Stochastic ground motion simulation with geological site effects in damage asessment

Damage assessment of structures with a mechanical non linear model demands the representation of seismic action in terms of an accelerogram (dynamic analysis) or a response spectrum (pushover analysis). Stochastic ground motion simulation is largely used in regions where seismic strong-motion records are available in insufficient number. In this work we present a variation of the stochastic finite-fault method with dynamic corner frequency that includes the geological site effects. The method was implemented in a computer program named SIMULSIS that generate time series (accelerograms) and response spectra. The program was tested with the MW= 7.3 Landers earthquake (June 28, 1992) and managed to reproduce its effects. In the present work we used it to reproduce the effects of the 1980’s Azores earthquake (January 1, 1980) in several islands, with different possible local site conditions. In those places, the response spectra are presented and compared with the buildings damage observed.

DNA damage in two bivalve species: Mytilus galloprovincialis and Ruditapes decussatus from the South coast of Portugal

Dissertação de mest., Estudos Marinhos e Costeiros, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2009

Detection of Incipient Thermal Damage Of Carbon Fiber/Epoxy Composites Using Fluorescent Thermal Damage Probes

Thesis (Ph.D.)--University of Washington, 2013

Translating proteomics into environmental health : biomarkers discovery for tobacco smoke-induced biological damage

Tese de doutoramento, Biologia (Biologia Molecular), Universidade de Lisboa, Faculdade de Ciências, 2015

Electrochemical DNA-sensor for evaluation of total antioxidant capacity of flavours and flavoured waters using superoxide radical damage

In this paper, a biosensor based on a glassy carbon electrode (GCE) was used for the evaluation of the total antioxidant capacity (TAC) of flavours and flavoured waters. This biosensor was constructed by immobilising purine bases, guanine and adenine, on a GCE. Square wave voltammetry (SWV) was selected for the development of this methodology. Damage caused by the reactive oxygen species (ROS), superoxide radical (O2·−), generated by the xanthine/xanthine oxidase (XOD) system on the DNA-biosensor was evaluated. DNA-biosensor encountered with oxidative lesion when it was in contact with the O2·−. There was less oxidative damage when reactive antioxidants were added. The antioxidants used in this work were ascorbic acid, gallic acid, caffeic acid, coumaric acid and resveratrol. These antioxidants are capable of scavenging the superoxide radical and therefore protect the purine bases immobilized on the GCE surface. The results demonstrated that the DNA-based biosensor is suitable for the rapid assess of TAC in beverages.

Electrocatalytic evaluation of DNA damage by superoxide radical for antioxidant capacity assessment

The integrity of DNA purine bases was herein used to evaluate the antioxidant capacity. Unlike other DNA-based antioxidant sensors reported so far, the damaging agent chosen was the O 2 radical enzymatically generated by the xanthine/xanthine oxidase system. An adenine-rich oligonucleotide was adsorbed on carbon paste electrodes and subjected to radical damage in the presence/absence of several antioxidant compounds. As a result, partial damage on DNA was observed. A minor product of the radical oxidation was identified by cyclic voltammetry as a diimine adenine derivative also formed during the electrochemical oxidation of adenine/guanine bases. The protective efficiency of several antioxidant compounds was evaluated after electrochemical oxidation of the remaining unoxidized adenine bases, by measuring the electrocatalytic current of NADH mediated by the adsorbed catalyst species generated. A comparison between O 2 and OH radicals as a source of DNA lesions and the scavenging efficiency of various antioxidant compounds against both of them is discussed. Finally, the antioxidant capacity of beverages was evaluated and compared with the results obtained with an optical method.

Damage evaluation of drilled carbon/epoxy laminates based on area assessment methods

The characteristics of carbon fibre reinforced laminates had widened their use, from aerospace to domestic appliances. A common characteristic is the need of drilling for assembly purposes. It is known that a drilling process that reduces the drill thrust force can decrease the risk of delamination. In this work, delamination assessment methods based on radiographic data are compared and correlated with mechanical test results (bearing test).

Using a cohesive damage model to predict the tensile behaviour of CFRP single-strap repairs

This work addresses both experimental and numerical analyses regarding the tensile behaviour of CFRP single-strap repairs. Two fundamental geometrical parameters were studied: overlap length and patch thickness. The numerical model used ABAQUS® software and a developed cohesive mixed-mode damage model adequate for ductile adhesives, and implemented within interface finite elements. Stress analyses and strength predictions were carried out. Experimental and numerical comparisons were performed on failure modes, failure load and equivalent stiffness of the repair. Good correlation was found between experimental and numerical results, showing that the proposed model can be successfully applied to bonded joints or repairs.

Adapting remote labs to learning scenarios: case studies using VISIR and RemotElectLab

Remote laboratories are an emergent technological and pedagogical tool at all education levels, and their widespread use is an important part of their own improvement and evolution. This paper describes several issues encountered on laboratorial classes, on higher education courses, when using remote laboratories based on PXI systems, either using the VISIR system or an alternate in-house solution. Three main issues are presented and explained, all reported by teachers, that gave support to students' use of remote laboratories. The first issue deals with the need to allow students to select the actual place where an ammeter is to be inserted on electric circuits, even incorrectly, therefore emulating real-world difficulties. The second one deals with problems with timing when several measurements are required at short intervals, as in the discharge cycle of a capacitor. In addition, the last issue deals with the use of a multimeter in dc mode when reading ac values, a use that collides with the lab settings. All scenarios are presented and discussed, including the solution found for each case. The conclusion derived from the described work is that the remote laboratories area is an expanding field, where practical use leads to improvement and evolution of the available solutions, requiring a strict cooperation and information-sharing between all actors, i.e., developers, teachers, and students.

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

MASCEM Restructuring: Ontologies For Scenarios Generation in Power Systems Simulators

The electricity market restructuring, along with the increasing necessity for an adequate integration of renewable energy sources, is resulting in an rising complexity in power systems operation. Various power system simulators have been introduced in recent years with the purpose of helping operators, regulators, and involved players to understand and deal with this complex environment. This paper focuses on the development of an upper ontology which integrates the essential concepts necessary to interpret all the available information. The restructuring of MASCEM (Multi-Agent System for Competitive Electricity Markets), and this system’s integration with MASGriP (Multi-Agent Smart Grid Platform), and ALBidS (Adaptive Learning Strategic Bidding System) provide the means for the exemplification of the usefulness of this ontology. A practical example is presented, showing how common simulation scenarios for different simulators, directed to very distinct environments, can be created departing from the proposed ontology.

Load Profiling Tool to Support Smart Grid Operation Scenarios

This paper presents the characterization of high voltage (HV) electric power consumers based on a data clustering approach. The typical load profiles (TLP) are obtained selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The choice of the best partition is supported using several cluster validity indices. The proposed data-mining (DM) based methodology, that includes all steps presented in the process of knowledge discovery in databases (KDD), presents an automatic data treatment application in order to preprocess the initial database in an automatic way, allowing time saving and better accuracy during this phase. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ consumption behavior. To validate our approach, a case study with a real database of 185 HV consumers was used.

Realistic Traffic Scenarios Using a Census Methodology: Vila Real Case Study

This paper presents the first phase of the redevelopment of the Electric Vehicle Scenario Simulator (EVeSSi) tool. A new methodology to generate traffic demand scenarios for the Simulation of Urban MObility (SUMO) tool for urban traffic simulation is described. This methodology is based on a Portugal census database to generate a synthetic population for a given area under study. A realistic case study of a Portuguese city, Vila Real, is assessed. For this area the road network was created along with a synthetic population and public transport. The traffic results were obtained and an electric buses fleet was evaluated assuming that the actual fleet would be replaced in a near future. The energy requirements to charge the electric fleet overnight were estimated in order to evaluate the impacts that it would cause in the local electricity network.

Data Mining Approach to support the Generation of Realistic Scenarios for Multi-Agent simulation of Electricity Markets

This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players’ characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.