spacedc-mdao

Multidisciplinary design analysis and optimization (MDAO) of orbital compute infrastructure, with terrestrial data-center baselines for comparison.

The package optimizes delivered useful compute, not nominal watts or nominal GPUs. It takes installed capacity and degrades it through power, thermal, network, reliability, and utilization limits, then reports where a design fails and which assumptions decide the outcome. Its job is to make the feasibility boundary visible — not to argue that space wins.

C_delivered = C_peak · f_software · f_power · f_thermal · f_network · f_availability · f_utilization

Every default number is a provenance-tagged assumption (value, units, source, date, confidence). For the bundled 1 MW text-inference scenario, Earth wins on levelized cost — the orbital design is limited by optical-downlink availability and the launch, radiator, and station-keeping mass it carries.

Where to go next

• Quick start — install and run the first comparison.
• User tiers — from a one-line CLI compare to custom catalogs.
• Model architecture — how the disciplines couple.
• Governing equations — the physics behind each factor.
• Assumptions & provenance — every default, sourced.
• API reference — the public Python surface.