Optimizing Simulations with Caedium ProfessionalComputational fluid dynamics (CFD) simulations are powerful tools for analyzing fluid flow, heat transfer, and related phenomena across engineering disciplines. Caedium Professional offers an integrated environment that simplifies building, running, and analyzing CFD models. This article explains practical techniques and workflows to optimize simulations with Caedium Professional so you can achieve accurate results faster while using computational resources efficiently.
Why optimization matters
Efficient simulations reduce turnaround time, lower computational cost, and enable more design iterations. Optimization covers several goals:
- Faster run times so you can explore more design options.
- Reliable convergence to ensure results are physically meaningful.
- Accurate results without unnecessary mesh refinement or solver overuse.
- Reproducibility so workflows can be automated and shared.
Plan before you model
Start by clarifying objectives and constraints:
- Define the key quantities of interest (pressure drop, lift, heat transfer coefficient, etc.).
- Determine acceptable accuracy and error tolerances for those quantities.
- Identify dominant physics (laminar/turbulent flow, compressibility, species transport, conjugate heat transfer).
- Select geometry fidelity: use simplified geometry where possible (remove small fillets, tiny holes, or fasteners that don’t affect answers).
This up-front scoping avoids over-simulation and helps you choose solver settings and mesh strategies accordingly.
Geometry and boundary simplification
Simplifying geometry is one of the highest-impact steps:
- Remove nonessential small features and fillets using Caedium’s CAD import and editing tools or external CAD cleanup before import.
- Use symmetry planes to model only a portion of the domain when appropriate (half or quarter models).
- Replace complex assemblies with equivalent porous media or simplified blocks when fine details don’t influence the flow field.
Simpler geometry reduces mesh count and speeds up solution times without sacrificing the accuracy of the quantities you care about.
Mesh strategy and quality
Mesh design strongly influences accuracy and performance.
- Choose an appropriate mesh type: Caedium supports structured/unstructured polyhedral and tetrahedral meshes. Polyhedral meshes often yield better convergence and accuracy per cell than tetrahedral meshes.
- Use local refinement only where needed: around boundary layers, wakes, or regions with large gradients. Avoid global refinement.
- Control boundary-layer resolution: for wall-bounded turbulent flows, ensure appropriate y+ values for your turbulence model. For common RANS models aim for y+ ≈ 30–300 with wall functions, or y+ ≈ 1 for low-Re treatments. Use prism/wedge layers near walls when possible.
- Check mesh quality metrics (skewness, aspect ratio, orthogonality). Poor-quality cells can slow convergence or cause divergence.
- Run a mesh independence study targeted to your key output variables rather than global field convergence.
Example mesh refinement workflow:
- Generate a baseline mesh with coarse resolution.
- Identify regions with high gradients from an initial run (velocity, pressure, temperature).
- Apply local refinement and add boundary-layer prisms only where they affect outputs.
- Repeat until metrics of interest change within tolerance.
Physics models and solver settings
Selecting appropriate physical models and tuning solver controls is critical.
- Start simple: use steady RANS turbulence models for many engineering problems. Move to unsteady (URANS, LES) only if necessary to capture transient or unsteady phenomena.
- Choose inlet/outlet boundary conditions that reflect the physical situation (velocity, mass flow, pressure, turbulent intensity). Improper BCs can produce artifacts that look like mesh or solver problems.
- For heat transfer problems, consider conjugate heat transfer only where solid conduction affects fluid behavior; otherwise, prescribe wall temperatures or heat fluxes.
- Use non-dimensional numbers (Reynolds, Mach, Peclet) to guide model choice and grid requirements.
Solver tuning tips:
- Start with relaxed solver settings (larger under-relaxation, larger time step for transient) to reach a reasonable solution quickly, then tighten controls for final runs.
- Use residuals plus integrated quantities (drag, lift, mass imbalance) to judge convergence. Residuals alone can be misleading.
- Enable multi-grid or algebraic multi-grid (AMG) if available to accelerate convergence for pressure and velocity.
- For steady flows that exhibit convergence difficulty, try pseudo-transient or local time-stepping approaches.
Use model reduction and surrogate methods
When many design evaluations are needed:
- Use reduced-order models (proper orthogonal decomposition, surrogate models) trained from a limited set of high-fidelity simulations.
- Employ response-surface methods or machine learning surrogates for rapid exploration of parameter space.
- For parameter sweeps, use coarser meshes or simplified physics to screen designs, then validate promising candidates with high-fidelity runs.
Caedium integrates scripting and automation that can help generate training datasets and manage parametric studies.
Parallel computing and resource management
Make efficient use of hardware:
- Run simulations in parallel when solving large meshes or unsteady cases. Caedium’s solver parallelization scales with core count but watch for diminishing returns as communication overhead rises.
- Balance memory and core allocation: too many cores with insufficient per-core memory can slow or crash runs.
- Use checkpointing for long transient runs so you can restart from intermediate states.
Profile runs to find bottlenecks (mesh generation, solver iterations, I/O) and address the most time-consuming step first.
Post-processing and result-driven refinement
Use quick post-processing to guide further improvement:
- Inspect key fields and integrated quantities early to detect modeling problems.
- Use line plots, probe points, and iso-surfaces to find where gradients suggest mesh or model changes.
- Quantify uncertainty: perform sensitivity analyses for input parameters and mesh resolution focused on your key outputs.
Refine only where result changes justify the extra cost.
Automation, scripting, and reproducibility
Automation saves time and reduces human error:
- Script pre-processing, solver runs, and post-processing to ensure repeatability. Caedium supports scripting via Python interfaces.
- Use version control for models and scripts so experiments are reproducible.
- Create templates for common setups (inlet profiles, turbulence settings, mesh controls) to standardize workflows across projects.
Common pitfalls and how to avoid them
- Over-refining globally: refine locally based on physics.
- Ignoring boundary conditions: set physically appropriate BCs and check mass/energy balances.
- Relying solely on residuals: monitor integrated quantities and ensure physical consistency.
- Using the most complex model by default: complexity should be driven by need, not by availability.
Checklist for an optimized Caedium Professional run
- Objectives and tolerances defined.
- Geometry simplified and symmetry used where possible.
- Mesh tailored with local refinement and boundary layers.
- Appropriate turbulence and physical models selected.
- Solver settings staged: coarse→refined.
- Parallel resources balanced for memory and speed.
- Post-processing used to guide targeted refinement.
- Scripts and templates created for reproducibility.
Optimizing simulations is an iterative process that balances accuracy, speed, and cost. By planning carefully, simplifying geometry, using targeted meshing, choosing appropriate physics models, leveraging parallel resources, and automating workflows, you can get reliable results from Caedium Professional with minimal computational expense.
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