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Solid Mechanics With Python: Non-cartesian Coordinates
Solid Mechanics With Python: Non-cartesian Coordinates
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This comprehensive volume covers the fundamental principles of solid mechanics, focusing on the mechanical behavior of solids in two and three dimensions under various types of loads. The solids are defined in non-Cartesian coordinate systems, such as polar, cylindrical, and spherical coordinate systems.The methodology and techniques to solve solid mechanics problems are explained in detail, formulated and demonstrated with numerous examples and illustrations. Two major approaches, the stress m…

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This comprehensive volume covers the fundamental principles of solid mechanics, focusing on the mechanical behavior of solids in two and three dimensions under various types of loads. The solids are defined in non-Cartesian coordinate systems, such as polar, cylindrical, and spherical coordinate systems.The methodology and techniques to solve solid mechanics problems are explained in detail, formulated and demonstrated with numerous examples and illustrations. Two major approaches, the stress method and the displacement method, are introduced. Python is used for formulation derivation, solving problems symbolically or numerically, and plotting results graphically.Written in Jupyter notebook format, this useful reference text offers a unified environment for theory description, code execution, and formulation, for easy reading and practicing.

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This comprehensive volume covers the fundamental principles of solid mechanics, focusing on the mechanical behavior of solids in two and three dimensions under various types of loads. The solids are defined in non-Cartesian coordinate systems, such as polar, cylindrical, and spherical coordinate systems.The methodology and techniques to solve solid mechanics problems are explained in detail, formulated and demonstrated with numerous examples and illustrations. Two major approaches, the stress method and the displacement method, are introduced. Python is used for formulation derivation, solving problems symbolically or numerically, and plotting results graphically.Written in Jupyter notebook format, this useful reference text offers a unified environment for theory description, code execution, and formulation, for easy reading and practicing.

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