Correcting for non-periodic behaviour in perturbative experiments: application to heat pulse propagation and modulated gas-puff experiments Article Swipe
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· 2020
· Open Access
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· DOI: https://doi.org/10.1088/1361-6587/ab9eaa
This paper introduces a recent innovation in dealing with non-periodic behavior often referred to as transients in perturbative experiments. These transients can be the result from the unforced response due to the initial condition and other slow trends in the measurement data and are a source of error when performing and interpreting Fourier spectra. Fourier analysis is particularly relevant in system identification used to build feedback controllers and the analysis of various pulsed experiments such as heat pulse propagation studies. The basic idea behind the methodology is that transients are continuous complex-valued smooth functions in the Fourier domain which can be estimated from the Fourier data. Then, these smooth functions can be subtracted from the data such that only periodic components are retained. The merit of the approach is shown in two experimental examples, i.e. heat pulse propagation (core transport analysis) and radiation front movement due to gas puffing in the divertor. The examples show that the quality of the data is significantly improved such that it allows for new interpretation of the results even for non-ideal measurements.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6587/ab9eaa
- https://iopscience.iop.org/article/10.1088/1361-6587/ab9eaa/pdf
- OA Status
- bronze
- Cited By
- 18
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3036576802
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3036576802Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1361-6587/ab9eaaDigital Object Identifier
- Title
-
Correcting for non-periodic behaviour in perturbative experiments: application to heat pulse propagation and modulated gas-puff experimentsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-06-19Full publication date if available
- Authors
-
M. van Berkel, Ricky van Kampen, Gerd Vandersteen, T. Kobayashi, T. Ravensbergen, H. Igami, J. T. Lammers, Gerardus W. Oosterwegel, C. Galperti, F. Felici, M.R. de BaarList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6587/ab9eaaPublisher landing page
- PDF URL
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https://iopscience.iop.org/article/10.1088/1361-6587/ab9eaa/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://iopscience.iop.org/article/10.1088/1361-6587/ab9eaa/pdfDirect OA link when available
- Concepts
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Fourier transform, Fourier analysis, Pulse (music), Frequency domain, Fourier series, Physics, Computational physics, Time domain, Computer science, Optics, Mathematical analysis, Mathematics, Computer vision, Quantum mechanics, DetectorTop concepts (fields/topics) attached by OpenAlex
- Cited by
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18Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 3, 2023: 3, 2022: 6, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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48Number 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.examples, | 133 |
| abstract_inverted_index.functions | 93, 109 |
| abstract_inverted_index.non-ideal | 176 |
| abstract_inverted_index.radiation | 142 |
| abstract_inverted_index.retained. | 122 |
| abstract_inverted_index.transport | 139 |
| abstract_inverted_index.components | 120 |
| abstract_inverted_index.continuous | 90 |
| abstract_inverted_index.innovation | 5 |
| abstract_inverted_index.introduces | 2 |
| abstract_inverted_index.performing | 49 |
| abstract_inverted_index.subtracted | 112 |
| abstract_inverted_index.transients | 15, 20, 88 |
| abstract_inverted_index.controllers | 66 |
| abstract_inverted_index.experiments | 73 |
| abstract_inverted_index.measurement | 40 |
| abstract_inverted_index.methodology | 85 |
| abstract_inverted_index.propagation | 78, 137 |
| abstract_inverted_index.experimental | 132 |
| abstract_inverted_index.experiments. | 18 |
| abstract_inverted_index.interpreting | 51 |
| abstract_inverted_index.non-periodic | 9 |
| abstract_inverted_index.particularly | 57 |
| abstract_inverted_index.perturbative | 17 |
| abstract_inverted_index.measurements. | 177 |
| abstract_inverted_index.significantly | 162 |
| abstract_inverted_index.complex-valued | 91 |
| abstract_inverted_index.identification | 61 |
| abstract_inverted_index.interpretation | 170 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5055218170, https://openalex.org/A5089469697 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 11 |
| corresponding_institution_ids | https://openalex.org/I4210147097, https://openalex.org/I83019370 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.6200000047683716 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.94646887 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |