Special Probabilistic Prediction Model for Temperature Characteristics of Dynamic Fluid Processes
Accurately predicting the temperature characteristics of a dynamic discharge process in different transportation conditions can improve the performance of reciprocating multiphase pumps in practice. However, an accurate model for the description of the complicated behavior is not available because o...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8697343/ |
| _version_ | 1819133231313715200 |
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| author | Hongying Deng Yang Zhang Bocheng Chen Yi Liu Shengchang Zhang |
| author_facet | Hongying Deng Yang Zhang Bocheng Chen Yi Liu Shengchang Zhang |
| author_sort | Hongying Deng |
| collection | DOAJ |
| description | Accurately predicting the temperature characteristics of a dynamic discharge process in different transportation conditions can improve the performance of reciprocating multiphase pumps in practice. However, an accurate model for the description of the complicated behavior is not available because of the unknown interphase interaction mechanisms and infeasible experiments. A probabilistic modeling method of automatically selecting prediction models is proposed for the dynamic discharge process. First, candidate computational fluid dynamics (CFD) models are empirically utilized to provide the training data for candidate Gaussian process models (GPMs). Then, a posterior probability index is proposed to assess the uncertainty of trained GPMs when the actual values are not available. With this information, the most suitable GPM and CFD models are selected sequentially for each new sample. Consequently, the developed special GPM (SGPM) can capture the main temperature characteristics. Moreover, the selection results of prediction models can provide useful information for the recognition of complicated flow patterns. The advantages of the proposed SGPM are demonstrated using a reciprocating multiphase pump under different transportation conditions. |
| first_indexed | 2024-12-22T09:44:00Z |
| format | Article |
| id | doaj.art-e9986e2e082b44059b4e3b06b770e870 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T09:44:00Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e9986e2e082b44059b4e3b06b770e8702022-12-21T18:30:36ZengIEEEIEEE Access2169-35362019-01-017550645507210.1109/ACCESS.2019.29129778697343Special Probabilistic Prediction Model for Temperature Characteristics of Dynamic Fluid ProcessesHongying Deng0Yang Zhang1Bocheng Chen2Yi Liu3https://orcid.org/0000-0002-4066-689XShengchang Zhang4Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou, ChinaInstitute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou, ChinaInstitute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou, ChinaInstitute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou, ChinaInstitute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou, ChinaAccurately predicting the temperature characteristics of a dynamic discharge process in different transportation conditions can improve the performance of reciprocating multiphase pumps in practice. However, an accurate model for the description of the complicated behavior is not available because of the unknown interphase interaction mechanisms and infeasible experiments. A probabilistic modeling method of automatically selecting prediction models is proposed for the dynamic discharge process. First, candidate computational fluid dynamics (CFD) models are empirically utilized to provide the training data for candidate Gaussian process models (GPMs). Then, a posterior probability index is proposed to assess the uncertainty of trained GPMs when the actual values are not available. With this information, the most suitable GPM and CFD models are selected sequentially for each new sample. Consequently, the developed special GPM (SGPM) can capture the main temperature characteristics. Moreover, the selection results of prediction models can provide useful information for the recognition of complicated flow patterns. The advantages of the proposed SGPM are demonstrated using a reciprocating multiphase pump under different transportation conditions.https://ieeexplore.ieee.org/document/8697343/Probabilistic modelinggaussian process modelcomputational fluid dynamicsmultiphase pump |
| spellingShingle | Hongying Deng Yang Zhang Bocheng Chen Yi Liu Shengchang Zhang Special Probabilistic Prediction Model for Temperature Characteristics of Dynamic Fluid Processes IEEE Access Probabilistic modeling gaussian process model computational fluid dynamics multiphase pump |
| title | Special Probabilistic Prediction Model for Temperature Characteristics of Dynamic Fluid Processes |
| title_full | Special Probabilistic Prediction Model for Temperature Characteristics of Dynamic Fluid Processes |
| title_fullStr | Special Probabilistic Prediction Model for Temperature Characteristics of Dynamic Fluid Processes |
| title_full_unstemmed | Special Probabilistic Prediction Model for Temperature Characteristics of Dynamic Fluid Processes |
| title_short | Special Probabilistic Prediction Model for Temperature Characteristics of Dynamic Fluid Processes |
| title_sort | special probabilistic prediction model for temperature characteristics of dynamic fluid processes |
| topic | Probabilistic modeling gaussian process model computational fluid dynamics multiphase pump |
| url | https://ieeexplore.ieee.org/document/8697343/ |
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