Uncertainty modelling in reliable preliminary space mission design

In the early phase of the design of a space mission, it is generally desirable to investigate as many feasible alternative solutions as possible. At this particular stage, an insufficient consideration for uncertainty would lead to a wrong decision on the feasibility of the mission. Traditionally a system margin approach is used in order to take into account the inherent uncertainties within the subsystem budgets. The reliability of the mission is then independently computed in parallel. An iteration process between the solution design and the reliability assessment should finally converge to an acceptable solution. By combining modern statistical methods to model uncertainties and global search techniques for multidisciplinary design, the present work proposes a way to introduce uncertainties in the mission design problem formulation. By minimising the effect of these uncertainties on both constraints and objective functions, while optimising the mission goals, the aim is to increase the reliability of the produced results

Reliable preliminary space mission design: Optimisation under uncertainties in the frame of evidence theory

In the early phase of the design of a space mission it is generally desirable to investigate as many feasible alternative solutions as possible. Traditionally a system margin approach is used in order to estimate the correct value of subsystem budgets. While this is a consolidated and robust approach, it does not give a measure of the reliability of any of the investigated solutions. In addition the mass budget is typically overdimensioned, where a more accurate design could lead to improvements in payload mass. This study will address two principal issues typically associated with the design of a space mission: (i) the effective and efficient generation of preliminary solutions by properly treating their inherent multi-disciplinary elements and (ii) the minimisation of the impact of uncertainties on the overall design, which in turn will lead to an increase in the reliability of the produced results. The representation and treatment of the uncertainties are key aspects of reliable design. An insufficient consideration of uncertainty or an unadapted mathematical representation leads to misunderstanding of the real issues of a design, to delay in the future development of the project or even potentially to its failure. The most common way to deal with uncertainty is the probabilistic approach. However, this theory is not suitable to represent epistemic uncertainties, arising from lack of knowledge. Alternative theories have been recently developed, amongst which we find Evidence Theory which is implemented in this work. Developed by Shafer from Dempster's original work, it is regarded by many as a suitable paradigm to accurately represent uncertainties. Evidence Theory is presented and discussed from an engineering point of view and special attention given to the implementation of this approach. Once mathematically represented, the uncertainties can be taken into account in the design optimisation problem. However, the computational complexity of Evidence Theory can be overwhelming and therefore more efficient ways to solve the reliable design problem are required. Existing methods are considered and improvements developed by the author, to increase the efficiency of the algorithm by making the most of the available data, are proposed and tested. Additionally, a new sample-based approximation technique to tackle large scale problems, is introduced in this thesis. Assuming that the uncertainties are modelled by means of intervals, the cluster approximation method, and especially implemented as a Binary Space Partition, appears to be very well-suited to the task. The performance of the various considered methods to solve the reliable design optimisation problem in the frame of Evidence Theory is tested and analysed. The dependency on the problem characteristics, such as dimensionality, complexity, or multitude of local solutions are carefully scrutinised. The conclusions of these tests enables the author to propose guidelines on how to tackle the problem depending on its specificity. Finally, two examples of preliminary space mission design are used to illustrate how the proposed methodology can be applied. Using realistic and current mission designs, the results show the benefits that could be achieved during the preliminary analyses and feasibility studies of space exploration

Preliminary space mission design under uncertainty

This paper proposes a way to model uncertainties and to introduce them explicitly in the design process of a preliminary space mission. Traditionally, a system margin approach is used in order to take the min to account. In this paper, Evidence Theory is proposed to crystallise the inherent uncertainties. The design process is then formulated as an optimisation under uncertainties(OUU). Three techniques are proposed to solve the OUU problem: (a) an evolutionary multi-objective approach, (b) a step technique consisting of maximising the belief for different levels of performance, and (c) a clustering method that firstly identifies feasible regions.The three methods are applied to the Bepi Colombo mission and their effectiveness at solving the OUU problem are compared

A non-deterministic planner for planetary rovers

This paper presents an integrated planning and scheduling algorithm based on co-evolutionary algorithms. This planner is the core of the delibera-tive level of a three-layer autonomous system (called Wisdom) for planetary rovers. The planner operates at two different levels: at a higher level reallocates and transforms mission goals, based on contingent events, in order to reach scientifically interesting targets and minimize the risk of a failure, at a lower level it generates scheduled sequences of actions that optimize a number of objectives. A particular implementation of co-evolutionary algorithms is used to generate sets of Pareto-optimal plans for every given sequence of goals. Some tests will illustrate the main characteristics of the algorithm

System engineering design optimisation under uncertainty for preliminary space mission

This paper proposes a way to model uncertainties and to introduce them explicitly in the design process of a preliminary space mission. Traditionally, a system margin approach is used in order to take them into account. In this paper, instead, evidence theory is proposed to crystallize the inherent uncertainties. The design process is then formulated as an optimisation under uncertainty (OUU) problem. An evolutionary multi-objective approach is used to solve the OUU. Two formulations of the OUU are analyzed: a bi-objective formulation and a complete belief function optimisation. The BepiColombo mission is used as a test case to investigate the benefits of the proposed method and to compare the two formulations

Preliminary mission analysis for the ESMO mission

The European Student Moon Orbiter, currently at preliminary phase A, is the first lunar spacecraft entirely designed by students, projected for launch in 2011 and to reach a stable lunar orbit around the Moon. This paper presents the trajectory analysis and design performed to accomplish the primary and secondary objectives of the mission. The requirements affecting the mission analysis are listed, including their effects on the target lunar orbit. The Outreach mission would send a spacecraft into a polar orbit around the Moon, the only payload on board being a Narrow Angle Camera. The Science mission will inject a nano satellite in a low, circular, polar orbit around the Moon. Two different transfer options were studied, making use of chemical and solar electric propulsion. Particular emphasis is put on the launch window analysis. In fact, being the spacecraft an auxiliary payload, the trajectory design must be compliant with any launch opportunity inside a three year launch window. Finally corollary studies, such as eclipse durations and ground station visibilities will be covered