Study of Composite Materials using Zigzag Theory on Timoshenko Beams

This final studies work target is to contribute two new beam theories to MAT-fem [6], an educational developed program by CIMNE [5]. Until now the MAT-fem Beams program only offered the Euler-Bernoulli and Timoshenko beam theories for homogeneous materials. With this work, the Timoshenko theory for composite materials and the refined zigzag theory are added. MAT-fem Beams application works by discretizing beams into two noded elements. This work offers a beam theories opportunity of contrasting with up to four kinematic variables. Timoshenko theory for composite materials works with three kinematics variables per node and zigzag theory works with four variables. Finally, the accuracy of zigzag theory must be remarked in comparison to other classic beam theories

Reliability estimation of the sheet stamping process using support vector machines

An important concern in sheet stamping is the risk of obtaining brittle final products that can be affected by fracture. Monte Carlo simulations presented herein show that this is governed by two main factors, namely static and dynamic friction coefficients. Whereas the latter correlates in a non-linear manner with minimum and maximum end thickness, the relationship of these design parameters to the former exhibits a bifurcation that is typical of highly non-linear phenomena, in which there is a sensitivity to small perturbations of the input values (chaos). In order to estimate the reliability of the process (i.e., the probability of obtaining brittle products due to low minimum and maximum thicknesses) with a reduced number of Monte Carlo runs, it is proposed to assimilate the problem to a pattern recognition task, due to the existence of two classes, namely robust and brittle. Among many pattern recognition algorithms that are useful to this end, use is made of support vector machines, as this incorporates the powerful tool of class margins that allow a drastic reduction of the number of simulations

Actuator and sensor fault estimation based on a proportional-integral quasi-LPV observer with inexact scheduling parameters

© 2019. ElsevierThis paper presents a method for actuator and sensor fault estimation based on a proportional-integral observer (PIO) for a class of nonlinear system described by a polytopic quasi-linear parameter varying (qLPV) mathematical model. Contrarily to the traditional approach, which considers measurable or unmeasurable scheduling parameters, this work proposes a methodology that considers inexact scheduling parameters. This condition is present in many physical systems where the scheduling parameters can be affected by noise, offsets, calibration errors, and other factors that have a negative impact on the measurements. A H8 performance criterion is considered in the design in order to guarantee robustness against sensor noise, disturbance, and inexact scheduling parameters. Then, a set of linear matrix inequalities (LMIs) is derived by the use of a quadratic Lyapunov function. The solution of the LMI guarantees asymptotic stability of the PIO. Finally, the performance and applicability of the proposed method are illustrated through a numerical experiment in a nonlinear system.Peer ReviewedPostprint (author's final draft

Contribution to the definition of non deterministic robust optimization in aeronautics accounting with variable uncertainties

Shape optimization is a largely studied problem in aeronautics. It can be applied to many disciplines in this field, namely efficiency improvement of engine blades, noise reduction of engine nozzles, or reduction of the fuel consumption of aircraft. Optimization for general purposes is also of increasing interest in many other fields. Traditionally, optimization procedures were based on deterministic methodologies as in Hamalainen et al (2000), where the optimum working point was fixed. However, not considering what happens in the vicinity of the defined working conditions can produce problems like loose of efficiency and performance. That is, in many cases, if the real working point differs from the original, even a little distance, efficiency is reduced considerably as pointed out in Huyse and Lewis (2001). Non deterministic methodologies have been applied to many fields (Papadrakakis, Lagaros and Tsompanakis, 1998; Plevris, Lagaros and Papadrakakis, 2005). One of the most extended nondeterministic methodologies is the stochastic analysis. The time consuming calculations required on Computational Fluid Dynamics (CFD) has prevented an extensive application of the stochastic analysis to shape optimization. Stochastic analysis was firstly developed in structural mechanics, several years ago. Uncertainty quantification and variability studies can help to deal with intrinsic errors of the processes or methods. The result to consider for design optimization is no longer a point, but a range of values that defines the area where, in average, optimal output values are obtained. The optimal value could be worse than other optima, but considering its vicinity, it is clearly the most robust regarding input variability. Uncertainty quantification is a topic of increasing interest from the last few years. It provides several techniques to evaluate uncertainty input parameters and their effects on the outcomes. This research presents a methodology to integrate evolutionary algorithms and stochastic analysis, in order to deal with uncertainty and to obtain robust solutions

Contribution to the Definition of Non Deterministic Robust Optimization in Aeronautics Accounting with Variable Uncertainties

Shape optimization is a largely studied problem in aeronautics. It can be applied to many disciplines in this field, namely efficiency improvement of engine blades, noise reduction of engine nozzles, or reduction of the fuel consumption of aircraft. Optimization for general purposes is also of increasing interest in many other fields. Traditionally, optimization procedures were based on deterministic methodologies as in Hamalainen et al (2000), where the optimum working point was fixed. However, not considering what happens in the vicinity of the defined working conditions can produce problems like loose of efficiency and performance. That is, in many cases, if the real working point differs from the original, even a little distance, efficiency is reduced considerably as pointed out in Huyse and Lewis (2001) Non deterministic methodologies have been applied to many fields (Papadrakakis, Lagaros and Tsompanakis, 1998; Plevris, Lagaros and Papadrakakis, 2005). One of the most extended nondeterministic methodologies is the stochastic analysis. The time consuming calculations required on Computational Fluid Dynamics (CFD) has prevented an extensive application of the stochastic analysis to shape optimization. Stochastic analysis was firstly developed in structural mechanics, several years ago. Uncertainty quantification and variability studies can help to deal with intrinsic errors of the processes or methods. The result to consider for design optimization is no longer a point, but a range of values that defines the area where, in average, optimal output values are obtained. The optimal value could be worse than other optima, but considering its vicinity, it is clearly the most robust regarding input variability. Uncertainty quantification is a topic of increasing interest from the last few years. It provides several techniques to evaluate uncertainty input parameters and their effects on the outcomes. This research presents a methodology to integrate evolutionary algorithms and stochastic analysis, in order to deal with uncertainty and to obtain robust solutions

Modelling and simulation of the effect loading on structures using and adaptive blending of discrete and finite element methods

We present a new computational model for predicting the effect of blast loading on structures. The model is based in the adaptive coupling of the finite element method (FEM) and the discrete element method (DEM) for the accurate reproduction of multifracturing and failure of structures under blast loading. In the paper we briefly describe the basis of the coupled DEM/FEM technology and demonstrate its efficiency in its application to the study of the effect of blast loading on a masonry wall, a masonry tunnel and a double curvature dam

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Condiciones Laborales Y La Autoestima De Los Estudiantes Del Centro De Educación Básica Alternativa Inca Garcilaso De La Vega, Cusco-2016

La presente investigación tiene como objetivo determinar qué relación existe entre las Condiciones laborales y la Autoestima de los estudiantes del Centro de Educación Básica Alternativa Inca Garcilaso de la Vega, Cusco-2016. Metodológicamente la investigación de tipo básico descriptivo, haciendo uso del diseño no experimental, correlacional, cuyos datos fueron recopilados en una sola oportunidad de corte transeccional, haciendo uso de la técnica encuesta, mediante el uso del cuestionario considerando para el planteamiento de preguntas la variable condiciones laborales y para la variable autoestima se utilizó el mismo instrumento, ambos fueron validados por juicio de expertos, lográndose obtener en el estadístico del alfa de Crombach el coeficiente de 0.874 y 0.905 de manera respectiva. El número de personas que conforman la población son 120 estudiantes del CEBA Inca Garcilaso de la Vega, la muestra un total de 60 estudiantes, elegidos mediante muestreo no probabilístico condicional, procesados mediante el uso de tablas y gráficos estadísticos realizados en el programa Excel y la prueba de hipótesis mediante el estadístico tau b de Kendall, hallado mediante el uso del software spss, versión 23. Los resultados del presente trabajo de investigación muestran que las Condiciones laborales se relacionan significativamente con la Autoestima de los estudiantes del Centro de Alternancia Inca Garcilaso de la Vega, Cusco, esto se asume de la tabla donde el 51.7% de los estudiantes que tienen condiciones laborales poco adecuadas su autoestima es baja y del valor tau b de Kendall que es 0.481 se asume una asociación moderada y del valor de p=0.001<0.05 se acepta la hipótesis alterna

Discrete Element Modelling of Rock Cutting Processes Interaction with Evaluation of Tool Wear

The document presents a numerical model of rocks and soils using spherical Discrete Elements, also called Distinct Elements. The motion of spherical elements is described by means of equations of rigid body dynamics. Explicit integration in time yields high computational efficiency. Spherical elements interact among one another with contact forces, both in normal and tangential directions. Efficient contact search scheme based on the octree structures has been implemented. Special constitutive model of contact interface taking into account cohesion forces allows us to model fracture and decohesion of materials. Numerical simulation predicts wear of rock cutting tools. The developed numerical algorithm of wear evaluation allows us us to predict evolution of the shape of the tool caused by wear. Results of numerical simulation are validated by comparison with experimental data