DSpace Collection:http://hdl.handle.net/10174/11172024-03-28T11:49:42Z2024-03-28T11:49:42ZStabilization of Oldroyd-B Fluid Flow Simulations Based On Eigenvalues of Conformation TensorPires, MariliaBodnár, Tomáshttp://hdl.handle.net/10174/364622024-03-19T16:57:51Z2023-03-01T00:00:00ZTitle: Stabilization of Oldroyd-B Fluid Flow Simulations Based On Eigenvalues of Conformation Tensor
Authors: Pires, Marilia; Bodnár, Tomás
Editors: Maria Amélia Loja, Mourad Bezzeghoud; Joaquim Infante Barbosa, José Alberto Rodrigues
Abstract: From a physical point of view, the molecular strain tensor can be represented
at continuous level in viscoelastic fluids by the conformation tensor. This symmetric tensor
should always be positive definite, however the positive-definiteness is sometimes lost
in numerical simulations of non-Newtonian viscoelastic fluids flows at larger values of
the Weissenberg number. This problem known as the High Weissenberg Number Problem
(HWNP) and is characterized by the breakdown of numerical solutions. In some cases
the HWNP can be avoided (or at least delayed) by adding a stress diffusion term to the
transport equations for viscoelastic tensors. In this work, numerical tests are presented,
demonstrating the HWNP problem and its possible cure based on stabilization method employing
local addition of an artificial stress diffusion term to the transport equations, in the
regions of the computational domain where the positive-definiteness of the conformation
tensor can be violated.2023-03-01T00:00:00ZMulti-objective Finite-Domain Constraint-Based Forest ManagementEloy, EduardoBushenkov, VladimirAbreu, Salvadorhttp://hdl.handle.net/10174/360972024-01-15T11:46:00Z2023-12-01T00:00:00ZTitle: Multi-objective Finite-Domain Constraint-Based Forest Management
Authors: Eloy, Eduardo; Bushenkov, Vladimir; Abreu, Salvador
Editors: Almeida, João Paulo; Alvelos, Filipe; Cerdeira, Jorge; Moniz, Samuel; Requejo, Cristina
Abstract: This paper describes an implementation of a Constraint Programming approach to the problem of multi-criteria forest management optimization. The goal is to decide when to harvest each forest unit while striving to optimize several criteria under spatial restrictions. With a large number of management units, the optimization problem becomes computationally intractable. We propose an approach for deriving a set of efficient solutions for the entire region. The proposed methodology was tested for Vale do Sousa region in the North of Portugal.2023-12-01T00:00:00ZAutomatic classification of ornamental stones using Machine Learning techniques - a study applied to limestone.Tereso, MarcoRato, LuisGonçalves, Teresahttp://hdl.handle.net/10174/340942023-02-10T11:24:25Z2020-05-31T23:00:00ZTitle: Automatic classification of ornamental stones using Machine Learning techniques - a study applied to limestone.
Authors: Tereso, Marco; Rato, Luis; Gonçalves, Teresa
Abstract: The industry of extraction and transformation of rock minerals has an enormous importance in the Portuguese trade balance. The export volume increases every year, and to maintain these results it is necessary to invest in the modernization and optimization of production processes, as well as, in the classification of raw materials. This study aims to implement a classification model of ornamental rocks through the analysis and classification of images, using machine learning algorithms. The recognition of the type of stone, through the capture of images and subsequent algorithmic analysis, will allow to define quality control scales in future processes, taking into account the different types of stone. In addition, it will also allow to develop models capable of helping in reducing the amount of raw material wasted. This work presents the steps taken to create a classification model, using a dataset of 2260 images distributed over four classes, three of which are very similar to color level and one with a different tone. In this study, the results of the application of three automatic classification algorithms are analyzed. In addition, a discussion of how types of images can improve results and the execution times of algorithms are presented.2020-05-31T23:00:00ZA Generalized Mean Under a Non-Regular Framework and Extreme Value Index EstimationGomes, Maria IvetteHenriques-Rodrigues, LígiaPestana, Dinishttp://hdl.handle.net/10174/335202023-01-17T12:15:35Z2021-01-01T00:00:00ZTitle: A Generalized Mean Under a Non-Regular Framework and Extreme Value Index Estimation
Authors: Gomes, Maria Ivette; Henriques-Rodrigues, Lígia; Pestana, Dinis
Editors: Skiadas, Christos H
Abstract: The Hill estimator, one of the most popular extreme value index (EVI) estimators under a heavy right-tail framework, i.e. for a positive EVI, here denoted by ξ, is an average of the log-excesses. Consequently, it can be regarded as the logarithm of the geometric mean or mean of order p = 0 of an adequate set of systematic statistics. We can thus more generally consider any real p, the mean of order p (MOp) of those same statistics and the associated MOp EVI-estimators, also called harmonic moment EVI-estimators. The normal asymptotic behaviour of these estimators has been obtained for p < 1/(2ξ), with consistency achieved for p < 1/ξ. The non-regular framework, i.e. the case p ≥ 1/(2ξ), will be now considered. Consistency is no longer achieved for p > 1/ξ, but an almost degenerate behavior appears for p = 1/ξ. Results are illustrated on the basis of large-scale simulation studies. An algorithm providing an almost degenerate MOp EVI-estimation is suggested.2021-01-01T00:00:00Z