A novel sparse reduced order formulation for modeling electromagnetic forces in electric motors

To ensure correct filling in the resin transfer molding (RTM) process, adequate numerical models have to be developed in order to correctly capture its physics, so that this model can be considered for process optimization. However, the complexity of the phenomenon often makes it impossible for numerical models to accurately predict its behavior, limiting its usage. To overcome this limitation, numerical models are enriched with measured data to ensure their correct predictability. Nevertheless, the data used is often limited due to practical constraints, such as a limited number of sensors or the high costs of experimental campaigns. In this context, the present paper demonstrates the implementation of a numerical model enriched with data, called Hybrid Twin applied to the RTM process when few sensors are considered in the mold to be injected. The performances of the developed hybrid twin are tested in a virtual test for the injection of a 2D mold, where the hybrid twin constructed using a simplified numerical model allows to accurately predict a complex model's resin flow-front over its entire time history.

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“…X m 1 (ν 1 )X m 2 (ν 2 ) 2 − X m 2 (ν 2 )f res (ν 1 , ν 2 ) δ((ν 1 ) i , (ν 2 ) i ) dν 1 dν 2 = 0 (35) Compute X m 2 (ν 2 ) by solving:…”

Section: Discussionmentioning

confidence: 99%

“…In this context, a new version of the PGD has been introduced in order to overcome this difficulty, the so-called sparse-PGD [33,34], allowing to compress and compute the PGD decomposition using unstructured and sparse data over a parametric space [35] while keeping the offline computations inexpensive.…”

Section: In Simple Terms This Meansmentioning

confidence: 99%

Hybrid twin of RTM process at the scarce data limit

et al. 2023

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“…It is a convenient method when there is no prior information. Moreover, in [33], the LHS is combined with a mesh constrained to Chebyshev nodes to take advantage of their properties minimizing the Runge's phenomenon. In addition, other sampling strategies can be designed to address a particular problem, thus improving performace.…”

Section: Regularized Regressions: the Regularized Sparse Pgd (Rs-pgd)...mentioning

confidence: 99%

“…By doing this, oscillations are reduced, since a higher-order basis will try to capture only what remains in the residual. 2 The MAS has proved to be a good strategy to improve significantly the s-PGD performance in many problems, see for instance [2,18,32,33]. However, it has some limitations.…”

Section: Theoretical Background: the S-pgdmentioning

confidence: 99%

Regularized regressions for parametric models based on separated representations

Sancarlos

Champaney

Chinesta

2023

Adv. Model. and Simul. in Eng. Sci.

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Regressions created from experimental or simulated data enable the construction of metamodels, widely used in a variety of engineering applications. Many engineering problems involve multi-parametric physics whose corresponding multi-parametric solutions can be viewed as a sort of computational vademecum that, once computed offline, can be then used in a variety of real-time engineering applications including optimization, inverse analysis, uncertainty propagation or simulation based control. Sometimes, these multi-parametric problems can be solved by using advanced model order reduction—MOR-techniques. However, solving these multi-parametric problems can be very costly. In that case, one possibility consists in solving the problem for a sample of the parametric values and creating a regression from all the computed solutions. The solution for any choice of the parameters is then inferred from the prediction of the regression model. However, addressing high-dimensionality at the low data limit, ensuring accuracy and avoiding overfitting constitutes a difficult challenge. The present paper aims at proposing and discussing different advanced regressions based on the proper generalized decomposition (PGD) enabling the just referred features. In particular, new PGD strategies are developed adding different regularizations to the s-PGD method. In addition, the ANOVA-based PGD is proposed to ally them.

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“…• The second one is to accelerate physics-based models using Model Order Reduction (MOR) techniques, as in Chinesta et al (2015), Chinesta et al (2011), or Sancarlos et al (2021).…”

Section: Introductionmentioning

confidence: 99%

Learning stable reduced-order models for hybrid twins

Sancarlos¹,

Cameron²,

Peuvedic³

et al. 2021

Preprint

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The concept of "Hybrid Twin" (HT) has recently received a growing interest thanks to the availability of powerful machine learning techniques. This twin concept combines physics-based models within a model-order reduction framework-to obtain real-time feedback rates-and data science. Thus, the main idea of the HT is to develop on-the-fly data-driven models to correct possible deviations between measurements and physics-based model predictions. This paper is focused on the computation of stable, fast and accurate corrections in the Hybrid Twin framework. Furthermore, regarding the delicate and important problem of stability, a new approach is proposed, introducing several sub-variants and guaranteeing a low computational cost as well as the achievement of a stable time-integration. The methodology is tested for an industrial case detailed in Section 4.1, where an excellent agreement was observed when employing the HT approach.

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A novel sparse reduced order formulation for modeling electromagnetic forces in electric motors

Cited by 10 publications

References 19 publications

Hybrid twin of RTM process at the scarce data limit

Hybrid twin of RTM process at the scarce data limit

Regularized regressions for parametric models based on separated representations

Learning stable reduced-order models for hybrid twins

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