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Quick Start by Role

PhD students & Postdocs — Start with Data Types and Use Cases. For HPC access, see Infrastructure and Containers.

PIs & Group Leaders — Review Metadata Standards and Repositories. See also Data Management Plans.

Data Stewards — Key cross-references: File Formats, Repositories, Infrastructure.

What is Simulation & Modeling Data Science?

Simulation and modeling encompass the use of mathematical models and numerical methods to predict the behavior of physical systems. From fluid dynamics and structural mechanics to multiphysics coupling and optimization, these approaches generate vast amounts of data that require careful management, storage, and analysis strategies.

The Need for Data Management in Simulation & Modeling​

Simulation workflows produce unique data management challenges:

  • Massive output volumes — A single CFD simulation can produce hundreds of gigabytes to terabytes of time-step data
  • Checkpoint and restart files — Long-running simulations require intermediate saves for fault tolerance
  • Parameter studies — Systematic variation of input parameters generates hundreds or thousands of related datasets
  • Mesh and geometry data — Complex 3D meshes must be versioned alongside simulation results
  • Reproducibility — Solver versions, compiler flags, hardware, and input parameters must all be tracked
  • Post-processing pipelines — Raw simulation output requires extensive processing before scientific insight can be extracted

Key Application Areas​

  • Computational fluid dynamics (CFD) — Aerodynamics, turbomachinery, combustion, weather modeling
  • Finite element analysis (FEA) — Structural mechanics, crash simulation, thermal analysis
  • Multiphysics — Fluid-structure interaction, electromagnetics, coupled thermal-mechanical problems
  • Optimization — Shape optimization, topology optimization, design of experiments
  • Digital twins — Real-time simulation models coupled with sensor data
  • Climate and weather modeling — Numerical weather prediction, climate projections

Common Tools and Software​

CFD​

  • OpenFOAM — Open-source CFD toolbox
  • ANSYS Fluent / CFX — Commercial CFD solvers
  • SU2 — Open-source multiphysics simulation

FEA​

  • Abaqus — Commercial FEA solver for structural and multiphysics problems
  • ANSYS Mechanical — Structural analysis
  • CalculiX — Open-source FEA solver
  • FEniCS (Python) — Open-source computing platform for PDEs

Pre/Post-Processing​

  • ParaView — Open-source visualization for large simulation datasets
  • Tecplot — Commercial visualization
  • Gmsh — Open-source mesh generation
  • SALOME — Open-source pre/post-processing platform

Workflow and Data Management​

  • Python (NumPy, SciPy, pandas) — Data analysis and automation
  • HDF5 / h5py — Hierarchical data format for large datasets
  • DVC (Data Version Control) — Version control for large data files
  • Snakemake / Nextflow — Workflow managers for reproducible pipelines

Infrastructure and Support​

Researchers working with simulation data at TUM can access:

  • LRZ Linux Cluster — For parallel simulations on thousands of cores
  • LRZ AI Systems — GPU computing for ML-accelerated simulations
  • NHR / EuroHPC — National and European HPC resources for extreme-scale simulations

Getting Started​

If you're new to simulation data management:

  1. Explore our resources — Check out the Data Types and Use Cases
  2. Access infrastructure — Apply for computing resources at LRZ
  3. Get support — Contact rdhub@mdsi.tum.de for data management guidance