Turbulence effect on crossflow around a circular cylinder at subcritical Reynolds numbers

An investigation of the effect of freestream turbulence on the flow around a smooth circular cylinder at subcritical Reynolds numbers from 5.2 x 10 to the 4th power to 2.09 x 10 to the 5th power was conducted. Measurements show that the interaction of incident turbulence with the initial laminar boundary layer: (1) modifies the characteristics of the mean surface pressure distribution; (2) induces an aft shift in the separation point ranging from 5 to 50 beyond the laminar separation angle of 80 degrees; and, (3) reduces the mean drag coefficient to values between 97 and 46% of its nearly constant laminar counterpart. The extent of these changes depends on the particular Reynolds number background turbulence combination. These results demonstrate that a boundary-layer flow similar to that found in critical, supercritical and/or transcritical flow regimes is induced by turbulence at subcritical Reynolds numbers and, hence, the effect of turbulence is equivalent to an effective increase in the Reynolds number. The change in the nature and properties of the boundary layer in the subcritical regime, consequent upon the penetration of turbulence into it, is in agreement with the model proposed by the vorticity-amplification theory

Phasing of dragonfly wings can improve aerodynamic efficiency by removing swirl

Dragonflies are dramatic, successful aerial predators, notable for their flight agility and endurance. Further, they are highly capable of low-speed, hovering and even backwards flight. While insects have repeatedly modified or reduced one pair of wings, or mechanically coupled their fore and hind wings, dragonflies and damselflies have maintained their distinctive, independently controllable, four-winged form for over 300 Myr. Despite efforts at understanding the implications of flapping flight with two pairs of wings, previous studies have generally painted a rather disappointing picture: interaction between fore and hind wings reduces the lift compared with two pairs of wings operating in isolation. Here, we demonstrate with a mechanical model dragonfly that, despite presenting no advantage in terms of lift, flying with two pairs of wings can be highly effective at improving aerodynamic efficiency. This is achieved by recovering energy from the wake wasted as swirl in a manner analogous to coaxial contra-rotating helicopter rotors. With the appropriate fore–hind wing phasing, aerodynamic power requirements can be reduced up to 22 per cent compared with a single pair of wings, indicating one advantage of four-winged flying that may apply to both dragonflies and, in the future, biomimetic micro air vehicles

Combinatorial Aspects of the Splitting Number

We define the strong splitting number, prove that it equals s when exists, and put some restrictions on the possibility that s is a singular carcinal

Extension and reconstruction theorems for the Urysohn universal metric space

We prove some extension theorems involving uniformly continuous maps of the universal Urysohn space. We also prove reconstruction theorems for certain groups of autohomeomorphisms of this space and of its open subsets.Comment: Final and shortened version, 25 pages, to appear in Czechoslovak Math.

Data enhanced predictive modeling for sales targeting

We describe and analyze the idea of data-enhanced predictive modeling (DEM). The term “enhanced ” here refers to the case that the data used for modeling is sampled not from the true target population, but from an alternative (closely related) population, from which much larger samples are available. This leads to a “bias-variance ” tradeoff, which implies that in some cases, DEM can improve predictive performance on the true target population. We theoretically analyze this tradeoff for the case of linear regression. We illustrate DEM on a problem of sales targeting for a set of software products. The “correct ” learning problem is to differentiate non-customers from newly acquired customers. The latter, however, are scarce. We illustrate how we can build better prediction models by using more flexible definitions of interesting targets, which give bigger learning samples. 1 Introducion A common situation in data modeling is when the available learning sample from the population of interest is relatively small, but a much larger sample is available from a similar population. The main question we address in this paper is, how can we leverage this abundant, relevant data towards improving predictive modeling? We have encountered this phenomenon in the context of targeting problems, where we are looking for potential customers among a large population of noncustomers. The learning problem we define involves differentiating customers that have recently bought the product for the first time (“positive examples”, which are usually quite rare) from non-customers. However, the population of veteran, established customers is being ignored completely in this approach. This population is often significantly larger than that of the recently converted “positive examples ” above, and since it represents customers, conceivably carries some information about what separates potential new customers from non customers. Many targeting applications do not mak