Modelling and simulation of self-regulating pneumatic valves

Pollok, Alexander; Casella, Francesco

doi:10.1080/13873954.2017.1298623

Mathematical and Computer Modelling of Dynamical Systems

2017

DOI: 10.1080/13873954.2017.1298623

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Modelling and simulation of self-regulating pneumatic valves

Alexander Pollok

Francesco Casella

Abstract: In conventional aircraft energy systems, self-regulating pneumatic valves (SRPVs) are used to control the pressure and mass flow of the bleed air. The dynamic behaviour of these valves is complex and dependent on several physical phenomena. In some cases, limit cycles can occur, deteriorating performance. This article presents a complex multi-physical model of SRPVs implemented in Modelica. First, the working principle is explained, and common challenges in control-system design-problems related to these valve… Show more

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Cited by 2 publications

References 11 publications

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Comparison of Control Strategies for Aircraft Bleed-Air Systems

Pollok

Casella

2017

IFAC-PapersOnLine

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Bleed-air systems in passenger aircraft are often prone to limit cycle oscillations. Tuning of such systems makes additional flight-tests necessary, generating large expenses. In this paper, several control approaches to improve the performance of bleed-air systems are compared. These approaches combine a base-controller with stiction-compensation techniques. The different approaches are implemented, optimized and evaluated using a high-fidelity bleedair system model, described in the equation-based modelling language Modelica. Results indicate that interaction between different valves should be reduced as much as possible. If a suitable base control approach is chosen, the influence of a superimposed stiction compensation technique seems to be small.

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Comparison of Control Strategies for Aircraft Bleed-Air Systems

Pollok

Casella

2017

IFAC-PapersOnLine

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Modeling Performance of Butterfly Valves Using Machine Learning Methods

Ekster¹,

Alchakov²,

Meleshin³

et al. 2021

Sustainability

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Control of airflow of activated sludge systems has significant challenges due to the non-linearity of the control element (butterfly valve). To overcome this challenge, some valve manufacturers developed valves with linear characteristics. However, these valves are 10–100 times more expensive than butterfly valves. By developing models for butterfly valves installed characteristics and utilizing these models for real-time airflow control, the authors of this paper aimed to achieve the same accuracy of control using butterfly valves as achieved using valves with linear characteristics. Several approaches were tested to model the installed valve’s characteristics, such as a formal mathematical model utilizing Simscape/Matlab software, a semi-empirical model, and several machine learning methods (MLM), including regression,support vector machine, Gaussian process, decision tree, and deep learning. Several versions of the airflow-valve position models were developed using each machine learning method listed above. The one with the smallest forecast error was selected for field testing at the 55.5×103 m3/day 12 MGD City of Chico activated sludge system. Field testing of the formal mathematical model, semi-empirical model, and the regularized gradient boosting machine model (the best among MLMs) showed that the regularized gradient boosting machine model (RGBMM) provided the best accuracy. The use of the RGBMMs in airflow control loops since 2019 at the City of Chico wastewater treatment plant showed that these models are robust and accurate (2.9% median error).

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Modelling and simulation of self-regulating pneumatic valves

Cited by 2 publications

References 11 publications

Comparison of Control Strategies for Aircraft Bleed-Air Systems

Comparison of Control Strategies for Aircraft Bleed-Air Systems

Modeling Performance of Butterfly Valves Using Machine Learning Methods

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