Zhang et al., 2023 - Google Patents
Analysis and identification of gas-liquid two-phase flow pattern based on multi-scale power spectral entropy and pseudo-image encodingZhang et al., 2023
- Document ID
- 2070996618910723353
- Author
- Zhang L
- Zhang S
- Publication year
- Publication venue
- Energy
External Links
Snippet
Gas-liquid two-phase flow is closely related to the production and transportation of energy industries. A flow pattern analysis and identification method based on multi-scale power spectral entropy (MPSE) with pseudo-image encoding (PIE) is proposed. The resistance …
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means
- G01N27/02—Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means by investigating the impedance of the material
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/02—Indexing codes associated with the analysed material
- G01N2291/028—Material parameters
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/02—Analysing fluids
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhang et al. | Analysis and identification of gas-liquid two-phase flow pattern based on multi-scale power spectral entropy and pseudo-image encoding | |
Sun et al. | Bearing fault diagnosis based on optimal convolution neural network | |
Dong et al. | Flow regimes identification-based multidomain features for gas–liquid two-phase flow in horizontal pipe | |
Li et al. | Flow regime identification in the subsea jumper based on electrical capacitance tomography and convolution neural network | |
Luo et al. | Hilbert–Huang transform, Hurst and chaotic analysis based flow regime identification methods for an airlift reactor | |
Mao et al. | Black-box real-time identification of sub-regime of gas-liquid flow using Ultrasound Doppler Velocimetry with deep learning | |
Zhang et al. | Gas/liquid two-phase flow pattern identification method using Gramian angular field and densely connected network | |
Wang et al. | Sectional void fraction measurement of gas-water two-phase flow by using a capacitive array sensor | |
Gao et al. | Characterization of chaotic dynamic behavior in the gas–liquid slug flow using directed weighted complex network analysis | |
Xu et al. | Flow pattern identification for gas-oil two-phase flow based on a virtual capacitance tomography sensor and numerical simulation | |
CN103487234A (en) | Gas-liquid two-phase flow dynamics representation and identification method based on multi-scale arrangement entropy | |
Dong et al. | Flow state monitoring of gas-water two-phase flow using multi-Gaussian mixture model based on canonical variate analysis | |
Wang et al. | Characterizing flow instability in oil-gas-water three-phase flow using multi-channel conductance sensor signals | |
Ye et al. | Multi-variable classification model for valve internal leakage based on acoustic emission time–frequency domain characteristics and random forest | |
Zhang et al. | Image reconstruction of electrical capacitance tomography based on optimal simulated annealing algorithm using orthogonal test method | |
Rossi et al. | Identification of suspension state using passive acoustic emission and machine learning in a solid–liquid mixing system | |
Chu et al. | Identification of boiling flow pattern in narrow rectangular channel based on TFA-CNN combined method | |
Wang et al. | Joint proper orthogonal decomposition: A novel perspective for feature extraction from multivariate cavitation flow fields | |
Xu et al. | Imaging of flow pattern of gas-oil flows with convolutional neural network | |
OuYang et al. | Soft measurement of oil–water two-phase flow using a multi-task sequence-based CapsNet | |
Jia et al. | Spatial and temporal characteristic information parameter measurement of interfacial wave using ultrasonic phased array method | |
Jiang et al. | A flow rate estimation method for gas–liquid two-phase flow based on transformer neural network | |
Wang et al. | Prediction of split-phase flow of low-velocity oil-water two-phase flow based on PLS-SVR algorithm | |
Geraldo et al. | Acoustic monitoring of sodium boiling in a liquid metal fast breeder reactor from autoregressive models | |
Zhang et al. | Cavitation state recognition method of centrifugal pump based on multi-dimensional feature fusion and convolutional gate recurrent unit |