Preparation and characterization of Pr0.6Sr0.4FeO3-δ-Ce0.9Pr0.1O2-δ nanofiber structured composite cathode for IT-SOFCs

In this work, Pr_0.6Sr_0.4FeO_3-δ -Ce_0.9Pr_0.1O_2-δ (PSFO-CPO) nanofibers were synthesized by a one-step electrospin technique for use in intermediate-temperature solid oxide fuel cell (IT-SOFC) applications. PSFO-CPO nanofibers were produced with a diameter of about 100nm and lengths exceeding tens of microns. The thermal expansion coefficient (TEC) matches with standard GDC electrolytes and the resulting conductivity also satisfies the needs of IT-SOFCs cathodes. EIS analysis of the nanofiber structured electrode gives a polarization resistance of 0.072Ωcm² at 800°C, smaller than that from the powdered cathode with the same composition. The excellent electrochemical performance can be attributed to the well-constructed microstructure of the nanofiber structured cathode, which promotes surface oxygen diffusion and charge transfer processes. All the results imply that the one-step electrospin method is a facile and practical way of improving the cathode properties and that PSFO-CPO is a promising cathode material for IT-SOFCs.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

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.

3D Analysis of Heat Transfer Intensification by Re-Entrance Flow Pin-Fins Microstructures with a Highly Thermal-Conductive Plate

Joule heat-induced hot-spot formation sets severe limits in the operation of continuous annular electrochromatography (CAEC), a new concept for preparative separation as an analog to analytical capillary electrochromatography (CEC). This may lead to eluent flow perturbance, even to boiling, which would massively weaken separation efficiency and may even hamper the stationary phase used for separation. For reasons of system integration and high-efficiency heat transfer, micro flow heat exchangers are considered with a separate coolant flow. A 3D numerical analysis of the heat transfer of water single-phase laminar flow in a square microchannel and different arrays of micro pin-fins was carried out using COMSOL Multiphysics. Several advanced materials with low electric conductivity and at the same time with high heat conductivity were put forward to be used in the CAEC system. As essential design point, it is proposed to constitute the micro heat exchanger from two different parts of the CAEC system, namely a microstructured pin-fins plate and a so-called conductive plate.

PIC simulation of a thermal anisotropy-driven Weibel instability in a circular rarefaction wave

The expansion of an initially unmagnetized planar rarefaction wave has recently been shown to trigger a thermal anisotropy-driven Weibel instability (TAWI), which can generate magnetic fields from noise levels. It is examined here whether the TAWI can also grow in a curved rarefaction wave. The expansion of an initially unmagnetized circular plasma cloud, which consists of protons and hot electrons, into a vacuum is modelled for this purpose with a two-dimensional particle-in-cell (PIC) simulation. It is shown that the momentum transfer from the electrons to the radially accelerating protons can indeed trigger a TAWI. Radial current channels form and the aperiodic growth of a magnetowave is observed, which has a magnetic field that is oriented orthogonal to the simulation plane. The induced electric field implies that the electron density gradient is no longer parallel to the electric field. Evidence is presented here that this electric field modification triggers a environments, which are needed to explain the electromagnetic emissions by astrophysical jets. It is outlined how this instability could be examined experimentally.second magnetic instability, which results in a rotational low-frequency magnetowave. The relevance of the TAWI is discussed for the growth of small-scale magnetic fields in astrophysical

Multiphysics Simulation of Thermal Plasma Produced by Lightning Strike on Aircraft Structures

A novel numerical technique is proposed to model thermal plasma of microseconds/milliseconds time-scale effect. Modelling thermal plasma due to lightning strike will allow the estimation of electric current density, plasma pressure, and heat flux at the surface of the aircraft structure. These input data can then be used for better estimation of the mechanical/thermal induced damage on the aircraft structures for better protection systems design. Thermal plasma generated during laser cutting, electric (laser) welding and other plasma processing techniques have been the focus of many researchers. Thermal plasma is a gaseous state that consists from a mixture of electrons, ions, and natural particles. Thermal plasma can be assumed to be in local thermodynamic equilibrium, which means the electrons and the heavy species have equal temperature. Different numerical techniques have been developed using a coupled Navier Stokes – Heat transfer – Electromagnetic equations based on the assumption that the thermal plasma is a single laminar gas flow. These previous efforts focused on generating thermal plasma of time-scale in the range of seconds. Lighting strike on aircraft structures generates thermal plasma of time-scale of milliseconds/microseconds, which makes the previous physics used not applicable. The difficulty comes from the Navier-Stokes equations as the fluid is simulated under shock load, this introducing significant changes in the density and temperature of the fluid.

In situ diagnostics and prognostics of solder fatigue in IGBT modules for electric vehicle drives

This paper proposes an in situ diagnostic and prognostic (D&P) technology to monitor the health condition of insulated gate bipolar transistors (IGBTs) used in EVs with a focus on the IGBTs' solder layer fatigue. IGBTs' thermal impedance and the junction temperature can be used as health indicators for through-life condition monitoring (CM) where the terminal characteristics are measured and the devices' internal temperature-sensitive parameters are employed as temperature sensors to estimate the junction temperature. An auxiliary power supply unit, which can be converted from the battery's 12-V dc supply, provides power to the in situ test circuits and CM data can be stored in the on-board data-logger for further offline analysis. The proposed method is experimentally validated on the developed test circuitry and also compared with finite-element thermoelectrical simulation. The test results from thermal cycling are also compared with acoustic microscope and thermal images. The developed circuitry is proved to be effective to detect solder fatigue while each IGBT in the converter can be examined sequentially during red-light stopping or services. The D&P circuitry can utilize existing on-board hardware and be embedded in the IGBT's gate drive unit.

Can the scaling behavior of electric conductivity be used to probe the self-organizational changes in solution with respect to the ionic liquid structure? The case of [C8MIM][NTf2]

Electrical conductivity of the supercooled ionic liquid [C8MIM][NTf2], determined as a function of temperature and pressure, highlights strong differences in its ionic transport behavior between low and high temperature regions. To date, the crossover effect which is very well known for low molecular van der Waals liquids has been rarely described for classical ionic liquids. This finding highlights that the thermal fluctuations could be dominant mechanisms driving the dramatic slowing down of ion motions near Tg. An alternative way to analyze separately low and high temperature dc-conductivity data using a density scaling approach was then proposed. Based on which a common value of the scaling exponent [gamma] = 2.4 was obtained, indicating that the applied density scaling is insensitive to the crossover effect. By comparing the scaling exponent [gamma] reported herein along with literature data for other ionic liquids, it appears that [gamma] decreases by increasing the alkyl chain length on the 1-alkyl-3-methylimidazolium-based ionic liquids. This observation may be related to changes in the interaction between ions in solution driven by an increase in the van der Waals type interaction by increasing the alkyl chain length on the cation. This effect may be related to changes in the ionic liquid nanostructural organization with the alkyl chain length on the cation as previously reported in the literature based on molecular dynamic simulations. In other words, the calculated scaling exponent [gamma] may be then used as a key parameter to probe the interaction and/or self-organizational changes in solution with respect to the ionic liquid structure.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.