Genetic algorithm; Interstellar exploration; Interstellar travel; Multi-target mission; Optimization; Spaceflight; Exploration strategies; Mission design; Multi-targets; Optimal strategies; Optimisations; Star model; Aerospace Engineering; Astronomy and Astrophysics; Geophysics; Atmospheric Science; Space and Planetary Science; Earth and Planetary Sciences (all); Multi -target mission
Abstract :
[en] In the past decade, the discovery of exoplanets has sparked new interests in the idea of interstellar travel and exploration. Despite various proposals for probe concepts and relevant technologies, there is a lack of extensive literature on viable exploration strategies for journeys beyond a single star system. Such exploration strategies might not only have implications on technology development strategies for achieving interstellar exploration but could also enrich existing models for galactic exploration feeding into solutions of the Fermi Paradox. This article presents optimal strategies for the exploration of a large number of near-by stars, using a dedicated, novel methodology, which sets it apart from existing literature: For the first time, the mission design problem of interstellar exploration is redefined as a bi-objective multi-vehicle open routing problem with profits. It is tackled by an adapted hybrid multi-objective genetic algorithm, which is further improved and modified according to the problem characteristics (e. g. large search space). The overall mission model assumes probes traveling on straight trajectories, utilizing flybys, and maintaining an average velocity of 10% of the speed of light. Surpassing prior research that typically relies on statistical models or restricted star data, the star models are founded on the second Gaia data release (Gaia DR2), which represents the most extensive star catalogue to the date of this study and is employed for the first time in the context of interstellar exploration. The resulting star model contains a maximum of 10,000 stars within a spherical region around Sol, covering a distance of 110 light years. It is found, that the number of explored stars J1 scales with mission duration J2 and probe number m according to J1∼J2m0.66, which provides an initial guidance for future interstellar mission design. Furthermore, the routes and selection of stars vary depending on the number of probes used: When conducting missions with a large number of probes, stars in close proximity to the Solar System are given more focus. On the other hand, missions with a small number of probes include more distant stars to facilitate shorter transfers along the route. Based on these findings, the following recommendations for interstellar exploration strategies can be drawn: When energy resources such as fuel reserves are scarce and the exploration mission is not limited to nearby stars, low probe numbers are more efficient. In contrast, high probe numbers enable faster exploration of nearby stars but involve less resource-efficient transfers, making them a suitable option for small, remotely propelled probe concepts. To address crowding effects in high probe number missions, swarm-based probe concepts are recommended based on the scaling law characteristics derived.
Disciplines :
Aerospace & aeronautics engineering
Author, co-author :
Lebert, Johannes; Technische Universität München, München, Germany
HEIN, Andreas ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SPASYS ; Initiative for Interstellar Studies (i4is), London, United Kingdom
Dziura, Martin; Technische Universität München, München, Germany
External co-authors :
yes
Language :
English
Title :
Optimal strategies for the exploration of near-by stars
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