On the use of OpenFOAM to model Oscillating wave surge converters

The Computational Fluid Dynamic (CFD) toolbox OpenFOAM is used to assess the applicability of Reynolds-Averaged Navier-Stokes (RANS) solvers to the simulation of Oscillating Wave Surge Converters (OWSC) in significant waves. Simulation of these flap type devices requires the solution of the equations of motion and the representation of the OWSC’s motion in a moving mesh. A new way to simulate the sea floor inside a section of the moving mesh with a moving dissipation zone is presented. To assess the accuracy of the new solver, experiments are conducted in regular and irregular wave traces for a full three dimensional model. Results of acceleration and flow features are presented for numerical and experimental data. It is found that the new numerical model reproduces experimental results within the bounds of experimental accuracy.

Hydrodynamic Loading on a Bottom Hinged Oscillating Wave Surge Converter

This paper presents experimental and numerical studies into the hydrodynamic loading of a bottom-hinged large buoyant flap held rigidly upright in waves. Possible applications and limitations of physical experiments, a linear potential analytical method, a linear potential numerical method, a weakly non-linear tool and RANS CFD simulations are discussed. Different domains of applicability of these research techniques are highlighted considering the validity of underlying assumptions, complexity of application and feasibility in terms of resources like time and computing power needed to obtain results. Conclusions are drawn regarding the future extension of the numerical methods to the case of a moving flap.

FE Simulation of Viscoplastic Consistency Model

Finite element (FE) simulations provides an inexpensive alternative for material testingof new metal alloys. Carrying out experimental testing is expensive. Nanoindentation is particularly costly due to the equipment needed to work on such a scale. FE simulations provide an inexpensive means of material testing if accurately carried out. This paper will demonstrate the applicability and accuracy of using FE modelling for basic material tests and will propose that the viscoplastic model may be used for nanoindentation testing. The simulations will test the Young’s modulus of materials during analysis when an Abaqus VUMAT is used. The viscoplastic model is incorporated into a subroutine and is tested at the macroscopic scale against previous published results.

Building pH Sensors into Paper-based Small-molecular Logic Sys-tems for Very Simple Detection of Edges of Objects

Genetically-engineered bacteria and reactive DNA networks detect edges of objects, as done in our retinas and as also found within computer vision. We now demonstrate that simple molecular logic systems (a combination of a pH sensor, a photo acid generator and a pH buffer spread on paper) without any organization can achieve this relatively complex computational goal with good-fidelity. This causes a jump in the complexity achievable by molecular logic-based computation and extends its applicability. The molecular species involved in light dose-driven 'off-on-off' fluorescence is diverted in the ‘on’ state by proton diffusion from irradiated to unirradiated regions where it escapes a strong quencher, thus visualizing the edge of a mask.

Discovering subgroups of patients from DNA copy number data using NMF on compacted matrices

In the study of complex genetic diseases, the identification of subgroups of patients sharing similar genetic characteristics represents a challenging task, for example, to improve treatment decision. One type of genetic lesion, frequently investigated in such disorders, is the change of the DNA copy number (CN) at specific genomic traits. Non-negative Matrix Factorization (NMF) is a standard technique to reduce the dimensionality of a data set and to cluster data samples, while keeping its most relevant information in meaningful components. Thus, it can be used to discover subgroups of patients from CN profiles. It is however computationally impractical for very high dimensional data, such as CN microarray data. Deciding the most suitable number of subgroups is also a challenging problem. The aim of this work is to derive a procedure to compact high dimensional data, in order to improve NMF applicability without compromising the quality of the clustering. This is particularly important for analyzing high-resolution microarray data. Many commonly used quality measures, as well as our own measures, are employed to decide the number of subgroups and to assess the quality of the results. Our measures are based on the idea of identifying robust subgroups, inspired by biologically/clinically relevance instead of simply aiming at well-separated clusters. We evaluate our procedure using four real independent data sets. In these data sets, our method was able to find accurate subgroups with individual molecular and clinical features and outperformed the standard NMF in terms of accuracy in the factorization fitness function. Hence, it can be useful for the discovery of subgroups of patients with similar CN profiles in the study of heterogeneous diseases.

Improving parameter learning of Bayesian nets from incomplete data

This paper addresses the estimation of parameters of a Bayesian network from incomplete data. The task is usually tackled by running the Expectation-Maximization (EM) algorithm several times in order to obtain a high log-likelihood estimate. We argue that choosing the maximum log-likelihood estimate (as well as the maximum penalized log-likelihood and the maximum a posteriori estimate) has severe drawbacks, being affected both by overfitting and model uncertainty. Two ideas are discussed to overcome these issues: a maximum entropy approach and a Bayesian model averaging approach. Both ideas can be easily applied on top of EM, while the entropy idea can be also implemented in a more sophisticated way, through a dedicated non-linear solver. A vast set of experiments shows that these ideas produce significantly better estimates and inferences than the traditional and widely used maximum (penalized) log-likelihood and maximum a posteriori estimates. In particular, if EM is adopted as optimization engine, the model averaging approach is the best performing one; its performance is matched by the entropy approach when implemented using the non-linear solver. The results suggest that the applicability of these ideas is immediate (they are easy to implement and to integrate in currently available inference engines) and that they constitute a better way to learn Bayesian network parameters.

Development of a doped titania immobilised thin film multi tubular photoreactor

This paper describes a novel doped titania immobilised thin film multi tubular photoreactor which has been developed for use with liquid, vapour or gas phase media. In designing photocatalytic reactors measuring active surface area of photocatalyst within the unit is one of the critical design parameters. This dictate greatly limits the applicability of any semi-conductor photocatalyst in industrial applications, as a large surface area equates to a powder catalyst. This demonstration of a thin film coating, doped with a rare earth element, novel photoreactor design produces a photocatalytic degradation of a model pollutant (methyl orange) which displayed a comparable degradation achieved with P25 TiO2. The use of lanthanide doping is reported here in the titania sol gel as it is thought to increase the electron hole separation therefore widening the potential useful wavelengths within the electromagnetic spectrum. Increasing doping from 0.5% to 1.0% increased photocatalytic degradation by ∼17% under visible irradiation. A linear relationship has been seen between increasing reactor volume and degradation which would not normally be observed in a typical suspended reactor system. © 2012 Elsevier B.V.