André Carvalho

Comparison of Surrogate Models in a Multidisciplinary Optimization Framework for Wing Design

The replacement of the analysis portion of an optimization problem by its equivalent metamodel usually results in a lower computational cost. In this paper, a conventional nonapproximative approach is compared against three differentmetamodels: quadratic-interpolation-based response surfaces,Kriging, and artificial neural networks. The results obtained from the solution of four different case studies based on aircraft design problems reinforces the idea that quadratic interpolation is only well-suited to very simple problems. At higher dimensionality, the usage of the more complex Kriging and artificial neural networks models may result in considerable performance benefits.

Laugh to me! implementing emotional escalation on autonomous agents for creating a comic sketch

The growing interest for Interactive Storytelling has lead the research into the exploration of this new media in classical story genres. In our research, we develop autonomous agents that act in a storytelling context with a comic purpose. We present a comic sketch model for autonomous agents with affective reasoning. The agents that use this model prepare the timing of the comic punchline by reasoning about emotional states in a process called Emotional Escalation. The punchline used for our test scenario, as well as the personality of the comic characters is based on the humour theory of incongruity-resolution.

A Comparison of Surrogate Models in the Framework of an MDO Tool for Wing Design

The replacement of the analysis portion of an optimization problem by its equivalent metamodel usually results in a lower computational cost. In this paper, three dierent metamodels are compared against the conventional non-approximative approach: quadratic interpolation based response surfaces, Kriging and Articial Neural Networks (ANN). The results obtained from the solution of three dierent case studies based on aircraft design problems reinforces the idea that quadratic interpolation is only well suited to very simple problems. At higher dimensionality, the usage of the more the complex Kriging and ANN models may result in considerable performance benets. Nomenclature b=2 Wing semispan, m c;ci Coecients for polynomial interpolation cbs Wing breakstation chord, m croot Wing root chord, m ctip Wing tip chord, m f (x) Regression model (Kriging) g (x) Constraint function nDV Number of design variables ns Number of samples nt Number of terms in polynomial interpolation/regression approximation qk(x) Values of regression functions at sample locations (Kriging) R (w; x; ) Correlation model (Kriging) sk Vector of independent variable samples (Kriging)

Simulation of rigid-body impact using the articulated-body algorithm

This paper presents a numerical algorithm to model the impact between articulated structures, or between an articulated structure and an object. The proposed algorithm, called impulse articulated-body algorithm (IABA), is based on the ABA but uses impulses and velocities rather than forces and accelerations. The algorithm also inherits the advantages of the ABA: versatility and low computational cost. The IABA provides a method to determine the impact impulse, without increasing the order of the algorithm.

Developments on finite element methods for medical image supported diagnostics

Variational image-processing models offer high-quality processing capabilities for imaging. They have been widely developed and used in the last two decades, enriching the fields of mathematics as well as information science. Mathematically, several tools are needed: energy optimization, regularization, partial differential equations, level set functions, and numerical algorithms. For this work we consider a second-order variational model for solving medical image problems. The aim is to obtain as far as possible fine features of the initial image and identify medical pathologies. The approach consists of constructing a regularized functional and to locally analyse the obtained solution. Some parameters selection is performed at the discrete level in the framework of the finite element method. We present several numerical simulations to test the efficiency of the proposed approach.

Wheels for Staircases

Our objective is to design innovative robot wheels capable of rolling on staircases, without sliding and without bouncing. This is the first step to reach the ultimate goal of building wheelchairs capable to overcome the obstacles imposed by staircases on people with limited mobility. We show that, given a staircase with equal steps, there is an infinite number of wheels that roll over it, with the constraints of no-sliding and no-bouncing. Some of these wheels appear to be very interesting for real applications. We also present an algorithm for the construction of a wheel, which depends only on the measures of the tread (the part of the staircase that is stepped on) and the riser (the vertical portion between each tread) of the step.

Structural Health Monitoring Suitable for Airborne Components Using the Speckle Pattern in Plastic Optical Fibers

Structural health monitoring (SHM) is a key factor for the correct operation in large dimension structures. In this paper, we propose and demonstrate the application of a distributed optical sensor, suitable for SHM, based on the evaluation of the speckle pattern at the output of a polymer optical fibers, aiming to evaluate the damage imposed to airborne metallic and composite structures. We propose also several algorithms to analyze the speckle data and access the imposed mechanical perturbation. The maximum sampling frequency of this method is limited by the image acquisition devices, yielding to a value of 15 Hz in our case.

A Robust and Reliability-Based Design Optimization Framework for Wing Design

This paper presents the outline of a framework for simultaneous analysis and robustness and reliability calculations in aircraft design optimization, with the option of employing surrogate models. Robust Design Optimization and Reliability Based Design Optimization are merged into a unified formulation which streamlines the setup of optimization problems and aims at preventing foreseeable implementation issues. The code in development expands upon and, in some cases, completely rewrites a previous version of a Multidisciplinary Design Optimization tool that was solely oriented to deterministic problems.