GAMM Mitteilungen最新文献

Brake squeal describes noise with different frequencies that can be emitted during the braking process. Typically, the frequencies are in the range of 1 to 16 kHz. Although the noise has virtually no effect on braking performance, strong attempts are made to identify and eliminate the noise as it can be very unpleasant and annoying. In the field of numerical simulation, the brake is typically modeled using the Finite Element method, and this results in a high-dimensional equation of motion. For the analysis of brake squeal, gyroscopic and circulatory effects, as well as damping and friction, must be considered correctly. For the subsequent analysis, the high-dimensional damped nonlinear equation system is linearized. This results in terms that are non-symmetric and dependent on the rotational frequency of the brake rotor. Many parameter points to be evaluated implies many evaluations to determine the relevant parameters of the unstable system. In order to increase the efficiency of the process, the system is typically reduced with a truncated modal transformation. However, with this method the damping and the velocity-dependent terms, which have a significant influence on the system, are neglected for the calculation of the eigenmodes, and this can lead to inaccurate reduced models. In this paper, we present results of other methods of model order reduction applied on an industrial high-dimensional brake model. Using moment matching methods combined with parametric model order reduction, both the damping and the various parameter-dependent terms of the brake model can be taken into account in the reduction step. Thus, better results in the frequency domain can be obtained. On the one hand, as usual in brake analysis, the complex eigenvalues are evaluated, but on the other hand also the transfer behavior in terms of the frequency response. In each case, the classical and the new reduction method are compared with each other.

In the present special volume, the German Association of Applied Mathematics and Mechanics (GAMM) Expert Committee “Experimental Solid Mechanics” was again given the opportunity to summarize the current scientific activities of individual participating working groups in Germany. Both optical measurement methods from surface information as well as radiation-based methods for detecting the internal states present in material bodies are receiving increasing interest. Nowadays, the required measurement systems can simply be purchased, or they can be developed in-house. In addition, there are still many scientific questions left open in the evaluation of the found measurement data. In the meantime, image correlation methods are often used to determine the surface deformation of components, the discrete data of which are now evaluated using proprietary software tools, or are coupled with infrared thermography systems, in particular to determine the dissipating energy of mechanically loaded components.

In the first issue of this special volume, four contributions are compiled proposing (1) a shear evaluation tool using digital image correlation (DIC) for plane problems [5], the coupling of infrared thermography (IRT) and 3D DIC for both (2) identifying material parameters in metal plasticity [6] as well as (3) applicability studies of foams and auxetic materials [3], and, finally, (4) identifying the heat conductivity parameters in transversal isotropy using IRT [8].

The second issue treats distance measurements of laminate layers using microscopical images [4], and three further contributions on μ-CT measurements. First, new in-situ measurements in granular media using μ-X-ray computer tomography (CT) combined with ultrasonic wave propagation is investigated [7]. A further contribution treats the influence of the pores on the fatigue properties of particular additively manufactured parts [2], and, finally, a study on the micro-structural characterization and stochastic modeling of open-cell foam using μ-CT image analysis [1]. All articles contribute to contact-less measurement sensing and evaluation in the field of solid mechanics.

Foam is a cellular material whose mechanical properties are strongly determined by its complex microstructure. To study the microstructure, at first a foam characterization based on $��\mu �$CT image processing is required. Here we present an image segmentation procedure and determine the foam's characteristics using the lattice cell-based concept of intrinsic volumes. Information like porosity, pore size distribution, and ligament shape are derived. These data are then employed as input for the generation of stochastic foam volume elements with the corresponding morphology. The introduced microstructural characterization and foam generation procedures are validated by an inverse analysis, that is, by a $��\mu �$CT image analysis of the stochastic foam volume element. Additionally, an example investigation of industrial polyurethane foam proves the concepts.

Pores are inherent to additively manufactured components and critical especially in technical components. Since they reduce the component's fatigue life, a reliable identification and description of pores is vital to ensure the component's performance. X-ray computed tomography (CT) is an established and non-destructive testing method to investigate internal defects. The CT scan process can induce noise and artefacts in the resulting images which afterwards have to be reduced through image processing. To reconstruct the internal defects of a component, the images need to be segmented in defect region and bulk material by applying a threshold. The application of the threshold as well as the previous image processing alter the geometry and size of the identified defects. This contribution aims to quantify the influence of selected commercial image processing and segmentation methods on identified pores in several additively manufactured components made of AlSi10Mg and Ti6Al4V as well as in an artificial CT scan. To that aim, gray value histograms and characteristic parameters thereof are compared for different image processing tools. After the segmentation of the processed images, particle characteristics are compared. The influence of image processing and segmentation on the predicted fatigue life of the material is evaluated through the change of the largest pore in each set of data applying Murakami's empirical $��\sqrt{��\text{area}��}�$-parameter model.

The article describes direct numerical simulations using an Euler–Lagrange approach with an immersed-boundary method to resolve the geometry and trajectory of particles moving in a flow. The presentation focuses on own work of the authors and discusses elements of physical and numerical modeling in some detail, together with three areas of application: microfluidic transport of spherical and nonspherical particles in curved ducts, flows with bubbles at different void fraction ranging from single bubbles to dense particle clusters, some also subjected to electro-magnetic forces, and bedload sediment transport with spherical and nonspherical particles. These applications with their specific requirements for numerical modeling illustrate the versatility of the approach and provide condensed information about main findings.

Cellular materials such as metal foams or auxetic metamaterials are interesting microheterogeneous materials used for lightweight construction and as energy absorbers. Their macroscopic behavior is related to their specific mesoscopic deformation by a strong structure-property-relationship. Digital image correlation and infrared thermography are two methods to visualize and study the local deformation behavior in materials. The present study deals with the full-field thermomechanical analysis of the mesomechanical deformation in Ni/PU hybrid foams and Ni/polymer hybrid auxetic structures performing a correlative digital image correlation and infrared thermography. Instead of comparing and correlating only the primary output variables of both methods, strain and temperature, also strain rates and temperature rates occurring during deformation were compared. These allow for a better correlation and more conclusive results than obtained using only the primary output variables.

Most technological advancements in medicine, process and energy engineering, life and food science, mobility and environmental engineering involve mastering fluid mechanical effects. In particular, compressible flow physics including shockwaves and phase-interface interactions exhibit multi-scale phenomena spanning several orders of magnitude upwards from nanometer and nanosecond time scales. Clearly, detailed analysis of such effects is impossible by means of experimental techniques. On the contrary, numerical modeling and simulations allow to capture the aforementioned mechanisms and provide non-invasive access to any quantity of interest. Yet, the complex fluid physics require powerful computational methods utilizing recent advancements for high-order schemes. In this work, we provide an overview on latest high-order low-dissipation schemes using level sets to model discontinuous phase-interface interactions.

Identifiability and sensitivity of thermal boundary coefficients identified alongside thermal material parameters by means of full field measurements during a simple tension test are shown empirically using a simple tension test with self heating as a proof of concept. The identification is started for 10 different initial guesses, all of which converge toward the same optimum. The solution appears to be locally unique and parameters therefore independent, but a comparison against a reference solution indicates high correlation between three model parameters and the prescribed external temperatures required to model heat exchange with either air or clamping jaws. This sensitivity is further analyzed by rerunning the identification with different prescribed external temperatures and by comparing the obtained optimal parameter values. Although the model parameters are independent, optimal values for heat conduction and the heat transfer coefficients are highly correlated as well as sensitive with respect to a change, respectively, measurement error of the external temperatures. A precise fit on the basis of a simple tension test therefore requires precise measurements and a suitable material model which is able to accurately predict dissipated energy.

The knowledge of the thermal conductivities is of particular interest for the thermo-mechanical modeling of transversely isotropic composite materials. Hence, the identification of these material parameters by solving an inverse problem is significant, as they cannot be directly measured. In this study, a suitable experimental setup is presented, where infrared thermography is used to measure the surface temperatures of thin specimens. Further, a local identifiability concept is employed to study whether locally unique parameters can be obtained. This leads to a particular step-wise identification concept. The parameter identification is performed applying a nonlinear least-square approach and finite elements. In the step-wise identification process the convection coefficient is required first, and, subsequently, the coefficients of the thermal conductivity tensor are determined. Due to the step-wise identification, the uncertainties of previously identified parameters have to be considered in the subsequent identification steps. The resulting uncertainties are estimated using the Gaussian error propagation concept. It turns out that the thermal conductivities of transversely isotropic materials are generally identifiable from surface temperature data. Furthermore, since all uncertainties have an essential influence on the results of real numerical simulations, their error propagation should be considered in resulting boundary-value problems. Thus, the uncertainty quantification is demonstrated by a validation experiment.