Simulation and Modeling Methodologies, Technologies and Applications

The paper addresses the issue of context-aware operational decision support in dynamic environments. The context model specifies conceptual knowledge describing the situation and problems to be solved in this situation. This model comprises knowledge captured from an application ontology, which is formalized by a set of constraints. The context aware decision support system (DSS) developed within the research has a service-oriented architecture. The Web-services constituting the architecture provide the DSS with the contextualized information from information resources, solve problems specified in the context, and participate in decision making. A decision making model overstepping the limits of the three-phase Simon’s model is offered. The paper proposes a set of technologies that can be used to implement the ideas behind the research. An application of these ideas is illustrated by an example of usage of the developed DSS for planning fire response actions.

Our goal was to realize a truly hybrid simulation system, which allows the simultaneous use of discreet event simulation and continuous 3D-simulation on a unified database. The key component is an active real-time simulation database, which is an object-oriented, self-reflecting graph database, with a powerful meta-information system. We achieve this by using State Oriented Modeling, which combines the ideas of object-oriented Petri-nets and supervisory control (using discreet event simulation as a control component). The object-oriented Petri-nets are formally described in the State Oriented Modeling Language, which is itself an extension scheme of the simulation database.

The evaluation of the numerous real-time scheduling algorithms is difficult without a real and complex implementation. Simulation allows to study the schedulers with more flexibility. This paper presents a simulation tool that uses a process-based discrete-event simulation engine. Compared to the other scheduling simulators, it is able to take into account the impact of the caches through statistical models and direct overheads such as context switches and scheduling decisions. The last Section shows how this tool can be used on concrete examples.

In times where urbanization becomes more important every day, epidemic outbreaks may be devastating. Powerful forecasting and analysis tools are of high importance for both, small and large scale examinations. Such tools provide valuable insight on different levels and help to establish and improve embankment mechanisms. Here, we present an agent-based algorithmic framework to simulate the spread of epidemic diseases on a national scope. Based on the population structure of Germany, we investigate parameters such as the impact of the number of agents, representing the population, on the quality of the simulation and evaluate them using real world data provided by the Robert Koch Institute [4, 22]. Furthermore, we empirically analyze the effects of certain non-pharmaceutical countermeasures as applied in the USA against the Influenza Pandemic in 1918–1919 [18]. Our simulation and evaluation tool partially relies on the probabilistic movement model presented in [8]. Our empirical tests show that the amount of agents in use may be crucial. Depending on the existing knowledge about the considered epidemic, this parameter alone may have a huge impact on the accuracy of the achieved simulation results. However, with the right choice of parameters—some of them being obtained from real world observations [10]—one can efficiently approximate the course of a disease in real world.

Tensor systems are a framework for modeling of multilinear hybrid systems with discrete and continuous valued signals. Two examples from building services engineering and multi-agent systems show applications of this framework. A tensor model of a heating system is derived and approximated by tensor decomposition methods first. Second, a tensor model of a multi-agent system with a structure already given in a decomposed form is reduced further by the same decomposition methods.

The aim of this paper is to improve scientific modeling of interdependent socio-technical networks. In these networks the interplay between technical or infrastructural elements on the one hand and social and behavioral aspects on the other hand, plays an important role. Examples include electricity networks, financial networks, residential choice networks. We propose an Agent-Based Modeling approach to simulate interdependent technical and social network behavior, the effects of potential policy measures and the societal impact when disturbances occur, where we focus on a specific use case: the smart grid, an intelligent system for matching supply and demand of electricity.

In this paper, we develop a heuristic bidding price decision algorithm in consideration of cost estimation accuracy under limited engineering Man-Hours (MH) in Engineering, Procurement, Construction (EPC) projects. It allocates engineering MH for cost estimation, which determines the cost estimation accuracy, to each order under the limited volume of MH, and then determines the bidding price for maximizing the expected profit based on cost estimation accuracy under the deficit order probability constraint. Numerical examples show that the bidding price decision in consideration of cost estimation accuracy and deficit order probability is essential for the contractor in making a stable profit in EPC projects, and that the developed algorithm is effective for making such bidding price decision.

Goal of the present paper is providing support to operations planning and management in complex scenarios. The authors are mainly focused on South Asia region, which is subject of experimental analysis by running an Intelligent Agents—driven HLA Federation. Simulation of investments and operations over an asymmetric mission environment with several parties, insurgents, terrorists and dynamic social framework is the aim. The scenario has various degrees of freedom and M&S enables evaluation of human behavior evolution and socio-psychological aspects. The presented models include Computer Generated Forces (CGF) driven by Intelligent Agents (IAs) that represents not only units on the battlefield, but also people and interest groups (i.e. Middle Class, Nomads, Clans). The study is focused on Civil Military Co-operations (CIMIC) and Psychological Operations (PSYOPs). The simulation is based on specific architecture that involves various federates playing different roles. Verification, Validation and Accreditation (VV&A) has been applied along the whole life cycle of the research, in order to determine the correctness and effectiveness of the results. The paper proposes experimental results obtained during the dynamic test of the federations.

Many earth, environmental, ecological, biological, physical, astrophysical and financial variables exhibit random space-time fluctuations; symmetric, non-Gaussian frequency distributions of increments characterized by heavy tails that often decay with separation distance or lag; nonlinear power-law scaling of sample structure functions (moments of absolute increments) in a midrange of lags, with breakdown in such scaling at small and large lags; extended power-law scaling at all lags; nonlinear scaling of power-law exponent with order of sample structure function; and pronounced statistical anisotropy. The literature has traditionally considered such variables to be multifractal. Previously we proposed a simpler and more comprehensive interpretation that views them as samples from stationary, anisotropic sub-Gaussian random fields or processes subordinated to truncated fractional Brownian motion or truncated fractional Gaussian noise. The variables thus represent mixtures of Gaussian components having random variances. We apply our novel approach to soil data collected at an Arizona field site and to corresponding hydraulic properties obtained by means of a neural network model and estimate their statistical scaling parameters by maximum likelihood. Our approach allows upscaling or downscaling statistical moments of such variables to fit diverse measurement or resolution and sampling domain scales.

A model was built using Adaptive Neuro Fuzzy Inference System (ANFIS) to determine the relationship between the natural suitability index of rainfed maize and yield per hectare and percentage of production area lost for the state of Puebla. The data used to build the model presented inconsistencies. The data of the INEGIs land use map presented more municipalities without rainfed maize agriculture than the database of SAGARPA. Also the SAGARPA data, in terms of the percentage of production area lost, do not mark any distinctions of the loss. Even with data inconsistencies ANFIS produced a coherent output reviewed by experts and local studies. The model shows that higher the percentage of production area lost and high yields, the higher the suitability index is. According to local studies this is due to the high degradation of the soils and confirmed with the second model built adding soil degradation information.

This study aimed to utilize a pressure swing adsorption (PSA) process to capture CO₂ from synthesis gas, which is the effluent stream of water-gas-shift reactor. The PSA process studied is a single-bed four-step process at mid-high temperature using K₂CO₃-promoted hydrotalcite as adsorbent. The breakthrough curve and desorption curve were verified against the simulation program which our group developed. It uses the method of lines combined with upwind differences, cubic spline approximation and LSODE of ODEPACK software to solve the equations. The optimal operating condition is obtained by varying the operating variables, such as feed pressure, bed length, etc. Furthermore, single-bed four-step process could achieve 98.49 % recovery of H₂ as the top product and 96.42 % purity and 96.57 % recovery of CO₂ as the bottom product.

Linear evolving emission scenarios are used instead of those of IPCC. They preserve, indeed they cover, the ranges of the corresponding IPCC values for the concentrations, forcings and global temperatures. Then, through fuzzy rules among concentrations, climate sensitivity and global temperature change, a fuzzy model has been conformed and used to explore uncertainties due to: not knowing what the emissions are going to be in the future, the one related to the climate sensitivity of the models (this has to do with different parameterizations of processes used in the models) and the uncertainties in the temperature maps produced by the models. Furthermore we show maps corresponding to 1, 2, etc., degrees centigrade of global and regional temperature increase and discuss the timing of exceeding them. Instead of talk about the uncertainty in temperature at a certain date we talk about the uncertainty in the date certain temperature is reached.

A design procedure for inline waveguide extracted pole filters with all-metal E-plane inserts is presented. To achieve acceptable modeling accuracy for this class of filters, an enhanced schematic-circuit-based surrogate model is developed, accounting for parasitic effects between the neighboring elements of the structure. The proposed circuit representation is used as a coarse model in a surrogate-based optimization of the filter structure with the space mapping technique as the main engine in the optimization process. Feasibility of the modeling approach is demonstrated by two filter design examples. The examples show that a low computational cost (corresponding to a few evaluations of the high-fidelity EM simulations of the filter structure) is required to obtain an optimized design.

In this work, we present a technique for low-cost surrogate modeling of waveguide filters. The proposed methodology is based on the decomposition of the filter structure. Some of the decomposed parts are modeled using response surface approximations (RSAs). The RSA models are subsequently combined with analytical models of the waveguide sections to form an initial filter surrogate. As a result of electromagnetic couplings between the decomposed parts, which are not accounted for by the initial surrogate, its accuracy is limited. This misalignment is reduced by applying space mapping at the level of the complete filter structure. Decomposition approach allows us to greatly reduce the computational cost of creating the surrogate because the time required to simulate the structure in parts is much lower than the time for simulating the entire filter. Moreover, the number of parameters describing each part is lower than for the entire filter. The presented technique is demonstrated using two test cases. Application examples are also given.

Four generic methods for quantile estimation have been compared: Monte Carlo (MC), Monte Carlo with Harrel-Davis weighting (WMC), quasi-Monte Carlo with Sobol sequence (QMC) and quasi-random splines (QRS). The methods are combined with RBF metamodel and applied to the analysis of morphodynamic—hydrodynamic simulations of the river bed evolution. The following results have been obtained. Harrel-Davis weighting gives a moderate 10–20 % improvement of precision at small number of samples N ~ 100. Quasi-Monte Carlo methods provide significant improvement of quantile precision, e.g. the number of function evaluations necessary to achieve rms ~ 10⁻⁴ precision is reduced from 1,000,000 for MC to 100,000 for QMC and to 6,000 for QRS. On the other hand, RBF metamodeling of bulky data allows to speed up the computation of one complete result in the considered problem from 45 min (on 32CPU) to 20 s (on 1CPU), providing rapid quantile estimation for the whole set of bulky data.

Axial flow pumps are one type of blade pump with great flux, lower head, highly fluids flow. This type of pump can be used in agriculture, irrigation and massive water project widely. Impellers are the main and highly sensitive part of the pumps which performs the function by transferring energy to the fluid there by increasing pressure and velocity. In axial flow pump design process, in order to get high performance pump, designers usually try to increase the efficiency (η) and decrease the required net positive suction head (NPSHr) simultaneously. In this paper, multi-objective optimization of axial flow pump based on modified Particle Swarm Optimization (MPSO) is performed. At first, the NPSHr and η in a set of axial flow pump are numerically investigated using commercial software ANSYS with the design variables concerning hub angle β_h, chord angle β_c, cascade solidity of chord σ_c, maximum thickness of blade H. And then, using the Group Method of Data Handling (GMDH) type neural networks in commercial software DTREG, the corresponding polynomial representation for NPSHr and η with respect to design variables are obtained. Finally, multi objective optimization based on modified Particle Swarm Optimization (MPSO) approach is used for Pareto based optimization. The result shows that an optimal solution of the axial flow pump impeller was obtained: NPSHr was decreased by 11.68 % and efficiency was increased by 4.24 % simultaneously. It means by using this method, better performance pump with higher efficiency and lower NPSHr can be got and this optimization is feasible.

Reports on the development of a system dynamics simulation model of initiation and nonmedical use of pharmaceutical opioids in the US. The study relies on historical trend data as well as expert panel recommendations that inform model parameters and structure. The model was used to assess simulated public health interventions for reducing initiation and nonmedical use of opioids. Results indicate that interventions which reduce the likelihood of informal sharing of leftover medicine could significantly reduce initiation and nonmedical use. Less effective are supply restrictions, such as drug take-back days, and interventions aimed at reducing the likelihood that nonusers would decide to initiate nonmedical use based on their interactions with nonmedical users. We conclude that system dynamics is an effective approach for evaluating potential interventions to this complex system where the use of pharmaceutical opioids to treat pain can lead to unintended distal outcomes in the public sphere.

Soft Computing techniques, such as Fuzzy Cognitive Maps (FCMs), can handle uncertainties in modeling complex situations using abstract inference mechanisms; they have been successfully used to select among different suggestions, to lead to a decision and to develop Medical Decision Support Systems for many medical-discipline applications. FCM models have great ability to handle complexity, uncertainty and abstract inference as is the case in the health care sector. Here is examined the case of the triage procedure in the Emergency Department (ED), where a decision supporting mechanism is quite invaluable. A Hierarchical structure is proposed within an integrated computerized health system where the Supervisor is modeled as an abstract FCM to support the triaging procedure and assessment of the health condition of people with communication difficulties such as the elderly arriving at the ED. There is also the lower level of the hierarchical structure where a FCM-ESI DSS has been developed and used to assign the Triage ESI level of every patient. Here a new methodology for designing and developing the FCM-ESI DSS is presented so to ensure the active involvement of human experts during the FCM-ESI construction procedure.