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Air-blast loading is a serious threat to military and civil vehicles, buildings, containers, and cargo. Applications of sandwich-structured composites have attracted increasing interest in modern lightweight design and in the construction of dynamic loading regimes due to their high resistance against blast and ballistic impacts. The functional properties of such composites are determined by the interplay of their face sheet material and the employed core topology. The core topology is the most important parameter affecting the structural behavior of sandwich composites. Therefore, this contribution presents a thorough numerical investigation of different core topologies in sandwich-structured composites subjected to blast loading. Special emphasis is put on prismatic and lattice core topologies displaying auxetic and classical non-auxetic deformation characteristics in order to illustrate the beneficial properties of auxetic core topologies. Their dynamic responses, elastic and plastic deformations, failure mechanisms, and energy absorption capabilities are numerically analyzed and compared. The numerical studies are performed by means of the commercial finite element code ABAQUS/Explicit, including a model for structural failure.
Purpose Langevin transducers are ultrasonic transducers that convert electrical into mechanical energy through the piezoelectric effect. This class of transducers achieves the highest efficiency in their mechanical resonance. Studies have shown that the resonant frequency changes with temperature. The aim of this contribution is to reproduce this temperature-dependence resonance frequency as accurately as possible with FEM simulations. Methods Therefore, the temperature-dependent resonance behavior of Langevin transducers is examined experimentally. A FEM model is created on the basis of temperature-dependent measured material coefficients. Using parameter correlations and optimization algorithms, the FEM model is fitted and validated by experimental results. Six variants of Langevin transducers are examined in the range from 30 °C to 80 °C with resonance frequencies between 34 and 38 kHz. They differ in three geometries and two materials. Results The experimental results show that the resonance frequencies decrease with increasing temperatures by 5.0–19.4 Hz/°C, depending on the material and geometry. As decisive parameters for the model fitting of the FEM results, three function-dependent stiffness coefficients of the piezoelectric material PZT8 and the Young’s moduli of the metallic materials are determined by parameter correlation. Conclusion Through the targeted fitting of these function-dependent parameters, the calculation of the resonance frequencies of Langevin transducers can be qualitatively and quantitatively improved, independent of shape and material.
This contribution provides a detailed comparison of the impact of various rheological models on the filling phase of injection molding simulations in order to enhance the accuracy of flow predictions and improve material processing. The challenge of accurately modeling polymer melt flow behavior under different temperature and shear rate conditions is crucial for optimizing injection molding processes. Therefore, the study examines commonly used rheological models, including Power-Law, Second-Order, Herschel-Bulkley, Carreau and Cross models. Using experimental data for validation, the accuracy of each model in predicting the flow front and viscosity distribution for a quadratic molded part with a PA66 polymer is evaluated. The Carreau-WLF Winter model showed the highest accuracy, with the lowest RMSE values, closely followed by the Carreau model. The Second-Order model exhibited significant deviations in the edge region from experimental results, indicating its limitations. Results indicate that models incorporating both shear rate and temperature dependencies, such as Carreau-WLF Winter, provide superior predictions compared to those including only shear rate dependence. These findings suggest that selecting appropriate rheological models can significantly enhance the predictive capability of injection molding simulations, leading to better process optimization and higher quality in manufactured parts. The study emphasizes the significance of comprehensive rheological analysis and identifies potential avenues for future research and industrial applications in polymer processing.