Active Debris Removal (ADR) for Mega-constellation Reliability
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Provided that the hazard of space debris in orbit can pose threats to space exploration missions and, thereby, influence the redundancy of Earth observation and telecommunication constellations, this chapter addresses the case for mega-constellation reliability. The space security challenge in this case does not only relate to the regulatory and legal framework thereof but also to the business development of technical solutions for space security. Although the current level of technologies enables active debris removal (ADR), its business applicability remains to be investigated. In this study, a multiparametric mega-constellation model has been developed to take into account orbital motion, coverage, ground communication, reliability, collision risks, and service consumption in the global telecommunication market. The research and simulations performed on the model allowed for the analysis of possible financial metrics (revenue, cash flows, total replenishment cost) of the company who operates the ADR, as well as replacement scenarios and weak points of the mega-constellation. All combined, the chapter provides insights into the market that exists for ADR technologies, by demonstrating the ADR business applicability for mega-constellations.
Since the inception of mega-constellation projects, the space debris problem got worse. Despite the fact that the history of spaceflight has witnessed cases of collisions between operating satellites and space junk, space debris objects have not been seriously considered in the business of aeronautics. Provided that the space telecommunication market utilizing low Earth orbit (LEO) and medium Earth orbit (MEO) becomes larger, the demand for new satellite systems and constellations, respectively, grows. Hence, the density of the satellites and other objects in orbit is growing as well. Eventually, by 2030 the number of the manmade objects in LEO and MEO is expected to grow ten times bigger than it is right now (European Space Operations Centre 2019). This definitely has repercussions for the market and as it constitutes a high financial risk.
Accordingly, this chapter examines the problem of space debris and how to potentially tackle it. The solution to the problem is what has been discussed for at least a decade: active debris removal (ADR), which is based on the mechanical process of returning the space object to the Earth atmosphere. Along with a passive debris removal, which is based on the phenomenon of atmospheric drag, active debris removal is one of the main space debris mitigation methods. The approach of this chapter is based on the premise that a satellite constellation operator and a company, aiming at the development of active space debris removal, could create a mutually beneficial situation. This would allow the operator to lower the risks of collisions, increase the stability and quality of the service, and improve its financial indicators while the ADR company would position itself in the market. At the same time, ADR can be of benefit to all mankind by ensuring the sustainably of the outer space environment.
Additionally, the analysis was based on the creation of a simulation environment that could facilitate in general the analysis of business reports for the whole telecommunication market. In particular, the simulation environment could become a quite effective instrument that enables risk assessment, market benchmarking, market specification, and selection of the appropriate business strategy. Hence, the simulation of in-orbit processes and the assessment of the data links were selected as the methodology of this chapter. For this purpose, the sustainable and stable simulation environment was created with valid and verified models. This environment has included different time scales, operating scenarios, constellation types, and satellite types to estimate financial metrics of the operator. As a final stage, the study applied the simulation environment developed to the specific business study. It was produced for the ADR company working along with the first echelon of the SpaceX Starlink constellation.
The results of the simulations and the results of the analysis showed that the loss of satellite could significantly influence the quality of the service reducing the coverage rate up to 20% and lead to extremely high financial losses for the operator (up to one billion dollars for a half of the lifetime of the satellite). That said and taking into account space technology readiness level and existing and developing business strategies, the ADR could successfully enter the space market and become profitable.
Simulation Model for Satellite Mega-constellation Reliability X
Concurrent Engineering Approach for SpaceX Starlink
SpaceX statements regarding service and project characteristics (SpaceX 2017)
Mass of the satellite, kg
Overall cost of the project, billion US$
Connection speed, MB/s
Service cost, $
<300 per subscription
Reliability Simulation Model for Satellite Constellation
The next step of the reliability assessment was to generate the array of satellite states – the matrix describing the status of the satellite during whole lifetime was “1” for operating, “2” for interrupted, “3” for failed, “4” for being on replacement, and “0” for not working. This provided for a simple and obvious understanding of the status of the satellite during every step of the simulation.
Orbital parameters of the SpaceX NGSO satellite constellation (SpaceX 2017)
Initial deployment (1,600 satellites)
Final deployment (2,825 satellites)
Satellites per plane
The satellites in NGSO are located in circular orbits and evenly distributed according to the Walker constellation design pattern (Larson and Wiley 1992). This allows placing the satellite within the constellation in a way that ensures evenly distributed Earth coverage and avoids possible collision between satellites. The main parameters of the constellation according to the Walker design pattern are the total number of spacecraft T, number of orbital planes P, and phasing parameter F (Larson and Wiley 1992). Due to the lack of information regarding the phasing parameter, it was set equal to one, which is suitable for constellation of this size.
To model the ground-track and Earth coverage of a satellite within the constellation, a projection of the satellite position was made. It was calculated in Earth-centered, Earth-fixed (ECEF), coordinate system to Earth sphere. All calculations of satellite motions made according to the previous paragraph are conducted in inertial reference frame such as Earth-centered inertial (ECI). Therefore, it should be converted to ECEF in order to calculate ground track and coverage. Transformation of coordinates is performed according to the International Earth Rotation and Reference Systems Service (IERS) 2010 conventions (Gérard and Luzum 2010) where such effects as precession and nutation of Earth rotation axis are considered.
- 1.Simulation related parameters:
The length of the simulation period
The size of the timestep of the simulation – which definitely is supposed to be selected corresponding to the parameters of the preprocessed files
- 2.Constellation-related parameters:
Accuracy of the ABGN grid
- 3.Satellite-related parameters:
Antenna field of view
- 4.Spare strategy type:
Two options are available: “none” for no strategy and “lod” for launch-on-demand strategy.
Optimal simulation parameters based on parameter-sensitivity analysis by the authors
CPU count, units
Step size, s
CPU frequency, GHz
Chunk size, steps
Marketing Model for Satellite Mega-constellation
As long as the satellite constellation model was created and it became clear how each satellite in the constellation moves and what was the coverage of each satellite, it became available to assess how each satellite influences the connectivity and, consequently, the company operator’s financial metrics. For that purpose, it was necessary to assess the market and allocate marketing data to the coordinates on the Earth surface.
Obviously, as an understanding of the possible business applications of the ADR is based on the financial metrics of the operators, the marketing analysis and model are taking the most important part in the research. It facilitates the calculation of possible revenue for the constellation through the implementation of economics and marketing.
Population model and market penetration
Verification error for different pricing models, based on the simulation assessments of the authors
Error rate, %
Population Map and Market Penetration
The population data is based on the information prepared by the NASA Socioeconomic Data and Applications Center (SEDAC) (Doxsey-Whitfield et al. 2015) based on the gridded population of the world (GPW). The fourth version of the data is the distribution of the human population across the Earth. The statistical data is generated based on the Earth observations and provides globally consistent data for any type of the researches and studies.
Theoretical three-parameter model describes three sides of the approach of estimation of the amount of the target audience: economic, marketing, and technical.
Agent-Based Ground Network
Considering the output of the economical modeling process, the agent-based ground network (ABGN) has been created. The term ABGN as well as the grid itself was created during the research specifically for the simulation to simplify the process of markets of the Earth-based consumers. The ABGN is the operational set of agents, or in this case subscribers of the Internet service, integrated to the peer-to-peer-linked network distributed by the Earth globe and having a set of parameters, enabling the assessment of the entire system. In the case, when the subscriber is a single user or a household, the agent network could be represented as the grid with number of the subscribers in surrounding area as the nod value.
Position. The positional argument, describing the position of the subscriber (or a set of subscribers) on the Earth globe. The distribution could be random, functional – set up with a function of time, evenly weighted – normally distributed nods of the grid, setting up a single subscriber or number of subscribers in the surrounding area, or single-located, the array of coordinate of the subscribers.
Money capacity. The parameter of money capacity is being calculated based on the amount of the target audience in the nod and the price of the service. This parameter is the permanent base for the calculations of the money flows of the operational company and dynamic coverage methods.
Traffic demand. The parameter is calculated based on the amount of target audience in the nod and the traffic demand in the location per capita. This parameter hardly influences the link budget and dynamic coverage methods.
The agent-based ground network is able to represent not only the large number of users but the single user as well. In other words, this system works with both the business to consumer (B2C) and business to business (B2B) strategy of operation problems. That, in particular, allows to solve the static and dynamic tasks (or a combination of such). This advantage allows the model to be applicable to other scenarios for various companies. The system works on a plug-and-go basis meaning that setting up the type of the ABGN does not require changing code in the core of the simulation.
As soon as the environment is ready, the verification takes place in order to determine the applicability of the model to real scenarios. The validation has been delivered as a two-step process: the first one was the validation of the model itself, performed with running the simulation for the constellation that already exists; the second was the case validation, determining whether the model is applicable to the particular case study.
Commercialization of ADR and Insurance Strategy
According to the baseline of the simulation, the propagation and coverage footprint were calculated. Based on the coverage and reliability model, the overall constellation coverage was calculated. Along with created ABGN model, the possible market coverage of the operator was calculated for the selected satellite constellation formation. The data for each timestep gave an opportunity to understand main financial metrics of the operator (such as positive cash flow from the servicing, income, and operational expenses).
This, consequently, enabled the research group to run some basic assessment scenarios of the telecommunication segment. The environment and the models have been verified using the existing cases of constellations and open information of the company at hand.
Following the assessments made in the beginning of the work, losing the satellite led to several huge impacts on the constellation operational indicators. It can be seen in Fig. 9 that operating with no replenishment strategy could be followed by revenue decrease (around 1.5 mln USD for the 1st year and nearly 1 bln USD for the first 5 years). In Fig. 10, the same data showed in percentage which is up to 6% revenue loss for the 1st year. Figure 11 also shows that the malfunction satellite can also lead to a coverage loss leading to the quality issues.
The assessment showed that active debris removal is critically important for the company-operator as it allows to increase revenues and decrease the risks of collisions. The main result of the outcome analysis is the fact that ADR is not only practically necessary to tackle the challenges of the mega-constellations, but it can also be commercialized.
Based on the research outcomes, a marketing analysis was followed to create a sustainable business strategy for the ADR company. For that purpose, the company was evaluated from a business perspective. The analysis showed the specifics of the business and possible strategy options. Three major business plans were assessed: the “flat insurance,” the “dynamic flat insurance,” and the “pay-as-you-go tariffs.” For each of the strategy, a separate simulation turn was run in order to understand the applicability of the strategy, its profitability, and positives and negatives of its use.
The flat insurance strategy is based on the business plans of the insurance and reinsurance companies providing the full insurance. This includes the complete ADR services, for a fixed reward. After simulating, this plan, however, appeared to be noncompetitive since the reward calculated is quite high and appears to be not advantageous for the operator.
The pay-as-you-go tariff, on the contrary, means to implement the system of rewarding the ADR company every time the satellite replacement takes place. However, in that case, things can get worse for the company itself. Since the satellite failure is highly connected with the reliability of the satellite, it seems that the company’s financial behavior can turn to be unpredictable. This can subsequently lead to investment overlaps and breaks – the incomes of the company are going to be strictly connected to the satellite loss, which is not equally distributed over time – the cash flow becomes “jumping.”
The objective of this chapter was to analyze a business case connected with an active debris removal (ADR) process. It aimed to understand its applicability and prove the market existence for ADR. In order to pursue this investigation, the analysis was based on the creation of a simulation environment for mega-constellations, the Starlink SpaceX first echelon constellation. The results of the study give insights into the success of an ADR company, being interconnected with the role of operators of these large constellations.
[Can you summarize here a bit more about the results? I think they are very important to highlight in relation to the mega-constellation challenge].
Perform a study for different constellation with different orbital parameters.
Perform a study for different altitudes (MEO and GSO).
Perform a study for other markets, such as Global Navigation Satellite System (GNSS), Earth observation (EO), or defense applications.
Apply different optimization methods.
Assess the replenishment times and the constellation sizing factors.
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