Ascent trajectory optimisation for a single-stage-to-orbit vehicle with hybrid propulsion

This paper addresses the design of ascent trajectories for a hybrid-engine, high performance, unmanned, single-stage-to-orbit vehicle for payload deployment into low Earth orbit. A hybrid optimisation technique that couples a population-based, stochastic algorithm with a deterministic, gradient-based technique is used to maximize the nal vehicle mass in low Earth orbit after accounting for operational constraints on the dynamic pressure, Mach number and maximum axial and normal accelerations. The control search space is first explored by the population-based algorithm, which uses a single shooting method to evaluate the performance of candidate solutions. The resultant optimal control law and corresponding trajectory are then further refined by a direct collocation method based on finite elements in time. Two distinct operational phases, one using an air-breathing propulsion mode and the second using rocket propulsion, are considered. The presence of uncertainties in the atmospheric and vehicle aerodynamic models are considered in order to quantify their effect on the performance of the vehicle. Firstly, the deterministic optimal control law is re-integrated after introducing uncertainties into the models. The proximity of the final solutions to the target states are analysed statistically. A second analysis is then performed, aimed at determining the best performance of the vehicle when these uncertainties are included directly in the optimisation. The statistical analysis of the results obtained are summarized by an expectancy curve which represents the probable vehicle performance as a function of the uncertain system parameters. This analysis can be used during the preliminary phase of design to yield valuable insights into the robustness of the performance of the vehicle to uncertainties in the specification of its parameters

Stochastic simulation framework for the Limit Order Book using liquidity motivated agents

In this paper we develop a new form of agent-based model for limit order books based on heterogeneous trading agents, whose motivations are liquidity driven. These agents are abstractions of real market participants, expressed in a stochastic model framework. We develop an efficient way to perform statistical calibration of the model parameters on Level 2 limit order book data from Chi-X, based on a combination of indirect inference and multi-objective optimisation. We then demonstrate how such an agent-based modelling framework can be of use in testing exchange regulations, as well as informing brokerage decisions and other trading based scenarios

Half a billion simulations: evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model

Multi-agent geographical models integrate very large numbers of spatial interactions. In order to validate those models large amount of computing is necessary for their simulation and calibration. Here a new data processing chain including an automated calibration procedure is experimented on a computational grid using evolutionary algorithms. This is applied for the first time to a geographical model designed to simulate the evolution of an early urban settlement system. The method enables us to reduce the computing time and provides robust results. Using this method, we identify several parameter settings that minimise three objective functions that quantify how closely the model results match a reference pattern. As the values of each parameter in different settings are very close, this estimation considerably reduces the initial possible domain of variation of the parameters. The model is thus a useful tool for further multiple applications on empirical historical situations

The management of intelligence-assisted finite element analysis technology

Artificial Intelligence (AI) approaches to Finite Element Analysis (FEA), have had tentative degrees of success over the last few years and some authors have argued that effective FEA can help in the manufacture reliability and safety aspects of engineered artefacts. The author of this paper reviews how such AI techniques have been applied and in this light, the author then uses a Fuzzy Cognitive Mapping (FCM), to develop a framework for the management of intelligence-assisted FEA