Domain Bundle Boundaries in Single Crystal BaTiO3 Lamellae: Searching for Naturally Forming Dipole Flux-Closure/Quadrupole Chains

Naturally occurring boundaries between bundles of 90° stripe domains, which form in BaTiO3 lamellae on cooling through the Curie Temperature, have been characterized using both piezoresponse force microscopy (PFM) and scanning transmission electron microscopy (STEM). Detailed interpretation of the dipole configurations present at these boundaries (using data taken from PFM) shows that in the vast majority of cases they are composed of simple zigzag 180° domain walls. Topological information from STEM shows that occasionally domain bundle boundaries can support chains of dipole flux closure and quadrupole nanostructures, but these kinds of boundaries are comparatively rare; when such chains do exist, it is notable that singularities at the cores of the dipole
structures are avoided. The symmetry of the boundary shows that diads and centers of inversion exist at positions where core singularities should have been expected.

Exotic Domain States in Ferroelectrics: Searching for Vortices and Skyrmions

Very recent experimental and theoretical work has sought to find out whether or not complex arrangements of dipoles (such as flux-closure, vortex or skyrmion patterns) exist in ferroelectrics. While there is undoubtedly still considerable work to be done, enough insight has now been gained to warrant a brief discussion of progress to date. This review article attempts to undertake such a discussion.

Parameter studies of fibre laser micro-welding of AISI 316L using Taguchi method

This paper discusses the application of the Taguchi experimental design approach in optimizing the key process parameters for micro-welding of thin AISI 316L foil using the 100W CW fibre laser. A L16 Taguchi experiment was conducted to systematically understand how the power, scanning velocity, focus position, gas flow rate and type of shielding gas affect the bead dimensions. The welds produced in the L16 Taguchi experiment was mainly of austenite cellular-dendrite structure with an average grain size of 5µm. An exact penetration weld with the largest penetration to fusion width ratio was obtained. Among those process parameters, the interaction between power and scanning velocity presented the strongest effect to the penetration to fusion width ratio and the power was found to be the predominantly important factor that drives the interaction with other factors to appreciably affect the bead dimensions.

Parameter study on laser welding of nitinol wire by Taguchi method

A L27 Taguchi experiment was done to investigate the effect of laser power, welding time, laser mode (CW and two pulsed modes), focus position, and their possible interactions on the weld-bead aspect ratio of laser-welded NiTi wires by using a 100W fibre laser. The optimized parameter setting to produce the full penetrated weldment with minimum welding defects is successfully determined in the Taguchi experiment. The laser mode is found to be the most important parameter that directly controls the weld-bead aspect ratio. The focus position is the secondly important parameter for the laser welding of NiTi wires. Strong interaction between the power and focus position is found in the Taguchi experiment. The optimized weldment produced by the Taguchi experiment is mainly of columnar dendritic structure in the weld zone (WZ) with the size of 1-3µm, while the HAZ exhibits equiaxed grain structure with the size of 5-10µm. The Vickers micro-hardness test indicted that the WZ and HAZ in the weldment are softened to certain extends after fibre laser welding.

Parameter estimation and equation formulation in Business Dynamics

System Dynamics enables modelling and simulation of highly non-linear feedback systems to predict future system behaviour. Parameter estimation and equation formulation are techniques in System Dynamics, used to retrieve the values of parameters or the equations for ?ows and/or variables. These techniques are crucial for the annotations and thereafter the simulation. This paper critically examines existing and well established approaches in parameter estimation and equation formulation along with their limitations, identifying performance gaps as well as providing directions for potential future research.

Parameter Optimization on Laser Welding of NiTi Wires by Taguchi Method

In this research, a preliminary study was done to find out the initial parameter window to obtain the full-penetrated NiTi weldment. A L27 Taguchi experiment was then carried out to statistically study the effects of the welding parameters and their possible interactions on the weld bead aspect ratio (or penetration over fuse-zone width ratio), and to determine the optimized parameter settings to produce the full-penetrated weldment with desirable aspect ratio. From the statistical results in the Taguchi experiment, the laser mode was found to be the most important factor that substantially affects the aspect ratio. Strong interaction between the power and focus position was found in the Taguchi experiment. The optimized weldment was mainly of columnar dendritic structure in the weld zone (WZ), while the HAZ exhibited equiaxed grain structure. The XRD and DSC results showed that the WZ remained the B2 austenite structure without any precipitates, but with a significant decrease of phase transformation temperatures. The results in the micro-hardness and tensile tests indicated that the mechanical properties of NiTi were decreased to a certain extent after fibre laser welding.

Using CAD parameter sensitivities for stack-up tolerance allocation

Tolerance allocation is an important step in the design process. It is necessary to produce high quality components cost-effectively. However, the process of allocating tolerances can be time consuming and difficult, especially for complex models. This work demonstrates a novel CAD based approach, where the sensitivities of product dimensions to changes in the values of the feature parameters in the CAD model are computed. These are used to automatically establish the assembly response function for the product. This information has been used to automatically allocate tolerances to individual part dimensions to achieve specified tolerances on the assembly dimensions, even for tolerance allocation in more than one direction simultaneously. It is also shown how pre-existing constraints on some of the part dimensions can be represented and how situations can be identified where the required tolerance allocation is not achievable. A methodology is also presented that uses the same information to model a component with different amounts of dimensional variation to simulate the effects of tolerance stack-up. © 2014 Springer-Verlag France.

Characterisation of Passive Intermodulation in Passive RF Devices with X-parameter

A novel approach to the modelling of passive intermodulation (PIM) generation in passive components with distributed weak nonlinearities is outlined. Based upon the formalism of X-parameters, it provides a unified framework for co-design of antenna beamforming networks, filters, combiners, phase shifters and other passive and active devices containing nonlinearities at RF front-end. The effects of discontinuities and complex circuit layouts can be efficiently evaluated with the aid of the equivalent networks of the canonical nonlinear elements. The main concepts are illustrated by examples of numerical simulations of PIM generation in the transmission lines and comparison with the measurement results.

Improving parameter learning of Bayesian nets from incomplete data

This paper addresses the estimation of parameters of a Bayesian network from incomplete data. The task is usually tackled by running the Expectation-Maximization (EM) algorithm several times in order to obtain a high log-likelihood estimate. We argue that choosing the maximum log-likelihood estimate (as well as the maximum penalized log-likelihood and the maximum a posteriori estimate) has severe drawbacks, being affected both by overfitting and model uncertainty. Two ideas are discussed to overcome these issues: a maximum entropy approach and a Bayesian model averaging approach. Both ideas can be easily applied on top of EM, while the entropy idea can be also implemented in a more sophisticated way, through a dedicated non-linear solver. A vast set of experiments shows that these ideas produce significantly better estimates and inferences than the traditional and widely used maximum (penalized) log-likelihood and maximum a posteriori estimates. In particular, if EM is adopted as optimization engine, the model averaging approach is the best performing one; its performance is matched by the entropy approach when implemented using the non-linear solver. The results suggest that the applicability of these ideas is immediate (they are easy to implement and to integrate in currently available inference engines) and that they constitute a better way to learn Bayesian network parameters.

Cavity-aided quantum parameter estimation in a bosonic double-well Josephson junction

We describe an apparatus designed to make non-demolition measurements on a Bose-Einstein condensate (BEC) trapped in a double-well optical cavity. This apparatus contains, as well as the bosonic gas and the trap, an optical cavity. We show how the interaction between the light and the atoms, under appropriate conditions, can allow for a weakly disturbing yet highly precise measurement of the population imbalance between the two wells and its variance. We show that the setting is well suited for the implementation of quantum-limited estimation strategies for the inference of the key parameters defining the evolution of the atomic system and based on measurements performed on the cavity field. This would enable {\it de facto} Hamiltonian diagnosis via a highly controllable quantum probe.

Energy Saving - Another Perspective for Parameter Optimization of P and PI Controllers

Modern control methods like optimal control and model predictive control (MPC) provide a framework for simultaneous regulation of the tracking performance and limiting the control energy, thus have been widely deployed in industrial applications. Yet, due to its simplicity and robustness, the conventional P (Proportional) and PI (Proportional–Integral) control are still the most common methods used in many engineering systems, such as electric power systems, automotive, and Heating, Ventilation and Air Conditioning (HVAC) for buildings, where energy efficiency and energy saving are the critical issues to be addressed. Yet, little has been done so far to explore the effect of its parameter tuning on both the system performance and control energy consumption, and how these two objectives are correlated within the P and PI control framework. In this paper, the P and PI controllers are designed with a simultaneous consideration of these two aspects. Two case studies are investigated in detail, including the control of Voltage Source Converters (VSCs) for transmitting offshore wind power to onshore AC grid through High Voltage DC links, and the control of HVAC systems. Results reveal that there exists a better trade-off between the tracking performance and the control energy through a proper choice of the P and PI controller parameters.

An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models

Clean and renewable energy generation and supply has drawn much attention worldwide in recent years, the proton exchange membrane (PEM) fuel cells and solar cells are among the most popular technologies. Accurately modeling the PEM fuel cells as well as solar cells is critical in their applications, and this involves the identification and optimization of model parameters. This is however challenging due to the highly nonlinear and complex nature of the models. In particular for PEM fuel cells, the model has to be optimized under different operation conditions, thus making the solution space extremely complex. In this paper, an improved and simplified teaching-learning based optimization algorithm (STLBO) is proposed to identify and optimize parameters for these two types of cell models. This is achieved by introducing an elite strategy to improve the quality of population and a local search is employed to further enhance the performance of the global best solution. To improve the diversity of the local search a chaotic map is also introduced. Compared with the basic TLBO, the structure of the proposed algorithm is much simplified and the searching ability is significantly enhanced. The performance of the proposed STLBO is firstly tested and verified on two low dimension decomposable problems and twelve large scale benchmark functions, then on the parameter identification of PEM fuel cell as well as solar cell models. Intensive experimental simulations show that the proposed STLBO exhibits excellent performance in terms of the accuracy and speed, in comparison with those reported in the literature.