高熵形状记忆合金的研究进展

高熵形状记忆合金是在等原子比NiTi合金的基础上,结合高熵合金的概念,逐渐发展起来的一种新型高温形状记忆合金。近年来,已开发出了综合性能优异的(TiZrHf)50(NiCoCu)50系和(TiZrHf)50(NiCuPd)50系高熵形状记忆合金,引起了广泛的关注和研究兴趣。本文从物相组成、微观组织、马氏体相变行为、形状记忆效应和超弹性等角度出发,综述了高熵形状记忆合金的研究进展,并对高熵形状记忆合金未来的研究重点进行了展望。     

Thermodynamic modeling of selected ternary systems containing Y and CALPHAD simulation of CoNiCrAlY metallic coatings

Metallic coatings can improve the high temperature resistance of superalloys serving in the gas turbines. In general they are Al–Co–Cr–Ni alloys with small Y additions to improve oxide scale adherence.In order to complete the construction of a thermodynamic database for coatings, thermodynamic assessments of four ternary systems have been performed by means of the CALPHAD method, namely Al–Co–Y, Al–Ni–Y, Al–Cr–Y and Co–Ni–Y. All of the experimental phase diagrams and thermodynamic data available in the literature were critically reviewed. The liquid, fcc, bcc and hcp phases were modeled as substitutional solutions. The order-disorder model has been adopted to describe the A1/L1₂ and A2/B2 phase relations. A series of ternary compounds have been modeled during the present work according to the crystal structure or composition. As a result a satisfactory agreement was obtained between our calculations and the experimental data used in the assessment.Finally, interaction parameters calculated in this work have been merged in the thermodynamic database for the simulation of Al–Co–Cr–Ni–Y alloys. This has been validated by comparing our calculations with experimental data regarding selected Ni-based and Co-based alloy coatings.     

A Dynamic Memory Allocation Optimization Mechanism Based on Spark

Spark is a distributed data processing framework based on memory. Memory allocation is a focus question of Spark research. A good memory allocation scheme can effectively improve the efficiency of task execution and memory resource utilization of the Spark. Aiming at the memory allocation problem in the Spark2.x version, this paper optimizes the memory allocation strategy by analyzing the Spark memory model, the existing cache replacement algorithms and the memory allocation methods, which is on the basis of minimizing the storage area and allocating the execution area according to the demand. It mainly including two parts: cache replacement optimization and memory allocation optimization. Firstly, in the storage area, the cache replacement algorithm is optimized according to the characteristics of RDD Partition, which is combined with PCA dimension. In this section, the four features of RDD Partition are selected. When the RDD cache is replaced, only two most important features are selected by PCA dimension reduction method each time, thereby ensuring the generalization of the cache replacement strategy. Secondly, the memory allocation strategy of the execution area is optimized according to the memory requirement of Task and the memory space of storage area. In this paper, a series of experiments in Spark on Yarn mode are carried out to verify the effectiveness of the optimization algorithm and improve the cluster performance.     

多金属3D打印的SLM设备工艺参数库试验研究

针对特定的金属3D打印机,加工不同金属均需反复实验,因此建立成形工艺参数数据库十分必要。以某国产金属打印设备为研究对象,以加工Al Si10Mg合金的工艺方法为例,采用正交试验方法和微观组织分析法验证其选择最佳工艺参数组合的正确性,最终通过多种金属的加工得到该设备的激光功率、扫描间距、扫描速度对打印不同金属的致密度、抗拉强度以及断后伸长率的影响的规律。结果显示选择激光功率时要充分考虑金属的熔点、易氧化、反光率等特性,扫描间距对成形不同金属的力学性能影响敏感度最大,为建立更多金属成形工艺数据库提供了参考。     

Persistence Analysis of Multi-scale Planar Structure Graph in Point Clouds

Modern acquisition techniques generate detailed point clouds that sample complex geometries. For instance, we are able to produce millimeter-scale acquisition of whole buildings. Processing and exploring geometrical information within such point clouds requires scalability, robustness to acquisition defects and the ability to model shapes at different scales. In this work, we propose a new representation that enriches point clouds with a multi-scale planar structure graph. We define the graph nodes as regions computed with planar segmentations at increasing scales and the graph edges connect regions that are similar across scales. Connected components of the graph define the planar structures present in the point cloud within a scale interval. For instance, with this information, any point is associated to one or several planar structures existing at different scales. We then use topological data analysis to filter the graph and provide the most prominent planar structures. Our representation naturally encodes a large range of information. We show how to efficiently extract geometrical details (e.g. tiles of a roof), arrangements of simple shapes (e.g. steps and mean ramp of a staircase), and large-scale planar proxies (e.g. walls of a building) and present several interactive tools to visualize, select and reconstruct planar primitives directly from raw point clouds. The effectiveness of our approach is demonstrated by an extensive evaluation on a variety of input data, as well as by comparing against state-of-the-art techniques and by showing applications to polygonal mesh reconstruction.     

Study of devolatilization during chemical looping combustion of large coal and biomass particles

Chemical Looping Combustion (CLC) is one of the emerging technologies for carbon capture, with less energy penalty. The present way of using pulverized coals in a fluidized bed (FB)-CLC have limitations like loss of unconverted char and gaseous combustibles, which could be mitigated by use of coarser fuel particles. Devolatilization time is a critical input for the effective design of FB-CLC systems, primarily when large fuel particles are used. The present study investigates the devolatilization time and the char yield of three coals of two shapes, namely, two high ash Indian coals and a low ash Indonesian coal and a wood (Casuarina equisetifolia) in the size range of +8–25 mm, at different fuel reactor temperatures (800–950 °C) of a hematite based CLC unit. The devolatilization times of single fuel particles during CLC are determined using a visual method called ‘Color Indistinction Method’. Indonesian coal has the longest devolatilization time among the fuels, and biomass has the least. Increasing the bed temperature enhances the rate of volatile release, whereas this effect is less pronounced in larger particles. Devolatilization of Indonesian coal is found to be strongly influenced by the changes in operating conditions. With the decrease in sphericity, a maximum of 56% reduction in devolatilization time is observed for the +20–25 mm slender particles of Indonesian coals when compared to the near-round particles. The maximum average char yields at the end of the devolatilization phase for coal and biomass are about 55–76% and 16% respectively. Char yield in coal particles increases with an increase in particle size, whereas biomass particles show relatively consistent yield across all experimental conditions. Increase in bed temperature reduces the char yields of coal up to 12% and in biomass up to 30%. High volatile Indian coal is the most influenced fuel by the changes in fuels shape. A correlation for determining devolatilization time under CLC environment is presented, and it successfully fits most of the experimental values within ±20% deviation for coals (R² = 0.95) and within ±15% deviation for biomass (R² = 0.97).     

Thermodynamic assessments of the U–Nb–Mo and U–Nb–Cr ternary systems

The thermodynamic assessments of the U–Nb–Mo and U–Nb–Cr systems have been performed by using the CALPHAD (Calculation of Phase Diagrams) method on the basis of critical evaluation of phase diagram data reported in literature. The reported individual solution phases, i.e. liquid, (αU), (βU), γ, δ and two intermetallic compounds, i.e. MoU₂ and NbCr₂, have been modeled. The modeling covers the whole composition range and a wide temperature range. By utilizing the available thermodynamic parameters of the sub-binary systems, the U–Nb–Mo and U–Nb–Cr systems have been thermodynamically assessed and a series of self-consistent parameters have been obtained for the first time, which can reproduce most of the phase diagram and thermodynamic data to provide guidance for the design of nuclear fuels.     

Study of non-equilibrium dendrite growth in an undercooled alloy

ABSTRACT

Nonequilibrium thermodynamics and transportation kinetics near the propagating solid–liquid interface dominates the rapid solidification process, which is far from a thermodynamically stable state. Rapid solidification process can be described more precisely using quantitative thermodynamic calculation of phase diagram with nonlinear liquidus and solidus and evaluating the nonequilibrium effect in diffusion kinetics. Based on these basic principles, we have used a current nonequilibrium dendrite growth model to describe the rapid solidification process and the recalescence temperature of deeply undercooled alloys. Evolution of the key fundamental solidification parameters was also evaluated. The experimental data agree well with the model prediction.     

A two-stage vision-based method for measuring the key parameters of ball screws

Ball screws are crucial for improving the reliability and interchangeability of transmission mechanical systems; however, existing contact measurement methods that utilise stylus contact are not efficient, which precludes their use for rapid in-situ geometry evaluation. This paper presents a vision-based two-stage method for rapid measurement of key parameters (raceway arc radii and centre distance) of ball screws. The edge contour information is extracted from the acquired image using the dual-tree complex wavelet transform and non-maximal suppression. In the matching stage, a shape-matching algorithm is used for detecting approximate geometrical centres of raceway arcs. The refinement stage, on the other hand, is implemented for acquiring precise dimensional results. Furthermore, the method of averaging multiple measurements is performed to suppress random noise. A comparative experiment is presented to validate the robustness of the proposed method. Based on experimental results, the calculated mean absolute errors in the measurement of the two raceway arc radii and the centre distance are found to be 0.0082 mm, 0.0079 mm and 0.0055 mm, respectively. This study therefore paves the way for key parameter measurement without removing ball screws.     

Compiler compatible 5.66 Mb/mm2 8T 1R1W register file in 14 nm FinFET technology

1-read/1-write (1R1W) register file (RF) is a popular memory configuration in modern feature rich SoCs requiring significant amount of embedded memory. A memory compiler is constructed using the 8T RF bitcell spanning a range of instances from 32 b to 72 Kb. An 8T low-leakage bitcell of 0.106 μm² is used in a 14 nm FinFET technology with a 70 nm contacted gate pitch for high-density (HD) two-port (TP) RF memory compiler which achieves 5.66 Mb/mm² array density for a 72 Kb array which is the highest reported density in 14 nm FinFET technology. The density improvement is achieved by using techniques such as leaf-cell optimization (eliminating transistors), better architectural planning, top level connectivity through leaf-cell abutment and minimizing the number of unique leaf-cells. These techniques are fully compatible with memory compiler usage over the required span. Leakage power is minimized by using power-switches without degrading the density mentioned above. Self-induced supply voltage collapse technique is applied for write and a four stack static keeper is used for read Vmin improvement. Fabricated test chips using 14 nm process have demonstrated 2.33 GHz performance at 1.1 V/25 °C operation. Overall V_min of 550 mV is achieved with this design at 25 °C. The inbuilt power-switch improves leakage power by 12x in simulation. Approximately 8% die area of a leading 14 nm SoC in commercialization is occupied by these compiled RF instances.