Search Results

The Hybrid Finite Element Analysis (HFEA) method is based on combining conventional Finite Element Analysis (FEA) with analytical solutions and energy methods for mid-frequency computations. The method is appropriate for computing the vibration of structures which are comprised by stiff load bearing components and flexible panels attached to them; and for considering structure-borne loadings with the excitations applied on the load bearing members. In such situations, the difficulty in using conventional FEA at higher frequencies originates from requiring a very large number of elements in order to capture the flexible wavelength of the panel members which are present in a structure. In the HFEA the conventional FEA model is modified by de-activating the bending behavior of the flexible panels in the FEA computations and introducing instead a large number of dynamic impedance elements for representing the omitted bending behavior of the panels.

The Energy Finite Element Analysis (EFEA) has been developed for evaluating the vibro-acoustic behavior of complex systems. In the past EFEA results have been compared successfully to measured data for Naval, automotive, and aircraft systems. The main objective of this paper is to present information about the process of developing EFEA models for two configurations of a business jet, performing analysis for computing the vibration and the interior noise induced from exterior turbulent boundary layer excitation, and discussing the correlation between test data and simulation results. The structural EFEA model is generated from an existing finite element model used for stress analysis during the aircraft design process. Structural elements used in the finite element model for representing the complete complex aircraft structure become part of the EFEA structural model.

In this paper the development of statistical metamodels and statistical fast running models is presented first. They are utilized for propagating uncertainties in a multi-discipline design optimization process. Two main types of uncertainty can be considered in this manner: uncertainty due to variability in design variables or in random parameters; uncertainty due to the utilization of metamodels instead of the actual simulation models during the optimization process. The value of the new developments and their engagement in multi-discipline design optimization is demonstrated through a case study. An underwater vehicle is designed under four different disciplines, namely, noise radiation, self-noise due to TBL excitation, dynamic response due to propulsion impact loads, and response to an underwater detonation.