Thermal properties of hierarchical YSZ and LZO ceramic microspheres with multi-scaled voids investigated by using theoretical,experimental and simulation methods

Heat transfer within ceramic feedstock powders is still unclear, which impedes optimization of the thermal and mechanical properties of the thermal sprayed coatings. The microspheres (yttria-stabilized zirconia YSZ and lanthanum zirconate LZO) were prepared via the electro-spraying assisted phase inversion method (ESP). The thermal properties of the two ESP microspheres and a commercial hollow spherical powder (HOSP) were investigated by using theoretical, experimental, and simulation methods. Thermal conductivity of the single microsphere was estimated via a novel nest model that was derived from the Maxwell-Eucken 1 and the EMT model. Thermal conductivity of a single YSZ/LZO-ESP microsphere prepared at 1100–1200 °C was within 0.36–0.75 W/m K, which was ～ 20 % lower than that of a single YSZ-HOSP microsphere with a similar porosity. Heat flux simulation showed that high tortuosity around the multi-scaled voids of the ESP microsphere led to a more efficient decrease in thermal conductivity compared with total porosity.     

Input-to-state learning of recurrent neural networks with delay and disturbance

This article deals with the issue of input-to-state stabilization for recurrent neural networks with delay and external disturbance. The goal is to design a suitable weight-learning law to make the considered network input-to-state stable with a predefined -gain. Based on the solution of linear matrix inequalities, two schemes for the desired learning law are presented via using decay-rate-dependent and decay-rate-independent Lyapunov functionals, respectively. It is shown that, in the absence of external disturbance, the proposed learning law also guarantees the exponential stability of the network. To illustrate the applicability of the present weight-learning law, two numerical examples with simulations are given.     

An enhanced state estimation for linear parabolic PDE systems with mobile sensors

This paper studies an enhanced state estimation problem of distributed parameter processes modeled by a linear parabolic partial differential equation using mobile sensors. The proposed estimation scheme contains a state estimator and the guidance of mobile sensors, where the spatial domain is decomposed into multiple subdomains according to the number of sensors and each sensor is capable of moving within the respective subdomain. The state estimator is desired to make the state estimation error system exponentially stable while providing an performance bound. The mobile sensor guidance is used to enhance the transient performance of the error system. By the Lyapunov direct technique, an integrated design of state estimator and mobile sensor guidance laws is developed in the form of bilinear matrix inequalities (BMIs) to meet the desired design objectives. Moreover, to make the performance bound as small as possible, a suboptimal enhanced state estimation problem is formulated as a BMI optimization one, which can be solved via an iterative linear matrix inequality algorithm. Finally, numerical simulations are given to show the effectiveness of the proposed method.     

Determining Stability Constants Using the AKUFVE Technique

ABSTRACT

The AKUFVE techniques were developed by Rydberg and co-workers in the 1960s. The main aim was to be able to perform a series of liquid-liquid extraction data varying one or more parameters and at the same time achieve very pure phases. As such, this technique was later used for short-lived isotope studies in the SISAK system, but also as a standalone unit for a large number of thermodynamic studies of extraction systems both for fundamental understanding as well as more applied investigations. In this paper, the apparatus with modifications made over the decades is described. In addition, studies with stability constant determinations for the zirconium-water-acetylacetone system as well as lanthanide extraction using bromodecanoic acid are exemplified to demonstrate the potential use of the technique. The results shown clearly demonstrate the versatility and ability of the AKUFVE system.     

Novel high-energy ionic molecules deriving from new monovalent and divalent 4-oxyl-3,5-dinitropyrazolate moieties

ABSTRACT

In recent years, the exploration of a practical strategy for novel energetic molecules with high energy and low sensitivity is very desirable but highly challenging. Novel ionic energetic molecules have attracted much attention in this area due to their prominent advantages including low sensitivities, high thermal stability, and excellent energy performances. Herein, five different ionic energetic molecules based on new monovalent and divalent 4-oxyl-3,5-dinitropyrazolate moieties with enhanced oxygen balance have been synthesized, characterized and evaluated as potential high-energy materials. Thermal stability, sensitivities and energy output test were measured and studied in detail. The heats of formation and energetic parameters were calculated by using Gaussian 09 suite of programs and EXPLO 5 code. The results suggest that all as-prepared new molecules exhibit good thermal stability with high decomposition temperature (3, 231°C; 5, 160°C; 6, 185°C; 7, 180°C; 8, 213°C), and relative low sensitivity (IS > 20 J, FS = 324 N). Inheriting the significant oxygen content of monovalent and divalent 4-oxyl-3,5-dinitropyrazolate moieties, they also possess good energy properties (v _D = 8238 ~ 9208 m s^?1, P = 26.8 ~ 36.7 GPa, V _o = 481.8 ~ 959.4 L kg^?1), which make them competitive high-energy materials.     

LPNN-based approach for LASSO problem via a sequence of regularized minimizations

In compressive sampling theory, the least absolute shrinkage and selection operator (LASSO) is a representative problem. Nevertheless, the non-differentiable constraint impedes the use of Lagrange programming neural networks (LPNNs). We present in this article the -LPNN model, a novel algorithm that tackles the LASSO minimization together with the underlying theory support. First, we design a sequence of smooth constrained optimization problems, by introducing a convenient differentiable approximation to the non-differentiable -norm constraint. Next, we prove that the optimal solutions of the regularized intermediate problems converge to the optimal sparse signal for the LASSO. Then, for every regularized problem from the sequence, the -LPNN dynamic model is derived, and the asymptotic stability of its equilibrium state is established as well. Finally, numerical simulations are carried out to compare the performance of the proposed -LPNN algorithm with both the LASSO-LPNN model and a standard digital method.     

On monitoring the process mean of autocorrelated observations with measurement errors using the w-of-w runs-rules scheme

Runs-rules have been widely used since the 1950s in industrial and nonindustrial process monitoring applications to improve the performance of basic and other traditional monitoring schemes. However, none of the studies on runs-rules have accounted for a process with a combined effect of measurement errors and autocorrelation. Hence, in this paper, the use of the w-of-w runs-rules to improve the performance of the Shewhart $\overline{X}$ scheme using an additive model with a constant variance and a first-order autoregressive model is proposed. To reduce the combined negative effect of measurement errors and autocorrelation, we implement a sampling strategy based on rational subgroups in which (a) multiple measurements per item are taken (instead of a standard single measurement) and (b) non-neighboring observations are gathered. Moreover, the latter sampling strategy is incorporated into the values of probability elements of a Markov chain matrix which is used to derive some closed-form expressions for the zero- and steady-state run-length distribution. The main finding of this study is that, with respect to some overall performance measures, the proposed scheme outperforms the existing Shewhart $\overline{X}$ scheme by a significant margin. A real-life example is used to illustrate the practical implementation of the proposed scheme.     

Weighted power means of q-rung orthopair fuzzy information and their applications in multiattribute decision making

Weighted power means with weights and exponents serving as their parameters are generalizations of arithmetic means. Taking into account decision makers' flexibility in decision making, each attribute value is usually expressed by a $q$-rung orthopair fuzzy value ($q$-ROFV, $q\ge 1$), where the former indicates the support for membership, the latter support against membership, and the sum of their $q$th powers is bounded by one. In this paper, we propose the weighted power means of $q$-rung orthopair fuzzy values to enrich and flourish aggregations on $q$-ROFVs. First, the $q$-rung orthopair fuzzy weighted power mean operator is presented, and its boundedness is precisely characterized in terms of the power exponent. Then, the $q$-rung orthopair fuzzy ordered weighted power mean operator is introduced, and some of its fundamental properties are investigated in detail. Finally, a novel multiattribute decision making method is explored based on developed operators under the $q$-rung orthopair fuzzy environment. A numerical example is given to illustrate the feasibility and validity of the proposed approach, and it is shown that the power exponent is an index suggesting the degree of the optimism of decision makers.     

The influence of cryogenic rolling on a transformation-induced plasticity steel

ABSTRACT

Here, a novel cryogenic rolling plus intercritical annealing process was applied to a transformation-induced plasticity (TRIP) steel with a low chemical composition of carbon and manganese. Compared with traditional cold rolling, obvious grain refinement was observed, due to a high amount of dislocations retained. In addition, austenite volume fraction was increased, because of a unique nucleation mechanism. Subjected to cryogenic rolling, strength and ductility were increased, due to the enhanced austenite stability, which provided continuous and active TRIP effect. Consequently, tensile strength was increased to 1030?MPa, and elongation was increased to 38.2%. Thus, a great mechanical combination was obtained in a steel with a relatively low chemical composition with carbon and manganese, only by cryogenic rolling process.