Generation Of Simulation Models Research Articles

Modeler in a box: how can large language models aid in the simulation modeling process?

We examine the potential of prompting a large language model-based chatbot, ChatGPT, to generate functional simulation model code from a prose-based narrative. The simple narrative describes how the mode of transportation for elementary school students changed due to the COVID-19 pandemic and related factors, including a lack of available bus drivers, lack of mask enforcement on buses, and inclement weather. We document the process of providing this narrative to ChatGPT and further prompting the chatbot to generate model code to represent the narrative and to make it executable. We test ChatGPT’s ability to use prose descriptions of a phenomenon to generate simulation models using three paradigms: discrete event system, system dynamics, and agent-based modeling. Our findings reveal that ChatGPT could not produce concise or executable models, facing higher difficulty when asked to do so in programming languages it was less familiar with. This analysis underscores the strengths and limitations of the current state of this technology for modeling and simulation. Furthermore, we propose how future advancements in Large Language Models may aid the simulation modeling process, increasing transparency and participation in multidisciplinary team efforts.

A material handling system modeling framework: a data-driven approach for the generation of discrete-event simulation models

A material handling system modeling framework: a data-driven approach for the generation of discrete-event simulation models

An aircraft assembly process formalism and verification method based on semantic modeling and MBSE

The aircraft assembly system is highly complex involving different stakeholders from multiple domains. The design of such a system requires comprehensive consideration of various industrial scenarios aiming to optimize key performance indicators. Traditional design methods heavily rely on domain expert knowledge using documents to define assembly solutions which are later verified through simulations. However, these document-centric approaches cannot provide graphical notations for engineers to efficiently understand the entire assembly process. Moreover, it is difficult to analyze the performance of the designed assembly processes using simulations since the simulation models have to be developed based on the documents manually rather than be generated automatically from the design models. In this paper, a semantic-driven approach is proposed to support aircraft assembly process formalism and performance analysis. First, meta-models of aircraft assembly processes are developed based on SysML and discrete-event simulation models using a semantic modeling language named KARMA. Then an application ontology is defined for generating semantic models from KARMA architecture models to capture domain knowledge, system requirements and simulation model information of the aircraft assembly process. A model transformer is developed to transform the KARMA models to discrete-event simulation models based on the application ontology. Then the generated simulation models are executed to obtain the simulation results for verifying the designed assembly process. Finally, the obtained simulation results are used to support decision-making of selecting the optimal aircraft assembly process. A case study is conducted to verify the proposed method.

Automatic model generation for material flow simulations of Third-Party Logistics

AbstractThe use of Third-Party Logistics (TPL) is a common practice among manufacturing companies seeking to increase profitability. However, the tender process in selecting a TPL service provider can be challenging, requiring significant effort from both the tendering company and the service provider. The latter must meticulously plan processes and calculate pricing positions while running the risk of losing the bid. This risk impedes verifying logistical feasibility and comparing different logistic concepts extensively, such as layouts, which are often work-intensive. With the ongoing progress of research toward automatic simulation model generation for material flow, it is left to answer whether such approaches can improve the planning processes of TPL service providers by using planning data to generate simulation models. Therefore, this work presents a system with an underlying ontology to generate material flow simulations by developing a model transformation methodology. The system’s functions are tested to determine whether they can support the planning process using defined case studies that cover everyday planning decisions. As a result, the system is capable of verifying the performance of planned logistic systems with minimal manual modelling efforts. This encompasses the evaluation of alternative logistical concepts for configuring the planned systems.

Automated model generation framework for material flow simulations of production systems

Owing to increasingly important drivers for change, such as automation, digitisation, and dynamic demand patterns, simulation models of production systems become outdated rapidly. At the same time, building simulation models often requires much time, cost, and expertise, especially when dealing with complex job shop production systems. To address these challenges, an automated simulation model generation (ASMG) framework for material flow simulation of production systems is presented. This framework contains multiple approaches to infer routeing, control and temporal aspects from event-based data. To achieve this, methods from process mining (PM) and machine learning (ML) are applied. Additionally, the suitability of Coloured Petri Nets (CPNs) to serve as conceptual and operational simulation models is examined. The inferred simulation models have high validity when compared to the real system concerning the KPIs machine utilisation, throughput, and work in process. It is shown, that most model elements can be inferred very well, in particular process routes, processing times, and resource selection rules. This proof of concept serves as a foundation for research on detection approaches inferring further model elements such as setup times accurately.

A health equity framework to support the next generation of cancer population simulation models.

Over the past 2 decades, population simulation modeling has evolved as an effective public health tool for surveillance of cancer trends and estimation of the impact of screening and treatment strategies on incidence and mortality, including documentation of persistent cancer inequities. The goal of this research was to provide a framework to support the next generation of cancer population simulation models to identify leverage points in the cancer control continuum to accelerate achievement of equity in cancer care for minoritized populations. In our framework, systemic racism is conceptualized as the root cause of inequity and an upstream influence acting on subsequent downstream events, which ultimately exert physiological effects on cancer incidence and mortality and competing comorbidities. To date, most simulation models investigating racial inequity have used individual-level race variables. Individual-level race is a proxy for exposure to systemic racism, not a biological construct. However, single-level race variables are suboptimal proxies for the multilevel systems, policies, and practices that perpetuate inequity. We recommend that future models designed to capture relationships between systemic racism and cancer outcomes replace or extend single-level race variables with multilevel measures that capture structural, interpersonal, and internalized racism. Models should investigate actionable levers, such as changes in health care, education, and economic structures and policies to increase equity and reductions in health-care-based interpersonal racism. This integrated approach could support novel research approaches, make explicit the effects of different structures and policies, highlight data gaps in interactions between model components mirroring how factors act in the real world, inform how we collect data to model cancer equity, and generate results that could inform policy.

Simulation-Based Digital Twins Enabling Smart Services for Machine Operations: An Industry 5.0 Approach

The Industry 5.0 initiative seeks the sustainability and resilience of production systems through digital technologies. Derived from such an initiative, the Operator 5.0 concept surged to place the operator as the main value-creation contributor of sustainable and resilient human-machine systems. Smart service systems are data-driven services that provide intelligent capabilities to support decision-making in business processes. Despite previous research on smart services driven by multiple technologies in different contexts, further studies approaching Digital Twins as enablers of smart services in machine use phases are yet to be explored. Digital Twin, an Industry 5.0 enabler, is a technology that virtually represents physical assets that collect and analyze data from actual operations to make predictions for decision-making. Simulation-based Digital Twins generate simulation models that are continuously upgraded with asset data in real time, improving the accuracy of predictions. This paper aims to investigate how the Simulation-based Digital Twins (SDT) can enable the development of smart services in the Operator 5.0 context. We build on a multiple case study of 13 interviews from nine heavy mobile machinery manufacturers. We capture our results in categorizing SDT-enabled smart services for the use phases of the machine lifecycle. The categorization identifies the capabilities of each smart service for decision-making support and value co-creation.

Study on sound transmission characteristics and positioning strategy of gas insulated transmission line shell

Gas insulated transmission line (GIL) is widely used for large-capacity power transmission in complex environment, and has the characteristics of long distance and totally enclosed structure. When the insulation fault occurs inside GIL, its metal shell structure makes it more difficult to monitor through optical, electrical and other physical quantities. Considering the rapidity and accuracy of positioning, the propagation of sound waves along the GIL sheath is a commonly used method in GIL on-site PD testing and running state online monitoring for fault positioning. This paper establishes a high-pressure GIL acoustic propagation simulation model based on pipe acoustics theory, analyzes the frequency dispersion and mode distribution of acoustic waves along the GIL shell, determines an appropriate fault location strategy based on the propagation velocity and attenuation of the selected acoustic wave mode, and carries out experiments to verify it. The results of the research show that the low order bending wave F(1,1) propagated along the GIL shell has good positioning characteristics, and the propagation velocity conforms to the dispersion curve distribution. By taking into account the attenuation and delay of the acoustic wave passing through epoxy insulators or expansion joints in the fault localization strategy, the localization accuracy can be improved by 60%. And this positioning strategy was verified to be effective in the GIL insulation fault localization test conducted in the field.

Estimation of system delay based toll equivalency factors at toll plazas using simulation

The service time and time headway of the vehicles are used to define the equivalency factor of different vehicle classes at the toll plaza. Both the service time and time headway are point measures and do not account for the system delay (time difference when a vehicle enters the queue and leaves the tollbooth) caused to the vehicle. The present study aims to quantify the system delay incurred to the vehicles at electronic toll collection (ETC) lanes under mixed traffic conditions using a microsimulation approach. Field data collected from one toll plaza located on a National Highway are used for simulation model generation. A new terminology called system delay-based toll equivalency factor (DTEF) is introduced to convert the different vehicle classes into equivalent passenger cars. The DTEF variation for different approach volumes and heavy commercial vehicle (HCV) compositions were checked at different ETC penetration levels. A total of 288 scenarios have been worked out, and simulation runs have been made for all such scenarios to obtain the DTEF values. The average DTEF value of HCV was obtained as 2.20. Further, it is found that with an increase in approach volume and HCV share in the traffic stream, the DTEF value increases. The outcome of the present study will be useful to field practitioners and engineers to determine the capacity in equivalent DTEF/hr and level of service of a toll plaza in terms of system delay.

SHP2SIM: a python pipeline for Modelica based district and urban scale energy simulations

ABSTRACT Energy simulation models are crucial to estimate the energy demand of buildings, especially for prospective planning on a district or city scale. As required input data is not available in many cases, an automated model generation workflow is needed. Existing workflows have several disadvantages, including: (i) dependence on large input datasets of existing buildings; (ii) no 3D representation to support the planning process; (iii) they are proprietary solutions. The pipeline ‘SHP2SIM’ is an open-source python pipeline enabling enrichment and generation of building energy simulation models based on little input data for district and urban scale. The pipeline is tested by simulating the heat load for a district with 27 buildings and validated for one building: R squared is 0.9825, CV(RMSE) is 22.10%, and NMBE is 4.06% on a monthly basis. To enable reproducibility and encourage open science, input data, output models, and the pipeline are openly available (https://github.com/tug-cps/shp2sim).

In-situ empirical validation of common indoor climate parameters in an inhabited multizone dwelling

Combined building indoor climate and energy simulation models only recently gained vast popularity and their application has been moving from the research community to a broader audience. Yet, in-situ empirical validation of this new generation of complex multi-purpose dynamic simulation models has lagged behind. Using a dynamic multizone building indoor climate and energy simulation model in Modelica with the IDEAS library and buoyancy driven airflow components (validated with CONTAM), this research presents model validation results and lessons learned from an in-situ empirical validation study of common indoor climate parameters (i.e., indoor air temperature (), relative humidity (RH) and CO2 concentration (CO2)) for an inhabited and mechanically ventilated case study dwelling in The Netherlands. The simulation results show that the latest generation of building indoor climate and energy models in Modelica have great ability to accurately predict common indoor climate parameters in multizone inhabited dwellings (provided that user behavior info is available). Evaluation metrics for the three studied parameters show excellent calibration criteria (i.e., MAE between 0.60–0.78 °C (), 3.5–4.6% (RH) and 88–181 ppm (CO2)) and the accompanying graphs corroborate the findings. In the event that no motion sensor data is available, statistically generated occupancy profiles prove good representative alternatives on the condition that basic info is available about the number of inhabitants and the inhabitants’ lifestyle. In-situ monitoring for empirical model validation proves to be a real challenge full of (un)foreseen obstacles.

Modelling and control of hydrogen production processes based on electrolysis

Currently, hydrogen is considered as one of the most promising energy carriers, the production of which is possible from various raw materials, including water, natural gas, hydrogen sulphide, coal, etc. The article presents the main results of an analysis of global technological trends in the development of hydrogen generation methods in the period from 2010 to 2038, which aims to identify in-demand and popular technological solutions for hydrogen energy. The analysis is based on the International Energy Agency's database published in October 2022, which contains the most comprehensive information on the key characteristics of 990 hydrogen projects based in sixty countries: output, installed electrical power, carbon dioxide emissions, type of output, stage and timing of implementation. The analysis shows the steady leadership of electrolysis hydrogen generation technologies in the context of the search for the most widespread method of hydrogen production. At the same time, the global hydrogen energy industry has clearly expressed trends towards the increased introduction of alternative (non-electrolysis) technologies in large-scale industrial production. Based on the existing empirical Ullerberg model, a modified universal structural simulation model of hydrogen electrolysis generation in plants with alkaline electrolysers and with proton exchange membrane has been proposed. The modified model has been developed in MATLAB application package and Simulink dynamic simulation environment using Simscape physical simulation elements. Verification procedure of the developed model showed good agreement of simulation results with the experimental data available in the open sources and obtained at the alkaline electrolysis and proton exchange membrane electrolysis plants. To increase the energy efficiency of hydrogen production process, a single-loop system for automatic regulation of feed water temperature supplied to the proton-exchange membrane electrolysis unit was developed.

Automated generation of simulation model in context of industry 4.0

ABSTRACT Material flow simulation is a core technology of Industry 4.0. It can analyze and improve large-scale production systems through experimentation with digital simulation models. However, modeling in discrete event simulation is considered as an effortful and time-consuming activity and challenges especially small and medium-sized enterprises. Systematic experiments and what-if-analysis require a large number of models. Modeling and simulation becomes a repetitive activity and the ability to model and simulate instantly becomes crucial for industry, 4.0. However, model generation typically uses specific methods to build models with individual properties for specific physical systems. A general literature review cannot sufficiently describe the current state of model generation. This study aims to provide an analysis of model generation based on the modeling strategy, modeling view, and production system type, as well as model properties and limitations.

Development and application of computational methods for the study of protein dynamics with Pm-HMGR as a model system.

Continuous atomistic descriptions of enzyme mechanisms and atomistic dynamics are crucial to the characterization of protein function and dysfunction—a common cause of disease—but unattainable with current techniques, necessitating novel methods which leverage computational-experimental synergy. Computational studies can provide a wealth of information, but carry little weight without experimental validation. Thus, coupling time-resolved crystallographic studies of enzyme mechanisms with computational mechanistic investigation through molecular dynamics (MD) simulation and predictive model generation provides the opportunity to combine approaches. We demonstrate and refine a novel Markov State-informed Multilinear Singular Value Decomposition (MSiMSVD) method with the Pseudomonas mevalonii HMG CoA Reductase (PmHMGR) pathway as a model system. MSiMSVD couples time-resolved crystallographic studies of enzyme mechanisms with computational mechanistic investigation through MD simulation and predictive model generation. Application of the MSiMSVD method to slow dynamical events-such as the PmHMGR 2nd hydride transfer-is limited by the ability of force fields to reproduce atomistic behavior at a transition state. This need can be met via the generation of a Transition State Force Field (TSFF) with a program such as Quantum-guided Molecular Mechanics (Q2MM). However, a frequently encountered problem in the optimization of TSFFs for biomolecules is the high dimensionality of the optimization surface producing local rather than global minima. To address this, we implement a constrained particle swarm algorithm hybridized with a basic genetic algorithm to automate the optimization of TSFFs with Q2MM. The Q2MM and the MSiMSVD methods will become accessible to non-experts for a wide range of systems, such as large biomolecules. Thus far, initial Markov State Models generated from MD simulation of the PmHMGR 2nd hydride transfer indicate that more sampling is necessary to cover the transition state conformation, i.e. the structure of the activated complex.

Automated Generation of Discrete Event Simulation Models for the Economic Assessment of Interventions for Rare Diseases Using the RaDiOS Ontology

Collection and synthesis of evidence is a key task in the development of the simulation models required for health technology assessment (HTA). The implementation of some of these models, such as discrete event simulation (DES) models, presents technical difficulties and requires higher technical skills. This work presents a method to extract the knowledge stored in an ontology, Rare Disease Ontology for Simulation (RaDiOS), to generate a DES model. RaDiOS is a domain ontology focused on collecting evidence on rare diseases for simulation models. We reviewed and enhanced the ontology to increase its semantic expressiveness. Besides, we developed a transformation tool (RaDiOS-MTT) to automatically generate DES models from the knowledge stored in the ontology. We defined a set of “synthetic” diseases, with simple natural histories, represented them in RaDiOS, and compared the results of the automatically generated simulation models with their manually created counterparts. Afterwards, we used a case study on a real intervention (newborn screening for profound biotinidase deficiency) to validate our approach. The automatically generated models for the synthetic diseases mimicked their programmatic counterparts in structure and results. The same happened to the model for profound biotinidase deficiency.

Cost-effectiveness of miglustat versus symptomatic therapy of Niemann-Pick disease type C.

Niemann-Pick disease type C (NP-C) is a progressive neurodegenerative disorder with early infantile (< 2years), late infantile (2-6years), juvenile (7-15years) and adolescent (> 15years) onset. The mainstay of therapy for NP-C patients with neurological symptoms is miglustat, a drug that may modify the course of the disease. Our aim was to evaluate the cost-effectiveness of miglustat in comparison to symptomatic therapy in patients with NP-C in the socio-economic settings of the Republic of Serbia, an upper-middle-income European economy. The perspective of the Serbian Republic Health Insurance Fund was chosen for this study, and the time horizon was eighty years. The main outcomes of the study were quality-adjusted life years gained with miglustat and comparator, and direct costs of treatment. The study was conducted through the generation and simulation of the Discrete-Event Simulation model. The model results were obtained after Monte Carlo microsimulation of a sample with 1000 virtual patients. Treatment with miglustat was not cost-effective when compared with symptomatic therapy and was associated with negative values of net monetary benefit regardless of the onset of neurological manifestations (- 110,447,627.00 ± 701,614.00 RSD, - 343,871,695.00 ± 2,577,441.00 RSD, - 1,397,908,502.00 ± 23,084,235.00 RSD and - 2,953,680,879.00 ± 33,297,412.00 RSD) for early infantile, late infantile, juvenile and adolescent cohorts, respectively). When traditional pharmacoeconomic evaluation is employed, miglustat is not a cost-effective option in comparison to symptomatic therapy for the treatment of NP-C. However, given the proven efficacy of miglustat, there is a need to find ways to make this drug available to all patients with NP-C.

Synchronous Integration Method of System and Simulation Models for Mechatronic Systems Based on SysML

System architecture could be described clearly and unambiguously by a descriptive system model during the system-level design of mechatronic products. However, for system verification, it is necessary to integrate the system modeling tools and simulation tools for analysis. Most of the existing integration methods focus on the integration in the physical architecture phase of the system model. A model integration framework that can integrate simulation into the logical architecture phase of the entire model for synchronous model verification is proposed in this paper. In the integration process, a Model-to-Text method is employed for information extraction and model transformation from the SysML model to the simulation model. The system model can be transformed by reusing and updating the generated simulation model through the framework for model verification. A framework with the Simulink model as the transformation target is constructed as an example in this paper, and the capability of this framework is verified by a design process of a quadruped robot. The result shows that this framework can realize the system design paradigm of integrating system verification for the entire logical architecture, which can improve the design efficiency and provide a reference for other system design and integration work.

Dynamic simulation models as digital twins of logistics systems driven by data from multiple sources

In recent years, the development of manufacturing companies in the most dynamic industries, such as automotive, has been conditioned by the use of modern technologies, but also management concepts focused on quality and continuous improvement of processes and services. There was also a rapid increase in the level of production automation with a simultaneously growing level of flexibility in manufacturing systems, forced by the need to adapt the product to the customer’s needs (mass customization). These factors influenced the formation of a new concept for the development of industrial systems, referred to as Industry 4.0. It refers to production based on cyber-physical systems, integrating manufacturing, logistics, and distribution. One of the main components of an intelligent factory, based on cyber-physical systems, is the virtual equivalent of a real system in the form of the digital twin. Based on the feedback from the real system, a digital simulation supported by analytical software on the information platform is created. Dynamic simulation models should automatically adapt to changes in physical objects of the analysed system. Therefore, models of real objects reflecting their current state and logic of behaviour are required. The methods of automatic or semi-automatic generation of simulation models are the answer to these problems. They are based on approaches using data and information obtained from ERP/MES/CAD systems. In this paper, the proposed schemes for data exchange in the process of creation and simulation models and mapping of the dynamics of the actual system are shown.

Computer Simulation Model for Surface Generation in Turning process. (Dept. M(Production))

Computer Simulation Model for Surface Generation in Turning process. (Dept. M(Production))

Model-based automatic generation of digital twin models for the simulation of reconfigurable manufacturing systems for timber construction

Reconfigurable manufacturing systems adapt to changing requirements and therefore offer a solution to the challenges associated with increasing product variety and shorter product life cycles. This is especially helpful in the prefabrication in timber construction, as components and requirements vary widely between projects. However, reconfigurations must be quick to keep downtimes short. The planning of reconfigurations is a complex problem where digital twins and simulation models of the manufacturing systems are necessary for simulation and validation. In our timber prefabrication use case, building components and requirements change from project to project, and the manufacturing system needs to reconfigure frequently. The building components and the configuration of the manufacturing system are designed simultaneously during a co-design process to ensure the integrity of the manufacturing process in a changing manufacturing environment. Hence, this may require many iterations of the planning process. Each iteration needs validation through a simulation. However, the manual generation of simulation models is time-consuming and error-prone and thus poses an obstacle for the digital planning phase. This paper addresses this issue and presents a model-based approach to generate simulation models for reconfigurable manufacturing systems. Our system and its components are modeled in automation markup language as this domain-specific language allows us to model not only kinematics and geometry but also behavior, skills, and interfaces to check the compatibility of components. We apply the method to our use case with the IntCDC wood prefabrication system. The proposed method builds a component graph for a configuration of the manufacturing system and generates the required packages for the simulation in Gazebo and ROS. To build simulation models automatically, we apply model-to-model and model-to-text transformation techniques. The proposed method is suitable for integration in the digital co-design workflow of IntCDC and allows fast iterations with continuous simulations of machine configurations throughout the planning process.