Thermodynamic modelling of the ternary Bi-Ga-Te system for potential application in thermoelectric materials

Thermoelectric materials have drawn widespread attention because they can enable the direct conversion between electric and thermal energy. Over the years, different materials such as skutterudites, clathrates, intermetallic alloys, eutectic alloys, chalcogenides have been explored for Thermoelectric (TE) applications. Amongst the eutectic alloys, the Bi-Ga-Te system exhibits promising potential as a TE material. Accordingly, in this study, we performed the thermodynamic optimization and critical evaluation of binary Bi–Ga, Bi–Te, Ga–Te, and ternary Bi-Ga-Te systems using the CALPHAD method. It is observed that the Ga–Te system shows asymmetric liquid solution properties with strong negative enthalpy of mixing, whereas the Bi–Te liquid exhibits the symmetric regular solution behavior. Moreover, the Bi–Ga liquid solution has a positive enthalpy of mixing. Therefore, Modified Quasichemical Model (MQM) using pair approximation was utilized to describe the diversified thermodynamic properties of liquid solution in sub-binaries by taking into account the Short-Range Ordering (SRO). By merging the binary optimization results with a proper interpolation method, the liquid solution properties and phase diagram information in the Bi-Ga-Te ternary system were also reproduced successfully without any adjustable ternary parameter. Several ternary eutectic compositions were suggested for designing TE alloy with enhanced properties using the developed database.     

Sustainable optimization of waste management network over extended planning time horizon

This study proposes a multiperiod mixed integer linear programming model for the management of a single municipal solid waste (MSW) treatment plant with sustainability as the objective. Discrete and continuous variables define the capacity selections for diverse MSW technologies, and the operation of the MSW network, respectively. The economic target is considered to maximize the net present value. The environmental impact is the minimization of a normalized environmental objective function (NEOF). The social target is the maximization of jobs. An interesting feature about the research work is the requirement of biodrying technologies for MSW moisture content control. Due to the conflicted nature among the sustainability components, a multiobjective optimization (MO) is carried out to find the Pareto optimal solutions. The MO results show that the Pareto optimal solutions vary around profit range of $4.9–8.5 billion, NEOF impact range of 3.2–3.6 units, and social benefit range of 2700–4828 jobs.     

Optimization of flow in additively manufactured porous columns with graded permeability

Chemical engineering systems often involve a functional porous medium, such as in catalyzed reactive flows, fluid purifiers, and chromatographic separations. Ideally, the flow rates throughout the porous medium are uniform, and all portions of the medium contribute efficiently to its function. The permeability is a property of a porous medium that depends on pore geometry and relates flow rate to pressure drop. Additive manufacturing techniques raise the possibilities that permeability can be arbitrarily specified in three dimensions, and that a broader range of permeabilities can be achieved than by traditional manufacturing methods. Using numerical optimization methods, we show that designs with spatially varying permeability can achieve greater flow uniformity than designs with uniform permeability. We consider geometries involving hemispherical regions that distribute flow, as in many glass chromatography columns. By several measures, significant improvements in flow uniformity can be obtained by modifying permeability only near the inlet and outlet.     

Creep behaviour of an Al–Si–Al2O3 composite based on phase evolution at 1300oC

An Al–Si–Al₂O₃ composite was prepared with corundum, aluminium powder and silicon powder. A creep test was carried out at 1300°C under 0.2 MPa for 50 h in air. The results show that the Al–Si–Al₂O₃ composite performs a low constant creep rate and remain until the end of the 50-h test. This is attributed to the in-situ formation of the tough non-oxide reinforcements, whisker-like (AlN)_x(Al₂OC)_1-x solid solution and granular β-SiC, by reactions of Al and Si during creep test. The whisker-like (AlN)_x(Al₂OC)_1-x solid solution and granular β-SiC reinforcements are evenly filled in the pores, which play the role of bridging and pinning reinforcement, forming a strong network structure with corundum aggregates. Moreover, these non-oxide phases are not wetted by the liquid phases, which impel the liquid phase shrinks in the network structure in isolation during creep test. Thus, the adverse effect of the liquid phase on the high-temperature strength of the composites is eliminated, so the composites with strong network structure quickly get a stationary low-creep state. A creep mechanism model is established.     

超高层劲性结构及大型机械设备立体协同施工技术

基于某超高层建筑群项目的主体施工阶段,对超高层劲性结构建筑群项目施工过程中可能遇到的问题,诸如大型机械立体协同、钢结构与钢筋连接节点优化、钢结构桁架层施工等重难点进行总结分析,并提出相应的解决方案以供探讨,可为同类型工程提供参考经验。     

A novel adaptive convolutional neural network for fault diagnosis of hydraulic piston pump with acoustic images

As an essential part of hydraulic transmission systems, hydraulic piston pumps have a significant role in many state-of-the-art industries. Thus, it is important to implement accurate and effective fault diagnosis of hydraulic piston pumps. Owing to the heavy reliance of shallow machine learning models on the expertise and experience of engineers, fault diagnosis based on deep models has attracted significant attention from academia and industry. To construct a deep model with good performance, it is necessary and challenging to tune the hyperparameters (HPs). Since many existing methods focus on manual tuning and use common search algorithms, it is meaningful to explore more intelligent algorithms that can automatically optimize the HPs. In this paper, Bayesian optimization (BO) is employed for adaptive HP learning, and an improved convolutional neural network (CNN) is established for fault feature extraction and classification in a hydraulic piston pump. First, acoustic signals are transformed into time–frequency distributions by a continuous wavelet transform. Second, a preliminary CNN model is built by setting initial HPs. The range of each HP to be optimized is identified. Third, BO is employed to select the optimal combination of HPs. An improved model called CNN-BO is constructed. Finally, the diagnostic efficiency of CNN-BO is analyzed using a confusion matrix and t-distributed stochastic neighbor embedding. The classification performance of different models is compared. It is found that CNN-BO has a higher accuracy and better robustness in fault diagnosis for a hydraulic piston pump. This research will provide a basis for ensuring the reliability and safety of the hydraulic pump.     

Joint tracking coefficients and the time delay of a nonstationary linear system,preceded by a static nonlinearity

The paper considers a block-oriented nonlinear Hammerstein system that consists of a linear block with real-valued time-varying unknown coefficients and a pure time delay, preceded by the static nonlinearity. The joint tracking of linear block coefficients and the time delay while processing observation samples is under consideration. The difficulty of the tracking process consists in the initial nonconvexity of the objective function to be minimized, in respect of the time delay. To obtain current estimates of the true coefficients and the time delay it is necessary to overcome the multiextremality of the objective function. A global optimization problem could be solved by changing a form of the nonconvex objective function into a convex one. The optimization problem has been subdivided into two problems that are analyzed in this article, as well. The first one consists of removal of the objective function multiextremality in respect of the time delay, while hunting for the global extremum. Therefore, the well-known approach based on the method of corrective operators, which is used to transform the multiextremal criterion into a unimodal function for the nonstationary Wiener system, is developed here for the Hammerstein one. The other problem consists of the applicability of unified adaptive algorithms, used for Wiener systems in the case of Hammerstein systems. The recursive parametric identification technique, based on the adaptive algorithm procedures, is developed here for the nonstationary block-oriented Hammerstein systems, too. The applicability of algorithms is supported by various simulation tests solved by computer for distinct inputs and for two static nonlinearities.     

接触器触头系统振动机理分析及算法优化

为了解决合闸过程中动、静触头接触引起的振动弹跳问题.本文建立了接触系统的二自由度运动微分方程,并利用遗传算法对交流接触器吸合过程进行优化,同时通过高速摄影实验对接触弹跳的全过程进行了观察和分析.结果表明:理论与实验结果高度一致,铁心在触头分离前发生碰撞,进一步加剧触头弹跳;铁芯弹跳再次碰撞时,触头的弹跳不受影响;在接触器运行过程中,动铁芯的运动会引起系统轻微振动;采用遗传算法优化的接触器触头弹跳时间和最大振幅均减小.研究结果为进一步控制和减小接触弹跳提供了理论依据.     

An AW-HARIS Based Automated Segmentation of Human Liver Using CT Images

In the digestion of amino acids, carbohydrates, and lipids, as well as protein synthesis from the consumed food, the liver has many diverse responsibilities and functions that are to be performed. Liver disease may impact the hormonal and nutritional balance in the human body. The earlier diagnosis of such critical conditions may help to treat the patient effectively. A computationally efficient AW-HARIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human liver. The proposed approach can recognize the abnormalities with better accuracy without training, unlike in supervisory procedures requiring considerable computational efforts for training. In the earlier stages, the CT images are pre-processed through an Adaptive Multiscale Data Condensation Kernel to normalize the underlying noise and enhance the image’s contrast for better segmentation. Then, the preliminary phase’s outcome is being fed as the input for the Anisotropic Weighted–-Heuristic Algorithm for Real-time Image Segmentation algorithm that uses texture-related information, which has resulted in precise outcome with acceptable computational latency when compared to that of its counterparts. It is observed that the proposed approach has outperformed in the majority of the cases with an accuracy of 78%. The smart diagnosis approach would help the medical staff accurately predict the abnormality and disease progression in earlier ailment stages.     

基于响应曲面法的涡轮增压器废气阀门激光焊接工艺优化研究

激光焊接技术凭借其高效、高精度的特点已广泛应用在汽车制造产业,在涡轮增压器的生产中,激光焊接也逐步被应用于实现废气阀门与阀杆的连接。虽然当前对于激光焊接的工艺研究很多,但对工艺参数的优化也一直缺少一个科学有效的方法。本文以熔接深度、熔接宽度和内部缺陷作为响应变量,选取焊接功率、焊接速度作为因子,运用响应曲面法(RSM)设计实验方案,通过对响应结果进行分析,研究焊接参数对焊接质量的影响,拟合能够预测最优参数的数学模型,最终将获得的最优参数运用到生产中进行验证。