Fractionated solar power satellite for regional coverage

This paper presents a preliminary analysis of a fractionated solar power satellite system for regional coverage. The fractionated system is composed of a cluster of satellites, in different possible configurations, that concurrently beam energy to the ground through medium power lasers. The paper presents an analysis of the possible orbit solutions that can be adopted to provide power during the night time to local users in different regions of the world. The system is intended to serve mobile stations or local stations that can be hardly accessed by normal power lines or are cut off during disasters. A preliminary system analysis shows that with a limited number of small size satellite local users can be provided with a few kWh of energy every day

Hybrid behavioural-based multi-objective space trajectory optimization

In this chapter we present a hybridization of a stochastic based search approach for multi-objective optimization with a deterministic domain decomposition of the solution space. Prior to the presentation of the algorithm we introduce a general formulation of the optimization problem that is suitable to describe both single and multi-objective problems. The stochastic approach, based on behaviorism, combinedwith the decomposition of the solutions pace was tested on a set of standard multi-objective optimization problems and on a simple but representative case of space trajectory design

Special issue on evolutionary computation in aerospace sciences

The main objective of this special issue is to portray the diverse efforts made in the application of evolutionary computation techniques, or related methods, to aerospace problems. The issue collects articles, written by researchers from around the globe, for a stimulating and synergetic discussion on recent advances in evolutionary methods for the solution of aerospace problems

Multibody dynamics for biologically inspired smart space structure

Structures in space are often just serving a single purpose. By developing a structure which is able to change it properties and adapt to changing environmental conditions, the costs of space missions can be decreased significantly. The design and simulation of such a structure is presented in this paper. The developed structure consists of an array of interconnected cells which are each able to alter their volume due to internal pressure change. By coordinated cell actuation in a specific pattern, the global structure can be deformed to obtain a desired shape. A multibody code was developed which constantly solves the equation of motion with inputs from internal actuation and external perturbation forces. During the inflation and actuation of the structure, the entities of the mass matrix and the stiffness matrix are changed due to changing properties of the cells within the array based on their state and displacement. This paper outlines the principles behind the developed code and gives examples in two dimensional and three dimensional space

Extension of the proximity-quotient control law for low-thrust propulsion

In this paper, the proximity quotient control law, first developed by Petropoulos, is extended to account for both third body effects and solar radiation pressure. The perturbing effect of solar radiation pressure becomes relevant when dealing with solar sails, or large optics in space. Equations for the disturbing acceleration and disturbing potential function were derived for the perturbations, then analyzed to determine the minimum and maximum rate of changes of the Keplerian elements given the thrust vector and true anomaly of the spacecraft. These were then analytically incorporated into the Q-law feedback function. The complete mathematical derivations are presented. The extended Q-law is compared to the fully optimal control law, stemming from optimal control theory, for the same dynamical model. Two missions are used as test cases

Multi agent collaborative search based on Tchebycheff decomposition

This paper presents a novel formulation of Multi Agent Collaborative Search, for multi-objective optimization, based on Tchebycheff decomposition. A population of agents combines heuristics that aim at exploring the search space both globally (social moves) and in a neighborhood of each agent (individualistic moves). In this novel formulation the selection process is based on a combination of Tchebycheff scalarization and Pareto dominance. Furthermore, while in the previous implementation, social actions were applied to the whole population of agents and individualistic actions only to an elite sub-population, in this novel formulation this mechanism is inverted. The novel agent-based algorithm is tested at first on a standard benchmark of difficult problems and then on two specific problems in space trajectory design. Its performance is compared against a number of state-of-the-art multi objective optimization algorithms. The results demonstrate that this novel agent-based search has better performance with respect to its predecessor in a number of cases and converges better than the other state-of-the-art algorithms with a better spreading of the solutions

Impact cratering experiments into highly porous bodies

Asteroids represent both an opportunity and a risk. Their pristine environment captures the early collision evolution of the solar system; while their inherent ground impact potential could result in the mass extinction of life. Amongst the many possibilities of asteroid deflection, kinematic impactors have been theoretically proven to be a promising technique. However, this is primarily based on modelling rocky, brittle bodies. Little experimental consideration has been made for highly porous bodies. Therefore to advance current mitigation scenarios a series of experiments have been conducted. Under an accelerated reference frame this aimed to assess the impact cratering response of highly porous asteroids. All events were examined relative to the increasing levels of porosity and the impact’s resultant morphological profile. This included crater shape and depth, and the ejecta profile. The latter was considered critical in assessing the overall contribution to the momentum enhancement exchange of any kinematic impact event

Robust design of a reentry unmanned space vehicle by multifidelity evolution control

This paper addresses the preliminary robust design of a small-medium scale re-entry unmanned space vehicle. A hybrid optimization technique is proposed that couples an evolutionary multi-objective algorithm with a direct transcription method for optimal control problems. Uncertainties on the aerodynamic forces and vehicle mass are integrated in the design process and the hybrid algorithm searches for geometries that a) minimize the mean value of the maximum heat flux, b) maximize the mean value of the maximum achievable distance, and c) minimize the variance of the maximum heat flux. The evolutionary part handles the system design parameters of the vehicle and the uncertain functions, while the direct transcription method generates optimal control profiles for the re-entry trajectory of each individual of the population. During the optimization process, artificial neural networks are used to approximate the aerodynamic forces required by the direct transcription method. The artificial neural networks are trained and updated by means of a multi-fidelity, evolution control approach

A multidirectional modified Physarum solver for discrete decision making

In this paper, a bio-inspired algorithm able to incrementally grow decision graphs in multiple directions is presented. The heuristic draws inspiration from the behaviour of the slime mould Physarum Polycephalum. In its main vegetative state, the plasmodium, this large single-celled amoeboid organism extends and optimizes a net of veins looking for food. The algorithm is here used to solve classical problems in operations research (symmetric Traveling Salesman and Vehicle Routing Problems). Simulations on selected test cases demonstrate that a multidirectional modied Physarum solver performs better than a unidirectional one. The ability to evaluate decisions from multiple directions enhances the performance of the solver in the construction and selection of optimal decision sequences