orbitplan

Feasibility planner for inference in orbit — find the constraint that's actually stopping you.

Three things decide whether a model can run in space, and they fight each other: the array has to make the power, the radiator has to shed the heat that power becomes, and the ground link has to carry whatever data survives. Optimising one in isolation is how orbital compute plans die. orbitplan reconciles all three against a real sensor or serving load and names the binding constraint.

Part of a set:

• orbitherm — sizes the radiator (used automatically when installed)
• radshield — keeps the model correct under radiation-induced bit flips

Install

pip install orbitplan            # core
pip install orbitplan[thermal]   # + orbitherm for the full thermal model

Quick start

import orbitplan as op

plan = op.MissionPlan(
    sensor=op.Sensor(burst_gb_per_s=10, duty_cycle=0.002),      # SAR instrument
    workload=op.Workload.preset("sar_segmenter", input_mb=800, output_mb=2),
    accelerator=op.Accelerator.preset("jetson_agx_orin"),
    power=op.PowerBudget(array_area_m2=8),
    thermal=op.ThermalBudget(radiator_area_m2=2),
    link=op.LinkBudget(band="x_band"),
)

r = plan.evaluate()
print(r.bottleneck)     # -> 'none'
print(r.summary())

Drop the onboard inference (output_mb=800) and the same satellite becomes downlink-bound: 1,728 GB/day of raw data against 72 GB/day of X-band capacity. That gap is the entire business case for processing in orbit.

What it models

Power — solar array output derated for cell efficiency, packing, eclipse fraction, battery round-trip and PMAD losses, minus bus overhead.

Thermal — the heat-rejection ceiling on sustained compute. Delegates to orbitherm when installed; falls back to a bare Stefan-Boltzmann estimate otherwise.

Downlink — contact time × rate × passes, derated for protocol overhead, versus the data actually produced. Anything the payload can't process has to go down raw.

Workload — energy and latency per inference from real accelerator efficiency (peak TOPS derated by an achievable-utilisation factor, which is where most naive estimates go wrong).

Verdicts

evaluate() returns a PlanResult whose bottleneck is one of:

Every result carries the numbers behind the call — margins, watts required versus available, GB/day generated versus downlinked — so you can see how far off you are, not just that you failed.

Presets

Accelerators: jetson_orin_nx, jetson_agx_orin, nvidia_l4, nvidia_h100. Workloads: resnet50, vit_b16, yolov8m, sar_segmenter, llm_1b_token. Bands: s_band, x_band, ka_band, optical. All overridable.

Scope

First-order feasibility triage — for architecture trade studies, go/no-go calls and sanity-checking orbital compute claims. Not a substitute for detailed mission design: it assumes circular LEO, worst-case (β=0) eclipse geometry, an isothermal radiator, and steady-state operation.

License

Apache-2.0