Faculty of Engineering
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Publication Embargo Feasibility of hydrological modelling for intermittent streams using HEC-HMS: a process evaluation(Taylor and Francis, 2025-01-08) Perera, M. D.D; Gomes, P.I.AThe use of hydrological software in simulating the rainfall–runoff relationships of intermittent streams is rather unfound due to their dynamic flow regimes. This study assessed the feasibility of using Hydrologic Engineering Center–Hydrologic Modelling System (HEC-HMS), a widely used open-source hydrological modelling software, in discharge simulation of intermittent streams in a dry tropical zone. Individual calibration was required for each stream, even in adjoining sub-catchments with the same geology and climate. Event-based models with transitional periods captured seasonal variations in catchment characteristics. Strong correlations (Pearson’s r > 0.7, P < 0.05) between observed and simulated discharges indicated model success and, after calibration for one season, flows of the next (similar) season could be predicted without further adjustments. Baseflow and channel infiltration were the most sensitive parameters in the wet and dry seasons, respectively. This study demonstrated the possibility of building accurate hydrological models for intermittent streams by incorporating seasonal variations and extensive calibration.Publication Open Access Evaluation of mesoscale physical habitats in sediment and water quality improvement – a mesocosm study for urban canals(Inderscience Publishers, 2025) De Zoysa, S; Chandeep, K. A.T; Pathirathne, P.H.D.R; Gomes, P.I.AThis study investigated the applicability of different types of attenuation processes (i.e., aeration and stirring) with and without dilution in nutrients (nitrogen and phosphorous) and sulphide-polluted sediment cleanup via laboratory mesocosms. Attenuation refers to the decline in contaminant concentration, a phenomenon driven by processes like dilution, mixing, and dispersion. Dilution, a remedial method involving the blending of contaminated water with uncontaminated often happens with uncontaminated runoff or a tributary. Regardless of the seasons, aeration, stirring, combined aeration and stirring, and dilution generally resulted in better removal efficiency of pollutants. Aeration combined with stirring showed notable improvements across multiple water quality parameters, and parameters seemed to be treatment type dependent, but without any significant differences. Dilution reduced electrical conductivity and increased dissolved oxygen but did not influence ammoniacal nitrogen and phosphate. The energy consumption for a unit percentage improvement via aeration and stirring was 0.04–0.25 USD and 0.03–0.15 USD, respectively. Therefore, relying solely on attenuation processes without dilution is deemed economically infeasible in real or prototype applications. This research sheds light on potential applications including pros and cons, emphasising the need for a balanced approach, and setting the stage for future studies.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 Embargo Nonlocal strain gradient modeling of vibration energy harvesting in fluid-immersed bimorph sandwich nanoplates under thermal environment(American Institute of Physics, 2025-02-07) Roodgar Saffari, P; Senjuntichai, T; Rajapakse, NThis research details a method for mathematically simulating and assessing thermal vibration energy harvesting in laminated bimorph nanoplates in fluid contact. The model uses the piezoelectric characteristics of the outer layers and the functionally graded (FG) core material to transform thermal stresses into electrical energy efficiently. Nanostructures' size effects and nonclassical behavior are captured by the nonlocal strain gradient theory (NSGT). Combining the Navier-Stokes equations with the electromechanical equations obtained from Hamilton's principle, first-order shear deformation theory (FSDT), and Gauss's law yields an advanced multi-physics model. The FG core exhibits variations by the power law principle and is composed of both ceramic and metal components. Analytical solutions are obtained for the frequency response functions that relate the electrical power output to the external circuit load resistance by solving the coupled electromechanical-fluid equations. A thorough investigation is conducted to analyze how different elements impact energy harvesting performance using parametric studies. These factors include the configuration of the harvester (either parallel or series piezoelectric connections), nonlocal and strain gradient effects, temperature gradients, fluid depth, electrical load, geometric dimensions, and the material properties of the piezoelectric layers, and functionally graded core.Publication Embargo Durability and mechanical performance of glass and natural fiber-reinforced concrete in acidic environments(Elsevier Ltd, 2025-02-28) Justin, S; Thushanthan, K; Tharmarajah, GThis study investigates the mechanical and durability characteristics of fiber-reinforced concrete when exposed to acidic environments. The research focuses on the effects of adding 1 % of treated coir fibers (TCF), treated rice husk fibers (TRH), and glass fibers (GF), along with 5 % silica fume (SF), to concrete. Experimental results show that the inclusion of these fibers and SF enhances both compressive and tensile strengths, with the most significant improvements observed in GF-reinforced concrete. The durability of the concrete was tested by immersing samples in acidic solutions with pH values of 3 and 5 for 28 days. Ultrasonic Pulse Velocity (UPV) tests indicated that the concrete’s quality remained stable, while compressive strength tests revealed an increase in strength, particularly in samples exposed to pH 5. Sorptivity tests, which measure water absorption, indicated higher initial absorption rates due to the porous nature of fiber-reinforced concrete. However, as hydration progressed, the rate decreased. SEM images show that incorporating silica fume improves the microstructure of the specimens benefitting the strength of the structure. The study concludes that concrete reinforced with GF and SF exhibits superior mechanical properties and durability in acidic environments, making it a promising material for use in harsh conditionsPublication Embargo Framework for generating high-resolution Hong Kong local climate projections to support building energy simulations(American Institute of Physics, 2025-03-07) Wang, J; Kudagama, B.J; Perera, U.S; Li, S; Zhang, XFiner resolution climate model projections are essential for designing regional building energy consumption and adaptation strategies under changing climate conditions. However, projections from Global Climate Models (GCMs) are typically coarse in resolution and subject to biases and uncertainty. To address this, the present study uses bilinear interpolation and morphing statistical downscaling to obtain high spatial (around 10 km) and temporal (hourly) resolution weather data, for more accurate estimations of future residential building energy consumption under climate change. An empirical quantile mapping bias-correction technique is applied to adjust the projection data from 44 GCMs under four representative Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The bias-corrected data are validated against meteorological observations from the Hong Kong Observatory's King's Park station. The hourly data are then converted to typical meteorological year data and used as input for EnergyPlus to predict future energy consumption patterns in public rental housing in Hong Kong. Case studies under the four SSPs show that climate change will significantly impact residential building energy use. Energy consumption is projected to increase by up to 14.0% for harmony-type buildings, 12.8% for trident-type buildings, and 12.4% for slab-type buildings by the end of the century under the SSP5-8.5 scenario, highlighting the urgent need for adaptive building design and energy policy measures.Publication Embargo Autoencoder based data clustering for identifying anomalous repetitive hand movements, and behavioral transition patterns in children(Springer Science and Business Media Deutschland GmbH, 2025-01-21) Wedasingha, N; Samarasinghe, P; Senevirathna, L; Papandrea, M; Puiatti, AThe analysis of repetitive hand movements and behavioral transition patterns holds particular significance in detecting atypical behaviors in early child development. Early recognition of these behaviors holds immense promise for timely interventions, which can profoundly impact a child’s well-being and future prospects. However, the scarcity of specialized medical professionals and limited facilities has made detecting these behaviors and unique patterns challenging using traditional manual methods. This highlights the necessity for automated tools to identify anomalous repetitive hand movements and behavioral transition patterns in children. Our study aimed to develop an automated model for the early identification of anomalous repetitive hand movements and the detection of unique behavioral patterns. Utilizing autoencoders, self-similarity matrices, and unsupervised clustering algorithms, we analyzed skeleton and image-based features, repetition count, and frequency of repetitive child hand movements. This approach aimed to distinguish between typical and atypical repetitive hand movements of varying speeds, addressing data limitations through dimension reduction. Additionally, we aimed to categorize behaviors into clusters beyond binary classification. Through experimentation on three datasets (Hand Movements in Wild, Updated Self-Stimulatory Behaviours, Autism Spectrum Disorder), our model effectively differentiated between typical and atypical hand movements, providing insights into behavioral transitional patterns. This aids the medical community in understanding the evolving behaviors in children. In conclusion, our research addresses the need for early detection of atypical behaviors through an automated model capable of discerning repetitive hand movement patterns. This innovation contributes to early intervention strategies for neurological conditionsPublication Open Access Robust Distributed Collaborative Beamforming for WSANs in Dual-Hop Scattered Environments with Nominally Rectangular Layouts(Multidisciplinary Digital Publishing Institute (MDPI), 2025-03-19) Ben Smida, O; Affes, S; Jayakody, D; Nizam, YWe introduce a robust distributed collaborative beamforming (RDCB) approach for addressing channel estimation challenges in dual-hop transmissions within wireless sensor and actuator networks (WSANs) of K nodes. WSANs enhance wireless communication by reducing data transmission, latency, and energy consumption while optimizing network load through integrated sensing and actuation. The source S transmits signals to the WSAN, where nodes relay them to the destination D using beamforming weights to minimize noise and preserve signal integrity. These weights depend on channel state information (CSI), where estimation errors degrade performance. We develop RDCB solutions for three first-hop propagation scenarios—monochromatic [line-of-sight (LoS)] or “M”, bichromatic (moderately scattered) or “B”, and polychromatic (highly scattered) or “P”—while assuming a monochromatic LoS or “M” link for the second hop between the nodes and the far-field destination. Termed MM-RDCB, BM-RDCB, and PM-RDCB, respectively (“X” and “Y” in XY-RDCB—for X (Formula presented.) and Y (Formula presented.) —refer to the chromatic natures of the first- and second-hop channels, respectively, to which a specific RDCB solution is tailored), these solutions leverage asymptotic approximations for large K values and the nodes’ geometric symmetries. Our distributed solutions allow local weight computation, enhancing spectral and power efficiency. Simulation results show significant improvements in the signal-to-noise ratio (SNR) and robustness versus WSAN node placement errors, making the solutions well suited for emerging 5G and future 5G+/6G and Internet of Things (IoT) applications for different challenging environments.Publication Embargo Hydrodynamic adjustment of mean flow and turbulence around a sinking boulder during local scouring(Springer Science and Business Media Deutschland GmbH, 2025-04) Ye, C; Zhang, Q. Y; Wang, X. K; Lei, M; Gomes, P.I.A; Yan, X, FThe fact that on a live bed, boulders tend to sink during scouring is usually ignored, weakening the true understanding of hydrodynamics in boulder beds. In this paper, flume experiments were conducted to investigate the hydrodynamics around a boulder over a movable bed with a particle tracking velocimetry (PTV) system. By measuring the velocity field, the major flow characteristics, such as velocity distribution, turbulent kinetic energy (TKE) and bed shear stress, were analyzed. The results show that the sinking boulder apparently mediates the local flow structure and turbulence pattern. The near wake region is located in the range of 2D (D is the particle size of the boulder) downstream of the boulder. There is a near-bed countercurrent in the near wake region, the TKE increase sharply, and the velocity distribution deviates from the logarithmic distribution. Compared with the flat bed, the turbulent kinetic energy extreme point of the boulder riverbed in the near wake area deviate from the bed surface to the water depth at the top of the boulder, and the direction reversal and extreme point appear at the top of the boulder. The bed shear stress increases sharply in the near wake region of 1.5 ~ 2D.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.