Titlebook: Applied Structural Mechanics; Fundamentals of Elas H. Eschenauer,N. Olhoff,W. Schnell Textbook 1997 Springer-Verlag Berlin Heidelberg 1997

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书目名称Applied Structural Mechanics影响因子(影响力)

书目名称Applied Structural Mechanics影响因子(影响力)学科排名

书目名称Applied Structural Mechanics网络公开度

书目名称Applied Structural Mechanics网络公开度学科排名

书目名称Applied Structural Mechanics被引频次

书目名称Applied Structural Mechanics被引频次学科排名

书目名称Applied Structural Mechanics年度引用

书目名称Applied Structural Mechanics年度引用学科排名

书目名称Applied Structural Mechanics读者反馈

书目名称Applied Structural Mechanics读者反馈学科排名

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Constitutive laws of linearly elastic bodiese stress tensor, and we furthermore assume that these relations are time . The behaviour of the bodies is denoted as elastic, i.e. there are . permanent strains .. after removing the load of the body (Fig. 5.1). The bodies considered shall furthermore, as it is usual in the classical elasticity theo

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Disksl stresses are assumed uniformly distributed over the thickness, i.e. they do not depend on z. We therefore have a State of Plane Stress for which the most important basic equations in Cartesian and in polar coordinates are summarized in the following, where the thickness of the disk is assumed to b

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Platese . As shown in Fig. 9.1 a), the plate is subjected to surface loads p perpendicular to the mid—plane. An arbitrary load is resolved vertically and parallel to the surface. The in—plane forces can then be dealt with by means of the disk theory (Ch. 8). The interest in this chapter is restricted to t

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Fundamentals of structural optimization.e. calculation of deformations, stresses, natural vibration frequencies, buckling loads, etc. In view of the development and construction of machines and system components the question arises which measures must be taken in order to reduce costs . to improve quality and reliability; in other words

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Sensitivity analysis of structurestermination of the gradients of the objective function and the constraints is a highly important step in the optimization process (see Fig. 15.4), since these gradients are not only a prerequisite for the majority of optimization algorithms (see Chapter 16), but they also provide important informati

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