Kogut, Krzysztof
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inżynieria chemiczna
inżynieria środowiska, górnictwo i energetyka
inżynieria środowiska, górnictwo i energetyka
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Item type:Article, Access status: Open Access , Semi-active suspension system modelling and parameters identification(Wydawnictwa AGH, 2012) Kogut, KrzysztofThis paper presents a study of obtaining a model of the real quarter-car suspension device. The system is equipped with an automotive engineering magnetorheological (MR) rotary brake. Due to the complex mechanical structure of the apparatus the considered model contains several simplifications. In the parameter estimation process the grey-box method was used, while the process itself was split into two separate steps. In the first step, the parameters of the nonlinear model of suspension part are identified and the static profile of the MR damper is experimentally determined. The second step is to estimate the parameters of the model of the wheel-eccentricity part. Comparison of the modelled system trajectories and real-time experiments are presented. The identification results show that the obtained model is accurate and can be successfully applied to simulate the device.Item type:Doctoral Dissertation, Access status: Open Access , Analiza możliwości modelowania sieci przesyłowej gazu ziemnego z wykorzystaniem sztucznych sieci neuronowych(Data obrony: 2008-01-21) Kogut, Krzysztof
Wydział Paliw i EnergiiPresently used empirical formulas as well as commercially available computer programmes are burdened with errors resulting from consideration of not complete set of parameters (e.g. the change of real volume of gas) which are required to perform calculations precisely enough. The designed, studied and presented in the dissertation modelling method is unique (and not found in any literature). It is based on artificial neural networks elementary models corresponding to individual elements of gas transmission netrworks and their cascade connections in compound system. The aim of modelling was to find such neural network configuration, which would most exactly predict (in given point of natural gas transmission network) the value of final pressure. Using suitable computer programmes a database was created, which allowed teaching of the neural networks for estimating final pressure. Real measurements were obtained from natural gas transmission network operators. The measurements were required at stages of created neural networks models operation verification and their coaching. Two types of models were built: elementary networks model (where networks were joined using cascase-like connections to model complex structure of gas network) and neural networks model (which describes complex industrial gas network and which was teached on real measurements data, specifically to predict pressure in given point of the network). Using computer programmes for neural networks modelling, investigation of conformity of real measurements values with final pressures values predicted by created neural networks (of both types: Multi Layer Perceptron and Radial Basic Functions) was performed. Verification of elementary neural nets operation was conducted for available real fragments of natural gas transmission networks. The results of calculations derived from available suitable computer programmes were compared to data gathered from these transmission networks.