Complex systems topics in hydrodynamics and econophysics
Complex Systems, Computational Fluid Dynamics, Complex Networks, Motif
Synchronization, Alignment and Efficiency Index.
This work comprises investigations on two different aspects of complex systems, computati-
onal fluid dynamics and time series. In the first part, we resume the investigation, using the
Ansys-FLUENT environment, of a model for the control mechanism of the hydrodynamic ins-
tability in a radial Hele-Shaw (HS) cell through the time dependence of the injection rate of the
invading fluid of lower viscosity when compared to that of the resident fluid. The fluid-fluid
instability can be suppressed or strongly reduced by choosing the best time-dependent injection
rate. Three different functional forms for the injection are considered. Based on minimizing
the magnitude of the fluctuations of the invading interface towards the circular geometry, a
criterion is developed to identify the best injection rate. The second part is related to finan-
cial time series in the scope of econophysics, where two studies were performed. Initially, the
motifs synchronization formalism was used for the construction of complex networks based on
the correlations among the elements of a set of time-series for the prices of liquid fuels in the
retail market of Salvador, Bahia. In this part, we analyze, for any determined subnet, how its
throughput or gain compares to the network average. A possible application of this method is
the detection of systematic deviations from the average behavior, which may be related to an
alignment among the constituents of some subnetwork. The second study consists in evalua-
ting market efficiency by means of the efficiency index (IE) for renewable and non-renewable
energy price series traded on stock exchanges. The IE is based on a measure of distance (or
deviation) that the market can present in relation to the efficient market state. The proposed
use of IE determined from independently calculated values of the Hurst exponent, H, and the
fractal dimension, D, can lead to greater accuracy in evaluating market behavior by correcting
possible biases in the algorithms used to calculate H and D.