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The algorithmic efficiency of Newton-based methods for Free Flight Trajectory Optimization is heavily influenced by the size of the domain of convergence. We provide numerical evidence that the convergence radius is much larger in practice than what the theoretical worst case bounds suggest. The algorithm can be further improved by a convergence-enhancing domain decomposition.

Globally optimal free flight trajectory optimization can be achieved with a combination of discrete and continuous optimization. A key requirement is that Newton's method for continuous optimization converges in a sufficiently large neighborhood around a minimizer. We show in this paper that, under certain assumptions, this is the case.

Two-stage methods addressing continuous shortest path problems start local minimization from discrete shortest paths in a spatial graph. The convergence of such hybrid methods to global minimizers hinges on the discretization error induced by restricting the discrete global optimization to the graph, with corresponding implications on choosing an appropriate graph density. A prime example is flight planning, i.e., the computation of optimal routes in view of flight time and fuel consumption under given weather conditions. Highly efficient discrete shortest path algorithms exist and can be used directly for computing starting points for locally convergent optimal control methods. We derive a priori and localized error bounds for the flight time of discrete paths relative to the optimal continuous trajectory, in terms of the graph density and the given wind field. These bounds allow designing graphs with an optimal local connectivity structure. The properties of the bounds are illustrated on a set of benchmark problems. It turns out that localization improves the error bound by four orders of magnitude, but still leaves ample opportunities for tighter error bounds by a posteriori estimators.

The potential of mean force (PMF) between two nano crystals (NCs) represents an effective interaction potential that can be used to study the assembly of NCs to various superstructures. For a given temperature, the effective interaction is obtained best from molecular dynamics simulations. Based on a density functional approach, this study proposes three methods of predicting the PMF for any temperature based on a single molecular dynamics simulation for one temperature. The three methods construct the PMF by considering the ligands as an ideal gas, as hard-sphere chains, or as Lennard-Jones interaction sites. To apply this methodology, the density of the interaction centers must be extracted from the simulation data. For the ideal gas model, a straightforward sampling procedure with a fixed lattice in space leads to free energies that are too large in order to consistently explain the simulation data for different temperatures. Naive sampling does not account for the small momenta added to the NCs when coupled to a thermostat. A method is proposed that corrects for the unphysical steps during the simulation. The ideal gas contribution computed for the corrected density is significantly smaller than the one obtained from naive sampling and can thus explain the temperature dependence of the PMF correctly. For the hard-sphere chain model, where a weighted density is used, the correction of the particle density is not essential. However, the PMF calculated based on the corrected density confirms our approach. All three models predict PMF curves in very good agreement with simulation results, but they differ in the number of input parameters and the computational effort. Based on the modeling results, we predict the existence of an additional attractive force at small distances of the NCs - a depletion force.