GAMM Mitteilungen最新文献

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.

Investigations of biphasic monodisperse soft (rubber) and stiff (glass) particle mixtures under hydrostatic conditions show an interesting behavior with regard to the effective stiffness. P-wave modulus measured by acoustic wave propagation at ultrasonic frequencies showed a significant decline while more soft particles are added, that is, higher rubber volume fractions, due to a change in the microstructure of the granular medium. However, for small volume fractions of soft particles, it could be observed that the P-wave modulus is increasing. This result cannot be explained by classical mixture rules or effective medium theories. For the understanding of those effects, a detailed insight into the microstructure of the granular medium is necessary. To gain this information and link it later back to the measured effective mechanical properties, high-resolution micro X-ray computed tomography ($��\mu �$XRCT) imaging is a well-established tool. With $��\mu �$XRCT imaging, the granular microstructure can be visualized in 3D and characterized subsequently. Combining classical effective characterization methods with $��\mu �$XRCT imaging can help to solve a variety of multi-scale problems. Performing the characterization step in situ, meaning inside the laboratory-based $��\mu �$XRCT scanner, has the advantage that exactly the same samples are mechanically characterized and visualized. To address the mentioned observation above, we designed a low X-ray absorbing oedometer cell with integrated broadband piezoelectric P-wave transducers which enables this kind of investigation inside a laboratory-based $��\mu �$XRCT scanner. The focus of this contribution is on the general experimental methodology which can be transferred to other multi-scale problems. It starts with a description of the image acquisition and ends with the post-processing of the in situ acquired image data. To demonstrate this, cylindrical samples consisting of the same monodisperse rubber and glass particle mixtures that were studied before under hydrostatic stress conditions are considered. Selected results are presented to explain the single steps.

This contribution deals with a new shear device, which is applied for rubber measurements in simple shear in an extended temperature range. As a special feature for the loading system, no bonding technique, or vulcanization is used, but a form fit connection via pins. The measured stress–shear value curves for different temperature levels are evaluated with a standard method from GOM ARAMIS. In addition, a new approximation based evaluation method is introduced and applied for shear value determination including smoothing of the raw digital image correlation data with consideration of characteristic noise properties. This enables the analysis of noise induced error influences. It was proved by the approximation based method, that the standard ARAMIS evaluation provides shear value results, which are suitable for performed characterization tests. As a consequence, the nearly homogeneous measurement data can be used for parameter fitting of constitutive rubber models.

In sandwich laminates made of metal–polymer–metal (MPM) layers under forming conditions, the thickness changes along the thin-layered specimens are of particular interest. To measure this distribution, it is common to take microscopic images and determine manually the thicknesses at particular discrete points. We compare and provide several methods determining the thickness distribution from microscopic images. First, an interpolation scheme is chosen to obtain a continuous function of the interfaces between the layers instead of the discrete pixel data. Afterwards, the methods—ruled surface approach, orthogonal projection, embedded circle scheme, and a dull but simple method—are applied and compared with each other. For very smooth data, most of the schemes show equivalent results. However, the “brute force”—called the dull approach—turns out to be the most robust scheme, particularly, if noisy data is studied. The Gaussian error propagation concept is applied to study the uncertainties resulting from noisy pixel data. The schemes are adapted to finite element simulation results as well so that a direct comparison of experimental and numerical data would be possible.

The occurrence and shape of turbulent structures in mixed convection flows through a differently heated vertical channel are investigated in terms of thermally induced attenuation and amplification of turbulent velocity, pressure, and temperature fluctuations using direct numerical simulations. It is shown that the wall-normal momentum transport is decreased and increased near the heated and cooled wall, respectively, and that this leads to a reduced and elevated production of turbulent velocity fluctuations in the streamwise velocity component in the aiding and opposing flow, respectively. The corresponding flow structures are smoother, faster and warmer in the aiding flow and aligned along the main flow, while the colder structures in the opposing flow are more frayed and less directed. The warmer flow structures in the aiding flow are overall more stable than the colder structures in the opposing flow. Besides, the study reveals that the position of the maximum temperature fluctuations moves toward the heated wall, so that the sweeps produced at the two walls are affected differently by the former. As a consequence, the distance and time period over which the fluctuations develop in the aiding flow are shorter than in the opposing flow. It is further shown that vortex structures oriented in the streamwise direction usually arise with an offset to the right or left above a sweep or an ejection, whereby the decreasing values of the correlation coefficients with increasing Grashof number indicate a weakening of the vortex structures. Since none of the evaluated vortex criteria, that is, the distributions of the vorticity, $��{�\lambda �}_{�2�}�$- value or Rortex-value correlate well with the evaluated minima of the pressure fluctuations, they do not allow a clear identification of the vortex structures. Finally, analyzing the budget of the turbulent kinetic energy it is confirmed that the velocity fluctuations are only indirectly influenced by the buoyancy force. Thus, the attenuation and amplification of the turbulent velocity fluctuations is reflected in the reduction and exaggeration of the Reynolds shear stresses in the aiding and opposing flow, respectively.

This work presents direct numerical simulations (DNS) of a circular turbulent jet impinging on rough plates. The roughness is once resolved through an immersed boundary method (IBM) and once modeled through a parametric forcing approach (PFA) which accounts for surface roughness effects by applying a forcing term into the Navier–Stokes equations within a thin layer in the near-wall region. The DNS with the IBM setup is validated using optical flow field measurements over a smooth and a rough plate with similar statistical surface properties. In the study, IBM-resolved cases are used to show that the PFA is capable of reproducing mean flow features well at large wall-normal distances, while less accurate predictions are observed in the near-wall region. The demarcation between these two regions is approximately identified with the mean wall height $��{�k�}_{�m�}�$ of the surface roughness distribution. Based on the observed differences in the results between IBM- and PFA-resolved cases, plausible future improvements of the PFA are suggested.

Streaming Dynamic Mode Decomposition (sDMD) is a low-storage version of dynamic mode decomposition (DMD), a data-driven method to extract spatiotemporal flow patterns. Streaming DMD avoids storing the entire data sequence in memory by approximating the dynamic modes through incremental updates with new available data. In this paper, we use sDMD to identify and extract dominant spatiotemporal structures of different turbulent flows, requiring the analysis of large datasets. First, the efficiency and accuracy of sDMD are compared to the classical DMD, using a publicly available test dataset that consists of velocity field snapshots obtained by direct numerical simulation of a wake flow behind a cylinder. Streaming DMD not only reliably reproduces the most important dynamical features of the flow; our calculations also highlight its advantage in terms of the required computational resources. We subsequently use sDMD to analyse three different turbulent flows that all show some degree of large-scale coherence: rapidly rotating Rayleigh–Bénard convection, horizontal convection and the asymptotic suction boundary layer (ASBL). Structures of different frequencies and spatial extent can be clearly separated, and the prominent features of the dynamics are captured with just a few dynamic modes. In summary, we demonstrate that sDMD is a powerful tool for the identification of spatiotemporal structures in a wide range of turbulent flows.

Active turbulence control has been pursued continuously for the last decades, striving for an altered, energetically more favorable flow. In this article, our focus is on a promising method inducing a spanwise wall movement in order to reduce turbulence intensity and hence friction drag, investigated by means of direct numerical simulation. This approach transforms a previously time dependent oscillatory wall motion into a static spatial modulation with prescribed wavelength in the streamwise direction [48]. Most procedures related to turbulence control including the present one have been overwhelmingly applied to incompressible flow. This work is different and novel to the effect, that this control method is applied to compressible, supersonic channel flow up to a bulk Mach number of $��M�a�=�3�$. Due to substantial variations of viscosity, density, and temperature within the near-wall region in supersonic flow, the impact of the control method is altered compared to solenoidal flow conditions. By creating a data set of different Mach-/Reynolds numbers and control parameters, knowledge is gained in which way the effectiveness of oscillatory techniques and physical mechanisms change under the influence of compressibility. It is shown that the control method is able to effectively reduce turbulence levels and lead to large drag reduction levels in compressible supersonic flow. Variable property effects even enhance this behavior for the whole set of investigated parameters. Overall, the higher Mach number cases show a larger net power saving compared to the incompressible ones. Furthermore, we observe an increase of the optimum wavelength with increasing Mach number, which helps in guiding optimal implementations of such a control method.