Rapid synthesis of high-level architecture-based heterogeneous simulation: a model-based integration approach

Hemingway, Graham; Neema, Himanshu; Nine, Harmon; Sztipanovits, János; Karsai, Gábor

doi:10.1177/0037549711401950

Cited by 56 publications

(36 citation statements)

References 11 publications

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“…The advantage of HLA is the possibility of highly paralleled simulations for large-scale systems; whereas its disadvantage is the introduction of additional time-synchronization issues [44]. Thus, we can register various researches focusing on the optimization aspect of the synchronization techniques in HLA [79,80]. Table 3 provides a non-exhaustive list of examples of co-simulation frameworks orchestrated by a master algorithm.…”

Section: Federation Of Simulation-simulator Coupling With Orchestratormentioning

confidence: 99%

On Conceptual Structuration and Coupling Methods of Co-Simulation Frameworks in Cyber-Physical Energy System Validation

et al. 2017

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Co-simulation is an emerging method for cyber-physical energy system (CPES) assessment and validation. Combining simulators of different domains into a joint experiment, co-simulation provides a holistic framework to consider the whole CPES at system level. In this paper, we present a systematic structuration of co-simulation based on a conceptual point of view. A co-simulation framework is then considered in its conceptual, semantic, syntactic, dynamic and technical layers. Coupling methods are investigated and classified according to these layers. This paper would serve as a solid theoretical base for specification of future applications of co-simulation and selection of coupling methods in CPES assessment and validation.

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Section: Federation Of Simulation-simulator Coupling With Orchestratormentioning

confidence: 99%

On Conceptual Structuration and Coupling Methods of Co-Simulation Frameworks in Cyber-Physical Energy System Validation

et al. 2017

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“…The management and coupling of cosimulations have been recently addressed with FMI standard [11] and also with model based techniques [12]. However, the area of expertise of control application developers may still not be in simulation techniques.…”

Section: Related Workmentioning

confidence: 99%

Model-Driven Development of Automation and Control Applications: Modeling and Simulation of Control Sequences

Vepsäläinen

Kuikka

2014

Advances in Software Engineering

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The scope and responsibilities of control applications are increasing due to, for example, the emergence of industrial internet. To meet the challenge, model-driven development techniques have been in active research in the application domain. Simulations that have been traditionally used in the domain, however, have not yet been sufficiently integrated to model-driven control application development. In this paper, a model-driven development process that includes support for design-time simulations is complemented with support for simulating sequential control functions. The approach is implemented with open source tools and demonstrated by creating and simulating a control system model in closed-loop with a large and complex model of a paper industry process.

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“…The second layer, the Syntactic layer, ascertains that the right data are exchanged among the models. The C2WT [66] and SORASCS [67] achieve that. In the third layer, the Semantic layer, interoperation across models should be examined to ensure that the exchange of data is semantically correct with respect to the problem domain.…”

Section: Meta-models and Meta-modelingmentioning

confidence: 99%

“…The move towards supporting Multi-Modeling in various Modeling and Simulation platforms is already taking place. The Command and Control Wind Tunnel (C2WT) [66] developed by Vanderbilt University and the Service Oriented Architecture for Socio-Cultural Systems (SORASCS) [67] developed by Carnegie Mellon University are examples of MultiModeling capable platforms. While the first provides a federated approach, utilizing the High Level Architecture (HLA) [68] standard and the meta-programmable Generic Modeling Environment (GME) [69]; the second employs Service Oriented Architecture techniques in providing model interoperation capabilities.…”

Section: Introductionmentioning

confidence: 99%

Resilient Organizational Architectures for Command and Control

Levis¹

2014

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The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE fDD-MM-VyyYj ABSTRACTA formal definition of organizational resilience is articulated in which the three stages of avoidance, survival, and recovery are included. Three measures of performance. Capacity, Tolerance, and Flexibility are considered and several computable measures for each are defined. A case study of the resilience of a Maritime Operations Center operating in a contested cyber environment illustrate the research results. In addition, an approach for integrating and synchronizing the planning by functional components was developed. Finally, a domain specific approach was developed for using meta-modeling and multi-modeling to address the evaluation the design of C2 organizations. 5a. CONTRACT NUMBER. Enter all contract numbers as they appear in the report, e.g. F33315-86-C-5169. SUBJECT TERMS5b. GRANT NUMBER. Enter all grant numbers as they appear in the report, e.g. AFOSR-82-1234. 5c. PROGRAM ELEMENT NUMBER.Enter all program element numbers as they appear in the report, e.g. 61101 A.5e. TASK NUMBER. Enter all task numbers as they appear in the report, e.g. 05; RF0330201; T4112.5f. WORK UNIT NUMBER. Enter all work unit numbers as they appear in the report, e.g. 001; AFAPL30480105. AUTHOR(S). Enter name(s) of person(s)responsible for writing the report, performing the research, or credited with the content of the report. The form of entry is the last name, first name, middle initial, and additional qualifiers separated by commas, e.g. Smith, Richard, J, Jr. PERFORMING ORGANIZATION NAME(S) AND ADDRESS{ES). Self-explanatory. PERFORMING ORGANIZATION REPORT NUMBER.Enter all unique alphanumeric report numbers assigned by the performing organization, e.g. BRL-1234; AFWL-TR-85-4017-VOI-21-PT-2. " SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES).Enter the name and address of the organization(s) financially responsible for and monitoring the work. SPONSOR/MONITOR'S ACRONYM(S).Enter, if available, e.g. BRL, ARDEC, NADC. SPONSOR/MONITOR'S REPORT NUMBER(S).Enter report number as assigned by the sponsoring/ monitoring agency, if available, e.g. BRL-TR-829; -215. The overall objectives of the effo...

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Rapid synthesis of high-level architecture-based heterogeneous simulation: a model-based integration approach

Cited by 56 publications

References 11 publications

On Conceptual Structuration and Coupling Methods of Co-Simulation Frameworks in Cyber-Physical Energy System Validation

On Conceptual Structuration and Coupling Methods of Co-Simulation Frameworks in Cyber-Physical Energy System Validation

Model-Driven Development of Automation and Control Applications: Modeling and Simulation of Control Sequences

Resilient Organizational Architectures for Command and Control

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