https://hdl.handle.net/20.500.12960/18 2026-05-10T14:25:16Z 2026-05-10T14:25:16Z Oral, Ferhan https://hdl.handle.net/20.500.12960/1820 2026-04-16T06:49:33Z 2026-01-01T00:00:00Z

Use of unmanned surface vessels for search and rescue in the Arctic Oral, Ferhan The use of unmanned surface vessels (USVs) in the maritime industry has increased rapidly in recent years. At the same time, maritime activity in the Arctic is expanding as diminishing sea ice enables greater access for commercial shipping, offshore energy development, fisheries, and cruise tourism. This growth has heightened the risk of maritime accidents in a region characterized by harsh environmental conditions, vast distances, and limited infrastructure. Existing search and rescue (SAR) capabilities of Arctic states are widely regarded as insufficient to fully address the anticipated increase in maritime traffic, prompting efforts to enhance SAR preparedness. This study examines the potential role of USVs in supporting Arctic SAR operations from operational and legal perspectives, with particular reference to the Northern Sea Route. It addresses two research questions: 'how USVs can support SAR operations in the Arctic in light of existing SAR capabilities and increasing maritime activity', and 'what legal and regulatory constraints may limit their deployment'. A qualitative pilot study was conducted based on semi-structured interviews with domain experts, which were coded and analyzed using content and thematic analysis methods. The findings suggest that USVs can enhance SAR operations as complementary assets, provided that existing legal ambiguities are clarified.

2026-01-01T00:00:00Z Maruccio, Claudio Montegiglio, Pasquale Kendibilir, Abdullah Kefal, Adnan https://hdl.handle.net/20.500.12960/1791 2025-11-19T10:27:45Z

Adaptive modeling and parameter identification of piezoelectric structures under system uncertainty and nonlinear effects Maruccio, Claudio; Montegiglio, Pasquale; Kendibilir, Abdullah; Kefal, Adnan A novel robust synchronization-based computational strategy is developed capable of identifying material and system parameters of active flexible smart structures and modeling the nonlinear response due to faults and system reconfiguration even in the presence of noise in the experimental data and uncertainties. The sensitivity problem, resulting from the gradient-based algorithm, is addressed with a novel hybrid technique that exploits the advantages of both frequency and time-domain procedures. This hybrid approach ensures robust parameter identification and accurate modeling under challenging conditions making it highly suitable for real-time applications. Extensive numerical results confirm the intrinsic benefits of the proposed computational strategy, demonstrating its effectiveness for simulating the response of nonlinear electromechanical vibration-based devices. The proposed approach is validated through its application to parameter identification and damage diagnosis of a sandwich laminate, showcasing its practical relevance and versatility. These advancements represent a significant step forward in the modeling and control of piezoelectric smart structures, offering a robust solution for applications ranging from structural health monitoring to energy harvesting.

Aşan, Cihat https://hdl.handle.net/20.500.12960/1770 2025-04-11T12:14:14Z 2025-01-01T00:00:00Z

An exploratory alternative approach to potential incident simulation, control, and evaluation system simulation for prediction of oil spill behavior through supervised machine learning Aşan, Cihat The incidents occurring within the marine environment are supported by various Decision Support Systems (DSS), both in simulation and intervention. Accurate and real-time data inputs into these systems greatly contribute to the effective and prompt decision-making process. However, the absence of these systems in all situations or the inability to provide real-time data inputs can negatively impact the effectiveness of decision processes. This study aims to create a method that can enable reliable and accurate predictions regarding oil pollution and the cost-effective execution of certain decision processes. For this purpose, an exploratory study with various cases of ship-sourced oil pollution has been simulated using the Potential Incident Simulation Control and Evaluation System (PISCES). Random input values for each case have been utilized in PISCES simulation experiments. Afterward, supervised machine learning models were trained using the simulation experiments data set to predict oil dispersion amount and time of oil impact on shore. Model hyperparameters were optimized using cross-validated grid-searches. Through hyperparameter optimization using grid search, XGB, Random Forest, and Gradient Boosting emerged as the leading models for estimating oil dispersion. However, while Gradient Boosting yielded satisfactory outcomes, its performance could be further enhanced with additional data. Obtained results show that the proposed methodology has the potential for predicting the time of impact on shore, hence for rapid results for standard initial actions, they can be used as an alternative DSS to PISCES.

2025-01-01T00:00:00Z Acarer, Tayfun https://hdl.handle.net/20.500.12960/1683 2024-10-09T07:26:39Z 2024-01-01T00:00:00Z

Energy-Aware Path Planning by Autonomous Underwater Vehicle in Underwater Wireless Sensor Networks for Safer Maritime Transportation Acarer, Tayfun Throughout history, maritime transportation has been preferred for international and intercontinental trade thanks to its lower cost than other transportation ways, which have a risk of ship accidents. To avoid these risks, underwater wireless sensor networks can be used as a robust and safe solution by monitoring maritime environment where energy resources are critical. Energy constraints must be solved to enable continuous data collection and communication for environmental monitoring and surveillance so they can last. Their energy limitations and battery replacement difficulties can be addressed with a path planning approach.This paper considers the energy-aware path planning problem with autonomous underwater vehicles by five commonly used approaches, namely, Ant Colony Optimization-based Approach, Particle Swarm Optimization-based Approach, Teaching Learning-based Optimization-based Approach, Genetic Algorithm-based Approach, Grey Wolf Optimizer-based Approach. Simulations show that the system converges faster and performs better with genetic algorithm than the others. This paper also considers the case where direct traveling paths between some node pairs should be avoided due to several reasons including underwater flows, too narrow places for travel, and other risks like changing temperature and pressure. To tackle this case, we propose a modified genetic algorithm, the Safety-Aware Genetic Algorithm-based Approach, that blocks the direct paths between those nodes. In this scenario, the Safety-Aware Genetic Algorithm-based approach provides just a 3% longer path than the Genetic Algorithm-based approach which is the best approach among all these approaches. This shows that the Safety-Aware Genetic Algorithm-based approach performs very robustly. With our proposed robust and energy-efficient path-planning algorithms, the data gathered by sensors can be collected very quickly with much less energy, which enables the monitoring system to respond faster for ship accidents. It also reduces total energy consumption of sensors by communicating them closely and so extends the network lifetime.

2024-01-01T00:00:00Z