Adaptation of the Gauss-Newton Optimization Algorithm in Hilbert Diagnostics Using the Bunsen Flame as an Example | Sciact - Система учета научной деятельности ИМ СО РАН

Adaptation of the Gauss-Newton Optimization Algorithm in Hilbert Diagnostics Using the Bunsen Flame as an Example Научная публикация

Журнал

Научная визуализация (Scientific Visualization)
ISSN: 2079-3537

Вых. Данные

Год: 2024, Том: 16, Номер: 4, Страницы: 59-66 Страниц : 8 DOI: 10.26583/sv.16.4.06

Ключевые слова

Hilbert optics, Gauss-Newton algorithm, optimization, Bunsen flame, flame diagnostics.

Авторы

Arbuzov E.V. ^1,3 , Dubnishchev Yu.N. ^2,3 , Zolotukhina O.S. ^2,3

Организации

1	Sobolev Institute of Mathematics SB RAS
2	Kutateladze Institute of Thermophysics SB RAS
3	Novosibirsk State Technical University

Информация о финансировании (1)

Институт математики им. С.Л. Соболева СО РАН

FWNF-2022-0009

The paper suggests a new form of visualization of the current psycho-emotional and The possibility of using the Gauss-Newton optimization algorithm in Hilbert diagnostics of react-ing media (flames) is considered in the paper using the example of data on the temperature and main compounds of a laminar methane-air Bunsen flame. The method of reconstructing phase optical density fields is used to solve the inverse problem of Hilbert optics (it was pre-viously proposed and tested on numerical models): the hilbertogram is calculated in the con-sidered flame axisymmetric section, from which the initial values of the phase function and the medium refractive index are reconstructed. The applications scope expansion of the ob-tained results in the experimental data processing will be the work development.

Arbuzov E.V. , Dubnishchev Y.N. , Zolotukhina O.S.
Adaptation of the Gauss-Newton Optimization Algorithm in Hilbert Diagnostics Using the Bunsen Flame as an Example
Научная визуализация (Scientific Visualization). 2024. V.16. N4. P.59-66. DOI: 10.26583/sv.16.4.06 Scopus РИНЦ OpenAlex

Опубликована в печати:	2 дек. 2024 г.
Опубликована online:	2 дек. 2024 г.

Scopus:	2-s2.0-85210999072
РИНЦ:	79315002
OpenAlex:	W4404755458

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