Magnonic band structure of domain wall magnonic crystals

Magnonic crystals are prototype magnetic metamaterials designed for the control of spin wave propagation. Conventional magnonic crystals are composed of single domain elements. If magnetization textures, such as domain walls, vortices and skyrmions, are included in the building blocks of magnonic crystals, additional degrees of freedom over the control of the magnonic band structure can be achieved. We theoretically investigate the influence of domain walls on the spin wave propagation and the corresponding magnonic band structure. It is found that the rotation of magnetization inside a domain wall introduces a geometric vector potential for the spin wave excitation. The corresponding Berry phase has quantized value $4 n_w \pi$, where $n_w$ is the winding number of the domain wall. Due to the topological vector potential, the magnonic band structure of magnonic crystals with domain walls as comprising elements differs significantly from an identical magnonic crystal composed of only magnetic domains. This difference can be utilized to realize dynamic reconfiguration of magnonic band structure by a sole nucleation or annihilation of domain walls in magnonic crystals.Comment: 21 pages, 9 figure

Novel Formulation and Application of Model Predictive Control.

Model predictive control (MPC) has been extensively studied in academia and widely accepted in industry. This research has focused on the novel formulation of model predictive controllers for systems that can be decomposed according to their nonlinearity properties and several novel MPC applications including bioreactors modeled by population balance equations (PBE), gas pipeline networks, and cryogenic distillation columns. Two applications from air separation industries are studied. A representative gas pipeline network is modeled based on first principles. The full-order model is ill-conditioned, and reduced-order models are constructed using time-scale decomposition arguments. A linear model predictive control (LMPC) strategy is then developed based on the reduced-order model. The second application is a cryogenic distillation column. A low-order dynamic model based on nonlinear wave theory is developed by tracking the movement of the wave front. The low-order model is compared to a first-principles model developed with the commercial simulator HYSYS.Plant. On-line model adaptation is proposed to overcome the most restrictive modeling assumption. Extensions for multiple column modeling and nonlinear model predictive control (NMPC) also are discussed. The third application is a continuous yeast bioreactor. The autonomous oscillations phenomenon is modeled by coupling PBE model of the cell mass distribution to the rate limiting substrate mass balance. A controller design model is obtained by linearizing and temporally discretizing the ODES derived from spatial discretization of the PBE model. The MPC controller regulate the discretized cell number distribution by manipulating the dilution rate and the feed substrate concentration. A novel plant-wide control strategy is developed based on integration of LMPC and NMPC. It is motivated by the fact that most plants that can be decomposed into approximately linear subsystems and highly nonlinear subsystems. LMPCs and NMPCs are applied to the respective subsystems. A sequential solution algorithm is developed to minimize the amount of unknown information in the MPC design. Three coordination approaches are developed to reduce the amount of information unavailable due to the sequential MPC solution of the coupled subsystems and applied to a reaction/separation process. Furthermore, a multi-rate approach is developed to exploit time-scale differences in the subsystems