Aerodynamic Data Fusion Toward the Digital Twin Paradigm

Renganathan, S. Ashwin; Harada, Kohei; Mavris, Dimitri N.

doi:10.2514/1.j059203

Cited by 31 publications

(13 citation statements)

References 28 publications

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“…Apart from hardware requirements, collecting high quality from multiple heterogeneous data sources will significantly impact the following realisation of DTs functions. Data collection does not simply transform the data from the sensor to models but needs to fully consider every risk and uncertainty [69], e.g. sensor fault or extreme operating environment.…”

Section: A Modelling 1) Data Collection and Pre-processmentioning

confidence: 99%

Digital Twin in Aerospace Industry: A Gentle Introduction

Aslam

Wileman

et al. 2022

IEEE Access

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Digital twin (DT), primarily a virtual replica of any conceivable physical entity, is a highly transformative technology with profound implications. Whether it be a product development, design optimisation, performance improvement, or predictive maintenance, digital twins are changing the ways work is undertaken in various industries with multifarious business applications. Aerospace industry, including its manufacturing base, is one such keen adopter of digital twins with an unprecedented interest in their bespoke design, development, and implementation across wider operations and critical functions. This, however, comes with some misconceptions about the digital twin technology and lack of understanding with respect to its optimal implementation. For instance, equating digital twin to an intelligent model while ignoring the essential components of data acquisition and visualisation, misleads the creators into building digital shadow or digital models, instead of the actual digital twin. This paper unfolds such intricacies of digital twin technology for the aerospace community in particular and others in general so as to remove the fallacies that affect their effective realisation for safety-critical systems. It comprises a comprehensive survey of digital twins and their constituent elements. Elaborating their characteristic stateof-the-art composition along with corresponding limitations, three dimensions of the future digital twins for aerospace sector, termed as aero-Digital Twins (aero-DTs), are proposed as an outcome of this survey. These include the interactive, standardisation, and cognitive dimensions of digital twins, which if leveraged diligently could help the aero-DT research and development community quadruple the efficiency of existing and future aerospace systems as well as their associated processes.

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Section: A Modelling 1) Data Collection and Pre-processmentioning

confidence: 99%

Digital Twin in Aerospace Industry: A Gentle Introduction

Aslam

Wileman

et al. 2022

IEEE Access

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“…From Table 1, it can be concluded that most research focuses on turbines [24][25][26][27][28][29][30][31][32][33][34][35][36][37] and aeroengines [14,[45][46][47][48][49][50][51][52]. In terms of the application of turbines, research on wind turbines [30][31][32][33][34][35][36][37] accounts for a large proportion.…”

Section: Overview On Life Cycle Of Turbomachinery With Digital Twinmentioning

confidence: 99%

“…From the perspective of the life cycle, the most numerous phases are monitoring [30][31][32][34][35][36][37]47] and manufacturing [27,[39][40][41]45,51]. In addition, there is currently a certain amount of research in the prediction [14,25,26,44,46] and design [27,38,42] phases, and many scholars are also concerned about the operation and maintenance [29,43,51] phase.…”

Section: Overview On Life Cycle Of Turbomachinery With Digital Twinmentioning

confidence: 99%

“…Thus, in recent years, most of the research has been related to turbomachinery assets. There are some studies that have established a complete system or platform [26,30,[35][36][37][38]43,52]; however, more research has only proposed a framework or model of the DT [14,24,25,[27][28][29][31][32][33][34][39][40][41][42][44][45][46][47][48][49][50][51], and turning them into practical applications remains to be studied. A practical assembly site which is consisted by coordination element, coordination relationship, and control method for coordination accuracy Notes: "Yes" or "No" under Sustainability: The object has sustainability or not; "-": Related information is not available from the source.…”

Section: Overview On Life Cycle Of Turbomachinery With Digital Twinmentioning

confidence: 99%

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Digital Twin Technologies for Turbomachinery in a Life Cycle Perspective: A Review

et al. 2021

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Turbomachinery from a life cycle perspective involves sustainability-oriented development activities such as design, production, and operation. Digital Twin is a technology with great potential for improving turbomachinery, which has a high volume of investment and a long lifespan. This study presents a general framework with different digital twin enabling technologies for the turbomachinery life cycle, including the design phase, experimental phase, manufacturing and assembly phase, operation and maintenance phase, and recycle phase. The existing digital twin and turbomachinery are briefly reviewed. New digital twin technologies are discussed, including modelling, simulation, sensors, Industrial Internet of Things, big data, and AI technologies. Finally, the major challenges and opportunities of DT for turbomachinery are discussed.

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“…Then, the surface load distribution is recovered with improved accuracy. Renganathan et al [37] employed a Bayesian framework and an extension of the proper orthogonal decomposition with constraints to infer the true fields conditioned on measured quantities of interest. A multi-fidelity ROM based on manifold alignment, fusing inconsistent fields from high-fidelity and low-fidelity simulations, was constructed by Perron et al [38].…”

mentioning

confidence: 99%

Deep Learning for Multi-Fidelity Aerodynamic Distribution Modeling from Experimental and Simulation Data

Li¹,

Kou²,

Zhang³

2021

Preprint

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The wind-tunnel experiment plays a critical role in the design and development phases of modern aircraft, which is limited by prohibitive cost. In contrast, numerical simulation, as an important alternative paradigm, mimics complex flow behaviors but is less accurate compared to experiment. This leads to the recent development and emerging interest in applying data fusion for aerodynamic prediction. In particular, the accurate prediction of aerodynamic with lower computational cost can be achieved by fusing experimental (high-fidelity) and computational (low-fidelity) aerodynamic data. Currently, existing works on aerodynamic model using data fusion mainly concern integral data (lift, drag, etc.). In this paper, a multifidelity aerodynamic model based on Deep Neural Network (DNN), where both numerical and experimental data are introduced in the loss function with proper weighting factor to balance the overall accuracy, is developed for aerodynamic distribution over the wing surface.

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Aerodynamic Data Fusion Toward the Digital Twin Paradigm

Cited by 31 publications

References 28 publications

Digital Twin in Aerospace Industry: A Gentle Introduction

Digital Twin in Aerospace Industry: A Gentle Introduction

Digital Twin Technologies for Turbomachinery in a Life Cycle Perspective: A Review

Deep Learning for Multi-Fidelity Aerodynamic Distribution Modeling from Experimental and Simulation Data

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