Two Experiments in Dissipative Systems of Many Degrees of Freedom

我们从多自由度耗散系统中选取了两个典型的实验：一个是封闭小室中发展热湍流的实验;二是体外培养神经元网络。1.利用低普朗特数流体汞在Rayleigh-Benard位形中产生发展热湍流。得到的瑞利数（Ra）为10 μ g<11>，雷诺数为5x，这是目前实验室对低普朗特数流体的最高数值，与其它流体的最高数值是一致的。在热湍流中发现了一种新的状态，即两个边界层倒置，即粘性边界层完全包含在热边界层中。尽管有这种倒置，但发现穿过电池的净热传输（努塞尔数，Nu）与Ra^成比例，<0.29>这与理论预测相矛盾，即当两个边界层倒置时，Nu与Ra^&lt;1/2&gt;成比例。我们得出的结论是，所观察到的状态就是热湍流的渐进状态。2.我们从18天的大鼠胚胎中培养神经元。神经元网络在玻璃基板上再生。利用钙敏感荧光染料和共聚焦显微镜，对神经元内钙浓度的动态变化进行了测量和分析。我们发现神经网络的同步放电有三种类型：（1）周期性（2）非周期性（3）周期性同步放电模式。提出了一种新的多点神经活动时间序列分析方法。它使我们能够识别不同的神经元簇，以不同的同步模式放电。

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.