Drilling process of composite laminates – a tool based analysis

“Drilling of polymeric matrix composites structures”

Finite Element Analysis (FEA) applied to heat transfer optimization process of pultrusion die systems

The aim of this study is to optimize the heat flow through the pultrusion die assembly system on the manufacturing process of a specific glass-fiber reinforced polymer (GFRP) pultrusion profile. The control of heat flow and its distribution through whole die assembly system is of vital importance in optimizing the actual GFRP pultrusion process. Through mathematical modeling of heating-die process, by means of Finite Element Analysis (FEA) program, an optimum heater selection, die position and temperature control was achieved. The thermal environment within the die was critically modeled relative not only to the applied heat sources, but also to the conductive and convective losses, as well as the thermal contribution arising from the exothermic reaction of resin matrix as it cures or polymerizes from the liquid to solid condition. Numerical simulation was validated with basis on thermographic measurements carried out on key points along the die during pultrusion process.

Optimizing the embedded heaters position in the pultrusion process using finite element analysis

This study is based on a previous experimental work in which embedded cylindrical heaters were applied to a pultrusion machine die, and resultant energetic performance compared with that achieved with the former heating system based on planar resistances. The previous work allowed to conclude that the use of embedded resistances enhances significantly the energetic performance of pultrusion process, leading to 57% decrease of energy consumption. However, the aforementioned study was developed with basis on an existing pultrusion die, which only allowed a single relative position for the heaters. In the present work, new relative positions for the heaters were investigated in order to optimize heat distribution process and energy consumption. Finite Elements Analysis was applied as an efficient tool to identify the best relative position of the heaters into the die, taking into account the usual parameters involved in the process and the control system already tested in the previous study. The analysis was firstly developed with basis on eight cylindrical heaters located in four different location plans. In a second phase, in order to refine the results, a new approach was adopted using sixteen heaters with the same total power. Final results allow to conclude that the correct positioning of the heaters can contribute to about 10% of energy consumption reduction, decreasing the production costs and leading to a better eco-efficiency of pultrusion process.

Analysis of diffusion process in fractured reservoirs using fractional derivative approach

The fractal geometry is used to model of a naturally fractured reservoir and the concept of fractional derivative is applied to the diffusion equation to incorporate the history of fluid flow in naturally fractured reservoirs. The resulting fractally fractional diffusion (FFD) equation is solved analytically in the Laplace space for three outer boundary conditions. The analytical solutions are used to analyze the response of a naturally fractured reservoir considering the anomalous behavior of oil production. Several synthetic examples are provided to illustrate the methodology proposed in this work and to explain the diffusion process in fractally fractured systems.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Safety function analysis in an industrial production process

Dissertação para obtenção do Grau de Mestre em Engenharia e Gestão Industrial

A study on reducing digital piracy: an analytical network process approach with benefits, opportunities, costs and risks analysis

Dissertação para obtenção do Grau de Mestre em Engenharia e Gestão Industrial

Analysis of benefits of the process of certification on international standars: ISO 9001-2008 for Mapfre Assistencia Portugal

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Trust, alliance management capability and the alliance management function: A comparative analysis of Portuguese SMEs

Strategic alliances represent a key driver for internationalization and growth, being the purpose of this work project to better understand the intertwined relationship between trust and the existence of an alliance management position. Previous research supports the positive impact of such position in stock market returns. However, little attention has been given to the impact of such position on the level of trust in the collaborative arrangement, which is deemed to be a key driver for alliance success. A qualitative comparative case-study of three Portuguese SMEs is used to draw conclusions from the literature to real life business cases and it demonstrates the positive impact of an alliance management position on trust.

Analysis of chromium behaviour and speciation during the electrodialytic process

The interest in chromium (Cr) arises from the widespread use of this heavy metal in various industrial processes that cause its release as liquid, solid and gaseous waste into the environment. The impact of Cr on the environment and living organisms primarily depends on its chemical form, since Cr(III) is an essential micronutrient for humans, other animals and plants, and Cr(VI) is highly toxic and a known human carcinogen. This study aimed to evaluate if the electrodialytic process (ED) is an appropriate treatment for Cr removal, through a critical overview of Cr speciation, before and after the ED experiments, to assess possible Cr(III)-Cr(VI) interconversions during the treatment. ED was the treatment technique applied to two types of matrices containing Cr: chromate copper arsenate (CCA) contaminated soil and municipal solid waste incineration (MSWI) fly ash. In order to study Cr remediation, three EDR set-ups were used: a new set-up, the combined cell (2/3C or 3/2C), with three compartments, alternating current between two anodes and different initial experimental conditions, one set-up with three compartments (3C cell) and the other set-up with two compartments (2C cell). The Cr removal rates obtained in this study were between 10-36% for the soil, and 1-13% for the fly ash. The highest Cr removal rates were achieved in the 26 days experiments: 36% for the soil, 13% for the fly ash. Regarding the 13 days experiments, the highest Cr removal rates were attained with the 2/3C set-up: 24% for the soil, 5% for the fly ash. The analysis of Cr(VI) was performed before and after ED experiments to evaluate eventual changes in Cr speciation during the treatment. This analysis was conducted by two methods: USEPA Method 3060A, for the extraction of Cr(VI); and Hach Company Method 8023, for the detection of Cr(VI). Despite the differences in Cr total concentration, both matrices presented a similar speciation, with Cr(III) being the main species found and Cr(VI) less than 3% of Cr total, before and after the treatment. For fly ash, Cr(VI) was initially below the detection limit of the method and remained that way after the treatment. For soil, Cr(VI) decreased after the treatment. Oxidation of Cr(III) to Cr(VI) did not occur during the ED process since there was no increase in Cr(VI) in the matrices after the treatment. Hence, the results of this study indicate that ED is an appropriate technique to remediate matrices containing Cr because it contributes to Cr removal, without causing Cr(III)-Cr(VI) interconversions.

Analysis of a controversial decision process: the case of the pumped hydro storage power plant Atdorf in Germany

Based on the report for the unit “Métodos Interactivos de Participação e Decisão A” (Interactive methods of participation and decision A), coordinated by Prof. Lia Maldonado Teles de Vasconcelos and Prof. Nuno Miguel Ribeiro Videira Costa. This unit was provided for the PhD Program in Technology Assessment in 2015/2016.

Acoustic emission analysis of the effect of a 2D wedge shaped blade on the compact bone cutting process

Surgeons may use a number of cutting instruments such as osteotomes and chisels to cut bone during an operative procedure. The initial loading of cortical bone during the cutting process results in the formation of microcracks in the vicinity of the cutting zone with main crack propagation to failure occuring with continued loading. When a material cracks, energy is emitted in the form of Acoustic Emission (AE) signals that spread in all directions, therefore, AE transducers can be used to monitor the occurrence and development of microcracking and crack propagation in cortical bone. In this research, number of AE signals (hits) and related parameters including amplitude, duration and absolute energy (abs-energy) were recorded during the indentation cutting process by a wedge blade on cortical bone specimens. The cutting force was also measured to correlate between load-displacement curves and the output from the AE sensor. The results from experiments show AE signals increase substantially during the loading just prior to fracture between 90% and 100% of maximum fracture load. Furthermore, an amplitude threshold value of 64dB (with approximate abs-energy of 1500 aJ) was established to saparate AE signals associated with microcracking (41 – 64dB) from fracture related signals (65 – 98dB). The results also demonstrated that the complete fracture event which had the highest duration value can be distinguished from other growing macrocracks which did not lead to catastrophic fracture. It was observed that the main crack initiation may be detected by capturing a high amplitude signal at a mean load value of 87% of maximum load and unsteady crack propagation may occur just prior to final fracture event at a mean load value of 96% of maximum load. The author concludes that the AE method is useful in understanding the crack initiation and fracture during the indentation cutting process.

An instrumental analysis of the European Union's capability to act in conflict response

Descripció i anàlisi dels instruments polítics, econòmics, civils i militars de la Unió Europea per enfrontar qualsevol conflicte.