Dataset Viewer
Auto-converted to Parquet Duplicate
text
stringlengths
0
2.56k
Simulating homogeneous isotropic turbulence using the dnsFoam solver without explicit turbulence modeling. Initialization employs synthetic turbulence with boxTurbDict settings of Ea=10 and k0=5, supplemented by turbulenceProperties constants including UOsigma=0.090295 and UOKupper=10. The kinematic viscosity (nu) is 0...
The discretization uses Euler time stepping, Gauss linear gradients, and a higher-order Gauss cubic divergence scheme specifically for the velocity flux (div(phi,U)). Laplacian terms use Gauss linear corrected, while surface normal gradients use the corrected scheme. Pressure is solved with PCG using DIC preconditioner...
The simulation models steady-state heat conduction through a flange geometry using the laplacianFoam solver. This solver computes the temperature distribution governed by the Laplace equation for thermal diffusion. The thermal diffusivity coefficient (DT) is set to 4e-05 mΒ²/s in the constant/transportProperties diction...
The initial temperature field (0/T) is uniformly set to 273 K across the entire domain. For boundary conditions:
* Patches named "patch1" and "patch3" are insulated (zero temperature gradient, `zeroGradient`).
* Patch "patch2" is held at a constant temperature of 273 K (`fixedValue`).
* Patch "patch4" is held at a constant elevated temperature of 573 K (`fixedValue`).
Key numerical settings include:
* Spatial discretization for the Laplacian term (`laplacian(DT,T)`) employs second-order accuracy using the `Gauss linear corrected` scheme.
* The SIMPLE algorithm is used for solution control with 2 non-orthogonal correctors.
* The temperature field is solved using the Preconditioned Conjugate Gradient (PCG) method with a Diagonal Incomplete-Cholesky (DIC) preconditioner. The solution tolerances are an absolute tolerance of 1e-06 and zero relative tolerance.
* Surface normal gradient calculations at faces use the `corrected` scheme to handle potential mesh non-orthogonality.
Conduct a steady-state thermal diffusion simulation using the laplacianFoam solver to model heat conduction across coupled domains with implicit Arbitrary Mesh Interface (AMI) handling via non-blocking communication. The thermal diffusivity coefficient (DT) is set to 4e-05 mΒ²/s. The computational domain features multip...
Discretization employs Gauss-linear schemes for gradient terms and Gauss-linear-corrected schemes for Laplacian operators, with snGrad correction for surface-normal gradients. The solver configuration uses a geometric-algebraic multigrid (GAMG) approach for the temperature field, applying processor agglomeration and Ga...
Simulate steady-state heat conduction in a 2D geometry consisting of two interconnected blocks using the laplacianFoam solver. The domain features two Cyclic Arbitrary Mesh Interface (AMI) boundaries to connect the blocks, alongside top and bottom walls with zero temperature gradient (insulated conditions), a fixed tem...
Spatial discretization employs the Gauss linear scheme for gradients. The laplacian term uses the Gauss linear corrected scheme for accuracy, particularly handling non-orthogonal meshes. The simulation uses the steady-state approach (ignoring time derivatives). The temperature field is solved with the Preconditioned Co...
Utilize the potentialFoam solver to perform a steady-state simulation of inviscid, incompressible potential flow past a cylinder. The simulation solves the Laplace equation for velocity potential to obtain an irrotational velocity field.
An inlet boundary condition at the left domain face imposes a uniform freestream velocity of (1 0 0) m/s. Top and bottom boundaries use symmetry plane conditions. The right boundary employs a zero-gradient condition for pressure and velocity. Pressure is fixed at 0 on the right face, while its gradient is set to zero a...
The numerical approach employs the steady-state time scheme. Gradient terms are discretized using the least squares method. Velocity flux divergence uses the bounded Gauss linear scheme. Laplacian terms, relevant for solving the potential equation, use Gauss linear corrected interpolation. Surface normal gradient discr...
Solution of the primary scalar potential field (`Phi`) and the pressure field (`p`) leverages the Geometric Algebraic Multi-Grid (GAMG) linear solver. A Diagonal Incomplete-Cholesky (DIC) smoother is used, with solver tolerances set to an absolute tolerance of 1e-06 and a relative tolerance of 0.01. Up to 3 non-orthogo...
Conduct a two-dimensional steady-state potential flow simulation for the pitzDaily case using the potentialFoam solver without turbulence modeling. The flow is treated as incompressible and inviscid. For spatial discretization, employ Gauss linear schemes for gradients and corrected linear schemes for Laplacian terms. ...
Using scalarTransportFoam to perform a 2-dimensional simulation of passive scalar transport in a flow domain configured for the pitzDaily case. The velocity field is predefined with an inlet fixed at 10 m/s in the x-direction, no-slip conditions on the upper and lower walls, and a zero-gradient pressure outlet. The sca...
This is a simulation of steady incompressible laminar flow using the simpleFoam solver. The fluid is Newtonian with a constant kinematic viscosity. The computational domain features an Arbitrary Mesh Interface (AMI) with the implicit coupling setting enabled on the AMI1 and AMI2 boundaries, allowing for non-conformal g...
Simulate a compressible, non-reacting turbulent flow in a choked nozzle using the reactingFoam solver with the k-epsilon turbulence model. The simulation involves a gas mixture initialized at 292 K and 250 kPa, composed primarily of 70% Nβ‚‚ and 30% Oβ‚‚ by mass, with trace species set to zero. Gravity is included with a m...
For numerical discretization, employ an Euler scheme for time derivatives. Use cell-limited Gauss linear gradients for velocity and Gauss linear corrected for laplacian terms. Divergence schemes include limitedLinear for velocity, limitedLinear01 for species, and upwind for turbulence dissipation rate (Ξ΅) and turbulent...
The PIMPLE algorithm operates with 2 outer correctors, 2 pressure correctors, and 1 non-orthogonal corrector, without momentum prediction. Boundary conditions: inlet uses mass flow rate and temperature from CSV input with 2% turbulence intensity; outlet sets choked pressure at 101.325 kPa; walls apply no-slip velocity,...
The simulation employs the reactingFoam solver to model reactive gas flow involving mass transfer across a semi-permeable membrane using the k-epsilon RAS turbulence model. The setup features a mixture of CH4, O2, H2O, CO2, and N2, with an Arrhenius-based methane combustion reaction defined in the chemistry model, thou...
Initial velocity fields are set to zero throughout the domain. Initial mass fraction fields define distinct compositions: in the pipe region, CH4 and N2 constitute the mixture, whereas the sleeve region contains O2 and N2. The membrane interface between pipe and sleeve utilizes semiPermeableBaffle boundary conditions f...
The PIMPLE algorithm solves the flow, applying momentum prediction, 1 outer corrector, and 2 pressure correctors without non-orthogonal correction. Euler discretization handles time derivatives. Divergence schemes for convective fluxes are solved using the limitedLinear and limitedLinearV formats. Gradient and Laplacia...
This simulation employs the reactingFoam solver to model a two-dimensional laminar counter-flow methane-air diffusion flame. The domain features separate fuel (pure methane) and oxidizer (23% oxygen, 77% nitrogen) inlets with opposing velocities of 0.1 m/s and -0.1 m/s respectively, an outlet with fixed pressure, and a...
The chemistry model defines a single-step methane reaction (CH4 + 2O2 β†’ CO2 + 2H2O) using Arrhenius kinetics with pre-exponential factor 5.2e16 and activation temperature 14906 K. Thermophysical properties use JANAF thermodynamics, Sutherland transport, and perfect gas equations. Spatial discretization applies Gauss-li...
Solution algorithms employ PIMPLE with 2 pressure-velocity correctors per outer iteration and momentum prediction disabled. Pressure solves use PCG/DIC with relative tolerance 0.1, while velocity and scalars use PBiCGStab/DILU with relative tolerance 0.1. A post-processing function calculates Bilger mixture fraction at...
This simulation models a two-dimensional laminar counterflow methane-air diffusion flame using the reactingFoam solver with local time stepping (LTS). The laminar flow configuration involves opposed fuel and air streams meeting at the centerline. The left (fuel) inlet injects pure methane at 293 K with velocity (0.1, 0...
Numerical discretization employs the local Euler scheme for time derivatives. Gradients are discretized with Gauss linear schemes. Divergence terms for convection of velocity, species, and turbulence scalars use limitedLinear differencing (limited to preserve boundedness). Laplacian and interpolation schemes default to...
Solution settings use the PIMPLE algorithm with momentum prediction disabled and only one outer/corrector loop. Pressure is solved with the PCG method and DIC preconditioner. Velocities and scalars (temperature, species mass fractions, turbulence scalars) use the PBiCGStab method with DILU preconditioner. Solver tolera...
Employ the reactingFoam solver to perform a 2-dimensional laminar counterflow flame simulation with methane (CH4) as fuel and air as oxidizer. The simulation utilizes the LTS (Local Time Stepping) approach, the GRI-Mech 3.0 chemical mechanism (GRI), and TDAC (Tabulation of Dynamic Adaptive Chemistry) for chemistry acce...
Initial and boundary conditions are as follows:
* Fuel inlet (boundary 'fuel'): Velocity fixed at (0.1 0 0) m/s, temperature fixed at 293 K, CH4 mass fraction fixed at 1.0, O2, H2O, CO2, and default inert mass fractions fixed at 0.0.
* Air inlet (boundary 'air'): Velocity fixed at (-0.1 0 0) m/s, temperature fixed at 293 K, O2 mass fraction fixed at 0.23, N2 mass fraction fixed at 0.77, CH4, H2O, CO2, and default inert mass fractions fixed at 0.0.
* Outlet (boundary 'outlet'): Total pressure condition set to 1e5 Pa, inletOutlet conditions for velocity and species mass fractions (Yi) allowing outflow or reverting to internal field values for inflow.
* Initial uniform internal field: Pressure 1e5 Pa, Temperature 2000 K, Velocity (0 0 0) m/s. CH4, O2, H2O, CO2, and default inert mass fraction fields are uniform 0.0; N2 mass fraction field is uniform 1.0.
* Thermophysical properties: Perfect gas equation of state, JANAF thermodynamics, Sutherland transport model.
Discretization schemes defined in `fvSchemes` include:
* Time derivative: `localEuler`
* Gradient: `Gauss linear`
* Divergence: Primarily `limitedLinear`/`limitedLinearV 1` for velocity (`phi,U`), species/enthalpy (`phi,Yi_h`), kinetic energy (`phi,K`), dissipation rate (`phi,epsilon`), pressure correction (`phid,p`), and stress term; `linear` for turbulence stress term; `none` default overridden.
* Laplacian: `Gauss linear orthogonal`
* Interpolation: `linear`
* Surface normal gradient: `orthogonal`
Solution algorithms in `fvSolution`:
* Pressure (p): Solved using PCG (Preconditioned Conjugate Gradient) with DIC (Diagonal Incomplete-Cholesky) preconditioner (tolerance 1e-6, relTol 0.1).
* Velocity (U), enthalpy (h), species mass fractions (Yi): Solved using PBiCGStab (Preconditioned Bi-Conjugate Gradient Stabilized) with DILU (Diagonal Incomplete-LU) preconditioner (tolerance 1e-6, relTol 0.1).
* Density (rho) solved diagonally.
* PIMPLE (merged PISO-SIMPLE): Momentum prediction disabled (`momentumPredictor no`), outer correctors set to 1 (`nOuterCorrectors 1`), inner correctors set to 1 (`nCorrectors 1`), no non-orthogonal correction (`nNonOrthogonalCorrectors 0`), no equation relaxation (`relaxationFactors { equations { ".*" 1; } }`).
Conduct a 2D laminar counter-flow flame simulation using reactingFoam with the GRI chemical mechanism. The setup involves opposing fuel (pure CH4) and air (23% O2, 77% N2) streams flowing at 0.1 m/s each under atmospheric pressure (1e5 Pa), with initial temperatures at 293 K inlets and a hot internal field of 2000 K. S...
Discretization employs Euler time scheme, Gauss linear gradients, limited linear divergence for momentum/species/energy, and orthogonal surface normal gradients. Pressure is solved via PCG/DIC with 1e-6 tolerance; velocity/enthalpy use PBiCGStab/DILU with 1e-6 tolerance. PIMPLE coupling disables momentum prediction and...
Employ the reactingFoam solver to conduct a 2D laminar counter-flow methane-air flame simulation utilizing GRI-Mech 3.0 chemistry and TDAC (Tabulation of Dynamic Adaptive Chemistry) for combustion modeling. The setup features two opposing inlets: a fuel stream (pure CH4 at 293 K with velocity 0.1 m/s) and an oxidizer s...
Key discretization schemes:
- Temporal: Euler implicit
- Gradient: Gauss linear
- Divergence: limitedLinear for velocity and species fluxes
- Laplacian: Gauss linear orthogonal
Solution algorithms:
- Pressure solver: PCG with DIC preconditioner (tol 1e⁻⁢, relTol 0.1)
- Velocity/species/enthalpy solvers: PBiCGStab with DILU preconditioner (tol 1e⁻⁢, relTol 0.1)
- PIMPLE settings: 1 outer iteration, 2 pressure corrections, no non-orthogonal correction
Chemistry acceleration:
- TDAC reduction method: DAC with tolerance 1e⁻⁴ and initial species set {CO, CHβ‚„, HOβ‚‚}
- ISAT tabulation: tolerance 1e⁻⁴, max 2000 leafs, with species scaling
Initial fields:
- Uniform pressure 1e5 Pa
- Uniform temperature 2000 K
- Species mass fractions: CHβ‚„=0 (except fuel inlet), Oβ‚‚=0 (except oxidizer inlet), Nβ‚‚=1 in domain
Conduct a laminar, compressible, multi-species reactive flow simulation using the rhoReactingFoam solver to study methane (CH4) absorption at a porous base surface in gravity. The domain features an inlet with fixed CH4 concentration (1.0), temperature (300 K), and vertical velocity (0.01 m/s); an outlet/sides with pre...
This simulation employs the rhoCentralFoam solver to model a laminar, compressible gas flow for the LadenburgJet60psi case, representing a supersonic jet configuration. The fluid is air treated as a perfect gas with Sutherland viscosity transport and constant specific heat capacity. The 2D axisymmetric domain uses wedg...
This simulation employs the simpleFoam solver to perform a steady-state, incompressible Reynolds-Averaged Navier-Stokes (RANS) simulation of airflow around a 2D airfoil. The Spalart-Allmaras one-equation turbulence model is activated to calculate the eddy viscosity. The working fluid is a Newtonian fluid with a constan...
End of preview. Expand in Data Studio

ChatCFD Dataset: Benchmarks and Resources for Automated CFD Analysis

✨ Overview

This repository is the test dataset for the ChatCFD project. ChatCFD is an intelligent agent system driven by Large Language Models (LLM), designed to achieve end-to-end automation of Computational Fluid Dynamics (CFD) simulations through natural language instructions.

To support academic research and community development, we have open-sourced the core data used to evaluate ChatCFD's performance, including benchmark sets based on official OpenFOAM tutorials and complex simulation cases from academic papers.

πŸ“Š Dataset Structure

This dataset contains approximately 2.51 GB of resources, divided into three main parts:

1. OpenFOAM Case Descriptions (OF_case_description/)

Contains a series of natural language descriptions (typically in .txt format) based on official OpenFOAM tutorial cases. These descriptions simulate simulation requirements that users would input through spoken or written language in real-world scenarios, used to test the LLM agent's ability to extract physical scenarios and simulation parameters.

2. OpenFOAM Case Grids (OF_case_grids/)

Contains OpenFOAM-format mesh files corresponding to the basic cases mentioned above. These meshes have been validated and can be directly invoked by the ChatCFD system for simulation calculations after automatically configuring the cases.

3. Paper Reproduction Benchmark Cases (papers_and_grids/)

This directory showcases ChatCFD's ability to handle complex tasks, containing two typical cases (NACA0012, nozzle simulation):

  • Academic Papers: Original papers (PDF format) from web sources or academic databases, providing detailed physical setup instructions.
  • Mesh Files: .msh format meshes corresponding to the paper cases.

This part of the data is used to verify whether ChatCFD can directly understand complex CFD configurations from unstructured academic literature and drive simulation workflows.

πŸš€ Quick Start

This dataset can be used in conjunction with the ChatCFD source code. You can choose either of the following methods to download.

After downloading, please refer to the project configuration file guide to fill in the corresponding mesh and description file paths into the system parameters to start end-to-end automated simulation testing.

Download Method 1: Using Git LFS

Clone the entire repository using Git LFS:

git lfs install
git clone https://huggingface.co/datasets/ConMoo/ChatCFD_dataset

Download Method 2: Using Python + huggingface_hub

First, install the dependency package:

pip install huggingface_hub

Then run the following code to download the dataset to the downloaded_data folder in the current directory:

from huggingface_hub import snapshot_download
import time

# Download dataset to "downloaded_data" folder in current directory
max_retries = 5
for attempt in range(max_retries):
    try:
        snapshot_download(
            repo_id="ConMoo/ChatCFD_dataset",
            repo_type="dataset",
            local_dir="./downloaded_data",
            max_workers=8,  # Optionally reduce concurrency to avoid connection overload
        )
        break
    except Exception as e:
        if attempt < max_retries - 1:
            wait_time = (attempt + 1) * 5
            print(f"Download failed (attempt {attempt + 1}/{max_retries}): {e}")
            print(f"Retrying in {wait_time} seconds...")
            time.sleep(wait_time)
        else:
            raise

print("πŸŽ‰ Download complete! Please check the ./downloaded_data directory.")

πŸ“„ Citation

If you use this dataset or the ChatCFD system in academic research, please cite our preprint:

@article{fan2026chatcfd,
  title={ChatCFD: A Large Language Model-Driven Agent for End-to-End Computational Fluid Dynamics Automation with Structured Knowledge and Reasoning},
  author={Fan, E and Hu, Kang and Wu, Zhuowen and Ge, Jiangyang and Miao, Jiawei and Zhang, Yuzhi and Sun, He and Wang, Weizong and Zhang, Tianhan},
  journal={Advanced Intelligent Discovery},
  pages={e202500174},
  year={2026},
  publisher={Wiley Online Library}
}

πŸ’¬ Community & Contact

Project Homepage: ChatCFD - GitHub

Contact Email: 454114084@qq.com, hukang1scu@gmail.com

Downloads last month
80