ClarusC64/cascade-f1-suspension-rideheight-aerostall-porpoise-v0.1

F1 Suspension–RideHeight–AeroStall–Porpoise Cascade

A quad coupling model for high-speed aerodynamic instability.

This repository models how suspension stiffness, ride height, aero stall margin, and vertical oscillation interact to produce porpoising and stall-driven performance collapse.

It shifts analysis from component tuning to interaction boundary mapping.

What This Repo Demonstrates

You can:

• Score a setup state for stall cascade risk • Identify which parameters drive instability • Compare alternative ride height / stiffness setups • Estimate distance to aerodynamic stall boundary • Export structured stability reports

The dataset is synthetic. It demonstrates the geometry of instability surfaces.

Core Quad

• suspensionstiffness • rideheight • aerostallmargin • vertical_oscillation

These variables couple non-linearly at high speed.

The model captures how:

Lower ride height → increased downforce but reduced stall margin Higher stiffness → reduced compliance and higher sensitivity to track input Reduced stall margin → higher probability of aero detachment Oscillation → repeated stall crossing and amplification

Instability emerges from interaction, not from a single threshold.

Prediction Target

label_cascade

• 0 = Stable aerodynamic operating window • 1 = Stall-driven oscillation region reached

A cascade represents:

Sustained porpoising Aero detachment cycles Loss of platform stability High-speed performance degradation

Row Structure

Each row is a normalized setup snapshot (0.0–1.0 scale).

suspension_stiffness Higher values mean less compliance and higher transfer of track energy

ride_height Lower values increase downforce but reduce stall margin

aerostallmargin Lower values mean closer proximity to stall boundary

vertical_oscillation Higher values indicate sustained instability

Use Cases

Pre-Session Setup Validation

Evaluate ride height and stiffness combinations before track running.

Stall Margin Analysis

Quantify how close a setup sits to aerodynamic detachment.

Aero Package Comparison

Rank configurations by stability margin.

What-If Exploration

Test incremental ride height or stiffness changes. Measure impact on stall risk.

What Makes This Different

vs Static Ride Height Targets

Not “run 2mm higher.”

But:

“How does ride height behave under stiffness and oscillation coupling?”

vs Single-Parameter Monitoring

Ride height, stiffness, and stall margin must be evaluated together.

vs Lap-Time-Only Simulation

Simulation predicts pace. This predicts platform stability and collapse risk.

Example Output

Input Setup

{
  "suspension_stiffness": 0.58,
  "ride_height": 0.50,
  "aero_stall_margin": 0.54,
  "vertical_oscillation": 0.46
}
Risk Assessment
{
  "cascade_probability": 0.39,
  "risk_band": "AMBER"
}
Boundary Interpretation

Small decreases in ride_height combined with higher stiffness can:

• reduce stall margin
• increase oscillation amplitude
• push cascade probability above 0.70

Distance-to-RED can be estimated via L1 / L2 perturbation norms.

Batch Testing Capability

Compare multiple setups:

def batch_test(setups: list) -> list:
    results = []
    for s in setups:
        risk = score(s)
        results.append((s, risk))
    results.sort(key=lambda x: x[1]["cascade_probability"], reverse=True)
    return results

Applications:

• Compare low-rake vs higher ride configurations
• Evaluate suspension stiffness variants
• Rank aero balance configurations

Exportable Stability Reports

Structured reporting supports:

• Engineering review sessions
• Setup documentation
• Wind tunnel correlation analysis
• Post-session debriefs

Example concept:

def export_report(setup, risk, boundary_configs, mitigations):
    report = {
        "configuration": setup,
        "risk_assessment": risk,
        "boundary_configs": boundary_configs,
        "mitigations": mitigations
    }
    return report
Files

data/train.csv
Synthetic training data

data/tester.csv
Evaluation dataset

scorer.py
Outputs:

• accuracy
• precision
• recall
• f1
• confusion matrix

Evaluation

Run:

python scorer.py

Scope

This repository demonstrates quad coupling geometry using synthetic data.

It does not represent calibrated team telemetry.

Small samples reveal structure.
Production-scale data determines operational exposure.

Production Direction

Production deployment enables:

• 50K–1M row telemetry-calibrated datasets
• Real-time aero stability scoring
• Dynamic stall boundary monitoring
• Early warning before oscillation escalation
• Integration into race engineering dashboards

License

MIT

Structural Note
This dataset identifies a measurable coupling pattern associated with systemic instability.
The sample demonstrates the geometry.
Production-scale data determines operational exposure.

Enterprise & Research Collaboration
Clarus develops production-scale coherence monitoring infrastructure for motorsport, healthcare, finance, infrastructure, and AI systems.

team@clarusinvariant.com

Instability is detectable.
Boundaries are measurable.