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3.1  Topology correlation and test preparation

• Defining sensors in the FEM model
• Sensor/shaker placement

Topology correlation is the phase where one correlates test and model geometrical and sensor/shaker configurations. Most of this effort is handled by fe_sens with some use of femesh.

Starting with SDT 6.0, FEM sensors (see section 4.3) can be associated with wire frame model, the strategy where the two models where merged is thus obsolete.

As described in the following sections the three important phases of topology correlation are

• combining test and FEM model including coordinate system definition for the test nodes if there is a coordinate system mismatch,
• building of an observation matrix allowing the prediction of measurements based on FEM deformations,
• sensor and shaker placement.

3.1.1  Defining sensors in the FEM model

Given a FEM model (see section 4.2) and a test wire-frame model (see section 2.2.1 and  section 2.2.2), the first step is to declare the test as a SensDof sensor set entry in the case stack as done in the following example (see also the gartte demo). Section 4.3 gives many more details the sensor GUI, the available sensors (sensor trans, sensor triax, laser, ...). Section 4.3.3 discusses topology correlation variants in more details.

For this simple example, the FEM model must describe nodes, elements and DOFs. The test wire frame TEST must describe nodes and lines/elements and a .tdof field to declare sensors.

 cf=demosdt('demo gartfeplot'); % load FEM
 TEST=demosdt('demo garttewire');  % see sdtweb('pre#presen')
 cf.mdl=fe_case(cf.mdl,'sensdof','outputs',TEST)
 fecom(cf,'curtabCase','outputs');fecom('ProViewOn')
 fecom('TextStack') % display sensor text
 % now display FEM shape on sensors
 fe_case(cf.mdl,'sensmatch')
 cf.sel(2)='-outputs';
 cf.o(1)={'sel 2 def 1 ch 7 ty2 scc .25','edgecolor','r'};

In many practical applications, the coordinate systems for test and FEM differ. fe_sens supports the use of a local coordinate system for test nodes with the basis command. For an example use

 cf=demosdt('demo garttebasis');  
 fe_sens('basis estimate',cf,'sensors');
 % adjust origin manually with
 cf.CStack{'sensors'}.bas(4:6)=[-1 0 0];
 sens=cf.CStack{'sensors'}

Note that sensors defined using a sens.tdof DOF definition vector use the response coordinate system information given in column 3 of sens.Node while the 5 column format gives sensor directions in the global FEM coordinate system. In the example above, position and displacement coordinate systems for test nodes are set to 100. Thus the sensor 1011.02 (sens.tdof(1)) is a measurement in FEM direction z.

3.1.2  Sensor/shaker placement

In cases where an analytical model of a structure is available before the modal test, it is good practice to use the model to design the sensor/shaker configuration.

Typical objectives for sensor placement are

• Wire frame representations resulting from the placement should allow a good visualization of test results without expansion. Achieving this objective, enhances the ability of people doing the test to diagnose problems with the test, which is obviously very desirable.
• Seen at sensors, it is desirable that modes look different. This is measured by the condition number of [cφ]^T[cφ] (modeshape independence, see [14]) or by the magnitude of off-diagonal terms in the auto-MAC matrix (this measures orthogonality). Both independence and orthogonality are strongly related.
• sensitivity of measured modeshape to a particular physical parameter (parameter visibility)

Sensor placement capabilities are accessed using the fe_sens function as illustrated in the gartsens demo. This function supports the effective independence [14] and maximum sequence algorithms which seek to provide good placement in terms of modeshape independence.

It is always good practice to verify the orthogonality of FEM modes at sensors using the auto-MAC (whose off-diagonal terms should typically be below 0.1)

 cphi = fe_c(mdof,sdof)*mode; ii_mac('cpa',cphi,'mac auto plot')

For shaker placement, you typically want to make sure that

• you excite a set of target modes,
• or will have a combination of simultaneous loads that excites a particular mode and not other nearby modes.

The placement based on the first objective is easily achieved looking at the minimum controlability, the second uses the Multivariate Mode Indicator function (see ii_mmif). Appropriate calls are illustrated in the gartsens demo.