How do I set up an orbit simulation platform for maneuver planning and scenario testing across multiple mission versions?

To set up an orbit simulation platform for maneuver planning and scenario testing across multiple mission versions, build it as a modular toolchain rather than a single monolithic simulator.

1) Define the core use cases

Start by separating the platform into these functions:

• Orbit propagation: predict spacecraft state over time
• Maneuver design: apply impulsive or finite burns
• Scenario testing: compare mission versions, contingencies, and assumptions
• Analysis and visualization: plots, telemetry-like outputs, event timelines
• Batch runs / Monte Carlo: run many cases with different parameters

2) Choose the simulation fidelity

Decide what level of physics you need:

• Basic / fast: two-body dynamics, impulsive maneuvers
• Operational: J2 perturbations, drag, SRP, third-body effects
• High fidelity: attitude coupling, detailed force models, finite-burn thrust, numerical ephemerides

A good platform usually supports multiple fidelity levels so early design runs stay fast while final validation is more accurate.

3) Use a modular architecture

A practical structure is:

• Mission definition layer
  • spacecraft properties
  • orbit elements or ephemeris
  • constraints, goals, and version metadata
• Dynamics engine
  • orbit propagation
  • force models
  • maneuver execution
• Scenario manager
  • loads mission version A/B/C
  • applies assumptions and events
  • manages branches and contingencies
• Analysis layer
  • orbit differences
  • fuel usage
  • pointing/time-window checks
  • access/eclipse/interference metrics
• UI / API layer
  • web dashboard, CLI, or notebooks
  • upload mission configs
  • launch runs and retrieve results

4) Represent each mission version cleanly

For multiple mission versions, treat each version as a configuration package:

• spacecraft mass, area, drag coefficient, thrust limits
• initial orbit state
• maneuver plan
• environment settings
• timeline of events
• constraints and success criteria

Store these in version-controlled files such as:

• YAML / JSON for configuration
• Git tags or release IDs for mission versions
• database records for run history and outputs

Example structure:

missions/
  mission_A/
    config.yaml
    maneuvers.yaml
    constraints.yaml
  mission_B/
    config.yaml
    maneuvers.yaml
    constraints.yaml

5) Build a maneuver planning workflow

Support both planning and evaluation:

1. Initialize orbit state
2. Define target condition
   • altitude, RAAN, LTAN, phase, relative position, etc.
3. Compute maneuver
   • Hohmann, phasing, plane change, station-keeping, rendezvous
4. Propagate after maneuver
5. Check constraints
   • delta-v, fuel, eclipse, visibility, collision avoidance
6. Iterate

If needed, include an optimizer to find burn time, direction, and magnitude.

6) Support scenario testing and branching

This is essential for mission version comparison.

Include capabilities to:

• branch from a baseline mission
• perturb inputs
• swap force models
• inject failures
• test delayed burns or missed maneuvers
• simulate alternative launch dates or spacecraft states

Use a run identifier and scenario tree:

• baseline
• baseline_drag_high
• baseline_burn_delayed_30min
• mission_B_with_fuel_margin

7) Make outputs comparable across versions

Standardize outputs so every run produces the same artifacts:

• propagated ephemeris
• maneuver log
• fuel consumption
• event timeline
• constraint violations
• plots and summary tables

Then compare runs with:

• state deltas
• Δv deltas
• time-to-target differences
• resource margins
• pass/fail against requirements

8) Pick a software stack

Common options:

• Python for orchestration and analysis
• Orekit for high-quality orbital mechanics
• GMAT for mission analysis
• STK for scenario visualization and access analysis
• NumPy/SciPy for custom optimization and propagation
• PostgreSQL/SQLite for run tracking
• Docker for reproducible execution

A common pattern is:

• Python orchestrates
• Orekit/GMAT does the orbital math
• Database stores configurations and results
• Front end displays scenario comparisons

9) Include reproducibility features

To support multiple versions safely:

• version every input file
• log software version and force model settings
• capture random seeds for Monte Carlo
• archive run artifacts
• create immutable run IDs

This lets you reproduce any planning result later.

10) Add validation and test cases

Before trusting the platform, validate it with:

• analytical orbit cases
• known maneuver examples
• cross-checks against GMAT/STK/Orekit
• regression tests for each mission version
• boundary tests for high drag, low fuel, or missed burns

11) Recommended implementation path

A good phased approach:

Phase 1: MVP

• two-body propagation
• impulsive burns
• YAML mission configs
• simple plotting
• compare two mission versions

Phase 2: Operational

• J2, drag, SRP
• maneuver windows and constraints
• scenario branching
• batch simulations

Phase 3: Advanced

• finite-burn modeling
• optimization
• Monte Carlo uncertainty
• web dashboard and database-backed mission history

12) Minimal architecture example

[Config Repo] -> [Scenario Manager] -> [Propagation Engine] -> [Analysis] -> [Results DB/UI]

13) Practical recommendation

If you want something robust without reinventing everything:

• use Orekit for dynamics
• use Python for scenario orchestration
• store mission versions in Git + YAML
• store outputs in SQLite/PostgreSQL
• add a small web UI or notebook-based interface for planning reviews

If you want, I can also provide:

1. a reference architecture diagram,
2. a sample YAML mission config, or
3. a Python/Orekit starter template for this platform.