Coupled operational optimization of smart valve system subject to different approach angles of a pipe contraction

In this paper, we focus on interconnected trajectory optimization of two sets of solenoid actuated butterfly valves dynamically coupled in series. The system undergoes different approach angles of a pipe contraction as a typical profile of the so-called “Smart Valves” network containing tens of actuated valves. A high fidelity interconnected mathematical modeling process is derived to reveal the expected complexity of such a multiphysics system dealing with electromagnetics, fluid mechanics, and nonlinear dynamic effects. A coupled operational optimization scheme is formulated in order to seek the most efficient trajectories of the interconnected valves minimizing the energy consumed enforcing stability and physical constraints. We examine various global optimization methods including Particle Swarm, Simulated Annealing, Genetic, and Gradient based algorithms to avoid being trapped in several possible local minima. The effect of the approach angles of the pipeline contraction on the amount of energy saved is discussed in detail. The results indicate that a substantial amount of energy can be saved by an intelligent operation that uses flow torques to augment the closing efforts.

[1]  C. Bennett,et al.  Momentum, Heat, and Mass Transfer , 1962 .

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[4]  V. Cerný Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , 1985 .

[5]  Kwang Y. Lee,et al.  Optimal operation of large-scale power systems , 1988 .

[6]  D. Grierson,et al.  Optimal sizing, geometrical and topological design using a genetic algorithm , 1993 .

[7]  A. Mezyk,et al.  Minimization of transient forces in an electro-mechanical system , 1994 .

[8]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[9]  T. Kajima Dynamic model of the plunger type solenoids at deenergizing state , 1995 .

[10]  V. I. Klimovich On the optimal design of the form of hydroturbine impeller blades , 1997 .

[11]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[12]  Frédéric Messine,et al.  Optimal design of electromechanical actuators: a new method based on global optimization , 1998 .

[13]  Carl Tim Kelley,et al.  Iterative methods for optimization , 1999, Frontiers in applied mathematics.

[14]  Charles L. Karr,et al.  Genetic Algorithm Frequency Domain Optimization of an Anti-Resonant Electromechanical Controller , 2003, GECCO.

[15]  Kurt Maute,et al.  Topology optimization of electrostatically actuated microsystems , 2005 .

[16]  Myung Kyoon Chung,et al.  Study on Hydrodynamic Torque of a Butterfly Valve , 2006 .

[17]  Guojun Li,et al.  Research on Optimal Operation in Large-Scale Steam Piping System , 2007 .

[18]  Zachary Leutwyler,et al.  A CFD Study of the Flow Field, Resultant Force, and Aerodynamic Torque on a Symmetric Disk Butterfly Valve in a Compressible Fluid , 2008 .

[19]  Gürsel Sefkat The design optimization of the electromechanical actuator , 2009 .

[20]  Izhak Bucher,et al.  Optimal electrode shaping for precise modal electromechanical filtering , 2009 .

[21]  Lech Nowak Optimization of the electromechanical systems on the basis of coupled field‐circuit approach , 2010 .

[22]  Peiman Naseradinmousavi,et al.  A Chaotic Blue Sky Catastrophe of Butterfly Valves Driven by Solenoid Actuators , 2011 .

[23]  Peiman Naseradinmousavi,et al.  Nonlinear mathematical modeling of butterfly valves driven by solenoid actuators , 2011 .

[24]  Peiman Naseradinmousavi Nonlinear Modeling, Dynamic Analysis, and Optimal Design and Operation of Electromechanical Valve Systems , 2012 .

[25]  Peiman Naseradinmousavi,et al.  Transient chaos and crisis phenomena in butterfly valves driven by solenoid actuators , 2012 .

[26]  Peiman N. Mousavi,et al.  Optimal Design of Solenoid Actuators Driving Butterfly Valves , 2013 .

[27]  Dimitri N. Mavris,et al.  Electric Control Surface Actuator Design Optimization and Allocation for the More Electric Aircraft , 2013 .

[28]  Shayma'a A. Mahdi Optimization of PID Controller Parameters based on Genetic Algorithm for non-linear Electromechanical Actuator , 2014 .

[29]  Peiman Naseradinmousavi A Novel Nonlinear Modeling and Dynamic Analysis of Solenoid Actuated Butterfly Valves Coupled in Series , 2015 .

[30]  Peiman Naseradinmousavi,et al.  Design Optimization of Solenoid Actuated Butterfly Valves Dynamically Coupled in Series , 2015, HRI 2015.

[31]  Miroslav Krstic,et al.  Design Optimization of Dynamically Coupled Actuated Butterfly Valves Subject to a Sudden Contraction , 2016 .