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Model identification and measurement of parameters of model turbine JetCAT
Tlustoš, Jiří ; Dolák, Petr (referee) ; Klusáček, Stanislav (advisor)
The bachelor's thesis deals with measurement of microturbine engine parameters. The first part of the thesis covers theory of microturbine parameters measurement, data analysis, interpretation and utilization for model identification, engine control, individual functional block charts and characteristics generation. All possible methods for engine parameters measurement, sensors selection and installation, data acquisition are analyzed here. The focus is on the engine air flow measurement. Selected method of the air flow measurement is described here in detail and verified for the specific case. The Calibration was carried out by means of measuring route with continuous air flow adjustment. In the final part is concept evaluation and recommendation for air flow measurement in the real application on the model turbine JetCAT.
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Model identification and measurement of parameters of model turbine JetCAT
Tlustoš, Jiří ; Dolák, Petr (referee) ; Klusáček, Stanislav (advisor)
The bachelor's thesis deals with measurement of microturbine engine parameters. The first part of the thesis covers theory of microturbine parameters measurement, data analysis, interpretation and utilization for model identification, engine control, individual functional block charts and characteristics generation. All possible methods for engine parameters measurement, sensors selection and installation, data acquisition are analyzed here. The focus is on the engine air flow measurement. Selected method of the air flow measurement is described here in detail and verified for the specific case. The Calibration was carried out by means of measuring route with continuous air flow adjustment. In the final part is concept evaluation and recommendation for air flow measurement in the real application on the model turbine JetCAT.
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Normal and uniform noise - violation of the assumption on noise distribution in model identification
Jirsa, Ladislav ; Pavelková, Lenka
Mathematical modelling under uncertainty together with the field of applied statistics represent tools useful in many practical domains. Widely accepted assumption of normal (Gaussian) noise has created the basis for theoretical and algorithmic solutions of respective tasks. However, many continuous variables are strictly bounded and their uncertainty may have origin in various physical processes which causes a non-normal distribution of their noise. Furthermore, adaptation of algorithms based on normal model for identification of models with bounded noise can distort the estimates due to inconsistent handling of uncertainty. This report describes a study to compare results of estimation algorithms based on assumption of normal and uniform noise. Data sequences processed by the algorithms have normal noise bounded by a low limit with respect to standard deviation. We illustrate disparity between noise assumption and a true noise distribution and its influence on the quality of the estimates. It is a part of an effort to develop theory and fast algorithms for estimation with bounded noise, applicable in practice.
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