基于虚拟仿真技术的热分析实验教学探索与实践*

针对热分析实验教学,设计和开发了热分析仪虚拟仿真实训系统,提出了一种新的基于虚拟仿真技术的仪器分析测试类实验课教学模式,并将其应用于实际的实验教学中。对随堂实验教学质量进行了调查,结果表明此种实验教学模式很大程度上提升了学生自主学习的兴趣,提高了学生的综合实践能力。     

Computer-assisted Modelling and Analysis of Linear Programming Problems: Towards a Unified Framework

A framework for model formulation and analysis to support operationsand management of large-scale linear programs is developed fromthe combined capabilities of CAMPS and ANALYZE. Both the systemsare reviewed briefly and the interface which integrates thetwo systems is then described. The model formulation, matrixgeneration, and model management capability of CAMPS and thecomplementary model and solution analysis capability of ANALYZEare presented within a unified framework. Relevant generic functionsare highlighted, and an example is presented in detail to illustratethe level of integration achieved in the current prototype system.Some new results on discourse models and model management supportare given in a framework designed to move toward an ‘intelligent’system for linear programming modelling and analysis.     

An Optimal Extension of the Polak–Ribière–Polyak Conjugate Gradient Method

Based on a singular value analysis on an extension of the Polak–Ribière–Polyak method, a nonlinear conjugate gradient method with the following two optimal features is proposed: the condition number of its search direction matrix is minimum and also, the distance of its search direction from the search direction of a descent nonlinear conjugate gradient method proposed by Zhang et al. is minimum. Under proper conditions, global convergence of the method can be achieved. To enhance e?ciency of the proposed method, Powell’s truncation of the conjugate gradient parameters is used. The method is computationally compared with the nonlinear conjugate gradient method proposed by Zhang et al. and a modified Polak–Ribière–Polyak method proposed by Yuan. Results of numerical comparisons show e?ciency of the proposed method in the sense of the Dolan–Moré performance profile.     

Current trends and future directions in turbulent thermal convection

The system of turbulent thermal convection is introduced. Progresses in recent decades in the four major areas of research in turbulent convection are briefly reviewed. Some of the recent trends of the field are then discussed, which also serve to point out that the future directions in this important field of fluid mechanics lie in the extension to the non-standard or non-classical Rayleigh—Bénard configuration.     

Large-scale turbulence phenomena in compound rectangular channels

In this article we investigate turbulent flow of air through compound rectangular channels to experimentally investigate the turbulence phenomena in compound channels. Detailed experimental data of axial mean velocity, wall shear stresses, five of six Reynolds stresses, auto- and cross-spectral densities, and two-point space correlations were measured by hot-wire anemometry in 18 geometrical configurations.

The symmetry of the present flow appears to be better than that of previous measurements and the range of measurments is more extensive. The most interesting result is the existence of a quasi-periodic large-scale turbulence structure in most of the geometries investigated. This structure is stationary and independent of the axial position in the channel. It exists in any longitudinal slot or groove in a wall or a connecting gap between two flow channels, provided its depth is more than approximately twice its width. The frequency of this flow oscillation is determined by the geometry of the slot and is linearly dependent on the bulk velocity.     

大规模数据的L1惩罚分位数回归方法研究--基于特征筛选和随机抽样方法

为解决大规模数据在进行回归分析时存在的计算内存不足和运行时间较长的问题,提出两个新的回归分析方法:先筛选后抽样的大规模数据L₁惩罚分位数回归方法(FSSLQR)和先抽样后筛选的大规模数据L₁惩罚分位数回归方法(SFSLQR),其数值模拟和实际应用结果表明:FSSLQR和SFSLQR方法不仅能够显著降低计算内存和运行时间,而且其估计预测和变量选择的结果与全量L₁惩罚分位数回归基本一致。此外,与Xu等(2018)提出的大规模数据的L1惩罚分位数回归方法(SLQR)相比,FSSLQR和SFSLQR方法在估计预测、变量选择和运行时间等方面都更具优势。     

The Adjoint Newton Algorithm for Large-Scale Unconstrained Optimization in Meteorology Applications

A new algorithm is presented for carrying out large-scale unconstrained optimization required in variational data assimilation using the Newton method. The algorithm is referred to as the adjoint Newton algorithm. The adjoint Newton algorithm is based on the first- and second-order adjoint techniques allowing us to obtain the Newton line search direction by integrating a tangent linear equations model backwards in time (starting from a final condition with negative time steps). The error present in approximating the Hessian (the matrix of second-order derivatives) of the cost function with respect to the control variables in the quasi-Newton type algorithm is thus completely eliminated, while the storage problem related to the Hessian no longer exists since the explicit Hessian is not required in this algorithm. The adjoint Newton algorithm is applied to three one-dimensional models and to a two-dimensional limited-area shallow water equations model with both model generated and First Global Geophysical Experiment data. We compare the performance of the adjoint Newton algorithm with that of truncated Newton, adjoint truncated Newton, and LBFGS methods. Our numerical tests indicate that the adjoint Newton algorithm is very efficient and could find the minima within three or four iterations for problems tested here. In the case of the two-dimensional shallow water equations model, the adjoint Newton algorithm improves upon the efficiencies of the truncated Newton and LBFGS methods by a factor of at least 14 in terms of the CPU time required to satisfy the same convergence criterion.The Newton, truncated Newton and LBFGS methods are general purpose unconstrained minimization methods. The adjoint Newton algorithm is only useful for optimal control problems where the model equations serve as strong constraints and their corresponding tangent linear model may be integrated backwards in time. When the backwards integration of the tangent linear model is ill-posed in the sense of Hadamard, the adjoint Newton algorithm may not work. Thus, the adjoint Newton algorithm must be used with some caution. A possible solution to avoid the current weakness of the adjoint Newton algorithm is proposed.     

MODLER: Modeling by object-driven linear elemental relations

This paper describes a system to represent linear programming models and their instances. In addition to a modeling language, MODLER has an extensive query capability which includes a multi-view architecture. Further, randomization options provide rapid prototyping. The MODLER system is part of a workbench for building and managing decision support systems that are based on linear programming.This research was supported by a consortium of industries: Amoco Oil Company, Shell Development Company, Chesapeake Decision Science, GAMS Development Corp., Ketron Management Science, MathPro, Inc., Optimal Methods, Inc., and XMP Software, Inc. Partial support was also provided by the Office of Naval Research (Contract No. N-00014-88-K-0104).     

Feasible Interior Methods Using Slacks for Nonlinear Optimization

A slack-based feasible interior point method is described which can be derived as a modification of infeasible methods. The modification is minor for most line search methods, but trust region methods require special attention. It is shown how the Cauchy point, which is often computed in trust region methods, must be modified so that the feasible method is effective for problems containing both equality and inequality constraints. The relationship between slack-based methods and traditional feasible methods is discussed. Numerical results using the KNITRO package show the relative performance of feasible versus infeasible interior point methods.     

Shielded distribution approximation for a wall-bounded classical fluid

We investigate lower order distribution functions in classical fluids in the presence of large-scale inhomogeneities, in particular those imposed by wall contacts. The consequences of the effective shielding of a wall by the nearest particle of the set being considered are determined in the context of two distribution function hierarchies, kinematic and dynamic in origin. The effects of both flat and spherical, hard and soft walls are considered, as well as those of curved and double walls. A few correction sequences to the basic shielding approximation are discussed.Supported in part by the Department of Energy under contract DE-AC02-76 ERO 3077.