PARAMETER IDENTIFICATION BY OPTIMIZATION METHOD FOR A POLLUTION PROBLEM IN POROUS MEDIA

In the present work, we investigate the inverse problem of reconstructing the parameter of an integro-differential parabolic equation, which comes from pollution problems in porous media, when the final observation is given. We use the optimal control framework to establish both the existence and necessary condition of the minimizer for the cost func- tional. Furthermore, we prove the stability and the local uniqueness of the minimizer. Some numerical results will be presented and discussed.

Spectral properties and identification of aerostatic bearings

Modified rotor kit Bently Nevada was used for dynamic characteristics measurements of new developed aerostatic bearings.Mathematical model of these bearings is considered as linear.Model was identified with the help of harmonic force excitation independently from the speed of journal rotation.The stiffness and damping matrices were identified for different air inlet pressures.The calculated spectral properties allow to determine the stability boundary for suitable variation of model parameters.

On grey relation projection model based on projection pursuit

Multidimensional grey relation projection value can be synthesized as one-dimensional projection value by using projection pursuit model. The larger the projection value is,the better the model. Thus,according to the projection value,the best one can be chosen from the model aggregation. Because projection pursuit modeling based on accelerating genetic algorithm can simplify the implementation procedure of the projection pursuit technique and overcome its complex calculation as well as the difficulty in implementing its program,a new method can be obtained for choosing the best grey relation projection model based on the projection pursuit technique.

System Identification Modeling and Unstable Behavior of the Dynamics of Flows within the Tip Region of an Axial Compressor Blade Passage

In recent years, the correlation coefficient of pressure data from the same blade passage in an axial compressor unit has been used to characterize the state of flow in the blade passage. In addition, the correlation coefficient has been successfully used as an indicator for active control action using air injection. In this work, the correlation coefficient approach is extended to incorporate system identification algorithms in order to extract a mathematical model of the dynamics of the flows within a blade passage. The dynamics analyzed in this research focus on the flow streams and pressure along the rotor blades as well as on the unsteady tip leakage flow from the rotor tip gaps. The system identification results are used to construct a root locus plot for different flow coefficients, starting far away from stall to near stall conditions. As the compressor moves closer to stall, the poles of the identified models move towards the imaginary axis of the complex plane, indicating an impending instability. System frequency data is captured using the proposed correlation based system identification approach. Additionally, an oscillatory tip leakage flow is observed at a flow coefficient away from stall and how this oscillation changes as the compressor approaches stall is an interesting result of this research. Comparative research is analyzed to determine why the oscillatory flow behavior occurs at a specific sensor location within the tip region of the rotor blade.

Dynamic modeling framework for solid-gas sorption systems

A dynamic modeling framework based on an intelligent approach is proposed to identify the complex behaviors of solid-gas sorption systems.An experimental system was built and tested to assist in developing a model of the system performance during the adsorption and desorption processes.The variations in the thermal effects and gaseous environment accompanying the reactions were considered when designing the model.An optimization platform based on a multi-population genetic algorithm and artificial criteria was established to identify the mod-eling coefficients and quantify the effects of condition changes on the reactions.The calibration of the simulation results against the tested data showed good accuracy,where the coefficient of determination was greater than 0.988.The outcome of this study could provide a modeling basis for the optimization of solid-gas sorption systems and contribute a potential tool for uncovering key characteristics associated with materials and components.

Inverse problem for adaptive SIR model:Application to COVID-19 in Latin America

This work presents a method for solving an Adaptive Susceptible-Infected-Removed(ASIR)epidemic model with time-dependent transmission and removal rates.Available COVID-19 data as of March 2021 are used for identifying the rates from an inverse problem.The estimated rates are used to solve the adaptive SIR system for the spread of the infectious disease.This method simultaneously solves the problem for the time-dependent rates and the unknown functions of the A-SIR system.Presented results show the spread of COVID-19 in the World,Argentina,Brazil,Colombia,Dominican Republic,and Honduras.Comparisons of the reported affected by the disease individuals from the available real data and the values obtained with the A-SIR model demonstrate how well the model simulates the dynamic of the infectious disease.