This study has involved the investigation of the flows in a high efficiency cyclone separator and the performance characteristics of the cyclone when operating under the influence of base suction. It has long been accepted that a bleed taken from the base region of a cyclone could, generally, enhance the overall collection efficiency. However, detail analyses and investigations have been limited. This investigation has involved flow visualisation, laser Doppler anemometry, computational fluid Dynamics, as well as both model particle tests and full scale prototype tests to quantify the effect of base suction and cyclone performance. Flow visualisation has highlighted the extension of the vortices into the solid receiver at the base of the cyclone. The flow patterns in this region have been investigated and quantified using laser Doppler anemometry and this result compared to the predictions from computational fluid dynamics. Agreement between these results tends to be good in the inner vortex but less good in the outer vortex region. Model particle tests have shown that the extension of the vortices into the solid receiver results in the complete destruction of the dust layer in the receiver with the subsequent re-entrainment and carry over of particulate to the vortex finder. These particle tests have shown that this re-entrainment can be suppressed by the application of a suction in the base region and the overall collection efficiency improved. A bleed flow of 10% by volume is shown to give the maximum overall efficiency. Above this percentage the efficiency reduces. This trend in the results was also confirmed by full scale prototype tests.

This study addresses the challenge of identifying anomalies within multivariate time series data, focusing specifically on the operational parameters of gas turbine combustion systems. In search of an effective detection method, the research explores the application of three distinct machine learning methods: the Long Short-Term Memory (LSTM) autoencoder, the Self-Organizing Map (SOM), and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Through the experiment, these models are evaluated to determine their efficacy in anomaly detection. The findings show that the LSTM autoencoder not only surpasses its counterparts in performance metrics but also shows a unique capability to identify the underlying causes of detected anomalies. This paper delves into the comparative analysis of these techniques and discusses the implications of the models in maintaining the reliability and safety of gas turbine operations.

The complex multiphase flow physics of spray-swirl interaction in both reacting and non-reacting environment is of fundamental and applied significance for a wide variety of applications ranging from gas turbine combustors to pharmaceutical drug nebulizers. Understanding the intricate dynamics between this two phase flow field is pivotal for enhancing mixing characteristics, reducing pollutant emissions and increasing the combustion efficiency in next generation combustors. The present work experimentally investigates the near and far-field break-up, dispersion and coalescence characteristics of a hollow cone spray in an unconfined, co¬annular isothermal swirling air jet environment. The experiments were conducted using an axial-flow hollow cone spray nozzle having a 0.5 mm orifice. Nozzle injection pressure (PN = 1 bar) corresponding to a Reynolds number at nozzle exit ReN = 7900 used as the test setting. At this setting, the operating Reynolds number of the co-annular swirling air stream number (Res) was varied in four distinct steps, i.e. Res = 1600, 3200, 4800 and 5600. Swirl was imparted to the co¬axial flow using a guided vane swirler with blade angle of Ф=45° (corresponding geometric swirl number SG = 0.8). Two types of laser diagnostic techniques were utilized: Particle / Droplet imaging analysis (PDIA) and shadowgraph to study the underlying physical mechanisms involved in the primary breakup, dispersion and coalescence dynamics of the spray. Measurements were made in the spray in both axial and radial directions and they indicate that Sauter Mean Diameter (SMD) in radial direction is highly reliant on the intensity of swirl imparted to the spray. The spray is subdivided into two zones as function of swirl in axial and radial direction: (1) near field of the nozzle (ligament regime) where variation in SMD arises predominantly due to primary breakup of liquid films (2) far-field of the nozzle where dispersion and collision induced coalescence of droplets is dominant. Each regime has been analyzed meticulously, by computing probability of primary break-up, probability of coalescence and spatio-temporal distribution of droplets which gives probabilistic estimate of aforementioned governing phenomena. In addition to this, spray global length scale parameters such as spray cone angle, break-up length, wavelength of liquid film has been characterized by varying Res while maintaining constant ReN.

[ES] La búsqueda novedosa es un nuevo paradigma de los algoritmos de optimización, evolucionarios y bioinspirados, que está basado en la idea de forzar la búsqueda del óptimo global en aquellas partes inexploradas del dominio de la función que no son atractivas para el algoritmo, con la intención de evitar estancamientos en óptimos locales. La búsqueda novedosa se ha aplicado al algoritmo de optimización de enjambre de partículas, obteniendo un nuevo algoritmo denominado algoritmo de enjambre novedoso (NS). NS se ha aplicado al conjunto de pruebas sintéticas CEC2005, comparando los resultados con los obtenidos por otros algoritmos del estado del arte. Los resultados muestran un mejor comportamiento de NS en funciones altamente no lineales, a cambio de un aumento en la complejidad computacional. En lo que resta de trabajo, el algoritmo NS se ha aplicado en diferentes modelos, específicamente en el diseño de un motor de combustión interna, en la estimación de demanda de energía mediante gramáticas de enjambre, en la evolución del cáncer de vejiga de un paciente concreto y en la evolución del COVID-19. Cabe remarcar que, en el estudio de los modelos de COVID-19, se ha tenido en cuenta la incertidumbre, tanto de los datos como de la evolución de la enfermedad. / [CA] La cerca nova és un nou paradigma dels algoritmes d'optimització, evolucionaris i bioinspirats, que està basat en la idea de forçar la cerca de l'òptim global en les parts inexplorades del domini de la funció que no són atractives per a l'algoritme, amb la intenció d'evitar estancaments en òptims locals. La cerca nova s'ha aplicat a l'algoritme d'optimització d'eixam de partícules, obtenint un nou algoritme denominat algoritme d'eixam nou (NS). NS s'ha aplicat al conjunt de proves sintètiques CEC2005, comparant els resultats amb els obtinguts per altres algoritmes de l'estat de l'art. Els resultats mostren un millor comportament de NS en funcions altament no lineals, a canvi d'un augment en la complexitat computacional. En el que resta de treball, l'algoritme NS s'ha aplicat en diferents models, específicament en el disseny d'un motor de combustió interna, en l'estimació de demanda d'energia mitjançant gramàtiques d'eixam, en l'evolució del càncer de bufeta d'un pacient concret i en l'evolució del COVID-19. Cal remarcar que, en l'estudi dels models de COVID-19, s'ha tingut en compte la incertesa, tant de les dades com de l'evolució de la malaltia. / [EN] Novelty Search is a recent paradigm in evolutionary and bio-inspired optimization algorithms, based on the idea of forcing to look for those unexplored parts of the domain of the function that might be unattractive for the algorithm, with the aim of avoiding stagnation in local optima. Novelty Search has been applied to the Particle Swarm Optimization algorithm, obtaining a new algorithm named Novelty Swarm (NS). NS has been applied to the CEC2005 benchmark, comparing its results with other state of the art algorithms. The results show better behaviour in high nonlinear functions at the cost of increasing the computational complexity. During the rest of the thesis, the NS algorithm has been used in different models, specifically the design of an Internal Combustion Engine, the prediction of energy demand estimation with Grammatical Swarm, the evolution of the bladder cancer of a specific patient and the evolution of COVID-19. It is also remarkable that, in the study of COVID-19 models, uncertainty of the data and the evolution of the disease has been taken in account. / Martínez Rodríguez, D. (2021). Optimization Algorithm Based on Novelty Search Applied to the Treatment of Uncertainty in Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/178994