Cycle-to-cycle variations in cross-flow turbine performance and flow fields Article Swipe
Abigale Snortland
,
Isabel Scherl
,
Brian Polagye
,
Owen Williams
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.03218
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.03218
Cross-flow turbine performance and flow fields exhibit cycle-to-cycle variations, though this is often implicitly neglected through time- and phase-averaging. This variability could potentially arise from a variety of mechanisms -- inflow fluctuations, the stochastic nature of dynamic stall, and cycle-to-cycle hysteresis -- each of which have different implications for our understanding of cross-flow turbine dynamics. In this work, the extent and sources of cycle-to-cycle variability for both the flow fields and performance are explored experimentally under two, contrasting operational conditions. Flow fields, obtained through two-dimensional planar particle image velocimetry inside the turbine swept area, are examined in concert with simultaneously measured performance. Correlations between flow-field and performance variability are established by an unsupervised hierarchical flow-field clustering pipeline. This features a principal component analysis (PCA) pre-processor that allows for clustering based on all the dynamics present in the high-dimensional flow-field data in an interpretable, low-dimensional subspace that is weighted by contribution to overall velocity variance. We find that the flow-field clusters and their associated performance are correlated primarily with inflow fluctuations, despite relatively low turbulence intensity, that drive variations in the timing of the dynamic stall process. Further, we find no evidence of substantial cycle-to-cycle hysteresis. Clustering reveals persistent correlations between performance and flow-field variability during the upstream portion of the turbine rotation. The approach employed here provides a more comprehensive picture of cross-flow turbine flow fields and performance than aggregate, statistical representations.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2302.03218
- https://arxiv.org/pdf/2302.03218
- OA Status
- green
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319653576
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4319653576Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2302.03218Digital Object Identifier
- Title
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Cycle-to-cycle variations in cross-flow turbine performance and flow fieldsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-02-07Full publication date if available
- Authors
-
Abigale Snortland, Isabel Scherl, Brian Polagye, Owen WilliamsList of authors in order
- Landing page
-
https://arxiv.org/abs/2302.03218Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2302.03218Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2302.03218Direct OA link when available
- Concepts
-
Turbulence, Inflow, Turbine, Flow (mathematics), Stall (fluid mechanics), Mechanics, Physics, Engineering, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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47Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.that | 125, 145, 156, 175 |
| abstract_inverted_index.this | 10, 56 |
| abstract_inverted_index.two, | 76 |
| abstract_inverted_index.with | 98, 167 |
| abstract_inverted_index.(PCA) | 123 |
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| abstract_inverted_index.their | 161 |
| abstract_inverted_index.time- | 16 |
| abstract_inverted_index.under | 75 |
| abstract_inverted_index.which | 44 |
| abstract_inverted_index.work, | 57 |
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| abstract_inverted_index.during | 204 |
| abstract_inverted_index.extent | 59 |
| abstract_inverted_index.fields | 5, 69, 225 |
| abstract_inverted_index.inflow | 30, 168 |
| abstract_inverted_index.inside | 89 |
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| abstract_inverted_index.planar | 85 |
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| abstract_inverted_index.between | 103, 199 |
| abstract_inverted_index.concert | 97 |
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| abstract_inverted_index.dynamic | 36, 183 |
| abstract_inverted_index.exhibit | 6 |
| abstract_inverted_index.fields, | 81 |
| abstract_inverted_index.overall | 151 |
| abstract_inverted_index.picture | 220 |
| abstract_inverted_index.portion | 207 |
| abstract_inverted_index.present | 134 |
| abstract_inverted_index.reveals | 196 |
| abstract_inverted_index.sources | 61 |
| abstract_inverted_index.through | 15, 83 |
| abstract_inverted_index.turbine | 1, 53, 91, 210, 223 |
| abstract_inverted_index.variety | 26 |
| abstract_inverted_index.Further, | 186 |
| abstract_inverted_index.analysis | 122 |
| abstract_inverted_index.approach | 213 |
| abstract_inverted_index.clusters | 159 |
| abstract_inverted_index.dynamics | 133 |
| abstract_inverted_index.employed | 214 |
| abstract_inverted_index.evidence | 190 |
| abstract_inverted_index.examined | 95 |
| abstract_inverted_index.explored | 73 |
| abstract_inverted_index.features | 118 |
| abstract_inverted_index.measured | 100 |
| abstract_inverted_index.obtained | 82 |
| abstract_inverted_index.particle | 86 |
| abstract_inverted_index.process. | 185 |
| abstract_inverted_index.provides | 216 |
| abstract_inverted_index.subspace | 144 |
| abstract_inverted_index.upstream | 206 |
| abstract_inverted_index.velocity | 152 |
| abstract_inverted_index.weighted | 147 |
| abstract_inverted_index.component | 121 |
| abstract_inverted_index.different | 46 |
| abstract_inverted_index.dynamics. | 54 |
| abstract_inverted_index.neglected | 14 |
| abstract_inverted_index.pipeline. | 116 |
| abstract_inverted_index.primarily | 166 |
| abstract_inverted_index.principal | 120 |
| abstract_inverted_index.rotation. | 211 |
| abstract_inverted_index.variance. | 153 |
| abstract_inverted_index.Clustering | 195 |
| abstract_inverted_index.Cross-flow | 0 |
| abstract_inverted_index.aggregate, | 229 |
| abstract_inverted_index.associated | 162 |
| abstract_inverted_index.clustering | 115, 128 |
| abstract_inverted_index.correlated | 165 |
| abstract_inverted_index.cross-flow | 52, 222 |
| abstract_inverted_index.flow-field | 104, 114, 138, 158, 202 |
| abstract_inverted_index.hysteresis | 40 |
| abstract_inverted_index.implicitly | 13 |
| abstract_inverted_index.intensity, | 174 |
| abstract_inverted_index.mechanisms | 28 |
| abstract_inverted_index.persistent | 197 |
| abstract_inverted_index.relatively | 171 |
| abstract_inverted_index.stochastic | 33 |
| abstract_inverted_index.turbulence | 173 |
| abstract_inverted_index.variations | 177 |
| abstract_inverted_index.conditions. | 79 |
| abstract_inverted_index.contrasting | 77 |
| abstract_inverted_index.established | 109 |
| abstract_inverted_index.hysteresis. | 194 |
| abstract_inverted_index.operational | 78 |
| abstract_inverted_index.performance | 2, 71, 106, 163, 200, 227 |
| abstract_inverted_index.potentially | 22 |
| abstract_inverted_index.statistical | 230 |
| abstract_inverted_index.substantial | 192 |
| abstract_inverted_index.variability | 20, 64, 107, 203 |
| abstract_inverted_index.variations, | 8 |
| abstract_inverted_index.velocimetry | 88 |
| abstract_inverted_index.Correlations | 102 |
| abstract_inverted_index.contribution | 149 |
| abstract_inverted_index.correlations | 198 |
| abstract_inverted_index.hierarchical | 113 |
| abstract_inverted_index.implications | 47 |
| abstract_inverted_index.performance. | 101 |
| abstract_inverted_index.unsupervised | 112 |
| abstract_inverted_index.comprehensive | 219 |
| abstract_inverted_index.fluctuations, | 31, 169 |
| abstract_inverted_index.pre-processor | 124 |
| abstract_inverted_index.understanding | 50 |
| abstract_inverted_index.cycle-to-cycle | 7, 39, 63, 193 |
| abstract_inverted_index.experimentally | 74 |
| abstract_inverted_index.interpretable, | 142 |
| abstract_inverted_index.simultaneously | 99 |
| abstract_inverted_index.low-dimensional | 143 |
| abstract_inverted_index.two-dimensional | 84 |
| abstract_inverted_index.high-dimensional | 137 |
| abstract_inverted_index.phase-averaging. | 18 |
| abstract_inverted_index.representations. | 231 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8600000143051147 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.01658991 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |