Advanced Workflows with Sahand Engineering Toolbox for EngineersSahand Engineering Toolbox is a modular suite designed to streamline engineering tasks across design, analysis, simulation, and documentation. This article describes advanced workflows that leverage its tools to accelerate product development, improve collaboration, and reduce iteration time. Practical examples and best practices are included to help engineers integrate Sahand into real-world projects.
1. Overview: where Sahand fits in an engineering pipeline
Sahand combines CAD import/export, parameterized modeling, numerical analysis modules, automation scripting, and reporting. It is most useful in these stages:
- Concept and rapid prototyping (parameter sweeps, quick geometry generation).
- Detailed design and optimization (multi-physics simulation, constraint-based updates).
- Validation and verification (automated tests, batch simulations).
- Documentation and handoff (automated drawings, exported BOMs).
Key advantage: tight coupling between modeling, simulation, and automation reduces manual rework when design parameters change.
2. Setting up a repeatable project structure
A consistent folder structure and template project in Sahand saves time and avoids errors.
Recommended structure:
- project_root/
- models/ (native Sahand files, versioned)
- geometry/ (imported STEP/IGES)
- simulations/ (setup and results)
- scripts/ (automation and parameter files)
- docs/ (reports, drawings, BOMs)
- data/ (measurement or test inputs)
Start each new project from a template that includes:
- preconfigured units, material library, and standards
- default simulation templates (static, thermal, modal)
- example automation scripts for parameter sweeps and batch runs
3. Parameterized modeling and design intent
Use Sahand’s parameterization features to encode design intent so changes propagate predictably.
Workflow tips:
- Identify primary design parameters (lengths, thicknesses, hole positions). Keep them few and high-level.
- Link dependent features to parameters rather than to other features directly; this reduces fragile references.
- Use named parameter sets for different product variants (e.g., “base”, “heavy-duty”).
- Create constraints and checks (min/max values) to prevent invalid geometry during automation.
Example: a bracket model with parameters width W, thickness T, and hole offset O. Define fillet radius as a function of T to keep proportions consistent:
fillet_radius = 0.1 * T 4. Automation and scripting: reducing repetitive work
Sahand supports scripting (Python-like or native macro language) to automate tasks such as batch simulations, design of experiments (DoE), and report generation.
Common automated tasks:
- Parameter sweeps — vary W and T across ranges and record stress/deflection.
- Optimization loops — call an optimizer to minimize mass subject to stress constraints.
- Preprocessing pipelines — import CAD, heal geometry, apply materials, mesh, set loads.
- Postprocessing pipelines — extract key metrics, generate plots, produce PDF reports.
Example pseudo-script structure:
for params in parameter_list: load_model(template_model) set_parameters(params) run_simulation(sim_type="static") results = extract_results(["max_stress", "deflection"]) save_results(params, results) generate_summary_report(all_results) 5. Integrating simulation types for multi-physics workflows
Advanced products often require coupling across disciplines (structural, thermal, fluid). Sahand supports sequential and co-simulation workflows.
Sequential coupling example:
- Thermal simulation — obtain temperature field under operating load.
- Structural simulation — import temperature field as thermal load and compute thermal stresses.
- Fatigue analysis — use stress cycles to estimate life.
Co-simulation tips:
- Maintain consistent meshes or use robust mapping tools for field transfers.
- Automate data export/import between modules to remove manual copying.
- Validate mapping accuracy on smaller test cases before full runs.
6. Mesh strategies and accuracy control
Good meshes balance accuracy and runtime. Use Sahand’s meshing controls to tailor element size and type.
Guidelines:
- Start with coarse meshes for design exploration, refine critical regions later.
- Use boundary layer meshes for fluid-structure interfaces.
- Apply mesh convergence studies automatically in scripts: run with progressively finer meshes until key outputs stabilize.
- Use adaptive meshing where available to focus elements where errors are highest.
Convergence loop example:
mesh_size = initial while not converged: create_mesh(mesh_size) run_simulation() compute_change_in_key_output() if change < tolerance: converged = True else: mesh_size *= 0.7 7. Optimization workflows
Common optimization goals: minimize mass, maximize stiffness, meet fatigue life, or reduce cost. Sahand can connect with optimizers for gradient-based or global search.
Workflow:
- Define objective function and constraints based on extracted simulation outputs (e.g., max_stress < allowable).
- Choose optimizer type: gradient (fast, needs smooth design space) or global (Genetic Algorithms, Particle Swarm — handles discontinuities).
- Use surrogate models (response surfaces) when full simulations are expensive: build approximate models from a DoE and optimize on the surrogate, then validate with full runs.
Example DoE + surrogate loop:
- Run DoE (Latin Hypercube) with N samples.
- Fit surrogate (Kriging, polynomial).
- Optimize surrogate to find candidate optimum.
- Validate candidate in full simulation; iterate if needed.
8. Batch runs, HPC and cloud integration
For large parameter studies use batch processing or cloud/HPC integration.
Practical steps:
- Containerize Sahand runtime or use provided headless execution mode.
- Partition parameter space across nodes; ensure each job saves intermediate outputs.
- Use a centralized results database or cloud storage for aggregation.
- Monitor runs and implement automatic retries for transient failures.
9. Collaboration, versioning and traceability
Engineering teams require reproducibility.
Best practices:
- Use a version control system (git or PDM) for scripts, templates, and lightweight text files. Store large binaries in a dedicated LFS or PDM.
- Embed metadata in model files: creator, timestamp, baseline parameters, and linked simulation cases.
- Produce automated run logs and reports that list exact parameter values and software versions used.
10. Reporting and handoff
Automate generation of reports and manufacturing outputs.
Include:
- Summary table of variants with key metrics (mass, max stress, critical frequencies).
- Automated drawing exports (2D and annotated 3D views).
- BOM export with linked part IDs and materials.
- Packaged archive containing model, simulation inputs, and results for supplier handoff.
Example report sections:
- Executive summary (key numbers)
- Model description and assumptions
- Simulation setups and boundary conditions
- Results and sensitivity analysis
- Recommendations and next steps
11. Validation, testing, and continuous improvement
Validate workflows with physical tests and incorporate feedback.
Process:
- Start with a simplified test part; compare simulation to measured results.
- Quantify discrepancies and adjust material models, boundary conditions, or contact definitions.
- Keep a knowledge base of common fixes and assumptions for future projects.
12. Example end-to-end case study (compressive bracket)
- Template load: import bracket geometry, assign steel material.
- Parameterize key dimensions (W, T, hole_pos).
- Run a coarse parameter sweep for W and T to identify feasible region.
- Perform mesh convergence for three candidate geometries.
- Run thermal-structural sequential coupling if operating temperature varies.
- Optimize for mass subject to max_stress < 250 MPa and deflection < 2 mm using a surrogate model.
- Batch-validate optimum set across manufacturing tolerances and produce final drawings and BOM.
13. Common pitfalls and troubleshooting
- Over-parameterization — too many degrees of freedom makes optimization slow and unstable.
- Hidden references — avoid feature-to-feature links that break during redesigns.
- Ignoring mesh sensitivity — can produce misleading “optimal” designs.
- Poor data management — lost context leads to repeat work.
14. Final checklist for deploying advanced workflows
- Project template and folder structure in place.
- Parameterized models with named sets for variants.
- Automated scripts for preprocessing, simulation, and postprocessing.
- Mesh convergence and surrogate modeling where needed.
- Versioning, metadata, and automated reporting enabled.
- Validation plan with physical testing and model updates.
Sahand Engineering Toolbox supports robust, automated engineering workflows when used with disciplined project setup, parameterization, and automation. The combination of repeatable templates, scripting, multi-physics coupling, and optimization accelerates development while maintaining traceability and accuracy.