Design of Experiments (DoE)

BikeSim, CarSim, and TruckSim include powerful capabilities for systematic parameter variation and results analysis, allowing efficient optimization and validation through Design of Experiments methodologies.

Key Features

Parametric Studies

Easily set up and run systematic parametric studies by varying multiple parameters across specified ranges.

Batch Simulations

Run multiple simulations in sequence or parallel to efficiently explore the design space and identify optimal configurations.

Results Analysis

Analyze outputs across multiple simulation runs to identify trends, sensitivities, and optimal parameter combinations.

Optimization

Identify optimal parameter combinations to meet specific performance criteria or optimize multiple objectives simultaneously.

Benefits

Reduced Development Time

Quickly explore design alternatives and identify optimal configurations without the need for physical prototypes.

Improved Performance

Systematically refine designs to achieve optimal performance across multiple criteria.

Cost Savings

Identify issues early in the design process, reducing the need for costly physical prototypes and testing.

Robust Design

Evaluate performance across a range of conditions to ensure designs are robust against variations and uncertainties.

Integration with External Tools

The VehicleSim products support integration with external optimization and DoE tools:

MATLAB/Simulink

Connect with MATLAB's optimization and DoE toolboxes for advanced experimental design and analysis.

Python Integration

Use Python libraries like SciPy and PyDOE to design and analyze experiments with VehicleSim products.

Cloud Computing

Scale up experiments using cloud computing resources for parallel simulations and reduced computation time.

API Access

Use the VehicleSim API to integrate with third-party DoE and optimization tools for customized workflows.