Design automation and optimization of honeycomb structures for maximum sound transmission loss

Cellular materials with macro effective properties defined by repeated meso-structures are increasingly replacing conventional homogeneous materials due to their high strength to weight ratio, and controllable effective mechanical properties, such as negative Poisson's ratio and tailored orthotropic elastic properties. Honeycomb structures are a well-known cellular material that has been used extensively in aerospace and other industries where the premium on the weight reduction with high-strength is required. Common applications of honeycomb cellular structures are their use as the core material in sandwich plates and plates between two face sheets. Honeycomb structures are built from repetition of a common hexagonal unit cell tessellation defined by four independent geometric parameters; the hexagonal unit cell side lengths, h, and l, cell wall thickness, t and orientation of the angle between the cell walls&thetas;. These parameters can be controlled to achieve desirable effective properties.;Another important application of honeycomb sandwich structures is the ability to adjust the unit cell geometric parameters to increase the Sound Transmission Loss (STL); a metric for measurement of noise cancellation for acoustic waves passing though the panel structure, while maintaining a low mass, and controllable effective stiffness and strength properties. Previous research has been limited to parametric studies exploring the effect of change in a single unit cell parameter on the Sound Transmission Loss (STL). To obtain an optimal STL result and to determine sensitivities, the present work presents a novel technique to control all four of the unit cell parameters while maintaining constant overall dimensions and mass of the honeycomb sandwich plate. These two constraints are necessary to ascertain that the high STL occurs only due to the change in geometric properties of honeycomb unit cell, as the STL increases with increase in mass and change in overall dimensions.;An optimization problem has been set-up with the design variables as hexagonal interior angle, number of unit cells in the horizontal direction, and number of unit cells in the vertical direction for a representative plate model with in-plane acoustic pressure wave transmission analysis. The optimization process required a complete design automation workflow of geometry creation based on changes in number of cells, constraints on overall dimensions and mass, output results extraction, construction of response surface to expedite the optimization using genetic algorithms. The process involved a coupled structural-acoustic finite element model with direct steady-state analysis and natural frequency extraction created and solved using the commercial finite element software package ABAQUS. The model is used to obtain acoustic pressure values for calculation of the STL of the honeycomb sandwich plate. Quadratic Timoshenko beam elements have been used to discretize the thin-walled honeycomb cellular structures for increased accuracy at higher frequencies. The elastic structure model is coupled with acoustic elements by applying surface based tie-constraints to transfer normal plate surface accelerations as input to calculate radiated sound pressure. The entire process of finite element model creation and solution has been parameterized and automated by extensive use of Python scripts directly interfaced with the ABAQUS solver. A detailed workflow has been set-up in the optimization package modeFRONTIER that generates the input variables using a genetic algorithm, NSGA-II, controls the Python scripts to create and solve the finite element Abaqus model, calls the Python scripts to extract results for post-processing needed to generate the STL vs. Frequency plots and finally optimizes the geometric unit cell parameters to maximize STL over a typical frequency range, all while respecting constraints on overall dimensions and mass. The frequency range from 200 Hz to 400 Hz was used to demonstrate the design automation and optimization process developed. The same workflow can be used to optimize STL for other frequency ranges. (Abstract shortened by UMI.).