EFFECT – Efficient finite element code

The theme of this dissertation is the finite element method applied to mechanical structures. A new finite element program is developed that, besides executing different types of structural analysis, also allows the calculation of the derivatives of structural performances using the continuum method of design sensitivities analysis, with the purpose of allowing, in combination with the mathematical programming algorithms found in the commercial software MATLAB, to solve structural optimization problems. The program is called EFFECT – Efficient Finite Element Code. The object-oriented programming paradigm and specifically the C ++ programming language are used for program development. The main objective of this dissertation is to design EFFECT so that it can constitute, in this stage of development, the foundation for a program with analysis capacities similar to other open source finite element programs. In this first stage, 6 elements are implemented for linear analysis: 2-dimensional truss (Truss2D), 3-dimensional truss (Truss3D), 2-dimensional beam (Beam2D), 3-dimensional beam (Beam3D), triangular shell element (Shell3Node) and quadrilateral shell element (Shell4Node). The shell elements combine two distinct elements, one for simulating the membrane behavior and the other to simulate the plate bending behavior. The non-linear analysis capability is also developed, combining the corotational formulation with the Newton-Raphson iterative method, but at this stage is only avaiable to solve problems modeled with Beam2D elements subject to large displacements and rotations, called nonlinear geometric problems. The design sensitivity analysis capability is implemented in two elements, Truss2D and Beam2D, where are included the procedures and the analytic expressions for calculating derivatives of displacements, stress and volume performances with respect to 5 different design variables types. Finally, a set of test examples were created to validate the accuracy and consistency of the result obtained from EFFECT, by comparing them with results published in the literature or obtained with the ANSYS commercial finite element code.

EDP - A new estimation algorithm

The aim of this work project is to analyze the current algorithm used by EDP to estimate their clients’ electrical energy consumptions, create a new algorithm and compare the advantages and disadvantages of both. This new algorithm is different from the current one as it incorporates some effects from temperature variations. The results of the comparison show that this new algorithm with temperature variables performed better than the same algorithm without temperature variables, although there is still potential for further improvements of the current algorithm, if the prediction model is estimated using a sample of daily data, which is the case of the current EDP algorithm.

Generalized Dasymetric Mapping Algorithm for Accessing Land-Use Change

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ReedCob – An eco-efficient building technology for monolithic walls based on earth and reeds

4th Conference COST ACTION FP1303 – Designing with Bio-based Materials – Challenges and opportiunities. INIA – CSIC, Madrid, 24-25 February 2016. Book of abstracts, T.Troya, J.Galván, D.Jones (Eds.), INIA and IETcc – CSIS, pg. 79-80 (ISBN: 978-91-88349-16-3)

Quadratic programming versus second order con programming in portfolio optimization

Despite the extensive literature in finding new models to replace the Markowitz model or trying to increase the accuracy of its input estimations, there is less studies about the impact on the results of using different optimization algorithms. This paper aims to add some research to this field by comparing the performance of two optimization algorithms in drawing the Markowitz Efficient Frontier and in real world investment strategies. Second order cone programming is a faster algorithm, appears to be more efficient, but is impossible to assert which algorithm is better. Quadratic Programming often shows superior performance in real investment strategies.

Genetic algorithm for shipping route estimation with long-range tracking data : automatic reconstruction of shipping routes based on the historical ship positions for maritime safety applications

Ship tracking systems allow Maritime Organizations that are concerned with the Safety at Sea to obtain information on the current location and route of merchant vessels. Thanks to Space technology in recent years the geographical coverage of the ship tracking platforms has increased significantly, from radar based near-shore traffic monitoring towards a worldwide picture of the maritime traffic situation. The long-range tracking systems currently in operations allow the storage of ship position data over many years: a valuable source of knowledge about the shipping routes between different ocean regions. The outcome of this Master project is a software prototype for the estimation of the most operated shipping route between any two geographical locations. The analysis is based on the historical ship positions acquired with long-range tracking systems. The proposed approach makes use of a Genetic Algorithm applied on a training set of relevant ship positions extracted from the long-term storage tracking database of the European Maritime Safety Agency (EMSA). The analysis of some representative shipping routes is presented and the quality of the results and their operational applications are assessed by a Maritime Safety expert.

A decision support system for a waste collection vehicle routing problem

The selective collection of municipal solid waste for recycling is a very complex and expensive process, where a major issue is to perform cost-efficient waste collection routes. Despite the abundance of commercially available software for fleet management, they often lack the capability to deal properly with sequencing problems and dynamic revision of plans and schedules during process execution. Our approach to achieve better solutions for the waste collection process is to model it as a vehicle routing problem, more specifically as a team orienteering problem where capacity constraints on the vehicles are considered, as well as time windows for the waste collection points and for the vehicles. The final model is called capacitated team orienteering problem with double time windows (CTOPdTW).We developed a genetic algorithm to solve routing problems in waste collection modelled as a CTOPdTW. The results achieved suggest possible reductions of logistic costs in selective waste collection.

Parallelization of kinetic Monte Carlo algorithm to simulate AL3Sc precipitation

The present paper reports the precipitation process of Al3Sc structures in an aluminum scandium alloy, which has been simulated with a synchronous parallel kinetic Monte Carlo (spkMC) algorithm. The spkMC implementation is based on the vacancy diffusion mechanism. To filter the raw data generated by the spkMC simulations, the density-based clustering with noise (DBSCAN) method has been employed. spkMC and DBSCAN algorithms were implemented in the C language and using MPI library. The simulations were conducted in the SeARCH cluster located at the University of Minho. The Al3Sc precipitation was successfully simulated at the atomistic scale with the spkMC. DBSCAN proved to be a valuable aid to identify the precipitates by performing a cluster analysis of the simulation results. The achieved simulations results are in good agreement with those reported in the literature under sequential kinetic Monte Carlo simulations (kMC). The parallel implementation of kMC has provided a 4x speedup over the sequential version.

Eco-efficient self-compacting concrete with reduced Portland cement content and high volume of fly ash and metakaolin

The eco-efficient, self-compacting concrete (SCC) production, containing low levels of cement in its formulation, shall contribute for the constructions' sustainability due to the decrease in Portland cement use, to the use of industrial residue, for beyond the minimization of the energy needed for its placement and compaction. In this context, the present paper intends to assess the viability of SCC production with low cement levels by determining the fresh and hardened properties of concrete containing high levels of fly ash (FA) and also metakaolin (MK). Hence, 6 different concrete formulations were produced and tested: two reference concretes made with 300 and 500 kg/m3 of cement; the others were produced in order to evaluate the effects of high replacement levels of cement. Cement replacement by FA of 60% and by 50% of FA plus 20% of MK were tested and the addition of hydrated lime in these two types of concrete were also studied. To evaluate the self-compacting ability slump flow test, T500, J-ring, V-funnel and L-box were performed. In the hardened state the compressive strength at 3, 7, 14, 21, 28 and 90 days of age was determined. The results showed that it is possible to produce low cement content SCC by replacing high levels of cement by mineral additions, meeting the rheological requirements for self-compacting, with moderate resistances from 25 to 30 MPa after 28 days.

HEP-Frame: A software engineered framework to aid the development and efficient multicore execution of scientific code

This work was supported by FCT (Fundação para a Ciência e Tecnologia) within Project Scope (UID/CEC/00319/2013), by LIP (Laboratório de Instrumentação e Física Experimental de Partículas) and by Project Search-ON2 (NORTE-07-0162- FEDER-000086), co-funded by the North Portugal Regional Operational Programme (ON.2 - O Novo Norte), under the National Strategic Reference Framework, through the European Regional Development Fund.

Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.

Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm

The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.

A new competitive implementation of the electromagnetism-like algorithm for global optimization

The Electromagnetism-like (EM) algorithm is a population- based stochastic global optimization algorithm that uses an attraction- repulsion mechanism to move sample points towards the optimal. In this paper, an implementation of the EM algorithm in the Matlab en- vironment as a useful function for practitioners and for those who want to experiment a new global optimization solver is proposed. A set of benchmark problems are solved in order to evaluate the performance of the implemented method when compared with other stochastic methods available in the Matlab environment. The results con rm that our imple- mentation is a competitive alternative both in term of numerical results and performance. Finally, a case study based on a parameter estimation problem of a biology system shows that the EM implementation could be applied with promising results in the control optimization area.

Combining non-dominance, objective-order and spread metric to extend firefly algorithm to multi-objective optimization

In this paper, we propose an extension of the firefly algorithm (FA) to multi-objective optimization. FA is a swarm intelligence optimization algorithm inspired by the flashing behavior of fireflies at night that is capable of computing global solutions to continuous optimization problems. Our proposal relies on a fitness assignment scheme that gives lower fitness values to the positions of fireflies that correspond to non-dominated points with smaller aggregation of objective function distances to the minimum values. Furthermore, FA randomness is based on the spread metric to reduce the gaps between consecutive non-dominated solutions. The obtained results from the preliminary computational experiments show that our proposal gives a dense and well distributed approximated Pareto front with a large number of points.

New control algorithm for single-phase series active power filter

This paper presents a single-phase Series Active Power Filter (Series APF) for mitigation of the load voltage harmonic content, while maintaining the voltage on the DC side regulated without the support of a voltage source. The proposed series active power filter control algorithm eliminates the additional voltage source to regulate the DC voltage, and with the adopted topology it is not used a coupling transformer to interface the series active power filter with the electrical power grid. The paper describes the control strategy which encapsulates the grid synchronization scheme, the compensation voltage calculation, the damping algorithm and the dead-time compensation. The topology and control strategy of the series active power filter have been evaluated in simulation software and simulations results are presented. Experimental results, obtained with a developed laboratorial prototype, validate the theoretical assumptions, and are within the harmonic spectrum limits imposed by the international recommendations of the IEEE-519 Standard.