流动与传热数值模型的发展现状

流动与传热的数值模型发展至今已有三十多年的历史 ,该文拟结合炉内流动模型的特点 ,从差分格式、迭代组织形式、网格生成等三方面对流动、传热数值方法的发展现状作一简单介绍。参 9     

FRICTION LOSSES FOR FLOW OF SLURRIES IN PIPELINE BENDS,FITTINGS, AND VALVES

ABSTRACT

Resistance coefficients for flow of suspensions of well defined glass beads of narrow size fractions in 1-inch and 2-inch straight pipes, in standard 45^°, 90^° and 180^° bends, in 90^° smooth bends of various curvature radii, and also in gate and globe valves were measured. The measurements were made for two sizes of fine glass beads, -325 mesh and -200-325 mesh, covering wide ranges of turbulent Reynolds number and solids concentrations from 0 to 50 weight percent.

These friction loss data were analyzed with regard to the effects of Reynolds number and suspended solids concentration, and the calculated resistance coefficients were compared with those estimated from available design procedures recommended for turbulent single-phase Newtonian flow. Within the range of particle sizes examined in this work no particle size effects could be discerned. The effects of Reynolds number and suspended solids concentration on the friction loss measurement were calculated.     

Inference and uncertainty quantification of stochastic gene expression via synthetic models

Estimating uncertainty in model predictions is a central task in quantitative biology. Biological models at the single-cell level are intrinsically stochastic and nonlinear, creating formidable challenges for their statistical estimation which inevitably has to rely on approximations that trade accuracy for tractability. Despite intensive interest, a sweet spot in this trade-off has not been found yet. We propose a flexible procedure for uncertainty quantification in a wide class of reaction networks describing stochastic gene expression including those with feedback. The method is based on creating a tractable coarse-graining of the model that is learned from simulations, a synthetic model, to approximate the likelihood function. We demonstrate that synthetic models can substantially outperform state-of-the-art approaches on a number of non-trivial systems and datasets, yielding an accurate and computationally viable solution to uncertainty quantification in stochastic models of gene expression.     

Principles and methodology of fuzzy sets

The principles of fuzzy sets and their role in processing uncertain information will be discussed. The question of knowledge representation that is of significant importance in problems of system modelling will be formulated and considered at the level of fuzzy sets. Modelling and simulation realized with the aid of fuzzy sets are studied in a unified methodological framework. First a notion of the cognitive perspective is applied to articulate the problem in terms of specialized linguistic labels. Fuzzy models are constructed to capture logical relationships between the elements (linguistic labels) of the cognitive perspective. Several different classes of the models distinguished with regard to their structural dependencies will be analysed in depth. Finally a linguistic-numerical transformation constituting a type of model-environment interface will be studied.     

Examining end users’ ability to select business services: A conceptual framework and an empirical study

Software made from autonomous business services is gaining popularity. Now end users can build large applications by assembling a suite of services. Because some end users might have limited knowledge of their requirements and the functionality of available services, the key challenge is to find the services needed to build an application. The task of finding the services matching requirements requires specialized knowledge—knowledge of requirements and the functionality of available services—not just mere general competence. Moreover, the complexity of the requirements could also hinder the ability of end users to select services. However, there is little research into how the end users’ sophistication and requirement complexity affect their ability to avoid duplication (i.e., select the most cost-effective set of services) and select a set of services that satisfy their requirements. We provide a conceptual framework for the choice problem faced by the decision maker and develop a set of hypotheses on end user's sophistication and requirement complexity, and the impact of these factors on outcome performance—the ability to avoid duplication and select the appropriate services. Then we conduct an empirical study to test the hypotheses. Empirical results offer support for all hypotheses. Our work has several implications. We demonstrate both conceptually and empirically that end users’ naivety has a significant impact on service duplication. For a profit-maximizing service vendor, knowledge of the end user's sophistication/naivety allows there to be different pricing strategies: (1) a pure component strategy, (2) a pure bundling strategy, or (3) a mixed bundling strategy.     

3ds max地形建模研究

论述3ds max三种地形建模方法:等高线地形建模、置换修改器地形建模、DreamScape插件地形建模,并探讨这三种地形建模方法的优劣。     

A Recursive Orthogonal Least Squares Algorithm for Training RBF Networks

A recursive orthogonal least squares (ROLS) algorithm for multi-input, multi-output systems is developed in this paper and is applied to updating the weighting matrix of a radial basis function network. An illustrative example is given, to demonstrate the effectiveness of the algorithm for eliminating the effects of ill-conditioning in the training data, in an application of neural modelling of a multi-variable chemical process. Comparisons with results from using standard least squares algorithms, in batch and recursive form, show that the ROLS algorithm can significantly improve the neural modelling accuracy. The ROLS algorithm can also be applied to a large data set with much lower requirements on computer memory than the batch OLS algorithm.     

基于EMD的改进MFCC的语音情感识别

人在不同情感下的语音信号其非平稳性尤为明显,传统的MFCC只能反映语音信号的静态特征,经验模态分解能够精细地刻画语音信号的非平稳特性。为提取情感语音的非平稳特征,用经验模态分解将情感语音信号分解为一系列固有模态函数分量,通过Mel滤波器后取其对数能量,进行DCT反变换后得到改进的MFCC作为情感识别的新特征,采用支持向量机对高兴、生气、厌烦和恐惧等四种语音情感识别。仿真实验结果表明：改进的MFCC识别率达到77.17%,在不同的信噪比下,识别率最大可提高3.26%。     

PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation

Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical modelling. In this context, they are used to encode correlation structures over space and can generalize well in interpolation tasks. Despite their flexibility, off-the-shelf GPs present serious computational challenges which limit their scalability and practical usefulness in applied settings. Here, we propose a novel, deep generative modelling approach to tackle this challenge, termed PriorVAE: for a particular spatial setting, we approximate a class of GP priors through prior sampling and subsequent fitting of a variational autoencoder (VAE). Given a trained VAE, the resultant decoder allows spatial inference to become incredibly efficient due to the low dimensional, independently distributed latent Gaussian space representation of the VAE. Once trained, inference using the VAE decoder replaces the GP within a Bayesian sampling framework. This approach provides tractable and easy-to-implement means of approximately encoding spatial priors and facilitates efficient statistical inference. We demonstrate the utility of our VAE two-stage approach on Bayesian, small-area estimation tasks.     

A typology of different development and testing options for symbolic regression modelling of measured and calculated datasets

Data-driven modelling is used to develop two alternative types of predictive environmental model: a simulator, a model of a real-world process developed from either a conceptual understanding of physical relations and/or using measured records, and an emulator, an imitator of some other model developed on predicted outputs calculated by that source model. A simple four-way typology called Emulation Simulation Typology (EST) is proposed that distinguishes between (i) model type and (ii) different uses of model development period and model test period datasets. To address the question of to what extent simulator and emulator solutions might be considered interchangeable i.e. provide similar levels of output accuracy when tested on data different from that used in their development, a pair of counterpart pan evaporation models was created using symbolic regression. Each model type delivered similar levels of predictive skill to that other of published solutions. Input–output sensitivity analysis of the two different model types likewise confirmed two very similar underlying response functions. This study demonstrates that the type and quality of data on which a model is tested, has a greater influence on model accuracy assessment, than the type and quality of data on which a model is developed, providing that the development record is sufficiently representative of the conceptual underpinnings of the system being examined. Thus, previously reported substantial disparities occurring in goodness-of-fit statistics for pan evaporation models are most likely explained by the use of either measured or calculated data to test particular models, where lower scores do not necessarily represent major deficiencies in the solution itself.