Variabilidade temporal dos fluxos noturnos determinados a partir de duas diferentes metodologias no nível de 325 m acima da floresta Amazônica

The present study aimed to analyze and compare the temporal variability of the nocturnal fluxes of CO2, sensitive and latent heat, calculated from two different methodologies: one with a 5-minute temporal window (using the eddy covariance technique), and another with 109 minutes (from multiresolution decomposition). For this, night series of 25 nights were used between October and November 2015. The analyzes were made for two groups of distinct turbulence patterns: one with intermittent regime and the other with homogeneous turbulence. The results showed that the fluxes obtained by the classical method of eddy covariance were dependent on the intensity of the turbulence. On the other hand, the fluxes calculated from the multiresolution decomposition technique showed significant fluctuations in the temporal evolution of all scalars analyzed, with the largest percentage differences between the two approaches occurring in the homogeneous turbulence regime group, which was characterized by predominantly weak turbulent activity throughout the night. In the comparison made, the methodology employed in the 109-minute window showed greater efficiency in the estimates of exchanges at 325 m in the ATTO tower, especially during conditions of low turbulent activity.

Identification of time-varying systems with partial acceleration measurements by synthesis of wavelet decomposition and Kalman filter

Structural systems often exhibit time-varying dynamic characteristics during their service life due to serve hazards and environmental erosion, so the identification of time-varying structural systems is an important research topic. Among the previous methodologies, wavelet multiresolution analysis for time-varying structural systems has gained increasing attention in the past decades. However, most of the existing wavelet-based identification approaches request the full measurements of structural responses including acceleration, velocity, and displacement responses at all dynamic degrees of freedom. In this article, an improved algorithm is proposed for the identification of time-varying structural parameters using only partial measurements of structural acceleration responses. The proposed algorithm is based on the synthesis of wavelet multiresolution decomposition and the Kalman filter approach. The time-varying structural stiffness and damping parameters are expanded at multi-scale profile by wavelet multiresolution decomposition, so the time-varying parametric identification problem is converted into a time-invariant one. Structural full responses are estimated by Kalman filter using partial observations of structural acceleration responses. The scale coefficients by the wavelet expansion are estimated via the solution of a nonlinear optimization problem of minimizing the errors between estimated and observed accelerations. Finally, the original time-varying parameters can be reconstructed. To demonstrate the efficiency of the proposed algorithm, the identification of several numerical examples with various time-varying scenarios is studied.