Technical articles, research summaries, and project updates. Follow our progress as we work toward making a Dyson swarm a reality.
Monte Carlo simulation reveals that solar radiation pressure provides sufficient station-keeping for collectors at ≤0.7 AU, potentially eliminating propellant costs for inner-system swarm operations.
Monte Carlo cost modeling identifies the crossover point where in-space manufacturing becomes cheaper than Earth production plus launch—approximately 3,500 units under baseline assumptions.
Pareto frontier analysis comparing 8 orbital locations for Assembly Hub and depot placement. Sun-Earth L4/L5 emerges as optimal for Phase 1, with 0.7 AU heliocentric as a Phase 2 option.
Discrete event simulation demonstrates that hierarchical coordination scales to 1M+ nodes while centralized architectures bottleneck at ~10,000. Optimal coordinator duty cycle: 24-48 hours.
Discrete event logistics simulation reveals that 150,000-200,000 km depot spacing achieves <7 day mean time to repair with 85%+ fleet utilization for billion-unit maintenance operations.
Analysis reveals xenon demand would exceed global production by 15-20x. Alternative propellants and hybrid architectures offer the only viable path forward.
Discrete event simulation reveals the sweet spot for transport vehicle fleet sizing—not too few large vehicles, not too many small ones.
Monte Carlo simulation demonstrates that ground-based spectral analysis achieves only 47% survey efficiency due to decision latency—not bandwidth.
Monte Carlo analysis of 5,500+ simulated missions shows that electric propulsion choice matters more than constellation size for identifying high-value mining targets.
Recent peer-reviewed studies from 2022-2025 provide critical insights on habitability constraints, construction approaches, and material requirements.
How Gemini 3 Pro, GPT-5.2, and Claude Opus 4.5 differ in their analysis of Dyson swarm construction phases.
Side-by-side comparison of Phase 0 Bill of Materials from Gemini 3 Pro, GPT-5.2, and Claude Opus 4.5.
Analyzing the strengths and blind spots of AI analysis for asteroid mining and resource processing.
A review of solar sail technology and its applications for Dyson swarm satellite positioning and station-keeping.
Explaining our methodology for using multiple large language models to analyze complex engineering challenges in Dyson swarm construction.
A deep dive into the first phase of Dyson swarm construction: establishing space-based resource extraction and processing capabilities.
An overview of Project Dyson, our mission to make a Dyson swarm a reality, and how we plan to get there through collaborative planning and research.