Active Flow and Combustion Control 2014 by Rudibert King

By Rudibert King

The ebook reviews at the newest theoretical and experimental advances within the box of lively circulation and combustion keep watch over. It covers new advancements in actuator expertise and sensing, in strong and optimum open- and closed-loop keep watch over, in addition to in version aid for keep watch over. It collects contributions provided throughout the 3rd variation of the lively movement and Combustion regulate convention, held in September 10-12, 2014 on the Technische Universität Berlin (Germany). This convention, in addition to the learn provided within the e-book, were supported by means of the collaborative examine heart SFB 1029 -Substantial potency bring up in gasoline generators via direct use of coupled unsteady combustion and circulation dynamics, funded through the DFG (German learn Foundation).

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Comparison of Iterative Learning and Repetitive Control 43 H∞ – Optimal Repetitive Control. The H∞ – synthesis framework [26] has been successfully used for the synthesis of (robust) repetitive controllers in the past [6], [2], [21], [30]. e. all poles of Q(z) and L(z) lie inside the unit circle around the origin of the complex plane. e. Ei (z) = R(z) − Yi (z). Combining (19) and (20) leads to the error transition equation E∞ (z)−Ei (z) = Q(z) − G(z) z L(z) ΔEi (z)∈C M(z)∈C E∞ (z)−Ei−1 (z) , (21) ΔEi−1 (z)∈C where E∞ (z) = lim Ei (z) = lim Ei−1 (z) is the converged control error with i→∞ i→∞ E∞ (z) = 1 − M (z) −1 1 − Q(z) R(z) − D(z) .

ASME Paper 2000GT-268 (2000) 6. : The Application of Ultra High Lift Blading in the BR 715 LP Turbine. ASME J. Turbomach. 124, 45–51 (2002) 7. : On the Impact of Blade Count Reduction on Aerodynamic Performance and Loss Generation in a Three-Stage LP Turbine. ASME Paper 2001-GT-0197 (2001) 8. : Aerodynamic Design of Low-Pressure Turbines, AGARD Lecture Series No. 167 (1989) 9. : A Comparison of Three Low Pressure Turbine Designs. ASME J. Turbomach. 135, 051026 (2013) 22 R. Niehuis and M. Mack 10.

9. 4 jωTs )= max Δ∈H∞ , |Δ|≤1 Q(e jωTs ) − Gp (e jωTs )e jωTs as well as L(ejωTs ) . Iterative Learning Controller The ILC synthesis is based on the state space representation of the nominal plant model. The weighting matrices in (12), (13), and (14) are chosen as We = 102 ·I, Wu = 100 · I, and WΔ = 5 · 101 · I, respectively. 5 Closed-Loop Experiments This section presents results obtained from closed-loop experiments. The experiments were designed to demonstrate and to compare the capability of an optimal ILC against a robust H∞ – optimal RC for the purpose of rejecting periodic disturbances.

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