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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.

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Related Experiment Video

Updated: Jun 16, 2026

Structural Design and Manufacturing of a Cruiser Class Solar Vehicle

Structural Design and Manufacturing of a Cruiser Class Solar Vehicle

Published on: January 30, 2019

A comprehensive CFD lifecycle dataset for marine vessel hydrodynamics.

Song Wang¹, Chen Wang^2,3, Jianchun Wang⁴

¹Energy and Internet Research Institute, Tsinghua University, Beijing, China. wangsong@tsinghua-eiri.org.

Scientific Data

|June 13, 2026

Summary

This study introduces comprehensive datasets from industrial computational fluid dynamics (CFD) workflows, covering the entire simulation lifecycle. These resources aim to advance artificial intelligence (AI) adoption in CFD by providing essential data for model training.

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Last Updated: Jun 16, 2026

Structural Design and Manufacturing of a Cruiser Class Solar Vehicle

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Area of Science:

Naval Architecture and Ocean Engineering
Computational Fluid Dynamics (CFD)
Artificial Intelligence (AI) in Engineering

Background:

Industrial adoption of AI in computational simulation is hindered by a lack of comprehensive, real-world datasets.
Existing CFD datasets often cover only specific simulation phases, limiting AI model development for the full workflow.

Purpose of the Study:

To present novel, high-quality datasets spanning the complete CFD lifecycle from industrial workflows.
To facilitate the development of AI models capable of learning across different simulation phases.
To support research in CFD solver performance, data management, and flow visualization.

Main Methods:

Collected data from operational industrial CFD workflows at the China Ship Scientific Research Center.
Included data from three marine vessels (two tankers, one submarine) with complex stern flows.
Structured datasets to encompass pre-processing (geometries), solving (linear systems), and post-processing (flow solutions, meshes).

Main Results:

Developed three unique datasets covering the entire CFD simulation pipeline.
The datasets capture complex separated stern flows characteristic of low-speed, high-block-coefficient hull forms.
The data resource integrates interdependent stages, enabling holistic AI model training and analysis.

Conclusions:

The presented datasets address the critical need for comprehensive CFD data to advance AI in engineering simulations.
This resource enables AI model development across the full CFD lifecycle, improving robustness and applicability.
The datasets also serve as valuable benchmarks for solver evaluation, data management research, and flow visualization techniques.