Online Optimization Applied to a Shockless Explosion Combustor

Schäpel, Jan-Simon; Reichel, Thoralf G.; Klein, Rupert; Paschereit, Christian Oliver; King, Rudibert

doi:10.3390/pr4040048

Cited by 4 publications

(2 citation statements)

References 22 publications

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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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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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“…This special issue on real-time optimization (http://www.mdpi.com/journal/processes/special_ issues/real_time_optimization) includes both methodological and practical contributions [1][2][3][4][5][6][7][8][9][10][11][12][13]. All seven methodological contributions deal with explicit RTO schemes that repeat the optimization when new measurements become available.…”

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confidence: 99%

Special Issue “Real-Time Optimization” of Processes

Bonvin

2017

Processes

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Process optimization is the method of choice for improving the performance of industrial processes, while also enforcing the satisfaction of safety and quality constraints. Long considered an appealing tool but only applicable to academic problems, optimization has now become a viable technology. Still, one of the strengths of optimization-namely, its inherent mathematical rigor-can also be perceived as a weakness, since engineers might sometimes find it difficult to obtain an appropriate mathematical formulation to solve their practical problems. Furthermore, even when process models are available, the presence of plant-model mismatches and process disturbances makes the direct use of model-based optimal inputs hazardous. In the last 30 years, the field of real-time optimization (RTO) has emerged to help overcome the aforementioned modeling difficulties. RTO integrates process measurements into the optimization framework. This way, process optimization does not rely exclusively on a (possibly inaccurate) process model but also on process information stemming from measurements. Various RTO techniques are available in the literature and can be classified in two broad families depending on whether a process model is used (explicit optimization) or not (implicit optimization).This special issue on real-time optimization (http://www.mdpi.com/journal/processes/special_ issues/real_time_optimization) includes both methodological and practical contributions [1][2][3][4][5][6][7][8][9][10][11][12][13]. All seven methodological contributions deal with explicit RTO schemes that repeat the optimization when new measurements become available. The methods covered include modifier adaptation, economic MPC, and the two-step approach of parameter identification and numerical optimization. The six contributions that deal with applications cover various fields including refineries, well networks, combustion, and membrane filtration.This special issue shows that RTO is a very active area of research with excellent opportunities for applications. The guest editor would like to thank all authors for their timely collaboration on this project and excellent scientific contributions. References

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On the Influence of Fuel Stratification and Its Control on the Efficiency of the Shockless Explosion Combustion Cycle

Rähse

Stathopoulos

Schäpel

et al. 2018

Journal of Engineering for Gas Turbines and Power

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Constant volume combustion (CVC) cycles for gas turbines are considered a very promising alternative to the conventional Joule cycle and its variations. The reason is the considerably higher thermal efficiency of these cycles, at least for their ideal versions. Shockless explosion combustion (SEC) is a method to approximate CVC. It is a cyclic process that consists of four stages, namely wave propagation, fuel injection, homogeneous auto-ignition, and exhaust. A pressure wave in the combustion chamber is used to realize the filling and exhaust phases. During the fuel injection stage, the equivalence ratio is controlled in such a way that the ignition delay time of the mixture matches its residence time in the chamber before auto-ignition. This means that the fuel injected first must have the longest ignition delay time, and thus forms the leanest mixture with air. By the same token, fuel injected last must form the richest mixture with air (assuming that a rich mixture leads to a small ignition delay). The total injection time is equal to the time that the wave needs to reach the open combustor end and return as a pressure wave to the closed end. Up to date, fuel stratification has been neglected in thermodynamic simulations of the SEC cycle. The current work presents its effect on the thermal efficiency of the cycle and on the exhaust conditions (pressure, temperature, and Mach number) of shockless explosion combustion chambers. This is done by integrating a fuel injection control algorithm in an existing computational fluid dynamics code. The capability of this algorithm to homogenize the auto-ignition process by improving the injection process has been demonstrated in past experimental studies of the SEC. The numerical code used for the simulation of the combustion process is based on the time-resolved 1D-Euler equations with source terms obtained from a detailed chemistry model.

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Online Optimization Applied to a Shockless Explosion Combustor

Cited by 4 publications

References 22 publications

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

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

Special Issue “Real-Time Optimization” of Processes

On the Influence of Fuel Stratification and Its Control on the Efficiency of the Shockless Explosion Combustion Cycle

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