Explorative In-situ Analysis of Turbulent Flow Data Based on a Data-Driven Approach

The Proper Orthogonal Decomposition (POD) has been used for several years in the post-processing of highly-resolved Computational Fluid Dynamics (CFD) simulations. While the POD can provide valuable insights into the spatial-temporal behaviour of single transient flows, it can be challenging to evaluate and compare results when applied to multiple simulations. Therefore, we propose a workflow based on data-driven techniques, namely dimensionality reduction and clustering to extract knowledge from large simulation bundles from transient CFD simulations. We apply this workflow to investigate the flow around two cylinders that contain complex modal structures in the wake region. A special emphasis lies on the formulation of in-situ algorithms to compute the data-driven representations during run-time of the simulation. This can reduce the amount of data inand output and enables a simulation monitoring to reduce computational efforts. Finally, a classifier is trained to predict characteristic physical behaviour in the flow only based on the input parameters

In-situ Estimation of Time-averaging Uncertainties in Turbulent Flow Simulations

The statistics obtained from turbulent flow simulations are generally uncertain due to finite time averaging. The techniques available in the literature to accurately estimate these uncertainties typically only work in an offline mode, that is, they require access to all available samples of a time series at once. In addition to the impossibility of online monitoring of uncertainties during the course of simulations, such an offline approach can lead to input/output (I/O) deficiencies and large storage/memory requirements, which can be problematic for large-scale simulations of turbulent flows. Here, we designed, implemented and tested a framework for estimating time-averaging uncertainties in turbulence statistics in an in-situ (online/streaming/updating) manner. The proposed algorithm relies on a novel low-memory update formula for computing the sample-estimated autocorrelation functions (ACFs). Based on this, smooth modeled ACFs of turbulence quantities can be generated to accurately estimate the time-averaging uncertainties in the corresponding sample mean estimators. The resulting uncertainty estimates are highly robust, accurate, and quantitatively the same as those obtained by standard offline estimators. Moreover, the computational overhead added by the in-situ algorithm is found to be negligible. The framework is completely general and can be used with any flow solver and also integrated into the simulations over conformal and complex meshes created by adopting adaptive mesh refinement techniques. The results of the study are encouraging for the further development of the in-situ framework for other uncertainty quantification and data-driven analyses relevant not only to large-scale turbulent flow simulations, but also to the simulation of other dynamical systems leading to time-varying quantities with autocorrelated samples

In-situ Estimation of Time-averaging Uncertainties in Turbulent Flow Simulations

The statistics obtained from turbulent flow simulations are generally uncertain due to finite time averaging. The techniques available in the literature to accurately estimate these uncertainties typically only work in an offline mode, that is, they require access to all available samples of a time series at once. In addition to the impossibility of online monitoring of uncertainties during the course of simulations, such an offline approach can lead to input/output (I/O) deficiencies and large storage/memory requirements, which can be problematic for large-scale simulations of turbulent flows. Here, we designed, implemented and tested a framework for estimating time-averaging uncertainties in turbulence statistics in an in-situ (online/streaming/updating) manner. The proposed algorithm relies on a novel low-memory update formula for computing the sample-estimated autocorrelation functions (ACFs). Based on this, smooth modeled ACFs of turbulence quantities can be generated to accurately estimate the time-averaging uncertainties in the corresponding sample mean estimators. The resulting uncertainty estimates are highly robust, accurate, and quantitatively the same as those obtained by standard offline estimators. Moreover, the computational overhead added by the in-situ algorithm is found to be negligible. The framework is completely general and can be used with any flow solver and also integrated into the simulations over conformal and complex meshes created by adopting adaptive mesh refinement techniques. The results of the study are encouraging for the further development of the in-situ framework for other uncertainty quantification and data-driven analyses relevant not only to large-scale turbulent flow simulations, but also to the simulation of other dynamical systems leading to time-varying quantities with autocorrelated samples

Assessing the Distribution of Water Ice and Other Volatiles at the Lunar South Pole with LUVMI-X: A Mission Concept

The search for exploitable deposits of water and other volatiles at the Moon’s poles has intensified considerably in recent years, due to the renewed strong interest in lunar exploration. With the return of humans to the lunar surface on the horizon, the use of locally available resources to support long-term and sustainable exploration programs, encompassing both robotic and crewed elements, has moved into focus of public and private actors alike. Our current knowledge about the distribution and concentration of water and other volatiles in the lunar rocks and regolith is, however, too limited to assess the feasibility and economic viability of resource-extraction efforts. On a more fundamental level, we currently lack sufficiently detailed data to fully understand the origins of lunar water and its migration to the polar regions. In this paper, we present LUVMI-X, a mission concept intended to address the shortage of in situ data on volatiles on the Moon that results from a recently concluded design study. Its central element is a compact rover equipped with complementary instrumentation capable of investigating both the surface and shallow subsurface of illuminated and shadowed areas at the lunar south pole. We describe the rover and instrument design, the mission’s operational concept, and a preliminary landing-site analysis. We also discuss how LUVMI-X fits into the diverse landscape of lunar missions under development

Vernetzungen und Publikumskonstruktionen im Internet

Passoth J-H, Sutter T, Wehner J. Vernetzungen und Publikumskonstruktionen im Internet. In: Frank-Job B, Mehler A, Sutter T, eds. Die Dynamik sozialer und sprachlicher Netzwerke. Konzepte, Methoden und empirische Untersuchungen an Beispielen des WWW. Wiesbaden: Springer VS; 2013: 139-159