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Optimization of Turbofan Engine Performance through Blade Profile Modification

This thesis reports detailed research performed to optimize the geometries of blade rows in turbofan engines through analysis with computational fluid dynamics. The study was conducted systematically by employing theoretical analysis, along with numerical computation used to evaluate the aerodynamic performance of two different blade types. The methodology consisted of the design of blade geometries in SolidWorks, mesh generation using structured and unstructured elements, and CFD simulations on ANSYS Fluent with a focus on cascade analysis. The study commenced with the development of two designs below: Blade Design 1 having a height of 1300 mm, chord length of 294 mm at the base, and 700 mm at a height of 750 mm; Blade Design 2 at a similar height, differing only in leading edge diameters and chord lengths at different cross-sections. This enables detailed meshing such that near-wall regions and critical flow features are resolved with adequate resolution. Using a pressure-based solver, the CFD simulation was performed using the k-ω SST turbulence model, which is proper for capturing near-wall effects and handling adverse pressure gradients. Inlet velocities were considered between 1 and 40 m/s, thus analyzing performance under different operating conditions.

Author: Ajiksun Kumaradhas

Supervisor: Adham Ahmed Awad Elsayed Elmenshawy

Degree: Bachelor

Year: 2024

Work Language: English

Study programme: Aviation Engineering

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AI-driven Voice Recognition: Model Development and Application

In the course of the work is decided to develop a speech recognition model with the application with the system assistant capabilities. The result of the author's work is a model that is capable of speech recognition on a limited set of words and the application that will be the prototype of the concept. The software is implemented using Visual Studio Code/Jupyter, Python programming language with big framework such as Keras. The developed software fully meets the requirements and is ready for operation.

Author: Aleksejs Ņikiforovs

Supervisor: Dmitry Pavlyuk

Degree: Bachelor

Year: 2024

Work Language: English

Study programme: Computer Science

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Improvement of machine leaning algorithms performance by data set dimensionality reduction using cellular automata

A significant challenge in Machine Learning is dealing with high-dimensional data. Complexity knowns as the "curse of dimensionality" results in deterioration оf Machine Learning algorithms performance as the dimensionality and dataset size increases. Cellular automata are a dynamical discrete computational system with mathematical functions knows as rules that result in complex global behaviour. We used one-dimensional elementary cellular automata as a tool for dataset size. Model variables were selected for initial status vector generation and its further transformation to format that is suitable for cellular automata rules application known in cellular automata theory as configuration. Then model iterated through all possible cellular automata rules and various epochs variations were applied. Model performance for reduced dataset was compared with benchmark results of original dataset after standard dimensionality reduction technics used. It was concluded that applied cellular automata rules can be used as alternative methods for dataset size reduction without deteriorating model performance.

Author: Alexey Kuchvalskiy

Supervisor: Dmitry Pavlyuk

Degree: Master

Year: 2024

Work Language: English

Study programme: Computer Sciences

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Use of Artificial Intelligence in Human Resource Management

The integration of Artificial Intelligence into Human Resource Management processes presents an evolving domain of exploration and practical application. The widespread adoption of AI technologies presents the potential for significant transformations in products, innovation processes and business models. The research aim is to develop a framework for effective use of AI in Human Resource Management. The subject of the research is AI-based solutions in Human Resource Management. The object of the research is Human Resource Management processes in an enterprise.A multifaceted research approach was used to scrutinize the effective utilization of AI in HRM: Literature review, Case Studies, Employee Surveys, Interviews with HR experts.During this research, the author highlighted aspects of contemporary HRM that can be improved using AI technologies; made a list of the recommended practices for integrating AI-based solutions into HRM; developed a framework for AI tools implementation and found out that HR experts’ and employees’ overall perspective on AI within HRM appears optimistic.This research holds both theoretical and practical significance, driving advancements in theoretical knowledge, informing organizational practices, and shaping the future of work in the digital age.

Author: Ana Enache

Supervisor: Yulia Stukalina

Degree: Master

Year: 2024

Work Language: English

Study programme: Business and Management

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Development of Decision Support Tool for Transport Forwarding Company Operating within Netherlands and Italy

This paper investigates the development of a decision support tool for a Latvian transportation company operating in the Dutch and Italian markets. Models and algorithms for optimizing freight routes using Excel and Python are included. Digitalization of logistics processes is recognized as a key to improve efficiency and reduce costs. Two methods for solving the traveling salesman problem were examined and compared: Excel with Solver and Python with the NetworkX library.The methodology involved collecting data from Google My Maps, creating Excel spreadsheets, and developing Python software to automate route optimization. The results showed that both methods improved route planning, reducing time and cost, as well as reducing carbon footprint.The study emphasizes the importance of integrating technologies such as machine learning and big data into logistics to increase flexibility and adaptability. Recommendations were offered to further improve and implement these technologies for sustainable business development and increased competitiveness in international markets.

Author: Anastasija Škaduna

Supervisor: Berdymyrat Ovezmyradov

Degree: Professional Bachelor

Year: 2024

Work Language: English

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