A novel multi-fidelity neural network for response prediction using rotor dynamics and model reduction
Abstract Uncertainties in rotating machines are unavoidable, which affect their parameters and dynamic response. So, instead of employing deterministic models, data-driven meta-modeling techniques which incorporate unpredictability and randomness are necessary for the response variation analysis of...
Ausführliche Beschreibung
Autor*in: |
Khamari, Debanshu S. [verfasserIn] |
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E-Artikel |
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Englisch |
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2023 |
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Anmerkung: |
© The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Übergeordnetes Werk: |
Enthalten in: Journal of the Brazilian Society of Mechanical Sciences and Engineering - Berlin : Springer, 2003, 45(2023), 11 vom: 25. Okt. |
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Übergeordnetes Werk: |
volume:45 ; year:2023 ; number:11 ; day:25 ; month:10 |
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DOI / URN: |
10.1007/s40430-023-04521-2 |
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Katalog-ID: |
SPR053527631 |
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520 | |a Abstract Uncertainties in rotating machines are unavoidable, which affect their parameters and dynamic response. So, instead of employing deterministic models, data-driven meta-modeling techniques which incorporate unpredictability and randomness are necessary for the response variation analysis of rotating systems. The performance of the meta-model relies heavily on the quality and amount of the training dataset. In reality, however, only a tiny amount of high-fidelity data is obtainable from high-dimensional finite element simulation or experimental investigation, although low-cost low-fidelity data may be numerous. The objective of this paper is to develop a novel neural network model for multi-level response prediction by obtaining a high number of low-fidelity data quickly through model order reduction and a limited amount of high-fidelity data correctly from a full-order model. The accuracy of the meta-model is demonstrated by comparing against a classical deep neural network. Two different types of meta-model are established by using two model reduction techniques: Guyan reduction and modified system equivalent reduction expansion process. The performance of the model is demonstrated by employing frequency response variation characterization of a complex rotor as a case example. The results reveal that the multi-fidelity neural network performs better than the low-fidelity frequency response curves alone, which is observed to have a lot of inaccuracies. The deep neural network, on the other hand, is unable to reflect on the dynamic response of the full model. A regression of more than 90% shows that the meta-model has high effectiveness in properly predicting the frequency responses. The mean squared error values for the meta-model are found to be less than 0.1, which is typically regarded as acceptable. Frequency response curves of four test samples are selected at random for comparison. It is observed that the meta-model frequency response moves much closer to the full model than compared to that of the low-fidelity model reduction. The performance resilience of the model is tested by using five different training runs with random data splits. Minor changes in the values of logarithm mean absolute error and logarithm root mean squared error under different training runs show appropriate curve fitting and signify superior accuracy. It is concluded that the multi-fidelity neural network can reach a higher level of accuracy with a limited amount of high-fidelity data. The model effectively identifies both the linear and complex nonlinear correlation between the high-and low-fidelity data, resulting in enhanced efficacy in contrast to state-of-the-art methods. | ||
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650 | 4 | |a Multi-fidelity neural network |7 (dpeaa)DE-He213 | |
700 | 1 | |a Behera, Suraj K. |4 aut | |
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10.1007/s40430-023-04521-2 doi (DE-627)SPR053527631 (SPR)s40430-023-04521-2-e DE-627 ger DE-627 rakwb eng Khamari, Debanshu S. verfasserin (orcid)0000-0003-1534-2014 aut A novel multi-fidelity neural network for response prediction using rotor dynamics and model reduction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Uncertainties in rotating machines are unavoidable, which affect their parameters and dynamic response. So, instead of employing deterministic models, data-driven meta-modeling techniques which incorporate unpredictability and randomness are necessary for the response variation analysis of rotating systems. The performance of the meta-model relies heavily on the quality and amount of the training dataset. In reality, however, only a tiny amount of high-fidelity data is obtainable from high-dimensional finite element simulation or experimental investigation, although low-cost low-fidelity data may be numerous. The objective of this paper is to develop a novel neural network model for multi-level response prediction by obtaining a high number of low-fidelity data quickly through model order reduction and a limited amount of high-fidelity data correctly from a full-order model. The accuracy of the meta-model is demonstrated by comparing against a classical deep neural network. Two different types of meta-model are established by using two model reduction techniques: Guyan reduction and modified system equivalent reduction expansion process. The performance of the model is demonstrated by employing frequency response variation characterization of a complex rotor as a case example. The results reveal that the multi-fidelity neural network performs better than the low-fidelity frequency response curves alone, which is observed to have a lot of inaccuracies. The deep neural network, on the other hand, is unable to reflect on the dynamic response of the full model. A regression of more than 90% shows that the meta-model has high effectiveness in properly predicting the frequency responses. The mean squared error values for the meta-model are found to be less than 0.1, which is typically regarded as acceptable. Frequency response curves of four test samples are selected at random for comparison. It is observed that the meta-model frequency response moves much closer to the full model than compared to that of the low-fidelity model reduction. The performance resilience of the model is tested by using five different training runs with random data splits. Minor changes in the values of logarithm mean absolute error and logarithm root mean squared error under different training runs show appropriate curve fitting and signify superior accuracy. It is concluded that the multi-fidelity neural network can reach a higher level of accuracy with a limited amount of high-fidelity data. The model effectively identifies both the linear and complex nonlinear correlation between the high-and low-fidelity data, resulting in enhanced efficacy in contrast to state-of-the-art methods. Rotor dynamic response (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Model order reduction (dpeaa)DE-He213 Deep neural network (dpeaa)DE-He213 Multi-fidelity neural network (dpeaa)DE-He213 Behera, Suraj K. aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 45(2023), 11 vom: 25. Okt. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:45 year:2023 number:11 day:25 month:10 https://dx.doi.org/10.1007/s40430-023-04521-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 45 2023 11 25 10 |
spelling |
10.1007/s40430-023-04521-2 doi (DE-627)SPR053527631 (SPR)s40430-023-04521-2-e DE-627 ger DE-627 rakwb eng Khamari, Debanshu S. verfasserin (orcid)0000-0003-1534-2014 aut A novel multi-fidelity neural network for response prediction using rotor dynamics and model reduction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Uncertainties in rotating machines are unavoidable, which affect their parameters and dynamic response. So, instead of employing deterministic models, data-driven meta-modeling techniques which incorporate unpredictability and randomness are necessary for the response variation analysis of rotating systems. The performance of the meta-model relies heavily on the quality and amount of the training dataset. In reality, however, only a tiny amount of high-fidelity data is obtainable from high-dimensional finite element simulation or experimental investigation, although low-cost low-fidelity data may be numerous. The objective of this paper is to develop a novel neural network model for multi-level response prediction by obtaining a high number of low-fidelity data quickly through model order reduction and a limited amount of high-fidelity data correctly from a full-order model. The accuracy of the meta-model is demonstrated by comparing against a classical deep neural network. Two different types of meta-model are established by using two model reduction techniques: Guyan reduction and modified system equivalent reduction expansion process. The performance of the model is demonstrated by employing frequency response variation characterization of a complex rotor as a case example. The results reveal that the multi-fidelity neural network performs better than the low-fidelity frequency response curves alone, which is observed to have a lot of inaccuracies. The deep neural network, on the other hand, is unable to reflect on the dynamic response of the full model. A regression of more than 90% shows that the meta-model has high effectiveness in properly predicting the frequency responses. The mean squared error values for the meta-model are found to be less than 0.1, which is typically regarded as acceptable. Frequency response curves of four test samples are selected at random for comparison. It is observed that the meta-model frequency response moves much closer to the full model than compared to that of the low-fidelity model reduction. The performance resilience of the model is tested by using five different training runs with random data splits. Minor changes in the values of logarithm mean absolute error and logarithm root mean squared error under different training runs show appropriate curve fitting and signify superior accuracy. It is concluded that the multi-fidelity neural network can reach a higher level of accuracy with a limited amount of high-fidelity data. The model effectively identifies both the linear and complex nonlinear correlation between the high-and low-fidelity data, resulting in enhanced efficacy in contrast to state-of-the-art methods. Rotor dynamic response (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Model order reduction (dpeaa)DE-He213 Deep neural network (dpeaa)DE-He213 Multi-fidelity neural network (dpeaa)DE-He213 Behera, Suraj K. aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 45(2023), 11 vom: 25. Okt. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:45 year:2023 number:11 day:25 month:10 https://dx.doi.org/10.1007/s40430-023-04521-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 45 2023 11 25 10 |
allfields_unstemmed |
10.1007/s40430-023-04521-2 doi (DE-627)SPR053527631 (SPR)s40430-023-04521-2-e DE-627 ger DE-627 rakwb eng Khamari, Debanshu S. verfasserin (orcid)0000-0003-1534-2014 aut A novel multi-fidelity neural network for response prediction using rotor dynamics and model reduction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Uncertainties in rotating machines are unavoidable, which affect their parameters and dynamic response. So, instead of employing deterministic models, data-driven meta-modeling techniques which incorporate unpredictability and randomness are necessary for the response variation analysis of rotating systems. The performance of the meta-model relies heavily on the quality and amount of the training dataset. In reality, however, only a tiny amount of high-fidelity data is obtainable from high-dimensional finite element simulation or experimental investigation, although low-cost low-fidelity data may be numerous. The objective of this paper is to develop a novel neural network model for multi-level response prediction by obtaining a high number of low-fidelity data quickly through model order reduction and a limited amount of high-fidelity data correctly from a full-order model. The accuracy of the meta-model is demonstrated by comparing against a classical deep neural network. Two different types of meta-model are established by using two model reduction techniques: Guyan reduction and modified system equivalent reduction expansion process. The performance of the model is demonstrated by employing frequency response variation characterization of a complex rotor as a case example. The results reveal that the multi-fidelity neural network performs better than the low-fidelity frequency response curves alone, which is observed to have a lot of inaccuracies. The deep neural network, on the other hand, is unable to reflect on the dynamic response of the full model. A regression of more than 90% shows that the meta-model has high effectiveness in properly predicting the frequency responses. The mean squared error values for the meta-model are found to be less than 0.1, which is typically regarded as acceptable. Frequency response curves of four test samples are selected at random for comparison. It is observed that the meta-model frequency response moves much closer to the full model than compared to that of the low-fidelity model reduction. The performance resilience of the model is tested by using five different training runs with random data splits. Minor changes in the values of logarithm mean absolute error and logarithm root mean squared error under different training runs show appropriate curve fitting and signify superior accuracy. It is concluded that the multi-fidelity neural network can reach a higher level of accuracy with a limited amount of high-fidelity data. The model effectively identifies both the linear and complex nonlinear correlation between the high-and low-fidelity data, resulting in enhanced efficacy in contrast to state-of-the-art methods. Rotor dynamic response (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Model order reduction (dpeaa)DE-He213 Deep neural network (dpeaa)DE-He213 Multi-fidelity neural network (dpeaa)DE-He213 Behera, Suraj K. aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 45(2023), 11 vom: 25. Okt. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:45 year:2023 number:11 day:25 month:10 https://dx.doi.org/10.1007/s40430-023-04521-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 45 2023 11 25 10 |
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10.1007/s40430-023-04521-2 doi (DE-627)SPR053527631 (SPR)s40430-023-04521-2-e DE-627 ger DE-627 rakwb eng Khamari, Debanshu S. verfasserin (orcid)0000-0003-1534-2014 aut A novel multi-fidelity neural network for response prediction using rotor dynamics and model reduction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Uncertainties in rotating machines are unavoidable, which affect their parameters and dynamic response. So, instead of employing deterministic models, data-driven meta-modeling techniques which incorporate unpredictability and randomness are necessary for the response variation analysis of rotating systems. The performance of the meta-model relies heavily on the quality and amount of the training dataset. In reality, however, only a tiny amount of high-fidelity data is obtainable from high-dimensional finite element simulation or experimental investigation, although low-cost low-fidelity data may be numerous. The objective of this paper is to develop a novel neural network model for multi-level response prediction by obtaining a high number of low-fidelity data quickly through model order reduction and a limited amount of high-fidelity data correctly from a full-order model. The accuracy of the meta-model is demonstrated by comparing against a classical deep neural network. Two different types of meta-model are established by using two model reduction techniques: Guyan reduction and modified system equivalent reduction expansion process. The performance of the model is demonstrated by employing frequency response variation characterization of a complex rotor as a case example. The results reveal that the multi-fidelity neural network performs better than the low-fidelity frequency response curves alone, which is observed to have a lot of inaccuracies. The deep neural network, on the other hand, is unable to reflect on the dynamic response of the full model. A regression of more than 90% shows that the meta-model has high effectiveness in properly predicting the frequency responses. The mean squared error values for the meta-model are found to be less than 0.1, which is typically regarded as acceptable. Frequency response curves of four test samples are selected at random for comparison. It is observed that the meta-model frequency response moves much closer to the full model than compared to that of the low-fidelity model reduction. The performance resilience of the model is tested by using five different training runs with random data splits. Minor changes in the values of logarithm mean absolute error and logarithm root mean squared error under different training runs show appropriate curve fitting and signify superior accuracy. It is concluded that the multi-fidelity neural network can reach a higher level of accuracy with a limited amount of high-fidelity data. The model effectively identifies both the linear and complex nonlinear correlation between the high-and low-fidelity data, resulting in enhanced efficacy in contrast to state-of-the-art methods. Rotor dynamic response (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Model order reduction (dpeaa)DE-He213 Deep neural network (dpeaa)DE-He213 Multi-fidelity neural network (dpeaa)DE-He213 Behera, Suraj K. aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 45(2023), 11 vom: 25. Okt. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:45 year:2023 number:11 day:25 month:10 https://dx.doi.org/10.1007/s40430-023-04521-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 45 2023 11 25 10 |
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10.1007/s40430-023-04521-2 doi (DE-627)SPR053527631 (SPR)s40430-023-04521-2-e DE-627 ger DE-627 rakwb eng Khamari, Debanshu S. verfasserin (orcid)0000-0003-1534-2014 aut A novel multi-fidelity neural network for response prediction using rotor dynamics and model reduction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Uncertainties in rotating machines are unavoidable, which affect their parameters and dynamic response. So, instead of employing deterministic models, data-driven meta-modeling techniques which incorporate unpredictability and randomness are necessary for the response variation analysis of rotating systems. The performance of the meta-model relies heavily on the quality and amount of the training dataset. In reality, however, only a tiny amount of high-fidelity data is obtainable from high-dimensional finite element simulation or experimental investigation, although low-cost low-fidelity data may be numerous. The objective of this paper is to develop a novel neural network model for multi-level response prediction by obtaining a high number of low-fidelity data quickly through model order reduction and a limited amount of high-fidelity data correctly from a full-order model. The accuracy of the meta-model is demonstrated by comparing against a classical deep neural network. Two different types of meta-model are established by using two model reduction techniques: Guyan reduction and modified system equivalent reduction expansion process. The performance of the model is demonstrated by employing frequency response variation characterization of a complex rotor as a case example. The results reveal that the multi-fidelity neural network performs better than the low-fidelity frequency response curves alone, which is observed to have a lot of inaccuracies. The deep neural network, on the other hand, is unable to reflect on the dynamic response of the full model. A regression of more than 90% shows that the meta-model has high effectiveness in properly predicting the frequency responses. The mean squared error values for the meta-model are found to be less than 0.1, which is typically regarded as acceptable. Frequency response curves of four test samples are selected at random for comparison. It is observed that the meta-model frequency response moves much closer to the full model than compared to that of the low-fidelity model reduction. The performance resilience of the model is tested by using five different training runs with random data splits. Minor changes in the values of logarithm mean absolute error and logarithm root mean squared error under different training runs show appropriate curve fitting and signify superior accuracy. It is concluded that the multi-fidelity neural network can reach a higher level of accuracy with a limited amount of high-fidelity data. The model effectively identifies both the linear and complex nonlinear correlation between the high-and low-fidelity data, resulting in enhanced efficacy in contrast to state-of-the-art methods. Rotor dynamic response (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Model order reduction (dpeaa)DE-He213 Deep neural network (dpeaa)DE-He213 Multi-fidelity neural network (dpeaa)DE-He213 Behera, Suraj K. aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Berlin : Springer, 2003 45(2023), 11 vom: 25. Okt. (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:45 year:2023 number:11 day:25 month:10 https://dx.doi.org/10.1007/s40430-023-04521-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 45 2023 11 25 10 |
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Khamari, Debanshu S. |
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Khamari, Debanshu S. misc Rotor dynamic response misc Uncertainty misc Model order reduction misc Deep neural network misc Multi-fidelity neural network A novel multi-fidelity neural network for response prediction using rotor dynamics and model reduction |
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A novel multi-fidelity neural network for response prediction using rotor dynamics and model reduction Rotor dynamic response (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 Model order reduction (dpeaa)DE-He213 Deep neural network (dpeaa)DE-He213 Multi-fidelity neural network (dpeaa)DE-He213 |
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A novel multi-fidelity neural network for response prediction using rotor dynamics and model reduction |
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Journal of the Brazilian Society of Mechanical Sciences and Engineering |
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novel multi-fidelity neural network for response prediction using rotor dynamics and model reduction |
title_auth |
A novel multi-fidelity neural network for response prediction using rotor dynamics and model reduction |
abstract |
Abstract Uncertainties in rotating machines are unavoidable, which affect their parameters and dynamic response. So, instead of employing deterministic models, data-driven meta-modeling techniques which incorporate unpredictability and randomness are necessary for the response variation analysis of rotating systems. The performance of the meta-model relies heavily on the quality and amount of the training dataset. In reality, however, only a tiny amount of high-fidelity data is obtainable from high-dimensional finite element simulation or experimental investigation, although low-cost low-fidelity data may be numerous. The objective of this paper is to develop a novel neural network model for multi-level response prediction by obtaining a high number of low-fidelity data quickly through model order reduction and a limited amount of high-fidelity data correctly from a full-order model. The accuracy of the meta-model is demonstrated by comparing against a classical deep neural network. Two different types of meta-model are established by using two model reduction techniques: Guyan reduction and modified system equivalent reduction expansion process. The performance of the model is demonstrated by employing frequency response variation characterization of a complex rotor as a case example. The results reveal that the multi-fidelity neural network performs better than the low-fidelity frequency response curves alone, which is observed to have a lot of inaccuracies. The deep neural network, on the other hand, is unable to reflect on the dynamic response of the full model. A regression of more than 90% shows that the meta-model has high effectiveness in properly predicting the frequency responses. The mean squared error values for the meta-model are found to be less than 0.1, which is typically regarded as acceptable. Frequency response curves of four test samples are selected at random for comparison. It is observed that the meta-model frequency response moves much closer to the full model than compared to that of the low-fidelity model reduction. The performance resilience of the model is tested by using five different training runs with random data splits. Minor changes in the values of logarithm mean absolute error and logarithm root mean squared error under different training runs show appropriate curve fitting and signify superior accuracy. It is concluded that the multi-fidelity neural network can reach a higher level of accuracy with a limited amount of high-fidelity data. The model effectively identifies both the linear and complex nonlinear correlation between the high-and low-fidelity data, resulting in enhanced efficacy in contrast to state-of-the-art methods. © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstractGer |
Abstract Uncertainties in rotating machines are unavoidable, which affect their parameters and dynamic response. So, instead of employing deterministic models, data-driven meta-modeling techniques which incorporate unpredictability and randomness are necessary for the response variation analysis of rotating systems. The performance of the meta-model relies heavily on the quality and amount of the training dataset. In reality, however, only a tiny amount of high-fidelity data is obtainable from high-dimensional finite element simulation or experimental investigation, although low-cost low-fidelity data may be numerous. The objective of this paper is to develop a novel neural network model for multi-level response prediction by obtaining a high number of low-fidelity data quickly through model order reduction and a limited amount of high-fidelity data correctly from a full-order model. The accuracy of the meta-model is demonstrated by comparing against a classical deep neural network. Two different types of meta-model are established by using two model reduction techniques: Guyan reduction and modified system equivalent reduction expansion process. The performance of the model is demonstrated by employing frequency response variation characterization of a complex rotor as a case example. The results reveal that the multi-fidelity neural network performs better than the low-fidelity frequency response curves alone, which is observed to have a lot of inaccuracies. The deep neural network, on the other hand, is unable to reflect on the dynamic response of the full model. A regression of more than 90% shows that the meta-model has high effectiveness in properly predicting the frequency responses. The mean squared error values for the meta-model are found to be less than 0.1, which is typically regarded as acceptable. Frequency response curves of four test samples are selected at random for comparison. It is observed that the meta-model frequency response moves much closer to the full model than compared to that of the low-fidelity model reduction. The performance resilience of the model is tested by using five different training runs with random data splits. Minor changes in the values of logarithm mean absolute error and logarithm root mean squared error under different training runs show appropriate curve fitting and signify superior accuracy. It is concluded that the multi-fidelity neural network can reach a higher level of accuracy with a limited amount of high-fidelity data. The model effectively identifies both the linear and complex nonlinear correlation between the high-and low-fidelity data, resulting in enhanced efficacy in contrast to state-of-the-art methods. © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstract_unstemmed |
Abstract Uncertainties in rotating machines are unavoidable, which affect their parameters and dynamic response. So, instead of employing deterministic models, data-driven meta-modeling techniques which incorporate unpredictability and randomness are necessary for the response variation analysis of rotating systems. The performance of the meta-model relies heavily on the quality and amount of the training dataset. In reality, however, only a tiny amount of high-fidelity data is obtainable from high-dimensional finite element simulation or experimental investigation, although low-cost low-fidelity data may be numerous. The objective of this paper is to develop a novel neural network model for multi-level response prediction by obtaining a high number of low-fidelity data quickly through model order reduction and a limited amount of high-fidelity data correctly from a full-order model. The accuracy of the meta-model is demonstrated by comparing against a classical deep neural network. Two different types of meta-model are established by using two model reduction techniques: Guyan reduction and modified system equivalent reduction expansion process. The performance of the model is demonstrated by employing frequency response variation characterization of a complex rotor as a case example. The results reveal that the multi-fidelity neural network performs better than the low-fidelity frequency response curves alone, which is observed to have a lot of inaccuracies. The deep neural network, on the other hand, is unable to reflect on the dynamic response of the full model. A regression of more than 90% shows that the meta-model has high effectiveness in properly predicting the frequency responses. The mean squared error values for the meta-model are found to be less than 0.1, which is typically regarded as acceptable. Frequency response curves of four test samples are selected at random for comparison. It is observed that the meta-model frequency response moves much closer to the full model than compared to that of the low-fidelity model reduction. The performance resilience of the model is tested by using five different training runs with random data splits. Minor changes in the values of logarithm mean absolute error and logarithm root mean squared error under different training runs show appropriate curve fitting and signify superior accuracy. It is concluded that the multi-fidelity neural network can reach a higher level of accuracy with a limited amount of high-fidelity data. The model effectively identifies both the linear and complex nonlinear correlation between the high-and low-fidelity data, resulting in enhanced efficacy in contrast to state-of-the-art methods. © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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title_short |
A novel multi-fidelity neural network for response prediction using rotor dynamics and model reduction |
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https://dx.doi.org/10.1007/s40430-023-04521-2 |
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Behera, Suraj K. |
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10.1007/s40430-023-04521-2 |
up_date |
2024-07-03T20:10:58.601Z |
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|
score |
7.398053 |