Evaluation of behavior of the secondary columns in semi-supported steel shear walls

Semi-supported steel shear walls (SSSW), whose steel plate is connected to secondary columns rather than main columns of the frame, have been considered as an alternative steel shear walls to the traditional type. Many investigations have been made for proportionate designing of components of SSSW system. One of the important issues in this regard is the out of plane buckling of the secondary columns. In this paper, the plastic theory of structures is utilized to find out the axial force distribution, along the compressive column. Then, using energy method, for an assumed shear wall with specific geometry and material and a given shear force, the maximum overturning moment that makes the compressive secondary column buckles, can be determined. Repeating this method, for various shear forces, makes it possible to draw some interaction curves between overturning moments and shear forces. These curves can be used to analyze and design of semi-supported steel shear walls.     

Group search optimization for economic fuel scheduling

The economical use of fuel available for the generation of power has become a major concern of electric utilities. This paper presents an approach for economic fuel scheduling problem by using group search optimization. This is a minimization technique that includes the standard load constraints as well as the fuel constraints. The generation schedule is compared to that which would result if fuel constraints were ignored. The comparison shows that fuel consumed can be adequately controlled by adjusting the power output of various generating units so that the power system operates within its fuel limitations and within contractual constraints. It has been found that small additional amount of fuel may be required to serve the same power demand but the additional cost of this fuel may well compensate for the penalty that might otherwise be imposed for not maintaining the fuel contract. Numerical results for two test systems have been presented and the test results obtained from group search optimization are compared with those obtained from particle swarm optimization and evolutionary programming.     

Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.     

空间经济学视角下城郊型开发区产城融合路径

首先从空间经济学视角分析了产城关系演变中的土地价值损失的形成及弥补过程,提出产城冲突下价值损失的构成、影响因素及扩大机制。随后,围绕价值损失的补偿方式指出了产城融合的三条路径,并对现实中各类路径的应用给出了宏观及微观解释——我国城镇化和工业化发展的阶段性变化、不同类型产业的产出效率及其就业人群需求差异是路径选择的决定性因素。最后,本文指出了进一步的研究方向,即通过合理的制度安排,减少产城融合过程中不同主体之间利益再分配所需的交易成本。     

A meta-heuristic optimization approach to the scheduling of bag-of-tasks applications on heterogeneous clouds with multi-level arrivals and critical jobs

As cloud computing evolves, it is becoming more and more apparent that the future of this industry lies in interconnected cloud systems where resources will be provided by multiple “Cloud” providers instead of just one. In this way, the hosts of services that are cloud-based will have access to even larger resource pools while at the same time increasing their scalability and availability by diversifying both their computing resources and the geographical locations where those resources operate from. Furthermore the increased competition between the cloud providers in conjunction with the commoditization of hardware has already led to large decreases in the cost of cloud computing and this trend is bound to continue in the future. Scientific focus in cloud computing is also headed this way with more studies on the efficient allocation of resources and effective distribution of computing tasks between those resources. This study evaluates the use of meta-heuristic optimization algorithms in the scheduling of bag-of-tasks applications in a heterogeneous cloud of clouds. The study of both local and globally arriving jobs has been considered along with the introduction of sporadically arriving critical jobs. Simulation results show that the use of these meta-heuristics can provide significant benefits in costs and performance.     

A smooth curve evolution approach to the feedrate planning on five-axis toolpath with geometric and kinematic constraints

Feedrate planning with geometric and kinematic constraints is crucial for sculptured surface machining. Due to the non-linear relationship between the Cartesian space and the joint space, the feedrate planning method for a given five-axis toolpath is very limited compared with that in three-axis machining. To achieve the exact control of the chord error and the kinematic characteristics of cutter and machine tool, this paper presents a new feedrate planning method for five-axis parametric path using a smooth curve evolution strategy. The constraints in feedrate planning are first classified as two types of neighbor-independent (NI) constraints and neighbor-dependent (ND) constraints. Then for constraint violated region, the detailed formulas of determining the update feedrates of violated sampling points are given using a decoupled manner. As a result, NI and ND constraints are satisfied respectively with one step and multi-step smooth curve evolution technique, which can smoothly deform the target feedrate profile to the desired update positions. Simulations and experiments are performed on the given tool path to validate the effectiveness of the proposed feed planning method. The results show that the proposed method is robust and effective in the exact control of constraints in the feedrate planning on complex five-axis toolpath.     

测井曲线标准化方法研究

由于长岭凹陷区经过近60年的勘探开发,再加上井场井眼条件的差异、测井系列、仪器刻度、测量时间及操作者不同等因素,使得各测井曲线存在系统误差。因此在开展储层参数研究和有效厚度划分时,为消除这些非地层因素对原始资料的影响,有必要对测井曲线进行标准化,以保证解释结果的合理性与可靠性。测井曲线标准化方法可以分为定性和定量两种类型。其中定性方法主要包括直方图法标准化校正、均值法标准化校正和重叠图法标准化校正;定量方法则为趋势面分析校正法。本论文利用Matlab软件,采用趋势面方法对研究区域内的井进行测井曲线标准化。结果表明经过趋势面法标准化校正能够消除不利因素,准确的提取出测井曲线中的各种有用地质信息。     

Predictions of frame compliance and apex morphology in sharp nanoindentations

If all components in a nanoindentation system are well calibrated and a reference material has unique hardness, H and reduced modulus, E_r independent of the indentation depths, the load, L and the penetration depth, h in the indentation loading curve of the reference material can be correlated by L=Kh². Here the constant K is expressed by H, E_r and indenter geometry constants. By using H and E_r of a fused silica and the Berkovich geometry, an analytical expression for the indentation loading curve could be derived. To compare with this analytical loading curve, experimental indentation data were measured with two commercial nanoindenters. The experimental loading curves shifted leftward or rightward from the analytical loading curve and this depth deviation was attributed to improper calibration of the nanoindenters. Quantitative calibrations of frame compliance and indenter bluntness were tried for the raw nanoindentation data and this resulted in consistent nanoindentation data regardless of the used nanoindenters.     

Project scheduling under partially renewable resources and resource consumption during setup operations

There has been an increasing pressure on manufacturing industries to reduce energy consumption. In this study, we propose a new variant of RCPSP called RCPSP/πRC, which can deal with realistic energy constraints such as power restriction during peak hours, contract demand, and energy consumption during setup operations. First, we present an integer programming (IP) model and a constraint programming (CP) model of the RCPSP/πRC. Next, we present a heuristic mode restriction method called a mask calculation algorithm to achieve efficient searching by restricting selectable modes. Finally, through computational experiments, we evaluate the proposed methods and show their effectiveness.