A Navier–Stokes-Informed Neural Network for Simulating the Flow Behavior of Flowable Cement Paste in 3D Concrete Printing

A Navier–Stokes-Informed Neural Network for Simulating the Flow Behavior of Flowable Cement Paste in 3D Concrete Printing

In this work, we propose a Navier–Stokes-Informed Neural Network (NSINN) as a surrogate approach to predict the localized flow behavior of cementitious materials for advancing 3D additive construction technology to gain fundamental insights into multiscale mechanisms of cement paste rheology. NS equ...

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Bibliographic Details
Main Authors: Tianjie Zhang, Donglei Wang, Yang Lu
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Buildings
Subjects:
physics-informed neural network
cementitious material
three-dimensional concrete printing
Navier–Stokes equations
computational fluid dynamics
Online Access:https://www.mdpi.com/2075-5309/15/2/275
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Summary:In this work, we propose a Navier–Stokes-Informed Neural Network (NSINN) as a surrogate approach to predict the localized flow behavior of cementitious materials for advancing 3D additive construction technology to gain fundamental insights into multiscale mechanisms of cement paste rheology. NS equations are embedded into the NSINN to interpret the flow pattern in the 3D printing barrel. The results show that the presented NSINN has a higher accuracy compared to a traditional artificial neural network (ANN) as the Mean Square Errors (MSEs) of the u, v, and p predicted by NSINN are <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.25</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>4</mn></mrow></msup></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.85</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>5</mn></mrow></msup></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3.91</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></semantics></math></inline-formula>, respectively. Compared to the ANN, the MSE of the predictions are <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.88</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.17</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.72</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula>, respectively. Moreover, the mean prediction time used in the NSINN, the ANN, and Computational Fluid Dynamics (CFD) are 0.039 s, 0.014 s, and 3.37 s, respectively. That means the method is more computationally efficient at performing simulations compared to CFD which is mesh-based. The NSINN is also utilized in studying the relationship between geometry and extrudability. The ratio (R = 0.25, 0.5, and 0.75) between the diameter of the outlet and that of the domain is studied. It shows that a larger ratio (R = 0.75) can lead to better extrudability of the 3D concrete printing (3DCP).
ISSN:2075-5309

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