Solution of Lubrication Problems with Deep Neural Network

Yadav, Saurabh; Thakre, Gananath D.

doi:10.1007/978-981-19-4208-2_34

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…The initial publication on the application of PINNs to solve a simplified variant of the Reynolds equation was by Almqvist in 2021 [3]. This pioneering work was expanded upon by researchers such as Zhao et al, Li et al, and Yadav et al, who developed more advanced algorithms to address the 2D Reynolds equation in contexts ranging from linear sliders to gas bearings and journal bearings, respectively, [4][5][6]. A significant advancement was achieved by Rom, who became the first to implement PINNs for the stationary Reynolds equation, incorporating the Jakobsson-Floberg-Olsson (JFO) cavitation model.…”

Section: Physical-informed Lossmentioning

confidence: 99%

“…As a pioneer in this field, Almqvist investigated the interpolation with PINNs for the determination of hydrodynamic pressure, described by a simplified variant of the Reynolds equation [3]. In subsequent work, PINNs were applied to 2D problems [4][5][6]. The newest achievements consider the computation of pressure and cavitation in tribological systems [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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Extrapolation of Hydrodynamic Pressure in Lubricated Contacts: A Novel Multi-Case Physics-Informed Neural Network Framework

Brumand-Poor,

Bauer,

Plückhahn

et al. 2024

Lubricants

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In many technical applications, understanding the behavior of tribological contacts is pivotal for enhancing efficiency and lifetime. Traditional experimental investigations into tribology are often both costly and time-consuming. A more profound insight can be achieved through elastohydrodynamic lubrication (EHL) simulation models, such as the ifas-DDS, which determines precise friction calculations in reciprocating pneumatic seals. Similar to other distributed parameter simulations, EHL simulations require a labor-intensive resolution process. Physics-informed neural networks (PINNs) offer an innovative method to expedite the computation of such complex simulations by incorporating the underlying physical equations into the neural network’s parameter optimization process. A hydrodynamic PINN framework has been developed and validated for a variant of the Reynolds equation. This paper elucidates the framework’s capacity to handle multi-case scenarios—utilizing one PINN for various simulations—and its ability to extrapolate solutions beyond a limited training domain. The outcomes demonstrate that PINNs can overcome the typical limitation of neural networks in extrapolating the solution space, showcasing a significant advancement in computational efficiency and model adaptability.

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Section: Physical-informed Lossmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extrapolation of Hydrodynamic Pressure in Lubricated Contacts: A Novel Multi-Case Physics-Informed Neural Network Framework

Brumand-Poor,

Bauer,

Plückhahn

et al. 2024

Lubricants

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“…In contrast, when complete field physics values are directly provided, the data-driven method can accurately predict the flow field for unknown conditions without possessing physical interpretability. Yadav and Thakre [69] also employed a PINN to study the behavior of a fluidlubricated journal as well as a two-lobe bearing and compared the obtained results against an FEM model. Even though the authors provided few insights and details on the employed model and its implementation, they reported a quite good correlation between the PINN and FEM at various load cases, with errors below 6% and 5% with respect to the predicted eccentricity and friction coefficient.…”

Section: Lubrication Predictionmentioning

confidence: 99%

“…Using PINN to solve the 1D Reynolds BVP to predict the pressure distribution in a fluid-lubricated linear converging slider 2021 [64] Using PINN to solve the 2D Reynolds equation to predict the pressure and film thickness distribution considering load balance in a fluid-lubricated linear converging slider 2023 [66] Using supervised, semi-supervised, and unsupervised PINN to solve the 2D Reynolds equation to predict the pressure and film thickness distribution considering load balance and eccentricity in a gas-lubricated journal bearing 2022 [68] Using PINN to solve the 2D Reynolds equation to predict the behavior of fluid-lubricated journal as well as two-lobe bearings 2023 [69] Using PINN with soft and hard constraints to solve the 2D Reynolds equation to predict the pressure distribution in fluid-lubricated journal bearings at fixed eccentricity with constant and variable viscosity 2023 [70] Using PINN to solve the 2D Reynolds equation to predict the pressure and fractional film content distribution in fluid-lubricated journal bearings at fixed and variable eccentricity considering cavitation 2023 [71] Using PINN to solve the 2D Reynolds equation to predict the pressure and fractional film content distribution in fluid-lubricated journal bearings at fixed eccentricity considering cavitation 2023 [72] Wear and damage prediction Using semi PINN to find regression fitting parameters for Archard's wear law based upon small data from fretting wear experiments 2015 [77] Using hybrid PINN to predict wind turbine bearing fatigue based upon a physics-informed bearing damage model as well as data-driven grease degradation approach 2020 [78] Using physics-informed CNN with preceding threshold model for rolling bearing fault detection 2021 [79] Using physics-informed residual network for rolling bearing fault detection 2023 [80] Using PIML framework consisting of piecewise fitting, a hybrid physics-informed data-driven model, and meta-learning to predict tool wear 2022 [81] As such, PINNs have been employed for lubrication prediction by solving the Reynolds differential equation. Starting with the 1D Reynolds equation for a converging slider, in only two years, the complexity has already been tremendously increased, now covering the 2D Reynolds equation, journal bearings with load balance and variable eccentricity, and cavitation effects.…”

Section: Lubrication Predictionmentioning

confidence: 99%

Physics-Informed Machine Learning—An Emerging Trend in Tribology

Marian,

Tremmel

2023

Lubricants

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Physics-informed machine learning (PIML) has gained significant attention in various scientific fields and is now emerging in the area of tribology. By integrating physics-based knowledge into machine learning models, PIML offers a powerful tool for understanding and optimizing phenomena related to friction, wear, and lubrication. Traditional machine learning approaches often rely solely on data-driven techniques, lacking the incorporation of fundamental physics. However, PIML approaches, for example, Physics-Informed Neural Networks (PINNs), leverage the known physical laws and equations to guide the learning process, leading to more accurate, interpretable and transferable models. PIML can be applied to various tribological tasks, such as the prediction of lubrication conditions in hydrodynamic contacts or the prediction of wear or damages in tribo-technical systems. This review primarily aims to introduce and highlight some of the recent advances of employing PIML in tribological research, thus providing a foundation and inspiration for researchers and R&D engineers in the search of artificial intelligence (AI) and machine learning (ML) approaches and strategies for their respective problems and challenges. Furthermore, we consider this review to be of interest for data scientists and AI/ML experts seeking potential areas of applications for their novel and cutting-edge approaches and methods.

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“…This mesh-free nature of PINNs may, in some cases, outperform classical FEMs, as illustrated by Yadav and Thakre. 16…”

Section: Introductionmentioning

confidence: 99%

A hybrid data-driven approach for the analysis of hydrodynamic lubrication

Zhao,

Wong

2023

Proceedings of the Institution of Mechanical Engineers, Part J:

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The application of data mining technology has intensively advanced tribology research. While recent lubrication studies have highlighted the importance of data mining, researchers have not fully bridged the gap between massive lubrication data and intrinsic lubrication mechanisms. Thus, by revisiting lubrication modelling from the data-driven and physics-informed perspectives, we aim to construct a hybrid approach for hydrodynamic lubrication classification and prediction, where data-driven methods are combined with physics-informed approaches to achieve a fast and accurate prediction of the hydrodynamic lubrication scenario. Our approach will spur the application of data mining methods in lubrication studies.

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Solution of Lubrication Problems with Deep Neural Network

Cited by 5 publications

References 8 publications

Extrapolation of Hydrodynamic Pressure in Lubricated Contacts: A Novel Multi-Case Physics-Informed Neural Network Framework

Extrapolation of Hydrodynamic Pressure in Lubricated Contacts: A Novel Multi-Case Physics-Informed Neural Network Framework

Physics-Informed Machine Learning—An Emerging Trend in Tribology

A hybrid data-driven approach for the analysis of hydrodynamic lubrication

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