Physics-Informed Neural Network modeling developing laminar pipe flow in a circular pipe, trained on boundary layer equations and boundary conditions without labeled data.
-
Updated
Sep 23, 2026 - Python
Physics-Informed Neural Network modeling developing laminar pipe flow in a circular pipe, trained on boundary layer equations and boundary conditions without labeled data.
MATLAB code for order-of-magnitude analysis (OMA) and physics-informed symbolic regression (GPTIPS-2) to model turbulent pipe-flow friction/pressure drop using Nikuradse & Superpipe data, including custom constraints, fitness functions, and figure reproduction scripts.
Pipe flow and pressure drop calculator: Darcy-Weisbach, Colebrook/Swamee-Jain, fittings, elevation, multiple segments
Fluid mechanics for engineers in Python: a validated library, weekly teaching scripts for a full second-year course, notebooks and a showcase of advanced topics
Moody diagram svg generator
CFD simulation of laminar pipe flow development length using ANSYS Fluent and CFX.
Analytical Hagen–Poiseuille flow of a Carreau–Yasuda fluid — Python package, scripts and browser calculator (Wang, JNNFM 310, 2022)
Transparent, validation-backed workflow for preliminary pipe headloss and circular gravity-flow checks.
MATLAB scripts for generating asymmetric Reynolds-number cycles and post-processing Nusselt number and skin-friction data from turbulent pipe-flow DNS.
Hybrid neural-numerical warm-start correction framework for Colebrook-White pipe-flow equations with Newton refinement.
To associate your repository with the pipe-flow topic, visit your repo's landing page and select "manage topics."