A Finite Element Numerical Algorithm for Modelling and Data Fitting in Complex Systems

Numerical modelling methodologies are important by their application to engineering and scientific problems, because there are processes where analytical mathematical expressions cannot be obtained to model them. When the only available information is a set of experimental values for the variables that determine the state of the system, the modelling problem is equivalent to determining the hyper-surface that best fits the data. This paper presents a methodology based on the Galerkin formulation of the finite elements method to obtain representations of relationships that are defined a priori, between a set of variables: y = z(x1, x2,...., xd). These representations are generated from the values of the variables in the experimental data. The approximation, piecewise, is an element of a Sobolev space and has derivatives defined in a general sense into this space. The using of this approach results in the need of inverting a linear system with a structure that allows a fast solver algorithm. The algorithm can be used in a variety of fields, being a multidisciplinary tool. The validity of the methodology is studied considering two real applications: a problem in hydrodynamics and a problem of engineering related to fluids, heat and transport in an energy generation plant. Also a test of the predictive capacity of the methodology is performed using a cross-validation method

Influence of pressure and temperature on key physicochemical properties of corn stover-derived biochar

This study focuses on analyzing the effect of both the peak temperature and pressure on the properties of biochar produced through slow pyrolysis of corn stover, which is a common agricultural waste that currently has little or no value. The pyrolysis experiments were carried out in a fixed-bed reactor at different peak temperatures (400, 525 and 650 °C) and absolute pressures (0.1, 0.85 and 1.6 MPa). The inert mass flow rate (at NTP conditions) was adjusted in each test to keep the gas residence time constant within the reactor. The as-received corn stover was pyrolyzed into a biochar without any physical pre-treatment as a way to reduce the operating costs. The properties of biochars showed that high peak temperature led to high fixed-carbon contents, high aromaticity and low molar H:C and O:C ratios; whereas a high pressure only resulted in a further decrease in the O:C ratio and a further increase in the fixed-carbon content. Increasing the operating pressure also resulted in a higher production of pyrolysis gas at the expense of water formation

An algorithm to schedule water delivery in pressurized irrigation networks

This study presents a deterministic constrained optimisation algorithm developed for using in a pressurized irrigation network. In irrigation networks —or water networks supplied by a head tank— utility managers can fully adapt the delivery times to suit their needs. The program provides a strategy for scheduling water delivery at a constant flow rate (opening and closing of hydrants, units, and subunits) to minimise energy consumption. This technique improves on earlier approaches by employing a deterministic method with little computing time. This method has been tested in the University of Alicante pressurized irrigation network, where decision-makers have identified the need to diminish the energy expenditure for watering University’s gardens.This work was supported by the research project “DESENREDA” through the 2021 call “Estancias de movilidad en el extranjero Jose Castillejo” of the Ministerio de Universidades (CAS21/00085) and for the project “Hi-Edu Carbon” Erasmus Plus Programme, Key Action KA22021, action type (2021-1-SK01-KA220-HED-000023274

Parallel approach of a Galerkin-based methodology for predicting the compressive strength of the lightweight aggregate concrete

A methodology based on the Galerkin formulation of the finite element method has been analyzed for predicting the compressive strength of the lightweight aggregate concrete using ultrasonic pulse velocity. Due to both the memory requirements and the computational cost of this technique, its parallelization becomes necessary for solving this problem. For this purpose a mixed MPI/OpenMP parallel algorithm has been designed and different approaches and data distributions analyzed. On the other hand, this Galerkin methodology has been compared with multiple linear regression models, regression trees and artificial neural networks. Based on different measures of goodness of fit, the effectiveness of the Galerkin methodology, compared with these statistical techniques for data mining, is shown.This research was supported by the Spanish Ministry of Science, Innovation and Universities Grant RTI2018-098156-B-C54, co-financed by the European Commission (FEDER funds)

Are orchid bees useful indicators of the impacts of human disturbance?

Biodiversity and ecosystem functions are threatened by human disturbance, and tropical forests are one the most vulnerable habitats. Monitoring the impacts of disturbance and the success of conservation projects is crucial, and to do this effectively it is important to identify suitable measures that are sensitive to ecosystem disturbance. Orchid bees (Euglossini) are a specialist group with mutualistic relationships with many plant species and can fly long distances, making them important pollinators of widely dispersed plant species. A loss of specialist pollinators such as these could have severe consequences for the plants that rely on their services. We therefore aimed to answer the following question: are orchid bees useful indicators of the impacts of human disturbance? If so, what measures of orchid bee diversity are most sensitive? And do orchid bees provide any indication of changes in pollination services along a disturbance gradient? Orchid bees were collected from 18 sites across a gradient of disturbance in a tropical forest region in southeast Peru. Alpha diversity across the gradient was compared using Hills numbers. Beta diversity was assessed using community composition, species contributions to beta diversity, beta diversity partitioning and novel measures of redundancy and representativeness. The potential pollination services available at each site were measured using artificial flowers and counts of pollinator visits. Alpha diversity of orchid bees showed low sensitivity to disturbance. Beta diversity measures were more informative, with disturbed sites found to be highly redundant in the ecosystem compared to the less disturbed sites. However, the most sensitive measure across the gradient was abundance – there was a significant decrease in the number of bees caught as disturbance increased, with likely consequences for pollination services. These results suggest that orchid bees may be useful indicators of the impacts of human disturbance, but alpha diversity is a poor metric for this purpose. In order to understand how human disturbance is affecting biodiversity, multiple diversity indices should be considered, and in the case of orchid bees, redundancy and abundance could be useful for detecting sensitive responses to forest disturbance. © 2019 Elsevier Lt

Numerical Non-Linear Modelling Algorithm Using Radial Kernels on Local Mesh Support

Estimation problems are frequent in several fields such as engineering, economics, and physics, etc. Linear and non-linear regression are powerful techniques based on optimizing an error defined over a dataset. Although they have a strong theoretical background, the need of supposing an analytical expression sometimes makes them impractical. Consequently, a group of other approaches and methodologies are available, from neural networks to random forest, etc. This work presents a new methodology to increase the number of available numerical techniques and corresponds to a natural evolution of the previous algorithms for regression based on finite elements developed by the authors improving the computational behavior and allowing the study of problems with a greater number of points. It possesses an interesting characteristic: Its direct and clear geometrical meaning. The modelling problem is presented from the point of view of the statistical analysis of the data noise considered as a random field. The goodness of fit of the generated models has been tested and compared with some other methodologies validating the results with some experimental campaigns obtained from bibliography in the engineering field, showing good approximation. In addition, a small variation on the data estimation algorithm allows studying overfitting in a model, that it is a problematic fact when numerical methods are used to model experimental values.This research has been partially funded by the Spanish Ministry of Science, Innovation and Universities, grant number RTI2018-101148-B-I00

A methodology for the classification of gravel beaches

Beaches are highly flexible structures that can be deformed by several reasons, some natural as wind and swell and others not, as human actions. Gravel, considered as a component of the beach is not always separated from the rest of the materials. It is a part of the coastline sedimentary balance, usually with time and spatial scales much greater than those corresponding to the stretch of the coast under study. The conceptual and experimental difficulties of studying this kind of beach have meant that nowadays they are really unknown. In this paper, methodologies to classify and determinate the most important characteristics in gravel beaches are presented. The authors have studied 34 shingle beaches in the region of Alicante (Spain) from a database with their characteristics. Obtained data corresponds to the morphology of the beach, the materials that take part in its composition and the wave energy, considering its incidence, the wave height, the local period and its influence on the coastline. At the beginning, mathematical models are generated, allowing the expression of the relationships between the slope of berm and the rest of variables. To classify the beaches, a factor analysis has been used on the experimental data matrix, considering all the variables as predictive, obtaining in this way an index for beach classification with similar characteristics. Furthermore, to determine the predictive variables that allow characterizing the 34 beaches, a discriminant analysis has been applied over several sets of variables. In each case, a predictive model of cluster belonging is created, considering a discriminant function, and with the clustering function formed by different clusters. The methodologies developed in this paper will be applied later to other beaches as classification and variable selection methods

Benefits of a dance group intervention on institutionalized elder people: A Bayesian network approach

The present study aims to explore the effects of an adapted classical dance intervention on the psychological and functional status of institutionalized elder people using a Bayesian network. All participants were assessed at baseline and after the 9 weeks period of the intervention. Measures included balance and gait, psychological well-being, depression, and emotional distress. According to the Bayesian network obtained, the dance intervention increased the likelihood of presenting better psychological well-being, balance, and gait. Besides, it also decreased the probabilities of presenting emotional distress and depression. These findings demonstrate that dancing has functional and psychological benefits for institutionalized elder people. Moreover it highlights the importance of promoting serious leisure variety in the daily living of institutionalized elder adults

Galerkin's formulation of the finite elements method to obtain the depth of closure

Coastal erosion and lack of sediment supply are a serious global problem. It is therefore necessary to determine the depth of closure (DoC) of a beach—key parameter in the calculation of the sand volume and the location of the beach protection elements—in a precise way. For this reason, this work generates a numerical model based on Galerkin's formulation of finite elements that provides sufficient precision for the determination of DoC with a minimum investment. Thus, after the generation of three models in which the difference was the dependent variables, the least complex has been chosen. It is composed of the variables: median sediment size, wave height and period associated with the mean flow, as well as the angle that the mean flow forms with respect to the studied profile in absolute value (α). The selected model has been compared with the most commonly used models currently in use, having an average absolute error of 0.36 m and an average MAPE of 70% over current models. In addition, it presents a high stability, since after the random disturbance of all the input variables (up to 5%), the model error remains stable, increasing the MAPE by a maximum of 7.4% and the average absolute error by 0.15 m. Therefore, it is possible to use the model to infer the DoC in other study areas where the values of the variables are similar to those studied here, although the selected method can be extrapolated to other parts of the world.This work was partially supported by the Universidad de Alicante through the project “Estudio sobre el perfil de equilibrio y la profundidad de cierre en playas de arena” (GRE15-02)