http://hdl.handle.net/10174/1149 Sun, 05 Apr 2026 23:52:29 GMT 2026-04-05T23:52:29Z http://hdl.handle.net/10174/32947 Title: Assessing the obstacle effect on jet fan flow variability in car parks using computational fluid dynamics Authors: Cardoso, Ricardo; Cavaleiro Costa, Sérgio; Casaca, Cláudia; Carvalho, Alda; Malico, Isabel Abstract: The presence of obstacles may compromise the efficiency of the ventilation systems of underground car parks. One such case occurs when jet fans are close to beams or air ducts placed on the ceiling of the park. In this situation, understanding the extent and conditions at which the flow is disturb is of fundamental importance. To start addressing this question, in this study, Computational Fluid Dynamics (CFD) results were combined with Principal Component Analysis. The geometry under study was reduced to a simplified underground car park envelope, an impulse jet fan and a downstream obstacle placed on the ceiling of the park. As a first step, 34 computational fluid dynamics simulations of the isothermal incompressible turbulent flow were performed, randomly varying the distance of the obstacle to the fan and the jet fan discharge angle according to different distributions. Afterwards, representative results were selected and rewrote in the principal components space, with the aim of understanding the impact of the obstacle on the flow through identification and interpretation of the underlying factors. Because of the three-dimensionality and complexity of the flow, it is hard to select the output variables that are representative and can quantify the jet flow disturbance due to the obstacle. In this work, a first attempt was made, and the flow conditions (distance of fan to obstacle and jet fan angle) typified in accordance with these variables. Tue, 01 Nov 2022 00:00:00 GMT http://hdl.handle.net/10174/32947 2022-11-01T00:00:00Z http://hdl.handle.net/10174/30968 Title: First results from a novel multispecies predator-prey optimization algorithm Authors: Cavaleiro Costa, Sérgio; Janeiro, Fernando M.; Malico, Isabel Abstract: Optimization plays a central role in today's society. Different strategies have been proposed over the years, and their application is not independent of the problem. In this work, a novel predator-prey optimization algorithm (PPA) is presented and compared with genetic algorithms (GA). In the proposed algorithm, the search is based on the behaviour of a predefined number of species, some acting as predators and others as prey. The former pursue not only the latter to prey but also members of the same species to mate. On the other hand, preys run away from predators, but are attracted to other members of the same species. The performance of the proposed algorithm is tested with the help of four benchmark test functions: Goldstein-Price, Mishra's bird, Michalewicz and Eggholder functions. Each of these functions expose both PPA and GA to different difficulties. Five hundred runs were performed for each of the four test functions. Despite the better performance of the PPA compared to GA in all benchmarks analysed, the algorithm stands out when minimizing the Eggholder function, which has many local minima and the global minimum located near the edge of the search space. In the case of this function, the success rate is 75.6% for the PPA against 29.0% for the GA, when considering a tolerance of 1.0×10-2. The algorithm presented in this work has shown a resilient convergence to the global minimum with fewer iterations than the genetic algorithms, suggesting promising results in problems with similar characteristics. Fri, 01 Jan 2021 00:00:00 GMT http://hdl.handle.net/10174/30968 2021-01-01T00:00:00Z http://hdl.handle.net/10174/30966 Title: Geometric optimization of linear Fresnel reflector solar collector using a genetic algorithm Authors: Cavaleiro Costa, Sérgio; Santos, André; Canavarro, Diogo; Malico, Isabel; Janeiro, Fernando Abstract: In this work, the optimization of a Linear Fresnel Reflector solar concentrator through a parallelized Multi-offspring Genetic Algorithm is presented. An analytical description of the concentrator was developed and used to compute specific annual collected energy; this is the fitness function to be maximized. Selection, reproduction, and mutation operators were defined to produce diversity of individuals. Elitism was applied to the best subject of the population, so that it is kept in the following generations. The results show a tendency towards systems that privilege larger gaps between heliostats, as this reduces shading and blocking effects. Additionally, concentration was found to be at the lower bound of search, the receiver tends to be positioned as high as possible and mirrors to be as short as possible. So, for these constraints we found an optimal fitness of 1238.35 kWh/m2 for Évora. Fri, 01 Jan 2021 00:00:00 GMT http://hdl.handle.net/10174/30966 2021-01-01T00:00:00Z http://hdl.handle.net/10174/28832 Title: Reinforcement Learning for Dual-Resource Constrained Scheduling Authors: Martins, M.; Viegas, J.; Coito, T.; Firme, B.; Sousa, J.; Figueiredo, Joao; Vieira, S. Abstract: This paper proposes using reinforcement learning to solve scheduling problems where two types of resources of limited availability must be allocated. The goal is to minimize the makespan of a dual-resource constrained flexible job shop scheduling problem. Efficient practical implementation is very valuable to industry, yet it is often only solved combining heuristics and expert knowledge. A framework for training a reinforcement learning agent to schedule diverse dual-resource constrained job shops is presented. Comparison with other state-of-theart approaches is done on both simpler and more complex instances that the ones used for training. Results show the agent produces competitive solutions for small instances that can outperform the implemented heuristic if given enough time. Other extensions are needed before real-world deployment, such as deadlines and constraining resources to work shifts. Wed, 01 Jan 2020 00:00:00 GMT http://hdl.handle.net/10174/28832 2020-01-01T00:00:00Z