Coupled orbit and attitude dynamics of a reconfigurable spacecraft with solar radiation pressure

This work investigates the orbital and attitude dynamics of future reconfigurable multi-panel solar sails able to change their shape during a mission. This can be enabled either by changing the relative position of the individual panels, or by using articulated mechanisms and deployable, retractable and/or inflatable structures. Such a model introduces the concept of modular spacecraft of variable morphology to large gossamer spacecraft. However, this joint concept is complex in nature and requires equations for coupled orbit/attitude dynamics. Therefore, as a starting point, the system is modelled as a rigid-body dumbbell consisting of two tip masses connected by a rigid, massless panel. The system is subjected to a central gravitational force field under consideration of solar radiation pressure forces. Therefore, we assign reflectivity coefficients to the tip masses and a high area-to-mass ratio. An analytical Hamiltonian approach is used to describe the planar motion of the system in Sun-centred Keplerian and non-Keplerian circular orbits. The stability and controllability of the system is enabled through changing the reflectivity coefficients, for example through the use of electro-chromic coating on its surface. The creation of artificial unstable equilibria of the system due to the presence of solar radiation pressure and heteroclinic connections between the equilibria are investigated. We further derive a constraint for the solar radiation pressure forces to maintain the system on a circular Sun-centred orbit. It is planned that the structure is eventually capable of reconfiguring between the equilibria by a minimum actuation effort

Probing defects and correlations in the hydrogen-bond network of ab initio water

The hydrogen-bond network of water is characterized by the presence of coordination defects relative to the ideal tetrahedral network of ice, whose fluctuations determine the static and time-dependent properties of the liquid. Because of topological constraints, such defects do not come alone, but are highly correlated coming in a plethora of different pairs. Here we discuss in detail such correlations in the case of ab initio water models and show that they have interesting similarities to regular and defective solid phases of water. Although defect correlations involve deviations from idealized tetrahedrality, they can still be regarded as weaker hydrogen bonds that retain a high degree of directionality. We also investigate how the structure and population of coordination defects is affected by approximations to the inter-atomic potential, finding that in most cases, the qualitative features of the hydrogen bond network are remarkably robust

Using Gaussian Process Regression to Simulate the Vibrational Raman Spectra of Molecular Crystals

Vibrational properties of molecular crystals are constantly used as structural fingerprints, in order to identify both the chemical nature and the structural arrangement of molecules. The simulation of these properties is typically very costly, especially when dealing with response properties of materials to e.g. electric fields, which require a good description of the perturbed electronic density. In this work, we use Gaussian process regression (GPR) to predict the static polarizability and dielectric susceptibility of molecules and molecular crystals. We combine this framework with ab initio molecular dynamics to predict their anharmonic vibrational Raman spectra. We stress the importance of data representation, symmetry, and locality, by comparing the performance of different flavors of GPR. In particular, we show the advantages of using a recently developed symmetry-adapted version of GPR. As an examplary application, we choose Paracetamol as an isolated molecule and in different crystal forms. We obtain accurate vibrational Raman spectra in all cases with fewer than 1000 training points, and obtain improvements when using a GPR trained on the molecular monomer as a baseline for the crystal GPR models. Finally, we show that our methodology is transferable across polymorphic forms: we can train the model on data for one structure, and still be able to accurately predict the spectrum for a second polymorph. This procedure provides an independent route to access electronic structure properties when performing force-evaluations on empirical force-fields or machine-learned potential energy surfaces

Shape-changing solar sails for novel mission applications

In order to increase the range of potential mission applications of solar sail technology, this paper introduces the concepts of shape change and continuously variable optical properties to large gossamer spacecraft. Merging the two concepts leads to the idea of solar sails as multi-functional platforms that can have potential benefits over conventional solar sails by delivering additional key mission functions such as power collection, sensing and communications. To this aim, the paper investigates the static deflection of a thin inelastic circular sail film with a variable surface reflectivity distribution. The sail film is modelled as a single surface framed by a rigid supporting hoop structure. When changing the reflectivity coefficient across the sail surface, the forces acting on the sail can be controlled without changing the incidence angle relative to the Sun. In addition, by assigning an appropriate reflectivity function across the sail, the load distribution due to solar radiation pressure can also be manipulated to control the billowing of the film. By an appropriate choice of reflectivity across the sail, specific geometries can be generated, such as a parabolic reflector, thus enabling a multi-functional sail. This novel concept of optical reconfiguration can potentially extend solar sail mission applications

Ab initio Modelling of the Early Stages of Precipitation in Al-6000 Alloys

Age hardening induced by the formation of (semi)-coherent precipitate phases is crucial for the processing and final properties of the widely used Al-6000 alloys. Early stages of precipitation are particularly important from the fundamental and technological side, but are still far from being fully understood. Here, an analysis of the energetics of nanometric precipitates of the meta-stable $\beta''$ phases is performed, identifying the bulk, elastic strain and interface energies that contribute to the stability of a nucleating cluster. Results show that needle-shape precipitates are unstable to growth even at the smallest size $\beta''$ formula unit, i.e. there is no energy barrier to growth. The small differences between different compositions points toward the need for the study of possible precipitate/matrix interface reconstruction. A classical semi-quantitative nucleation theory approach including elastic strain energy captures the trends in precipitate energy versus size and composition. This validates the use of mesoscale models to assess stability and interactions of precipitates. Studies of smaller 3d clusters also show stability relative to the solid solution state, indicating that the early stages of precipitation may be diffusion-limited. Overall, these results demonstrate the important interplay among composition-dependent bulk, interface, and elastic strain energies in determining nanoscale precipitate stability and growth

The Gibbs free energy of homogeneous nucleation: from atomistic nuclei to the planar limit

In this paper we discuss how the information contained in atomistic simulations of homogeneous nucleation should be used when fitting the parameters in macroscopic nucleation models. We show how the number of solid and liquid atoms in such simulations can be determined unambiguously by using a Gibbs dividing surface and how the free energy as a function of the number of solid atoms in the nucleus can thus be extracted. We then show that the parameters of a model based on classical nucleation theory can be fit using the information contained in these free-energy profiles but that the parameters in such models are highly correlated. This correlation is unfortunate as it ensures that small errors in the computed free energy surface can give rise to large errors in the extrapolated properties of the fitted model. To resolve this problem we thus propose a method for fitting macroscopic nucleation models that uses simulations of planar interfaces and simulations of three-dimensional nuclei in tandem. We show that when the parameters of the macroscopic model are fitted in this way the numerical errors for the final fitted model are smaller and that the extrapolated predictions for large nuclei are thus more reliable

The inefficiency of re-weighted sampling and the curse of system size in high order path integration

Computing averages over a target probability density by statistical re-weighting of a set of samples with a different distribution is a strategy which is commonly adopted in fields as diverse as atomistic simulation and finance. Here we present a very general analysis of the accuracy and efficiency of this approach, highlighting some of its weaknesses. We then give an example of how our results can be used, specifically to assess the feasibility of high-order path integral methods. We demonstrate that the most promising of these techniques -- which is based on re-weighted sampling -- is bound to fail as the size of the system is increased, because of the exponential growth of the statistical uncertainty in the re-weighted average

Optimisation of Low-Thrust and Hybrid Earth-Moon Transfers

This paper presents an optimization procedure to generate fast and low-∆v Earth-Moon transfer trajectories, by exploiting the multi-body dynamics of the Sun-Earth-Moon system. Ideal (first-guess) trajectories are generated at first, using two coupled planar circular restricted three-body problems, one representing the Earth-Moon system, and one representing the Sun-Earth. The trajectories consist of a first ballistic arc in the Sun-Earth system, and a second ballistic arc in the Earth-Moon system. The two are connected at a patching point at one end (with an instantaneous ∆v), and they are bounded at Earth and Moon respectively at the other end. Families of these trajectories are found by means of an evolutionary optimization method. Subsequently, they are used as first-guess for solving an optimal control problem, in which the full three-dimensional 4-body problem is introduced and the patching point is set free. The objective of the optimisation is to reduce the total ∆v, and the time of flight, together with introducing the constraints on the transfer boundary conditions and of the considered propulsion technology. Sets of different optimal trajectories are presented, which represents trade-off options between ∆v and time of flight. These optimal transfers include conventional solar-electric low-thrust and hybrid chemical/solar-electric high/low-thrust, envisaging future spacecraft that can carry both systems. A final comparison is made between the optimal transfers found and only chemical high-thrust optimal solutions retrieved from literature

A self-learning algorithm for biased molecular dynamics

A new self-learning algorithm for accelerated dynamics, reconnaissance metadynamics, is proposed that is able to work with a very large number of collective coordinates. Acceleration of the dynamics is achieved by constructing a bias potential in terms of a patchwork of one-dimensional, locally valid collective coordinates. These collective coordinates are obtained from trajectory analyses so that they adapt to any new features encountered during the simulation. We show how this methodology can be used to enhance sampling in real chemical systems citing examples both from the physics of clusters and from the biological sciences.Comment: 6 pages, 5 figures + 9 pages of supplementary informatio