System Simulation Method Research Articles

Ab Initio Simulations and Materials Chemistry in the Age of Big Data.

In this perspective, we discuss computational advances in the last decades, both in algorithms as well as in technologies, that enabled the development, widespread use, and maturity of simulation methods for molecular and materials systems. Such advances led to the generation of large amounts of data, which required the creation of several computational databases. Within this scenario, with the democratization of data access, the field now encounters several opportunities for data-driven approaches toward chemical and materials problems. Specifically, machine learning methods for predictions of novel materials or properties are being increasingly used with great success. However, black box usage fails in many instances; several technical details require expert knowledge in order for the predictions to be useful, such as with descriptors and algorithm selection. These approaches represent a direction for further developments, notably allowing advances for both developed and emerging countries with modest computational infrastructures.

A new version of the Riccati transfer matrix method for multibody systems consisting of chain and branch bodies

Computational speed and stability are two important aspects in the dynamics analysis of large-scale complex multibody systems. In order to improve both in the context of the multibody system transfer matrix method, a new version of the Riccati transfer matrix method is presented. Based on the new version of the general transfer matrix method for multibody system simulation, recursive formulae are developed which not only retain all advantages of the transfer matrix method, but also reduce the truncation error. As a result, the computational speed, accuracy and efficiency are improved. Numerical computation results obtained by the proposed method and an ordinary multibody system formulation show good agreement. The successful computation for a spatial branch system with more than 100000 degrees of freedom validates that the proposed method is also working for huge systems.

Service-Oriented Model Encapsulation and Selection Method for Complex System Simulation Based on Cloud Architecture

With the rise in cloud computing architecture, the development of service-oriented simulation models has gradually become a prominent topic in the field of complex system simulation. In order to support the distributed sharing of the simulation models with large computational requirements and to select the optimal service model to construct complex system simulation applications, this paper proposes a service-oriented model encapsulation and selection method. This method encapsulates models into shared simulation services, supports the distributed scheduling of model services in the network, and designs a semantic search framework which can support users in searching models according to model correlation. An optimization selection algorithm based on quality of service (QoS) is proposed to support users in customizing the weights of QoS indices and obtaining the ordered candidate model set by weighted comparison. The experimental results showed that the parallel operation of service models can effectively improve the execution efficiency of complex system simulation applications, and the performance was increased by 19.76% compared with that of scatter distribution strategy. The QoS weighted model selection method based on semantic search can support the effective search and selection of simulation models in the cloud environment according to the user’s preferences.

Nonadiabatic quantum dynamics without potential energy surfaces

We present an efficient \textit{ab initio} algorithm for quantum dynamics simulations of interacting systems that is based on the conditional decomposition of the many-body wavefunction [Phys. Rev. Lett. 113, 083003 (2014)]. Starting with this formally exact approach, we develop a stochastic wavefunction ansatz using a set of interacting conditional wavefunctions as a basis. We show that this technique achieves quantitative accuracy for a photo-excited proton-coupled electron transfer problem and for nonequilibrium dynamics in a cavity bound electron-photon system in the ultra-strong coupling regime, using two orders of magnitude fewer trajectories than the corresponding mean field calculation. This method is highly parallelizable, and constitutes a practical and efficient alternative to available quantum-classical simulation methods for systems of interacting fermions and bosons.

Повышение информационной безопасности автоматизированных систем диспетчерского управления в электроэнергетических системах

The main trends in the development of automated dispatch control systems (also known as SCADA — supervisory control and data acquisition systems) in the electric power industry are considered, and the shift to the concept of remotely controlled unattended substations is analyzed. It is shown that the introduction of smart computerized control systems in the electric power industry results in that the matters concerned with ensuring cybersecurity of power facilities are coming at the forefront. Possible information-related impacts and cyber- attacks on electric power system facilities can upset the operation of not only automated systems and secondary intelligent equipment, but also the main power equipment of power plants and substations. Unauthorized control of digital substation equipment, failures and malfunctions of primary and secondary substation equipment caused by cyber attacks are the main challenges and threats arising from the fitting of the electric power industry with information technologies. These new challenges determine the relevance of assessing the risks of upsetting the operation of automated dispatch control systems in the electric power industry. It is proposed to assess the risks of upsetting the automated dispatch control system operation in addition to evaluating the reliability of electric power systems and their components. The relevance of developing the above-mentioned risk assessment methodology is shown, and one of possible versions of its practical implementation is proposed. The proposed version implies application of the real-time power system simulation method with connecting the secondary equipment (relay protection and automatic control terminals, controllers and intelligent devices) into the simulation loop. The rest part of the system (primary substation equipment) can be represented by a numerical model implemented on an RTDS simulator (this approach is called hardware-in-the-loop simulation). Simulation of an electric power system area with the relay protection, automatic, and process control system devices connected in the simulation loop will make it possible to estimate the consequences for various scenarios of disturbing the operation of a complex electric power system, also as a result of possible unauthorized information attacks. The developed methodology for assessing the risks of disturbing the operation of an automated dispatch control system makes it possible to obtain the integral risk values for the considered plant or system. The analysis results can be used for taking management decisions, and a correct and timely choice of measures for managing the identified makes it possible to ensure the reliability and safety of both individual electric power industry facilities and the electric power system as a whole.

Simulation of Knowledge Transfer Process Model Between Universities: A Perspective of Cluster Innovation Network

Combined with the basic properties of the cluster innovation network, with the cluster innovation network, which can be composed of different universities that have knowledge potential difference as the research object, the knowledge transfer process is divided into four stages: knowledge externalization, knowledge sharing, knowledge innovation, and knowledge internalization, and the article constructs a knowledge transfer process model through introducing explicit knowledge and tacit knowledge conversion effect mechanism. According to the theory of complex adaptive system, the principle of network connection oriented the knowledge potential difference and the characteristic of the explicit knowledge and tacit knowledge within universities. We research the knowledge transfer process of universities using the system simulation method and focus on the evolution mechanism of the cluster innovation network’s knowledge level at knowledge externalization and knowledge sharing stage. It further reveals the basic topology structure and dynamic evolution law of universities cluster innovation network. We find that both knowledge externalization efficiency and knowledge learning ability have positive correlation with the general knowledge level of network. The evident small-world network characteristic emerges during the dynamic evolution of universities cluster innovation network. Meanwhile, there exists a coupling evolution between the knowledge level of universities and the topology structure of the cluster innovation network.

Evolutionary Model and Simulation Research of Collaborative Innovation Network: A Case Study of Artificial Intelligence Industry

Based on integrating the fundamental attribute and the unique property of the collaborative innovation network, this paper establishes a collaborative innovation network model of Artificial Intelligence industry through depicting external stimulus conversion progresses and behaviors of network heterogeneous agents. Heterogeneous agents are the network elements of the model which regards the stimulus response as the evolutionary mechanism. Tencent is one of the largest Internet integrated service providers and one of the Internet companies with the largest number of service users in China, which has also set its sights on the development of the AI industry. Taking Tencent’s patent cooperation network in the field of Artificial Intelligence as an example and using system simulation method, we analyze the evolutionary law of the collaborative innovation network topology structure, the coupling evolution phenomenon of the knowledge and the network topology structure, distinct roles that agents play in the network, and relationship between the agents’ openness and the knowledge flow efficiency. We find the phenomenon of small world emergence more than once through the evolution of collaborative innovation network, whose degrees and reasons are also distinctive. There exists coupling evolution between the technological knowledge and the network structure. The collaborative innovation network is always oriented towards competitive industries. The agents’ openness has an essential influence on the lifting range of the technological knowledge. Strengthening the main position of enterprises in AI technological innovation and enhancing the degree of openness among heterogeneous agents are a powerful guarantee for improving the performance of collaborative innovation.

ИССЛЕДОВАНИЕ ЛОГИСТИКИ УПРАВЛЕНИЯ ПРОИЗВОДСТВОМ ВЫСОКОТЕХНОЛОГИЧЕСКОЙ ПРОДУКЦИИ ВИРТУАЛЬНОГО ПРЕДПРИЯТИЯ

It is stated and solved the complex task related to the arrangement and management of the virtual enterprise (VE) in the aerospace industry where high-tech innovative products produced. The complex task divided into several stages. At the first stage, the task of building a portfolio of VE orders solved. In order to do this, the state of the high-tech products market is analyzed to identify the competitive products. The alternatives of the order portfolio compared and evaluated considering the costs, timing, and risks of output. The composition of the VE enterprises selected at the second stage of the built portfolio of orders. For this purpose, the enterprises selected according to the principle of subject orientation considering the possible costs associated with the adaptation of the selected enterprises to fulfill the order portfolio. The info-communication links to manage the VE formed at the third stage. The cost and capacity of the communication equipment for transmission of control and technological information within the framework of the VE taken into consideration. The production cycle of the VE formed at the fourth stage considering the logistics requirements. The characteristics of the logistics chain "supply-production-sale" for each enterprise in the composition of the VE taken into consideration. At the fifth stage, the quality management system of the VE production considered. In order to ensure the quality, the corresponding activities that affect not only the quality of the product in the logistics chain but also the whole production processes (both basic and auxiliary) are carried out. At the sixth stage, the logistics processes of VE supplying and marketing optimized. The optimization of supply and marketing processes provides a reduction of product cost as well as the increased competitiveness in the market of high-tech products. The proposed tasks are logically related to each other and aimed at ensuring the competitiveness of domestic machine-building enterprises (aircraft construction, shipbuilding, etc.). In order to solve the proposed complex problem the methods of system analysis, integer optimization, simulation, agent and knowledge-oriented modeling are used. The proposed approach allows to justify the order portfolio, make the organizational structure of the enterprise, build the info-communication interaction between the executors, justify the quality requirements and also to organize the logistics chain of supply, supply, production and marketing of high-tech products, taking into account the terms of fulfilling the order portfolio and optimizing costs that is very important for domestic aircraft building

Regenerative simulation methods for multiserver systems

Regenerative simulation methods for multiserver systems

On the stochastic modelling of surface reactions through reflected chemical Langevin equations

Modelling of small-scale heterogeneous catalytic systems with master equations captures the impact of molecular noise, but can be computationally expensive. On the other hand, the chemical Fokker–Planck approximation offers an excellent alternative from an efficiency perspective. The Langevin equation can generate stochastic realisations of the Fokker–Planck equation; yet, these realisations may violate the conditions 0 ≤ θ ≤ 1 (where θ is surface coverage). In this work, we adopt Skorokhod’s formulations to impose reflective boundaries that remedy this issue. We demonstrate the approach on a simple system involving a single species and describing adsorption, desorption, reaction and diffusion processes on a lattice. We compare different numerical schemes for the solution of the resulting reflected Langevin equation and calculate rates of convergence. Our benchmarks should guide the choice of appropriate numerical methods for the accurate and efficient simulation of chemical systems in the catalysis field.

Identification of Parameters of Nonlinear Dynamical Systems Simulated by Volterra Polynomials

We study the identification methods for the nonlinear dynamical systems described by Volterra series. One of the main problems in the dynamical system simulation is the problem of the choice of the parameters allowing the realization of a desired behavior of the system. If the structure of the model is identified in advance, then the solution to this problem closely resembles the identification problem of the system parameters. We also investigate the parameter identification of continuous and discrete nonlinear dynamical systems. The identification methods in the continuous case are based on application of the generalized Borel Theorem in combination with integral transformations. To investigate discrete systems, we use a discrete analog of the generalized Borel Theorem in conjunction with discrete transformations. Using model examples, we illustrate the application of the developed methods for simulation of systems with specified characteristics.

Optimization of solar aided coal-fired power plant layouts using multi-criteria assessment

This paper presents an integration methodology for medium and high temperature solar aided coal-fired power generation systems. A set of preferred integration methods is proposed using analysis of pinch point, exergy and solar share and the application of system simulation methods. These methods can be used for hybrid (solar/fossil) systems screening, pre-assessment and decision-making. Power plant annual performance is analyzed to confirm the validity of the abovementioned methods. The presented methodology has been applied into a 100 + 1000 MWe grade solar tower aided coal-fired power generation plant (STAPG). According to the proposed methodology, the best integration scheme is found for a scenario where high-temperature solar thermal energy is used for steam reheating, while the remaining solar thermal energy is used for feedwater heating providing the maximum utilization of solar energy. The analysis reveals that as much as 214.7 MWe could be generated using solar thermal energy under the best integration scheme. This accounts for 21.3% of total electricity produced, while representing more than twice as much the power produced by an equivalent stand-alone solar tower power plant of the same solar field size. Furthermore, solar-to-electricity efficiency of the hybrid system may reach 36.8%, with power block efficiency of 43.3%, and the coal consumption rate is 203.5 g/kW h. All these parameters are representative of the best scenario amongst the seven integration schemes studied in this paper.

Manufacturing system simulation method using simulated annealing to minimize electric power cost for production line including injection molding machine

Manufacturing system simulation method using simulated annealing to minimize electric power cost for production line including injection molding machine

Evaluation of SME (Small Medium Enterprise) production system with discrete system simulation method

Like manufacturing companies that have flowshop production lines, SME facing the amount of flow time and makespan. Unfulfilled production targets, buildup on some machines that result in other machines being idle, increasing waiting times across multiple machines, and poor performance of workers are a series of problems facing SME. This article uses a discrete system simulation method to analyze and evaluate SME production lines to improve performance. Simulation is an appropriate tool used when experiments are needed in order to find the best response from system components. From the results of modeling and simulation done found the root of the problem is due to accumulation that occurs in one machine and the lack of utility of the operator in producing bags. So do the experimental design with 3 scenarios on the system that have been modeled and obtained some solutions that can be offered to solve the problem. Furthermore, after the alternative selection is obtained the best scenario based on the alternative selection test is the scenario3 by adding 2 new machines and 1 new operator on the production line obtained a significant output increase compared to other scenarios of approximately 30%. While the best scenario based on the minimum cost is the scenario2 by adding 2 new operators obtained output that is not much different than the scenario3. However, it has not been able to solve a series of problems facing SME. Therefore, the results of this study to solve a series of problems faced by SME is to add facilities in the production line of 2 new machines and 1 operator (scenario3).

Evaluation of integration methods for hybrid simulation of complex structural systems through collapse

This study examines the performance of integration methods for hybrid simulation of large and complex structural systems in the context of structural collapse due to seismic excitations. The target application is not necessarily for real-time testing, but rather for models that involve large-scale physical sub-structures and highly nonlinear numerical models. Four case studies are presented and discussed. In the first case study, the accuracy of integration schemes including two widely used methods, namely, modified version of the implicit Newmark with fixed-number of iteration (iterative) and the operator-splitting (non-iterative) is examined through pure numerical simulations. The second case study presents the results of 10 hybrid simulations repeated with the two aforementioned integration methods considering various time steps and fixed-number of iterations for the iterative integration method. The physical sub-structure in these tests consists of a single-degree-of-freedom (SDOF) cantilever column with replaceable steel coupons that provides repeatable highlynonlinear behavior including fracture-type strength and stiffness degradations. In case study three, the implicit Newmark with fixed-number of iterations is applied for hybrid simulations of a 1:2 scale steel moment frame that includes a relatively complex nonlinear numerical substructure. Lastly, a more complex numerical substructure is considered by constructing a nonlinear computational model of a moment frame coupled to a hybrid model of a 1:2 scale steel gravity frame. The last two case studies are conducted on the same porotype structure and the selection of time steps and fixed number of iterations are closely examined in pre-test simulations. The generated unbalance forces is used as an index to track the equilibrium error and predict the accuracy and stability of the simulations.

Model order reduction for dynamic simulation of a flexible multibody system via absolute nodal coordinate formulation

The Absolute Nodal Coordinate Formulation (ANCF) can be used to model a flexible multibody system subject to both overall motions and large deformations. With an increasing number of finite elements of ANCF to mesh flexible bodies, however, the computation cost will become extremely high. To improve the computational efficiency of ANCF for a large-scaled flexible multibody system, a systematic method is proposed in this study for the model order reduction based on the proper orthogonal decomposition and the Galerkin projection. At first, an approach for the selection of reduced constraint equations is proposed to deal with the singularity of the coefficient matrix of the Reduced-Order Model (ROM). Then, the computation of the reduced stiffness matrix and generalized force vector of the ROM are parallelized via the OpenMP directives. Afterwards, two parametric approaches are presented to make the ROM be adaptive to the change of system parameters. One is the interpolation approach of reduced-order basis vectors on a manifold to obtain the parametric ROM, and the other is the fast-to-slow ROM approach via the reduced-order basis vectors extracted from the fast dynamic responses to simulate slow dynamic responses. Finally, four numerical examples are given to validate the efficacy of the proposed method for the dynamic simulations of both rigid and flexible multibody systems.

Comparison of Tensor Decomposition Methods for Simulation of Multilinear Time-Invariant Systems with the MTI Toolbox

Several tools are available for linear state space models. But linear models may fail for applications where nonlinear effects are essential. Multilinear time-invariant (MTI) systems extend this class of systems and can be represented in a tensor framework. Tensor decomposition techniques reduce the storage effort for MTI systems and allow an efficient computation, e.g. during simulation. The paper proposes the application of four different decomposition techniques, canonic polyadic, Tucker, Tensor Train and Hierarchical Tucker decomposition to MTI systems. The methods are compared according to the application to a complex HVAC system example. The introduced MTI Toolbox implements methods for representation, simulation or linearization of MTI systems with MATLAB.

Modelling of organic Rankine cycle power systems in off-design conditions: An experimentally-validated comparative study

Because of environmental issues and the depletion of fossil fuels, the world energy sector is undergoing many changes toward increased sustainability. Among the many fields of research and development, power generation from low-grade heat sources is gaining interest and the organic Rankine cycle (ORC) is seen as one of the most promising technologies for such applications. In this paper, it is proposed to perform an experimentally-validated comparison of different modelling methods for the off-design simulation of ORC-based power systems. To this end, three types of modelling paradigms (namely a constant-efficiency method, a polynomial-based method and a semi-empirical method) are compared both in terms of their fitting and extrapolation capabilities. Post-processed measurements gathered on two experimental ORC facilities are used as reference for the models calibration and evaluation. The study is first applied at a component level (i.e. each component is analysed individually) and then extended to the characterization of the entire organic Rankine cycle power systems. Benefits and limitations of each modelling method are discussed. The results show that semi-empirical models are the most reliable for simulating the off-design working conditions of ORC systems, while constant-efficiency and polynomial-based models are both demonstrating lack of accuracy and/or robustness.

Compact Electro-Mechanical-Fluidic Model for Actuated Fluid Flow System

This paper presents a compact electro-mechanical-fluidic system-modeling method for multidomain system simulation based on multidomain physics that considers the total energy conservation condition, in terms of respective potential and flow quantities. Models for electrical, mechanical, and fluidic domains are developed to design the example of a blood pumping system, where the blood flow is driven by electrically controlled organic actuators. The electrical domain includes an organic mosfet -based control circuit, the mechanical domain includes organic actuators, and the fluidic domain includes a flexible fluid-flow channel. Control circuit, actuators, and fluid models are coupled through equivalent circuits, where interconnection relationships between two neighboring domains are expressed using the energy conservation concept. The model accuracy is verified with finite element method (FEM) based numerical simulation. Significantly faster simulation speed than with FEM and good accuracy were achieved.

The relative entropy is fundamental to adaptive resolution simulations.

Adaptive resolution techniques are powerful methods for the efficient simulation of soft matter systems in which they simultaneously employ atomistic and coarse-grained (CG) force fields. In such simulations, two regions with different resolutions are coupled with each other via a hybrid transition region, and particles change their description on the fly when crossing this boundary. Here we show that the relative entropy, which provides a fundamental basis for many approaches in systematic coarse-graining, is also an effective instrument for the understanding of adaptive resolution simulation methodologies. We demonstrate that the use of coarse-grained potentials which minimize the relative entropy with respect to the atomistic system can help achieve a smoother transition between the different regions within the adaptive setup. Furthermore, we derive a quantitative relation between the width of the hybrid region and the seamlessness of the coupling. Our results do not only shed light on the what and how of adaptive resolution techniques but will also help setting up such simulations in an optimal manner.