In today's industrial landscape, the accuracy of numerical simulations has become a determining factor for innovation and competitiveness. Yet, a major obstacle persists: the excessive complexity of native CAD models that significantly slows down FEA, CFD, and electromagnetic analyses. The consequences are direct: increased calculation times, risk of errors, and inefficient use of computing resources. Faced with this challenge, intelligent simplification of CAD models is emerging as a critical step in the digital engineering process.
Recent studies show that proper preparation of CAD models for simulation can reduce calculation times by up to 70% while preserving result accuracy in critical areas of interest. This optimized approach radically transforms the efficiency of engineering departments.
Table of contents
- Technical challenges in preparing CAD models for simulation
- Simplification techniques and model adaptation
- Methodologies and efficient industrial processes
- CADfix DX: optimal solution for model preparation
- Case studies and industrial applications
- Trends and future developments
Technical challenges in preparing CAD models for simulation
The direct use of CAD models in simulation environments encounters several fundamental obstacles that compromise the efficiency and accuracy of numerical analyses. Understanding these challenges is essential to implement effective simplification and optimization strategies.
Excessive complexity of design models
CAD models are typically designed with an extremely high level of detail to meet manufacturing needs. This precision, while essential for production, becomes problematic during numerical simulations:
- Computational overload due to details irrelevant to the analysis
- Exponentially longer calculation times
- Increased risks of solver non-convergence
- Excessive consumption of computing resources
For example, a mechanical component with hundreds of small fillets or detailed threads can multiply calculation time by 10 without providing significant precision to structural analysis results.
CAD-CAE interoperability problems
The diversity of software environments used in the product development chain creates significant data exchange difficulties. As CADfix documentation shows, a typical engineering scenario often involves several systems:
- Designers using CATIA or NX
- Production engineers working on ProENGINEER or SolidWorks
- Analysis teams using proprietary mesh formats
- External suppliers preferring IGES, STEP, or Solid Edge
This heterogeneity leads to format incompatibilities, geometric integrity losses, and conversion errors that require time-consuming manual interventions.
Specific requirements by simulation type
Each type of numerical analysis imposes particular constraints on model preparation:
| Simulation type | Specific requirements | Common issues |
|---|---|---|
| Finite Element Analysis (FEA) | "Watertight" geometry, simplification of small features | Poor quality meshing, geometric singularities |
| Computational Fluid Dynamics (CFD) | Negative volume (fluid), closed surfaces without intersection | Leaks in external envelope, details disturbing flow |
| Electromagnetics (EM) | Precise definition of interfaces between materials | Discontinuities at junctions, convergence problems |
Simplification techniques and model adaptation
In response to the identified challenges, various technical approaches allow optimization of CAD models to make them more suitable for numerical simulation constraints while preserving accuracy in areas of interest.
Intelligent feature removal (defeaturing)
Defeaturing is a fundamental approach to reducing model complexity before simulation. This technique consists of identifying and removing geometric features irrelevant to the specific analysis:
- Elimination of small holes, fillets, and chamfers
- Removal of cosmetic details (logos, markings)
- Simplification of non-critical areas for analysis
- Selective preservation of features influencing results
The effectiveness of defeaturing relies on the delicate balance between simplification and precision preservation. Excessive simplification can alter results, while insufficient simplification will not significantly reduce calculation times.
Geometric repair and cleaning
The intrinsic quality of geometric models is essential to ensure reliable simulations. Repair operations include:
- Correction of gaps and overlaps between surfaces
- Resolution of G0/G1 continuity problems
- Treatment of unconnected surfaces
- Correction of topological problems (invalid faces, degenerate edges)
These operations, often laborious when performed manually, can be automated using specialized tools like CADfix that systematically detect and resolve geometric anomalies.
Specific adaptations by simulation type
Depending on the type of analysis targeted, different geometric transformations can be applied:
| Adaptation type | Application | Benefits |
|---|---|---|
| Creation of external envelopes | CFD, thermal analyses | Clean definition of fluid volumes, simplified interfaces |
| Extraction of medial surfaces | Thin components (sheet metal, shells) | Dimensional reduction, faster calculations |
| Mid-surfacing | Injected parts, thin structures | 3D to 2D transition, significant reduction in elements |
| Connection idealization | Mechanical assemblies | Simplified but faithful representation of junctions |
Methodologies and efficient industrial processes
Beyond specific techniques, integrating model preparation into a methodical process is a key success factor for digital engineering departments.
Standardized workflows for model preparation
Establishing consistent workflows ensures the quality and repeatability of the simplification process:
- Preliminary model assessment and identification of simulation objectives
- Import and initial diagnosis of geometric problems
- Repair of critical defects affecting model integrity
- Simplification adapted to the specific needs of the analysis
- Qualitative and quantitative verification of the simplified model
- Export to the format required by the target solver
This structured approach minimizes the risk of errors and ensures optimal preparation for each type of analysis.
Performance indicators and metrics
To evaluate the effectiveness of model preparation processes, several key metrics can be tracked:
- Reduction in preparation time (up to 90% according to CADfix customer testimonials)
- Decrease in model volume (number of geometric entities)
- Acceleration of calculation times
- Improvement in mesh quality (aspect criteria, distortion)
- Solver convergence rate
Systematic monitoring of these indicators allows continuous optimization of practices and quantification of the benefits obtained.
CADfix DX: optimal solution for model preparation
Faced with the complex challenges of model preparation for simulation, CADfix DX stands as a reference solution, offering a comprehensive set of tools dedicated to optimizing CAD geometries for numerical analyses.
Key features of CADfix DX
CADfix DX offers a set of powerful tools specifically designed to meet the requirements of model preparation for simulation:
- Automated geometric repair detecting and correcting common problems
- Intelligent defeaturing with automatic identification of non-essential features
- Controlled mesh generation adapted to different solvers
- Extended interoperability with support for over 30 CAD and CAE formats
- Specialized tools for the specific needs of FEA, CFD, and EM analyses
The user interface organized around the "CADfix Wizard" effectively guides users through the import, repair, and export process, minimizing manual intervention and significantly accelerating workflows.
Optimization process with CADfix DX
The integrated workflow of CADfix DX unfolds in several key steps:
- Initial diagnosis: Identification and clear visualization of geometric problems
- Guided resolution: Proposals for solutions adapted to each type of anomaly
- Targeted simplification: Defeaturing tools adapted to the planned analysis type
- Continuous validation: Automatic updating of diagnostics after each operation
- Specific preparation: Geometric adaptations dedicated to the simulation type
- Optimized export: Generation of files in the format required by the solver
This methodical approach ensures obtaining models perfectly adapted to the constraints of different numerical analysis tools.
Quantifiable technical benefits
The adoption of CADfix DX in engineering processes generates significant measurable advantages:
| Benefit | Quantified impact | Added value |
|---|---|---|
| Reduction in preparation time | Up to 90% according to customer testimonials | Acceleration of development cycles |
| Decrease in manual interventions | Automation of 70% of repair tasks | Liberation of engineering resources |
| Improvement in mesh quality | 65% reduction in poor quality elements | Faster and more reliable convergence |
| Optimization of computing resources | Models 30-70% lighter depending on complexity | Faster simulations, infrastructure savings |
As David Merrit, Senior Engineer at Dana Glacier Vandervell testifies: "By using CADfix, the amount of model reworking has been reduced by around 90 percent and the total model set-up time reduced by around 50 percent."
Case studies and industrial applications
The adoption of advanced methodologies for simplifying CAD models for simulation has demonstrated its effectiveness in many industrial sectors, with concrete and measurable benefits.
Aerospace and defense
The aerospace sector, particularly demanding in terms of simulation precision and reliability, benefits considerably from intelligent simplification approaches:
- Simplification of complex structural components for strength analyses
- Preparation of aerodynamic models for high-fidelity CFD simulations
- Topological optimization based on pre-simplified geometries
According to Chris Jones from BAE SYSTEMS: "We use CADfix as the hub of our operation, a central resource for all the geometry we have to work on. Whatever kind of analysis we need to perform, whatever mesh we need, the starting point is always the clean geometry that's been assembled inside CADfix."
Automotive and transportation
The automotive industry, faced with increasingly shorter development cycles, takes advantage of advanced model preparation techniques to accelerate its digital validation processes:
- Optimization of complex models for virtual crash test simulations
- Preparation of geometries for aerodynamic and thermal analyses
- Simplification of mechanical assemblies for vibration analyses
The experience of Comau Systèmes France perfectly illustrates these advantages: "At the beginning of the project, CADfix allowed us to process 150 files automatically in just five days. The files were engine parts and were up to 30 Mb in size. Today, the use of different CAD systems is no longer an obstacle and CADfix gives us the opportunity to accept new projects."
Energy and heavy industry
The energy sector, characterized by large-scale equipment and strict safety requirements, particularly benefits from simplification approaches:
- Analysis of pressure equipment with optimized geometries
- Complex flow simulations in turbomachinery
- Thermal studies on simplified but representative assemblies
These applications demonstrate that intelligent model simplification is not just a technical step, but a true strategic lever to accelerate innovation while reducing development costs.
Trends and future developments
CAD model optimization for simulation is a constantly evolving field, driven by technological advances and growing needs in numerical simulation.
Artificial intelligence and advanced automation
AI technologies are rapidly transforming model simplification approaches:
- Machine learning algorithms for intelligent feature detection
- Contextual recognition of critical areas to preserve
- Automatic optimization of simplification level according to analysis type
- Prediction of optimal preparation parameters based on case history
These technologies allow us to envision systems capable of automatically proposing the optimal level of simplification based on the specific context of each analysis.
Smoother CAD-CAE integration
The evolution of engineering platforms tends toward more transparent integration of design and analysis environments:
- Real-time model preparation during the design phase
- Immediate feedback on the "simulability" of geometries
- Bidirectional synchronization between design and analysis models
- Unified platforms reducing format conversion needs
This progressive convergence of CAD and CAE environments should significantly reduce current obstacles to the intensive use of simulation as a design tool.
Towards democratization of numerical simulation
The growing automation of model preparation contributes to greater accessibility of simulation tools:
- Cloud solutions making simplification capabilities accessible without heavy infrastructure
- Simplified user interfaces for non-specialists
- Democratization of early simulation approaches in the design process
This evolution could fundamentally transform engineering processes by allowing systematic integration of simulation from the earliest design phases, even for modestly sized engineering structures.
Conclusion
Intelligent simplification of CAD models for simulation represents a major strategic lever for industrial companies seeking to accelerate their innovation cycles while optimizing their resources. The balance between geometric simplification and preservation of analysis precision constitutes a technical challenge that requires specialized tools and proven methodologies.
Solutions like CADfix DX now offer advanced capabilities to largely automate this critical process, generating substantial productivity gains while improving the quality and reliability of numerical simulations. Industrial testimonials confirm that investment in these technologies translates into significant reductions in preparation time, better utilization of engineering resources, and overall acceleration of development cycles.
As artificial intelligence and automation technologies continue to advance, model preparation for simulation will become even more integrated and transparent, contributing to a democratization of numerical simulation as a strategic tool for industrial innovation.
