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Based on Patent application DE 198 22 125.8-52, we present a technical description of a new temporal and spatial high resolving anemometer for gas and liquid flows. The measurement principle is based on the technique of an atomic force microscope where microstructured cantilevers are used to detect extreme small forces. We show the sensor as a small compact unit and present first measurements and characterizations.

Two series of visualization experiments were performed in the boundary layer of Rayleigh—Bénard convection with the goal to get more information on the behavior and characteristics of streaks elongated in the streamwise direction observed in the boundary layers. These streamwise streaks are a new type of coherent structures in the Rayleigh—Bénard convection.

Two series of visualization experiments were performed in the boundary layer of Rayleigh—Bénard convection with the goal to get more information on the behavior and characteristics of streaks elongated in the streamwise direction observed in the boundary layers. These streamwise streaks are a new type of coherent structures in the Rayleigh—Bénard convection.

The understanding of the complex statistics of fully developed turbulence in detail is still an open problem. One of the central points is to understand intermittency, i.e. to find exceptionally strong fluctuations on small scales. In the last years, the intermittency in different directions has attracted considerable interest. It has been controversial whether there are significant differences in intermittency between the different directions. More specifically one looks at the statistics of increments [( + ) − ()] , i.e. at the projection of the differences between two velocities separated by the vector in a certain direction . Here we denotes longitudinal increments with , for which and are parallel and transverse increments with for which is perpendicular to .

In a first step, one commonly investigates the statistics with the moments of the increments, the so-called structure functions, and assumes that, according to Kolmogorov, the structure functions obey a scaling law <> ∝ at least for sufficient high Reynolds number. The intermittency problem is then expressed by the deviation of the exponent ξ from the value /3, the well-known Kolmogorov (1941) scaling.

Lie group analysis is used to derive (exponential laws) for ZPG turbulent boundary layer flow. A new scaling group was found in the two-point correlation equations. DNS of such a flow was performed at = 2240 using a spectral method with up to 160 million grid points. The results of the numerical simulations are compared with the new scaling laws and good agreement is achieved.

Lie group analysis is used to derive (exponential laws) for ZPG turbulent boundary layer flow. A new scaling group was found in the two-point correlation equations. DNS of such a flow was performed at = 2240 using a spectral method with up to 160 million grid points. The results of the numerical simulations are compared with the new scaling laws and good agreement is achieved.

One of the common views is that the behavior of passive objects in turbulent flows is in many respects similar to that of genuine turbulence. However, there exist a number of essential qualitative differences which require caution in promoting analogies between the two and which may be misleading. Some of these differences were summarized in [1]. It is the purpose of this note to provide a critical update of these essential differences with the emphasis on the aspects not given in [1] including the new results appeared after 2001.

Two series of visualization experiments were performed in the boundary layer of Rayleigh—Bénard convection with the goal to get more information on the behavior and characteristics of streaks elongated in the streamwise direction observed in the boundary layers. These streamwise streaks are a new type of coherent structures in the Rayleigh—Bénard convection.

In this chapter, we make an inventory of the tools suitable for supporting exploratory data analysis. Our major point is that the primary tool for analysis is the human imaginative mind, and that all other tools are supplementary. Only the human mind actually does the analysis; the other tools supply it with the necessary material, appropriately prepared and presented. The most appropriate form for the presentation of such material is visual, since the mind, as most scientists tend to agree, operates predominantly with images.

The techniques and software tools usable in exploratory data analysis are currently very numerous, and new tools continue to appear. It would be completely unfeasible to survey all of them. Therefore, we have tried instead to set out the major tool categories and describe the key functions and properties of each category. The resulting classification looks as follows:

We divide the visual expressive means into display dimensions and visual, or retinal, variables. Display dimensions provide a set of positions within a display at which graphical elements, or marks, can be placed. Retinal variables represent various properties of the marks: shape, size, colour, texture, orientation, etc. In addition to the visual dimensions of a display, such as width, height, or depth, we consider also the display time, which can be used, for example, in animated presentations.

In exploratory data analysis, it is usually not enough to use a single tool. Various tools need to be combined. We consider two basic modes of tool combination, sequential and concurrent, and discuss the various mechanisms used for tool combination. Visualisation is an essential component of any tool ensemble. Initial data visualisation is used in order to understand what tools should be used for further work. Results produced by any non-visual tool need to be visualised so that the analyst can see and interpret them

Throughout this chapter, we provide many examples of various tools. Even when discussing non-visual tools such as data manipulation or computational methods, we use visualisation intensively to illustrate the examples. Readers can easily note that we have taken every opportunity to stress the great role of visualisation in exploratory data analysis. At the beginning of the chapter, we make an attempt to substantiate the importance of visualisation.

In this paper we describe a new method for predicting PDFs of observable quantities driven by stochastic processes with a local Markov property. The method deals with large class of nonstationarities by overembeding the vector in the conditional part of the conditional probabilities of the Markov chain which approximates the Markov process. This allows an application of a Farmer-Sidorowich-like prediction scheme [1] in the obtained vector space. Thus the conditional PDF of the investigated quantity for the next time step can be estimated and various forecasts can be performed. As an illustration the method is applied to the problem for the short-term prediction of turbulent wind gusts which are the major danger for the safe operation of wind energy turbines. Predicted gusts can be made innocent by a simple change of the pitch angle of the rotor blades. Within a prediction horizon of few seconds which is sufficient for this purpose the discussed method produces meaningful results.