Monte Carlo Risk Analysis

Managing Uncertainty with Monte Carlo Risk Analysis

In the real world, aerodynamic coefficients, mass/inertia properties, and initial conditions are never known with full certainty. CFTools' Monte Carlo module models this uncertainty directly, revealing the risk distribution across the full range of possible scenarios.

Why Model Uncertainty at All?

A single nominal 6-DOF run doesn't reflect the deviations present in real flight conditions. Without jointly accounting for aerodynamic coefficient tolerances, manufacturing-driven mass/inertia variation, and initial velocity/angle deviations, borderline-but-critical scenarios can be missed.

Defining Input Parameters as Distributions

Each source of uncertainty (aerodynamic coefficients, mass, moments of inertia, initial conditions) is defined as a probability distribution, tunable to your engineering tolerances or test data.

Deriving the Risk Distribution from Many Runs

Sampling from the defined distributions, a large number of 6-DOF runs are generated and executed automatically, producing a risk distribution that spans the full range of possible separation behavior.

Automatic Flagging of Critical Scenarios

Runs near or violating the safe clearance boundary are flagged automatically, so engineers don't have to manually sweep through thousands of runs to find which scenarios need attention.

Speed and Coverage Advantage Over Manual Review

Large-volume scenario sweeps run automatically instead of being reviewed one by one — shortening analysis time and increasing the chance of catching edge-case scenarios that manual review could miss. See other use cases.

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