International Journal of Modeling, Simulation, and Scientific Computing

In this paper, we present an algorithm for multivariate interpolation of scattered data sets lying in convex domains $\mathrm{\Omega }\subseteq {ℝ}^N$, for any $N\ge 2$. To organize the points in a multidimensional space, we build a $kd$-tree space-partitioning data structure, which is used to efficiently apply a partition of unity interpolant. This global scheme is combined with local radial basis function (RBF) approximants and compactly supported weight functions. A detailed description of the algorithm for convex domains and a complexity analysis of the computational procedures are also considered. Several numerical experiments show the performances of the interpolation algorithm on various sets of Halton data points contained in $\mathrm{\Omega }$, where $\mathrm{\Omega }$ can be any convex domain, like a 2D polygon or a 3D polyhedron. Finally, an application to topographical data contained in a pentagonal domain is presented.

The Lewisian turning-point obviously increase the labor costs of Chinese manufacturing firms, it made the employee turnover to become the key problem that how to keep the enterprise human resource stability. Here, the conceptual catastrophe model of employee turnover is built from the new perspective of empirical study and catastrophe theory. In particular, we developed a parameter estimation method of the Cusp catastrophe model based on qualitative simulation and fuzzy math, and then to demonstrate the rationality and robustness of this method by a practical case on a manufacturing enterprise. In the end, a series of virtual experiments are carried out for the employee turnover in the period of China’s economic transformation. The main results are that, how to measure the warning, critical and mutation area of employee turnover; the real relationship between the complex economic environment and the demission of employees; the control policy from the macroscopic governmental measures and microscopic enterprise ways.

The design process of new air- and rotorcraft is commonly separated into three different consecutive phases. In the conceptual design phase, the viability of different designs is investigated with respect to customer requirements and/or the market situation. It usually ends with the identification of a basic aircraft lay-out. In the subsequent preliminary design stage the various disciplines are introduced, thus redefining the design process as a multidisciplinary optimization (MDO) task. The objective of this design stage is to enhance the initial aircraft configuration by establishing an advanced design comprising a loft provided with primary structure. This updated aircraft configuration represents a global optimum solution for the specified requirements which will then be optimized on a local level in the concluding detailed design phase with particular regard to manufacturing aspects. The investigations in the preliminary design phase comprise the generation of numerous similar but still different analytical and finite element (FE) models. Even though computational power is constantly increasing the model generation process is still a time-consuming task. Moreover, it is also a potential source of errors which — in the worst case — may lead to time- and cost-intensive redesign activities during the detailed design. As the preliminary design stage, therefore, is of particular importance during the overall design process the model generation process benefits from parametric models and automated process chains. The presented paper overviews the tools used for the automated generation of FE models developed and used at the Institute of Structures and Design (BT) of the German Aerospace Center (DLR) for the subsequent use in numerical simulations. Furthermore, basic requirements for the effective use of parametrization and automation like a common data format and infrastructure will be introduced. Exemplary models and applications will be presented to illustrate the positive impact on efficiency in aircraft design. Concluding, future development steps and possible applications will be discussed.

Material handling has become one of the major challenges in modern production management. Consequently, this paper intends to investigate the part delivery of mixed-model assembly lines with decentralized supermarkets and tow trains. Besides, uncertain exception disturbances, including tow train failures and adjustments of the production sequence, are also considered. To solve this problem, a heuristic-based dynamic delivery strategy is proposed, which dynamically schedules the route, departure time, quantities and types of loaded parts for each tour. To evaluate the performance of this strategy, it is used to solve an instance in comparison with the periodic delivery strategy, experimental results are reported and their performances are compared under different metrics. Moreover, a multi-scenario analysis is employed to determinate the long-term decisions, including the number of tow trains and the route layout. Finally, the critical storage is suggested to be set for each station to avoid part starvation resulting from disturbances, and its effect on the delivery performance is investigated.

With the development of micromachining technology, it is very important to study the mechanism of micromachining, determine the micromachining parameters and ensure the products’ quality during the micromachining process. Combined with the micro-mechanism between tool and workpiece during micromachining process, the sources of the micro-cutting force were analyzed, the micro-cutting physical model was constructed, and the microstress model interacted between the cutting arc edge of the tool and the material of the workpiece was analyzed. Combined with the surface friction and elastic extrusion mechanism between the cutting tool and workpiece, the micro-cutting force model was constructed from two aspects. The micro-cutting depth is deeper than the minimum cutting depth and the micro-cutting depth is shallower than the minimum cutting depth, then the minimum cutting depth value was calculated. Combined with the dislocation properties and microcrystal structure of workpiece’s material, the internal stress of the micromachining force model based on the gradient plasticity theory was calculated, and the force model of the micro-cutting process was studied too. It is significant to control the precision of micromachining process during the micromachining process by constructing the micromachining process force model through studying the small deformation of the material and the mechanism of micromachining.

Forest resources are important natural resources for all living beings but they are continuously depleting due to overgrowth of human population and their development activities. Therefore, conservation of forest resources is an important problem for sustainable development. In view of this, in this paper, we have proposed and analyzed a nonlinear mathematical model to study the effects of economic and technological efforts on the conservation of forest resources. In the modeling process, it is assumed that due to increase in population size, the demand of population (population pressure) for forest products, lands, etc., increases and to reduce this population pressure, economic efforts are employed proportional to the population pressure. Further, it is assumed that technological efforts in the form of genetically engineered plants are applied proportional to the depleted level of forest resources to conserve them. Model analysis reveals that increase in economic and technological efforts increases the density of forest resources but further increase in these efforts destabilizes the system. Numerical simulation is carried out to verify analytical findings and explore the effect of different parameters on the dynamics of model system.

Intuitionistic fuzzy sets (IFSs) are well established as a tool to handle the hesitation in the decision system. In this research paper, fuzzy sets induced by IFS are used to develop a fuzzy time series forecasting model to incorporate degree of hesitation (nondeterminacy). To improve the forecasting accuracy, induced fuzzy sets are used to establish fuzzy logical relations. To verify the performance of the proposed model, it is implemented on one of the benchmarking time series data. Further, developed forecasting method is also tested and validated by applying it on a financial time series data. In order to show the accuracy in forecasting, the method is compared with other forecasting methods using different error measures.

Recently, Chinese megacities have suffered serious air pollution. Previous studies have pointed out that transportation systems have become one of the major sources of air pollution and on-road pollutant concentrations are significantly higher than off-road. Electric vehicle (EV) introduction is proposed as a method to alleviate the current situation. In order to better understand the benefit of the use of EVs in Beijing, a simulation platform has been developed to evaluate the improvement of air quality with the use of EVs quantitatively within the selected area. Four scenarios with different EV penetration rates are proposed and the results revealed 5%, 10%, 15% EV penetration rates which will bring about improvement of 0.86%, 9.01% and 12.23% for PM2.5, 0.92%, 9.01% and 13.32% for nitrogen oxides (NO_x), 0.95%, 8.86% and 13.73% for CO, respectively. The results revealed a promising improvement of air quality with the introduction of EVs.