VOID FRACTION ESTIMATION IN VERTICAL GAS-LIQUID FLOW BY PLURAL LONG SHORT-TERM MEMORY WITH SPARSE MODEL IMPLEMENTED IN MULTIPLE CURRENT-VOLTAGE SYSTEM
Plural long short-term memory (pLSTM) with sparse model (SM) has been implemented in a developed multiple current-voltage (MCV) system (called pLSTM-SM-MCV) for the a estimation in upward vertical gas-liquid flow. The new MCV system injects constant current to measure the voltage vnk at multiple ele...
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| Format: | Article |
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Begell House Inc.
2022
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| author | Tanaka, Koji Prayitno, Yosephus Ardean Kurnianto Sejati, Prima Asmara Kawashima, Daisuke Takei, Masahiro |
| author_facet | Tanaka, Koji Prayitno, Yosephus Ardean Kurnianto Sejati, Prima Asmara Kawashima, Daisuke Takei, Masahiro |
| author_sort | Tanaka, Koji |
| collection | UGM |
| description | Plural long short-term memory (pLSTM) with sparse model (SM) has been implemented in a developed multiple current-voltage (MCV) system (called pLSTM-SM-MCV) for the a estimation in upward vertical gas-liquid flow. The new MCV system injects constant current to measure the voltage vnk at multiple electrode pairs k at measurement time n with lower noise than the conventional multiple voltage-current (MVC) system. The measured voltage vector Vn is processed to the extracted voltage vector Ven by SM under the assumption in Vn. The Ven is trained for the flow regime identification by the 1st LSTM and the a estimation by the 2nd LSTM in pLSTM-SM-MCV. Experiments were conducted in a vertical gas-liquid experimental setup under the conditions of liquid single-phase flow, bubbly flow, and slug flow. As a result, the Ven was successfully extracted by determining the regularization parameter value λ in the sparse model as λ = 0.01 and λ = 0.042 in bubbly and slug flows, respectively. Under the λ, pLSTM-SM-MCV identifies the flow regime with zero percentage error and estimates the void fraction with a total mean of mean relative error hεi = 0.0275 which is reduced by 42 compared to the former pLSTM without SM (pLSTM-MCV) and by 51 compared to the former pLSTM implemented in conventional MVC system (pLSTM-MVC). |
| first_indexed | 2024-03-14T00:07:28Z |
| format | Article |
| id | oai:generic.eprints.org:283297 |
| institution | Universiti Gadjah Mada |
| last_indexed | 2024-03-14T00:07:28Z |
| publishDate | 2022 |
| publisher | Begell House Inc. |
| record_format | dspace |
| spelling | oai:generic.eprints.org:2832972023-11-21T05:38:13Z https://repository.ugm.ac.id/283297/ VOID FRACTION ESTIMATION IN VERTICAL GAS-LIQUID FLOW BY PLURAL LONG SHORT-TERM MEMORY WITH SPARSE MODEL IMPLEMENTED IN MULTIPLE CURRENT-VOLTAGE SYSTEM Tanaka, Koji Prayitno, Yosephus Ardean Kurnianto Sejati, Prima Asmara Kawashima, Daisuke Takei, Masahiro Engineering Plural long short-term memory (pLSTM) with sparse model (SM) has been implemented in a developed multiple current-voltage (MCV) system (called pLSTM-SM-MCV) for the a estimation in upward vertical gas-liquid flow. The new MCV system injects constant current to measure the voltage vnk at multiple electrode pairs k at measurement time n with lower noise than the conventional multiple voltage-current (MVC) system. The measured voltage vector Vn is processed to the extracted voltage vector Ven by SM under the assumption in Vn. The Ven is trained for the flow regime identification by the 1st LSTM and the a estimation by the 2nd LSTM in pLSTM-SM-MCV. Experiments were conducted in a vertical gas-liquid experimental setup under the conditions of liquid single-phase flow, bubbly flow, and slug flow. As a result, the Ven was successfully extracted by determining the regularization parameter value λ in the sparse model as λ = 0.01 and λ = 0.042 in bubbly and slug flows, respectively. Under the λ, pLSTM-SM-MCV identifies the flow regime with zero percentage error and estimates the void fraction with a total mean of mean relative error hεi = 0.0275 which is reduced by 42 compared to the former pLSTM without SM (pLSTM-MCV) and by 51 compared to the former pLSTM implemented in conventional MVC system (pLSTM-MVC). Begell House Inc. 2022 Article PeerReviewed Tanaka, Koji and Prayitno, Yosephus Ardean Kurnianto and Sejati, Prima Asmara and Kawashima, Daisuke and Takei, Masahiro (2022) VOID FRACTION ESTIMATION IN VERTICAL GAS-LIQUID FLOW BY PLURAL LONG SHORT-TERM MEMORY WITH SPARSE MODEL IMPLEMENTED IN MULTIPLE CURRENT-VOLTAGE SYSTEM. Multiphase Science and Technology, 34 (2). 25 – 42. ISSN 02761459 https://www.dl.begellhouse.com/journals/5af8c23d50e0a883,61671ff85f6610d1,2853602852edf477.html http://dx.doi.org/10.1615/MultScienTechn.2021039801 |
| spellingShingle | Engineering Tanaka, Koji Prayitno, Yosephus Ardean Kurnianto Sejati, Prima Asmara Kawashima, Daisuke Takei, Masahiro VOID FRACTION ESTIMATION IN VERTICAL GAS-LIQUID FLOW BY PLURAL LONG SHORT-TERM MEMORY WITH SPARSE MODEL IMPLEMENTED IN MULTIPLE CURRENT-VOLTAGE SYSTEM |
| title | VOID FRACTION ESTIMATION IN VERTICAL GAS-LIQUID FLOW BY PLURAL LONG SHORT-TERM MEMORY WITH SPARSE MODEL IMPLEMENTED IN MULTIPLE CURRENT-VOLTAGE SYSTEM |
| title_full | VOID FRACTION ESTIMATION IN VERTICAL GAS-LIQUID FLOW BY PLURAL LONG SHORT-TERM MEMORY WITH SPARSE MODEL IMPLEMENTED IN MULTIPLE CURRENT-VOLTAGE SYSTEM |
| title_fullStr | VOID FRACTION ESTIMATION IN VERTICAL GAS-LIQUID FLOW BY PLURAL LONG SHORT-TERM MEMORY WITH SPARSE MODEL IMPLEMENTED IN MULTIPLE CURRENT-VOLTAGE SYSTEM |
| title_full_unstemmed | VOID FRACTION ESTIMATION IN VERTICAL GAS-LIQUID FLOW BY PLURAL LONG SHORT-TERM MEMORY WITH SPARSE MODEL IMPLEMENTED IN MULTIPLE CURRENT-VOLTAGE SYSTEM |
| title_short | VOID FRACTION ESTIMATION IN VERTICAL GAS-LIQUID FLOW BY PLURAL LONG SHORT-TERM MEMORY WITH SPARSE MODEL IMPLEMENTED IN MULTIPLE CURRENT-VOLTAGE SYSTEM |
| title_sort | void fraction estimation in vertical gas liquid flow by plural long short term memory with sparse model implemented in multiple current voltage system |
| topic | Engineering |
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