Research Papers - Department of Mechanical Engineering
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Publication Open Access Self-starting characteristics and dynamic response of a free-spinning cross-flow air turbine for oscillating water columns under irregular wave conditions(Elsevier Ltd, 2026-02-24) Baddegamage B.H.B.P.D; Bae, S.J; Gunawardane S.D.G.S.P.; Lee, Y.H; Kim, K; Yoon, MThe cross-flow air turbine (CFAT) has been proposed as a self-rectifying device for oscillating water column (OWC) wave energy converters as an alternative to conventional Wells and impulse turbines. While previous studies have primarily focused on steady or regular flow conditions, the self-starting behavior and transient response of a free-spinning CFAT under irregular, bidirectional inflow representative of realistic sea states have not yet been investigated. This study presents a fully transient computational fluid dynamics analysis of a free-spinning CFAT operating under irregular airflow conditions derived from the JONSWAP spectrum. The simulations were performed under no-load conditions to isolate the intrinsic aerodynamic torque generation and evaluate self-starting capability. The effects of significant wave height and spectral peak period on turbine startup and unsteady aerodynamic response were systematically examined in both the time and frequency domains. The CFAT consistently initiates rotation without external assistance and reaches quasi-steady operation within 25–30 oscillation cycles. For significant wave heights ranging from 0.0375 m to 0.05 m, the mean instantaneous efficiency varies between 0.24 and 0.52, while efficiencies between 0.30 and 0.59 are obtained for spectral peak periods from 1.50 s to 1.88 s. Furthermore, wave-grouping effects play a decisive role in accelerating the turbine toward its equilibrium speed. Torque and pressure fluctuations closely follow the inflow velocity profile, with hysteresis-like behavior observed during flow reversals. These findings confirm the CFAT's suitability for practical OWC applications, demonstrating robust self-starting and stable performance under irregular conditions.Publication Embargo Evaluating and prioritizing delay factors in naval ship maintenance using the analytic hierarchy process: a Sri Lanka navy shipyard case study(Taylor and Francis Ltd., 2026-02-18) Fernando, W. J; Silva, N; Perera, CTimely maintenance of ships and craft is critical for ensuring operational readiness, safety, and economic sustainability in the maritime sector. However, scheduled docking delays remain a persistent challenge globally, incurring significant financial losses and reducing fleet availability. This study presents a systematic, quantitative approach to identify and prioritize 22 critical factors causing delays in scheduled docking. Using the Analytic Hierarchy Process (AHP), the study evaluates the relative importance of these factors to support informed decision-making. A case study of the Sri Lanka Navy (SLN) demonstrates the application of the proposed framework, revealing that 97% of docking delays occur before vessels enter the dock, with 31.8% of these delays attributable to deficiencies in the procurement of materials and spare parts. While the findings are based on a single case study of the SLN shipyard, they offer context-specific insights into the unique challenges faced by naval maintenance operations in developing regions.Publication Embargo Evaluating and prioritizing quality culture elements in the tire manufacturing industry: A case-based DEMATEL approach(Taylor and Francis Ltd., 2026-01-16) Silva, N; Bandara, S; Perera, CIn today’s competitive manufacturing environment, developing a robust quality culture that supports continuous improvement and defect prevention is critical for long-term operational excellence and customer satisfaction. This study applies the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to evaluate and prioritize the key elements shaping quality culture in a leading tire manufacturing company in Sri Lanka, offering a rare empirical application of DEMATEL in this context. Using a two-phase approach, Phase 1 involved a survey of 127 employees to identify seven critical elements, while Phase 2 analyzed expert evaluations from 16 professionals to establish causal relationships among them. The findings reveal that leadership (R + C = 14.40) and employee empowerment (R + C = 13.31) are the most influential drivers, followed by teamwork (R + C = 12.67), while focus on customer satisfaction, planning for quality, improvements and innovation, and standardized processes approach are primarily dependent elements. The built-in quality maturity framework and relative importance index were applied to assess the current implementation level of each element and identify performance gaps. The gap analysis highlights that leadership and employee empowerment are underperforming relative to expected levels, requiring immediate strategic enhancement. Managers should prioritize leadership and empowerment development to strengthen organizational quality culture and sustain competitiveness.Publication Embargo Performance improvement of a cross-flow air turbine for oscillating water column wave energy converter by nozzle and blade optimization(Elsevier Ltd, 2025-01-15) Baddegamage B.H.B.P.D; Bae, S.J; Jang, S.H; Gunawardane S.D.G.S.P; Lee, Y.O; Kim, K; Yoon, MThe global pursuit of renewable energy solutions has highlighted the potential of wave energy converters (WECs), particularly oscillating water column (OWC) systems, as viable clean energy sources. This study focuses on optimizing a cross-flow air turbine (CFAT) through comprehensive numerical simulations, aimed at enhancing its performance as the power take-off system in OWC applications. The influences of various geometrical parameters, such as including nozzle entry arc angle, nozzle starting angle, and angle of attack, on the performance of the CFAT are investigated. The optimized turbine model achieves a peak efficiency (η) of 0.71 in unidirectional flow at a flow coefficient (Φ) of 0.41, representing a significant improvement over the reference model (η = 0.61 at Φ = 0.29). In addition, the performance of the optimized CFAT is evaluated under regular wave conditions, simulating the bidirectional flow typical of real-world OWC applications. Although the peak efficiency in reciprocating flow slightly decreases, shifting to 0.68 at Φ = 0.53, the turbine maintains a high mean efficiency throughout the operating cycle. The results demonstrate that the optimized CFAT model provides robust performance in both unidirectional and bidirectional flows, making it a promising candidate for enhancing the efficiency of OWC-based WECs.Publication Open Access Forecasting renewable energy for microgrids using machine learning(Springer Nature, 2025-05-03) Sudasinghe, P; Herath, D; Karunarathne, I; Weeratunge, H; Jayasuriya, LMicrogrids, comprised of interconnected loads and distributed energy resources, function as single controllable entities with respect to the main grid. However, the inherent variability of distributed wind and solar generation within microgrids presents operational stability challenges concerning voltage regulation and frequency stability. Accurate forecasting of renewable generation is crucial for mitigating these challenges. This work proposes a one-dimensional Convolutional Neural Network (1-D CNN) based approach to forecast photovoltaic (PV) generation and wind energy, using data from the University of California, San Diego microgrid and San Diego Airport weather records. The proposed method is evaluated against various forecasting methods using key metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared value. Results show that the 1-D CNN model achieves an improvement of up to 229.8 times in MSE and a 24.47 fold improvement in MAE compared to baseline models that use traditional statistical methods in forecasting. This demonstrates the potential of machine learning for enhancing microgrid management, particularly in short-term forecasting of renewable generation. © The Author(s) 2025. Evaluated ML-based renewable energy forecasting models by implementing 1-D CNN and LSTM models using real-world data. Proposed 1-D CNN performs better than LSTM and baseline models, achieving higher accuracy and computational efficiency. Accurate forecasting of PV and wind energy generation enhances grid stability, reduces backup power dependency, and supports sustainable energy integration.Publication Open Access Interpretable SHAP-bounded Bayesian optimization for underwater acoustic metamaterial coating design(Springer Science and Business Media Deutschland GmbH, 2025-09-08) Weeratunge, H; Robe, D. M; Hajizadeh, EWe present an interpretability-informed Bayesian optimization framework for the inverse design of underwater acoustic coatings composed of polyurethane elastomers with embedded metamaterial features. A data-driven model was used to capture the relationship between acoustic performance, specifically, sound absorption and the corresponding geometrical design variables. To interpret these relationships, we applied SHapley Additive exPlanations (SHAP), enabling the identification of key parameters influencing the objective function and providing both global and local insights into their effects. The insights from the SHAP analysis were used to automatically refine the bounds of the design space, guiding the optimization process toward more promising regions. This approach was tested on two polyurethane materials with different hardness levels and yielded improved optimal designs compared to standard Bayesian optimization without increasing the number of simulations. This work underscores the effectiveness of combining interpretability techniques with optimization for the efficient and cost-effective design of underwater acoustic metamaterials under strict computational constraints and can be generalized towards other materials and engineering optimization problems