SIMULATION AND SCALE-UP OF PNEUMATIC CONVEYING AND CASCADING ROTARY DRYERS

ABSTRACT

This paper presents a unified model for simulation of cocurrent and countercurrent dispersion-type dryers. The main industrial applications are to pneumatic conveying (flash) dryers and cascading (direct) rotary dryers. The basic model is a one-dimensional incremental (stepwise) simulation, which has been developed over a number of years Equations for particle motion, heat and mass transfer, heat and mass balances and local gas conditions are solved simultaneously over a small increment along the dryer. All workers have previously had considerable difficulty in obtaining a good fit between simulations and actual results from pilot-plants or large-scale industrial dryers. A new “fitting mode” calculation overcomes this by identifying the parameters which need to be adjusted, concentrating on those which cannot be measured accurately. Excellent agreement has been obtained between the model and experimental data by this method. The paper also presents revised formulations for particle motion and heat transfer in rotary dryers. The model has been incorporated into two computer programs for flash and rotary dryers respectively, and results from the former are shown for a case study.     

有限变形理论能量释放率增率形式

基于有限变形理论中的能量原理和变分原理,考虑以有裂纹的瞬时位形为参考,建立增量变形引起的裂纹扩展方程能够更真实的描述裂纹尖端的扩展机制,在含有裂纹物体的瞬时变形的基础上,推导了裂纹体的能量释放率和增率的形式,提出了裂纹扩展判据.该判据反映了单位时间内裂纹扩展单位面积可以提供的能量与单位时间内裂纹扩展单位面积所需要的能量...     

Direct displacement-based assessment with nonlinear soil–structure interaction for multi-span reinforced concrete bridges

A practical and readily implementable seismic assessment procedure for multi-span reinforced concrete bridges is introduced in this paper. The procedure is based on an existing direct displacement-based assessment (DDBA) approach, and accounts for nonlinear dynamic soil–structure interaction (NLSSI) effects. Several simplified bridge structures lying on shallow foundations have been used as application examples. The validation has been done by comparing DDBA+NLSSI with the results of finite-element nonlinear time-history simulations by means of incremental dynamic analysis. Moreover, the influence of NLSSI on the assessment procedure has been evaluated by considering the same bridges with fixed base and with NLSSI effects. In spite of its simplicity that presently prevents its use for complex bridge structures, the proposed procedure is found to provide fast and reliable results, useful to give a first-level screening on a large set of bridges for highlighting the most critical situations, as well as to carry out fast parametric analyses to produce fragility curves in the framework of performance-based vulnerability/risk assessment.     

Accelerating EM for Large Databases

The EM algorithm is a popular method for parameter estimation in a variety of problems involving missing data. However, the EM algorithm often requires significant computational resources and has been dismissed as impractical for large databases. We present two approaches that significantly reduce the computational cost of applying the EM algorithm to databases with a large number of cases, including databases with large dimensionality. Both approaches are based on partial E-steps for which we can use the results of Neal and Hinton (In Jordan, M. (Ed.), Learning in Graphical Models, pp. 355–371. The Netherlands: Kluwer Academic Publishers) to obtain the standard convergence guarantees of EM. The first approach is a version of the incremental EM algorithm, described in Neal and Hinton (1998), which cycles through data cases in blocks. The number of cases in each block dramatically effects the efficiency of the algorithm. We provide amethod for selecting a near optimal block size. The second approach, which we call lazy EM, will, at scheduled iterations, evaluate the significance of each data case and then proceed for several iterations actively using only the significant cases. We demonstrate that both methods can significantly reduce computational costs through their application to high-dimensional real-world and synthetic mixture modeling problems for large databases.     

Incremental Testability Analysis for Partial Scan Selection and Design Transformations

This paper presents an efficient estimation method for incremental testability analysis, which is based partially on explicit testability re-calculation and partially on gradient techniques. The analysis results have been used successfully to guide design transformations and partial scan selection. Experimental results on a variety of benchmarks show that the quality of our incremental testability analysis is similar to those of the conventional explicit testability re-calculation methods and the technique can be used efficiently for improving the testability of a design during the high-level test synthesis and partial scan selection processes.     

A solid transportation problem for an item with fixed charge, vechicle cost and price discounted varying charge using genetic algorithm

In this paper, discount in transportation cost on the basis of transportated amount is extended to a solid transportation problem. In a transportation model, the available discount is normally offered on items/criteria, etc., in the form AUD (all unit discount) or IQD (incremental quantity discount) or combination of these two. Here transportation model is considered with fixed charges and vechicle costs where AUD, IQD or combination of AUD and IQD on the price depending upon the amount is offered and varies on the choice of origin, destination and conveyance. To solve the problem, genetic algorithm (GA) based on Roulette wheel selection, arithmetic crossover and uniform mutation has been suitably developed and applied. To illustrate the models, numerical examples have been presented. Here, different types of constraints are introduced and the corresponding results are obtained. To have better customer service, the entropy function is considered and it is displayed by a numerical example. To exhibit the efficiency of GA, another method—weighted average method for multi-objective is presented, executed on a multi-objective problem and the results of these two methods are compared.     

A recurrent stochastic binary network

Stochastic neural networks are usually built by introducing random fluctuations into the network. A natural method is to use stochastic connections rather than stochastic activation functions. We propose a new model in which each neuron has very simple functionality but all the connections are stochastic. It is shown that the stationary distribution of the network uniquely exists and it is approxi-mately a Boltzmann-Gibbs distribution. The relationship between the model and the Markov random field is discussed. New techniques to implement simulated annealing and Boltzmann learning are pro-posed. Simulation results on the graph bisection problem and image recognition show that the network is powerful enough to solve real world problems.     

增量法在深基坑支护结构计算中的应用

通过应用增量法的原理，用ＳＡＰ８４程序对深基坑支护结构进行内力分析，说明增量法的使用方法和其科学性、合理性、安全性，并希望该法的应用能编入《深基坑支护技术规程（送审稿）》和进入相关的应用程序，以期该法能够在更大的范围内推广应用。     

Reducing partition skew on MapReduce: an incremental allocation approach

MapReduce, a parallel computational model, has been widely used in processing big data in a distributed cluster. Consisting of alternate map and reduce phases, MapReduce has to shuffle the intermediate data generated by mappers to reducers. The key challenge of ensuring balanced workload on MapReduce is to reduce partition skew among reducers without detailed distribution information on mapped data. In this paper, we propose an incremental data allocation approach to reduce partition skew among reducers on MapReduce. The proposed approach divides mapped data into many micro-partitions and gradually gathers the statistics on their sizes in the process of mapping. The micropartitions are then incrementally allocated to reducers in multiple rounds. We propose to execute incremental allocation in two steps, micro-partition scheduling and micro-partition allocation. We propose a Markov decision process (MDP) model to optimize the problem of multiple-round micropartition scheduling for allocation commitment. We present an optimal solution with the time complexity of O(K · N²), in which K represents the number of allocation rounds and N represents the number of micro-partitions. Alternatively, we also present a greedy but more efficient algorithm with the time complexity of O(K · N ln N). Then, we propose a minmax programming model to handle the allocation mapping between micro-partitions and reducers, and present an effective heuristic solution due to its NP-completeness. Finally, we have implemented the proposed approach on Hadoop, an open-source MapReduce platform, and empirically evaluated its performance. Our extensive experiments show that compared with the state-of-the-art approaches, the proposed approach achieves considerably better data load balance among reducers as well as overall better parallel performance.