Entropy generation and thermodynamic optimization of fully developed laminar convection in a helical coil

The present paper analyses the entropy generation of the fully developed laminar convection in a helical coil with constant wall heat flux and presents the optimal design based on the minimum entropy generation principal. The important design parameters, including Reynolds number (Re), coil-to-tube radius ratio (δ) and nondimensional coil pitch (λ) are varied to investigate their influences on the entropy generation. The results presented in this paper cover Re range of 100–10,000, δ and λ range from 0.01 to 0.3. Compared with Re and δ, the coil pitch λ is found to have minor influence on the entropy generation. For a demonstrated case, the minimum entropy generation occurs in the range bounded by Re from 2271 to 4277 and δ from 0.17 to 0.3, within which the irreversibility of the system is lowest and the system performance would be optimum. The details show that there is an optimal Re for a helical coil with a fixed δ; meanwhile for a helical coil flow with a specified Re, the smaller δ should be selected when the Re is larger than 5000, and the larger δ should be selected when the Re is less than 5000. These results provide worthwhile information for heat exchanger designers to find the optimal helical coil design from the viewpoint of the thermodynamic second law.     

First-order modeling and stability analysis of illusory contours

In visual cognition, illusions help elucidate certain intriguing latent perceptual functions of the human vision system, and their proper mathematical modeling and computational simulation are therefore deeply beneficial to both biological and computer vision. Inspired by existent prior works, the current paper proposes a first-order energy-based model for analyzing and simulating illusory contours. The lower complexity of the proposed model facilitates rigorous mathematical analysis on the detailed geometric structures of illusory contours. After being asymptotically approximated by classical active contours, the proposed model is then robustly computed using the celebrated level-set method of Osher and Sethian [S. Osher, J.A. Sethian, Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulations, J. Comput. Phys., 79 (12) (1988) 12–49] with a natural supervising scheme. Potential cognitive implications of the mathematical results are addressed, and generic computational examples are demonstrated and discussed.     

On voltage collapse and apparent-power losses

The paper proposes a new concept for assessing vulnerably of a power system in terms of voltage stability. The concept is based on the apparent-power losses and uses only local data, i.e. voltage and current phasors at every line end in the system. In the paper, we prove that, at the voltage-collapse point, the entire increase in loading of the most critical line is due to increased transmission losses, which also coincides with the singularity of the Jacobian matrix. It is shown that this information can be used as an indication of a voltage-instability proximity. The proposed local method has several advantages over the existing static and dynamic methods, which is also shown in the paper.     

Use of the time constant to detect corrosion speed in reinforced concrete structures

The relatively simple to measure ‘time constant’ is presented as an index for characterizing the level of corrosion experienced by steel in concrete. This communication, largely in the form of a technical note, explicitly illustrates that the time constant determined from electrochemical testing has significant merit for monitoring corrosion of steel in concrete, and appears to be insensitive to the area of electrode being probed. In this work, the time constant, κ, is determined following a galvanostatic excitation signal, revealing a good correlation between the value of κ and corrosion status across specimens of widely varying geometry. Although this notion has been suggested in the past, this work presents both a survey and consolidated review to indicate the utility of the parameter as an index to corrosion rate on-site.     

Development of a small DMFC bipolar plate stack for portable applications

The direct methanol fuel cell (DMFC) is regarded as a promising candidate in portable electronic power applications. Bipolar plate stacks were systematically studied by controlling the operating conditions, and by adjusting the stack structure design parameters, to develop more commercial DMFCs. The findings indicate that the peak power of the stack is influenced more strongly by the flow rate of air than by that of the methanol solution. Notably, the stack performance remains constant even as the channel depth is decreased from 1.0 to 0.6 mm, without loss of the performance in each cell. Furthermore, the specific power density of the stack was increased greatly from ∼60 to ∼100 W l⁻¹ for stacks of 10 and 18 cells, respectively. The current status of the work indicates that the power output of an 18-cell short stack reaches 33 W in air at 70 °C. The outer dimensions of this 18-cell short stack are only 80 mm × 80 mm × 51 mm, which are suitable for practical applications in 10–20 W DMFC portable systems.     

Thermoelastic evaluation of composite laminates using digital imaging processing

The study of the variation of the curvature of non-symmetric composite laminates with temperature provides a measure of the magnitude of the thermal stresses and the mechanical behavior of the material with temperature. In the present work, an experimental method based on the pioneer use of a digital camera and image processing method is proposed to perform such measurements. Taking advantage of the method and by comparing experimental and CLT, extended-CLT and FEM simulation results, an evaluation of the residual stresses is described and an approach providing more precise determination of the residual thermal stresses in non-symmetric laminates is proposed.     

Noble metal water gas shift catalysis: Kinetics study and reactor design

Based on the water gas shift (WGS) catalytic mechanism on precious metal catalyst, a Langmuir–Hinshelwood (LH) kinetics model was derived for the operating conditions of syngas from natural gas reforming at near-ambient pressure. A power law kinetics model was also presented for comparative purpose. These two kinetics models were integrated in a dynamic distributed reactor model for design of full-scale WGS reactors for a natural gas fuel processing system. Modeling results indicated that the LH kinetics model gives predictions of reactor performance closer to the experimental data. Using the LH kinetics model, optimization of operating conditions for the high-temperature shift (HTS) and low-temperature shift (LTS) reactors was also attempted.     

CAE技术在汽轮机高温强度计算中的应用

为分析某百万千瓦超超临界汽轮机高压缸旋转部件在高温以及高离心力载荷下的强度状况,建立高压缸第一级动叶片的模型.根据蠕变试验数据,采用最小二乘法拟合幂蠕变方程中的各个参数,并详细介绍各参数的拟合、优化和验算过程;用CFD分析软件计算流体域内的对流传热系数并插值到有限元计算程序中,分析叶根倒角处危险区域的蠕变应力状况.数据验算结果表明,利用拟合数据计算得到的蠕变应力值与试验值比较接近,证明该方法可靠;CAE计算结果表明,叶根倒角处危险区域蠕变应力考核合格,并且有充足的安全裕量.     

New Paradigm of Data-Driven Smart Customisation through Digital Twin

Big data is one of the most important resources for the promotion of smart customisation. With access to data from multiple sources, manufacturers can provide on-demand and customised products. However, existing research of smart customisation has focused on data generated from the physical world, not virtual models. As physical data is constrained by what has already occurred, it is limited in the identification of new areas to improve customer satisfaction. A new technology called digital twin aims to achieve this integration of physical and virtual entities. Incorporation of digital twin into the paradigm of existing data-driven smart customisation will make the process more responsive, adaptable and predictive. This paper presents a new framework of data-driven smart customisation augmented by digital twin. The new framework aims to facilitate improved collaboration of all stakeholders in the customisation process. A case study of the elevator industry illustrates the efficacy of the proposed framework.     

Making costly manufacturing smart with transfer learning under limited data: A case study on composites autoclave processing

The integration of advanced manufacturing processes with ground-breaking Artificial Intelligence methods continue to provide unprecedented opportunities towards modern cyber-physical manufacturing processes, known as smart manufacturing or Industry 4.0. However, the “smartness” level of such approaches closely depends on the degree to which the implemented predictive models can handle uncertainties and production data shifts in the factory over time. In the case of change in a manufacturing process configuration with no sufficient new data, conventional Machine Learning (ML) models often tend to perform poorly. In this article, a transfer learning (TL) framework is proposed to tackle the aforementioned issue in modeling smart manufacturing. Namely, the proposed TL framework is able to adapt to probable shifts in the production process design and deliver accurate predictions without the need to re-train the model. Armed with sequential unfreezing and early stopping methods, the model demonstrated the ability to avoid catastrophic forgetting in the presence of severely limited data. Through the exemplified industry-focused case study on autoclave composite processing, the model yielded a drastic (88%) improvement in the generalization accuracy compared to the conventional learning, while reducing the computational and temporal cost by 56%.