7. Small displacement - Statics - Weak formulation and “energy” theorems#

This section presents different weak formulations in the elastic problem, for both equilibrium conditions and congruence conditions, for different models/structural elements.

Models. In this section, weak formulations for different models of elastic structures are explicitly derived:

Approaches. The results shown here can be classified into two different approaches, depending on the independent variables of the methods:

  • displacement approach: 1) weak form of the equilibrium equations, 2) principle of virtual work, and 3) stationariety of the total potential energy

  • force approach: 1) weak form of the congruence conditionss, 2) principle of complementary virtual work, and 3) stationariety of the total complementary potential energy.

A force approach can be efficiently used to evaluate hyperstatics and point displacements/rotations, common questions in simple problems/exercises of the introductory courses in structural mechanics, as a result of low-dimensional linear systems that can be solved with a pen-and-paper approach. Displacement methods usually require some assumptions about the displacement field, and provides a common general methods for solving the whole structure. As an example, finite element method can be formulated as a discrete counterpart (usually a projection over a finite-dimensional basis) of the the weak form of equilibrium equations: FEM usually results in a “higher”-dimensional linear system1 (if compared with those of a force approach), that makes from little to no sense at all to solve by-hand, but can be easily built and solved with a computer.

Examples, problems and exercises. Some problems about beam structures can be conveniently approached with the contents of this section.


1

Finite element methods for linear structural problems usually provide a linear problem, \(\mathbf{K} \mathbf{u} = \mathbf{f}\), with some useful properties that can be exploited while building and solving the problem: the stiffness matrix \(\mathbf{K}\) is usually sparse (this properties allows to save memory, using sparse matrix format to avoid storing in an inefficient way lots of information — lots of zero; efficients algorithms exist for matrix operations with sparse matrices) and symmetric (many algorithms work well with symmetric matrices).