Signal decomposition for surrogate modeling of a constrained ultrasonic design space Article Swipe
Laura Homa
,
Daniel Sparkman
,
John Wertz
,
John T. Welter
,
John C. Aldrin
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1063/1.5031601
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1063/1.5031601
The U.S. Air Force seeks to improve the methods and measures by which the lifecycle of composite structures are managed. Nondestructive evaluation of damage – particularly internal damage resulting from impact – represents a significant input to that improvement. Conventional ultrasound can detect this damage; however, full 3D characterization has not been demonstrated. A proposed approach for robust characterization uses model-based inversion through fitting of simulated results to experimental data. One challenge with this approach is the high computational expense of the forward model to simulate the ultrasonic B-scans for each damage scenario. A potential solution is to construct a surrogate model using a subset of simulated ultrasonic scans built using a highly accurate, computationally expensive forward model. However, the dimensionality of these simulated B-scans makes interpolating between them a difficult and potentially infeasible problem. Thus, we propose using the chirplet decomposition to reduce the dimensionality of the data, and allow for interpolation in the chirplet parameter space. By applying the chirplet decomposition, we are able to extract the salient features in the data and construct a surrogate forward model.
Related Topics
Concepts
Curse of dimensionality
Computer science
Ultrasonic sensor
Interpolation (computer graphics)
Surrogate model
Salient
Construct (python library)
Decomposition
Algorithm
Inversion (geology)
Inverse problem
Artificial intelligence
Acoustics
Machine learning
Mathematics
Ecology
Structural basin
Programming language
Mathematical analysis
Paleontology
Physics
Motion (physics)
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1063/1.5031601
- https://aip.scitation.org/doi/pdf/10.1063/1.5031601
- OA Status
- bronze
- Cited By
- 7
- References
- 9
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2800751151
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2800751151Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1063/1.5031601Digital Object Identifier
- Title
-
Signal decomposition for surrogate modeling of a constrained ultrasonic design spaceWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-01-01Full publication date if available
- Authors
-
Laura Homa, Daniel Sparkman, John Wertz, John T. Welter, John C. AldrinList of authors in order
- Landing page
-
https://doi.org/10.1063/1.5031601Publisher landing page
- PDF URL
-
https://aip.scitation.org/doi/pdf/10.1063/1.5031601Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://aip.scitation.org/doi/pdf/10.1063/1.5031601Direct OA link when available
- Concepts
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Curse of dimensionality, Computer science, Ultrasonic sensor, Interpolation (computer graphics), Surrogate model, Salient, Construct (python library), Decomposition, Algorithm, Inversion (geology), Inverse problem, Artificial intelligence, Acoustics, Machine learning, Mathematics, Ecology, Structural basin, Programming language, Mathematical analysis, Paleontology, Physics, Motion (physics), BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2019: 2, 2018: 5Per-year citation counts (last 5 years)
- References (count)
-
9Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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