Efficient MAPoD via Least Angle Regression based Polynomial Chaos Expansion Metamodel for Eddy Current NDT Article Swipe
Yang Bao
,
Jiahao Qiu
,
Praveen Gurrala
,
Jiming Song
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.13052/2024.aces.j.390510
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.13052/2024.aces.j.390510
In this article, a metamodeling approach based on non-intrusive polynomial chaos expansion (PCE) with least angle regression (LAR) method is used in boundary element analysis for a model-assisted probability of detection (MAPoD) study of eddy current nondestructive testing (NDT) systems. The LAR-PCE metamodel represents the NDT system model responses by a set of coefficients with the polynomial basis functions in lieu of pure kernel degeneration accelerated boundary element method (KD-BEM) based physical model. Both the computational accuracy and efficiency of the LAR-PCE metamodel over the ordinary least squares (OLS) based PCE metamodel are demonstrated by testing the 3D eddy current NDT benchmarks with different system setups, flaw lengths and widths. The simulation results show two digits accuracy of the PoD metrics compared with the ones achieved by the KD-BEM based physical model as the benchmark. The LAR-PCE metamodel has remarkable improvements in computational efficiency over the OLS-PCE metamodel and accelerates the MAPoD study.
Related Topics
Concepts
Metamodeling
Nondestructive testing
CHAOS (operating system)
Eddy current
Polynomial chaos
Polynomial regression
Polynomial
Current (fluid)
Regression
Mathematics
Computer science
Engineering
Physics
Statistics
Mathematical analysis
Electrical engineering
Monte Carlo method
Computer security
Quantum mechanics
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.13052/2024.aces.j.390510
- OA Status
- diamond
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402971747
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402971747Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.13052/2024.aces.j.390510Digital Object Identifier
- Title
-
Efficient MAPoD via Least Angle Regression based Polynomial Chaos Expansion Metamodel for Eddy Current NDTWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-05-31Full publication date if available
- Authors
-
Yang Bao, Jiahao Qiu, Praveen Gurrala, Jiming SongList of authors in order
- Landing page
-
https://doi.org/10.13052/2024.aces.j.390510Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.13052/2024.aces.j.390510Direct OA link when available
- Concepts
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Metamodeling, Nondestructive testing, CHAOS (operating system), Eddy current, Polynomial chaos, Polynomial regression, Polynomial, Current (fluid), Regression, Mathematics, Computer science, Engineering, Physics, Statistics, Mathematical analysis, Electrical engineering, Monte Carlo method, Computer security, Quantum mechanics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 1Per-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.different | 103 |
| abstract_inverted_index.expansion | 11 |
| abstract_inverted_index.functions | 58 |
| abstract_inverted_index.metamodel | 42, 82, 91, 137, 147 |
| abstract_inverted_index.responses | 48 |
| abstract_inverted_index.benchmark. | 134 |
| abstract_inverted_index.benchmarks | 101 |
| abstract_inverted_index.efficiency | 78, 143 |
| abstract_inverted_index.polynomial | 9, 56 |
| abstract_inverted_index.regression | 16 |
| abstract_inverted_index.remarkable | 139 |
| abstract_inverted_index.represents | 43 |
| abstract_inverted_index.simulation | 111 |
| abstract_inverted_index.accelerated | 65 |
| abstract_inverted_index.accelerates | 149 |
| abstract_inverted_index.probability | 28 |
| abstract_inverted_index.coefficients | 53 |
| abstract_inverted_index.degeneration | 64 |
| abstract_inverted_index.demonstrated | 93 |
| abstract_inverted_index.improvements | 140 |
| abstract_inverted_index.metamodeling | 4 |
| abstract_inverted_index.computational | 75, 142 |
| abstract_inverted_index.non-intrusive | 8 |
| abstract_inverted_index.model-assisted | 27 |
| abstract_inverted_index.nondestructive | 36 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.85066294 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |