Data-driven prediction of critical collapse pressure of flexible pipeline carcass layer
Carcass layers in flexible pipelines are mainly used to resist the radial external pressure. The large hydrostatic pressure in deep-water environments often leads to collapse failure of the carcass layer, thus causing the destruction of the entire flexible pipeline. Therefore, the critical collapse...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yan, Jun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy - Chang, Guanru ELSEVIER, 2015, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:249 ; year:2022 ; day:1 ; month:04 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.oceaneng.2022.110948 |
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ELV057150885 |
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| 520 | |a Carcass layers in flexible pipelines are mainly used to resist the radial external pressure. The large hydrostatic pressure in deep-water environments often leads to collapse failure of the carcass layer, thus causing the destruction of the entire flexible pipeline. Therefore, the critical collapse pressure of the carcass layer under external pressure must be evaluated in engineering design. At present, the established equivalent theoretical model or numerical simulation method used to study the critical collapse pressure of the carcass layer has certain limitations, such as low accuracy or excessive calculation time. To overcome these problems, a Kriging-modified buckling model is proposed with radius R and thickness t as design variables, based on data-driven technology, to predict the critical collapse pressure of the carcass layer accurately. First, sample points are selected in the design domain based on the optimal Latin hypercube sampling, and the weight between the sample points and predicted points is calculated using the Kriging algorithm. Then, the above-mentioned weights are used to modify the buckling theory to predict the critical collapse pressure of the carcass layer. The numerical results indicate that the modified model has a higher prediction accuracy than that of the traditional buckling theory. Additionally, the effects of the number and pattern of sample points on the modified model are discussed. This paper presents an efficient design method and implementation technology for the design of deep-water flexible pipeline carcass. | ||
| 520 | |a Carcass layers in flexible pipelines are mainly used to resist the radial external pressure. The large hydrostatic pressure in deep-water environments often leads to collapse failure of the carcass layer, thus causing the destruction of the entire flexible pipeline. Therefore, the critical collapse pressure of the carcass layer under external pressure must be evaluated in engineering design. At present, the established equivalent theoretical model or numerical simulation method used to study the critical collapse pressure of the carcass layer has certain limitations, such as low accuracy or excessive calculation time. To overcome these problems, a Kriging-modified buckling model is proposed with radius R and thickness t as design variables, based on data-driven technology, to predict the critical collapse pressure of the carcass layer accurately. First, sample points are selected in the design domain based on the optimal Latin hypercube sampling, and the weight between the sample points and predicted points is calculated using the Kriging algorithm. Then, the above-mentioned weights are used to modify the buckling theory to predict the critical collapse pressure of the carcass layer. The numerical results indicate that the modified model has a higher prediction accuracy than that of the traditional buckling theory. Additionally, the effects of the number and pattern of sample points on the modified model are discussed. This paper presents an efficient design method and implementation technology for the design of deep-water flexible pipeline carcass. | ||
| 650 | 7 | |a Data driven |2 Elsevier | |
| 650 | 7 | |a Kriging algorithm |2 Elsevier | |
| 650 | 7 | |a Carcass layer |2 Elsevier | |
| 650 | 7 | |a Critical collapse pressure |2 Elsevier | |
| 650 | 7 | |a Flexible pipeline |2 Elsevier | |
| 700 | 1 | |a Li, Wenbo |4 oth | |
| 700 | 1 | |a Du, Hongze |4 oth | |
| 700 | 1 | |a Zhang, Hengrui |4 oth | |
| 700 | 1 | |a Huo, Sixu |4 oth | |
| 700 | 1 | |a Lu, Qingzhen |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Chang, Guanru ELSEVIER |t Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy |d 2015 |g Amsterdam [u.a.] |w (DE-627)ELV01276728X |
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10.1016/j.oceaneng.2022.110948 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica (DE-627)ELV057150885 (ELSEVIER)S0029-8018(22)00379-1 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Yan, Jun verfasserin aut Data-driven prediction of critical collapse pressure of flexible pipeline carcass layer 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Carcass layers in flexible pipelines are mainly used to resist the radial external pressure. The large hydrostatic pressure in deep-water environments often leads to collapse failure of the carcass layer, thus causing the destruction of the entire flexible pipeline. Therefore, the critical collapse pressure of the carcass layer under external pressure must be evaluated in engineering design. At present, the established equivalent theoretical model or numerical simulation method used to study the critical collapse pressure of the carcass layer has certain limitations, such as low accuracy or excessive calculation time. To overcome these problems, a Kriging-modified buckling model is proposed with radius R and thickness t as design variables, based on data-driven technology, to predict the critical collapse pressure of the carcass layer accurately. First, sample points are selected in the design domain based on the optimal Latin hypercube sampling, and the weight between the sample points and predicted points is calculated using the Kriging algorithm. Then, the above-mentioned weights are used to modify the buckling theory to predict the critical collapse pressure of the carcass layer. The numerical results indicate that the modified model has a higher prediction accuracy than that of the traditional buckling theory. Additionally, the effects of the number and pattern of sample points on the modified model are discussed. This paper presents an efficient design method and implementation technology for the design of deep-water flexible pipeline carcass. Carcass layers in flexible pipelines are mainly used to resist the radial external pressure. The large hydrostatic pressure in deep-water environments often leads to collapse failure of the carcass layer, thus causing the destruction of the entire flexible pipeline. Therefore, the critical collapse pressure of the carcass layer under external pressure must be evaluated in engineering design. At present, the established equivalent theoretical model or numerical simulation method used to study the critical collapse pressure of the carcass layer has certain limitations, such as low accuracy or excessive calculation time. To overcome these problems, a Kriging-modified buckling model is proposed with radius R and thickness t as design variables, based on data-driven technology, to predict the critical collapse pressure of the carcass layer accurately. First, sample points are selected in the design domain based on the optimal Latin hypercube sampling, and the weight between the sample points and predicted points is calculated using the Kriging algorithm. Then, the above-mentioned weights are used to modify the buckling theory to predict the critical collapse pressure of the carcass layer. The numerical results indicate that the modified model has a higher prediction accuracy than that of the traditional buckling theory. Additionally, the effects of the number and pattern of sample points on the modified model are discussed. This paper presents an efficient design method and implementation technology for the design of deep-water flexible pipeline carcass. Data driven Elsevier Kriging algorithm Elsevier Carcass layer Elsevier Critical collapse pressure Elsevier Flexible pipeline Elsevier Li, Wenbo oth Du, Hongze oth Zhang, Hengrui oth Huo, Sixu oth Lu, Qingzhen oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:249 year:2022 day:1 month:04 pages:0 https://doi.org/10.1016/j.oceaneng.2022.110948 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 249 2022 1 0401 0 |
| spelling |
10.1016/j.oceaneng.2022.110948 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica (DE-627)ELV057150885 (ELSEVIER)S0029-8018(22)00379-1 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Yan, Jun verfasserin aut Data-driven prediction of critical collapse pressure of flexible pipeline carcass layer 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Carcass layers in flexible pipelines are mainly used to resist the radial external pressure. The large hydrostatic pressure in deep-water environments often leads to collapse failure of the carcass layer, thus causing the destruction of the entire flexible pipeline. Therefore, the critical collapse pressure of the carcass layer under external pressure must be evaluated in engineering design. At present, the established equivalent theoretical model or numerical simulation method used to study the critical collapse pressure of the carcass layer has certain limitations, such as low accuracy or excessive calculation time. To overcome these problems, a Kriging-modified buckling model is proposed with radius R and thickness t as design variables, based on data-driven technology, to predict the critical collapse pressure of the carcass layer accurately. First, sample points are selected in the design domain based on the optimal Latin hypercube sampling, and the weight between the sample points and predicted points is calculated using the Kriging algorithm. Then, the above-mentioned weights are used to modify the buckling theory to predict the critical collapse pressure of the carcass layer. The numerical results indicate that the modified model has a higher prediction accuracy than that of the traditional buckling theory. Additionally, the effects of the number and pattern of sample points on the modified model are discussed. This paper presents an efficient design method and implementation technology for the design of deep-water flexible pipeline carcass. Carcass layers in flexible pipelines are mainly used to resist the radial external pressure. The large hydrostatic pressure in deep-water environments often leads to collapse failure of the carcass layer, thus causing the destruction of the entire flexible pipeline. Therefore, the critical collapse pressure of the carcass layer under external pressure must be evaluated in engineering design. At present, the established equivalent theoretical model or numerical simulation method used to study the critical collapse pressure of the carcass layer has certain limitations, such as low accuracy or excessive calculation time. To overcome these problems, a Kriging-modified buckling model is proposed with radius R and thickness t as design variables, based on data-driven technology, to predict the critical collapse pressure of the carcass layer accurately. First, sample points are selected in the design domain based on the optimal Latin hypercube sampling, and the weight between the sample points and predicted points is calculated using the Kriging algorithm. Then, the above-mentioned weights are used to modify the buckling theory to predict the critical collapse pressure of the carcass layer. The numerical results indicate that the modified model has a higher prediction accuracy than that of the traditional buckling theory. Additionally, the effects of the number and pattern of sample points on the modified model are discussed. This paper presents an efficient design method and implementation technology for the design of deep-water flexible pipeline carcass. Data driven Elsevier Kriging algorithm Elsevier Carcass layer Elsevier Critical collapse pressure Elsevier Flexible pipeline Elsevier Li, Wenbo oth Du, Hongze oth Zhang, Hengrui oth Huo, Sixu oth Lu, Qingzhen oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:249 year:2022 day:1 month:04 pages:0 https://doi.org/10.1016/j.oceaneng.2022.110948 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 249 2022 1 0401 0 |
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10.1016/j.oceaneng.2022.110948 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica (DE-627)ELV057150885 (ELSEVIER)S0029-8018(22)00379-1 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Yan, Jun verfasserin aut Data-driven prediction of critical collapse pressure of flexible pipeline carcass layer 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Carcass layers in flexible pipelines are mainly used to resist the radial external pressure. The large hydrostatic pressure in deep-water environments often leads to collapse failure of the carcass layer, thus causing the destruction of the entire flexible pipeline. Therefore, the critical collapse pressure of the carcass layer under external pressure must be evaluated in engineering design. At present, the established equivalent theoretical model or numerical simulation method used to study the critical collapse pressure of the carcass layer has certain limitations, such as low accuracy or excessive calculation time. To overcome these problems, a Kriging-modified buckling model is proposed with radius R and thickness t as design variables, based on data-driven technology, to predict the critical collapse pressure of the carcass layer accurately. First, sample points are selected in the design domain based on the optimal Latin hypercube sampling, and the weight between the sample points and predicted points is calculated using the Kriging algorithm. Then, the above-mentioned weights are used to modify the buckling theory to predict the critical collapse pressure of the carcass layer. The numerical results indicate that the modified model has a higher prediction accuracy than that of the traditional buckling theory. Additionally, the effects of the number and pattern of sample points on the modified model are discussed. This paper presents an efficient design method and implementation technology for the design of deep-water flexible pipeline carcass. Carcass layers in flexible pipelines are mainly used to resist the radial external pressure. The large hydrostatic pressure in deep-water environments often leads to collapse failure of the carcass layer, thus causing the destruction of the entire flexible pipeline. Therefore, the critical collapse pressure of the carcass layer under external pressure must be evaluated in engineering design. At present, the established equivalent theoretical model or numerical simulation method used to study the critical collapse pressure of the carcass layer has certain limitations, such as low accuracy or excessive calculation time. To overcome these problems, a Kriging-modified buckling model is proposed with radius R and thickness t as design variables, based on data-driven technology, to predict the critical collapse pressure of the carcass layer accurately. First, sample points are selected in the design domain based on the optimal Latin hypercube sampling, and the weight between the sample points and predicted points is calculated using the Kriging algorithm. Then, the above-mentioned weights are used to modify the buckling theory to predict the critical collapse pressure of the carcass layer. The numerical results indicate that the modified model has a higher prediction accuracy than that of the traditional buckling theory. Additionally, the effects of the number and pattern of sample points on the modified model are discussed. This paper presents an efficient design method and implementation technology for the design of deep-water flexible pipeline carcass. Data driven Elsevier Kriging algorithm Elsevier Carcass layer Elsevier Critical collapse pressure Elsevier Flexible pipeline Elsevier Li, Wenbo oth Du, Hongze oth Zhang, Hengrui oth Huo, Sixu oth Lu, Qingzhen oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:249 year:2022 day:1 month:04 pages:0 https://doi.org/10.1016/j.oceaneng.2022.110948 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 249 2022 1 0401 0 |
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10.1016/j.oceaneng.2022.110948 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica (DE-627)ELV057150885 (ELSEVIER)S0029-8018(22)00379-1 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Yan, Jun verfasserin aut Data-driven prediction of critical collapse pressure of flexible pipeline carcass layer 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Carcass layers in flexible pipelines are mainly used to resist the radial external pressure. The large hydrostatic pressure in deep-water environments often leads to collapse failure of the carcass layer, thus causing the destruction of the entire flexible pipeline. Therefore, the critical collapse pressure of the carcass layer under external pressure must be evaluated in engineering design. At present, the established equivalent theoretical model or numerical simulation method used to study the critical collapse pressure of the carcass layer has certain limitations, such as low accuracy or excessive calculation time. To overcome these problems, a Kriging-modified buckling model is proposed with radius R and thickness t as design variables, based on data-driven technology, to predict the critical collapse pressure of the carcass layer accurately. First, sample points are selected in the design domain based on the optimal Latin hypercube sampling, and the weight between the sample points and predicted points is calculated using the Kriging algorithm. Then, the above-mentioned weights are used to modify the buckling theory to predict the critical collapse pressure of the carcass layer. The numerical results indicate that the modified model has a higher prediction accuracy than that of the traditional buckling theory. Additionally, the effects of the number and pattern of sample points on the modified model are discussed. This paper presents an efficient design method and implementation technology for the design of deep-water flexible pipeline carcass. Carcass layers in flexible pipelines are mainly used to resist the radial external pressure. The large hydrostatic pressure in deep-water environments often leads to collapse failure of the carcass layer, thus causing the destruction of the entire flexible pipeline. Therefore, the critical collapse pressure of the carcass layer under external pressure must be evaluated in engineering design. At present, the established equivalent theoretical model or numerical simulation method used to study the critical collapse pressure of the carcass layer has certain limitations, such as low accuracy or excessive calculation time. To overcome these problems, a Kriging-modified buckling model is proposed with radius R and thickness t as design variables, based on data-driven technology, to predict the critical collapse pressure of the carcass layer accurately. First, sample points are selected in the design domain based on the optimal Latin hypercube sampling, and the weight between the sample points and predicted points is calculated using the Kriging algorithm. Then, the above-mentioned weights are used to modify the buckling theory to predict the critical collapse pressure of the carcass layer. The numerical results indicate that the modified model has a higher prediction accuracy than that of the traditional buckling theory. Additionally, the effects of the number and pattern of sample points on the modified model are discussed. This paper presents an efficient design method and implementation technology for the design of deep-water flexible pipeline carcass. Data driven Elsevier Kriging algorithm Elsevier Carcass layer Elsevier Critical collapse pressure Elsevier Flexible pipeline Elsevier Li, Wenbo oth Du, Hongze oth Zhang, Hengrui oth Huo, Sixu oth Lu, Qingzhen oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:249 year:2022 day:1 month:04 pages:0 https://doi.org/10.1016/j.oceaneng.2022.110948 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 249 2022 1 0401 0 |
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10.1016/j.oceaneng.2022.110948 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica (DE-627)ELV057150885 (ELSEVIER)S0029-8018(22)00379-1 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ BIODIV DE-30 fid 42.13 bkl Yan, Jun verfasserin aut Data-driven prediction of critical collapse pressure of flexible pipeline carcass layer 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Carcass layers in flexible pipelines are mainly used to resist the radial external pressure. The large hydrostatic pressure in deep-water environments often leads to collapse failure of the carcass layer, thus causing the destruction of the entire flexible pipeline. Therefore, the critical collapse pressure of the carcass layer under external pressure must be evaluated in engineering design. At present, the established equivalent theoretical model or numerical simulation method used to study the critical collapse pressure of the carcass layer has certain limitations, such as low accuracy or excessive calculation time. To overcome these problems, a Kriging-modified buckling model is proposed with radius R and thickness t as design variables, based on data-driven technology, to predict the critical collapse pressure of the carcass layer accurately. First, sample points are selected in the design domain based on the optimal Latin hypercube sampling, and the weight between the sample points and predicted points is calculated using the Kriging algorithm. Then, the above-mentioned weights are used to modify the buckling theory to predict the critical collapse pressure of the carcass layer. The numerical results indicate that the modified model has a higher prediction accuracy than that of the traditional buckling theory. Additionally, the effects of the number and pattern of sample points on the modified model are discussed. This paper presents an efficient design method and implementation technology for the design of deep-water flexible pipeline carcass. Carcass layers in flexible pipelines are mainly used to resist the radial external pressure. The large hydrostatic pressure in deep-water environments often leads to collapse failure of the carcass layer, thus causing the destruction of the entire flexible pipeline. Therefore, the critical collapse pressure of the carcass layer under external pressure must be evaluated in engineering design. At present, the established equivalent theoretical model or numerical simulation method used to study the critical collapse pressure of the carcass layer has certain limitations, such as low accuracy or excessive calculation time. To overcome these problems, a Kriging-modified buckling model is proposed with radius R and thickness t as design variables, based on data-driven technology, to predict the critical collapse pressure of the carcass layer accurately. First, sample points are selected in the design domain based on the optimal Latin hypercube sampling, and the weight between the sample points and predicted points is calculated using the Kriging algorithm. Then, the above-mentioned weights are used to modify the buckling theory to predict the critical collapse pressure of the carcass layer. The numerical results indicate that the modified model has a higher prediction accuracy than that of the traditional buckling theory. Additionally, the effects of the number and pattern of sample points on the modified model are discussed. This paper presents an efficient design method and implementation technology for the design of deep-water flexible pipeline carcass. Data driven Elsevier Kriging algorithm Elsevier Carcass layer Elsevier Critical collapse pressure Elsevier Flexible pipeline Elsevier Li, Wenbo oth Du, Hongze oth Zhang, Hengrui oth Huo, Sixu oth Lu, Qingzhen oth Enthalten in Elsevier Science Chang, Guanru ELSEVIER Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy 2015 Amsterdam [u.a.] (DE-627)ELV01276728X volume:249 year:2022 day:1 month:04 pages:0 https://doi.org/10.1016/j.oceaneng.2022.110948 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 42.13 Molekularbiologie VZ AR 249 2022 1 0401 0 |
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Enthalten in Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy Amsterdam [u.a.] volume:249 year:2022 day:1 month:04 pages:0 |
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Enthalten in Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy Amsterdam [u.a.] volume:249 year:2022 day:1 month:04 pages:0 |
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Self-healable hydrogel on tumor cell as drug delivery system for localized and effective therapy |
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data-driven prediction of critical collapse pressure of flexible pipeline carcass layer |
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Data-driven prediction of critical collapse pressure of flexible pipeline carcass layer |
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Carcass layers in flexible pipelines are mainly used to resist the radial external pressure. The large hydrostatic pressure in deep-water environments often leads to collapse failure of the carcass layer, thus causing the destruction of the entire flexible pipeline. Therefore, the critical collapse pressure of the carcass layer under external pressure must be evaluated in engineering design. At present, the established equivalent theoretical model or numerical simulation method used to study the critical collapse pressure of the carcass layer has certain limitations, such as low accuracy or excessive calculation time. To overcome these problems, a Kriging-modified buckling model is proposed with radius R and thickness t as design variables, based on data-driven technology, to predict the critical collapse pressure of the carcass layer accurately. First, sample points are selected in the design domain based on the optimal Latin hypercube sampling, and the weight between the sample points and predicted points is calculated using the Kriging algorithm. Then, the above-mentioned weights are used to modify the buckling theory to predict the critical collapse pressure of the carcass layer. The numerical results indicate that the modified model has a higher prediction accuracy than that of the traditional buckling theory. Additionally, the effects of the number and pattern of sample points on the modified model are discussed. This paper presents an efficient design method and implementation technology for the design of deep-water flexible pipeline carcass. |
| abstractGer |
Carcass layers in flexible pipelines are mainly used to resist the radial external pressure. The large hydrostatic pressure in deep-water environments often leads to collapse failure of the carcass layer, thus causing the destruction of the entire flexible pipeline. Therefore, the critical collapse pressure of the carcass layer under external pressure must be evaluated in engineering design. At present, the established equivalent theoretical model or numerical simulation method used to study the critical collapse pressure of the carcass layer has certain limitations, such as low accuracy or excessive calculation time. To overcome these problems, a Kriging-modified buckling model is proposed with radius R and thickness t as design variables, based on data-driven technology, to predict the critical collapse pressure of the carcass layer accurately. First, sample points are selected in the design domain based on the optimal Latin hypercube sampling, and the weight between the sample points and predicted points is calculated using the Kriging algorithm. Then, the above-mentioned weights are used to modify the buckling theory to predict the critical collapse pressure of the carcass layer. The numerical results indicate that the modified model has a higher prediction accuracy than that of the traditional buckling theory. Additionally, the effects of the number and pattern of sample points on the modified model are discussed. This paper presents an efficient design method and implementation technology for the design of deep-water flexible pipeline carcass. |
| abstract_unstemmed |
Carcass layers in flexible pipelines are mainly used to resist the radial external pressure. The large hydrostatic pressure in deep-water environments often leads to collapse failure of the carcass layer, thus causing the destruction of the entire flexible pipeline. Therefore, the critical collapse pressure of the carcass layer under external pressure must be evaluated in engineering design. At present, the established equivalent theoretical model or numerical simulation method used to study the critical collapse pressure of the carcass layer has certain limitations, such as low accuracy or excessive calculation time. To overcome these problems, a Kriging-modified buckling model is proposed with radius R and thickness t as design variables, based on data-driven technology, to predict the critical collapse pressure of the carcass layer accurately. First, sample points are selected in the design domain based on the optimal Latin hypercube sampling, and the weight between the sample points and predicted points is calculated using the Kriging algorithm. Then, the above-mentioned weights are used to modify the buckling theory to predict the critical collapse pressure of the carcass layer. The numerical results indicate that the modified model has a higher prediction accuracy than that of the traditional buckling theory. Additionally, the effects of the number and pattern of sample points on the modified model are discussed. This paper presents an efficient design method and implementation technology for the design of deep-water flexible pipeline carcass. |
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Li, Wenbo Du, Hongze Zhang, Hengrui Huo, Sixu Lu, Qingzhen |
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