International Journal of Modeling, Simulation, and Scientific Computing

Cloud manufacturing is a new manufacturing paradigm which creates an open environment for transactions among the enterprises. Research on transaction modes and regularities in a cloud manufacturing environment is important for promoting the applications of cloud manufacturing. To this end, we design and implement a simulation platform according to the typical transaction processes of enterprises in the cloud manufacturing environment. In the simulation platform, enterprises are encapsulated into Service Agents, and thus the activities of service agents can be used to describe enterprise behaviors. By defining different rules, simulations for different business models can be conducted. Detailed descriptions of the platform architecture, functions, and key technologies are presented. The feasibility of the simulation platform is verified through a case study.

In order to establish an accurate peak over threshold (POT) model for reasonable load extrapolation, a new threshold selection method based on multiple criteria decision making (MCDM) technology is proposed. The fitting test criterion is taken into consideration in the method. For each candidate threshold, the fitting values of several fitting test criteria are integrated into a comprehensive evaluation value through entropy method and MCDM technology. The threshold corresponding to the minimum comprehensive evaluation value is assumed as the optimal threshold. A random simulation study is carried out to evaluate the performance of the method and to compare them with other literature methods. Genuine load data are applied the proposed method. Both the results shown that the proposed method could be seen as an additional method that complements existing threshold selection methods.

Development of an Advection Upstream Splitting Method (AUSM${}^{+}$-up) scheme-based Unstructured Finite Volume (UFVM) solver for the simulation of two-dimensional axisymmetric/planar high speed compressible turbulent reacting shear layers is presented. The inviscid numerical flux is evaluated using AUSM${}^{+}$-up upwind scheme. An eight-step hydrogen–oxygen finite rate chemistry model is used to model the development of chemical species in a supersonic reacting flow field. The chemical species terms are alone solved implicitly in this explicit flow solver by rescaling the equation in time. The turbulence modeling has been done using RNG-based $k$–$𝜖$ model. Three-stage Runge–Kutta method has been used for explicit time integration. The nonreacting two-dimensional Cartesian version of the same solver has been successfully validated against experimental and numerical results reported for the wall static pressure data in sonic slot injection to supersonic stream. Detailed validation studies for reacting flow solver has been done using experimental results reported for a coaxial supersonic combustor, in which species profile at various axial locations has been compared. Present numerical solver is useful in simulating combustors of high speed air-breathing propulsion devices.

Due to widespread growth of cloud technology, virtual server accomplished in cloud platform may collect useful data from a client and then jointly disclose the client’s sensitive data without permission. Hence, from the perspective of cloud clients, it is very important to take confident technical actions to defend their privacy at client side. Accordingly, different privacy protection techniques have been presented in the literature for safeguarding the original data. This paper presents a technique for privacy preservation of cloud data using Kronecker product and Bat algorithm-based coefficient generation. Overall, the proposed privacy preservation method is performed using two important steps. In the first step, PU coefficient is optimally found out using PUBAT algorithm with new objective function. In the second step, input data and PU coefficient is then utilized for finding the privacy protected data for further data publishing in cloud environment. For the performance analysis, the experimentation is performed with three datasets namely, Cleveland, Switzerland and Hungarian and evaluation is performed using accuracy and DBDR. From the outcome, the proposed algorithm obtained the accuracy of 94.28% but the existing algorithm obtained only the 83.64% to prove the utility. On the other hand, the proposed algorithm obtained DBDR of 35.28% but the existing algorithm obtained only 12.89% to prove the privacy measure.

In this paper, two wavelet neural network (WNN) frames which depend on Morlet wavelet function and Gaussian wavelet function were established. In order to improve the efficiency of model training, the momentum term was applied to modify the weights and thresholds, and the output of the network was summed up by function transformation of output layer nodes. When the Gaussian Wavelet Neural Networks (GWNN) and Morlet Wavelet Neural Networks (MWNN) were applied to coal consumption rate (CCR) estimation in a thermal power plant, the results confirmed their potency in function approximation. In addition, the influence of learning rate on the models was also discussed through the orthogonal experiment.

Heat transfer using air jet impingement technique is one of the conspicuous tasks in the looming world of electronic packaging system. Here, the material selection of heat sink becomes one of the prior and important assignments to construct a heat sink with desired characteristic cooling rate. In order to study the material effect of heat sink over the cooling characteristic, the present work takes an initiative in plotting the Nusselt magnitude over the radial distance for different material of heat sink. This is done by computing the flow regime and heat transfer characteristic of a 2D axis symmetric geometry in commercial simulating software, ANSYS CFX. The computation of cooling characteristic in form of Nusselt profile is done using SST + Gamma–theta turbulence model. Since the prediction of heat interaction due to the intermediacy and transition in the flow regime is a unique issue of this problem. The results for Nusselt curve signifies a tangible elevation in local Nusselt value (nonuniformity) with decrease in thermal diffusivity of target surface. Also the nonuniformity is observed to vanish above a critical range (66.76mm²/s) of thermal diffusivity. This happens due to presences of abnormal turbulence of heat flow which occurs inside the target surface. Since the variation in thermal diffusivity causes some imbalance competition between the heat storage and dissipation capabilities. Above all the target surface carrying thermal diffusivity less than 66.76mm²/s possesses a dominating heat storage capability, on behalf of which some heat transfer occurring in near jet and far jet regions are being restricted. These are transferred towards stagnation region in radial direction.

In this paper, we study a new numerical technique for a class of 2D nonlinear fractional diffusion-wave equations with the Caputo-type temporal derivative and Riesz-type spatial derivative. Galerkin finite element scheme is used for the discretization in the spatial direction, and the temporal component is discretized by a new alternating direction implicit (ADI) method. Next, we strictly prove that the numerical method is stable and convergent. Finally, to confirm our theoretical analysis, some numerical examples in 2D space are presented.

Cloud simulation derived data is defined as the data related to service version, characteristics, relationships, runtime environments and cross-domain communication during service execution in cloud simulation environment, collectively. It is of great value and significance in cloud simulation for service description, service composition and resource management. The types of derived data are abundant and the amount of it is huge. Existing studies on cloud simulation usually assume all of the derived data required for a specific task is well organized and available anytime, which is of course impossible. Derived data needs to be expressed and managed in terms of knowledge to make sure the smooth execution of cloud simulation platform. Therefore, this paper presents a derived data management method in cloud simulation platform to enable derived data collection and dynamic knowledge storage. The prototype system of the proposed method are established and a virtual prototype of double girder crane is taken as an example to verify the effectiveness of the method.

Online sequential extreme learning machine (OS-ELM) for single-hidden layer feedforward networks (SLFNs) is an effective machine learning algorithm. But OS-ELM has some underlying weaknesses of neglecting time series timeliness and being incapable to provide quantitative uncertainty for prediction. To overcome these shortcomings, a time series prediction method is presented based on the combination of OS-ELM with adaptive forgetting factor (AFF-OS-ELM) and bootstrap (B-AFF-OS-ELM). Firstly, adaptive forgetting factor is added into OS-ELM for adjusting the effective window length of training data during OS-ELM sequential learning phase. Secondly, the current bootstrap is developed to fit time series prediction online. Then associated with improved bootstrap, the proposed method can compute prediction interval as uncertainty information, meanwhile the improved bootstrap enhances prediction accuracy and stability of AFF-OS-ELM. Performances of B-AFF-OS-ELM are benchmarked with other traditional and improved OS-ELM on simulation and practical time series data. Results indicate the significant performances achieved by B-AFF-OS-ELM.

In this paper, we present an attempt at analyzing the seasonal pattern of the Babesiosis transmission dynamics in bovine and tick populations. The infestation rate plays an important role in the variation of infection. In this logic, we set out a mathematical model with variable infestation rate for the evolution of babesiosis disease. Using the (0,2)-Jacobi multi-wavelets method combined with the decoupling and quasi-linearization technique, we demonstrate the validity and applicability of our model. Then, a set of experimental data is used to validate the proposed model under the same operating conditions. The results of numerical simulations are provided to show the impact of seasonality on the transmission dynamics of Babesiosis. Eventually, a numerical study of the model varying the control parameters of babesiosis shows different scenarios about the spread of the disease.

Considering both the high complexity of urban traffic flow systems and the bounded rationality of travelers, providing traffic information to all travelers is an effective method to induce each individual to make a more rational route-choice decision. Within Advanced Traveler Information System (ATIS) working environment, temporal and spatial evolution processes of traffic flow in urban road networks are closely related to strategies of providing traffic information and contents of information. In view of the day-to-day route-choice situations, this study constructs original updating models of the cognitive travel time of travelers under four conditions, including not providing any route travel time, only providing the most rapid route travel time, only providing the most congested route travel time, and providing all the routes travel times. The disaggregate route-choice approach is adopted for simulation to reveal the relationship between the evolution process of network traffic flow and the strategy of providing traffic information. The simulation shows that providing traffic information to all travelers cannot improve the operational efficiency of road networks. It is noteworthy that an inappropriate information feedback strategy would lead to intense variation in various routes traffic flow. Compared with incomplete information feedback strategies, it is inefficient and superfluous to provide complete traffic information to all travelers.

Programming terminal high-low collaborative intercepting strategy scientifically and constructing assistant decision-making model with self-determination and intellectualization is onekey problem to enhance operational efficiency. Assistant decision-making model has been constructed after analysis on collaborative intercepting principle; then Improved Clonal Selection Algorithm Optimizing Neural Network (ICLONALG-NN) is designed to solve the terminal anti-missile collaborative intercepting assistant decision-making model through introducing crossover operator to increase population diversity, introducing modified combination operator to make use of the information before crossover and mutation, introducing population update operator into traditional CLONALG to optimize Neural Network parameters. Experimental simulation confirms the superiority and practicability of the assistant decision-making model solved by ICLONALG-NN.

The modeling of distillation column process is a very challenging problem because of the complex dynamic behavior. This paper investigates a Nonlinear Autoregressive Moving Average with eXogenous input (NARMAX) model, and a Hammerstein model to approximate the evolution of the overhead temperature in a separation system. The model development and validation are studied through experiments carried out on a distillation plant of laboratory scale. Three model order selection criteria such as Aikeke’s Information Criterion (AIC), Root Mean Square Error (RMSE) and Nash–Sutcliffe Efficiency (NSE) are used to evaluate the prediction performance of the process behavior. The results illustrate that both models produce acceptable predictions but the NARMAX model outperforms the Hammerstein model.

Combination of computer sciences and electronics has resulted in one of the most remarkable technologies of the recent years called internet of things, considered as a challenge in electronic health systems for taking care of patients. Internet of things presents a promising paradigm for management of digital identification in the form of service customization. The effect of internet of things on healthcare is still in its preliminary stages and requires a substantial development. Various equipment and services are developed and utilized for health systems by providing different things to establish communication and information provision to users at any conditions or places. In this paper, attempts have been made to detect electrocardiogram (ECG) signal through a wireless simple sensing network of body using internet of things operating based on classification and feature extraction.

Cloud Computing has become the popular paradigm for accessing the various scalable and on-demand computing services over the internet. Nowadays, individual Cloud Service Providers (CSPs) offering specialized services to the customers collaborate to form the Cloud Federation, in order to reap the real benefits of Cloud Computing. By collaboration, the member CSPs of the federation achieve better resource utilization and Quality of Service (QoS), thereby increasing their business prospects. When a CSP runs out of resources in the Cloud Federation, in order to offload the customer requests for resources to other CSP(s), identifying a suitable partner is a challenging task due to the lack of global coordination among them. In this paper, we propose the design and implementation of an efficient partner selection mechanism in the Cloud Federation, using the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods, and also considering the trust values of various CSPs in the federation. The AHP method is used to calculate the weights of the QoS parameters used in the TOPSIS method which is used to rank the various CSPs in the Cloud Federation according to the user requirements. Simulation results show the effectiveness of this approach in order to efficiently select the trustworthy partners in large scale federations to ensure the required QoS to the cloud consumers.

Recently the Kirchhoff rod and the methods of dynamical analogue have been widely used in modeling DNA. The features of a DNA such as its super slender and super large deformation raise new challenges in modeling and numerical simulations of a Kirchhoff rod. In this paper, Euler parameters are introduced to set up the quasi-Hamilton system of an elastic rod, then a symplectic algorithm is applied in its numerical simulations. Finally, a simplified surface model of the rod is given based on the hypothesis of rigid cross-section.

Molecular mechanics-based finite element (FE) models of graphene sheet and single-walled zigzag and armchair carbon nanotubes (CNTs) are developed on the basis of the assumption that the carbon nanostructures, when loaded, behave like frame structures. The behavior of carbon–carbon bonds, which are represented by beam elements, is simulated using the many-body second generation reactive empirical bond order (REBO) potential. By means of the FE models, the tensile behavior of carbon nanostructures is simulated. The FE models are verified against molecular dynamics simulations. The computed results in terms of tensile stress–strain curves and fracture patterns are compared with results obtained using the pairwise modified-Morse potential. Different tensile properties and fracture patterns are predicted using the two potentials. This is mainly attributed to the deviations in the force–bond length curves and to the contribution of bond angle variation which is present in REBO. The present work is the first attempt to implement the REBO potential into a continuum model of carbon nanostructures and paves the way for a more systematic incorporation of atomistic simulation methods into continuum models.

The analysis of the filamentary structure of the cosmo as well as that of the internal structure of the polar ice suggests the development of models based on three-dimensional (3D) point processes. A point process, regarded as a random measure, can be expressed as a sum of Delta Dirac measures concentrated at some random points. The integration with respect to the point process leads the continuous wavelet transform of the process itself. As possible mother wavelets, we propose the application of the Mexican hat and the Morlet wavelet in order to implement the scale-angle energy density of the process, depending on the dilation parameter and on the three angles which define the direction in the Euclidean space. Such indicator proves to be a sensitive detector of any variation in the direction and it can be successfully implemented to study the isotropy or the filamentary structure in 3D point patterns.

In view of the disastrous consequences of tailings dam break and its unique evolutionary process in complex areas, this paper constructs two-dimensional shallow water equations, rheological equations and mathematical models of tailings sand flows on the basis of Navier–Stokes equations (N–S equations). It performs total variation diminishing (TVD) discretization on these equations, develops forward simulation programs in MATLAB2016 and conducts numerical analyses on three kinds of dam breaks (ideal dam break, asymmetric dam break and dam break with obstacles in the downstream area). The results show that TVD discretization is effective in capturing shock waves. According to the analysis on consequences of Huangmailing Tailings Dam break, the author obtains the maximum distance of tailings sand flow, the flow rate of tailings and the time that tailings reach destinations in the downstream area, thereby providing scientific basis for disaster analyses on similar tailings dam breaks and supplying technical support for emergency rescues after disasters.