Analysis and prediction of force characteristics of tubular turbine based on Hankel-DMD-LSTM Article Swipe
Yanzhao Wu
,
Ran Tao
,
Di Zhu
,
Ruofu Xiao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1080/19942060.2024.2443122
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1080/19942060.2024.2443122
In order to analyse and master the force characteristics of the prototype bulb turbine under critical cavitation condition, this study compared the force characteristics of the turbine under different operating conditions, and finally the problems of the DMD method in the low dimensional reduction application of one-dimensional time-series signals were clarified. A Hankel-DMD method was proposed to analyse the force characteristics under different operating conditions, and the linearization advantage of the DMD method was utilized, Further temporal prediction was conducted on the variation law of the force characteristics of the tubular turbine. In order to accurately predict the future changes in the force characteristics of the turbine, this study analysed the prediction error of the linear Hankel-DMD method and proposed a new time series prediction model based on the Hankel-DMD-LSTM method, which provides a new means for predicting the force characteristics of tubular turbine. To a certain extent, it could provide diagnostic insights into abnormal events with distinct periodic changes in time-series signals, and provide certain assistance in signal interpretability.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/19942060.2024.2443122
- OA Status
- gold
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- 4
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All OpenAlex metadata
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https://openalex.org/W4405603090Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1080/19942060.2024.2443122Digital Object Identifier
- Title
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Analysis and prediction of force characteristics of tubular turbine based on Hankel-DMD-LSTMWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-18Full publication date if available
- Authors
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Yanzhao Wu, Ran Tao, Di Zhu, Ruofu XiaoList of authors in order
- Landing page
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https://doi.org/10.1080/19942060.2024.2443122Publisher landing page
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1080/19942060.2024.2443122Direct OA link when available
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Turbine, Computer science, Artificial intelligence, Engineering, Mechanical engineering, Computational science, SimulationTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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54Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.model | 125 |
| abstract_inverted_index.order | 1, 93 |
| abstract_inverted_index.study | 19, 108 |
| abstract_inverted_index.under | 14, 27, 61 |
| abstract_inverted_index.which | 131 |
| abstract_inverted_index.events | 155 |
| abstract_inverted_index.future | 98 |
| abstract_inverted_index.linear | 115 |
| abstract_inverted_index.master | 5 |
| abstract_inverted_index.method | 38, 53, 72, 117 |
| abstract_inverted_index.series | 123 |
| abstract_inverted_index.signal | 168 |
| abstract_inverted_index.Further | 75 |
| abstract_inverted_index.analyse | 3, 57 |
| abstract_inverted_index.certain | 146, 165 |
| abstract_inverted_index.changes | 99, 159 |
| abstract_inverted_index.extent, | 147 |
| abstract_inverted_index.finally | 32 |
| abstract_inverted_index.method, | 130 |
| abstract_inverted_index.predict | 96 |
| abstract_inverted_index.provide | 150, 164 |
| abstract_inverted_index.signals | 48 |
| abstract_inverted_index.tubular | 90, 142 |
| abstract_inverted_index.turbine | 13, 26 |
| abstract_inverted_index.abnormal | 154 |
| abstract_inverted_index.analysed | 109 |
| abstract_inverted_index.compared | 20 |
| abstract_inverted_index.critical | 15 |
| abstract_inverted_index.distinct | 157 |
| abstract_inverted_index.insights | 152 |
| abstract_inverted_index.periodic | 158 |
| abstract_inverted_index.problems | 34 |
| abstract_inverted_index.proposed | 55, 119 |
| abstract_inverted_index.provides | 132 |
| abstract_inverted_index.signals, | 162 |
| abstract_inverted_index.temporal | 76 |
| abstract_inverted_index.turbine, | 106 |
| abstract_inverted_index.turbine. | 91, 143 |
| abstract_inverted_index.advantage | 68 |
| abstract_inverted_index.conducted | 79 |
| abstract_inverted_index.different | 28, 62 |
| abstract_inverted_index.operating | 29, 63 |
| abstract_inverted_index.prototype | 11 |
| abstract_inverted_index.reduction | 43 |
| abstract_inverted_index.utilized, | 74 |
| abstract_inverted_index.variation | 82 |
| abstract_inverted_index.Hankel-DMD | 52, 116 |
| abstract_inverted_index.accurately | 95 |
| abstract_inverted_index.assistance | 166 |
| abstract_inverted_index.cavitation | 16 |
| abstract_inverted_index.clarified. | 50 |
| abstract_inverted_index.condition, | 17 |
| abstract_inverted_index.diagnostic | 151 |
| abstract_inverted_index.predicting | 137 |
| abstract_inverted_index.prediction | 77, 111, 124 |
| abstract_inverted_index.application | 44 |
| abstract_inverted_index.conditions, | 30, 64 |
| abstract_inverted_index.dimensional | 42 |
| abstract_inverted_index.time-series | 47, 161 |
| abstract_inverted_index.linearization | 67 |
| abstract_inverted_index.Hankel-DMD-LSTM | 129 |
| abstract_inverted_index.characteristics | 8, 23, 60, 87, 103, 140 |
| abstract_inverted_index.one-dimensional | 46 |
| abstract_inverted_index.interpretability. | 169 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8299999833106995 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.82250426 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |