A Van der Pol-Mathieu equation for the dynamics of dust grain charge in dusty plasmas

The chaotic profile of dust grain dynamics associated with dust-acoustic oscillations in a dusty plasma is considered. The collective behaviour of the dust plasma component is described via a multi-fluid model, comprising Boltzmann distributed electrons and ions, as well as an equation of continuity possessing a source term for the dust grains, the dust momentum and Poisson's equations. A Van der Pol–Mathieu-type nonlinear ordinary differential equation for the dust grain density dynamics is derived. The dynamical system is cast into an autonomous form by employing an averaging method. Critical stability boundaries for a particular trivial solution of the governing equation with varying parameters are specified. The equation is analysed to determine the resonance region, and finally numerically solved by using a fourth-order Runge–Kutta method. The presence of chaotic limit cycles is pointed out.

Difference equation sensor characterization algorithms for two-thermocouple probes

The characterization of thermocouple sensors for temperature measurement in variable flow environments is a challenging problem. In this paper, novel difference equation-based algorithms are presented that allow in situ characterization of temperature measurement probes consisting of two-thermocouple sensors with differing time constants. Linear and non-linear least squares formulations of the characterization problem are introduced and compared in terms of their computational complexity, robustness to noise and statistical properties. With the aid of this analysis, least squares optimization procedures that yield unbiased estimates are identified. The main contribution of the paper is the development of a linear two-parameter generalized total least squares formulation of the sensor characterization problem. Monte-Carlo simulation results are used to support the analysis.

P(rho)T Measurements of Imidazolium-Based Ionic Liquids

Experimental density measurements are reported, and the derived thermodynamic properties, such as the isothermal compressibility, the isobaric expansivity, and the thermal pressure coefficient are presented as Supporting Information for several imidazolium-based ionic liquids (ILs), namely, 1-ethyl-3-methyl-imidazolium bis(trifluoromethylsulfonyl)imide [C2mim][NTf2], 1-heptyl-3-methyl-imidazolium bis(trifluoromethylsulfonyl)imide [C7mim][NTf2], 1-octyl-3-methyl-imidazolium bis(trifluoromethylsulfonyl)imide [C8mim][NTf2], 1-ethyl-3-methyl-imidazolium tetrafluoroborate [C2mim][BF4], and 1-butyl-3-methyl-imidazolium tricyanomethane [C4mim][C(CN)3] in the pressure (0.10 <p/MPa <30.00) and temperature (293.15 <T/K <393.15) domains. These ILs were chosen to provide an understanding of the influence of the cation alkyl chain length and the anion influence on the properties under study. Experimental densities are correlated with the Tait equation with an average absolute deviation (AAD) less than 0.04 %. Experimental densities are in good agreement with the densities obtained by some recent predictive methods proposed in the literature.

Densities and Derived Thermodynamic Properties of Imidazolium-, Pyridinium-, Pyrrolidinium-, and Piperidinium-Based Ionic Liquids

In the present work, experimental density measurements are reported along with the derived thermodynamic properties, such as the isothermal compressibility (?T), the isobaric expansivity (ap), and the thermal pressure coefficient (?v) for imidazolium-, pyridinium-, pyrrolidinium-, and piperidinium-based ionic liquids (ILs), namely, 1-ethyl-3-methylimidazolium trifluoromethanesulfonate [C2mim][CF3SO3], 3-methyl-1-propylpyridinium bis(trifluoromethylsulfonyl)imide [C3mpy][NTf2], 1-methyl-1-propylpyrrolidinium bis(trifluoromethylsulfonyl)imide [C3mpyr][NTf2], 1-butyl-1-methylpyrrolidinium bis(trifluoromethylsulfonyl)imide [C4mpyr][NTf2], and 1-methyl-1-propylpiperidinium bis(trifluoromethylsulfonyl)imide [C3mpip][NTf2] in the pressure (0.10 <P/MPa <35.00) and temperature (293.15 <T/K <393.15) domains. These ILs were chosen to provide an understanding of the influence of the cation and anion on the properties under study. Experimental densities are correlated with the Tait equation with an average absolute relative deviation (AARD) better than 0.02 %. It is shown that experimental densities are in good agreement with the densities obtained by the predictive method previously proposed by us.

A hierarchical physical model-based approach to predictive control of a thermal power plant for efficient plant-wide disturbance rejection

Extending the work presented in Prasad et al. (IEEE Proceedings on Control Theory and Applications, 147, 523-37, 2000), this paper reports a hierarchical nonlinear physical model-based control strategy to account for the problems arising due to complex dynamics of drum level and governor valve, and demonstrates its effectiveness in plant-wide disturbance handling. The strategy incorporates a two-level control structure consisting of lower-level conventional PI regulators and a higher-level nonlinear physical model predictive controller (NPMPC) for mainly set-point manoeuvring. The lower-level PI loops help stabilise the unstable drum-boiler dynamics and allow faster governor valve action for power and grid-frequency regulation. The higher-level NPMPC provides an optimal load demand (or set-point) transition by effective handling of plant-wide interactions and system disturbances. The strategy has been tested in a simulation of a 200-MW oil-fired power plant at Ballylumford in Northern Ireland. A novel approach is devized to test the disturbance rejection capability in severe operating conditions. Low frequency disturbances were created by making random changes in radiation heat flow on the boiler-side, while condenser vacuum was fluctuating in a random fashion on the turbine side. In order to simulate high-frequency disturbances, pulse-type load disturbances were made to strike at instants which are not an integral multiple of the NPMPC sampling period. Impressive results have been obtained during both types of system disturbances and extremely high rates of load changes, right across the operating range, These results compared favourably with those from a conventional state-space generalized predictive control (GPC) method designed under similar conditions.

Plant-wide predictive control for a thermal power plant based on a physical plant model

A constrained non-linear, physical model-based, predictive control (NPMPC) strategy is developed for improved plant-wide control of a thermal power plant. The strategy makes use of successive linearisation and recursive state estimation using extended Kalman filtering to obtain a linear state-space model. The linear model and a quadratic programming routine are used to design a constrained long-range predictive controller One special feature is the careful selection of a specific set of plant model parameters for online estimation, to account for time-varying system characteristics resulting from major system disturbances and ageing. These parameters act as nonstationary stochastic states and help to provide sufficient degrees-of-freedom to obtain unbiased estimates of controlled outputs. A 14th order non-linear plant model, simulating the dominant characteristics of a 200 MW oil-fired pou er plant has been used to test the NPMPC algorithm. The control strategy gives impressive simulation results, during large system disturbances and extremely high rate of load changes, right across the operating range. These results compare favourably to those obtained with the state-space GPC method designed under similar conditions.