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

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)

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

Dose-dependent differential effect of neurotrophic factors on in vitro and in iivo regeneration of motor and sensory neurons

Although peripheral axons can regenerate after nerve transection and repair, functional recovery is usually poor due to inaccurate reinnervation. Neurotrophic factors promote directional guidance to regenerating axons and their selective application may help to improve functional recovery. Hence, we have characterized in organotypic cultures of spinal cord and dorsal root ganglia the effect of GDNF, FGF-2, NGF, NT-3, and BDNF at different concentrations on motor and sensory neurite outgrowth. In vitro results show that GDNF and FGF-2 enhanced both motor and sensory neurite outgrowth, NGF and NT-3 were the most selective to enhance sensory neurite outgrowth, and high doses of BDNF selectively enhanced motor neurite outgrowth. Then, NGF, NT-3, and BDNF (as the most selective factors) were delivered in a collagen matrix within a silicone tube to repair the severed sciatic nerve of rats. Quantification of Fluorogold retrolabeled neurons showed that NGF and NT-3 did not show preferential effect on sensory regeneration whereas BDNF preferentially promoted motor axons regeneration. Therefore, the selective effects of NGF and NT-3 shown in vitro are lost when they are applied in vivo, but a high dose of BDNF is able to selectively enhance motor neuron regeneration both in vitro and in vivo

An Octahedric Regression Model of Energy Efficiency on Residential Buildings

System modeling is a main task in several research fields. The development of numerical models is of crucial importance at the present because of its wide use in the applications of the generically named machine learning technology, including different kinds of neural networks, random field models, and kernel-based methodologies. However, some problems involving the reliability of their predictions are common to their use in the real world. Octahedric regression is a kernel averaged methodology developed by the authors that tries to simplify the entire process from raw data acquisition to model generation. A discussion about the treatment and prevention of overfitting is presented and, as a result, models are obtained that allow for the measurement of this effect. In this paper, this methodology is applied to the problem of estimating the energetic needs of different buildings according to their principal characteristics, a problem that has importance in architecture and civil and environmental engineering due to increasing concerns about energetic efficiency and ecological footprint

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

Effects of recent cooling in the Antarctic Peninsula on snow density and surface mass balance

The Antarctic Peninsula region has experienced a recent cooling for about 15 years since the beginning of the 21st century. In Livingston Island, this cooling has been of 0.8°C over the 12-yr period 2004–2016, and of 1.0°C for the summer average temperatures over the same period. In this paper, we analyse whether this observed cooling has implied a significant change in the density of the snowpack covering Hurd and Johnsons glaciers, and whether such a density change has had, by itself, a noticeable impact in the calculated surface mass balance. Our results indicate a decrease in the snow density by 22 kg m-3 over the study period. The density changes are shown to be correlated with the summer temperature changes. We show that this observed decrease in density does not have an appreciable effect on the calculated surface mass balance, as the corresponding changes are below the usual error range of the surface mass balance estimates. This relieves us from the need of detailed and time-consuming snow density measurements at every mass-balance campaign.This research was funded by the Spanish State Plan for Research and Development projects CTM2014-56473-R and CTM2017-84441-R

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

On the volatility of aromatic hydrocarbons in ionic liquids: Vapor-liquid equilibrium measurements and theoretical analysis

The use of ionic liquids (ILs) as solvent in the liquid-liquid extraction of aromatic compounds is one of their most studied applications. Nevertheless, the recovery of the extracted hydrocarbons has been much less investigated, being a required task to complete the global separation process. Taking into account the negligible vapor pressure of the ILs, this step could be easily carried out by flash distillation, which requires the study of vapor-liquid equilibrium (VLE). In order to study this topic deeper, in this work a systematic analysis of the VLE and vapor-liquid-liquid equilibrium (VLLE) data for {aromatic hydrocarbon + IL} binary mixtures was carried out, from both an experimental and computational point of view. For that, new experimental VLE and VLLE data of 24 {toluene + IL} binary mixtures were measured at 323.15 K using a technique based on the static headspace gas chromatography (HS-GC), providing relevant information on the toluene retained in the liquid depending on the cation/anion structure of the IL in the mixture. Furthermore, the quantum chemical Conductor-like Screening Model for Real Solvents (COSMO-RS) method was applied to better understand the structure-property relationship determining the phase behavior of {aromatic hydrocarbon + IL} binary systems. First, the suitability of COSMO-RS to predict VLE and VLLE data of {toluene + IL} binary mixtures was evaluated by comparison to 225 experimental data at 323.15 K, including 24 different ILs over the whole composition range. Valuable conclusions were achieved respect to the molecular model of IL needed to adequately predict VLE and VLLE data of the {aromatic hydrocarbon + IL} binary mixtures. Once the computational approach was stated, COSMO-RS methodology was used to analyze the influence of the intermolecular interactions between the toluene and the IL component on the phase behavior of their mixtures. As a result, COSMO-RS was demonstrated as a useful tool for the rational design of ILs with optimized properties for the separation of aromatic + aliphatic hydrocarbon binary mixtures, considering both liquid-liquid extraction and solvent regeneration stepsThe authors are grateful to Ministerio de Economía y Competitividad (MINECO) of Spain for financial support of Projects CTQ2014-52288-R and CTQ2014–53655-R and to Comunidad Autónoma de Madrid for the Project S2013/MAE-2800. Pablo Navarro thanks Fundação para a Ciência e a Tecnologia for awarding him a postdoctoral grant (Reference SFRH/BPD/117084/2016). Marcos Larriba also thanks MINECO for awarding him a Juan de la Cierva-Formación Contract (Reference FJCI-2015-25343)

A parallel methodology using radial basis functions versus machine learning approaches applied to environmental modelling

Parallel nonlinear models using radial kernels on local mesh support have been designed and implemented for application to real-world problems. Although this recently developed approach reduces the memory requirements compared with other methodologies suggested over the last few years, its computational cost makes parallelisation necessary, especially for big datasets with many instances or attributes. In this work, several strategies for the parallelisation of this methodology are proposed and compared. The MPI communication protocol and the OpenMP application programming interface are used to implement the algorithm. The performance of this methodology is compared with various machine learning methods, with particular consideration of techniques using radial basis functions (RBF). Different methods are applied to model the daily maximum air temperature from real meteorological data collected from the Agroclimatic Station Network of the Phytosanitary Alert and Information Network of Andalusia, an autonomous community of southern Spain. The obtained goodness-of-fit measures illustrate the effectiveness of this nonlinear methodology, and its training process is shown to be simpler than those of other powerful machine learning methods.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), and by the University of Alicante