乙酸乙酯皂化反应和碘钟反应实验改进

针对乙酸乙酯皂化反应和碘钟反应存在溶液恒温效率低、温度不均匀以及溶液混合不均匀、反应不均匀、数据偏差大等问题,从反应器和反应物的混合方式两方面进行改进,即采用夹套烧杯配合反应瓶作为反应器,使用多点位磁力搅拌器快速混合反应物。改进后使溶液恒温均匀稳定、高效,同时反应物分散均匀、反应快速,可以准确地测量溶液的电导率和变蓝时间,从而得到准确的反应级数、速率常数、活化能等数据。

A Genuinely Two-Dimensional HLL-Type Approximate Riemann Solver for Hypo-Elastic Plastic Flow

In this work,a genuinely two-dimensional HLL-type approximate Riemann solver is proposed for hypo-elastic plastic flow.To consider the effects of wave interaction from both the x-and y-directions,a corresponding 2D elastic-plastic approximate solver is constructed with elastic-plastic transition embedded.The resultant numerical flux combines one-dimensional numerical flux in the central region of the cell edge and two-dimensional flux in the cell vertex region.The stress is updated separately by using the velocity obtained with the above approximate Riemann solver.Several numerical tests,including genuinely two-dimensional examples,are presented to test the performances of the proposed method.The numerical results demonstrate the credibility of the present 2D approximate Riemann solver.

A Cell-Centered Lagrangian Scheme with an Elastic-Perfectly Plastic Solid Riemann Solver for Wave Propagations in Solids

A cell-centered Lagrangian scheme is developed for the numerical simula-tion of wave propagations in one dimensional(1D)elastic-plasticflow.The classical elastic-plastic material model initially proposed by Wilkins is adopted.The linear elas-tic model(Hooke’s Law),perfectly plastic model and von Mises yield criterion are used to describe the constitutive relationship of elastic-plastic solid.The second-order ex-tension of this scheme is achieved by a linear reconstruction method.Various numer-ical tests are simulated to check the capability of this scheme in capturing nonlinear elastic-plastic waves.Compared with the well-developed operator splitting method used in simulating elastic-plasticflow,this scheme is more accurate due to the con-sideration of a list of 64 different types of the nonlinear elastic-plastic waves when constructing the elastic-perfectly plastic Riemann solver.The numerical simulations of typical examples show competitive results.

Linearized Double-Shock Approximate Riemann Solver for Augmented Linear Elastic Solid

In this work,in order to capture discontinuities correctly in linear elastic solid,augmented internal energy is defined according to the first law of thermodynamics and Hooke’s law.The non-conservative linear elastic system is then rewritten into a conservative form with the help of an augmented total energy equation.We find that the non-physical oscillations occur to the popular HLL and HLLC approximate Riemann solvers when directly applied to simulate the augmented linear elastic solid.We analyze the intrinsic reason by defining a discrepancy factor which can be used to estimate the difference of the total stress across a contact discontinuity,where it is physically required to be continuous.We discover that non-physical oscillations inevitably appear in the vicinity of the contact discontinuity if this factor is away from zero for an approximate Riemann problem solver.In order to overcome this difficulty,we propose an approximate Riemann solver based on the linearized double-shock technique.Theoretical analysis and numerical results show that in comparison to the HLL and HLLC approximate Riemann solvers,the present linearized double-shock Riemann solver can eliminate the non-physical oscillations effectively.

Regression-Based Face Pose Estimation with Deep Multi-modal Feature Loss

Image-based face pose estimation tries to estimate the facial direction with 2D images.It provides important information for many face recognition applications.However,it is a difficult task due to complex conditions and appearances.Deep learning method used in this field has the disadvantage of ignoring the natural structures of human faces.To solve this problem,a framework is proposed in this paper to estimate face poses with regression,which is based on deep learning and multi-modal feature loss(M2FL).Different from current loss functions using only a single type of features,the descriptive power was improved by combining multiple image features.To achieve it,hypergraph-based manifold regularization was applied.In this way,the loss of face pose estimation was reduced.Experimental results on commonly-used benchmark datasets demonstrate the performance of M2FL.