Low-Earth Orbit space has become progressively more contested in recent years, mostly due to fast-paced advancements in space technology enabling easier access to it. Commercial satellites, often providing mass-market services in the form of constellations, represent one of the largest categories of resident space objects, contributing to the overall congestion of this highly strategical orbital regime.
Not only does this situation affect satellite operations, but also the deployment and mission analysis of any new satellite, leading to the inclusion of conjunction assessment and simulation as a crucial part of the process. 
As regards preliminary mission analysis, ESA MASTER and ESA DRAMA are often used as tools to provide a reliable overall picture of the environment a specific mission has to be resilient to, also by reproducing its effects on the target. MASTER models the flux of collisions by testing a complex representative population of resident space objects against a volume or a specific orbit. As for DRAMA, on the other hand, it is composed of several tools, some of them related to conjunction analysis and mitigation (e.g., ARES for prediction of collision avoidance effort and MIDA for damage assessment), some others linked to different aspects of space traffic management (e.g., CROC for cross-section computation and SARA for re-entry analysis).
The current work took place in the frame of a preliminary mission analysis study performed for the Commandement de l’espace to investigate the consequences of the space environment on a future LEO mission. Consequently, the main focus lies on using the ARES tool from DRAMA in order to assess the possible collision avoidance effort that can be expected throughout the operational life span of the asset, given a reference MASTER population. There are multiple levels of detail that ARES can provide as output, from the average velocity expense related to the expected collision avoidance maneuvers to the corresponding propellant mass (by choosing a thruster type from the list provided in the software). In this specific case, this effort consists of an expected number of predicted collision avoidance maneuvers and corresponding average velocity expense over a mission-specific time frame. 
The main limitation to what the current ARES version can take into account, given how the space object population is evolving in LEO, is that custom constellations cannot be included in the preloaded population that ARES employs to compute collision avoidance effort. They can instead be injected into a reference population as far as MASTER is concerned.
The main novelty of the study thus lies in a method devised to integrate this missing contribution to ARES analyses, customizing the 3 standard scenarios foreseen by ESA MASTER (business as usual, intermediate mitigation, full mitigation).
The technique is based on the manipulation of the basic outputs provided by MASTER, i.e. collision events counts expressed as 2D and 3D fluxes over a set of variables. The aim is that of retrieving the approximate number of additional conjunction events due to custom constellations only. By using a miss distance-based method of conjunction detection, the flux of objects impacting the target is converted to a number of conjunction over a given period of time. This is achieved by properly integrating the flux over the desired time span and over a surface that is no more related to the asset’s physical properties required by MASTER, but representing instead the surface of a control volume used to detect conjunctions.
To take into account combined limitations from sensors and targets, usually preventing some conjunctions from being detected, the same simplified radar equation used in ARES is employed to reduce the flux to the one complying with this limitation. To accomplish this, the flux over the target diameter and semi-major axis is integrated over the radar equation non-compliance region to understand how much of it is lost.
Thanks to these two steps, an augmented number of collision avoidance maneuvers and their corresponding average expense can be retrieved by simply post processing ARES outputs, thus avoiding the computational costs involved in running it multiple times.
The method was tested on a realistic use-case scenario involving an Earth-observation mission spanning 10 years, showing promising results in terms of both sensitivity to any combination of custom constellations, and flexibility on worst to best-case scenario definitions.