ESA GNC Conference Papers Repository

The work presented in this paper is part of the European H2020 ASCenSIon program. In this context, the overall purpose of this research is the definition and development of a Mission Analysis and GNC missionisation tool for autonomous re-entry vehicles. In recent years, space agencies and private firms are investing in reusable spacecraft and launch vehicles to make space access and in-orbit studies more economically and environmentally sustainable. The re-flight capability, requested by a reusable space system, motivates the need for a dedicated missionisation tool. The objective of missionisation is, indeed, the minimization of the tailoring effort during the mission design phase of each flight. One approach, which can address this aim, is to provide solutions for re-entry vehicles that are qualified for multiple missions. For this reason, a crucial step for missionisation is the computation of the set of missions that a vehicle can perform concerning its design parameters. The focus of this paper is on the preliminary mission design and the evaluation of the mission capabilities of a winged re-entry vehicle by means Multidisciplinary Design Optimization (MDO) approach available within the proposed MA and GNC missionisation tool. One of the key challenges in missionizing a re-entry vehicle is the need to balance the trade-offs between a broad set of system and mission requirements and constraints to obtain robust trajectories. The missionisation in this context is, indeed, a complex and challenging task that requires a thorough understanding of the multidisciplinary aspects of the system and mission design. For this reason, the paper reports the Multidisciplinary Design Analysis (MDA) framework with an overview of the set of disciplines embedded in the tool. For this scenario, the disciplines deal with weight, geometry, and aerodynamic estimation, evaluation of the static longitudinal equilibrium and stability, computation of the aerothermal-mechanical domain, and range capability. The disciplines numerically quantify the related performance and assess the feasible space domain concerning the design variables. Then, the MDA problem is exploited by the optimization routine to optimize the design variables while maximizing targeted performance. The MDO is a crucial feature because allows for the simultaneous optimization of multiple design parameters and objectives to ensure the overall performance and feasibility of the re-entry vehicle. By using this technique, the goal is to evaluate different design options to perform trade-off studies and identify the optimal design, especially when a multi-objectives approach is considered and nondominated optimal Pareto solutions are obtained. Within the MDO process, indeed, the solution space domain is explored through the variation of the design parameters. In this research, metamodeling techniques have been adopted to reduce the computational cost. One of the main challenges faced by MDO concerns, indeed, the efficiency in solving the optimization problem due to the relatively expensive evaluation of the MDA. In particular, in this work, the MDA has been integrated with the DEIMOS Space proprietary tool (EDLS/GNC Sizing and Analysis Too) ESAT, which employs Radial Basis Functions (RBFs) to create a surrogate model of the original problem. The metamodel is built by evaluating the MDA in different design points (samples). Consequently, the metamodel predicts the performance at any point of the domain space by interpolating the outputs of the known points. ESAT, then, uses the metamodel to perform the optimization and create performance maps. One of the drawbacks of adopting a surrogate model is given by the flaw in the accuracy of the solution, especially if a large number of design parameters is taken into account and a limited number of sample points is used. To validate the optimization process, the obtained results for the scenario analysed in this paper are compared with the outcomes achieved by solving the original problem with a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. The solutions are critically discussed both in terms of accuracy and computational time. The results obtained within the case study reported in this paper show that the proposed multidisciplinary tool embedding an MDO process is a powerful tool for enhancing the missionisation of re-entry vehicles. In particular, the MDA-MDO framework is essential for efficiently missionizing re-entry vehicles, providing a solution qualified for multiple missions. The MDO, indeed, handles the trade-offs between several solutions by considering a broad set of mission and system requirements, and it ensures that the final design is optimized across all relevant disciplines.