Structural dependence of microwave dielectric properties of spinel structured Mg2(Ti1-xSnx)O4 solid solutions: Crystal structure refinement,Raman spectra study and complex chemical bond theory

Mg₂(Ti_1-xSn_x)O₄ (x?=?0–1) ceramics were prepared through conventional solid-state method. This paper focused on the dependence of microwave dielectric properties on crystal structural characteristics via crystal structure refinement, Raman spectra study and complex chemical bond theory. XRD spectrums delineated the phase information of a spinel structure, and structural characteristic of these compositions were achieved with the help of Rietveld refinements. Raman spectrums were used to depict the correlations between vibrational phonon modes and dielectric properties. The variation of permittivity is ascribed to the Mg₂(Ti_1-xSn_x)O₄ average bond covalency. The relationship among the B-site octahedral bond energy, tetrahedral bond energy and temperature coefficient are discussed by defining on the change rate of bond energy and the contribution rate of octahedral bond energy. The quality factor is affected by systematic total lattice energy, and the research of XPS patterns illustrated that oxygen vacancies can be effectively restrained in rich oxygen sintering process. Obviously, the microwave dielectric properties of Mg₂(Ti_1-xSn_x)O₄ compounds were obtained (${\epsilon }_r$= 12.18, $Q\times f$?=?170,130?GHz, ${\tau }_f$?=??53.1?ppm/°C, x?=?0.2).     

Numerical integration for the inverse dynamics of a large class of cranes

Multibody System Dynamics - A recently developed index reduction method is applied to comparatively complicated underactuated mechanical systems in the context of inverse dynamics. The inverse...     

An image segmentation method based on Mumford–Shah model with mask factor and neighborhood factor

Pattern Analysis and Applications - A novel image segmentation model is proposed to improve the stability of existing segmentation methods. In the proposed model, we introduce two factors into the...     

A Two-Dimensional Numerical Analysis for Thermal Performance of an Intermittently Operated Radiant Floor Heating System in a Transient External Climatic Condition

Abstract

Building thermal inertia and operation control strategies have impacted on the thermal performance of a radiant floor heating system. This study conducts a two-dimensional numerical analysis of an intermittently operated radiant floor heating system using the Re-Normalization Group model with Discrete Ordinates Radiation model. A detailed numerical simulation setups and various analyses are provided, including grid independency analysis, initial condition, time step sizes and external boundary conditions. Three different weekend day intermittent operation strategies are investigated. The results showed that Case 3 designed with pre-heating of 20?h has better performance compared to Case 1 designed with pre-heating of 8?h and Case 2 designed with pre-heating of 14?h. The average indoor air temperature differences of approximate 2.1, 1.6 and 1.2 K are observed for Case 1, Case 2 and Case 3, respectively, when comparing two-time slot at 8:00am on Friday morning and Monday morning. This significantly highlights the effect of thermal inertia and the potential of energy saving due to the utilization of intermittent operation. Therefore, the current study presents numerical simulation potential in evaluating the radiant floor heating effects on indoor thermal environment, taking into account building thermal inertia and transient external climatic conditions.     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.