Phase transition and near-zero thermal expansion of Zr_(0.5)Hf_(0.5)VPO_7

The Zr（0.5）Hf（0.5）VPO7 is successfully synthesized by the solid-state method with near-zero thermal expansion. Powder x-ray diffraction（XRD）, Raman spectroscopy, thermal dilatometry, and scanning electron microscopy（SEM） are used to investigate the structure, the phase transition, and the coefficient of thermal expansion（CTE） of Zr（0.5）Hf（0.5）VPO7. The investigation results show that the samples are of the single cubic type with a space group of Pa3ˉ at room temperature（RT）.It can be inferred that the superstructure is transformed from the 3 × 3 × 3 superstructure to the 1 × 1 × 1 ideal crystal in a temperature range between 310 K and 323 K. The CTE is measured by a dilatometer to be 0.59 × 10^（-6） K^（-1）（310 K–673 K）. The values of intrinsic（XRD） and extrinsic（dilatometric） thermal expansion are both near zero. The results show that Zr（0.5）Hf（0.5）VPO7 has near-zero thermal expansion behavior over a wide temperature range.

Radial Basis Function Interpolation and Galerkin Projection for Direct Trajectory Optimization and Costate Estimation

This work presents a novel approach combining radial basis function(RBF)interpolation with Galerkin projection to efficiently solve general optimal control problems.The goal is to develop a highly flexible solution to optimal control problems,especially nonsmooth problems involving discontinuities,while accounting for trajectory accuracy and computational efficiency simultaneously.The proposed solution,called the RBF-Galerkin method,offers a highly flexible framework for direct transcription by using any interpolant functions from the broad class of global RBFs and any arbitrary discretization points that do not necessarily need to be on a mesh of points.The RBF-Galerkin costate mapping theorem is developed that describes an exact equivalency between the Karush-Kuhn-Tucker(KKT)conditions of the nonlinear programming problem resulted from the RBF-Galerkin method and the discretized form of the first-order necessary conditions of the optimal control problem,if a set of discrete conditions holds.The efficacy of the proposed method along with the accuracy of the RBF-Galerkin costate mapping theorem is confirmed against an analytical solution for a bang-bang optimal control problem.In addition,the proposed approach is compared against both local and global polynomial methods for a robot motion planning problem to verify its accuracy and computational efficiency.

Experimental and numerical study of chaff cloud kinetic performance under impact of high speed airflow

To solve the kinetic and diffusion problem of surface-type infrared decoy, multi-chaff kinetic models are established and chaff cloud holistic kinetic performance are analyzed under the impact of high speed airflow in this work. Chaffs rotate rapidly during the motion under the impact of high speed airflow. The rotation speed is correlated with lift, position of pressure center and aerodynamic damping. Computational Fluid Dynamics(CFD) is used to compute the aerodynamic coefficients of chaff. It is found that there exists serious aerodynamic interference which mainly relates to the overlapping area and distance among chaffs during the diffusion of chaff cloud. The chaff wind tunnel test and rocket sled experiment are carried out to verify the credibility of the models in this work. Then, the variation of chaff cloud expectation and extremum are analyzed to achieve the holistic kinetic and diffusing performance of chaff cloud. Simulation results demonstrate that the chaffs diffuse rapidly under the impact of high speed airflow and chaff cloud can be formed rapidly within 0.5 s. The shape of the chaff cloud is similar to cone that forms a certain angle with the horizontal plane and most chaffs focus on the second half.

Densification behavior of yttria-stabilized zirconia powders for solid oxide fuel cell electrolytes

Yttria-stabilized zirconia(YSZ) is the most common electrolyte material for solid oxide fuel cells. Herein, we conducted a comparative study on the densification behavior of three different kinds of commercial 8 mol% YSZ powders:(i) TZ-8Y(Tosoh, Japan),(ii) MELox 8Y(MEL Chemicals, UK), and(iii) YSZ-HT(Huatsing Power, China). The comparison was made on both the selfsupporting pellets and thin-film electrolytes coated onto a NiO–YSZ anode support. For the pellets, MELox 8Y showed the highest densification at lower sintering temperatures with 93% and 96% of the theoretical density at 1250 and 1300 ℃, respectively. Although YSZ-HT showed a higher sintering rate than TZ-8Y, a sintering temperature of 1350 ℃ was required for both the powders to reach 95% of the theoretical density. For the thin-film electrolytes, on the other hand, YSZ-HT showed the highest sintering rate with a dense microstructure at a co-sintering temperature of 1250 ℃. Our results indicate that besides the average particle size, other factors such as particle size distribution and post-processing play a significant role in determining the sintering rate and densification behavior of the YSZ powders. Additionally, a close match in the sintering shrinkage of the electrolyte and anode support is important for facilitating the densification of the thin-film electrolytes.

Building Load Forecasting Using Deep Neural Network with Efficient Feature Fusion

The energy consumption of buildings has risen steadily in recent years. It is vital for the managers and owners of the building to manage the electric energy demand of the buildings. Forecasting electric energy consumption of the buildings will bring great profits, which is influenced by many factors that make it very difficult to provide an advanced forecasting. Recently, deep learning techniques are widely adopted to solve this problem. Deep neural network offers an excellent capability in handling complex non-linear relationships and competence in exploring regular patterns and uncertainties of consumption behaviors at the building level. In this paper, we propose a deep convolutional neural network based on Res Net for hour-ahead building load forecasting. In addition, we design a branch that integrates the temperature per hour into the forecasting branch. To enhance the learning capability of the model, an innovative feature fusion is presented. At last, sufficient ablation studies are conducted on the point forecasting, probabilistic forecasting, fusion method, and computation efficiency.The results show that the proposed model has the state-of-theart performance, which reflects a promising prospect in application of the electricity market.