Multi-paradigm Modeling Research Articles

Multi-paradigm modelling and control of microgrid systems for better power stability in the Rockaways

The Rockaways Peninsula faces issues related to congestion and power outages during times of peak usage. Additionally, it is susceptible to disruptions caused by disasters such as hurricanes and storms. In this paper, we propose a new methodology that employs multi-paradigm modelling and control for the design and implementation of interconnected microgrid systems in the Rockaways. Microgrids are small-scale power networks that incorporate renewable energy technologies for power generation and distribution to enhance the control of energy supply and demand. Multi-paradigm modelling is employed to describe microgrids’ dynamic behavior more accurately by integrating system dynamics, agent-based modelling, as well as discrete event and continuous time simulation. We use agent-based models to describe the behavior of separate microgrid elements and the microgrid as a whole. Discrete event/continuous time simulation is used to analyze real-time operation of electrical parameters, such as voltage, current and frequency. Thus, the design, analysis and performance of microgrids are improved. Also, control strategies are used for the purpose of enabling the microgrids to operate effectively by responding to changes in power supply and demand and minimizing the effects of disturbances. The findings of this study demonstrate the feasibility and resilience benefits of incorporating multi-paradigm modelling and control in the design and management of microgrid systems in the Rockaways, which can result in the development of more durable, efficient, and sustainable energy systems in the region.

A Framework of Modeling and Simulation Based on Swarm Ontology for Autonomous Unmanned Systems

For the emerging autonomous swarm technology, from the perspective of systems science and Systems Engineering (SE), there must be novel methodologies and elements to aggregate multiple systems into a group, which distinguish the general components with specific functions. Here, we expect to provide a presentation of their existence in swarm development processes. The inspiration for our approach originates from the integration of swarm ontology, multiparadigm modeling, multiagent systems, cyber-physical systems, etc. Therefore, we chose the model-driven architecture as a framework to provide a method of model representation across the multiple levels of abstraction and composition. The autonomous strategic mechanism was defined and formed in parallel with Concept of Operations (ConOps) analysis and systems design, so as to effectively solve the cognitive problem of emergence caused by nonlinear causation among individual and whole behaviors. Our approach highlights the use of model-based processes and their artifacts in the swarm mechanism to integrate operational and functional models, which means connecting the macro- and micro-aspects in formalism to synthesize a whole with its expected goals, and then to verify and validate within an L-V-C simulation environment.

Architecting decentralized control in large-scale self-adaptive systems

Architecting a self-adaptive system with decentralized control is challenging. Indeed, architects shall consider several different and interdependent design dimensions and devise multiple control loops to coordinate and timely perform the correct adaptations. To support this task, we propose Decor, a reasoning framework for architecting and evaluating decentralized control. Decor provides (i) multi-paradigm modeling support, (ii) a modeling environment for MAPE-K style decentralized control, and (iii) a co-simulation environment for simulating the decentralized control together with the managed system and estimating the quality attributes of interest. We apply the Decor in three case studies: an intelligent transportation system, a smart power grid, and a cloud computing application. The studies demonstrate the framework’s capabilities to support informed architectural decisions on decentralized control and adaptation strategies.

Multiparadigm Modeling Framework to Evaluate the Impacts of Travel Patterns on Electric Vehicle Battery Lifespan

The widespread adoption of electric vehicles (EVs) can help attain economic and environmental sustainability by reducing oil dependency and greenhouse gas emissions. However, several issues need to be addressed before EVs can become a popular vehicle choice among the general public. A key issue is the perpetual reduction in EV battery capacity caused by battery degradation over time with usage. This can lead to a reduced driving range and cause “range anxiety” for EV drivers. This becomes even more critical in developing countries where consumers are highly sensitive to battery replacement costs. Thus, to promote EVs in developing economies, policymakers and vehicle manufacturers need to develop attractive incentive schemes and warranty strategies preceded by a thorough assessment of the useable EV battery lifespan for a wide range of users. This paper develops a multiparadigm modeling framework to compute battery degradation for a large population of EVs by capturing the effects of travel patterns, traffic conditions, and ambient temperature. The proposed framework consists of four different building blocks: (i) a microscopic traffic simulation model to generate speed profiles, (ii) an EV power consumption model, (iii) a battery equivalent circuit model, and (iv) a semiempirical battery degradation model. The proposed framework can also be used to assess the battery life-cycle of electric-powered automated vehicles by adjusting their travel patterns accordingly. A case study is presented using travel diary data of around 700 households from the U.S. National Household Travel Survey of 2009 to simulate household travel patterns and corresponding battery lifespan distribution.

Multi-scale Simulation for Energy Flexible Factories and Factory Networks: A System of Systems Perspective

The continuously increasing integration of volatile renewable energy sources requires an increasing amount of energy flexibility and organizational effort on the demand side to improve the synchronization of demand and supply. Within factories, energy flexibility potentials can be beneficially exploited to increase the utilization of on-site renewable energy. Excess renewable energy is usually fed into the grid for an ever decreasing feed-in compensation. Through the formation of local factory network as a collaborative systems of systems, factories can enhance the potentials of energy flexibility and on-site renewable energy. In this paper, the effects of two individual factories forming a collaborative factory network are evaluated regarding their potential to induce additional or amplified functionality for both factories. This functionality is quantified using environmental and economic indicators. A multi-scale multi-paradigm modeling approach is conducted to simulate and evaluate combined energy flexibility measures on single-factory level and on a collaborative factory network level. The results are discussed before the background of the definition of emergent behavior within systems of systems. Alongside the quantified results that support decision making for collaborative systems of systems, this work discusses the pain points of system of systems boundary definition and its impact on the definition and quantification of emergent behavior.

Multi-paradigm modeling for cyber–physical systems: A systematic mapping review

Cyber–Physical Systems (CPS) are heterogeneous and require cross-domain expertise to model. The complexity of these systems leads to questions about prevalent modeling approaches, their ability to integrate heterogeneous models, and their relevance to the application domains and stakeholders. The methodology for Multi-Paradigm Modeling (MPM) of CPS is not yet fully established and standardized, and researchers apply existing methods for modeling of complex systems and introducing their own. No systematic review has been previously performed to create an overview of the field on the methods used for MPM of CPS. In this paper, we present a systematic mapping study that determines the models, formalisms, and development processes used over the last decade. Additionally, to determine the knowledge necessary for developing CPS, our review studied the background of actors involved in modeling and authors of surveyed studies. The results of the survey show a tendency to reuse multiple existing formalisms and their associated paradigms, in addition to a tendency towards applying transformations between models. These findings suggest that MPM is becoming a essential approach to model CPS, and highlight the importance of future integration of models, standardization of development process and education.

Multi-paradigm modelling for cyber\u2013physical systems: a descriptive framework

The complexity of cyber–physical systems (CPSs) is commonly addressed through complex workflows, involving models in a plethora of different formalisms, each with their own methods, techniques, and tools. Some workflow patterns, combined with particular types of formalisms and operations on models in these formalisms, are used successfully in engineering practice. To identify and reuse them, we refer to these combinations of workflow and formalism patterns as modelling paradigms. This paper proposes a unifying (Descriptive) Framework to describe these paradigms, as well as their combinations. This work is set in the context of Multi-Paradigm Modelling (MPM), which is based on the principle to model every part and aspect of a system explicitly, at the most appropriate level(s) of abstraction, using the most appropriate modelling formalism(s) and workflows. The purpose of the Descriptive Framework presented in this paper is to serve as a basis to reason about these formalisms, workflows, and their combinations. One crucial part of the framework is the ability to capture the structural essence of a paradigm through the concept of a paradigmatic structure. This is illustrated informally by means of two example paradigms commonly used in CPS: Discrete Event Dynamic Systems and Synchronous Data Flow. The presented framework also identifies the need to establish whether a paradigm candidate follows, or qualifies as, a (given) paradigm. To illustrate the ability of the framework to support combining paradigms, the paper shows examples of both workflow and formalism combinations. The presented framework is intended as a basis for characterisation and classification of paradigms, as a starting point for a rigorous formalisation of the framework (allowing formal analyses), and as a foundation for MPM tool development.

Multi-paradigm model of the region’s innovative climate development

The relevance of the innovative development of regional economies is dictated by both an internal factor (balanced development of territories) and an external factor (the country’s competitiveness in the world market). The authors highlight the threats that may arise in different areas of the region’s economic activity. The rating system for assessing the innovation climate in the region has been substantiated and developed. The methodology of the author’s rating assessment of the innovation climate in the region allows a systematic approach to the qualitative and quantitative characteristics of the state of the innovation climate in the region (good, normal, pre-crisis, crisis). On the basis of the developed assessment system, a multi-paradigm model of the formation of an innovation climate in the region is proposed. The proposed author’s model, which reflects the system of factors of the innovation climate in the region, makes it possible to form ways of forming an innovation climate in the context of the selected areas. It seems that the proposed rating system of assessment and the model interconnected with it have applied significance, an integrated approach will allow assessing the level of the innovation climate in the region, and the proposed model will help to develop effective measures to form an innovative climate. The results of the work are the proposed rating system of assessment and a multi-paradigm model interconnected with it. In conclusion, the authors made a number of proposals for the formation of an innovative climate in the region.

Critical realism and constructivism: merging research paradigms for a deeper qualitative study

PurposeTraditional approaches in qualitative research have adopted one research paradigm linked to an established typology. This paper addresses the unconventional application of two research paradigms in one study. A critical realist approach was used to augment a constructivist analysis of data in a research project seeking to explore the meaning that managers in small to medium enterprises (SMEs) attach to hazard identification, the construction of a hazard profile reflective of the business and its use in assisting to manage hazards within the SME's safety management system framework. Critical realism offered a complementary but essential framework to explore causal mechanisms that led to a deeper understanding of the findings by searching for the processes and causality that lay beneath the social and organizational phenomena observed.Design/methodology/approachThis paper compares the two research paradigms in order to seek junctures and apply them to a research project. Analytical tools applied to each research paradigm within the project are presented, followed by a new multiparadigm conceptual model that integrates critical realism and constructivism, providing an original contribution of knowledge to this field of qualitative research.FindingsThe adoption of a multiparadigm model enabled not only the interpretation of social phenomena but also the determination of its causality, enabling a more insightful answering of the research question and leading to a deeper insight into the phenomenology that was studied. This research approach widens the boundaries of qualitative inquiry within organizational research by promoting strategies that challenge more traditionally anchored research typologies, and consequently contributes to better research outcomes.Research limitations/implicationsThis study was conducted across four organizations. Similar research is encouraged across a greater number of case studies to validate the process of using a constructivist and critical realist paradigm to gain a more insightful understanding of events and their causality.Practical implicationsThe comparison of two research paradigms and consequent provision of a conceptual model (Figure 3) provides potential for the development of further multiparadigm models for research projects within the field of organizational management.Social implicationsThis paper has the potential to promote engagement and collaboration between research scholars seeking to explore the use of multiple research paradigms.Originality/valueSuch an approach has not previously been widely discussed or adopted to examine qualitative data, and advances theory in qualitative research. The application of two research paradigms using such an approach can be applied to businesses in a number of different contexts to gain a more insightful understanding of research participant perspectives, observable events arising from those perspectives and their associated causality.

Multi-Paradigm Modeling Approach to Simulate the Link between the Fabrication Process and the Performance of Li-Ion Batteries

In this work we present a multi-paradigm modeling approach simulating the link between the fabrication process of lithium-ion battery electrodes and their electrochemical performance at the cell level. The approach combines discrete particle (Coarse Grained Molecular Dynamics) and continuum (PDE-based) mathematical models. Discrete particle models were used to simulate slurry preparation, drying, and calendering step in strong correlation with experimental results. Experimental data such as the slurry formulation, the viscosity vs. the shear rate, the density and the porosity of the dry electrode were used to validate our model for the slurry and the dried electrode state. Different compositions were studied, in order to reproduce experimental trends, and validate our force fields parametrization. The final electrode structures obtained after drying were compared to tomography characterizations and used to simulate the electrochemical performance of the electrodes, and compared with experimental results. In this presentation, for demonstration purpose, we focus on the case of the NMC as active material.

A modeling framework for the application of multi-paradigm simulation methods

Decisions about modeling and simulation (M&S) of real-world systems need to be evaluated prior to implementation. Discrete Event, System Dynamics, and Agent Based are three different modeling and simulation approaches widely applied to enhance decision-making of M&S of these systems. Combining and/or integrating these methods can provide solutions to a plethora of systems’ problems. However, current solutions and frameworks do not provide guidance for selecting and deploying M&S models. Hence, the aim of this work is to present a generic modeling framework for combining and/or integrating Discrete Event, System Dynamics, and Agent Based simulation approaches. The framework is termed multi-paradigm modeling framework (MPMF). In this paper, we describe the research methodology that was followed for the development of MPMF, the different phases of MPMF, and the generic relationships of forming and deploying multi-paradigm simulation models. Then we evaluate the framework by using it for the implementation of a universal task analysis simulation model (UTASiMo). The MPMF provided guidance on what methods need to be incorporated into the UTASiMo models, what information is exchanged among those models, and how these models are connected and interact with each other.

A Categorical Model of Process Co-Simulation

A set of dynamic systems in which some entities undergo transformations, or receive certain services in successive phases, can be modeled by processes. The specification of a process consists of a description of the properties of this process as a mathematical object in a suitable modeling language. The language chosen for specifying a process should facilitate the writing of this specification in a very clear and simple form. This raises the need for the use of various types of formalisms that are faithful to the component subsystems of such a system and which are capable of mimicking their varied dynamics. Often in practice, the development of domain specific languages is used to provide building blocks adapted to the processes. Thus, the concept of multi-paradigm modeling arises which involves the combination of different types of models, the decomposition and composition of heterogeneous specified models as well as their simulation. Multi-paradigm modeling presents a variety of challenges such as coupling and transforming the models described in various formalisms, the relationship between models at different levels of abstraction, and the creation of metamodels to facilitate the rapid development of varied formalisms for model specification. The simulation can be seen as a set of state variables that evolve over time. Co-simulation is a synthesis of all simulations of the components of the system, coordinated and synchronized based on interactions between them. The theory of categories provides a framework for organizing and structuring formal systems in which heterogeneous information can be transferred, thus allowing for the building of rigorous cohesion bridges between heterogeneous components. This paper proposes a new model of co-simulation of processes based on the category theory.

A Multi-Paradigm Modelling approach to live modelling

To develop complex systems and tackle their inherent complexity, (executable) modelling takes a prominent role in the development cycle. But whereas good tool support exists for programming, tools for executable modelling have not yet reached the same level of functionality and maturity. In particular, live programming is seeing increasing support in programming tools, allowing users to dynamically change the source code of a running application. This significantly reduces the edit–compile–debug cycle and grants the ability to gauge the effect of code changes instantly, aiding in debugging and code comprehension in general. In the modelling domain, however, live modelling only has limited support for a few formalisms. In this paper, we propose a Multi-Paradigm Modelling approach to add liveness to modelling languages in a generic way, which is reusable across multiple formalisms. Live programming concepts and techniques are transposed to (domain-specific) executable modelling languages, clearly distinguishing between generic and language-specific concepts. To evaluate our approach, live modelling is implemented for three modelling languages, for which the implementation of liveness substantially differs. For all three cases, the exact same structured process was used to enable live modelling, which only required a “sanitization” operation to be defined.

Bio-IR-M: A Multi-Paradigm Modelling for Bio-Inspired Multi-Agent Systems

Nowadays bio-inspired approaches are widely used. Some of them became paradigms in many domains, such as Ant Colony Optimization (ACO) and Genetic Algorithms (GA). Despite the inherent challenges of surviving, in the natural world, biological organisms evolve, self-organize and self-repair with only local knowledge and without any centralized control. The analogy between biological systems and Multi-Agent Systems (MAS) is more than evident. In fact, every entity in real and natural systems is easily identified as an agent. Therefore, it will be more efficient to model them with agents. In a simulation context, MAS has been used to mimic behavioural, functional or structural features of biological systems. In a general context, bio-inspired systems are carried out with ad hoc design models or with a one target feature MAS model. Consequently, these works suffer from two weaknesses. The first is the use of dedicated models for restrictive purposes (such as academic projects). The second one is the lack of a design model. In this paper, our contribution aims to propose a generic multi-paradigms model for bio-inspired systems. This model is agent-based and will integrate different bio-inspired paradigms with respect of their concepts. We investigate to which extent is it possible to preserve the main characteristics of both natural and artificial systems. Therefore, we introduce the influence/reaction principle to deal with these bio-inspired multi-agent systems.

Multi-paradigm deception modeling for cyber defense

Security-critical systems demand multiple well-balanced mechanisms to detect ill-intentioned actions and protect valuable assets from damage while keeping costs in acceptable levels. The use of deception to enhance security has been studied for more than two decades. However, deception is still included in the software development process in an ad-hoc fashion, typically realized as single tools or entire solutions repackaged as honeypot machines. We propose a multi-paradigm modeling approach to specify deception tactics during the software development process so that conflicts and risks can be found in the initial phases of the development, reducing costs of ill-planned decisions. We describe a metamodel containing deception concepts that integrates other models, such as a goal-oriented model, feature model, and behavioral UML models to specify static and dynamic aspects of a deception operation. The outcome of this process is a set of deception tactics that is realized by a set of deception components integrated with the system components. The feasibility of this multi-paradigm approach is shown by designing deception defense strategies for a students’ presence control system for the Faculty of Science and Technology of Universidade NOVA de Lisboa.

A model-driven framework for multi-paradigm modeling and holistic simulation of healthcare systems

The domain of healthcare is characterized by a high degree of complexity and a diversity of perspectives, and modelers are often confronted with the challenge of formulating a simulation model that captures this complexity in a systematic and manageable manner. Most often, the diverse perspectives of healthcare systems are studied in isolation and using specific formalisms. As it turns out, answering questions concerning behavioral properties of the overall system becomes difficult and therefore not sufficient for an efficient design and analysis of the system under study. In this article, we propose a framework for multi-paradigm modeling and holistic simulation of healthcare systems. We present a modeling methodology with a plethora of formalisms to allow the modeler to choose an appropriate formalism at a given level of abstraction while model transformation relates the different formalisms. Furthermore, we develop an integrative approach for the interactions between models of different perspectives through dynamic update of model output-to-parameter integration during concurrent simulations. Such an approach provides multiple levels of explanation for the same system, while offering, at the same time, an integrated view of the whole. The framework has successfully been applied to study part of the Nigerian healthcare system.

EPICE-HIV: An Epidemiologic Cost-Effectiveness Model for HIV Treatment.

The goal of this research was to establish a new and innovative framework for cost-effectiveness modeling of HIV-1 treatment, simultaneously considering both clinical and epidemiological outcomes. EPICE-HIV is a multi-paradigm model based on a within-host micro-simulation model for the disease progression of HIV-1 infected individuals and an agent-based sexual contact network (SCN) model for the transmission of HIV-1 infection. It includes HIV-1 viral dynamics, CD4+ T cell infection rates, and pharmacokinetics/pharmacodynamics modeling. Disease progression of HIV-1 infected individuals is driven by the interdependent changes in CD4+ T cell count, changes in plasma HIV-1 RNA, accumulation of resistance mutations and adherence to treatment. The two parts of the model are joined through a per-sexual-act and viral load dependent probability of disease transmission in HIV-discordant couples. Internal validity of the disease progression part of the model is assessed and external validity is demonstrated in comparison to the outcomes observed in the STaR randomized controlled clinical trial. We found that overall adherence to treatment and the resulting pattern of treatment interruptions are key drivers of HIV-1 treatment outcomes. Our model, though largely independent of efficacy data from RCT, was accurate in producing 96-week outcomes, qualitatively and quantitatively comparable to the ones observed in the STaR trial. We demonstrate that multi-paradigm micro-simulation modeling is a promising tool to generate evidence about optimal policy strategies in HIV-1 treatment, including treatment efficacy, HIV-1 transmission, and cost-effectiveness analysis.

Reliability evaluation of integrated energy systems based on smart agent communication

Reliability evaluation of Integrated Energy Systems (IESs) based on Smart Agent Communication (SAC) is studied in this paper. The typical structure and reliability evaluation modeling for IESs is firstly introduced. A new reliability evaluation approach is then presented, in which SAC based system reconfiguration is innovatively integrated into the reliability evaluation process. Based on this combination, state evaluation (key procedure of the reliability evaluation) along with system reconfiguration can be conducted autonomously in reliability evaluation. Algorithm and procedures of the system reconfiguration based on a decentralized agent communication algorithm (K−1 algorithm) is described. The system reconfiguration does not depend on global information of whole system, which can effectively improve reliability evaluation and modeling efficiency. The presented models and approaches are conducted on the multi-paradigm modeling and simulation platform-AnyLogic, and validated by extensive cases studies.

A Multiparadigm Modeling Investigation of Membrane Chemical Degradation in PEM Fuel Cells

We report a multi-paradigm model of the membrane chemical degradation in Polymer Electrolyte Membrane Fuel Cells (PEMFCs), by combining Coarse-Grained Molecular Dynamics (CGMD) and a multiscale cell performance model. CGMD is used to generate structural databases that relate the amount of detached (degraded) ionomer sidechains with the water content and the resulting PEM meso-microporous structure. The multiscale cell performance model describes the electrochemical reactions and transport mechanisms occuring in the electrodes from an on-the-fly coupling between Kinetic Monte Carlo (KMC) sub-models parametrized with Density Functional Theory (DFT) data and (partial differential equations-based) continuum sub-models. Furthermore, the performance model includes a kinetic PEM degradation sub-model which integrates the CGMD database. The cell model also predicts the instantaneous PEM sidechain content and conductivity evolution at each time step. The coupling of these diverse modeling paradigms allows one to describe the feedback between the instantaneous cell performance and the intrinsic membrane degradation processes. This provides detailed insights on the membrane degradation (sidechain detachment as well as water reorganization within the PEM) during cell operation. This novel modeling approach opens interesting perspectives in engineering practice to predict materials degradation and durability as a function of the initial chemical composition and structural properties in electrochemical energy conversion and storage devices.

Simulation Design: Trans-Paradigm Best-Practice from Software Engineering

There are growing initiatives to apply software engineering (SE) best-practice to computational science, which includes simulation. One area where the simulation literature appears to be particularly light is in the overall structural design of simulations, and what architectures and features are valuable for what reasons. (Part of the problem is that parts of this knowledge are abstracted away in simulation toolkits which are often not easily comparable, and have different conceptual aims.) To address this, I outline three key software properties which embody SE best-practices, and then define an 'idealised' software architecture for simulation—what SE would call a reference architecture—which strongly exhibits them. I show that this is universal to all simulations (largely because modelling-paradigm-specific detail is encapsulated into a 'single black box' layer of functionality) but that simulation toolkits tend to differ in how they map to them; this relates to the aims of the toolkits, which I provide a useful categorisation of. I show that, interestingly, there are several core features of this architecture that are not fully represented in any simulation toolkit that I am aware of. I present a library—JSIT—which provides some proof-of-concept implementations of them for Java-based toolkits. This library, and other ideas in the reference architecture, are put into practice on a published, multi-paradigm model of health and social care which uses the AnyLogic toolkit. I conclude with some thoughts on why this area receives so little focus, how to take it forwards, and some of the related cultural issues.