On the Probabilistic-Statistical Approach to the Analysis of Nonlocality Parameters of Plasma Density Full article
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Computational Mathematics and Mathematical Physics
ISSN: 0965-5425 , E-ISSN: 1555-6662 |
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| Output data | Year: 2024, Volume: 64, Number: 3, Pages: 441–452 Pages count : 12 DOI: 10.1134/S0965542524030047 | ||||
| Tags | plasma de-nsityspectral analysis time and space nonlocality time series of physical quantities | ||||
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| Affiliations |
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Funding (1)
| 1 | Sobolev Institute of Mathematics | FWNF-2024-0001 |
Abstract:
A sample of values of plasma density in a thermonuclear facility is studied. A methodology for processing experimental data that makes it possible to establish correspondence between this sample and a model of nonstationary noise is proposed. This model is formed as convolution of a stationary sequence and a memory function, and it makes it possible to simulate the competition between space and time nonlocalities. A physical interpretation of the nonlocality parameters is described.
Cite:
Arkashov N.S.
, Seleznev V.A.
On the Probabilistic-Statistical Approach to the Analysis of Nonlocality Parameters of Plasma Density
Computational Mathematics and Mathematical Physics. 2024. V.64. N3. P.441–452. DOI: 10.1134/S0965542524030047 WOS Scopus РИНЦ OpenAlex
On the Probabilistic-Statistical Approach to the Analysis of Nonlocality Parameters of Plasma Density
Computational Mathematics and Mathematical Physics. 2024. V.64. N3. P.441–452. DOI: 10.1134/S0965542524030047 WOS Scopus РИНЦ OpenAlex
Original:
Аркашов Н.С.
, Селезнев В.А.
О вероятностно-статистическом подходе к анализу параметров нелокальности плотности плазмы
Журнал вычислительной математики и математической физики. 2024. Т.64. №3. С.473-485. DOI: 10.31857/S0044466924030086 РИНЦ OpenAlex
О вероятностно-статистическом подходе к анализу параметров нелокальности плотности плазмы
Журнал вычислительной математики и математической физики. 2024. Т.64. №3. С.473-485. DOI: 10.31857/S0044466924030086 РИНЦ OpenAlex
Dates:
| Submitted: | Dec 20, 2022 |
| Accepted: | Nov 20, 2023 |
| Published print: | Apr 22, 2024 |
| Published online: | Apr 22, 2024 |
Identifiers:
| Web of science: | WOS:001206341400013 |
| Scopus: | 2-s2.0-85191098333 |
| Elibrary: | 66808890 |
| OpenAlex: | W4395015598 |