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Effect of ring baffle configuration in a self-priming venturi scrubber using CFD simulations

Particuology 2019年第6期17卷 63-69页

作者： Shuai Yang Xiangdi Zhao Wanfu Sun Jiwu Yuan Zheng Wang State Key Laboratory of Safety and Control for Chemicals SINOPEC Research Institute of Safety EngineeringQingdao 266071China

This study uses computational fluid dynamics to survey the gas‒liquid flow hydrodynamics of a self-priming venturi scrubber with and without ring *** on the simulation results,we explore how the structure and configuration parameters such as baffle height,opening-area ratio,and baffle thickness affect the injection flow rate and the homogeneity of the gas‒liquid *** addition,we report the best structure and configuration parameters for the ring *** best position for the ring baffle is at nearly 7/16 of the length of the diffuser above the *** the baffle too near the throat causes large pressure loss,decreasing the injection flow *** decrease in opening-area ratio leads to a lower injection flow rate and a more homogeneous gas‒liquid *** optimal value depends on the requirement of the injection flow *** the baffle thickness,5 mm is recommended;an overly thick baffle decreases the homogeneity of the gas‒liquid mixture near the *** structure parameters and configuration parameters must satisfy the requirement imposed by the injection flow rate and scrubbing efficiency.

关键词： Self-priming mode Venturi scrubber Computational fluid dynamics Injection flow rate mixing degree Scrubbing efficiency

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Realization and Improvement Algorithm of GMM-UBM Model in Voiceprint Recognition

Realization and Improvement Algorithm of GMM-UBM Model in Vo...

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第30届中国控制与决策会议

作者： Jing Zhang School of Information Science and Technology Guangdong University of Foreign Studies

For the speaker recognition algorithm, the necessary training voice for the traditional voiceprint recognition model based on Gaussian mixture model(GMM) usually takes tens of seconds, and it is difficult to cover all the language features of the speaker, thus causing the phenomenon of low recognition rate. The paper proposed Gaussian mixture model-Universal background model(GMM-UBM), by which to train the common features and the proprietary features respectively. A large number of speaker voice was taken as a training sample, and the voice features covered most of the voice features was trained into a Universal background model, then the voice feature of specific human could be adaptively obtained through the Universal background model, so that a model with good performance could be trained out from a shorter voice, and the length of the voice was reduced and the recognition accuracy was improved. At the same time, in order to improve the accuracy, the improvement and solution to select the initial value and determine the mixing degree of GMM-UBM model were proposed. After the improvement, the test speech length was controlled at 4-7 s, and the recognition rate could still reach 96%.

关键词： GMM-UBM voice length mixing degree

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