STAR-CD
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| STAR-CD | |
|---|---|
| Name | STAR-CD |
| Author | Computational Engineering International (CEI); later CD-adapco; Siemens PLM Software |
| Released | 1980s |
| Latest release version | proprietary |
| Programming language | Fortran, C++ |
| Operating system | Unix, Linux, Windows |
| Genre | Computational fluid dynamics |
| License | Proprietary |
STAR-CD is a commercial computational fluid dynamics (CFD) software package originally developed for industrial flow simulation and multiphysics analysis. It has been used across automotive, aerospace, energy, and chemical engineering sectors for turbulence, combustion, multiphase flow, and heat transfer problems. The code evolved through corporate transitions involving Rolls-Royce plc, Ford Motor Company, British Aerospace, CD-adapco, and Siemens AG acquisitions and integrations.
History
STAR-CD traces its origins to research groups associated with Rolls-Royce plc and the University of Cambridge in the 1980s, where engineers sought tools for internal combustion and turbomachinery design. Early industrial adopters included Ford Motor Company and Renault, while academic collaborations linked to Imperial College London and ETH Zurich advanced numerical methods. The software's corporate stewardship moved from its founders to CD-adapco in the 1990s, followed by acquisition by Siemens AG in 2016, aligning it with NX Siemens and Simcenter. STAR-CD's development history intersects with CFD milestones such as the rise of finite-volume methods used in codes like FLUENT and OpenFOAM, and with international programs sponsored by agencies like European Space Agency and NASA for propulsion and combustion modelling.
Features and Capabilities
STAR-CD provides finite-volume solvers for compressible and incompressible flows, incorporating models for Reynolds-averaged Navier–Stokes turbulence approaches used in ANSYS workflows and for large eddy simulation strategies similar to those in LES research communities. It supports multiphase approaches analogous to methods in Delft University of Technology publications, including Eulerian–Eulerian and Lagrangian particle tracking employed by teams at Argonne National Laboratory and Sandia National Laboratories. Combustion modelling capabilities align with mechanisms developed by Sandia National Laboratories and Lawrence Livermore National Laboratory, enabling detailed chemical kinetics and reduced mechanisms used in aviation work by Boeing and Airbus. Heat transfer, conjugate heat transfer, rotating reference frame treatment for turbomachinery design used by General Electric and Siemens Energy, and conjugated porous media models similar to those in Fraunhofer Society projects are available. Pre- and post-processing integrate with CAD systems from Dassault Systèmes and PTC, and visualization workflows familiar to users of ParaView and Tecplot.
Applications
Industry use cases include internal combustion engine development pursued by Toyota and Volkswagen Group, turbocharger and turbine analysis for Rolls-Royce plc and Siemens Energy, and aerodynamics studies relevant to McLaren and Red Bull Racing. In energy sectors, STAR-CD has been used for gas turbine combustor design in projects with GE Aviation and for nuclear reactor cooling investigations linked to EDF (Électricité de France). Chemical process modelling and reactor scale-up efforts have involved BASF and Dow Chemical Company, while maritime hydrodynamics studies intersect with work by Wärtsilä and MAN Energy Solutions. Academic and national laboratory research applications include droplet dynamics and spray modelling at Stanford University and MIT, and multi-physics coupling in fusion and plasma research associated with Culham Centre for Fusion Energy.
Architecture and Numerical Methods
STAR-CD's architecture is built around a cell-centered finite-volume discretization on structured and unstructured grids, paralleling approaches in legacy solvers like CFD-General Notation System adaptations. Numerical solvers include pressure–velocity coupling schemes related to SIMPLE family algorithms developed by researchers at Imperial College London and multigrid acceleration techniques used in high-performance computing centers such as Oak Ridge National Laboratory. Turbulence closures encompass k–epsilon and k–omega variants with near-wall treatments comparable to methods advanced at NASA Glenn Research Center, and advanced Reynolds stress closures researched at ETH Zurich. Combustion schemes employ detailed chemistry integration strategies inspired by Lawrence Berkeley National Laboratory work, and spray/atomization models follow Lagrangian paradigms common to studies at Sandia National Laboratories. Parallelization is implemented via MPI standards used at Cray Research and optimized for clusters produced by Hewlett-Packard and IBM.
Licensing and Development
STAR-CD is distributed under proprietary licensing managed historically by CD-adapco and subsequently by Siemens Digital Industries Software as part of the Simcenter portfolio. Commercial licenses target original equipment manufacturers such as Ford Motor Company and consulting firms like Arup, with support agreements and training programs delivered through authorized channels including ANSYS-era partners and independent consultancy networks. Development roadmaps have been influenced by integration strategies with NX Siemens CAD/PLM suites and interoperability standards promoted by organizations like ISO and IEEE.
Reception and Comparative Software
In comparative benchmarks, STAR-CD has been evaluated alongside commercial packages such as ANSYS Fluent, CONVERGE CFD, and CD-adapco's STAR-CCM+, as well as open-source projects like OpenFOAM. Reviews in industry journals and reports from engineering consultancies in the SMMT and IET communities highlight its strengths in engine and turbomachinery simulations, while competitors are noted for meshing automation or multiphysics integration in different contexts. Adoption by major manufacturers including Toyota, Rolls-Royce plc, and Siemens Energy underscores its reputation in regulated sectors where traceability and vendor support matter, whereas academic preference often trends toward OpenFOAM for transparent code access.