Gas-liquid two phase flow through a vertical 90deg elbow bend

Pressure drop data are reported for two phase air-water flow through a vertical to horizontal 90° elbow bend set in 0.026 m i.d. pipe. The pressure drop in the vertical inlet tangent showed some significant differences to that found for straight vertical pipe. This was caused by the elbow bend partially choking the inflow resulting in a build-up of pressure and liquid in the vertical inlet riser and differences in the structure of the flow regimes when compared to the straight vertical pipe. The horizontal outlet tangent by contrast gave data in general agreement with literature even to exhibiting a drag reduction region at low liquid rates and gas velocities between 1 and 2 m s -1. The elbow bend pressure drop was best correlated in terms of le/d determined using the actual pressure loss in the inlet vertical riser. The data showed a general increase with fluid rates that tapered off at high fluid rates and exhibited a negative pressure region at low rates. The latter was attributed to the flow being smoothly accommodated by the bend when it passed from slug flow in the riser to smooth stratified flow in the outlet tangent. A general correlation was presented for the elbow bend pressure drop in terms of total Reynolds numbers. A modified Lockhart-Martinelli model gave prediction of the data.

Robust Speaker Recognition in Noisy Conditions

This paper investigates the problem of speaker identi-fication and verification in noisy conditions, assuming that speechsignals are corrupted by environmental noise, but knowledgeabout the noise characteristics is not available. This research ismotivated in part by the potential application of speaker recog-nition technologies on handheld devices or the Internet. Whilethe technologies promise an additional biometric layer of securityto protect the user, the practical implementation of such systemsfaces many challenges. One of these is environmental noise. Due tothe mobile nature of such systems, the noise sources can be highlytime-varying and potentially unknown. This raises the require-ment for noise robustness in the absence of information about thenoise. This paper describes a method that combines multicondi-tion model training and missing-feature theory to model noisewith unknown temporal-spectral characteristics. Multiconditiontraining is conducted using simulated noisy data with limitednoise variation, providing a “coarse” compensation for the noise,and missing-feature theory is applied to refine the compensationby ignoring noise variation outside the given training conditions,thereby reducing the training and testing mismatch. This paperis focused on several issues relating to the implementation of thenew model for real-world applications. These include the gener-ation of multicondition training data to model noisy speech, thecombination of different training data to optimize the recognitionperformance, and the reduction of the model’s complexity. Thenew algorithm was tested using two databases with simulated andrealistic noisy speech data. The first database is a redevelopmentof the TIMIT database by rerecording the data in the presence ofvarious noise types, used to test the model for speaker identifica-tion with a focus on the varieties of noise. The second database isa handheld-device database collected in realistic noisy conditions,used to further validate the model for real-world speaker verifica-tion. The new model is compared to baseline systems and is foundto achieve lower error rates.