Self-similarity of Mean Flow in Pipe Turbulence

Based on our previous modified log-wake law in turbulent pipe ‡flows, we invent two compound similarity numbers (Y;U), where Y is a combination of the inner variable y+ and outer variable , and U is the pure exect of the wall. The two similarity numbers can well collapse mean velocity profile data with different moderate and large Reynolds numbers into a single universal profile. We then propose an arctangent law for the buffer layer and a general log law for the outer region in terms of (Y;U). From Milikan’s maximum velocity law and the Princeton superpipe data, we derive the von Kármán constant = 0:43 and the additive constant B=6. Using an asymptotic matching method, we obtain a self-similarity law that describes the mean velocity profile from the wall to axis; and embeds the linear law in the viscous sublayer, the quartic law in the bursting sublayer, the classic log law in the overlap, the sine-square wake law in the wake layer, and the parabolic law near the pipe axis. The proposed arctangent law, the general log law and the self-similarity law have been compared with the high-quality data sets, with diffrent Reynolds numbers, including those from the Princeton superpipe, Loulou et al., Durst et al., Perry et al., and den Toonder and Nieuwstadt. Finally, as an application of the proposed laws, we improve the McKeon et al. method for Pitot probe displacement correction, which can be used to correct the widely used Zagarola and Smits data set

Multi-pion correlations in high energy collisions

Any-order pion inclusive distribution for a chaotic source in high energy collisions are given which can be used in both theory and experiment to analyze any-order pion interferometry. Multi-pion correlations effects on two-pion and three-pion interferometry are discussed.Comment: Eq.(25) and Eq.(26) are correcte

Exact Solution of Photon Equation in a Nonstationary Godel-Type Cosmological Universe

This paper has excessive overlap with the following papers also written by the authors or their collaborators: gr-qc/0207026, gr-qc/0502059, gr-qc/0502061, and gr-qc/0510038.Comment: This submission has been withdrawn by arXiv administrators due to inappropriate text reuse from external source

Strategy for the selection of input ground motion for inelastic structural response analysis based on naïve Bayesian classifier

International audienceAn application of the naïve Bayesian classifier for selecting strong motion datain terms of the deformation probably induced on a given structural system is presented. Themain differences between the proposed method and the “standard” procedure based on theinference of a polynomial relationship between a single intensity measure and the engineeringdemand parameter are: the discrete description of the engineering demand parameter; theuse of an array of intensity measures; the combination of the information issued from thetraining phase via a Bayesian formulation. Six non-linear structural systems with initialfundamental frequency of 1, 2 and 5 Hz and with different strength reduction factors aremodelled. Their behaviour is described using the Takeda hysteretic model and the engineeringdemand parameter is expressed as the relative drift. A database of 6,373 strong motion recordsis built from worldwide catalogues and is described by a set of “classical” intensity measures;it constitutes the “training dataset” used to feed the Bayesian classifier. The structural systemresponse is reduced to a description of three possible classes: elastic, if the induced driftis lower than the yield displacement; plastic, if the drift ranges between the yield and theultimate drift values; fragile if the drift reaches the ultimate drift. The goal is to evaluate theconditional probability of observing a given status of the system as a function of the intensitymeasure array. To validate the presented methodology and evaluate its prediction capability,a blind test on a second dataset, completely disjointed from the training one, composed of7,000 waveforms recorded in Japan, is performed. The Japanese data are classed using theprobability distribution functions derived on the first data set. It is shown that, by combiningseveral intensity measures through the likelihood product, a stable result is obtained whereby most of the data (>75 %) are well classed. The degree of correlation between the intensitymeasure and the engineering demand parameter controls the reliability of the probabilitycurves associated to each intensity measure