Hossain Muhammad Muctadir, David A. Manrique Negrin, Raghavendran Gunasekaran, Loek Cleophas, Mark van den Brand, Boudewijn R. Haverkort
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引用次数: 0
摘要
数字孪生(DT)通常被定义为物理实体与相应虚拟实体(VE)的配对,根据使用情况模仿前者的某些方面。近年来,这一概念促进了从大型和小型高科技系统的设计、验证和预测性维护等众多用例。对于这些系统来说,各种异构的跨领域模型是必不可少的,而模型驱动工程在这些模型的设计、开发和维护中发挥着举足轻重的作用。我们认为,模型和模型驱动工程在 DT 的虚拟环境中也发挥着同样重要的作用。由于 DT 及其在不同领域和用例中的应用迅速普及,设计、开发和维护相应 VE 的方法、工具和实践也大相径庭。为了更好地了解这些异同,我们对来自产业界和学术界的 19 位与数字孪生不同生命周期阶段密切相关的专业人士进行了半结构式访谈研究。在本文中,我们将根据七个研究问题,介绍我们的分析和研究结果。总的来说,我们发现在对数字孪生的理解以及开发和维护相应虚拟环境所使用的工具、技术和方法方面,总体上缺乏统一性。此外,考虑到数字孪生是软件密集型系统,我们认为在数字孪生生命周期的各个阶段采用更多的软件工程实践、流程和专业知识具有巨大的发展潜力。
Current trends in digital twin development, maintenance, and operation: an interview study
Digital twins (DTs) are often defined as a pairing of a physical entity and a corresponding virtual entity (VE), mimicking certain aspects of the former depending on the use-case. In recent years, this concept has facilitated numerous use-cases ranging from design to validation and predictive maintenance of large and small high-tech systems. Various heterogeneous cross-domain models are essential for such systems, and model-driven engineering plays a pivotal role in the design, development, and maintenance of these models. We believe models and model-driven engineering play a similarly crucial role in the context of a VE of a DT. Due to the rapidly growing popularity of DTs and their use in diverse domains and use-cases, the methodologies, tools, and practices for designing, developing, and maintaining the corresponding VEs differ vastly. To better understand these differences and similarities, we performed a semi-structured interview research with 19 professionals from industry and academia who are closely associated with different lifecycle stages of digital twins. In this paper, we present our analysis and findings from this study, which is based on seven research questions. In general, we identified an overall lack of uniformity in terms of the understanding of digital twins and used tools, techniques, and methodologies for the development and maintenance of the corresponding VEs. Furthermore, considering that digital twins are software intensive systems, we recognize a significant growth potential for adopting more software engineering practices, processes, and expertise in various stages of a digital twin’s lifecycle.
期刊介绍:
We invite authors to submit papers that discuss and analyze research challenges and experiences pertaining to software and system modeling languages, techniques, tools, practices and other facets. The following are some of the topic areas that are of special interest, but the journal publishes on a wide range of software and systems modeling concerns:
Domain-specific models and modeling standards;
Model-based testing techniques;
Model-based simulation techniques;
Formal syntax and semantics of modeling languages such as the UML;
Rigorous model-based analysis;
Model composition, refinement and transformation;
Software Language Engineering;
Modeling Languages in Science and Engineering;
Language Adaptation and Composition;
Metamodeling techniques;
Measuring quality of models and languages;
Ontological approaches to model engineering;
Generating test and code artifacts from models;
Model synthesis;
Methodology;
Model development tool environments;
Modeling Cyberphysical Systems;
Data intensive modeling;
Derivation of explicit models from data;
Case studies and experience reports with significant modeling lessons learned;
Comparative analyses of modeling languages and techniques;
Scientific assessment of modeling practices