Study of Helium II heat transport phenomena in superconducting rutherford type cables

This thesis is a study of how heat is transported in non-steady-state conditions from a superconducting Rutherford cable to a bath of superfluid helium (He II). The same type of superconducting cable is used in the dipole magnets of the Large Hadron Collider (LHC). The dipole magnets of the LHC are immersed in a bath of He II at 1.9 K. At this temperature helium has an extremely high thermal conductivity. During operation, heat needs to be efficiently extracted from the dipole magnets to keep their superconducting state. The thermal stability of the magnets is crucial for the operation of the LHC, therefore it is necessary to understand how heat is transported from the superconducting cables to the He II bath. In He II the heat transfer can be described by the Landau regime or by the Gorter-Mellink regime, depending on the heat flux. In this thesis both measurements and numerical simulation have been performed to study the heat transfer in the two regimes. A temperature increase of 8 2 mK of the superconducting cables was successfully measured experimentally. A new numerical model that covers the two heat transfer regimes has been developed. The numerical model has been validated by comparison with existing experimental data. A comparison is made between the measurements and the numerical results obtained with the developed model.

A public transport bus assignment problem: parallel metaheuristics assessment

Combinatorial Optimization Problems occur in a wide variety of contexts and generally are NP-hard problems. At a corporate level solving this problems is of great importance since they contribute to the optimization of operational costs. In this thesis we propose to solve the Public Transport Bus Assignment problem considering an heterogeneous fleet and line exchanges, a variant of the Multi-Depot Vehicle Scheduling Problem in which additional constraints are enforced to model a real life scenario. The number of constraints involved and the large number of variables makes impracticable solving to optimality using complete search techniques. Therefore, we explore metaheuristics, that sacrifice optimality to produce solutions in feasible time. More concretely, we focus on the development of algorithms based on a sophisticated metaheuristic, Ant-Colony Optimization (ACO), which is based on a stochastic learning mechanism. For complex problems with a considerable number of constraints, sophisticated metaheuristics may fail to produce quality solutions in a reasonable amount of time. Thus, we developed parallel shared-memory (SM) synchronous ACO algorithms, however, synchronism originates the straggler problem. Therefore, we proposed three SM asynchronous algorithms that break the original algorithm semantics and differ on the degree of concurrency allowed while manipulating the learned information. Our results show that our sequential ACO algorithms produced better solutions than a Restarts metaheuristic, the ACO algorithms were able to learn and better solutions were achieved by increasing the amount of cooperation (number of search agents). Regarding parallel algorithms, our asynchronous ACO algorithms outperformed synchronous ones in terms of speedup and solution quality, achieving speedups of 17.6x. The cooperation scheme imposed by asynchronism also achieved a better learning rate than the original one.