Tradespace exploration applied to an interplanetary CubeSat mission

The work presented in this paper can be framed in the field of operational research. The paper deals with the early stages of design (phase 0/A) of a new space mission that includes innovative technologies and novel system concepts. The very early phases of the system lifecycle are of paramount importance for the cost of the system and of the enterprise as a whole. The pre-conceptual and conceptual design phases of a complex system are critical to the system’s lifecycle and crucial for the success of the system and its intended mission. Up to 70% of the costs are locked-in during these phases, and most of the decisions taken at this stage will heavily affect the entire life of the system. On the other hand, a very limited amount of resources is allocated for the completion of these delicate phases. Moreover, the information available at this stage about the future system is still very limited, and affected by high uncertainty. Typically, the following problems can be identified, among others: 1. a priori design selections without analysis or consideration of other options 2. inadequate technical feasibility studies in early design stages 3. pursuit of a detailed design without understanding the effects on the global system. All these problems are particularly serious when we deal with the definition of brand-new systems, for which no or little heritage is available. This is the case of micro- and nano-satellites for science missions to deep space, such as the case of interplanetary CubeSats missions. The need for methods to support the engineering team during the early stage of the design of these systems emerged. Some theories can be applied for the purpose of improving the overall quality of a mission feasibility study, in terms of both quantity and accuracy of the output. Among these, multi attribute utility theory and multi attribute tradespace exploration, along with epoch-era analysis can support the design process effectively. CubeSat missions represent a good test case for the application of these techniques. CubeSats are a growing reality, and an increasing number of available components can be considered during the design. In particular, many commercial off-the-shelf items are available, but also space-qualified components are beginning to appear on the market. In addition, exploiting the flexibility of distributed systems, several different mission concepts can be defined for this type of platforms. Due to this, the design space is extremely vast, and intelligent exploration methods are needed to select the solutions that provide the highest utility. This paper presents an algorithm for tradespace exploration, in which the methodology of multi-attribute utility theory is integrated with an optimal exploration of the design-space performed with genetic algorithms. The proposed methodology is applied to the case of an interplanetary CubeSat science mission to an asteroid. The paper describes the definition of the design vector, the tuning of the genetic algorithm, and shows the results of the exploration process by analysing one of the optimal solutions found by the algorithm.