A novel minimum weight formulation of topology optimization implemented with reanalysis approach

In this paper, we develop an efficient diagonal quadratic optimization formulation for minimum weight design problem subject to multiple constraints. A high-efficiency computational approach of topology optimization is implemented within the framework of approximate reanalysis. The key point of the formulation is the introduction of the reciprocal-type variables. The topology optimization seeking for minimum weight can be transformed as a sequence of quadratic program with separable and strictly positive definite Hessian matrix, thus can be solved by a sequential quadratic programming approach. A modified sensitivity filtering scheme is suggested to remove undesirable checkerboard patterns and mesh dependence. Several typical examples are provided to validate the presented approach. It is observed that the optimized structure can achieve lighter weight than those from the established method by the demonstrative numerical test. Considerable computational savings can be achieved without loss of accuracy of the final design for 3D structure. Moreover, the effects of multiple constraints and upper bound of the allowable compliance upon the optimized designs are investigated by numerical examples.     

基于UG的采煤机防爆盖板三维标注及数控工艺研究

随着三维CAD技术的发展,产品制造信息在三维模型和二维图纸之间的反复传递,严重影响了产品制造信息的正确、快速传递。基于三维产品制造信息技术,以采煤机防爆盖板为例,在UG NX10.0中建立了防爆盖板的三维模型并进行了三维标注,设计了加工工艺路线,规划了数控加工刀具轨迹,进行了加工仿真,验证了数控加工的正确性,在此基础上完成了采煤机防爆盖板的三维工艺式设计。该研究对基于模型定义的数字化设计与制造研究,实现制造业信息化具有重要意义。     

爆炸防控技术在爆破工程警务规划和技术评估中应用

通过介绍爆炸防控技术概念的内涵和外延,说明了爆破工程警务规划设计和技术评估的目的,就是有效防止或遏制涉爆事故,维护社会公共安全。在对一项复杂环境下的基础开挖爆破工程实施警务规划设计和技术评估时,警务技术民警对照爆破作业设计施工单位技术方案中的参数,现场核实需要保护的建筑和重要设施,准确测量保护目标与爆区的实际距离,分析是否存在影响爆破工程实施的不稳定因素,确认上述参数符合安全要求后做出准予爆破许可的建议。爆破工程警务规划设计和技术评估的要点就是要通过现场核查,核实爆破有害效应是否可控,能否确保爆破施工时周边保护目标的安全。     

考虑关节角加速度约束的仿人机器人偏摆力矩控制方法

针对仿人机器人步行过程中存在的机器人关节角加速度约束影响控制性能的问题，提出一种考虑关节角加速度约束的仿人机器人偏摆力矩控制方法.该方法充分考虑了双臂在摆动过程中对偏摆力矩的影响，根据力矩平衡条件得到需要抵消的偏摆力矩的大小与方向，将偏摆力矩的控制问题转化为带约束条件的二次规划问题，并设计了一种在线变步长迭代算法计算得到优化后的双臂摆动轨迹.实验表明，该方法能有效抵消机器人步行中产生的偏摆力矩，避免控制过程中的"削峰"现象，有效提高机器人的步行稳定性.     

The generalized discrete ‐centroid problem

The ‐centroid problem or leader–follower problem is generalized considering different customer choice rules where a customer may use facilities belonging to different firms, if the difference in travel distance (or time) is small enough. Assuming essential goods, some particular customer choice rules are analyzed. Linear programming formulations for the generalized ‐medianoid and ‐centroid problems are presented and an exact solution approach is applied. Some computational examples are included.     

Sustainable optimization of waste management network over extended planning time horizon

This study proposes a multiperiod mixed integer linear programming model for the management of a single municipal solid waste (MSW) treatment plant with sustainability as the objective. Discrete and continuous variables define the capacity selections for diverse MSW technologies, and the operation of the MSW network, respectively. The economic target is considered to maximize the net present value. The environmental impact is the minimization of a normalized environmental objective function (NEOF). The social target is the maximization of jobs. An interesting feature about the research work is the requirement of biodrying technologies for MSW moisture content control. Due to the conflicted nature among the sustainability components, a multiobjective optimization (MO) is carried out to find the Pareto optimal solutions. The MO results show that the Pareto optimal solutions vary around profit range of $4.9–8.5 billion, NEOF impact range of 3.2–3.6 units, and social benefit range of 2700–4828 jobs.     

An efficient approach for the S‐shaped penalty function

Interval goal programming (IGP) with a marginal penalty function (PF) was first proposed by Charnes and Collomb in 1972, and further improved by Kvanli and other researchers. Recently, Lu and Chen proposed an efficient logarithmic method to formulate IGP with an S‐shaped PF. However, their method requires adding many binary variables when the problem size becomes large, which increases the computational burden in the solution process. This study proposes an efficient approach for the S‐shaped PF. The arbitrary PF frequently appears in the fields of business and industry. However, none of the previous approaches have addressed arbitrary PFs without adding binary variables. The proposed approach can be easily extended to formulate an arbitrary PF in which binary variables are no longer required, regardless of the number of break points. The proposed method can improve the efficiency of IGP for solving large size management and decision problems in considering PFs. In order to demonstrate the correctness, usefulness of the proposed model, illustrative examples are provided.     

Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming

Prediction of mode I fracture toughness (K_IC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression (LMR) and gene expression programming (GEP) methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and elastic modulus (E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets. Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of K_IC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) were utilized herein. In the case of testing datasets, the values of R², RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156, respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R² value and lower errors.     

Decentralized optimal tracking control for large-scale nonlinear systems with tracking error constraints

In this article, a decentralized optimal tracking control strategy is proposed for a class of nonlinear systems with tracking error constraints by utilizing adaptive dynamic programming (ADP). It should be noted that ADP technology cannot be directly used to solve decentralized optimal tracking problem of large-scale interconnected nonlinear system with nonzero equilibrium points, since that an infinite domain performance index function may result in an unsolvable solution. In addition, by introducing a smooth function, the constrained tracking error is transformed into an unconstrained one. Then, the error dynamics and a new infinite domain performance index function are designed, such that ADP technology can be used. Following the designed performance index function, the tracking error can be ensured within a small neighborhood of zero. Finally, the feasibility and the effectiveness of the proposed decentralized optimal control scheme are verified through two simulation examples.