Signed Directed Graph based Simulation Model Validation Framework for Petrochemical Process

We review the state of the art of process modeling for discrete event simulation, make a number of observations and identify a number of issues that have to be tackled for promoting the use of process modeling in simulation. Process models are of particular interest in model-based simulation engineering approaches where the executable simulation model (code) is obtained with the help of textual or visual models. We present an illustrative example of model-based simulation development.

Test-driven simulation modelling: A case study using agent-based maritime search-operation simulation

Background and Purpose: Business process simulation (BPS) model is based on real-life data form sources like databases, observations and interviews. It acts as “as-is” business scenario can used for reengineering. The main challenge is to gather relevant data and to develop simulation model. Research aims to elaborate BPS model and to systematically assess how close to reality it is. Design/Methodology/Approach: The research has been performed in Polish telecommunications company. Authors investigate technical process of expanding cellular network. After elaborating “as-is” model, authors use ADONIS simulation tool to run a series simulations and confront simulation results with actual historical events. After this, assessment whether computer simulation model can precisely map real-life business process - and consequently act as credible basis for process improvement - is made. Results: The simulation model has been constructed with data from the WfMS database, observations, staff knowledge and their experience. Fully equipped simulation model is found to allow reconstructing the historical execution of business activity with low margin for error. Some limitation were identified and discussed. Conclusion: BPS is not a popular approach for process reengineering and improvement yet. Data collection issues for BPS that require adopting process mining techniques and additional information sources are among the reasons for that. In our study, computer simulation outputs are compatible with historical events. Hence the model reflects the business reality and can be taken as a reference model while redesigning the process.

The paper presents a new game theoretic approach towards the validation of discrete event air combat simulation models. In the approach, statistical techniques are applied for estimating game models based on simulation data. The estimation procedure is presented in cases involving games with both discrete and continuous decision variables. The validity of the simulation model is assessed by comparing the properties of the estimated games to actual practices in air combat. The approach enhances existing methods for the validation of discrete event simulation models by incorporating the inherent game setting of air combat into the analysis. The estimated games also provide a novel game theoretic perspective to simulation metamodeling which is used to facilitate simulation analysis. The utilization of the game theoretic approach is illustrated by analyzing simulation data produced with an existing air combat simulation model.

This paper presents a new game-theoretic approach toward the validation of discrete-event air combat (AC) simulation models and simulation-based optimization. In this approach, statistical techniques are applied for estimating games based on data produced by a simulation model. The estimation procedure is presented in cases involving games with both discrete and continuous decision variables. The validity of the simulation model is assessed by comparing the properties of the estimated games to actual practices in AC. These games are also applied for simulation-based optimization in a two-sided setting in which the action of the opponent is taken into account. In optimization, the estimated games enable the study of effectiveness of AC tactics as well as aircraft, weapons, and avionics configurations. The game-theoretic approach enhances existing methods for the validation of discrete-event simulation models and techniques for simulation-based optimization by incorporating the inherent game setting of AC into the analysis. It also provides a novel game-theoretic perspective to simulation metamodeling which is used to facilitate simulation analysis. The utilization of the game-theoretic approach is illustrated by analyzing simulation data obtained with an existing AC simulation model.

Software process simulation models (SPSMs) that are based on descriptive process models offer the executability that can demonstrate dynamic changes of software processes over time. Verification and validation (V&V) is critical in SPSMs for guaranteeing the quality and reliability of models. V&V of dynamic software process models is more complex and challenging than for static software process models. This work systematically summarizes and maps V&V studies in SPSM to provide guidelines for future research and practice. Specifically, this study aims at identifying the focus of research on V&V, the methods used for V&V, and how to implement V&V of SPSMs in software engineering research. We conducted a systematic mapping study on studies of SPSMs that report on their V&V activities. Under the guidance of a V&V meta‐model for SPSMs, we study four research questions about V&V process. We identified 107 primary studies from a pool of 313 papers on SPSMs until 2021. There are two main results of our study. The first one presents the relationship between quality aspects of SPSMs and the V&V methods to assure them. The second result reveals the relationships among the modeling process, three modeling steps, five quality aspects, and 10 V&V methods. Generally, researchers do not pay sufficient attention to V&V, as 65.8% ( ) failed to mention or elaborate on their V&V process. We systematically summarize and map the state‐of‐the‐art V&V research in software process modeling field to support modelers' practice and improve their V&V process.

Proactive prevention and fighting fire at industrial facilities, often located in urbanized clusters, should include the use of modern methods for modeling danger zones that appear during the spread of the harmful combustion products of various chemicals. Simulation modeling is a method that allows predicting the parameters of a danger zone, taking into account a number of technological, landscape, and natural-climatic factors that have a certain variability. The purpose of this research is to develop a mathematical simulation model of the formation process of a danger zone during an emergency at an industrial facility, including an explosion of a container with chemicals and fire, with the spread of an aerosol and smoke cloud near residential areas. The subject of this study was the development of a simulation model of a danger zone of combustion gases and its graphical interpretation as a starting point for timely decision making on evacuation by an official. The mathematical model of the process of danger zone formation during an explosion and fire at an industrial facility presented in this article is based on the creation of a GSL library from data on the mass of explosion and combustion products, verification using the Wald test, and the use of algorithms for calculating the starting and ending points of the danger zone for various factor values’ variables, constructing ellipses of the boundaries of the distribution of pollution spots. The developed model makes it possible to calculate the linear dimensions and area of the danger zone under optimistic and pessimistic scenarios, constructing a graphical diagram of the zones of toxic doses from the source of explosion and combustion. The results obtained from the modeling can serve as the basis for making quick decisions about evacuating residents from nearby areas.

Network-based computer simulation models are powerful tools for analyzing and guiding policy formation related to the actual systems being modeled. However, the inherent data and computationally intensive nature of this model class gives rise to fundamental challenges when it comes to executing typical experimental designs. In particular this applies to model validation. Manual management of the complex simulation work-flows along with the associated data will often require a broad combination of skills and expertise. Examples of skills include domain expertise, mathematical modeling, programming, high-performance computing, statistical designs, data management as well as the tracking all assets and instances involved. This is a complex and error-prone process for the best of practices, and even small slips may compromise model validation and reduce human productivity in significant ways. In this paper, we present a novel framework that addresses the challenges of model validation just mentioned. The components of our framework form an ecosystem consisting of (i) model unification through a standardized model configuration format, (ii) simulation data management, (iii) support for experimental designs, and (iv) methods for uncertainty quantification, and sensitivity analysis, all ultimately supporting the process of model validation. (Note that our view of validation is much more comprehensive than simply ensuring that the computational model can reproduce instance of historical data.) This is an extensible design where domain experts from e.g. experimental design can contribute to the collection of available algorithms and methods. Additionally, our solution directly supports reproducible computational experiments and analysis, which in turn facilitates independent model verification and validation. Finally, to showcase our design concept, we provide a sensitivity analysis for examining the consequences of different intervention strategies for an influenza pandemic.

Fused deposition modeling (FDM) offers many advantages over conventional manufacturing methods, but it is limited by the number of materials available. Extending FDM technology to semicrystalline polymers has been challenging due to the crystallization that occurs during cooling which results in FDM part warpage. Previous work used process simulation models to study the effects of material parameters and FDM process variables on the part warpage seen using polypropylene (PP). In this work, the process simulation models were adapted to investigate warpage of FDM parts made with a high-performance semicrystalline polymer, polyphenylene sulfide (PPS). Material parameters in the PPS process simulation models were individually changed to the PP values to investigate which material parameters cause PP to exhibit higher warpage than PPS. Material parameters of interest included coefficient of thermal expansion (CTE), thermal conductivity, heat capacity, and Young’s modulus. Additional material parameters based on material property modification through the addition of fillers were investigated in order to establish the relationship between material parameters and warpage values. The simulation models suggested that the CTE has the largest impact on FDM part warpage. Decreasing the CTE in the simulation model resulted in a decrease in the FDM part warpage by the same factor.

Computer programs designed for traffic microsimulation have gained considerable popularity and widespread use, both in practical application as well as in research works. The number of traffic simulation models has increased significantly and until the end of the last century there were more then 50 simulation models available already. Their advantage is the ability to predict behavior in everyday traffic, as well as in emergency situations. Unlike conventional analytical methods for traffic analysis, simulation models can also display the dynamics of traffic systems and 2D or 3D animations, as a result of their modeling, are much more understandable to incompetent population. The aim of this paper is to briefly describe the basic settings of microsimulation computer programs. The AIMSUN computer program will be described in details and the process of making the AIMSUN (Advanced Interactive Microscopic Simulator for Urban and Non-Urban Networks) microsimulation model for roundabout in Radnicka Street in Rijeka will be shown. Since the traffic simulation computer programs are commonly used to estimate characteristics of traffic flow, such as the average travel time, the capacity of roads or the average speed of vehicles, models are based on adjusting the input parameters in order to reach a reasonable matching between model results and field data. This process is called calibration, and it is necessary in proving the authenticity of the simulation model and model validation. For the calibration and validation of the planned simulation model in this case, the obtained results were compared with those obtained by another analytical capacity method (Austrian method) and the SIDRA INTERSECTION (Signalized & Unsignalized Intersection Design and Research Aid) computer program which is based on the Australian calculation method for roundabout capacities.

For the verification and validation of microscopic simulation models of pedestrian flow, we have performed experiments for different kind of facilities and sites where most conflicts and congestion happens e.g. corridors, narrow passages, and crosswalks. The validity of the model should compare the experimental conditions and simulation results with video recording carried out in the same condition like in real life e.g. pedestrian flux and density distributions. The strategy in this technique is to achieve a certain amount of accuracy required in the simulation model. This method is good at detecting the critical points in the pedestrians walking areas. For the calibration of suitable models we use the results obtained from analysing the video recordings in Hajj 2009 and these results can be used to check the design sections of pedestrian facilities and exits. As practical examples, we present the simulation of pilgrim streams on the Jamarat bridge (see Figure 5). The objectives of this study are twofold: first, to show through verification and validation that simulation tools can be used to reproduce realistic scenarios, and second, gather data for accurate predictions for designers and decision makers.

This study shows that, for many manufacturing processes (such as welding and injection moulding), a machine breakdown will result in the scrapping of a component, and goes on to explain the effect that this can have on process simulation models. Breakdown-related scrapping events are ignored by commercially available process simulation packages and this reduces their usefulness in decision support. This paper explains the different types of breakdown/scrapping dynamic possible and clearly demonstrates the scrapping dynamics effect on one of the major performance measures in LEAN-based manufacturing systems, namely the Overall Equipment Effectiveness. The paper also demonstrates the effect on model accuracy of a miss interpretation of part scrap behaviour or none deliberately simplified modelling of part scrap. This uncertainty in model accuracy will reduce the usefulness of the simulation model as a decision support tool.

Simulation software Tecnomatix Plant Simulation was originally created for a modelling and subsequent simulation of production and logistics processes. Its variability, however, opens its use also in other areas such as transport in urban agglomerations. Based on that, research was implemented to verify the program’s application in urban transport, specifically to visualize and simulate traffic processes at the traffic node. The paper describes a methodology which made it possible to create a simulation program for traffic light intersections, and presents examples of the simulation model application. The proposed methodology will enable the application of Tecnomatix Plant Simulation to create a complex simulation model of the logistics process. It will mainly enable to simulate the field of production logistics and city logistics within one simulation model.

The relevance of the study is due to the fact that despite the sustainable development of methods and technologies for creating models of various classes and purposes, the tasks of managing their quality (the so-called inverse problems of theoretical qualimetry) are often neglected. For example, in the field of information technology, much more attention is paid to assessing the quality of software products, which is reflected in the corresponding standards. These tasks become especially relevant when models reflect a complex process (CP), and such processes may be unique, and obtaining complete objective data on such CPs prior to modeling is difficult or impossible (accidents and disasters models, military operations, natural disasters, and others like that). The article’s main purpose is to consider the issues of solving the inverse qualimetric problem by parametric adaptation of analytical models to unpredictable possible changes in the complex processes’ nature. The solution to this problem is based on the combined (analytical and simulation) modeling of unique processes. Theoretical qualimetry involves a quantitative assessment of any product's qualitative properties; to ensure the adequacy level of analytical models (based on the results of their parametric adaptation), it is planned to evaluate with the use of well-known metrology principles regarding relative measurement deviations. In this case, detailed simulation modelling results are used as a reference for combined modelling. The complexity of the processes conditiond by the expediency of their polymodel description in order to mutually compensate for the objective deficiencies of heterogeneous models, as well as the use of simulation capabilities to adjust the parameters of analytical models in cases where the use of the latter is due to strict time constraints on obtaining calculation results and developing control actions based onthem. A variant of the formalized statement of the problem of parametric adaptation of analytical models is presented. A presented example that considers the adaptation of parameters of the Lanchestertype analytical model conducted on simulation data. The adaptation of parameters could be carried out in different ways, and the expediency of choosing a certain one is determined by the assessment of mutual deviations of the results of heterogeneous modeling. The results of the calculations witnesses that parametric adaptation can be an effective element in solving one of the key tasks of model qualimetry, namely, ensuring their required qualitative properties. The prospect of further research is to consider the issues of structural adaptation of simulation models to the given conditions for displaying complex processes.

Energy system optimization model for tissue papermaking process