| Project/Area Number |
14550152
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Fluid engineering
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| Research Institution | KYOTOINSTITUTE OF TECHNOLOGY |
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Principal Investigator |
MATSUNO Kenichi KYOTOINSTITUTE OF TECHNOLOGY, FACULTY OF ENGINEERING AND DESIGN, PROFESSOR, 工芸学部, 教授 (70252541)
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| Project Period (FY) |
2002 – 2003
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| Project Status |
Completed (Fiscal Year 2003)
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| Budget Amount *help |
¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 2003: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2002: ¥1,300,000 (Direct Cost: ¥1,300,000)
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| Keywords | shock wave / self-induced oscillation / compressible flow / supersonic flow / jet / numerical analysis / CFD / spectral analysis / 解適合格子形成法 |
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| Research Abstract |
The purpose of this project is to progress noticeably a adaptive-grid method by incorporating the approach developed in the field of the Artificial intelligence (AT). The project consists of two phases. At the first phase, the adaptive grid method using elliptic equation is incorporated into a new finite-volume method in moving grid system. At the same time, a neural network model of spatiotemporal visual processing has been applied to detect discontinuities, such as shock wave and contact surfaces, in a numerical solution of a supersonic flow. The present shock wave detection method using neural network approach has been successfully incorporated into the adaptive grid method since the shock waves are automatically detected by neural network and using the information of flow field.
The present approach to detect discontinuities in the flow field has been proved surely a way to know the flow physics in the flow field. the method has been tried to extend in order to detect general flow properties, such as vortex, boundary layer and so on, and, however, it is still under studies since the neural network technology is also developing.
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