Revisiting the ‘Venturi effect’ in passage ventilation between two non-parallel buildings

A recent study conducted by Blocken et al. (Numerical study on the existence of the Venturi effect in passages between perpendicular buildings. Journal of Engineering Mechanics, 2008,134: 1021-1028) challenged the popular view of the existence of the ‘Venturi effect’ in building passages as the wind is exposed to an open boundary. The present research extends the work of Blocken et al. (2008a) into a more general setup with the building orientation varying from 0° to 180° using CFD simulations. Our results reveal that the passage flow is mainly determined by the combination of corner streams. It is also shown that converging passages have a higher wind-blocking effect compared to diverging passages, explained by a lower wind speed and higher drag coefficient. Fluxes on the top plane of the passage volume reverse from outflow to inflow in the cases of α=135°, 150° and 165°. A simple mathematical expression to explain the relationship between the flux ratio and the geometric parameters has been developed to aid wind design in an urban neighborhood. In addition, a converging passage with α=15° is recommended for urban wind design in cold and temperate climates since the passage flow changes smoothly and a relatively lower wind speed is expected compared with that where there are no buildings. While for the high-density urban area in (sub)tropical climates such as Hong Kong where there is a desire for more wind, a diverging passage with α=150° is a better choice to promote ventilation at the pedestrian level.

An approximate dynamic programming approach for improving accuracy of lossy data compression by Bloom filters

Bloom filters are a data structure for storing data in a compressed form. They offer excellent space and time efficiency at the cost of some loss of accuracy (so-called lossy compression). This work presents a yes-no Bloom filter, which as a data structure consisting of two parts: the yes-filter which is a standard Bloom filter and the no-filter which is another Bloom filter whose purpose is to represent those objects that were recognised incorrectly by the yes-filter (that is, to recognise the false positives of the yes-filter). By querying the no-filter after an object has been recognised by the yes-filter, we get a chance of rejecting it, which improves the accuracy of data recognition in comparison with the standard Bloom filter of the same total length. A further increase in accuracy is possible if one chooses objects to include in the no-filter so that the no-filter recognises as many as possible false positives but no true positives, thus producing the most accurate yes-no Bloom filter among all yes-no Bloom filters. This paper studies how optimization techniques can be used to maximize the number of false positives recognised by the no-filter, with the constraint being that it should recognise no true positives. To achieve this aim, an Integer Linear Program (ILP) is proposed for the optimal selection of false positives. In practice the problem size is normally large leading to intractable optimal solution. Considering the similarity of the ILP with the Multidimensional Knapsack Problem, an Approximate Dynamic Programming (ADP) model is developed making use of a reduced ILP for the value function approximation. Numerical results show the ADP model works best comparing with a number of heuristics as well as the CPLEX built-in solver (B&B), and this is what can be recommended for use in yes-no Bloom filters. In a wider context of the study of lossy compression algorithms, our researchis an example showing how the arsenal of optimization methods can be applied to improving the accuracy of compressed data.

A parallel data assimilation model for oceanographic observations

In this paper we describe the development of a program that aims at the optimal integration of observed data in an oceanographic model describ