- PII
- S30345340S1024708425030034-1
- DOI
- 10.7868/S3034534025030034
- Publication type
- Article
- Status
- Published
- Authors
- Volume/ Edition
- Volume / Issue number 3
- Pages
- 30-36
- Abstract
- The problems of laminar jets that admit self-similar solutions are considered. A method for determining the self-similarity parameter is proposed based on the condition of existence of a solution to equations in self-similar variables under given boundary conditions with only a single self-similarity parameter. In problems of plane free and wall jets the self-similarity parameters are determined analytically. In the problem of a three-dimensional wall jet, the self-similarity parameter is determined using a neural network.
- Keywords
- пристенные струи автомодельность параметр автомодельности нейронные сети ламинарная струя
- Date of publication
- 23.03.2025
- Year of publication
- 2025
- Number of purchasers
- 0
- Views
- 38
References
- 1. Schlichting H. Laminare Strahlausbreitung // ZAMM-Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik. 1933. Jg. 13. No. 4. S. 260–263.
- 2. Шлихтинг Г. Теория пограничного слоя. М.: Наука, 1969. 744 с.
- 3. Bickley W.G. LXXIII. The plane jet // The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science. 1937. V. 23. No. 156. P. 727–731.
- 4. Акатинов Н. И. Распространение плоской ламинарной струи вязкой жидкости вдоль твердой стенки // Тр. Ленинградского политех. ин-та. 1953. № 5. С. 24–31.
- 5. Glauert M.B. The wall jet // Journal of Fluid Mechanics. 1956. V. 1. No. 6. P. 625–643.
- 6. Бут И. И., Гайфуллин А. М., Жанк В. В. Дальнее поле трехмерной пристенной ламинарной струи // Изв. РАН. Механика жидкости и газа. 2021. № 6. С. 51–61.
- 7. Gaifullin A.M., Shcheglov A.S. Self-Similarity of a Wall Jet with Swirl // Lobachevskii Journal of Mathematics. 2022. V. 43. No. 5. P. 1098–1103.
- 8. Лойцянский Л. Г. Механика жидкости и газа. М.: Наука, 1978. 736 с.
- 9. Raissi M., Perdikaris P., Karniadakis G. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations // Journal of Computational Physics. 2019. V. 378. P. 686–707.