Hybrid Analytical and Data-Driven Modeling Techniques for Digital Twin Applications

Wunderlich, Andrew; Booth, Kristen; Santi, Enrico

doi:10.1109/ests49166.2021.9512364

Cited by 16 publications

(9 citation statements)

References 6 publications

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“…Modelling methods used for DT development in the reviewed literature vary based on scope of application, starting from conventional mathematical modelling and statistical analysis used in [13,15,17,18,21,22], the finite elements method (FEM) used in [11,12,16,20,21,26], data-driven modelling (DDM) used in [8,[12][13][14], artificial neural networks (ANN) used in [8][9][10][11], grey-box modelling (GBM) in [10,27], cycle mean value (CMV) in [15,19], boundary element method (BEM) used in [24], model based systems engineering (MBSE) used in [23], and model-based design (MBD) as used in [25].…”

Section: Modelling and Modelling Methodsmentioning

confidence: 99%

“…BBM represents the input-output relation based on acquired data, thus being based entirely on empirical data, while WBM represents purely theoretical modelling based on system equations, as stated in [27]. In between the two, GBM combines acquired data with the theoretical model, and its use has been proposed in [10] to develop a DT predicting fuel consumption and ship speed.…”

Section: Modelling and Modelling Methodsmentioning

confidence: 99%

“…In between the two, GBM combines acquired data with the theoretical model, and its use has been proposed in [10] to develop a DT predicting fuel consumption and ship speed. Moreover, a GBM method has been proposed in [27] to develop a DT model of a boost converter as part of a shipboard power system. In [27] also, an artificial neural network (ANN) and the available data were used to develop GBM, which was validated to successfully predict fuel consumption and ship speed.…”

Section: Modelling and Modelling Methodsmentioning

confidence: 99%

“…Moreover, a GBM method has been proposed in [27] to develop a DT model of a boost converter as part of a shipboard power system. In [27] also, an artificial neural network (ANN) and the available data were used to develop GBM, which was validated to successfully predict fuel consumption and ship speed.…”

Section: Modelling and Modelling Methodsmentioning

confidence: 99%

“…SDT applications found in papers [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] were analysed according to the standards of the DT formulation process set in this paper. For every case study, methods used to form a DT, applications, and steps of the DT formulation process were analysed.…”

Section: Introductionmentioning

confidence: 99%

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Ship’s Digital Twin—A Review of Modelling Challenges and Applications

2022

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The Ship’s Digital Twin (SDT) is a digital record of a ship’s behaviour or a software clone, which can be used to simulate scenarios that are expensive or hardly feasible to perform on a real object and especially in real time. The purpose of the SDT is to achieve cost reduction, obtain timely warnings of irregularities, and optimise individual ship system performances or the operation of the whole ship and to assist ship management. The aim of this paper is to describe the concept of the SDT and clarify some perplexities that may occur from initial introduction to concept. To that end, the paper identifies the steps in the SDT formulation process and methods used in each step of the process. Furthermore, a four-step iterative procedure for the SDT development is proposed. The applications of the concept are numerous, and some of them are presented in a review analysis in this paper. The presented analysis leads to a conclusion that should give some direction to future research in this area.

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