Transport Engineering

Joint doctoral studies with Vilnius Gediminas Technical University (coordinating institution), Klaipėda University, and Vytautas Magnus University.

Joint doctoral studies in the research field of transport engineering are being carried out in the following areas: dynamics of weapons systems, creation of means of transportation, analysis of dynamic processes of trains, analysis of transport systems, mechanics of aircraft design. Most of doctoral students complete part of their studies or research at foreign research and educational institutions.

Topic title	Possible scientific supervisors	Source of funding
Application of artificial intelligence in characterization of the performance-emission profile of an ICE operating with hydrogen	prof. dr. Laurencas Raslavičius	state-funded	Research Topic Summary. The primary aim of this research is to develop an AI-based framework for characterizing and optimizing the performance-emission profile of an internal combustion engine (ICE) operating with hydrogen fuel. By integrating machine learning algorithms, particularly artificial neural networks (ANNs), the study seeks to establish a data-driven methodology for accurately predicting engine performance and emissions under varying operational conditions. The key objectives of the research include: 1. Development of AI Models: Designing and training ANN-based predictive models to estimate key engine parameters, including power output, thermal efficiency, and brake-specific fuel consumption, based on real-world and simulated data. 2. Emission Characterization: Employing AI to analyze and predict the relationship between operating conditions (e.g., ignition timing, air-fuel ratio, and load) and emissions, with a specific focus on NOx formation, a major concern in hydrogen combustion. 3. Optimization of Engine Performance: Implementing AI-driven optimization techniques that adjust control parameters dynamically to achieve an optimal balance between performance and emissions. 4. Comparison with Conventional Methods: Evaluating the effectiveness of the AI-based approach against traditional empirical and physics-based models to demonstrate improvements in prediction accuracy, computational efficiency, and adaptability
Development of a Methodology for Efficient Energy Use and Comfort Level Assurance of Electric Buses Operating in Various Climate Conditions	doc. dr. Rolandas Makaras	state-funded	Research Topic Summary. Electric buses are becoming increasingly popular as an environmentally friendly alternative to traditional public transport; however, their operation under various climate conditions presents significant challenges. The main issues are related to energy consumption efficiency and ensuring passenger comfort. Low temperatures reduce battery performance and increase energy consumption due to the additional heating demand, while high temperatures require intensive air conditioning. Heating, ventilation, and air conditioning (HVAC) systems can account for up to 50% of total energy consumption under extreme climate conditions, significantly limiting the bus’s range on a single charge. Additionally, frequently opening doors on city routes cause temperature fluctuations, further increasing energy demand. These challenges highlight the need for new methodologies that optimize energy consumption without compromising passenger comfort
Research of Aeroelastic Behavior in High Aspect Ratio Aircraft Composite Wings	prof. dr. Sigitas Kilikevičius	state-funded