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

The Zr_(0.5)Hf_(0.5)VPO_7 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)VPO_7. 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)VPO_7 has near-zero thermal expansion behavior over a wide temperature range.

High-order discontinuous Galerkin solver on hybrid anisotropic meshes for laminar and turbulent simulations

Efficient and robust solution strategies are developed for discontinuous Galerkin(DG) discretization of the Navier-Stokes(NS) and Reynolds-averaged NS(RANS)equations on structured/unstructured hybrid meshes. A novel line-implicit scheme is devised and implemented to reduce the memory gain and improve the computational efficiency for highly anisotropic meshes. A simple and effective technique to use the modified Baldwin-Lomax(BL) model on the unstructured meshes for the DG methods is proposed. The compact Hermite weighted essentially non-oscillatory(HWENO) limiters are also investigated for the hybrid meshes to treat solution discontinuities. A variety of compressible viscous flows are performed to examine the capability of the present highorder DG solver. Numerical results indicate that the designed line-implicit algorithms exhibit weak dependence on the cell aspect-ratio as well as the discretization order. The accuracy and robustness of the proposed approaches are demonstrated by capturing complex flow structures and giving reliable predictions of benchmark turbulent problems.

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.

Hermite WENO-based limiters for high order discontinuous Galerkin method on unstructured grids

A novel class of weighted essentially nonoscillatory (WENO) schemes based on Hermite polynomials,termed as HWENO schemes,is developed and applied as limiters for high order discontinuous Galerkin (DG) method on triangular grids.The developed HWENO methodology utilizes high-order derivative information to keep WENO reconstruction stencils in the von Neumann neighborhood.A simple and efficient technique is also proposed to enhance the smoothness of the existing stencils,making higher-order scheme stable and simplifying the reconstruction process at the same time.The resulting HWENO-based limiters are as compact as the underlying DG schemes and therefore easy to implement.Numerical results for a wide range of flow conditions demonstrate that for DG schemes of up to fourth order of accuracy,the designed HWENO limiters can simultaneously obtain uniform high order accuracy and sharp,essentially non-oscillatory shock transition.

Implicit high-order discontinuous Galerkin method with HWENO type limiters for steady viscous flow simulations

Two types of implicit algorithms have been improved for high order discontinuous Galerkin (DG) method to solve compressible Navier-Stokes (NS) equations on triangular grids. A block lower-upper symmetric Gauss-Seidel (BLU-SGS) approach is implemented as a nonlinear iterative scheme. And a modified LU-SGS (LLU-SGS) approach is suggested to reduce the memory requirements while retain the good convergence performance of the original LU-SGS approach. Both implicit schemes have the significant advantage that only the diagonal block matrix is stored. The resulting implicit high-order DG methods are applied, in combination with Hermite weighted essentially non-oscillatory (HWENO) limiters, to solve viscous flow problems. Numerical results demonstrate that the present implicit methods are able to achieve significant efficiency improvements over explicit counterparts and for viscous flows with shocks, and the HWENO limiters can be used to achieve the desired essentially non-oscillatory shock transition and the designed high-order accuracy simultaneously.

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.

非结构网格间断Galerkin方法的一种简单有效后验激波指示器

针对间断Galerkin(DG)方法的激波捕捉问题,在三角形网格上提出了一种基于后验方法的简单有效的问题单元指示器.方法利用了von Neumann单元中不同时刻的离散解来保持DG格式的紧凑性.本文采用有效的技术来提供有关问题单元的进一步信息,并且可以对不同的问题单元应用不同的限制方法,因此可以获得良好的数值特性,包括保正性质和振荡抑制性质.本文采用了TVB限制器和Hermite WENO限制器,对比了提出的指示器与TVB指示器的性能.数值结果表明了当前后验激波指示器的有效性和鲁棒性.

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.