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，一个是关于封闭格子中发展的热湍流的实验。二是体外培养神经元网络。在Rayleigh-Benard构型中，通过使用低普朗特数流体水银，产生了发展的热湍流。达到的瑞利数(Ra)为10；lt；11；gt；箱体的雷诺数为5倍，这是已有的低普朗特数流体实验室实验中的最高值，与其他流体的最高雷诺数相匹配。我们发现了热湍流中两个边界层倒置的新状态，即热边界层内完全包含粘性边界层。尽管存在这种反转，但通过该单元的净热量传输(Nu)被发现与Ra^&lt；0.29&gt；成正比，这与当两个边界层倒置时Nu与Ra^&lt；1/2&gt；成正比的理论预测是相反的。我们得出结论：观测到的状态就是热湍流的渐近状态。我们用18天的大鼠胚胎进行了神经元的培养。神经元网络在玻璃衬底上再生。应用钙敏感荧光染料和共聚焦显微镜，对神经元内钙浓度活动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)周期性的(2)非周期性的(3)传播型的同步放电模式。我们还提出了一种分析多点神经活动时间序列的新方法。它使我们能够识别具有不同同步模式的不同神经元簇。

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.