a pictorial essay

The radiological features of lobar and segmental liver atrophy and compensatory hypertrophy associated with biliary obstruction are important to recognise for diagnostic and therapeutic reasons. Atrophied lobes/segments reduce in volume and usually contain crowded dilated bile ducts extending close to the liver surface. There is often a “step” in the liver contour between the atrophied and non-atrophied parts. Hypertrophied right lobe or segments enlarge and show a prominently convex or “bulbous” visceral surface. The atrophied liver parenchyma may show lower attenuation on pre-contrast computed tomography (CT) and CT intravenous cholangiography (CT-IVC) and lower signal intensity on T1-weighted magnetic resonance imaging (MRI). Hilar biliary anatomical variants can have an impact on the patterns of lobar/segmental atrophy, as the cause of obstruction (e.g. cholangiocarcinoma) often commences in one branch, leading to atrophy in that drainage region before progressing to complete biliary obstruction and jaundice. Such variants are common and can result in unusual but explainable patterns of atrophy and hypertrophy. Examples of changes seen with and without hilar variants are presented that illustrate the radiological features of atrophy/hypertrophy

Software platform virtualization in chemistry research and university teaching

<p>Abstract</p> <p>Background</p> <p>Modern chemistry laboratories operate with a wide range of software applications under different operating systems, such as Windows, LINUX or Mac OS X. Instead of installing software on different computers it is possible to install those applications on a single computer using Virtual Machine software. Software platform virtualization allows a single guest operating system to execute multiple other operating systems on the same computer. We apply and discuss the use of virtual machines in chemistry research and teaching laboratories.</p> <p>Results</p> <p>Virtual machines are commonly used for cheminformatics software development and testing. Benchmarking multiple chemistry software packages we have confirmed that the computational speed penalty for using virtual machines is low and around 5% to 10%. Software virtualization in a teaching environment allows faster deployment and easy use of commercial and open source software in hands-on computer teaching labs.</p> <p>Conclusion</p> <p>Software virtualization in chemistry, mass spectrometry and cheminformatics is needed for software testing and development of software for different operating systems. In order to obtain maximum performance the virtualization software should be multi-core enabled and allow the use of multiprocessor configurations in the virtual machine environment. Server consolidation, by running multiple tasks and operating systems on a single physical machine, can lead to lower maintenance and hardware costs especially in small research labs. The use of virtual machines can prevent software virus infections and security breaches when used as a sandbox system for internet access and software testing. Complex software setups can be created with virtual machines and are easily deployed later to multiple computers for hands-on teaching classes. We discuss the popularity of bioinformatics compared to cheminformatics as well as the missing cheminformatics education at universities worldwide.</p

Differential responses of zooplankton assemblages to environmental variation in temporary and permanent ponds

Permanent and temporary wetlands in Mediterranean shrublands represent unique repositories of biodiversity, which are increasingly threatened by human-induced habitat loss. The zooplankton of a permanent (P1) and a temporary pond (T35) in the Natural Reserve of Castelporziano, a rare residual stretch of such a shrubland in Central Italy (Latium), was investigated to: (1) expand and deepen knowledge of these endangered freshwater habitats, which represent a crucial component of Mediterranean biodiversity; (2) identify environmental controls regulating the development of zooplankton communities of each environment; and (3) highlight differences in the adaptive responses of the zooplankton community in relation to the different ecological conditions experienced by permanent and temporary habitats. Despite summer desiccation in T35, the two ponds exhibited a relative homogeneity in hydrological and physico-chemical dynamics. Zooplankton assemblages contained 41 total taxa, of which 32 were found in P1 and 28 in T35. Out of the 41 taxa identified, 22 (> 50%) were exclusively present in one of the two ponds. On a yearly basis, the community dynamics of P1 seemed to be conditioned by physical and chemical factors and by hydrological cycle characteristics, while the community of T35 responded to algal blooms, food competition and predator/prey equilibria rather than correlating to abiotic factors. The main differences amongst zooplankton assemblages were observed over short time scales and occurred both within and between seasons, highlighting the role of some structural taxa that dominated the average composition of the community throughout the year, and the importance of "quick-response" taxa in determining the short-term composition and structure variation of pond zooplankton. A year-round cyclic community succession peculiar to each pond is described