Percorrer por autor "Martins, Ana"
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- An application of time series clustering using a combined distancePublication . Martins, Ana; Vaz, Daniel; Silva, Tiago; Cardoso, Margarida; Carvalho, Alda; Loja, AméliaClustering time series aims at uncovering diverse longitudinal patterns. In this study we analyse time series of various parameters collected atop wind turbines in a wind farm in Portugal. Considering wind data in clustering may reveal differences in operation between neighbouring turbines, due to their position relative to one another and to terrain features. In this work, we use an approach to wind speed time series clustering based on a convex combination of distance measures between time series. For visualizing the resulting groups, we propose a graphical representation, Distance Matrix, which is quite more informative than the classical Multidimensional Scaling (MDS) map. This representation allows for quick comparisons between pairs of turbines for (dis)similarities. Our approach provides distinct insights regarding the differences between time series, emphasizing differences in values (Euclidean distance), trends (Pearson-based distance), and cyclical behaviours (Euclidean distance between periodograms and/or autocorrelation structures). In most cases, we found two groups, which were not always coincident with the geographical groups, but the proposed approach could also find the rationale behind the clusters that were formed. The results obtained may help identifying undesirable aerodynamic loads that the blades of a particular wind turbine may be subjected to, thereby shortening its time in-service.
- Clustering of wind speed time series as a tool for wind farm diagnosisPublication . Martins, Ana; Vaz, Daniel; Silva, Tiago; Cardoso, Margarida; Carvalho, AldaIn several industrial fields, environmental and operational data are acquired with numerous purposes, potentially generating a huge quantity of data containing valuable information for management actions. This work proposes a methodology for clustering time series based on the K-medoids algorithm using a convex combination of different time series correlation metrics, the COMB distance. The multidimensional scaling procedure is used to enhance the visualization of the clustering results, and a matrix plot display is proposed as an efficient visualization tool to interpret the COMB distance components. This is a general-purpose methodology that is intended to ease time series interpretation; however, due to the relevance of the field, this study explores the clustering of time series judiciously collected from data of a wind farm located on a complex terrain. Using the COMB distance for wind speed time bands, clustering exposes operational similarities and dissimilarities among neighboring turbines which are influenced by the turbines’ relative positions and terrain features and regarding the direction of oncoming wind. In a significant number of cases, clustering does not coincide with the natural geographic grouping of the turbines. A novel representation of the contributing distances—the COMB distance matrix plot—provides a quick way to compare pairs of time bands (turbines) regarding various features.
- A statistical assessment of drilling effects on glass fiber-reinforced polymeric compositesPublication . Martins, Ana; Carvalho, Alda; Bragança, Ivo; Barbosa, Inês; Barbosa, Joaquim; Loja, AméliaFiber-reinforced composites are extensively used in many components and structures in various industry sectors, and the need to connect and assemble such types of components may require drilling operations. Although drilling is a common machining process; when dealing with fiber-reinforced composite materials, additional and specific problems may arise that can com-promise mechanical integrity. So, the main goal of this work is to assess how various input variables impact two main outcomes in the drilling process: the exit-adjusted delamination factor and the maximum temperature on the bottom surface where the drilling tool exits. The input variables include the type of drilling tools used, the operating speeds, and the thickness of the plates being drilled. By using Analysis of Variance (ANOVA), the analysis aims to identify which factors significantly influence damage and exit temperature. The results demonstrate that the influence of tools and drilling parameters is critical, and those selections impact the quality of the hole and the extent of the induced damage to the surrounding area. In concrete, considering the initially selected set of tools, the BZT03 tool does not lead to high-quality holes when drilling medium- and high-thickness plates. In contrast, the Dagger tool shows potential to reduce exit hole damage while also lowering temperature.
- Variability on functionally graded plates’ deflection due to uncertainty on carbon nanotubes’ propertiesPublication . Carvalho, Alda; Martins, Ana; Mota, Ana; Loja, AméliaCarbon nanotubes are widely used as material reinforcement in diverse fields of engineering. Being that their contribution is significant to improving the mean properties of the resulting materials, it is important to assess the influence of the variability on carbon nanotubes’ material and geometrical properties to structures’ responses. This work considers functionally graded plates constituted by an aluminum continuous phase reinforced with single-walled or multi-walled carbon. The nanotubes' weight fraction evolution through the thickness is responsible for the plates’ functional gradient. The plates’ samples are simulated considering that only the nanotubes’ material and geometrical characteristics are affected by uncertainty. The results obtained from the multiple regression models developed allow us to conclude that the length of the nanotubes has no impact on the maximum transverse displacement of the plates in opposition to the carbon nanotubes’ weight fraction evolution, their internal and external diameters, and the Young’s modulus. The multiple regression models developed can be used as alternative prediction tools within the domain of the study.