Description of the thermodynamic properties and fluid-phase behavior of aqueous solutions of linear,branched, and cyclic amines

The SAFT-γ Mie group-contribution equation of state is used to represent the fluid-phase behavior of aqueous solutions of a variety of linear, branched, and cyclic amines. New group interactions are developed in order to model the mixtures of interest, including the like and unlike interactions between alkyl primary, secondary, and tertiary amine groups (NH₂, NH, N), cyclic secondary and tertiary amine groups (cNH, cN), and cyclic methine-amine groups (cCHNH, cCHN) with water (H₂O). The group-interaction parameters are estimated from appropriate experimental thermodynamic data for pure amines and selected mixtures. By taking advantage of the group-contribution nature of the method, one can describe the fluid-phase behavior of mixtures of molecules comprising those groups over broad ranges of temperature, pressure, and composition. A number of aqueous solutions of amines are studied including linear, branched aliphatic, and cyclic amines. Liquid–liquid equilibria (LLE) bounded by lower critical solution temperatures (LCSTs) have been reported experimentally and are reproduced here with the SAFT-γ Mie approach. The main feature of the approach is the ability not only to represent accurately the experimental data employed in the parameter estimation, but also to predict the vapor–liquid, liquid–liquid, and vapor–liquid–liquid equilibria, and LCSTs with the same set of parameters. Pure compound and binary phase diagrams of diverse types of amines and their aqueous solutions are assessed in order to demonstrate the main features of the thermodynamic and fluid-phase behavior.     

On the approximation of the canard explosion point in singularly perturbed systems without an explicit small parameter

A canard explosion is the dramatic change of period and amplitude of a limit cycle of a system of nonlinear ODEs in a very narrow interval of the bifurcation parameter. It occurs in slow–fast systems and is well understood in singular perturbation problems where a small parameter epsilon defines the time-scale separation. We present an iterative algorithm for the determination of the canard explosion point which can be applied for a general slow–fast system without an explicit small parameter. We also present assumptions under which the algorithm gives accurate estimates of the canard explosion point. Finally, we apply the algorithm to the van der Pol equations, a Templator model for a self-replicating system and a model for intracellular calcium oscillations with no explicit small parameters and obtain very good agreement with results from numerical simulations.     

MHD flow in a rectangular duct with a perturbed boundary

The unsteady magnetohydrodynamic (MHD) flow of a viscous, incompressible and electrically conducting fluid in a rectangular duct with a perturbed boundary, is investigated. A small boundary perturbation $\epsilon$ is applied on the upper wall of the duct which is encountered in the visualization of the blood flow in constricted arteries. The MHD equations which are coupled in the velocity and the induced magnetic field are solved with no-slip velocity conditions and by taking the side walls as insulated and the Hartmann walls as perfectly conducting. Both the domain boundary element method (DBEM) and the dual reciprocity boundary element method (DRBEM) are used in spatial discretization with a backward finite difference scheme for the time integration. These MHD equations are decoupled first into two transient convection–diffusion equations, and then into two modified Helmholtz equations by using suitable transformations. Then, the DBEM or DRBEM is used to transform these equations into equivalent integral equations by employing the fundamental solution of either steady-state convection–diffusion or modified Helmholtz equations. The DBEM and DRBEM results are presented and compared by equi-velocity and current lines at steady-state for several values of Hartmann number and the boundary perturbation parameter.     

Influences of chitosan on freeze–thaw stability of Arenga pinnata starch

This study aims to investigate chitosan (CS) with five different molecular weight (Mw) on freeze–thaw stability of Arenga pinnata starch (APS) gel subjected to five freeze–thaw cycles (FTC). The syneresis of APS gels was reduced by adding CS and the APS gel with high Mw CS had lower syneresis duo to a higher water holding capacity (P < 0.05). The addition of CS significantly decreased the hardness and molecular ordered structure of APS gel. In addition, CS could improve the microstructural stability. The results suggested that CS could effectively improve the freeze–thaw stability of APS gel, and CS with higher Mw might have more practical utility to improve stability of APS gel.     

对抗样本生成技术综述

如今,深度学习已被广泛应用于图像分类和图像识别的问题中,取得了令人满意的实际效果,成为许多人工智能应用的关键所在.在对于模型准确率的不断探究中,研究人员在近期提出了“对抗样本”这一概念.通过在原有样本中添加微小扰动的方法,成功地大幅度降低原有分类深度模型的准确率,实现了对于深度学习的对抗目的,同时也给深度学习的攻方提供了新的思路,对如何开展防御提出了新的要求.在介绍对抗样本生成技术的起源和原理的基础上,对近年来有关对抗样本的研究和文献进行了总结,按照各自的算法原理将经典的生成算法分成两大类——全像素添加扰动和部分像素添加扰动.之后,以目标定向和目标非定向、黑盒测试和白盒测试、肉眼可见和肉眼不可见的二级分类标准进行二次分类.同时,使用MNIST数据集对各类代表性的方法进行了实验验证,以探究各种方法的优缺点.最后总结了生成对抗样本所面临的挑战及其可以发展的方向,并就该技术的发展前景进行了探讨.     

Adaptive robust boundary control of shaft vibrations under perturbations with unknown upper bounds

This paper presents robust and adaptive boundary control designs to stabilize the two‐dimensional vibration of hybrid shaft model. The hybrid shaft is mathematically represented by a set of partial differential equations, governing the shaft vibrations, coupled to ordinary differential equations, describing rigid body spinning and dynamic boundary conditions. The control objective is to stabilize the transverse vibrations of the perturbed shaft while regulating the spinning rate. To achieve this, the paper first establishes robust boundary control laws that fulfil the control objective in the presence of modeling uncertainties and external disturbances operating over the shaft domain and boundary. Lyapunov‐based analyses show that the proposed robust control exponentially stabilizes the shaft with vanishing distributive perturbations, while assuring ultimately bounded vibrations in the case of nonvanishing perturbations. Then, adaptive control philosophy is utilized to achieve redesigned robust controllers that only use online adaptation of control gains without acquiring the knowledge of bounds on perturbations, as well as dynamic parameters. An advantage of this design is avoiding an overconservative robust control law, which may induce poor stability and chattering in tackling system perturbations with unknown upper bounds. Simulations through finite element method illustrate the results. Copyright © 2014 John Wiley & Sons, Ltd.     

大型MOX燃料快堆钠空泡反应性微扰理论研究

钠空泡反应性效应是钠冷快堆核设计和安全分析的重要内容。本文基于多群节块扩散法，采用微扰理论推导出钠空泡反应性的计算方法，对1 000 MWe钠冷快堆MOX燃料堆芯的总钠空泡反应性、空间分布、物理分项进行了计算。结果表明，钠空泡反应性主要来源于中子泄漏的增加和能谱的硬化，两者一正一负，且空间分布规律相反，导致钠空泡反应性具有强烈的空间依赖性；对于所计算的MOX燃料堆芯钠空泡反应性高达3 $左右。计算和分析结果阐明了钠空泡反应性的产生机理和分布规律，可为低钠空泡的设计提供参考。     

Flame Front Deformation Instabilities of Filtration Combustion for Initial Thermal Perturbation

The flame front deformation instability of low-velocity filtration combustion within an inert packed bed is studied based on the initial preheating non-uniformity. Based on the experimental phenomena, an initial thermal perturbation model is numerically proposed so as to predict the deformation behaviors of the flame front instabilities. The numerical prediction indicates that the assumption of an initial thermal perturbation is a feasible explanation as the cause of the flame front inclination instability. As the initial thermal perturbation increases, the phenomena of the flame front break and shrinking instabilities could easily occur at high filtration velocity or low equivalence ratio. Moreover, the evolutions of the flame front break rate and the shrinking rate are quantitatively analyzed.     

Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation

Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. To strengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samples and re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporated into the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%.