Data driven reduced modeling for fluidized bed with immersed tubes based on PCA and Bi-LSTM neural networks

Fang, Jiabin; Cu, Wenkai; Liu, Huang; Zhang, Huixin; Liu, Hanqing; Wei, Jinjia; Ma, Xiang; Zheng, Nan

doi:10.1016/j.partic.2023.12.012

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…To extend the prepared reduced-basis applicability to non-linear parameterized systems, we construct the reduced-order model by using artificial neural networks as a data-driven replacement for standard projection techniques. First, the usage of artificial intelligence in the field of MOR is growing, with more complex architectures allowing more complex tasks, see, e.g., [6,12,13]. Second, we have already introduced an analogous framework for non-parameterized systems in our previous contributions [14,15].…”

Section: Introductionmentioning

confidence: 99%

Model Order Reduction of Parameter-Dependent Particle Laden Flows

Kovárnová,

Isoz

2024

Topical Problems of Fluid Mechanics 2024

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In this contribution, we extend our previously-developed framework for model order reduction of transport-dominated systems towards parameterized cases. The method combines the shifted proper orthogonal decomposition with interpolation via artificial neural networks. The resulting framework is an a-posteriori one, i.e., for parameterized systems, time-dependent solutions for several parameter values are required for the construction of the reduced-order model (ROM). The ROM can then be cheaply evaluated to predict the solution for previously unseen parameter values. The method is suitable for industry-relevant transport-dominated systems, such as particle-laden flows, where traditional mode-based methods, such as proper orthogonal decomposition (POD), often fail.

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Section: Introductionmentioning

confidence: 99%

Model Order Reduction of Parameter-Dependent Particle Laden Flows

Kovárnová,

Isoz

2024

Topical Problems of Fluid Mechanics 2024

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A novel data-driven reduced-order model for the fast prediction of gas-solid heat transfer in fluidized beds

Li,

Xu,

Wang

et al. 2024

Applied Thermal Engineering

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Analysis of Dynamic Behavior of Gravity Model Using the Techniques of Road Saturation and Hilbert Curve Dimensionality Reduction

Yang,

He,

Wang

et al. 2024

Sustainability

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In this study, we investigate the relationship between parameters and the dynamic behavior of traffic flow in road traffic systems, and we propose a segmented cost function to describe the effects of this flow on the dynamic gravity model at different saturation levels. We use single-parameter bifurcation analysis, maximum Lyapunov exponent calculation, and three-parameter bifurcation analysis to reveal the effects of parameter variations on the nonlinear dynamical behaviors of the modified gravity model, and we investigate the evolution laws of the traffic system in depth. In order to solve the problems of low efficiency and poor visualization ability in traditional dynamics analysis techniques, this paper proposes the Hilbert curve dimensionality reduction technique, which can completely retain the original data features. The three-dimensional pseudo-Hilbert curve is used to traverse the three-parameter bifurcation data, realizing the transformation of data from three- to one-dimensional. Then, the two-dimensional pseudo-Hilbert curve is used to traverse the reduced one-dimensional data, and the two-dimensional visualization of the three-parameter bifurcation diagram is successfully realized. The dimensionality reduction technique provides a new way of thinking for parameter analysis in the engineering field. By analyzing the two-dimensional bifurcation plan obtained after this reduction, it is found that the modified gravity model is more stable compared with the original model, and this conclusion is also verified by the wavelet transform results. Finally, a new robustness evaluation index is defined based on the dynamics of the model, and the simulation results reveal the intrinsic correlation between the saturation parameter and road congestion, which provides an important basis for promoting sustainable transportation in the road network.

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Data driven reduced modeling for fluidized bed with immersed tubes based on PCA and Bi-LSTM neural networks

Cited by 3 publications

References 41 publications

Model Order Reduction of Parameter-Dependent Particle Laden Flows

Model Order Reduction of Parameter-Dependent Particle Laden Flows

A novel data-driven reduced-order model for the fast prediction of gas-solid heat transfer in fluidized beds

Analysis of Dynamic Behavior of Gravity Model Using the Techniques of Road Saturation and Hilbert Curve Dimensionality Reduction

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