Archetypes of Digital Twins

Valk, Hendrik van der; Haße, Hendrik; Möller, Frederik; Otto, Boris

doi:10.1007/s12599-021-00727-7

Cited by 92 publications

(52 citation statements)

References 55 publications

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“…To meet the characteristics of a real twin, communication must be in a bivalent way. Van der Falk et al [ 26 ] deduce DT archetypes from characteristics as well as industry interviews. Starting from Basic Digital Twins which, similar to a digital shadow, just represent the state of a physical object, up to increasingly complex twin variations, the following archetypes are further differentiated: Enriched Digital Twin, Autonomous Control Twin, Enhanced Autonomous Control Twin, Exhaustive Twin.…”

Section: Related Workmentioning

confidence: 99%

Establishing Reliable Research Data Management by Integrating Measurement Devices Utilizing Intelligent Digital Twins

Lehmann

Schorz

Rache

et al. 2023

Sensors

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One of the main topics within research activities is the management of research data. Large amounts of data acquired by heterogeneous scientific devices, sensor systems, measuring equipment, and experimental setups have to be processed and ideally be managed by Findable, Accessible, Interoperable, and Reusable (FAIR) data management approaches in order to preserve their intrinsic value to researchers throughout the entire data lifecycle. The symbiosis of heterogeneous measuring devices, FAIR principles, and digital twin technologies is considered to be ideally suited to realize the foundation of reliable, sustainable, and open research data management. This paper contributes a novel architectural approach for gathering and managing research data aligned with the FAIR principles. A reference implementation as well as a subsequent proof of concept is given, leveraging the utilization of digital twins to overcome common data management issues at equipment-intense research institutes. To facilitate implementation, a top-level knowledge graph has been developed to convey metadata from research devices along with the produced data. In addition, a reactive digital twin implementation of a specific measurement device was devised to facilitate reconfigurability and minimized design effort.

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Section: Related Workmentioning

confidence: 99%

Establishing Reliable Research Data Management by Integrating Measurement Devices Utilizing Intelligent Digital Twins

Lehmann

Schorz

Rache

et al. 2023

Sensors

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“…However, the concept of DT is still loose and broad and is constantly evolving while DT enablers are under ongoing development and DT applications are spreading to many sectors. In view of this, several works focused on reviewing the characteristics and achievements of the DT [85,303,[333][334][335][336][337][338][339][340][341][342][343][344][345].…”

Section: Digital Twinmentioning

confidence: 99%

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Cunha¹,

Droz²,

Zine³

et al. 2022

Preprint

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The use of Machine Learning (ML) has rapidly spread across several fields, having encountered many applications in Structural Dynamics and Vibroacoustic (SD&V). The increasing capabilities of ML to unveil insights from data, driven by unprecedented data availability, algorithms advances and computational power, enhance decision making, uncertainty handling, patterns recognition and real-time assessments. Three main applications in SD&V have taken advantage of these benefits. In Structural Health Monitoring, ML detection and prognosis lead to safe operation and optimized maintenance schedules. System identification and control design are leveraged by ML techniques in Active Noise Control and Active Vibration Control. Finally, the so-called ML-based surrogate models provide fast alternatives to costly simulations, enabling robust and optimized product design. Despite the many works in the area, they have not been reviewed and analyzed. Therefore, to keep track and understand this ongoing integration of fields, this paper presents a survey of ML applications in SD&V analyses, shedding light on the current state of implementation and emerging opportunities. The main methodologies, advantages, limitations, and recommendations based on scientific knowledge were identified for each of the three applications. Moreover, the paper considers the role of Digital Twins and Physics Guided ML to overcome current challenges and power future research progress. As a result, the survey provides a broad overview of the present landscape of ML applied in SD&V and guides the reader to an advanced understanding of progress and prospects in the field.

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“…Nor does a DT correspond to a digital shadow, depicting the real-time states and changes of a PT that can just be manually modified. The changes in a DT automatically mirror and affect the status of its PT: the data flows bi-directionally (Van der Valk et al, 2020 ) and in real time between twins in digital and physical worlds, possibly without any human intervention (Liu et al, 2022 ) through the DT-driven control of an actuated PT. However, a DT is typically “played” by experts like managers, engineers, and designers as a complex interactive simulation to predict future issues in the PT according to its past and current behavior (Semeraro et al, 2021 ).…”

Section: Approaching and Adopting Digital Twinsmentioning

confidence: 99%

Beyond Digital Twins: Phygital Twins for Neuroergonomics in Human-Robot Interaction

et al. 2022

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Archetypes of Digital Twins

Cited by 92 publications

References 55 publications

Establishing Reliable Research Data Management by Integrating Measurement Devices Utilizing Intelligent Digital Twins

Establishing Reliable Research Data Management by Integrating Measurement Devices Utilizing Intelligent Digital Twins

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Beyond Digital Twins: Phygital Twins for Neuroergonomics in Human-Robot Interaction

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