Modelling photovoltaic modules with neural networks using angle of incidence and clearness index

A neural network for modelling photovoltaic modules using angle of incidence and clearness index is proposed. Engineers require methods to estimate the output of a photovoltaic plant depending on meteorological conditions. Therefore, models for the grid inverter and the generator must be provided, and their outputs must be combined. The connection between both models is related to the maximum power point of the generator and how it is tracked by the inverter. That maximum power point under specific conditions of irradiance and module temperature is determined by the I–V curve of the module, which must be simulated under those conditions. Algebraic procedures were used to simulate the I–V curve. Recently, neural networks have been used for the same purpose. Previous methods only take into account the irradiance and the module temperature. The model proposed is based on neural networks, and it uses not only the irradiance and the module temperature but also the angle of incidence and the instantaneous clearness index as additional inputs. The normalised clearness replaces the standard clearness index because it allows the removal of the hourly trend found in this last index. This new model improves the results obtained with previous ones as it can distinguish amongst samples with the same solar irradiance and temperature values but with different angle of incidence and instantaneous clearness index. Results show that this model could be used to improve the accuracy of the tools used to forecast the output of photovoltaic plants. Copyright © 2014 John Wiley & Sons, Ltd.     

AVScreen: a real-time video augmentation method

Journal of Real-Time Image Processing - We present a tool for video augmentation in real-time, which we name the augmentation virtual screen (AVScreen). AVScreen is useful for developing...     

Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding

Magnetic resonance imaging (MRI) is frequently used to detect and segment multiple sclerosis lesions due to the detailed and rich information provided. We present a modified expectation-maximisation algorithm to segment brain tissues (white matter, grey matter, and cerebro-spinal fluid) as well as a partial volume class containing fluid and grey matter. This algorithm provides an initial segmentation in which lesions are not separated from tissue, thus a second step is needed to find them. This second step involves the thresholding of the FLAIR image, followed by a regionwise refinement to discard false detections. To evaluate the proposal, we used a database with 45 cases comprising 1.5T imaging data from three different hospitals with different scanner machines and with a variable lesion load per case. The results for our database point out to a higher accuracy when compared to two of the best state-of-the-art approaches.     

A volume element model (VEM) for energy systems engineering

This work presents a simplified modeling and simulation approach for energy systems engineering that is capable of providing quick and accurate responses during system design. For that, the laws of conservation are combined with available empirical and theoretical correlations to quantify the diverse types of flows that cross the system and produce a simplified tridimensional mathematical model, namely a volume element model (VEM). The physical domain of interest is discretized in space, thus producing a system of algebraic and ODEs with respect to time, whose solution delivers the project variables spatial distribution and dynamic response. In order to illustrate the application of the VEM in energy systems engineering, three example problems are considered: (i) a regenerative heat exchanger; (ii) a power electronic building block (PEBB); and (iii) a notional all‐electric ship. The same mathematical model was used to analyze problems (ii) and (iii), that is, the thermal management of heat‐generating equipment packaging. In the examples, the converged mesh had a total of 20, 2000, and 7725 volume elements. The third problem led to the largest simulation, which for steady‐state cases took between 5 and 10 min of computational time to reach convergence and for the ship dynamic response 50 min (i.e., 80,000 s of real time). The regenerative heat exchanger model demonstrated how VEM allowed for the coexistence of different phases (subsystems) within the same volume element. The thermal management model was adjusted and experimentally validated for the PEBB system, and it was possible to perform a parametric and dynamic analysis of the PEBB and of the notional all‐electric ship. Therefore, because of the observed combination of accuracy and low computational time, it is expected that the model could be used as an efficient tool for design, control, and optimization in energy systems engineering. Copyright © 2014 John Wiley & Sons, Ltd.     

Using interactive multiobjective methods to solve DEA problems with value judgements

Data envelopment analysis (DEA) is a performance measurement tool that was initially developed without consideration of the decision maker (DM)'s preference structures. Ever since, there has been a wide literature incorporating DEA with value judgements such as the goal and target setting models. However, most of these models require prior judgements on target or weight setting. This paper will establish an equivalence model between DEA and multiple objective linear programming (MOLP) and show how a DEA problem can be solved interactively without any prior judgements by transforming it into an MOLP formulation. Various interactive multiobjective models would be used to solve DEA problems with the aid of PROMOIN, an interactive multiobjective programming software tool. The DM can then search along the efficient frontier to locate the most preferred solution where resource allocation and target levels based on the DM's value judgements can be set. An application on the efficiency analysis of retail banks in the UK is examined. Comparisons of the results among the interactive MOLP methods are investigated and recommendations on which method may best fit the data set and the DM's preferences will be made.     

On the sizing of a solar thermal electricity plant for multiple objectives using evolutionary optimization

Design, implementation and operation of solar thermal electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks.     

Optimal Experimental Designs in the Flow Rate of Particles

This article focuses on analyzing the process of jam formation during the discharge by gravity of granular material stored in a two-dimensional silo. The aim of the article is two-fold. First, optimal experimental designs are computed, in which four approaches are considered: D-optimality, a combination of D-optimality and a cost/gain function, Bayesian D-optimality, and sequential designing. These results reveal that the efficiency of the design used by the experimenters can be improved dramatically. A sensitivity analysis with respect to the most important parameter is also performed. Second, estimation of the unknown parameters is done using least squares, that is, assuming normality, and also via maximum likelihood assuming the exponential distribution. Simulations for the designs considered in this article show that the variance, the mean squared error, and the bias of the estimators using maximum likelihood are in most cases lower than those using least squares. Supplementary materials for this article are available online.     

A Review of Element-Based Galerkin Methods for Numerical Weather Prediction: Finite Elements,Spectral Elements,and Discontinuous Galerkin

Numerical weather prediction (NWP) is in a period of transition. As resolutions increase, global models are moving towards fully nonhydrostatic dynamical cores, with the local and global models using the same governing equations; therefore we have reached a point where it will be necessary to use a single model for both applications. The new dynamical cores at the heart of these unified models are designed to scale efficiently on clusters with hundreds of thousands or even millions of CPU cores and GPUs. Operational and research NWP codes currently use a wide range of numerical methods: finite differences, spectral transform, finite volumes and, increasingly, finite/spectral elements and discontinuous Galerkin, which constitute element-based Galerkin (EBG) methods. Due to their important role in this transition, will EBGs be the dominant power behind NWP in the next 10 years, or will they just be one of many methods to choose from? One decade after the review of numerical methods for atmospheric modeling by Steppeler et al. (Meteorol Atmos Phys 82:287–301, 2003), this review discusses EBG methods as a viable numerical approach for the next-generation NWP models. One well-known weakness of EBG methods is the generation of unphysical oscillations in advection-dominated flows; special attention is hence devoted to dissipation-based stabilization methods. Since EBGs are geometrically flexible and allow both conforming and non-conforming meshes, as well as grid adaptivity, this review is concluded with a short overview of how mesh generation and dynamic mesh refinement are becoming as important for atmospheric modeling as they have been for engineering applications for many years.     

Computational aspects of the mechanics of complex materials

Summary Bodies with exotic properties display material substructural complexity from nano to meso-level. Various models have been built up in condensed matter physics to represent the behavior of special classes of complex bodies. In general, they fall within the setting of an abstract model building framework which is not only a unifying structure of existing models but—above all—atool to construct special models of new exotic materials. We describe here basic elements of this framework, the one ofmultifield theories, trying to furnish a clear idea of the subtle theoretical and computational problems arising within it. We present the matter in a form that allows one to construct appropriate algorithms in special cases of physical interest. We discuss also issues related to the construction of compatible and mixed finite elements in linearized setting, the extension of extended finite element methods to analyze the influnce of material substructures on crack growth, the evolution of sharp discontinuity surfaces in complex bodies. Concrete examples of complex bodies are also presented with a number of details. Dedicated to Gianfranco Capriz on occasion of his eightieth birthday