Physically sound, self-learning digital twins for sloshing fluids

Moya, Beatriz; Alfaro, Icíar; González, David; Chinesta, Francisco

doi:10.1371/journal.pone.0234569

Cited by 25 publications

(14 citation statements)

References 41 publications

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“…e intervehicle tuning of the cloud platform is a complete process from the beginning of users' request for orders, through the decomposition of orders, to the generation of the initial scheduling scheme, to the adjustment of the initial scheme in the face of disturbances, and finally to the end of after-sales service after the delivery of orders [27]. All manufacturing resources such as equipment, software, status, and data of different factories will be uploaded to the scheduling cloud platform for storage, and technologies such as Computer-Aided Engineering Planning (CAPP), Product Data Management (PDM), Manufacturing Execution System (MES), and Product Lifecycle Management (PLM) will be integrated.…”

Section: E Scheduling Platform Has Two Scheduling Processmentioning

confidence: 99%

Job Shop Scheduling Based on Digital Twin Technology: A Survey and an Intelligent Platform

Han

Yang

et al. 2021

Complexity

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The concept of digital twinning has become a hot topic in the manufacturing industry in recent years. The emerging digital twin technology is an intelligent technology that makes full use of multimodels, big data, and interdisciplinary knowledge, which provides some new approaches for the field of the intelligent manufacturing industry. The job shop scheduling problem has been an important research field in the discrete manufacturing industry. Digital twin technology is adopted to solve the problem of job shop scheduling, which provides the possibility for the intelligent development of workshops. Based on digital twin technology and combined with the actual problem of production line scheduling, we propose a new intelligent scheduling platform to solve the shop scheduling problems above. Meanwhile, based on the prediction and diagnosis of multisource dynamic interference in the workshop production process by big data analysis technology, the corresponding interference strategy is formulated in advance by the scheduling cloud platform. The model simulation experiment of intelligent dispatching cloud platform was carried out, and some enterprises in intelligent manufacturing workshop were taken as examples to verify the superiority of the dispatching cloud platform. Finally, we look forward to the future research direction of intelligent manufacturing based on digital twin technology.

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Section: E Scheduling Platform Has Two Scheduling Processmentioning

confidence: 99%

Job Shop Scheduling Based on Digital Twin Technology: A Survey and an Intelligent Platform

Han

Yang

et al. 2021

Complexity

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“…Indeed, the approach proposed in [21] involves some experimental tests or a finite-element model of the container, whereas the formulations used in [19] and [20] are only valid for rectangular container. Also the methods based on machine learning algorithms shown in [22] and [23] require experimental tests or complex fluidodynamic simulations of the liquid behavior. In [18] and [1], the sloshing height is estimated by using the pendulum model (reported in Appendix A), but the latter does not give accurate results for increasing values of the container acceleration (see Section IV).…”

Section: Sloshing Height Computationmentioning

confidence: 99%

“…In [21], a two-degree-of-freedom mass-spring-damper is introduced and the sloshing height is computed by solving the dynamic equations of the model: however, some experimental tests or a finite-element model of the container is necessary to obtain all parameters. Experimental or pseudo-experimental results (obtained by fluido-dynamical simulations) are also used in [22] and [23], where the liquid behaviour is predicted by a model constructed by machine-learning techniques from available data. In [24], an accurate study explicitly related to the evaluation of the sloshing height for intermittent motions in packaging machines is conducted, by using the pendulum model for rectangular containers.…”

Section: Introductionmentioning

confidence: 99%

A Simple Model-Based Method for Sloshing Estimation in Liquid Transfer in Automatic Machines

Guagliumi

Berti²,

Monti³

et al. 2021

IEEE Access

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This paper studies the phenomenon of sloshing in the field of automatic machines for packaging of liquid products, with specific reference to containers with planar motions. After introducing two equivalent discrete models based on a mass-spring-damper system borrowed from the literature (one linear and one non-linear), a novel method is proposed to evaluate the sloshing height of the liquid, namely the deviation of its free surface at the wall of the container from the equilibrium condition. The merits of this method are that it is easy to use, requiring no experimental evaluation of the system parameters or computationally-expensive fluidodynamical simulations, and it gives good results also for highly dynamical motions. Moreover, though this paper focuses on cylindrical containers performing rectilinear movements, the technique therein presented can be extended to containers of arbitrary shape and generic planar motions. The method is validated by experimental tests using cylindrical containers of different dimensions and a large number of rectilinear motion laws with maximum accelerations up to 12m/s 2 . The results are compared with those that may obtained by using other methods of equal complexity available in the literature, showing the effectiveness of the proposed technique.

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“…It is noted that a rigorous numerical model is still needed when violent wave motions and long-time predictions are involved. Very recently, Moya et al [34,35] examined the learning ability of locally linear embedding and topological data analysis in constructing a reduced-order model for describing wave motions. This perspective relies on the wealth of information from previous measurements to guide forecasting, which can be data-intensive.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-based Characterization Framework for Parametric Representation of Nonlinear Sloshing

Luo¹,

Kareem²,

Yu³

et al. 2022

Preprint

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The growing interest in creating a parametric representation of liquid sloshing inside a container stems from its practical applications in modern engineering systems. The resonant excitation, on the other hand, can cause unstable and nonlinear water waves, resulting in chaotic motions and non-Gaussian signals. This paper presents a novel machine learning-based framework for nonlinear liquid sloshing representation learning. The proposed method is a parametric modeling technique that is based on sequential learning and sparse regularization. The dynamics are categorized into two parts: linear evolution and nonlinear forcing. The former advances the dynamical system in time on an embedded manifold, while the latter causes divergent behaviors in temporal evolution, such as bursting and switching. The proposed framework's merit is demonstrated using an experimental dataset of liquid sloshing in a tank under horizontal excitation with a wide frequency range and various vertical slat screen settings.

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Physically sound, self-learning digital twins for sloshing fluids

Cited by 25 publications

References 41 publications

Job Shop Scheduling Based on Digital Twin Technology: A Survey and an Intelligent Platform

Job Shop Scheduling Based on Digital Twin Technology: A Survey and an Intelligent Platform

A Simple Model-Based Method for Sloshing Estimation in Liquid Transfer in Automatic Machines

A Machine Learning-based Characterization Framework for Parametric Representation of Nonlinear Sloshing

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