6th-order finite volume approximations for the stokes equations with a curved boundary

A new solver for the Stokes equations based on the finite volume method is proposed using very accurate polynomial reconstruction to provide a 6th-order scheme. We face two main difficulties: the gradient-divergence duality where the divergence free condition will impose the pressure gradient, and on the other hand, we assume that the domain has a regular curved boundary. The last point implies that a simple approximation of the boundary using piecewise segment lines dramatically reduces the scheme accuracy to at most a second-order one. We propose a new and simple technology which enables to restore the full scheme accuracy based on a specific polynomial reconstruction only using the Gauss points of the curved boundary and does not require any geometrical transformation.Fundação para a Ciência e a Tecnologia (FCT)This research was financed by FEDER Funds through Programa Operational Fatores de Competitividade — COMPETE and by Portuguese Funds FCT — Fundação para a Ciência e a Tecnologia, within the Projects PEst-C/MAT/UI0013/2014, PTDC/MAT/121185/2010 and FCT-ANR/MAT-NAN/0122/201

New cell-vertex reconstruction for finite volume scheme : application to the convection-diffusion-reaction equation

The design of efficient, simple, and easy to code, second-order finite volume methods is an important challenge to solve practical problems in physics and in engineering where complex and very accurate techniques are not required. We propose an extension of the original Frink's approach based on a cell-to-vertex interpolation to compute vertex values with neighbor cell values. We also design a specific scheme which enables to use whatever collocation point we want in the cells to overcome the mass centre point restrictive choice. The method is proposed for two- and three-dimension geometries and a second-order extension time-discretization is given for time-dependent equation. A large number of numerical simulations are carried out to highlight the performance of the new method.Fundação para a Ciência e a Tecnologia (FCT

Computational framework to model the selective laser sintering process

Selective laser sintering (SLS) is one of the most well-regarded additive manufacturing (AM) sub-processes, whose popularity has been increasing among numerous critical and demanding industries due to its capabilities, mainly manufacturing parts with highly complex geometries and desirable mechanical properties, with potential to replace other, more expensive, conventional processes. However, due to its various underlying multi-physics phenomena, the intrinsic complexity of the SLS process often hampers its industrial implementation. Such limitation has motivated academic interest in obtaining better insights into the process to optimize it and attain the required standards. In that regard, the usual experimental optimization methods are time-consuming and expensive and can fail to provide the optimal configurations, leading researchers to resort to computational modeling to better understand the process. The main objective of the present work is to develop a computational model capable of simulating the SLS process for polymeric applications, within an open-source framework, at a particle-length scale to assess the main process parameters’ impact. Following previous developments, virgin and used polymer granules with different viscosities are implemented to better represent the actual process feedstock. The results obtained agree with the available experimental data, leading to a powerful tool to study, in greater detail, the SLS process and its physical parameters and material properties, contributing to its optimization.This work was funded by National Funds through FCT, The Portuguese Foundation for Science and Technology, References UID/CTM/50025/2019 and UIDB/04436/2020, project SIFA— Sistema Inteligente de Fabricação Aditiva (POCI-01-0247-FEDER-047108) and European Regional Development Fund through the Operational Competitiveness and Internationalization Programme (COMPETE 2020)

Avaliação do custo vs benefício na introdução de medidas de sustentabilidade na reabilitação de edifícios antigos – estudo de caso

Tendo por base os novos desafios com que o sector da construção, nomeadamente este artigo tem como objetivo a avaliação dos custos económicos e dos benefícios em termos energéticos e de sustentabilidade decorrentes da implementação de três cenários de reabilitação (básica, energética e sustentável) num edifício unifamiliar situado no centro histórico de Viana do Castelo

Computational modelling of the selective laser sintering process

Additive Manufacturing (AM) has increased in popularity in numerous important and demanding industries due to the capability of manufacturing parts with complex geometries and reduced wastage. As one of its most popular techniques, selective laser sintering (SLS) is sought after by several industries that aim to replace conventional and more expensive processes. However, the SLS process is intrinsically complex due to the various underlying multi-physics phenomena and more studies are needed to obtain more insights about it. This has resulted in many academical interests to optimize the process and allow it to achieve industrial standards. Most of these optimization attempts are performed through experimental methods that are time-consuming, expensive and do not always provide the optimal configurations. This has led researchers to resort to computational modelling, aiming at better understanding the process to anticipate and fix the defects. The main objective of the present work was to develop a computational model capable of simulating the SLS process for polymeric applications, within an open-source framework, at particle length scale. Since distinct approaches are required for accurately simulating each step of the SLS process, different numerical methods were employed to develop a tool capable of studying the impact, in a representative section of the powder bed, of the physical parameters that can be adjusted in the process. The developed work comprises several steps, starting with an extensive study of the theoretical aspects of the SLS process, which aimed at the acquaintance with the underlying phenomena, process unwind, its parameters and their influence, as well as evaluating the existing limitations and challenges. This step was then followed by a detailed analysis of the most common employed models to represent the major phenomena and of the accuracy level of the approaches, based on the employed simplifications. A set of computational tools was then assessed and their built-in models were selected, when possible, according to the precedent literature review. Lastly, various tests were carried to obtain an experimental qualitative validation of the used code, to assure that the undetaken approach was adequate to simulate the process. The achieved developments represent a significant advance towards the detailed SLS process simulation. With the use of open-source software (LIGGGHTS e OpenFOAM), several studies were performed on a realistic powder bed section and, despite the absence of enough and more detailed experimental data, the simulation results are in agreement with the ones used for comparison. Overall, the accomplished work allowed to conclude that the employed tools constitute a great potential to study, in detail, the SLS process and its parameters influence and, therefore, contribute to its optimization.This work was funded by National Funds through FCT - Portuguese Foundation for Science and Technology, Reference UID/CTM/50025/2019 and UIDB/04436/2020, and project SIFA - Sistema Inteligente de Fabricação Aditiva (POCI-01-0247-FEDER-047108). The authors also acknowledge the support of the computational clusters Search-ON2 (NORTE-07-0162-FEDER-000086) and Minho Advanced Computing Center (MACC

Reabilitação sustentável de edifícios antigos : contribuição para os edifícios de balanço energético nulo (nZEB) e otimização do nível de sustentabilidade

O aumento do consumo dos recursos energéticos não renováveis tornou-se num problema de grande relevo, tanto em termos económicos como ambientais. Para minimizar este problema foi publicada em 2002 uma diretiva europeia, a EPBD, cuja reformulação em 2010 obriga a que todos os edifícios novos e grandes reabilitações sejam caracterizados, a partir de 2020, por um balanço energético quase nulo. Em Portugal, a transposição desta diretiva deu-se através da implementação dos Decreto-Lei n.º 78/2006 (SCE), Decreto-Lei n.º 79/2006 (RSECE) e Decreto-Lei n.º 80/2006 (RCCTE). Contudo, apesar de estes regulamentos terem contribuído para uma melhoria das condições do parque habitacional português, não foi verificada uma melhoria significativa, uma vez que a maioria do edificado é anterior à implementação destes. Torna-se assim indispensável pensar na reabilitação como estratégia fundamental para reduzir as necessidades energéticas no país. No presente trabalho é efetuada a análise energética e de sustentabilidade de um caso de estudo através de três cenários de reabilitação diferentes, sendo eles, Reabilitação Básica, Reabilitação Energética e Reabilitação Sustentável. A análise energética foi realizada segundo o RCCTE com o apoio da ferramenta informática CYPE e a avaliação de sustentabilidade foi efetuada com recurso à metodologia SBToolPT-H

Using the fraction of transpirable soil water to estimate grapevine leaf water potential: comparing the classical statistical regression approach to machine learning algorithms

Context and purpose of the study – Weather uncertainty is forcing Mediterranean winegrowers to adopt new irrigation strategies to cope with water scarcity while ensuring a sustainable yield and improved berry and wine quality standards. Therefore, more accurate and high-resolution monitoring of soil water content and vine water status is a major concern. Leaf water potential measured at pre-dawn (YPD) is considered to be in equilibrium with soil water potential and is highly correlated with soil water content at the soil depth where roots extract water. The aim of this study is to evaluate a dataset of eco-physiological data collected in a 3-year vineyard irrigation trial to assess the explanatory power of the fraction of transpirable soil water (FTSW) to predict YPD by comparing the classical statistical regression approach with a machine learning algorithm (MLA). Material and methods – Deficit irrigation trials were conducted from 2013 to 2015 in a commercial vineyard in the Alentejo (southern Portugal). Trial plot was planted with Vitis vinifera (L.) cv. Aragonez (ARA)(syn. Tempranillo), grafted onto 1103 Paulsen rootstock and spaced 1.5 m within and 3.0 m between N-S oriented rows. The experimental layout was a randomized complete block design with two treatments: sustained deficit irrigation (SDI – control; ~30% Etc) and regulated deficit irrigation (RDI; ~15% Etc) and 4 replicates per treatment. The YPD and soil water content were measured the day before and the day after each irrigation event by using a capacitance probe down to a soil depth of 1 m and a Scholander pressure chamber. Models predicting YPD from FTSW were trained on 600 data cases and validated on an independent dataset (10% of all available data) using MATLAB R2022b (Mathworks, USA) and STATISTICA 13 (Tibco, USA). Results – Our results show that 87.6% of the observed YPD variability is explained by the FTSW using a linear regression model (LRM) with a linear-logarithmic transformation of the independent variables. The accuracy of the prediction model, as measured by root mean squared error (RMSE), in the independent validation dataset, was 0.08 MPa. These results were compared to the estimation accuracy of a set of MLAs. Two support vector machine (SVM) algorithms with a quadratic and a medium Gaussian kernel function, and three Gaussian process regression (GPR) algorithms with an exponential, a squared exponential and a rational quadratic kernel functions were tested. All trained MLAs showed an accuracy in explaining the variability of the YPD (86-87%) similar to the LRM. An increase in the model explained variability of the independent dataset from 89 to 91% was observed in all MLAs, with an accuracy of 0.087 to 0.096 MPa as measured by the RMSE. Both statistical methods indicate that YPD can be estimated with good accuracy using FTSW as an explanatory variable. Regarding the comparative performance of the two types of statistical models no differences were found in the ability of the tested models to estimate YPD.N/