1. Locations requiring the removal of paneling to access internal components. 2. Locations requiring technical support personnel to operate a manlift. 3. Locations that are not easily referenced to identifiable features of the test article such as rivet lines, edges or corners; with additional time and effort, such locations may still be identified by a template or laser tracker. This paper discusses the compromises that can be made when developing the TAM in order to successfully extract all target modes while minimizing the difficulty of installing the sensors. The ultimate figure of merit for a successful pretest is the pseudo-orthogonality, defined in the following section. Holding the number of accelerometers constant, a purely analytically derived TAM will produce the best possible pretest pseudo-orthogonality. A TAM that has been adjusted to allow for an easily installed and maintained ASET may have a slightly degraded, but still sufficient, pseudo-orthogonality. The following sections describe the pretest analysis process, including the iterative residual kinetic energy (IRKE) method to select the candidate accelerometer locations, the genetic algorithm for down-selecting to an optimal set of accelerometers, and how these relate to the construction of the test display model (TDM). The specific test article studied in this paper is the “iron bird,” which was fabricated by ATA Engineering, Inc., (ATA) as an internal development and training tool that simulates the dynamics and form factor of a fighter jet. While only the FEM of the iron bird is studied in this paper, the physical test article is depicted in Figure 1 undergoing a modal test. DESCRIPTION OF ANALYTIC PRETEST PROCESS A successful pretest analysis results in an optimized ASET that captures all pretest target modes, as evidenced by the pseudo-orthogonality: [ ] [ ] [ ][ ]2 1 12 = Φ Φ AA T M O Equation 1 Figure 1. ATA “iron bird” test article. 80
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