Data-Driven Modeling for On-Demand Flow Prescription in Fan-Array Wind Tunnels Article Swipe
Alejandro Stefan-Zavala
,
Isabel Scherl
,
Ioannis Mandralis
,
Steven L. Brunton
,
Morteza Gharib
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.12309
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.12309
Fan-array wind tunnels are an emerging technology to design bespoke wind fields through grids of individually controllable fans. This design is especially suited for the turbulent, dynamic, non-uniform flow conditions found close to the ground, and has enabled applications from entomology to flight on Mars. However, due to the high dimensionality of fan-array actuation and the complexity of unsteady fluid flow, the physics of fan arrays are not fully characterized, making it difficult to prescribe arbitrary flow fields. Accessing the full capability of fan arrays requires resolving the map from time-varying grids of fan speeds to three-dimensional unsteady flow fields, which remains an open problem. This map is unfeasible to span in a single study, but it can be partitioned and studied in subsets. In this paper, we study the special case of constant fan-speeds and time-averaged streamwise velocities with one homogeneous spanwise axis. We produce a proof-of-concept surrogate model by fitting a regularized linear map to a dataset of fan-array measurements. We use this model as the basis for an open-loop control scheme to design flow profiles subject to constraints on fan speeds. In experimental validation, our model scored a mean prediction error of 1.02 m/s and our control scheme a mean tracking error of 1.05 m/s in a fan array with velocities up to 12 m/s. We empirically conclude that the physics relating constant fan speeds to time-averaged streamwise velocities are dominated by linear dynamics, and present our method as a foundational step to fully resolve fan-array wind tunnel control.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2412.12309
- https://arxiv.org/pdf/2412.12309
- OA Status
- green
- Cited By
- 2
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- 10
- OpenAlex ID
- https://openalex.org/W4405561864
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4405561864Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2412.12309Digital Object Identifier
- Title
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Data-Driven Modeling for On-Demand Flow Prescription in Fan-Array Wind TunnelsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-16Full publication date if available
- Authors
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Alejandro Stefan-Zavala, Isabel Scherl, Ioannis Mandralis, Steven L. Brunton, Morteza GharibList of authors in order
- Landing page
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https://arxiv.org/abs/2412.12309Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2412.12309Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2412.12309Direct OA link when available
- Concepts
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Wind tunnel, Flow (mathematics), Marine engineering, Medical prescription, Geology, Engineering, Aerospace engineering, Mechanics, Physics, Medicine, PharmacologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.surrogate | 148 |
| abstract_inverted_index.capability | 81 |
| abstract_inverted_index.complexity | 56 |
| abstract_inverted_index.conditions | 29 |
| abstract_inverted_index.entomology | 40 |
| abstract_inverted_index.especially | 21 |
| abstract_inverted_index.fan-speeds | 134 |
| abstract_inverted_index.prediction | 192 |
| abstract_inverted_index.streamwise | 137, 230 |
| abstract_inverted_index.technology | 6 |
| abstract_inverted_index.turbulent, | 25 |
| abstract_inverted_index.unfeasible | 108 |
| abstract_inverted_index.velocities | 138, 213, 231 |
| abstract_inverted_index.constraints | 180 |
| abstract_inverted_index.empirically | 219 |
| abstract_inverted_index.homogeneous | 141 |
| abstract_inverted_index.non-uniform | 27 |
| abstract_inverted_index.partitioned | 119 |
| abstract_inverted_index.regularized | 153 |
| abstract_inverted_index.validation, | 186 |
| abstract_inverted_index.applications | 38 |
| abstract_inverted_index.controllable | 16 |
| abstract_inverted_index.experimental | 185 |
| abstract_inverted_index.foundational | 243 |
| abstract_inverted_index.individually | 15 |
| abstract_inverted_index.time-varying | 90 |
| abstract_inverted_index.measurements. | 161 |
| abstract_inverted_index.time-averaged | 136, 229 |
| abstract_inverted_index.characterized, | 69 |
| abstract_inverted_index.dimensionality | 50 |
| abstract_inverted_index.proof-of-concept | 147 |
| abstract_inverted_index.three-dimensional | 96 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |