Optimization method for the determination of material parameters in damaged composite structures

An optimization method to identify the material parameters of composite structures using an inverse method is proposed. This methodology compares experimental results with their numerical reproduction using the finite element method in order to obtain an estimation of the error between the results. This error estimation is then used by an evolutionary optimizer to determine, in an iterative process, the value of the material parameters which result in the best numerical fit. The novelty of the method is in the coupling between the simple genetic algorithm and the mixing theory used to numerically reproduce the composite behavior. The methodology proposed has been validated through a simple example which illustrates the exploitability of the method in relation to the modeling of damaged composite structures.Peer ReviewedPostprint (author’s final draft

Optimization method for the determination of material parameters in damaged composite structures

Abstract An optimization method to identify the material parameters of composite structures using an inverse method is proposed. This methodology compares experimental results with their numerical reproduction using the finite element method in order to obtain an estimation of the error between the results. This error estimation is then used by an evolutionary optimizer to determine, in an iterative process, the value of the material parameters which result in the best numerical fit. The novelty of the method is in the coupling between the simple genetic algorithm and the mixing theory used to numerically reproduce the composite behavior. The methodology proposed has been validated through a simple example which illustrates the exploitability of the method in relation to the modeling of damaged composite structures

Designing EDAs by using the Elitist Convergent EDA Concept and the Boltzmann Distribution

ABSTRACT This paper presents a theoretical definition for designing EDAs called Elitist Convergent Estimation of Distribution Algorithm (ECEDA), and a practical implementation: the Boltzmann Univariate Marginal Distribution Algorithm (BUMDA). This proposal computes a Gaussian model which approximates a Boltzmann distribution via the minimization of the Kullback Leibler divergence. The resulting approach needs only one parameter: the population size. A set of problems is presented to show advantages and comparative performance of this approach with state of the art continuous EDAs

A bayesian network based estimation of distribution algorithm for topological structure optimization

The topological optimization problema is stated as follows: to find the best shape of a mechanical structure subject to certain service conditions. Usually, the topological optimization problem is tackled by starting from an initial structure which is modified by adding parts, generating gaps, modifying dimensions or the shape contour. This work presents a novel proposal on topological optimization which uses an Estimation of Distribution Algorithm (EDA) based on a Bayesian network. The EDA works as follows: propose a set of candidate solutions (population),the candidate solutions are generated according to a probability distribution, the population is evaluated on the objective function and contraints, and finally, the best structures are selected and used to recomputed the search distribution, and so on. The objective function is the structure weight, and the constraints are maximum Von Mises Stress,the node displacement and practical conditions such as connectivity of all the parts of the structure. The results show that the proposal is capable of designing low weight structures which fulfill the service conditions.El problema de optimización topológica es encontrar la mejor forma de una estructura mecánica sujeta a ciertas condiciones de servicio. Partiendo de una estructura inicial, se pueden agregar y quitar partes, modificar contornos, y dimensiones. En el presente trabajo se aborda el problema de optimización topológica utilizando un Algoritmo de Estimación de Distribución (AED) que utiliza una red bayesiana. La forma de trabajo del algoritmo es la siguiente: propone un conjunto de soluciones (población de estructuras) generadas por el muestreo de cierta distribución de probabilidad, estas son evaluadas para conocer su valor de función objetivo y restricciones. Las estructuras que tienen un mejor desempeño de acuerdo a su evaluación son seleccionadas para recalcular la distribución de probabilidad, con la cual será generada una nueva población. De esta forma se espera generar en cada iteración mejores estructuras. El objetivo a minimizar es el peso de la estructura, las restricciones son el máximo esfuerzo Von Mises, el desplazamiento en los nodos con carga, el desplazamiento máximo en cualquier nodo, y condiciones prácticas y estéticas de la estructura como: el tamaño de los agujeros presentes y la conectividad de sus piezas. La evaluación se realiza utilizando primordialmente el Método del Elemento finito. Los resultados obtenidos muestran la capacidad de la propuesta de proveer soluciones factibles de bajo costo.Peer Reviewe

A bayesian network based estimation of distribution algorithm for topological structure optimization

The topological optimization problema is stated as follows: to find the best shape of a mechanical structure subject to certain service conditions. Usually, the topological optimization problem is tackled by starting from an initial structure which is modified by adding parts, generating gaps, modifying dimensions or the shape contour. This work presents a novel proposal on topological optimization which uses an Estimation of Distribution Algorithm (EDA) based on a Bayesian network. The EDA works as follows: propose a set of candidate solutions (population),the candidate solutions are generated according to a probability distribution, the population is evaluated on the objective function and contraints, and finally, the best structures are selected and used to recomputed the search distribution, and so on. The objective function is the structure weight, and the constraints are maximum Von Mises Stress,the node displacement and practical conditions such as connectivity of all the parts of the structure. The results show that the proposal is capable of designing low weight structures which fulfill the service conditions.El problema de optimización topológica es encontrar la mejor forma de una estructura mecánica sujeta a ciertas condiciones de servicio. Partiendo de una estructura inicial, se pueden agregar y quitar partes, modificar contornos, y dimensiones. En el presente trabajo se aborda el problema de optimización topológica utilizando un Algoritmo de Estimación de Distribución (AED) que utiliza una red bayesiana. La forma de trabajo del algoritmo es la siguiente: propone un conjunto de soluciones (población de estructuras) generadas por el muestreo de cierta distribución de probabilidad, estas son evaluadas para conocer su valor de función objetivo y restricciones. Las estructuras que tienen un mejor desempeño de acuerdo a su evaluación son seleccionadas para recalcular la distribución de probabilidad, con la cual será generada una nueva población. De esta forma se espera generar en cada iteración mejores estructuras. El objetivo a minimizar es el peso de la estructura, las restricciones son el máximo esfuerzo Von Mises, el desplazamiento en los nodos con carga, el desplazamiento máximo en cualquier nodo, y condiciones prácticas y estéticas de la estructura como: el tamaño de los agujeros presentes y la conectividad de sus piezas. La evaluación se realiza utilizando primordialmente el Método del Elemento finito. Los resultados obtenidos muestran la capacidad de la propuesta de proveer soluciones factibles de bajo costo.Peer Reviewe

Parameter estimation for empirical and semi-empirical models in a direct ethanol fuel cell

Experimental data from a Direct Ethanol Fuel Cell (DEFC) provides a general perspective about its performance; nevertheless, it does not provide information about the cell’s physical characteristics nor information to improve its performance. On the other hand, numerical simulation can be used to test the cell’s design and boost its performance but requires a set of physical parameters. In this proposal, we introduce a novel modification to an empirical model for a Direct Methanol Fuel Cell to make it suitable for DEFC simulations at different temperatures by a new semi-empirical mathematical model. In addition, we introduce temperature-depending parametric forms of several terms to reduce the number of possible parameters to estimate from the DEFC. Then, we combined the models with an estimation of distribution algorithm to find the numerical simulation that best reproduces the experimental polarization curve. The method is validated by estimating the parameters to reproduce the experimental data at different temperatures reported in the literature, and with data obtained in an in-house open-cathode DEFC, recorded at a scan rate of 10 mVs−1, using as fuel CH3CH2OH1 M at 25 °C and 60 °C. From the estimation results at temperature set T1→=(T1a,T1c)∘C, the same parameters are used for a simulation at T2→=(T2a,T2c)∘C, demonstrating that it reproduces the two experimental polarization curves. Hence, the models and methods presented here can be used to reduce physical experimentation and to test different designs and operation settings

Síntesis Óptima de Mecanismos utilizando un EDA basado en la Distribución Normal

The problem of synthesis of mechanisms is to find the adequate dimensions in the elements in order to perform a given task. The task is defined by a set of points named: precision points. The problem is defined as minimizing the distance among the precision points and the actual position of the mechanism, the decision variables are the elements dimensions, the position and rotation of the relative coordinate system and two parameters related with the initial position and velocity. The proposal of this work is an algorithm for automatized synthesis of mechanisms. We use an Estimation of Distribution Algorithm (EDA), to approximate the optimum, by sampling and estimating a Normal multivariate probability distribution. Each sample is a candidate solution (a candidate mechanism), then we measure the difference between the precision points and the actual positions of the mechanism, the best candidate solutions are used to update the probability distribution, and the process is repeated until convergence is reached. We apply the proposed method for the synthesis of a Four-bar planar mechanism with closed chain. The obtained results are near-optimal designs that are better than others reported in the literature. The proposed method is a competitive alternative for the automatized synthesis of mechanism. We obtain high quality results when compare our approach with other reported in the literature. Additionally, the proposal presents other interesting features: the number of precision points can be increased without the need of increasing the dimension of the search space, in addition, the algorithm can be used for optimizing any mechanism. The results suggest that EDAs can successfully approach this kind of problems, hence, future work contemplate to use the same strategy for tackling more complex problems, possibly involving control parameters, or intending to design singularity-free closed kinematic chains.El problema de síntesis de mecanismos consiste en determinar las dimensiones de los eslabones a S fin de que éstos puedan completar una tarea, la cual es definida por el posicionamiento en un conjunto de puntos denominados puntos de precisión. En este trabajo se aborda este problema como un ejercicio de optimización, que minimiza la distancia entre la posición de un mecanismo propuesto y los puntos de precisión, tomando como variables de decisión las dimensiones del mecanismo, su sistema coordenado relativo y parámetros que determinan la velocidad del mismo y la posición inicial. La propuesta final es un algoritmo para el diseño automatizado de mecanismos que cumplan en lo mayor posible con la tarea deseada. Se utilizó un Algoritmo de Estimación de Distribución (EDA, por sus siglas en inglés), el cual aproxima el óptimo mediante muestreos subsecuentes de una distribución Normal multivariada, donde cada muestra representa una propuesta de un mecanismo. Después, se simula y se mide la diferencia entre los puntos de precisión y las posiciones del mecanismo propuesto, los mejores mecanismos se utilizan para actualizar la distribución de búsqueda en cada generación (iteración) del algoritmo hasta que converge a un punto de bajo costo de la función objetivo. Se utilizó el método propuesto para la síntesis automatizada de un mecanismo plano de 4 barras de cadena cinemática cerrada. Los resultados obtenidos son diseños óptimos aproximados, que superan a otros reportados en la literatura especializada. El método propuesto representa una alternativa eficaz para la síntesis automatizada de mecanismos. Los resultados son altamente competitivos con otros reportados en la literatura. La propuesta presenta además ventajas contra otras similares, en el sentido de que puede utilizarse para aproximar un número arbitrario de puntos de precisión, sin aumentar la dimensionalidad del problema, adicionalmente puede ser utilizado para la síntesis de otros mecanismos. Los resultados sugieren que los EDAs pueden utilizarse en este tipo de problemas de manera exitosa, por lo que a futuro se podría atacar problemas muchos más complejos, que involucren otras características deseadas como parámetros de control, o disminución de singularidades en sistemas de cadena cinemática cerrada

Modeling hind-limb kinematics using a bio-inspired algorithm with a local search

Abstract Background Laboratory rats play a critical role in research because they provide a biological model that can be used for evaluating the affectation of diseases and injuries, and for the evaluation of the effectiveness of new drugs and treatments. The analysis of locomotion in laboratory rats facilitates the understanding of motor defects in many diseases, as well as the damage and recovery after peripheral and central nervous system injuries. However, locomotion analysis of rats remains a great challenge due to the necessity of labor intensive manual annotations of video data required to obtain quantitative measurements of the kinematics of the rodent extremities. In this work, we present a method that is based on the use of a bio-inspired algorithm that fits a kinematic model of the hind limbs of rats to binary images corresponding to the segmented marker of images corresponding to the rat’s gait. The bio-inspired algorithm combines a genetic algorithm for a group of the optimization variables with a local search for a second group of the optimization variables. Results Our results indicate the feasibility of employing the proposed approach for the automatic annotation and analysis of the locomotion patterns of the posterior extremities of laboratory rats. Conclusions The adjustment of the hind limb kinematic model to markers of the video frames corresponding to rat’s gait sequences could then be used to analyze the motion patterns during the steps, which, in turn, can be useful for performing quantitative evaluations of the effect of lesions and treatments on rats models