DBbun/artemis-ii-simulator-v1.0

🚀 Artemis II Human-in-the-Loop Crew Performance Simulator

Forward-looking synthetic modeling of crew physiology and cognitive reliability for NASA’s planned Artemis II lunar flyby mission.

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🧭 Overview

This project simulates human cognitive performance across the full Artemis II mission timeline.

Unlike traditional aerospace simulations that focus on hardware systems, this framework models the human system:

• Sleep pressure & circadian rhythm
• Cognitive performance
• Error probability (CREAM)
• CO₂ cognitive effects
• Radiation exposure
• Countermeasures (caffeine, naps, light therapy)
• Team workload fairness
• Mission-phase stress

All outputs are fully synthetic and generated via Monte Carlo simulation.

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🛰 Figure 1 — Artemis II Mission Trajectory with Crew Performance Overlay

What this shows

Top panel:

• Distance from Earth across the 15-step Artemis II sequence
• Outbound transit
• Lunar flyby (6,513 km)
• Trans-Earth return
• Magnetosphere boundary

Bottom panel:

• Cognitive performance of each crew member
• Concern threshold (70%)
• Critical threshold (65%)
• Mission phase shading

Why it matters

This figure demonstrates that:

• Performance dips align with circadian troughs
• High-workload phases amplify fatigue
• Lunar flyby timing interacts with biological rhythms
• Deep-space segments show stable but cyclic cognitive oscillations

This is a direct example of orbital context interacting with human reliability.

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🧠 Figure 2 — Peak Performance Windows (Top 20)

What this shows

• Top 20 predicted high-performance windows
• Role-differentiated optimal task intervals
• Ranked across mission time

Why it matters

This enables:

• Scheduling cognitively demanding tasks
• Aligning procedures with peak biological readiness
• Pre-mission task optimization

It transforms physiology into operational decision-support.

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⚖ Figure 3 — Workload Distribution Fairness (Gini Coefficient)

What this shows

• Gini coefficient across mission time
• 0 = equal workload distribution
• 1 = highly unequal workload
• Concern threshold at 0.30

Why it matters

Unequal workload distribution:

• Increases cognitive stress on specific crew members
• Amplifies fatigue accumulation
• Raises downstream error probability

This introduces team-level reliability modeling, not just individual modeling.

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📊 Figure 4 — Sensitivity Analysis (Countermeasures & Environment)

Scenarios tested:

• Baseline
• High CO₂
• No caffeine
• No countermeasures
• Sleep shift ±2 hours

Outputs shown:

• Mean cognitive performance
• % time below critical threshold
• Mean error probability

Why it matters

This shows:

• Removing countermeasures significantly increases risk
• High CO₂ environments measurably degrade reliability
• Sleep phase misalignment increases error exposure

This supports countermeasure evaluation before flight.

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🔥 Figure 5 — Mission Risk Heatmap (CREAM Error Probability)

What this shows

• Hour-of-day vs mission-day error probability
• Higher intensity = higher predicted error risk

Why it matters

This reveals:

• Circadian-linked reliability bands
• Early-mission vulnerability
• Risk clustering during workload spikes

It provides a temporal reliability fingerprint of the mission.

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👨‍🚀 Figure 6 — Role-Differentiated Workload & Performance

What this shows

For each role (CDR, PLT, MS1, MS2):

• Workload curve
• Cognitive performance
• Nap interventions
• Threshold overlays

Why it matters

This demonstrates:

• Different roles accumulate fatigue differently
• Workload asymmetry affects performance
• Countermeasure timing changes recovery curves

This supports role-aware mission planning.

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📈 Figure 7 — Mission Timeline Physiological Overview

Includes:

A. Mean performance + 90% CI
B. Homeostatic sleep drive
C. Allostatic load
D. Caffeine pharmacokinetics
E. Cabin CO₂ levels
F. CREAM control mode by mission phase

Why it matters

This integrates:

• Biological rhythms
• Environmental stressors
• Countermeasures
• Reliability modes

into a single systems-level view of the mission.

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🧠 What Makes This Structurally Unique

This simulator integrates:

• Sleep science
• Human reliability analysis (CREAM)
• Environmental modeling
• Team fairness metrics
• Monte Carlo uncertainty
• Mission-phase annotation

There is no known open-source framework that combines:

Human physiology + reliability modeling + orbital context + countermeasure sensitivity + team fairness

for Artemis-class missions.

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🎯 Intended Users

🎓 Students

Human factors, aerospace systems, Monte Carlo modeling.

🧠 Human Factors Engineers

Fatigue-aware task scheduling, countermeasure evaluation.

📊 Data Scientists

Synthetic multi-agent time-series dataset.

🛰 Space Systems Engineers

Human-in-the-loop risk modeling.

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⚠ Disclaimer

This is a research-grade simulation framework.

It is not:

• A NASA operational tool
• A medical diagnostic system
• A flight safety authority

All data is synthetic.

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👤 Author

DBbun LLC
2026