Peristaltic transport of a particulate suspension in the small intestine

Sinnott, Matthew D.; Cleary, Paul W.; Harrison, Simon

doi:10.1016/j.apm.2017.01.034

Cited by 68 publications

(31 citation statements)

References 59 publications

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“…SPH is a promising approach to model multiphase flows specifically in the gastric lumen [ 68 ]. SPH has also been successfully applied in computational modeling of the small intestine [ 69 ].…”

Section: Applicationsmentioning

confidence: 99%

Fluid–Structure Interaction Analyses of Biological Systems Using Smoothed-Particle Hydrodynamics

Toma

Chan-Akeley

Arias

et al. 2021

Biology

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Due to the inherent complexity of biological applications that more often than not include fluids and structures interacting together, the development of computational fluid–structure interaction models is necessary to achieve a quantitative understanding of their structure and function in both health and disease. The functions of biological structures usually include their interactions with the surrounding fluids. Hence, we contend that the use of fluid–structure interaction models in computational studies of biological systems is practical, if not necessary. The ultimate goal is to develop computational models to predict human biological processes. These models are meant to guide us through the multitude of possible diseases affecting our organs and lead to more effective methods for disease diagnosis, risk stratification, and therapy. This review paper summarizes computational models that use smoothed-particle hydrodynamics to simulate the fluid–structure interactions in complex biological systems.

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Section: Applicationsmentioning

confidence: 99%

Fluid–Structure Interaction Analyses of Biological Systems Using Smoothed-Particle Hydrodynamics

Toma

Chan-Akeley

Arias

et al. 2021

Biology

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“…Current multiphysics models (e.g. [22][23][24]) only account for fixed v WAVE , whose propagation pattern is hardcoded and does not adapt to the luminal environment. However, this is not how our body works.…”

Section: The Multiphysics Model Without An Artificial Neural Network (Non-adaptive Model)mentioning

confidence: 99%

The virtual physiological human gets nerves! How to account for the action of the nervous system in multiphysics simulations of human organs

Alexiadis

Simmons

Stamatopoulos

et al. 2021

J. R. Soc. Interface.

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This article shows how to couple multiphysics and artificial neural networks to design computer models of human organs that autonomously adapt their behaviour to environmental stimuli. The model simulates motility in the intestine and adjusts its contraction patterns to the physical properties of the luminal content. Multiphysics reproduces the solid mechanics of the intestinal membrane and the fluid mechanics of the luminal content; the artificial neural network replicates the activity of the enteric nervous system. Previous studies recommended training the network with reinforcement learning. Here, we show that reinforcement learning alone is not enough; the input–output structure of the network should also mimic the basic circuit of the enteric nervous system. Simulations are validated against in vivo measurements of high-amplitude propagating contractions in the human intestine. When the network has the same input–output structure of the nervous system, the model performs well even when faced with conditions outside its training range. The model is trained to optimize transport, but it also keeps stress in the membrane low, which is exactly what occurs in the real intestine. Moreover, the model responds to atypical variations of its functioning with ‘symptoms’ that reflect those arising in diseases. If the healthy intestine model is made artificially ill by adding digital inflammation, motility patterns are disrupted in a way consistent with inflammatory pathologies such as inflammatory bowel disease.

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“…Using FEM-based models often leads to problems with numerical convergence due to the large deformations involved for compliant systems such as foods. These can be overcome through the use of the Coupled Eulerian – Lagrangian Finite Elements Analysis [ 50 ] or alternative ‘meshless’ methods such as the Discrete Element [ 51 , 52 ] or the Smoothed Particle Hydrodynamics [ 53 , 54 ] methods.…”

Section: Modelling Dryingmentioning

confidence: 99%

Modelling Processes and Products in the Cereal Chain

Carvalho

Charalambides

Đjekić

et al. 2021

Foods

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In recent years, modelling techniques have become more frequently adopted in the field of food processing, especially for cereal-based products, which are among the most consumed foods in the world. Predictive models and simulations make it possible to explore new approaches and optimize proceedings, potentially helping companies reduce costs and limit carbon emissions. Nevertheless, as the different phases of the food processing chain are highly specialized, advances in modelling are often unknown outside of a single domain, and models rarely take into account more than one step. This paper introduces the first high-level overview of modelling techniques employed in different parts of the cereal supply chain, from farming to storage, from drying to milling, from processing to consumption. This review, issued from a networking project including researchers from over 30 different countries, aims at presenting the current state of the art in each domain, showing common trends and synergies, to finally suggest promising future venues for research.

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Peristaltic transport of a particulate suspension in the small intestine

Cited by 68 publications

References 59 publications

Fluid–Structure Interaction Analyses of Biological Systems Using Smoothed-Particle Hydrodynamics

Fluid–Structure Interaction Analyses of Biological Systems Using Smoothed-Particle Hydrodynamics

The virtual physiological human gets nerves! How to account for the action of the nervous system in multiphysics simulations of human organs

Modelling Processes and Products in the Cereal Chain

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