Parametric study of unsteady-flow-induced volute casing vibro-acoustics in a centrifugal fan

A numerical parametric analysis of a vibro-acoustic coupling method that considered the influence of vibro-acoustic coupling was carried out to investigate the casing vibrations and feathers of vibrational noise induced by unsteady flow of the centrifugal fan at the best-efficiency point (BEP). There are three important aspects of this method. First, an unsteady flow-field with a whole impeller-volute configuration was solved based on three-dimensional incompressible Navier-Stokes equations and a standard k-ε turbulence mode to obtain the source of the vibro-acoustics. Second, a one-way-flow structural acoustic coupling method was implemented to study the volute vibrations and behaviors of vibrational noise by adoption. The generation mechanism of vibrational noise of the volute casing was revealed. Third, the parametric analysis method was used to explore the parametric relationship between the panel thicknesses (such as front-panel thickness [FT], side-panel thickness [ST], and back-panel thickness [BT]) and the outlet acoustical power of the volute casing surface. The parametric analysis provides a reasonable range of values of three panel thicknesses that result in minimal vibrational sound radiation

A high performance ultra-wideband low cost SMA-to-GCPW transition

This letter presents a novel low cost through-the-wall SMA connector and the transition structures from the SMA to a grounded coplanar waveguide (GCPW). The SMA connector has two short metal legs extended from the outer conductor used to reduce the discontinuity of the transition. The parameters of the GCPW are designed to match the geometry of the coaxial. A matching stub is introduced in the center conductor line to further improve the performance of the transition. A prototype device is developed and measured. The measurement results show the return loss of the proposed transition is better than 20dB up to 26.5GHz

Cost-effective online trending topic detection and popularity prediction in microblogging

Identifying topic trends on microblogging services such as Twitter and estimating those topics’ future popularity have great academic and business value, especially when the operations can be done in real time. For any third party, however, capturing and processing such huge volumes of real-time data in microblogs are almost infeasible tasks, as there always exist API (Application Program Interface) request limits, monitoring and computing budgets, as well as timeliness requirements. To deal with these challenges, we propose a cost-effective system framework with algorithms that can automatically select a subset of representative users in microblogging networks in offline, under given cost constraints. Then the proposed system can online monitor and utilize only these selected users’ real-time microposts to detect the overall trending topics and predict their future popularity among the whole microblogging network. Therefore, our proposed system framework is practical for real-time usage as it avoids the high cost in capturing and processing full real-time data, while not compromising detection and prediction performance under given cost constraints. Experiments with real microblogs dataset show that by tracking only 500 users out of 0.6 million users and processing no more than 30,000 microposts daily, about 92% trending topics could be detected and predicted by the proposed system and, on average, more than 10 hours earlier than they appear in official trends lists