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1. Modeling A UAV Surveillance Scenario- An Applied MBSE Approach

   https://doi.org/10.1109/SysCon53073.2023.10131074  

   Lopez, Viviana ; Akundi, Aditya ( April 2023 , IEEE International Systems Conference (SysCon))

   As the complexity of both products and systems increases across a wide range of industry sectors, there has been an influx in demand for methods of system organization and optimization. MBSE enhances the ability to obtain, analyze, communicate, and manage data on a comprehensive architecture of a system. In this study, a military combat surveillance scenario is modeled using SysML generating state machine diagrams and activity diagrams using the Magic Model Analyst execution framework plugin. This study seeks to prove the feasibility of an MBSE-enabled framework using SysML to create and simulate a surveillance system that monitors and reports on the health status and performance of an armored fighting vehicle (combat tank) through an Unmanned Ariel Vehicle (UAV). The Magic System of Systems Architect, which actively promotes system development architectural frameworks, was used to construct SysML-compliant models, allowing the creation of intricate model diagrams. The construction of the UAV surveillance scenario emphasized the capability of modifying a diagram feature and ensuring that the alteration is communicated to all linked model diagrams. This study builds on a previously published MBSE-enabled conceptual framework for creating digital twins. The purpose of this research is to test and validate the framework's procedures. Keywords—MBSE, SysML, MBSE framework, UAV, Surveillance 

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2. Reservoir computing as digital twins for nonlinear dynamical systems

   https://doi.org/10.1063/5.0138661  

   Kong, Ling-Wei ; Weng, Yang ; Glaz, Bryan ; Haile, Mulugeta ; Lai, Ying-Cheng ( March 2023 , Chaos: An Interdisciplinary Journal of Nonlinear Science)

   We articulate the design imperatives for machine learning based digital twins for nonlinear dynamical systems, which can be used to monitor the “health” of the system and anticipate future collapse. The fundamental requirement for digital twins of nonlinear dynamical systems is dynamical evolution: the digital twin must be able to evolve its dynamical state at the present time to the next time step without further state input—a requirement that reservoir computing naturally meets. We conduct extensive tests using prototypical systems from optics, ecology, and climate, where the respective specific examples are a chaotic CO2 laser system, a model of phytoplankton subject to seasonality, and the Lorenz-96 climate network. We demonstrate that, with a single or parallel reservoir computer, the digital twins are capable of a variety of challenging forecasting and monitoring tasks. Our digital twin has the following capabilities: (1) extrapolating the dynamics of the target system to predict how it may respond to a changing dynamical environment, e.g., a driving signal that it has never experienced before, (2) making continual forecasting and monitoring with sparse real-time updates under non-stationary external driving, (3) inferring hidden variables in the target system and accurately reproducing/predicting their dynamical evolution, (4) adapting to external driving of different waveform, and (5) extrapolating the global bifurcation behaviors to network systems of different sizes. These features make our digital twins appealing in applications, such as monitoring the health of critical systems and forecasting their potential collapse induced by environmental changes or perturbations. Such systems can be an infrastructure, an ecosystem, or a regional climate system.

    

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3. Model based systems engineering—A text mining based structured comprehensive overview

   https://doi.org/10.1002/sys.21601  

   Akundi, Aditya ; Mondragon, Oscar ( September 2021 , Systems Engineering)

   null (Ed.)

   An observed increase in systems scale and complexity has led to a significant momentum in exploring, identifying, and adopting model based systems engineering (MBSE) tools and techniques amongst research communities and industry practitioners. Several attempts to transform systems design and engineering practices through the use of MBSE in academia and industry has led to a considerable increase in the number of articles published containing the keyword “MBSE.” This growth serves as the motivation in this paper to explore the MBSE landscape with the help of text mining techniques to identify the most often used key terms, tools, and languages, in the context of research in MBSE and the thematic aspects defining the use of MBSE by researchers and practitioners. The objective of this paper is to provide a structured comprehensive overview of research contributions across the MBSE landscape by employing text mining techniques for: (a) identifying the concepts and methodologies inferred upon in relation to MBSE, and (b) classifying the literature published to identify commonalities across academic researchers and practitioners using MBSE tools and methods. For this purpose, the abstracts of 2380 relevant articles published in the period of the last two decades from five different databases are mined. It is found that the terms “SysML,” “Cyber Physical Systems,” and “Production” are the most used terms among researchers across the MBSE landscape with SysML being the most widely used modeling language. Further, six major thematic topics are identified that classify articles from over the last two decades with an increasing interest observed in the use of MBSE to support manufacturing and production engineering activities, especially in the cyber physical systems domain. The contributions of this paper provide a leeway on using text mining techniques to understand the research directions that are currently of interest in the field of MBSE and thereby identify potential future research directions. 

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4. Building digital twins of the human immune system: toward a roadmap

   https://doi.org/10.1038/s41746-022-00610-z  

   Laubenbacher, R. ; Niarakis, A. ; Helikar, T. ; An, G. ; Shapiro, B. ; Malik-Sheriff, R. S. ; Sego, T. J. ; Knapp, A. ; Macklin, P. ; Glazier, J. A. ( May 2022 , npj Digital Medicine)

   Digital twins, customized simulation models pioneered in industry, are beginning to be deployed in medicine and healthcare, with some major successes, for instance in cardiovascular diagnostics and in insulin pump control. Personalized computational models are also assisting in applications ranging from drug development to treatment optimization. More advanced medical digital twins will be essential to making precision medicine a reality. Because the immune system plays an important role in such a wide range of diseases and health conditions, from fighting pathogens to autoimmune disorders, digital twins of the immune system will have an especially high impact. However, their development presents major challenges, stemming from the inherent complexity of the immune system and the difficulty of measuring many aspects of a patient’s immune state in vivo. This perspective outlines a roadmap for meeting these challenges and building a prototype of an immune digital twin. It is structured as a four-stage process that proceeds from a specification of a concrete use case to model constructions, personalization, and continued improvement.

    

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5. Digital Twins for Materials

   https://doi.org/10.3389/fmats.2022.818535  

   Kalidindi, Surya R. ; Buzzy, Michael ; Boyce, Brad L. ; Dingreville, Remi ( March 2022 , Frontiers in Materials)

   Digital twins are emerging as powerful tools for supporting innovation as well as optimizing the in-service performance of a broad range of complex physical machines, devices, and components. A digital twin is generally designed to provide accurate in-silico representation of the form (i.e., appearance) and the functional response of a specified (unique) physical twin. This paper offers a new perspective on how the emerging concept of digital twins could be applied to accelerate materials innovation efforts. Specifically, it is argued that the material itself can be considered as a highly complex multiscale physical system whose form (i.e., details of the material structure over a hierarchy of material length) and function (i.e., response to external stimuli typically characterized through suitably defined material properties) can be captured suitably in a digital twin. Accordingly, the digital twin can represent the evolution of structure, process, and performance of the material over time, with regard to both process history and in-service environment. This paper establishes the foundational concepts and frameworks needed to formulate and continuously update both the form and function of the digital twin of a selected material physical twin. The form of the proposed material digital twin can be captured effectively using the broadly applicable framework of n-point spatial correlations, while its function at the different length scales can be captured using homogenization and localization process-structure-property surrogate models calibrated to collections of available experimental and physics-based simulation data. 

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