Engineering Design with Digital Thread

Singh, Victor; Willcox, Karen

doi:10.12783/shm2017/13943

Cited by 9 publications

(12 citation statements)

References 25 publications

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“…We adopt a Bayesian approach to state estimation in which we maintain a probabilistic estimate for the digital twin, which is updated as new data are acquired 35 . In particular, we treat the digital twin, d t , as a discrete random variable, with probability mass function given by the posterior distribution

b_{t} (M_{j}) : = p (d_{t} = M_{j} | u_{1}, \dots, u_{t}, o_{1}, \dots, o_{t}), \forall M_{j} \in ℳ

…”

Section: Data‐driven Digital Twinsmentioning

confidence: 99%

Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models

Kapteyn

Knezevic²,

Huynh³

et al. 2020

Numerical Meth Engineering

131

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This work proposes an approach that combines a library of component-based reduced-order models with Bayesian state estimation in order to create data-driven physics-based digital twins. Reduced-order modeling produces physics-based computational models that are reliable enough for predictive digital twins, while still being fast to evaluate. In contrast with traditional monolithic techniques for model reduction, the component-based approach scales efficiently to large complex systems, and provides a flexible and expressive framework for rapid model adaptation-both critical features in the digital twin context. Data-driven model adaptation and uncertainty quantification is formulated as a Bayesian state estimation problem, in which sensor data is used to infer which models in the model library are the best candidates for the digital twin. This approach is demonstrated through the development of a digital twin for a 12ft wingspan unmanned aerial vehicle. Offline, we construct a library of pristine and damaged aircraft components. Online, we use structural sensor data to rapidly adapt a physics-based digital twin of the aircraft structure. The data-driven digital twin enables the aircraft to dynamically replan a safe mission in response to structural damage or degradation.

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b_{t} (M_{j}) : = p (d_{t} = M_{j} | u_{1}, \dots, u_{t}, o_{1}, \dots, o_{t}), \forall M_{j} \in ℳ

…”

Section: Data‐driven Digital Twinsmentioning

confidence: 99%

Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models

Kapteyn

Knezevic²,

Huynh³

et al. 2020

Numerical Meth Engineering

131

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show abstract

“…MBSE approaches, such as SysML [16], can offer the defined specifications to formalize the system artifacts. Moreover, digital thread is a main approach to manage interoperability between digital twins and traceability between system development process and digital twin assets, because MBSE provides a unified descriptions of multiple domain knowledge and a specification to formalize system architecture views for the complex systems [20]. Furthermore, the unified specification enables stakeholders to develop their own models and provide them with the information in black box.…”

Section: Related Workmentioning

confidence: 99%

Towards a Decentralized Digital Engineering Assets Marketplace: Empowered by Model-based Systems Engineering and Distributed Ledger Technology

Lu¹,

Zheng²,

Hu³

et al. 2020

Preprint

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Model-based Systems Engineering (MBSE) has been widely utilized to formalize system artifacts and facilitate their development throughout the entire lifecycle. During complex system development, MBSE models need to be frequently exchanged across stakeholders. Concerns about data security and tampering using traditional data exchange approaches obstruct the construction of a reliable marketplace for digital assets. The emerging Distributed Ledger Technology (DLT), represented by blockchain, provides a novel solution for this purpose owing to its unique advantages such as tamper-resistant and decentralization. In this paper, we integrate MBSE approaches with DLT aiming to create a decentralized marketplace to facilitate the exchange of digital engineering assets (DEAs). We first define DEAs from perspectives of digital engineering objects, development processes and system architectures. Based on this definition, the Graph-Object-Property-Point-Role-Relationship (GOPPRR) approach is used to formalize the DEAs. Then we propose a framework of a decentralized DEAs marketplace and specify the requirements, based on which we select a Directed Acyclic Graph (DAG) structured DLT solution. As a proof-of-concept, a prototype of the proposed DEAs marketplace is developed and a case study is conducted to verify its feasibility. The experiment results demonstrate that the proposed marketplace facilitates free DEAs exchange with a high level of security, efficiency and decentralization.

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“…In product planning, these insights are considered especially valuable, as they help to reduce the characteristic uncertainties of this phase (Holler et al, 2016b). The analysis of use phase data in product planning is an exemplary application of the ideas of the digital thread, which describes the linking of information of all product lifecycle stages for design, evaluation, and life cycle management (Maybury, 2013;Singh and Willcox, 2018).…”

Section: Introductionmentioning

confidence: 99%

Potentials and challenges of analyzing use phase data in product planning of manufacturing companies

Meyer

Fichtler

Koldewey

et al. 2022

AIEDAM

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The successful planning of future product generations requires reliable insights into the actual products’ problems and potentials for improvement. A valuable source for these insights is the product use phase. In practice, product planners are often forced to work with assumptions and speculations as insights from the use phase are insufficiently identified and documented. A new opportunity to address this problem arises from the ongoing digitalization that enables products to generate and collect data during their utilization. Analyzing these data could enable their manufacturers to generate and exploit insights concerning product performance and user behavior, revealing problems and potentials for improvement. However, research on analyzing use phase data in product planning of manufacturing companies is scarce. Therefore, we conducted an exploratory interview study with decision-makers of eight manufacturing companies. The result of this paper is a detailed description of the potentials and challenges that the interviewees associated with analyzing use phase data in product planning. The potentials explain the intended purpose and generic application examples. The challenges concern the products, the data, the customers, the implementation, and the employees. By gathering the potentials and challenges through expert interviews, our study structures the topic from the perspective of the potential users and shows the needs for future research.

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Engineering Design with Digital Thread

Cited by 9 publications

References 25 publications

Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models

Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models

Towards a Decentralized Digital Engineering Assets Marketplace: Empowered by Model-based Systems Engineering and Distributed Ledger Technology

Potentials and challenges of analyzing use phase data in product planning of manufacturing companies

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