Physics-informed neural networks for understanding shear migration of particles in viscous flow

Lu, Daihui; Christov, Ivan

doi:10.1016/j.ijmultiphaseflow.2023.104476

Cited by 8 publications

(5 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Cahn–Hillard equation and Navier–Stokes equations were encoded directly into the residuals of a fully connected neural network. Lu and Christov used PINNs for modeling particle migration in a non-Brownian suspension from Couette flow, with results revealing that the inferred values of the empirical model’s parameters vary with the shear Peclet Number as well as particle bulk volume fraction of the suspension.…”

Section: Tomorrow’s Tools (>2020)mentioning

confidence: 99%

Fluidized Bed Scale-Up for Sustainability Challenges. 1. Tomorrow’s Tools

Cocco,

Chew

2024

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

The scaling up of fluidized beds has been purposefully pursued for more than 100 years. Yet, over that time, scale-up tools have not significantly changed. Data analysis is typically a standard analysis of variances statistical exercise, perhaps reinforced with a design of experimental procedure. Flowsheeting and equipment design are based on institutional knowledge, albeit graphical user interface-based process flow models make that job more manageable. Advanced models such as computational fluid dynamics are used but often as a supplement and not a primary driver. As a result, the scale-up process for a fluidized bed can take more than 10 years. Fluidized beds remain at the forefront of the present time-critical sustainability challenges, e.g., carbon capture by particulate sorbents, methane-to-hydrogen, plastic-to-chemicals, etc. In view of the exigency toward net zero, today's scale-up efforts need to be accelerated, leveraging the advanced new tools that have become readily available. The problem is that such tools are often neglected, inadequately implemented, ineffectively resourced, and/or poorly understood. This motivated the current effort, which is targeted at reviewing how scale-up tools have evolved over the years and the promising new tools, addressing some of the barriers of these tools in the design and scale-up of fluidized beds, as well as contemplating what can be done to circumvent these barriers. As a follow up, a companion part 2 (Cocco, R. A.; Chew, J. W. Ind. Eng. Chem. Res., submitted for publication) proposes a new scale-up path leveraging the advanced tools to achieve timely implementation of the new green fluidized bed processes.

show abstract

Section: Tomorrow’s Tools (>2020)mentioning

confidence: 99%

Fluidized Bed Scale-Up for Sustainability Challenges. 1. Tomorrow’s Tools

Cocco,

Chew

2024

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

“…Shaban et al 14 based shrinkage of a plant cell during drying. Lu and Christov 16 employed PINN approach to extend the utility of phenomenological models, which can simulate the particle migration in shear flow.…”

Section: Introductionmentioning

confidence: 99%

“…Batuwatta-Gamage et al 15 proposed a PINN-based surrogate framework to simulate the time-based moisture concentration and moisture-content-based shrinkage of a plant cell during drying. Lu and Christov 16 employed PINN approach to extend the utility of phenomenological models, which can simulate the particle migration in shear flow.…”

Section: Introductionmentioning

confidence: 99%

PINN Model of Diffusion Coefficient Identification Problem in Fick’s Laws

Li,

Yan,

Gao

et al. 2024

ACS Omega

View full text Add to dashboard Cite

This study tackles the complex task of determining diffusion coefficients in inverse problems, addressing the challenges of instability and computational demands. The primary objective is to introduce an efficient model for estimating diffusion coefficients under specific conditions. Through a unique fusion of Fick's laws and a Neural Network framework, a physicsinformed neural network (PINN) is developed for the diffusion coefficient identification problem. The model accommodates scenarios where both diffusion flux and concentration gradient are known, where diffusion flux is known while the concentration gradient is unknown, and where diffusion flux is unknown while the concentration gradient is known. Results demonstrate the model's efficiency, obtaining diffusion coefficients in less than 1000, 2000, and 3000 iterations for the respective scenarios. Sensitivity analysis underscores the model's validity across conditions, highlighting the positive impact of a higher proportion of effective data on convergence and alignment with general diffusion coefficient patterns. In conclusion, the PINN model stands out as a powerful tool for accurately estimating diffusion coefficients under varying conditions.

show abstract

“…Indeed, a PINN approach has already been taken for modeling flow in complex geometries such as blood vessels, 46 turbulent flow hydrodynamics sans a turbulence model, 47 two-phase flow, 48 and non-Brownian suspensions from Couette flow. 49 Many of these AI-based tools offer a significant opportunity to reduce the scale-up time without a significant increase in investments. Still, the methodology of scale-up needs to change as well.…”

Section: ■ Introductionmentioning

confidence: 99%

“…For instance, physics-informed neural networks (PINNs) hybridize a black-box neural network to white-box physical governing equations by converging the two parts to a single loss function, thereby enforcing adherence to physics. Indeed, a PINN approach has already been taken for modeling flow in complex geometries such as blood vessels, turbulent flow hydrodynamics sans a turbulence model, two-phase flow, and non-Brownian suspensions from Couette flow …”

Section: Introductionmentioning

confidence: 99%

Fluidized Bed Scale-Up for Sustainability Challenges. 2. New Pathway

Chew,

Cocco

2024

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Despite more than 100 years of commercialization of wide-ranging fluidized bed reactors, scale-up tools and methods have remained quite similar. To exploit the benefits of fluidized beds for the time-critical sustainability challenges, scale-up has to be implemented quicker and better. Correspondingly, a companion Part 1 (Ind. Eng. Chem. Res. 2024, 63, 2519−2533 reviewed the evolution of the tools used in scaling up fluidized beds. Leveraging that, the current Part 2 aims to first overview the traditional pathway for scale-up and then propose a new pathway. Notably, instead of the traditional way of focusing on a linear sequence of progressively larger units, the emphasis is on the Phases of Discovery, Research, and Development, which apply the new tools consistently, as well as address risk mitigation and economics throughout. Based on an acrylonitrile case study, a Monte Carlo analysis indicates the new proposed pathway offers more promising economic feasibility, with net present value over 20 years (NPV 20 ) of $310MM higher, along with 35% and 42% reductions in start-up and break-even times, respectively. The increase in costs for incorporating modeling efforts is insignificant compared to the overall benefits with respect to time and economics.

show abstract

Physics-informed neural networks for understanding shear migration of particles in viscous flow

Cited by 8 publications

References 45 publications

Fluidized Bed Scale-Up for Sustainability Challenges. 1. Tomorrow’s Tools

Fluidized Bed Scale-Up for Sustainability Challenges. 1. Tomorrow’s Tools

PINN Model of Diffusion Coefficient Identification Problem in Fick’s Laws

Fluidized Bed Scale-Up for Sustainability Challenges. 2. New Pathway

Contact Info

Product

Resources

About