CFD软件及源程序辅助工程流体力学课程教学的探索与实践

工程流体力学课程具有较强的理论性,涉及的知识面也较宽广,给该门课程的教与学带来较大的影响。文章针对工程流体力学课程的这些特点,结合多年来的教学实践,将CFD软件和源程序的强大数值模拟功能,引入工程流体力学课程辅助教学中,通过利用CFD软件和源程序进行数值模拟教学这一环节,探讨流体力学课程对接科研成果的教学模式。实践证明,这一模式获得了较好的教学效果。     

S30408不锈钢高温材料性能及本构模型综述

精确掌握材料高温下的性能参数,对结构抗火设计的准确性具有十分重要的意义。以S30408奥氏体不锈钢为研究对象,在现有S30408不锈钢高温材料性能试验的基础上,研究发现S30408不锈钢在同种条件下,相比碳钢具有较低的升温速率,且S30408不锈钢在600℃时仍能保持50%以上的强度与刚度。但不锈钢具有更大的热膨胀率,高温给结构的稳定性带来的威胁更大。其次,对比分析了国内外学者所提出的常温及高温下不锈钢的本构模型,根据S30408不锈钢高温下性能参数的变化规律,欧洲规范给出的建议值偏不安全,建议在不锈钢结构设计中采用Rasmussen模型,在结构抗火的数值模拟分析中采用Chen和Young的模型。     

三元层状固体润滑Ti3SiC2复合材料的制备与摩擦学研究进展

Ti_3SiC_2作为一种新型的陶瓷材料,兼具金属和陶瓷的双重性能,同时也由于具有良好的导电导热性、低密度、机械可加工性、优异的抗热震性能、高熔点、高热稳定性、耐高温氧化、耐腐蚀性能,近年来受到了越来越多研究者的关注。首先介绍了Ti_3SiC_2内部晶体学结构,指出其与二元碳化物Ti C有着紧密的晶体学关系,接着详细叙述了各种Ti_3SiC_2制备工艺及其复合材料的研究现状,阐述了化学气相沉积(CVD)、磁控溅射(MS)、脉冲激光沉积(PLD)、自蔓延高温合成(SHS)、热等静压(HIP)、热压烧结(HP)、火花等离子烧结(SPS)等技术在制备Ti_3SiC_2及其复合材料方面的优点和不足。随后重点分析了温度、滑动速度、载荷、添加组分含量、对偶材料种类和润滑环境等因素对Ti_3SiC_2及其复合材料摩擦学性能的影响。最后总结了在Ti_3SiC_2及其复合材料研究中存在的一些问题,并指出进一步提高摩擦磨损性能,添加多元增强相,改进现有技术以及采取新型制备工艺,是未来发展的重要方向。     

Study of the potential effect of spark plasma sintering on the preparation of complex FGM/laminated WC-based cermet

In the present work, the capability of SPS process for the production of a complex composite possessing different forms of materials based on WC-Ni-Ti-CNT FGM/laminated composite is investigated. With regard to the unique properties of SPS in producing and densifying a large number of materials, a complex material was prepared as a sandwich structure based on the reaction among Ti, CNT, Ni and WC in various forms consisting of powders, sheets and nano-tubes. The starting materials were initially placed in the graphite mold in the order of Ni powders, WC-10Ni, CNTs coated Ti sheet, WC-15Ni and Ti sheet from bottom to top. The microstructure study showed that the layer of Ni powders was adequately densified as an integrated layer, the second layer, WC-Ni, was attached to the Ni layer from the bottom and, at the top, it was connected to the highly concentrated WC layer formed as a consequence of Ni consumed by Ti to form NiTi and Ni₃Ti compounds. Moreover, the Ti sheets were changed into TiC particles as a result of reaction with WC and CNTs, and the WC-15Ni was placed at the top of this layer by bonding the structure properly; and eventually, the Ti sheet remained at the top of the specimen. The micro-hardness examination of the sample revealed the highest hardness related to the position of Ti-based layer due to the formation of intermetallic compounds and carbides.     

Upgrading the lubricity of bio-oil via homogeneous catalytic esterification under vacuum distillation conditions

In order to accelerate the application of bio-oil in the internal combustion engines, homogeneous catalytic esterification technology under vacuum distillation conditions was used to upgrade the crude bio-oil. The lubricities of the crude bio-oil (BO) and refined bio-oil with homogeneous catalytic esterification (RBO_hce) or refined bio-oil without catalyst but with distillation operation (RBO_wc) were evaluated by a high frequency reciprocating test rig according to the ASTM D 6079 standard. The basic physiochemical properties and components of the bio-oils were analyzed. The surface morphology, contents and chemical valence of active elements on the worn surfaces were investigated by scanning electron microscopy, energy dispersive spectroscopy and X-ray photoelectron spectroscopy, respectively. The results show that RBO_hce has better lubricities than those of BO, but RBO_wc has worse lubricities than those of BO. The tribological mechanisms of the bio-oils are attributed to the combined actions of lubricating films and factors that will break the film. Compared with BO, plenty of phenols in RBO_wc results in corrosion of the substrate and destroys the integrity of the lubricating films, which is responsible for its corrosive wear. However, more esters and alkanes in RBO_hce contribute to forming a complete boundary lubricating film on the rubbed surfaces which result in its excellent antifriction and antiwear properties.     

An improved scatter search algorithm for the single machine total weighted tardiness scheduling problem with sequence-dependent setup times

In this paper, a scatter search algorithm with improved component modules is proposed to solve the single machine total weighted tardiness problem with sequence-dependent setup times. For diversification generation module, both random strategy based heuristics and construction heuristic are adopted to generate the diversified population. For improvement module, variable neighborhood search based local searches are embedded into the algorithm to improve the trial solutions and the combined solutions. For reference set update module, the number of edges by which the two solutions differ from each other is counted to measure the diversification value between two solutions. We also propose a new strategy in which the length of the reference set could be adjusted adaptively to balance the computing time and solving ability. In addition, a discrete differential evolution operator is proposed with another two operators constitute the combination module to generate the new trial solutions with the solutions in the subsets. The proposed algorithm is tested on the 120 benchmark instances from the literature. Computational results indicate that the average relative percentage deviations of the improved algorithm from the ACO_AP, DPSO, DDE and GVNS are −5.16%, −3.33%, −1.81% and −0.08%, respectively. Comparing with the state-of-the-art and exact algorithms, the proposed algorithm can obtain 78 optimal solutions out of 120 instances within a reasonable computational time.     

Cross grouping strategy based 2DPCA method for face recognition

Grouping strategy exactly specifies the form of covariance matrix, therefore it is very essential. Most 2DPCA methods use the original 2D image matrices to form the covariance matrix which actually means that the strategy is to group the random variables by row or column of the input image. Because of their grouping strategies these methods have two main drawbacks. Firstly, 2DPCA and some of its variants such as A2DPCA, DiaPCA and MatPCA preserve only the covariance information between the elements of these groups. This directly implies that 2DPCA and these variants eliminate some covariance information while PCA preserves such information that can be useful for recognition. Secondly, all the existing methods suffer from the relatively high intra-group correlation, since the random variables in a row, column, or a block are closely located and highly correlated. To overcome such drawbacks we propose a novel grouping strategy named cross grouping strategy. The algorithm focuses on reducing the redundancy among the row and the column vectors of the image matrix. While doing this the algorithm completely preserves the covariance information of PCA between local geometric structures in the image matrix which is partially maintained in 2DPCA and its variants. And also in the proposed study intra-group correlation is weak according to the 2DPCA and its variants because the random variables spread over the whole face image. These make the proposed algorithm superior to 2DPCA and its variants. In order to achieve this, image cross-covariance matrix is calculated from the summation of the outer products of the column and the row vectors of all images. The singular value decomposition (SVD) is then applied to the image cross-covariance matrix. The right and the left singular vectors of SVD of the image cross-covariance matrix are used as the optimal projective vectors. Further in order to reduce the dimension LDA is applied on the feature space of the proposed method that is proposed method + LDA. The exhaustive experimental results demonstrate that proposed grouping strategy for 2DPCA is superior to 2DPCA, its specified variants and PCA, and proposed method outperforms bi-directional PCA + LDA.     

Efficient multi-criteria optimization on noisy machine learning problems

Recent research revealed that model-assisted parameter tuning can improve the quality of supervised machine learning (ML) models. The tuned models were especially found to generalize better and to be more robust compared to other optimization approaches. However, the advantages of the tuning often came along with high computation times, meaning a real burden for employing tuning algorithms. While the training with a reduced number of patterns can be a solution to this, it is often connected with decreasing model accuracies and increasing instabilities and noise. Hence, we propose a novel approach defined by a two criteria optimization task, where both the runtime and the quality of ML models are optimized. Because the budgets for this optimization task are usually very restricted in ML, the surrogate-assisted Efficient Global Optimization (EGO) algorithm is adapted. In order to cope with noisy experiments, we apply two hypervolume indicator based EGO algorithms with smoothing and re-interpolation of the surrogate models. The techniques do not need replicates. We find that these EGO techniques can outperform traditional approaches such as latin hypercube sampling (LHS), as well as EGO variants with replicates.