One of the biggest problems in an R&D process is the acquisition of information about the structure dynamic loads, which are needed to reliably prove the structure's durability. This paper aims to present an innovative method for simulating stationary Gaussian random processes, which is based on the conditional probability density function (PDF) approach. Design/methodology/approach - The basic information on the structure dynamic loads is first obtained by short-duration measurements on prototypes or the structure itself. These data are then used to simulate the expected structure load states during operations. A theoretical background is presented first, which is followed by the application of the method. Findings - The results show that the spectral characteristics of the original and simulated Gaussian random processes are very similar, if the influential range of the conditional PDF is properly chosen. Practical implications - The method can be applied for simulating random loads of structures, and excitations of dynamic systems, for example. Originality/value - The innovative simulation approach could be helpful to engineers in the early phases of the new product development process.
COBISS.SI-ID: 11971867
The fatigue damage to polymers generally depends on the material properties as well as on the mechanical, thermal, chemical, and other environmental influences. In this article, a methodology for modeling the dependence of the PA66 S-N curves on the material parameters, the material state, and the operating conditions is presented. The core of the presented methodology is a multilayer perceptron neural network combined with an analytical model of the PA66 S-N curve. Such a hybrid approach simultaneously utilizes the good approximation capabilities of the multilazer perceptron and knowledge of the phenomenon under consideration, because the analytical model for the S-N curves was estimated on the basis of the existing experimental data from the literature. The article presents the theoretical background of the applied methodology. The applicability and uncertainty of the presented methodology were assessed for the available data from the literature. The results show that it was possible to approximate the PA66 S-N curves for different input parameters if the space of the input parameters was adequately covered by the corresponding S-N curves.
COBISS.SI-ID: 11844123
The paper presents a comparison of different optimisation methods to estimate material parameters with the method of reverse engineering. Procedures were used to estimate parameters of nonlinear material model. The most appropriate procedure in terms of quality of the parameters and CPU time cost was chosen.
COBISS.SI-ID: 11919899