Industry is nowadays using composite materials more and more for automotive, aeronautic and space applications because of their good mechanical properties and low weight. This trend is shifting the traditional design paradigm dealing with metal structures to a modified paradigm involving hybrid metal/polymer structures. The new design process, needed to build these structures, requires also a shift from the traditional design approach to a new approach that integrates all the variabilities that hybrid designs introduce, mainly because of the high scattering related with composites manufacturing and assembling process. In this paper, the application of a Reliability-based Design Optimization procedure (RBDO) is proposed to address the variability modeling issues of an acoustic problem within the design process loop.
In the RBDO approach, the probability of failure for a particular design can be estimated using a methodology based on reliability analysis and design optimization through probabilistic models of relevant design parameters. In this way, deterministic designs using finite element models can be extended and optimized to fit much better the industrial requirements in terms of the structure’s weight and material costs and, at the same time, to ensure that output levels will not exceed a given threshold.
For demonstration purposes, an analysis project has been built around a finite element model of a composite aerospace panel of 24 by 8 inches with a viscoelastic damping layer embedded, simply supported along the edges and coupled to a model of an acoustic chamber. The plate and the volume were meshed in order to model the relative wavelengths up to 1500 Hz. A frequency response analysis has been performed by considering a mechanical excitation that simulates a plane wave acting on the panel and by evaluating radiated pressure in the fluid volume. The analyzed model can be representative of an acoustic enclosure such as an aircraft cabin, where cited variabilities can have a dramatic effect on noise characteristics.
Based on the addressed example, the main strengths and weaknesses of the probabilistic design methodology will be explained. The algorithmic framework used in this case study includes improvements in the trade-off of computation time vs. attained accuracy.
