Model Order Reduction a Key Technology for Digital Twins

Hartmann, Dirk; Herz, Matthias; Wever, Utz

doi:10.1007/978-3-319-75319-5_8

Cited by 69 publications

(45 citation statements)

References 12 publications

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“…Another advantage can be realized for developing surrogate models stationary parameterized systems, where full order models typically require many pseudoiterations to converge to a steady-state at each control parameter [279]- [281]. Therefore, reduced order modeling has been considered as a key enabler to compress high fidelity models into much lower dimensions to alleviate heavy computational demand in digital twin technologies [282]. ROM enables reusing simulation models from the early phases of product development in later product lifetime phases, especially during the product operation phase [283].…”

Section: Nonintrusive Data-driven Modelingmentioning

confidence: 99%

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

2020

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A digital twin can be defined as an adaptive model of a complex physical system. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

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Section: Nonintrusive Data-driven Modelingmentioning

confidence: 99%

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

2020

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“…Having ability to facilitate dynamic data exchange easier between different components, these non-intrusive models can arguably be more promising and impactful in numerous interdisciplinary fields. Moreover, with the advent of digital twin technologies 16 , the collection of data from sensors has become possible at different stages of product's lifecycle, and model order reduction might be considered as a key enabler for this digital twin vision in many emerging cyber-physical systems 17 .…”

Section: Introductionmentioning

confidence: 99%

A deep learning enabler for nonintrusive reduced order modeling of fluid flows

et al. 2019

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In this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows. We propose various deep neural network architectures which numerically predict evolution of dynamical systems by learning from either using discrete state or slope information of the system. Our approach has been demonstrated using both residual formula and backward difference scheme formulas. However, it can be easily generalized into many different numerical schemes as well. We give a demonstration of our framework for three examples: (i) Kraichnan-Orszag system, an illustrative coupled nonlinear ordinary differential equations, (ii) Lorenz system exhibiting chaotic behavior, and (iii) a non-intrusive model order reduction framework for the two-dimensional Boussinesq equations with a differentially heated cavity flow setup at various Rayleigh numbers. Using only snapshots of state variables at discrete time instances, our data-driven approach can be considered truly non-intrusive, since any prior information about the underlying governing equations is not required for generating the reduced order model. Our a posteriori analysis shows that the proposed data-driven approach is remarkably accurate, and can be used as a robust predictive tool for non-intrusive model order reduction of complex fluid flows.

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“…This direction in the service phase is strongly supported by the digital twin trend. Digital twins are developed based on the products installed, which are being monitored, the collected data are analyzed, and predictive capabilities are advanced [21].…”

Section: Literature Reviewmentioning

confidence: 99%

Configuration Lifecycle Management – Future of Product Configurators

Myrodia¹,

Randrup²,

Hvam

2018

2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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Model Order Reduction a Key Technology for Digital Twins

Cited by 69 publications

References 12 publications

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

A deep learning enabler for nonintrusive reduced order modeling of fluid flows

Configuration Lifecycle Management – Future of Product Configurators

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