Combined shape and sizing optimization of truss structures

 In this paper, an evolutionary optimization method is presented for weight minimum problem of a 3-dimensional truss structure in terms of nodal coordinates and element cross-sectional areas. The structure is subject to stress, local buckling and displacement constraints. Two types of design variables with different natures are optimized separately: (1) a fully stressed design (FSD) and scaling techniques are applied to sizing variables and (2) the evolutionary node shift method is applied to shape variables. Alternating procedure is utilized to couple the two types of variables and to combine the results. The optimum solution is achieved gradually from the initial configuration design. Two typical truss structures are examined to illustrate the validity of the method. Received: 22 October 2001 / Accepted: 04 June 2002 This research work is supported by the National Natural Science Foundation of China under the guarantees no. 10072050 and 10172072, respectively.     

Characterisation of inter-particle forces within agglomerated metallurgical powders

The strength of agglomerates of nickel flash furnace concentrate and dust was determined from experimental observations of agglomerates forming under controlled conditions, combined with mathematical equations from the literature. It was found that the agglomerates had a tensile strength ranging from 0.01 Pa to 38.7 Pa, while inter-particle forces ranged from 2.2 × 10^− 12 N to 1.5 × 10^− 10 N. These values were compared to the expected magnitude of van der Waals, electrostatic, magnetic and capillary forces within the agglomerates, and it was found that both electrostatic and van der Waals forces are likely to contribute to the cohesion of agglomerates, although sub-micron particles and the presence of sufficiently large asperities on the surface of particles limit the magnitude of van der Waals forces. Magnetic forces are large enough to contribute to the cohesion of dust agglomerates, which is in keeping with the high magnetite content of the recycle dust. It is postulated that electrostatic forces, acting over a longer range than van der Waals forces, may be responsible for initially bringing particles together. The methodology for determining inter-particle forces can be applied to the computer modelling of flash smelting systems, as well as other gas/particle systems such as fluidized beds.     

Optimal Control of Solar Sludge Dryers

Optimal control (OC) methodology is used to develop a control policy for a batch-operated solar sludge dryer with ventilation as the dominant control. Batch performance criteria and instantaneous optimization criteria are developed for two economic environments: quota limited and capital limited. The general formulation, as well as simplifications resulting from the multiplicative form of the evaporation (drying) rate model, are presented. The multiplicative form results in a single constant number, called here “control intensity,” which is used to guide the on-line control decisions. These decisions turn out to be independent of the dry solids content (DSC) of the sludge. Further simplification, by assuming a strictly constant weather, is used to demonstrate the general effects of the economic and physical (weather) environment on the solution.

The OC approach is used to address, via simulation under realistic weather conditions, several design, operation, and pricing problems. In particular, the following have been considered: sizing of the ventilation fans, determination of a fair fee for sludge disposal, finding the best final DSC, and evaluating the effect of the price of electricity. Regarding the particular location represented by the data, it has been shown that the installed capacity of the ventilation fans should be increased and that there seems to be sufficient economic incentive for solar drying.     

铝箔纸胶粘剂的制备

由醋酸乙烯和丙烯酸酯所制得的共聚物，能够极大地提高乳液对铝箔和纸张之间的粘合力，通过调整保护胶体聚乙烯醇的用量和增塑剂邻苯二甲酸二丁酯在反应过程中的不同加入方式，可以控制和生产所需要的乳液粘度，流平剂的加入，则改良了乳液在铝箔上胶时的流平性。     

使用扩展逻辑效力的逻辑路径尺寸优化方法

为解决集成电路物理设计中考虑互连线影响的逻辑路径延迟优化问题,提出一个计入互连线负载的扩展的逻辑效力(ELE),并针对ELE给出一个可同时优化逻辑路径中各个逻辑门尺寸及各段互连线长度的优化流程.ELE在保留原有逻辑效力参数的同时,使用互连寄生参数提取软件获得的Ⅱ型互连线参数,实现对带有互连线负载的逻辑门的传播延迟的描述和估计;逻辑路径优化流程采用效力延迟分配策略作为初始条件来表示各段互连线负载对总效力延迟的影响,将所用目标单元库和制造工艺的物理尺寸信息作为限制条件,以ELE表达式为核心展开优化计算,辅以动态规划办法,无需迭代运算,仅通过一轮计算即可求得全部结果.实验结果表明,该流程计算任务简单,资源耗费少,可以准确、快速地获得所需的逻辑门尺寸和互连线长度;结果清晰合理,与目标单元库和工艺库完全兼容.     

基于单片机的在线颗粒分析仪智能控制系统设计

针对国内粉末生产过程中粒度分布检验的滞后性,提出了在线颗粒分析仪智能控制系统设计。通过控制采样速度和喂料速度实现样品的在线分析,并实现分析数据的实时采集和传输,实现在线监控。实验结果表明,该系统测试范围达到4-300μm,可持续工作8小时,延时小于1分钟,测试准确度,重复性均达到在线控制的需要。     

Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain.     

Solving a multi-level capacitated lot sizing problem with multi-period setup carry-over via a fix-and-optimize heuristic

This paper presents a new algorithm for the dynamic multi-level capacitated lot sizing problem with setup carry-overs (MLCLSP-L). The MLCLSP-L is a big-bucket model that allows the production of any number of products within a period, but it incorporates partial sequencing of the production orders in the sense that the first and the last products produced in a period are determined by the model. We solve a model which is applicable to general bill-of-material structures and which includes minimum lead times of one period and multi-period setup carry-overs. Our algorithm solves a series of mixed-integer linear programs in an iterative so-called fix-and-optimize approach. In each instance of these mixed-integer linear programs a large number of binary setup variables is fixed whereas only a small subset of these variables is optimized, together with the complete set of the inventory and lot size variables. A numerical study shows that the algorithm provides high-quality results and that the computational effort is moderate.     

An efficient optimal algorithm for the quantity discount problem in material requirement planning

An optimal algorithm based on branch-and-bound approach is presented in this paper to determine lot sizes for a single item in material requirement planning environments with deterministic time-phased demand and constant ordering cost with zero lead time, where all-units discounts are available from vendors and backlog is not permitted. On the basis of the proven properties of optimal order policy, a tree-search procedure is presented to construct the sequence of optimal orders. Some useful fathom rules have been proven, which make the algorithm very efficient. To compare the performance of this algorithm with the other existing optimal algorithms, an experimental design with various environments has been developed. Experimental results show that the performance of our optimal algorithm is much better than the performance of other existing optimal algorithms. Considering computational time as the performance measure, this algorithm is considered the best among the existing optimal algorithms for real problems with large dimensions (i.e. large number of periods and discount levels).     

Economic lot sizing in a production system with random demand

An extended economic production quantity model that copes with random demand is developed in this paper. A unique feature of the proposed study is the consideration of transient shortage during the production stage, which has not been explicitly analysed in existing literature. The considered costs include set-up cost for the batch production, inventory carrying cost during the production and depletion stages in one replenishment cycle, and shortage cost when demand cannot be satisfied from the shop floor immediately. Based on renewal reward process, a per-unit-time expected cost model is developed and analysed. Under some mild condition, it can be shown that the approximate cost function is convex. Computational experiments have demonstrated that the average reduction in total cost is significant when the proposed lot sizing policy is compared with those with deterministic demand.