This lecture discusses models for predicting the evolution of space debris. Simple "particles-in-a-box" models are transparent but lack accuracy and flexibility. More complex "evolutionary" models are needed to answer important questions by simulating individual objects and their interactions over time while accounting for uncertainties. Key properties like object types and orbits must be included to predict the effects of mitigation measures and new operations on the debris environment. Monte Carlo simulation is introduced as the approach used by evolutionary models.

1. Space Environment
Lecture 38 – Space Debris (Vol. 4)
Simple to complex
Professor Hugh Lewis
SESA3038 Space Environment

2. Overview of lecture 38
• In this lecture we look at the advantages and disadvantages of the simple
(systems/PIB) models we introduced last time, addressing their
transparency, accuracy and flexibility
• We then introduce the concept of ‘evolutionary’ models that include the
key heterogeneities needed to answer important questions about the
evolution of the space debris population and objects in it
– We identify some of the key properties needed in these models
Space Environment – Space Debris (Vol. 4)

3. Space debris Space Environment – Space Debris (Vol. 4)
• The space debris environment: update
• Space surveillance/measurement of space debris
• Impacts on space operations
• Modelling of space debris
• Environment/evolutionary modelling
• Impact modelling
• Breakup modelling
• Re-entry modelling
• On-ground risk modelling
• Clean Space
Volume 4 (Week 8)

4. Particles-in-a-box models Space Environment – Space Debris (Vol. 4)
Advantages:
• Fast
• Easy analysis (equilibria, stability)
• Can capture the key characteristics of the debris environment
– Number of objects
– Number of collisions
Disadvantages:
• Assumes objects can interact with all other objects in the same
box
• Assumes objects in a population have the same characteristics
– Size, mass, area, collision probability, collisions
– Behaviour is identical
• Do not generally take account of inclination or eccentricity
• Adding ‘boxes’ and species adds complexity/opacity

5. Our simple PIB model
This simple model…
• Is a useful tool that explains how our system of objects (the debris population)
will behave
• Can be implemented/solved easily without expensive computer hardware or
software (e.g. we can use Microsoft Excel)
But…
• Cannot be easily adapted or modified
• Doesn’t capture key heterogeneities that are needed to answer important
research questions, e.g.:
• “What can we do to reduce the amount of debris?”
• “What mitigation measures are the most cost-effective?”
Space Environment – Space Debris (Vol. 4)

6. Our simple PIB model
So, our model is TRANSPARENT but not ACCURATE or sufficiently FLEXIBLE
How can we increase ACCURACY and FLEXIBILITY?
• Include important heterogeneities
• Different object types and properties
• Different object behaviours depending on the phase of their life
What problems might there be?
• Parameterisation: if we add more complexity, is there information available to
us for parameterising our model?
• Computation: if we add more complexity, will Microsoft Excel continue to be an
appropriate tool for our simulations?
Space Environment – Space Debris (Vol. 4)

7. Evolutionary models Space Environment – Space Debris (Vol. 4)
We want to be able to understand the following (and more):
• How do mitigation measures influence the debris environment?
• How effective are mitigation measures?
• Are there any negative effects of mitigation measures?
• Can mitigation measures be focused on particular objects/species?
• What objects should be removed?
• Are there any negative effects of debris removal?
• How will new space systems influence the debris environment?
• How will new space systems affect other space users?
• How will new space operations (e.g. on-orbit servicing) influence the debris environment?
Can’t answer these questions easily with PIB models

8. Evolutionary models Space Environment – Space Debris (Vol. 4)
To answer these questions:
• We need to simulate individual objects
• Size, mass, area
• Launch (time)
• Orbit and orbit evolution (including state uncertainty)
• Concept of operations – CONOPS
• We need to simulate the interactions (e.g. collisions) between these
individual objects
Adds complexity and computational challenges

9. Evolutionary models Space Environment – Space Debris (Vol. 4)
The simulation also needs to take account of uncertainty:
• Future solar activity  atmospheric drag  state uncertainty
• Future launch activity
• Future explosions
• Collision geometry
– ‘Body-to-body’, appendage, ‘glancing’
• Fragmentation
– Number of fragments
– Characteristics of fragments
• Others?
Monte Carlo Simulation

10. Recap of lecture 38
• In this lecture we identified some of the advantages and disadvantages of
the simple PIB models we introduced last time, and related these to the
topics of model transparency, accuracy and flexibility
• We then introduced ‘evolutionary’ models and highlighted the key
heterogeneities needed to answer important questions about the evolution
of the space debris population and objects in it
• In the next couple of lectures we introduce the Monte Carlo simulation
approach that is used by these evolutionary models to capture the
important uncertainties that are inherent to predictions of the future
situation, and look at the issues/limitations arising from this approach
Space Environment – Space Debris (Vol. 4)