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Noise diagnostics and analysis of steady combustion based on artificial intelligence
Leading article: 2021. November (MIAU No. 279.)
(Previous article: MIAU No. 278.)
Keywords: physics, model, similarity
Abstract: Prior activities: Before this study two other studies have been published (one
study for the Scientific Students' Associations Conference 2020, other for
Bachelor thesis 2021). The results of the next steps prescribed through the
previous research activities will be presented here and now.
Challenges: Regulations for combustion chambers are continuously stringent,
especially for pollutant emissions. The emission can be influenced through
passive elements like advanced nozzle geometry and/or diverging nozzle/quarl.
The effect of these passive elements can be evaluated from pollutant emission
analysis data. Besides geometry, the flow field also largely influences emissions.
The corresponding features include, e.g., flame shape, pressure, and temperature
fields. Control of these parameters is usually realized by active systems.
However, the reaction time of pollutant emission sensors is in the range of 60
seconds, hence, an alternative solution is necessary for online control.
Tasks: Online combustion control needs real-time input about the features of
combustion. Pollutant emission can however be derived from the acoustical signs
of the combustion. Both the aggregated index values about the ideality of the
emitted gases are calculated through artificial intelligence (AI), ensuring the
expected real-time characteristics. The AI is necessary to ensure a high-levelled
quality management during the automated derivation processes of production
function like Y=f(X1;...;Xn).
Results: Based on a relatively fast Fourier transformation of the acoustic signs,
the most characteristic amplitudes and frequencies make possible a quasi
arbitrary fitting of models concerning the physical attributes like pressure,
temperature, volumetric flow. The relevance of the acoustic signals will be visible
in case of a noise-based control process without any other sensor data.
The model correlations concerning the emitted gases started from a high level
of ~0.90, and these correlation values could be increased through model
hybridisation close to the maximum. It means, 3 (NO, CO2, O2) from 4 gases
(+ CO) could be modelled with a fitting near to the measurement accuracies.
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