A Dynamic Data Driven Applications Systems (DDDAS)-Based Digital Twin IoT Framework

Malik, Sarah; Rouf, Rakeen; Mazur, Krzysztof; Kontsos, Antonios

doi:10.1007/978-3-030-61725-7_6

Cited by 4 publications

(13 citation statements)

References 15 publications

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“…Digital Twin evolution -from the static interrelation between model and physical system towards a system dynamically integrated with its instrumentation, can be synergistically align with multimodal and information fusion [5] and engineering support [6]. One related concept is the Dynamic Data Driven Applications Systems (DDDAS) [7].…”

Section: Information Fusion and Digital Engineering / Digital Twinsmentioning

confidence: 99%

“…Dynamic Digital Twins are being applied to mechanical systems (automobiles and aircraft), power equipment (generating and transmitting power), manufacturing (material design and fabrication) as well as structures (intelligent buildings and hospitals). The use of DT with DDDAS was formulated for aerospace DTs in [65], and as amplified in the work by for Aerospace Digital Twins testbeds. The DDDAS DT concept extends cognitive awareness [66,67], as well as supporting security communications [68], power grid analysis [69,70], predictive maintenance [71], and structural health monitoring.…”

Section: Aerospace Systemsmentioning

confidence: 99%

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Digital twin meets information fusion: panel summary

Blasch,

Chen,

Chen

et al. 2024

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII

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Within the recent years, the concept of Digital Twins (DT) emerged to support digital engineering physical-systems design in the processing and analysis of device components, data flows, and networked systems. As an element of systems engineering, the DT serves as a method for life cycle management for operations and maintenance through monitoring, diagnostics, and prognostics. Key to DT methods is the use of distributed sensors to monitor the system and determine the functioning with the use of physical design information, such as a series of distributed edge sensors and the layout of a physical electrical grid. While industries like industrial manufacturing, electrical power, space systems, and healthcare maintenance have embraced DT; other groups are utilizing the concept for analysis. Given that a large number of sensors are used to gather data on the health of system, it is natural that data fusion, estimation theory, and signals processing support digital twin fusion (DTwF); but there are a variety of challenges such as big data, distribution fusion, and edge analytics. The panel will discuss areas in which data and information fusion techniques enhance DT applications.

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Section: Information Fusion and Digital Engineering / Digital Twinsmentioning

confidence: 99%

Section: Aerospace Systemsmentioning

confidence: 99%

Digital twin meets information fusion: panel summary

Blasch,

Chen,

Chen

et al. 2024

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII

View full text Add to dashboard Cite

show abstract

“…In the context of digital twins, the concept of state spaces refers to the representation of the current or past states of the Physical and Digital Twins. The Physical State ( S ) space encompasses all the relevant variables and parameters that describe the parameterized state of the physical asset at a given time in the Physical Twin (Malik et al, 2020) (Figure 1). These variables may include physical properties, environmental conditions, operational parameters, and any other factors that influence the behaviour of the physical asset .…”

Section: Physical State Smentioning

confidence: 99%

“…The Digital State ( D ) space refers to the virtual representation of the physical twin within the digital twin (Malik et al, 2020 2).…”

Section: Digital State Dmentioning

confidence: 99%

TwinEco: A Unified Framework for Dynamic Data-Driven Digital Twins in Ecology

Khan,

de Koning,

Endresen

et al. 2024

Preprint

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The burgeoning interest in digital twin (DT) technology presents a transformative potential for ecological modelling, offering new ways to model the complex dynamics of ecosystems. This paper introduces the TwinEco framework, designed to mitigate fragmentation in the development and deployment of DT applications in ecology. Compared to traditional modelling frameworks, TwinEco emphasises modularity and flexibility by introducing "layers" and "components" for DTs, accommodating diverse ecological applications without necessitating the deployment of all components. This modular approach ensures adaptability and scalability, promoting interoperability and integration with broader initiatives like Destination Earth. Digital twins in ecology offer significant advancements over traditional approaches by explicitly modelling changing processes and states over time, integrating extensive data, and enabling real-time feedback loops by actuating events or policies in the "real-world". The framework's capacity to adjust to changing environmental conditions enhances its predictive accuracy and responsiveness. This paper highlights the necessity for a unified framework to prevent divergent interpretations and ensure the interoperability of DT applications across ecological domains. Future recommendations include expanding case studies to demonstrate the framework's applicability, assessing the potential of Dynamic Data-Driven Application Systems (DDDAS) paradigm within ecological DTs, and exploring interactions between components to optimise performance. Emphasising model-data fusion and fostering a shared terminology within the ecological community are crucial for the framework's success. TwinEco aims to provide a robust foundation for ecological digital twins, enabling timely, data-driven decision-making to address global environmental challenges.

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“…DDDAS has the instrumentation loop and the data assimilation loop, where the data assimilation comes from a virtual model. Examples of DTs include internet of things (IoT) [60], the proof of work as a miner digital twin [61] and using DL for power grids as a DT concept [62,63]. There are many advantages of DT to support supply chain and decision making [64] as with predictive maintenance [65].…”

Section: Digital Twinmentioning

confidence: 99%

Human-machine cooperative AI decision-making with heterogeneous data

Blasch,

Bastian,

Aved

et al. 2023

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII

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Many techniques have been developed for sensor and information fusion, machine and deep learning, as well as data and machine analytics. Currently, many groups are exploring methods for human-machine teaming using saliency and heat maps, explainable and interpretable artificial intelligence, as well as user-defined interfaces. However, there is still a need for standard metrics for test and evaluation of systems utilizing artificial intelligence (AI), such as deep learning (DL), to support the AI principles. In this paper, we explore the elements associated with the opportunities and challenges emerging from designing, testing, and evaluating such future systems. The paper highlights the MAST (multi-attribute scorecard table), and more specifically the MAST criteria ―analysis of alternatives‖ by measuring the risk associated with an evidential DL-based decision. The concept of risk includes the probability of a decision as well as the severity of the choice, from which there is also a need for an uncertainty bound on the decision choice which the paper postulates a risk bound. Notional analysis for a cyber networked system is presented to guide to interactive process for test and evaluation to support the certification of AI systems as to the decision risk for a human-machine system that includes analysis from both the DL method and a user.

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A Dynamic Data Driven Applications Systems (DDDAS)-Based Digital Twin IoT Framework

Cited by 4 publications

References 15 publications

Digital twin meets information fusion: panel summary

Digital twin meets information fusion: panel summary

TwinEco: A Unified Framework for Dynamic Data-Driven Digital Twins in Ecology

Human-machine cooperative AI decision-making with heterogeneous data

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