PRESSURE FLUCTUATIONS ANALYSIS OF GAS-SOLID FLUIDIZED BED USING THE WIGNER DISTRIBUTION

黄海; 黄轶伦; 张卫东

您当前的位置：

首页 >

文章列表页 >

PRESSURE FLUCTUATIONS ANALYSIS OF GAS-SOLID FLUIDIZED BED USING THE WIGNER DISTRIBUTION

更新时间：2025-12-16

- PRESSURE FLUCTUATIONS ANALYSIS OF GAS-SOLID FLUIDIZED BED USING THE WIGNER DISTRIBUTION
- CIESC Journal Issue 4, (1999)
- 作者机构：
  
  浙江大学仪器工程系!杭州310027,浙江大学仪器工程系!杭州310027,浙江大学仪器工程系,杭州,310027
- 作者简介：
- 基金信息：
- DOI：
  CLC： Q021
- Online First：25 August 1999，
  
  Published：25 August 1999
- 稿件说明：
移动端阅览
黄海, 黄轶伦, 张卫东. PRESSURE FLUCTUATIONS ANALYSIS OF GAS-SOLID FLUIDIZED BED USING THE WIGNER DISTRIBUTION[J]. CIESC Journal, 1999, (4).
DOI：

黄海, 黄轶伦, 张卫东. PRESSURE FLUCTUATIONS ANALYSIS OF GAS-SOLID FLUIDIZED BED USING THE WIGNER DISTRIBUTION[J]. CIESC Journal, 1999, (4). DOI：

摘要

用Wigner谱首次对气固流化床压力脉动信号进行时频二维分析

压力脉动信号的强非平稳性得到了证实.在Wigner谱平面中定义了局部频峰加权平均频率参数

从实验角度研究了流化床气速、静床高对压力脉动信号加权平均频率的影响.结果表明

它具有比传统的功率谱主频好得多的统计重复性

能更准确地反映气泡相的特性.

Abstract

In this paper

the signal s strong non-stationary feature is demonstrated with time-frequency two-dimensional analysis by using the Wigner Distribution (WD) for pressure fluctuation signal in gas-solid fluidized beds. By a parameter defined as the Local Peak Weighted Average (LPWA) in WD space

the effect of velocity and probe height of fluidized beds on LPWA of pressure fluctuation is shown experimentally. The results indicate that the LPWA has much better reproducibility and accuracy than the major frequency of power spectral density in reflecting the properties of bubble phase.

关键词

Keywords

references

Views

367

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Mixed matrix membranes based on nitrogen-rich Schiff base network polymer and their CO2 separation performance

Study on synergistic modification of nitrogen-doped carbon coating and Ti⁴⁺ co-doping for lithium vanadium phosphate cathode materials

Prediction of biomass pyrolysis product yield based on machine learning algorithms

Study on dissolution kinetics of erythromycin thiocyanate

A two-stage dual-channel deep learning model for chemical fault classification

Related Author

No data

Related Institution

No data

⁰