Optimal Control for Firing Synchronization in an Annular Pulsed Detonation Combustor Mockup by Mixed-Integer Programming

Wolff, Sascha; King, Rudibert

doi:10.2514/6.2019-1742

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…In the future, it would be of some interest to adapt the presented control approach to a high-speed stator cascade in the compressible flow regime. Furthermore, to exploit new findings regarding optimal firing patterns of the combustion tubes [45], a more realistic disturbance generator could be used.…”

Section: Discussionmentioning

confidence: 99%

Binary Repetitive Model Predictive Active Flow Control Applied to an Annular Compressor Stator Cascade With Periodic Disturbances

Fietzke

Mihalyovics

King

et al. 2021

Journal of Engineering for Gas Turbines and Power

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Novel pressure gain combustion concepts invoke periodic flow disturbances in a gas turbine's last compressor stator row. This contribution presents studies of mitigation efforts on the effects of periodic disturbances on an annular compressor stator rig. The passages were equipped with pneumatic active flow control influencing the stator blade's suction side, and a rotating throttling disc downstream of the passages inducing periodic disturbances. For steady blowing, it is shown that with increasing actuation amplitudes $c_\mu$, a hub corner vortex's extension deteriorating the suction side flow can be reduced, resulting in an increased static pressure rise coefficient~$C_p$ of a passage. The effects of the induced periodic disturbances could not be addressed, by using steady blowing actuation. Considering a corrected total pressure loss coefficient $\zeta^*$, which includes the actuation effort, the stator row's efficiency decreases with higher $c_\mu$. Therefore, a closed-loop approach is presented to address the effects of the disturbances more specifically, thus lowering the actuation effort. For this, a Repetitive Model Predictive Control (RMPC) was applied, taking advantage of the disturbance's periodic nature. The presented RMPC formulation is restricted to a binary control domain to account for the used solenoid valves' switching character. An efficient implementation of the optimization within the RMPC is presented, which ensures real-time capability. As a result, $C_p$ increases in a similar magnitude but with a lower actuation mass flow of up to 66\,\%, resulting in a much lower~$\zeta^*$ for similar values of $c_\mu$.

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Section: Discussionmentioning

confidence: 99%

Binary Repetitive Model Predictive Active Flow Control Applied to an Annular Compressor Stator Cascade With Periodic Disturbances

Fietzke

Mihalyovics

King

et al. 2021

Journal of Engineering for Gas Turbines and Power

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“…Here, it should be pointed out that we focus on applications where there is not arbitrary time available for resetting a process. Rather, we look at situations such as in a pulsed detonation engine, where detonations have to follow one after the other without much time for recomputing a new firing pattern (Rähse et al, 2018;Schäpel et al, 2016;Wolff & King, 2019).…”

Section: Parallel Real-valued Ilcmentioning

confidence: 99%

Norm-optimal iterative learning control in an integer-valued control domain

Arnold

King

2021

International Journal of Control

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Formulating a control design for integer-valued inputs might be advantageous over, for example, rounding strategies based on a real-valued solution. However, the speed of convergence, computational cost, and stability are challenges to be addressed. This is especially true for optimisation-based approaches such as the norm-optimal iterative learning control (ILC). In this contribution, the convergence properties of integer-valued ILC are derived and compared against the known real-valued ILC. After an evaluation of two recently presented solutions, a new optimal set controller synthesis method for integer-valued normoptimal ILC is presented. The approach guarantees monotonic convergence and ensures that each iteration of the ILC will require a deterministic computational effort to find the suboptimal solution, with an upper bound for the objective function of the optimisation. This deterministic computational effort makes the setup applicable for real-time implementation. Finally, an example using the introduced new approach is given to highlight its advantages.

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Reduction of Pressure Fluctuations in an Annular Pulsed Detonation Combustor Mockup by Iterative Learning Control Using Eigenvector-Based Binary Solution Sets and Iterative Model Identification

Topalovic

Arnold

King

2021

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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Optimal Control for Firing Synchronization in an Annular Pulsed Detonation Combustor Mockup by Mixed-Integer Programming

Cited by 6 publications

References 7 publications

Binary Repetitive Model Predictive Active Flow Control Applied to an Annular Compressor Stator Cascade With Periodic Disturbances

Binary Repetitive Model Predictive Active Flow Control Applied to an Annular Compressor Stator Cascade With Periodic Disturbances

Norm-optimal iterative learning control in an integer-valued control domain

Reduction of Pressure Fluctuations in an Annular Pulsed Detonation Combustor Mockup by Iterative Learning Control Using Eigenvector-Based Binary Solution Sets and Iterative Model Identification

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