Multi-fidelity modeling to predict the rheological properties of a suspension of fibers using neural networks and Gaussian processes

• Published in: Physics of fluids (1994) Vol. 34; no. 5
• Main Authors: Boodaghidizaji, Miad, Khan, Monsurul, Ardekani, Arezoo M.
• Format: Journal Article
• Language: English
• Published: Melville American Institute of Physics 01.05.2022; American Institute of Physics (AIP)
• Subjects: Accuracy; Algorithms; artificial neural networks; Aspect ratio; CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; computational fluid dynamics; Computer simulation; Constitutive equations; constitutive relations; Constitutive relationships; Differential equations; Direct numerical simulation; fiber suspension; Fluid dynamics; Gaussian process; Gaussian processes; Industrial applications; Machine learning; Mathematical models; Neural networks; Physics; Rheological properties; Rheology; rheology and fluid dynamics; Simulation
• ISSN: 1070-6631; 1089-7666; 1527-2435; 1089-7666
• DOI: 10.1063/5.0087449

Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications. There are multiple factors, such as fiber aspect ratio and volume fraction, that play a significant role in altering the rheological behavior of suspensions. Three-dimensional (3D) numerical simulations of coupled differential equations of the suspension of fibers are computationally expensive and time-consuming. Machine learning algorithms can be trained on the available data and make predictions for the cases where no numerical data are available. However, some widely used machine learning surrogates, such as neural networks, require a relatively large training dataset to produce accurate predictions. Multi-fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified constitutive equations, can pave the way for more accurate predictions. Here, we focus on neural networks and the Gaussian processes with two levels of fidelity, i.e., high and low fidelity networks, to predict the steady-state rheological properties, and compare them to the single-fidelity network. High-fidelity data are obtained from direct numerical simulations based on an immersed boundary method to couple the fluid and solid motion. The low-fidelity data are produced by using constitutive equations. Multiple neural networks and the Gaussian process structures are used for the hyperparameter tuning purpose. Results indicate that with the best choice of hyperparameters, both the multi-fidelity Gaussian processes and neural networks are capable of making predictions with a high level of accuracy with neural networks demonstrating marginally better performance.