Generative Reconstruction of Spatiotemporal Wall-Pressure in Turbulent Boundary Layers via Patchwise Latent Diffusion Article Swipe
Fan, Xiantao
,
Parikh, Meet Hemant
,
Liu Yi
,
Liu-Xin Yang
,
Guo Jun-yi
,
Wang Meng
,
Wang, Jian-Xun
·
YOU?
·
· 2025
· Open Access
·
YOU?
·
· 2025
· Open Access
·
Wall-pressure fluctuations in turbulent boundary layers drive flow-induced noise, structural vibration, and hydroacoustic disturbances, especially in underwater and aerospace systems. Accurate prediction of their wavenumber-frequency spectra is critical for mitigation and design, yet empirical/analytical models rely on simplifying assumptions and miss the full spatiotemporal complexity, while high-fidelity simulations are prohibitive at high Reynolds numbers. Experimental measurements, though accessible, typically provide only pointwise signals and lack the resolution to recover full spatiotemporal fields. We propose a probabilistic generative framework that couples a patchwise (domain-decomposed) conditional neural field with a latent diffusion model to synthesize spatiotemporal wall-pressure fields under varying pressure-gradient conditions. The model conditions on sparse surface-sensor measurements and a low-cost mean-pressure descriptor, supports zero-shot adaptation to new sensor layouts, and produces ensembles with calibrated uncertainty. Validation against reference data shows accurate recovery of instantaneous fields and key statistics.
Related Topics
Concepts
Pointwise
Boundary (topology)
Probabilistic logic
Generative model
Turbulence
Computer science
Algorithm
Diffusion
Field (mathematics)
Generative grammar
Artificial intelligence
Pattern recognition (psychology)
Statistical physics
Data-driven
Representation (politics)
Diffusion map
Data assimilation
Flow (mathematics)
Physics
Sonar
Geology
Statistical model
Metadata
- Type
- article
- Landing Page
- http://arxiv.org/abs/2511.12455
- https://arxiv.org/pdf/2511.12455
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7106093403
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W7106093403Canonical identifier for this work in OpenAlex
- Title
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Generative Reconstruction of Spatiotemporal Wall-Pressure in Turbulent Boundary Layers via Patchwise Latent DiffusionWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
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2025-11-16Full publication date if available
- Authors
-
Fan, Xiantao, Parikh, Meet Hemant, Liu Yi, Liu-Xin Yang, Guo Jun-yi, Wang Meng, Wang, Jian-XunList of authors in order
- Landing page
-
https://arxiv.org/abs/2511.12455Publisher landing page
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https://arxiv.org/pdf/2511.12455Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2511.12455Direct OA link when available
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Pointwise, Boundary (topology), Probabilistic logic, Generative model, Turbulence, Computer science, Algorithm, Diffusion, Field (mathematics), Generative grammar, Artificial intelligence, Pattern recognition (psychology), Statistical physics, Data-driven, Representation (politics), Diffusion map, Data assimilation, Flow (mathematics), Physics, Sonar, Geology, Statistical modelTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.Wall-pressure | 0 |
| abstract_inverted_index.disturbances, | 13 |
| abstract_inverted_index.high-fidelity | 46 |
| abstract_inverted_index.hydroacoustic | 12 |
| abstract_inverted_index.instantaneous | 133 |
| abstract_inverted_index.mean-pressure | 110 |
| abstract_inverted_index.measurements, | 55 |
| abstract_inverted_index.probabilistic | 75 |
| abstract_inverted_index.wall-pressure | 94 |
| abstract_inverted_index.spatiotemporal | 43, 70, 93 |
| abstract_inverted_index.surface-sensor | 105 |
| abstract_inverted_index.pressure-gradient | 98 |
| abstract_inverted_index.(domain-decomposed) | 82 |
| abstract_inverted_index.empirical/analytical | 33 |
| abstract_inverted_index.wavenumber-frequency | 24 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.81652163 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |