Predicting filling efficiency of composite resin injection repair
We propose to develop a two-dimensional reduced-order reconstruction, simulation and injection strategy to model resin injection repair which is scalable and practical for use with available equipment. The proposed method involves reconstructing a damaged composite laminate using ultrasonic C-scans...
Ausführliche Beschreibung
Autor*in: |
Asiliskender, Ahmed [verfasserIn] Peiró, Joaquim [verfasserIn] Lee, Koon-Yang [verfasserIn] Parlamas, Apostolos [verfasserIn] Falzon, Brian [verfasserIn] Kazancı, Zafer [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Composites / A - Amsterdam [u.a.] : Elsevier, 1996, 174 |
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Übergeordnetes Werk: |
volume:174 |
DOI / URN: |
10.1016/j.compositesa.2023.107708 |
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Katalog-ID: |
ELV062420402 |
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245 | 1 | 0 | |a Predicting filling efficiency of composite resin injection repair |
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520 | |a We propose to develop a two-dimensional reduced-order reconstruction, simulation and injection strategy to model resin injection repair which is scalable and practical for use with available equipment. The proposed method involves reconstructing a damaged composite laminate using ultrasonic C-scans to determine the damage zone geometry and porosity. The damage zone permeability is calculated via semi-empirical constitutive equations, and used as input data for the CFD simulation of a resin injection process through the composite. The ultimate aim is to guide repair operators by identifying suitable injection configurations in order to improve cavity filling and thus repair efficiency. After establishing the methodology basis, we verify simulations through comparison to a proposed and analytically solved problem. Validation results show a 70+% simulation accuracy. Finally, we create a case study where cavity filling is improved by applying knowledge of the damage zone. This method’s ability to predict filling efficacy offers a viable, quantitative and more consistent alternative to existing intuition-based practices for resin injection repair. | ||
650 | 4 | |a Laminate repair | |
650 | 4 | |a Permeability | |
650 | 4 | |a Computational modelling | |
650 | 4 | |a Process modelling | |
700 | 1 | |a Peiró, Joaquim |e verfasserin |0 (orcid)0000-0001-8859-4912 |4 aut | |
700 | 1 | |a Lee, Koon-Yang |e verfasserin |0 (orcid)0000-0003-0777-2292 |4 aut | |
700 | 1 | |a Parlamas, Apostolos |e verfasserin |0 (orcid)0000-0001-5001-9752 |4 aut | |
700 | 1 | |a Falzon, Brian |e verfasserin |4 aut | |
700 | 1 | |a Kazancı, Zafer |e verfasserin |0 (orcid)0000-0003-2127-8389 |4 aut | |
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2023 |
allfields |
10.1016/j.compositesa.2023.107708 doi (DE-627)ELV062420402 (ELSEVIER)S1359-835X(23)00284-1 DE-627 ger DE-627 rda eng 660 VZ 51.75 bkl Asiliskender, Ahmed verfasserin (orcid)0000-0002-3808-9966 aut Predicting filling efficiency of composite resin injection repair 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose to develop a two-dimensional reduced-order reconstruction, simulation and injection strategy to model resin injection repair which is scalable and practical for use with available equipment. The proposed method involves reconstructing a damaged composite laminate using ultrasonic C-scans to determine the damage zone geometry and porosity. The damage zone permeability is calculated via semi-empirical constitutive equations, and used as input data for the CFD simulation of a resin injection process through the composite. The ultimate aim is to guide repair operators by identifying suitable injection configurations in order to improve cavity filling and thus repair efficiency. After establishing the methodology basis, we verify simulations through comparison to a proposed and analytically solved problem. Validation results show a 70+% simulation accuracy. Finally, we create a case study where cavity filling is improved by applying knowledge of the damage zone. This method’s ability to predict filling efficacy offers a viable, quantitative and more consistent alternative to existing intuition-based practices for resin injection repair. Laminate repair Permeability Computational modelling Process modelling Peiró, Joaquim verfasserin (orcid)0000-0001-8859-4912 aut Lee, Koon-Yang verfasserin (orcid)0000-0003-0777-2292 aut Parlamas, Apostolos verfasserin (orcid)0000-0001-5001-9752 aut Falzon, Brian verfasserin aut Kazancı, Zafer verfasserin (orcid)0000-0003-2127-8389 aut Enthalten in Composites / A Amsterdam [u.a.] : Elsevier, 1996 174 Online-Ressource (DE-627)320501078 (DE-600)2012223-8 (DE-576)094531404 nnns volume:174 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2411 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 51.75 Verbundwerkstoffe Schichtstoffe VZ AR 174 |
spelling |
10.1016/j.compositesa.2023.107708 doi (DE-627)ELV062420402 (ELSEVIER)S1359-835X(23)00284-1 DE-627 ger DE-627 rda eng 660 VZ 51.75 bkl Asiliskender, Ahmed verfasserin (orcid)0000-0002-3808-9966 aut Predicting filling efficiency of composite resin injection repair 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose to develop a two-dimensional reduced-order reconstruction, simulation and injection strategy to model resin injection repair which is scalable and practical for use with available equipment. The proposed method involves reconstructing a damaged composite laminate using ultrasonic C-scans to determine the damage zone geometry and porosity. The damage zone permeability is calculated via semi-empirical constitutive equations, and used as input data for the CFD simulation of a resin injection process through the composite. The ultimate aim is to guide repair operators by identifying suitable injection configurations in order to improve cavity filling and thus repair efficiency. After establishing the methodology basis, we verify simulations through comparison to a proposed and analytically solved problem. Validation results show a 70+% simulation accuracy. Finally, we create a case study where cavity filling is improved by applying knowledge of the damage zone. This method’s ability to predict filling efficacy offers a viable, quantitative and more consistent alternative to existing intuition-based practices for resin injection repair. Laminate repair Permeability Computational modelling Process modelling Peiró, Joaquim verfasserin (orcid)0000-0001-8859-4912 aut Lee, Koon-Yang verfasserin (orcid)0000-0003-0777-2292 aut Parlamas, Apostolos verfasserin (orcid)0000-0001-5001-9752 aut Falzon, Brian verfasserin aut Kazancı, Zafer verfasserin (orcid)0000-0003-2127-8389 aut Enthalten in Composites / A Amsterdam [u.a.] : Elsevier, 1996 174 Online-Ressource (DE-627)320501078 (DE-600)2012223-8 (DE-576)094531404 nnns volume:174 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2411 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 51.75 Verbundwerkstoffe Schichtstoffe VZ AR 174 |
allfields_unstemmed |
10.1016/j.compositesa.2023.107708 doi (DE-627)ELV062420402 (ELSEVIER)S1359-835X(23)00284-1 DE-627 ger DE-627 rda eng 660 VZ 51.75 bkl Asiliskender, Ahmed verfasserin (orcid)0000-0002-3808-9966 aut Predicting filling efficiency of composite resin injection repair 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose to develop a two-dimensional reduced-order reconstruction, simulation and injection strategy to model resin injection repair which is scalable and practical for use with available equipment. The proposed method involves reconstructing a damaged composite laminate using ultrasonic C-scans to determine the damage zone geometry and porosity. The damage zone permeability is calculated via semi-empirical constitutive equations, and used as input data for the CFD simulation of a resin injection process through the composite. The ultimate aim is to guide repair operators by identifying suitable injection configurations in order to improve cavity filling and thus repair efficiency. After establishing the methodology basis, we verify simulations through comparison to a proposed and analytically solved problem. Validation results show a 70+% simulation accuracy. Finally, we create a case study where cavity filling is improved by applying knowledge of the damage zone. This method’s ability to predict filling efficacy offers a viable, quantitative and more consistent alternative to existing intuition-based practices for resin injection repair. Laminate repair Permeability Computational modelling Process modelling Peiró, Joaquim verfasserin (orcid)0000-0001-8859-4912 aut Lee, Koon-Yang verfasserin (orcid)0000-0003-0777-2292 aut Parlamas, Apostolos verfasserin (orcid)0000-0001-5001-9752 aut Falzon, Brian verfasserin aut Kazancı, Zafer verfasserin (orcid)0000-0003-2127-8389 aut Enthalten in Composites / A Amsterdam [u.a.] : Elsevier, 1996 174 Online-Ressource (DE-627)320501078 (DE-600)2012223-8 (DE-576)094531404 nnns volume:174 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2411 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 51.75 Verbundwerkstoffe Schichtstoffe VZ AR 174 |
allfieldsGer |
10.1016/j.compositesa.2023.107708 doi (DE-627)ELV062420402 (ELSEVIER)S1359-835X(23)00284-1 DE-627 ger DE-627 rda eng 660 VZ 51.75 bkl Asiliskender, Ahmed verfasserin (orcid)0000-0002-3808-9966 aut Predicting filling efficiency of composite resin injection repair 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose to develop a two-dimensional reduced-order reconstruction, simulation and injection strategy to model resin injection repair which is scalable and practical for use with available equipment. The proposed method involves reconstructing a damaged composite laminate using ultrasonic C-scans to determine the damage zone geometry and porosity. The damage zone permeability is calculated via semi-empirical constitutive equations, and used as input data for the CFD simulation of a resin injection process through the composite. The ultimate aim is to guide repair operators by identifying suitable injection configurations in order to improve cavity filling and thus repair efficiency. After establishing the methodology basis, we verify simulations through comparison to a proposed and analytically solved problem. Validation results show a 70+% simulation accuracy. Finally, we create a case study where cavity filling is improved by applying knowledge of the damage zone. This method’s ability to predict filling efficacy offers a viable, quantitative and more consistent alternative to existing intuition-based practices for resin injection repair. Laminate repair Permeability Computational modelling Process modelling Peiró, Joaquim verfasserin (orcid)0000-0001-8859-4912 aut Lee, Koon-Yang verfasserin (orcid)0000-0003-0777-2292 aut Parlamas, Apostolos verfasserin (orcid)0000-0001-5001-9752 aut Falzon, Brian verfasserin aut Kazancı, Zafer verfasserin (orcid)0000-0003-2127-8389 aut Enthalten in Composites / A Amsterdam [u.a.] : Elsevier, 1996 174 Online-Ressource (DE-627)320501078 (DE-600)2012223-8 (DE-576)094531404 nnns volume:174 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2411 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 51.75 Verbundwerkstoffe Schichtstoffe VZ AR 174 |
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10.1016/j.compositesa.2023.107708 doi (DE-627)ELV062420402 (ELSEVIER)S1359-835X(23)00284-1 DE-627 ger DE-627 rda eng 660 VZ 51.75 bkl Asiliskender, Ahmed verfasserin (orcid)0000-0002-3808-9966 aut Predicting filling efficiency of composite resin injection repair 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose to develop a two-dimensional reduced-order reconstruction, simulation and injection strategy to model resin injection repair which is scalable and practical for use with available equipment. The proposed method involves reconstructing a damaged composite laminate using ultrasonic C-scans to determine the damage zone geometry and porosity. The damage zone permeability is calculated via semi-empirical constitutive equations, and used as input data for the CFD simulation of a resin injection process through the composite. The ultimate aim is to guide repair operators by identifying suitable injection configurations in order to improve cavity filling and thus repair efficiency. After establishing the methodology basis, we verify simulations through comparison to a proposed and analytically solved problem. Validation results show a 70+% simulation accuracy. Finally, we create a case study where cavity filling is improved by applying knowledge of the damage zone. This method’s ability to predict filling efficacy offers a viable, quantitative and more consistent alternative to existing intuition-based practices for resin injection repair. Laminate repair Permeability Computational modelling Process modelling Peiró, Joaquim verfasserin (orcid)0000-0001-8859-4912 aut Lee, Koon-Yang verfasserin (orcid)0000-0003-0777-2292 aut Parlamas, Apostolos verfasserin (orcid)0000-0001-5001-9752 aut Falzon, Brian verfasserin aut Kazancı, Zafer verfasserin (orcid)0000-0003-2127-8389 aut Enthalten in Composites / A Amsterdam [u.a.] : Elsevier, 1996 174 Online-Ressource (DE-627)320501078 (DE-600)2012223-8 (DE-576)094531404 nnns volume:174 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2411 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 51.75 Verbundwerkstoffe Schichtstoffe VZ AR 174 |
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Predicting filling efficiency of composite resin injection repair |
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predicting filling efficiency of composite resin injection repair |
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Predicting filling efficiency of composite resin injection repair |
abstract |
We propose to develop a two-dimensional reduced-order reconstruction, simulation and injection strategy to model resin injection repair which is scalable and practical for use with available equipment. The proposed method involves reconstructing a damaged composite laminate using ultrasonic C-scans to determine the damage zone geometry and porosity. The damage zone permeability is calculated via semi-empirical constitutive equations, and used as input data for the CFD simulation of a resin injection process through the composite. The ultimate aim is to guide repair operators by identifying suitable injection configurations in order to improve cavity filling and thus repair efficiency. After establishing the methodology basis, we verify simulations through comparison to a proposed and analytically solved problem. Validation results show a 70+% simulation accuracy. Finally, we create a case study where cavity filling is improved by applying knowledge of the damage zone. This method’s ability to predict filling efficacy offers a viable, quantitative and more consistent alternative to existing intuition-based practices for resin injection repair. |
abstractGer |
We propose to develop a two-dimensional reduced-order reconstruction, simulation and injection strategy to model resin injection repair which is scalable and practical for use with available equipment. The proposed method involves reconstructing a damaged composite laminate using ultrasonic C-scans to determine the damage zone geometry and porosity. The damage zone permeability is calculated via semi-empirical constitutive equations, and used as input data for the CFD simulation of a resin injection process through the composite. The ultimate aim is to guide repair operators by identifying suitable injection configurations in order to improve cavity filling and thus repair efficiency. After establishing the methodology basis, we verify simulations through comparison to a proposed and analytically solved problem. Validation results show a 70+% simulation accuracy. Finally, we create a case study where cavity filling is improved by applying knowledge of the damage zone. This method’s ability to predict filling efficacy offers a viable, quantitative and more consistent alternative to existing intuition-based practices for resin injection repair. |
abstract_unstemmed |
We propose to develop a two-dimensional reduced-order reconstruction, simulation and injection strategy to model resin injection repair which is scalable and practical for use with available equipment. The proposed method involves reconstructing a damaged composite laminate using ultrasonic C-scans to determine the damage zone geometry and porosity. The damage zone permeability is calculated via semi-empirical constitutive equations, and used as input data for the CFD simulation of a resin injection process through the composite. The ultimate aim is to guide repair operators by identifying suitable injection configurations in order to improve cavity filling and thus repair efficiency. After establishing the methodology basis, we verify simulations through comparison to a proposed and analytically solved problem. Validation results show a 70+% simulation accuracy. Finally, we create a case study where cavity filling is improved by applying knowledge of the damage zone. This method’s ability to predict filling efficacy offers a viable, quantitative and more consistent alternative to existing intuition-based practices for resin injection repair. |
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Predicting filling efficiency of composite resin injection repair |
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Peiró, Joaquim Lee, Koon-Yang Parlamas, Apostolos Falzon, Brian Kazancı, Zafer |
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