| Project/Area Number |
09440146
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
物性一般(含基礎論)
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| Research Institution | Tohoku University |
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Principal Investigator |
SANO Masaki Tohoku University, Associated Professor, 電気通信研究所, 助教授 (40150263)
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| Project Period (FY) |
1997 – 1998
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| Project Status |
Completed (Fiscal Year 1998)
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| Budget Amount *help |
¥11,800,000 (Direct Cost: ¥11,800,000)
Fiscal Year 1998: ¥5,700,000 (Direct Cost: ¥5,700,000)
Fiscal Year 1997: ¥6,100,000 (Direct Cost: ¥6,100,000)
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| Keywords | Dissipative System / Rayleigh-Benard system / Developed Turbulence / Hard turbulence / cultured neural Network / synchronous firing / 熱乱流 / 境界層 / 培養神経細胞 / 集団振動 |
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| Research Abstract |
We chose two typical experiments from Dissipative Systems with many degrees of freedom.
The one is an experiment on developed thermal turbulence in a closed cell. The second is cultured neuron network in vitro.
1. Developed thermal turbulence is created in Rayleigh-Benard configuration by using a low Prandtl number fluid, mercury. Achieved Rayleigh number (Ra) is 10^<11> and Reynolds number of the box is 5x which is the highest value among ever existing laboratory experiments for low Prandtl number fluid and compatible with the highest one for other fluid. We found a new state in thermal turbulence in which two boundary layers are inverted, i.e. viscous boundary layer is totally included within thermal boundary layer. In spite of this inversion, net heat transport across the cell (Nusselt number, Nu) was found to be proportional to Ra^<0.29> which contradicts to the theoretical prediction that Nu is proportional to Ra^<1/2> when two boundary layers are inverted. We concluded that the observed state is the asymptotic state of thermal turbulence.
2. We made cultures of neurons from 1 8days rat embryos. Network of neurons are regenerated on a glass substrate. By using Ca sensitive fluorescent dye and confocal microscope, dynamics of Ca concentration activity in neurons are measured and analyzed. We found three types of synchronous firing of neural network ; (1) Periodic (2)Non-periodic (3) Propagating synfiring pattern. We also proposed a new way of analyzing multi-point time series of neural activities. It enabled us to identify different clusters of neurons firing with different synchronous modes.
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