The use of structural mechanics models during the design process often leads to the development of models of varying fidelity. Often low-fidelity models are efficient to simulate but lack accuracy, while the high-fidelity counterparts are accurate with less efficiency. This paper presents a multifidelity surrogate modeling approach that combines the accuracy of a high-fidelity finite element model with the efficiency of a low-fidelity model to train an even faster surrogate model that parameterizes the design space of interest. The objective of these models is to predict the nonlinear frequency backbone curves of the Tribomechadynamics research challenge benchmark structure which exhibits simultaneous nonlinearities from frictional contact and geometric nonlinearity. The surrogate model consists of an ensemble of neural networks that learn the mapping between low and high-fidelity data through nonlinear transformations. Bayesian neural networks are used to assess the surrogate model’s uncertainty. Once trained, the multifidelity neural network is used to perform sensitivity analysis to assess the influence of the design parameters on the predicted backbone curves. Additionally, Bayesian calibration is performed to update the input parameter distributions to correlate the model parameters to the collection of experimentally measured backbone curves.
Engineers are interested in the ability to compare dynamic environments for many reasons. Current methods of comparing environments compare the measured acceleration at the same physical point via a direct measurement during the two environments. Comparing the acceleration at a defined point only provides a comparison of response at that location. However, the stress and strain of the structure are defined by the global response of all the points in a structure. This chapter uses modal filtering to transform a set of measurements at physical degrees of freedom into modal degrees of freedom that quantify the global response of the structure. Once the global response of the structure is quantified, two environments can be more reliably and accurately compared. This chapter compares the response of an aerospace component in a service environment to the response of the same component in a laboratory test environment. The comparison first compares the mode shapes between the two environments. Once it is determined that the same mode shapes are present in both configurations, the modal accelerations are compared in order to determine the similarity of the global response of the component.
Traditional electronics assemblies are typically packaged using physically or chemically blown potted foams to reduce the effects of shock and vibration. These potting materials have several drawbacks including manufacturing reliability, lack of internal preload control, and poor serviceability. A modular foam encapsulation approach combined with additively manufactured (AM) silicone lattice compression structures can address these issues for packaged electronics. These preloaded silicone lattice structures, known as foam replacement structures (FRSs), are an integral part of the encapsulation approach and must be properly characterized to model the assembly stresses and dynamics. In this study, dynamic test data is used to validate finite element models of an electronics assembly with modular encapsulation and a direct ink write (DIW) AM silicone FRS. A variety of DIW compression architectures are characterized, and their nominal stress-strain behavior is represented with hyperfoam constitutive model parameterizations. Modeling is conducted with Sierra finite element software, specifically with a handoff from assembly preloading and uniaxial compression in Sierra/Solid Mechanics to linear modal and vibration analysis in Sierra/Structural Dynamics. This work demonstrates the application of this advanced modeling workflow, and results show good agreement with test data for both static and dynamic quantities of interest, including preload, modal, and vibration response.
Calibrating a finite element model to test data is often required to accurately characterize a joint, predict its dynamic behavior, and determine fastener fatigue life. In this work, modal testing, model calibration, and fatigue analysis are performed for a bolted structure, and various joint modeling techniques are compared. The structure is designed to test a single bolt to fatigue failure by utilizing an electrodynamic modal shaker to axially force the bolted joint at resonance. Modal testing is done to obtain the dynamic properties, evaluate finite element joint modeling techniques, and assess the effectiveness of a vibration approach to fatigue testing of bolts. Results show that common joint models can be inaccurate in predicting bolt loads, and even when updated using modal test data, linear structural models alone may be insufficient in evaluating fastener fatigue.
Calibrating a finite element model to test data is often required to accurately characterize a joint, predict its dynamic behavior, and determine fastener fatigue life. In this work, modal testing, model calibration, and fatigue analysis are performed for a bolted structure, and various joint modeling techniques are compared. The structure is designed to test a single bolt to fatigue failure by utilizing an electrodynamic modal shaker to axially force the bolted joint at resonance. Modal testing is done to obtain the dynamic properties, evaluate finite element joint modeling techniques, and assess the effectiveness of a vibration approach to fatigue testing of bolts. Results show that common joint models can be inaccurate in predicting bolt loads, and even when updated using modal test data, linear structural models alone may be insufficient in evaluating fastener fatigue.