Faculty Publications (Engineering)

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    Distributed empirical risk minimization with differential privacy
    (Automatica, 2024) Liu, Changxin; Johansson, Karl H.; Shi, Yang
    This work studies the distributed empirical risk minimization (ERM) problem under differential privacy (DP) constraint. Standard distributed algorithms achieve DP typically by perturbing all local subgradients with noise, leading to significantly degenerated utility. To tackle this issue, we develop a class of private distributed dual averaging (DDA) algorithms, which activates a fraction of nodes to perform optimization. Such subsampling procedure provably amplifies the DP guarantee, thereby achieving an equivalent level of DP with reduced noise. We prove that the proposed algorithms have utility loss comparable to centralized private algorithms for both general and strongly convex problems. When removing the noise, our algorithm attains the optimal O(1/t) convergence for non-smooth stochastic optimization. Finally, experimental results on two benchmark datasets are given to verify the effectiveness of the proposed algorithms.
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    Quantifying the value of building demand response: Introducing a cross-sectoral model framework to optimize demand response scheduling
    (Energy Reports, 2024) Stanislaw, Lauren; Seatle, Madeleine; McPherson, Madeleine
    Co-optimization of demand-side electrification and supply-side variable renewable energy integration in electricity systems can lead to dramatically reduced emissions due to synergies between the two sectors. However, few models exist that represent both sectors in sufficient operational detail. To bridge this gap, this paper proposes a novel framework for transferring information from a building stock model to an electricity system model. First, demand response (DR) events are simulated within a model of building stock energy use. Then, the energy characteristics of these events are used to inform a series of constraints within the electricity system model. At the same time, hourly electricity use predictions from the building stock are also incorporated into the total demand met by the electricity system. This allows the electricity system model to determine the grid-optimal times for the building stock to enact DR. To demonstrate the utility of this framework, a case study into the effects of building efficiency increases, DR, and variable renewable capacity expansion in the city of Regina, Saskatchewan is performed, and various ways to reduce the costs and emissions associated with electricity use in Regina are compared.
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    Numerical investigation of effect of mechanical compression on the transport properties of fuel cell microporous layer using a pore-scale model
    (International Journal of Hydrogen Energy, 2024) Zhang, Heng; Hu, Hao; Sarker, Mrittunjoy; Shao, Xuanyu; Zhan, Zhigang; Sui, Pang-Chieh; Chuang, Po-Ya Abel
    The microporous layer (MPL) plays an important role in water and thermal management of proton exchange membrane fuel cells (PEMFCs). An in-depth investigation of the mechanical compression effect on transport properties in the MPL can help optimize cell performance. In this work, the microstructure of the MPL is numerically reconstructed and the finite element method is applied to simulate mechanical behavior. Besides, the distribution of stress-strain, porosity, and pore size in the MPL under ten different levels of mechanical compression strains are studied. Lastly, the pore-scale model is employed to investigate the effective transport properties of the MPL as a function of compression strain. The analysis reveals that as the MPL strain increases from 0% to 40%, there is a 29% decrease in porosity, a 50% reduction in average pore diameter, a 60% decrease in effective gas diffusivity, a 100% increase in tortuosity, and an 80% increase in electrical and thermal conductivity. With the escalation of mechanical compression, both the magnitude and uniformity of stress-strain-displacement concurrently rise. Mechanical compression strains below 20% exhibit a lesser impact on transport properties. Beyond this threshold, exceeding the 20% compression strain point, mechanical stress assumes a critical role in influencing MPL transport properties.
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    Hydrogen from food waste: Energy potential, economic feasibility, and environmental impact for sustainable valorization
    (Energy Reports, 2024) Hossain, Md. Sanowar; Wasima, Fairuz; Shawon, Md. Sharul Islam Khan; Das, Barun K.; Das, Pronob; Paul, Sanjay
    Globally, inefficient management of municipal solid waste, composed primarily of food waste poses concern for human and environmental well-being. Food waste can be converted into hydrogen gas, which can be utilized to generate power without emitting any harmful pollutants. This solution would also help with the issue of disposing of food waste. The conversion of food waste into hydrogen is a practical energy source with potential financial benefits. This study explores the transformative potential of converting food waste into renewable energy through hydrogen production, focusing on Bangladesh from 2023 to 2042. Notably, the study forecasts a surge in food waste from 23 million tons in 2023–110 million tons by 2042. By 2042, food waste is expected to generate 2480 MW of power, a rise from 489 MW in 2023. Based on the results of the economic study, the food waste into hydrogen via gasification project is financially viable in all of Bangladesh's main cities. Metrics such as internal rate of return, payback period, levelized cost of energy, net present value, and total life cycle cost were used to assess economic viability. The hydrogen production cost, payback period, and internal rate of return are 2.05 $/kg, 11 years and 14% respectively. It was discovered that using the available electricity from hydrogen gas may displace 1428 M liters of diesel fuel combustion. The quantity of diesel fuel saved can cut carbon dioxide emissions by 3.85 million tons. It was also found that using hydrogen as a source of energy generation has an attractive ecological efficiency of 99.98%. This research provides novel and pertinent data for investors contemplating gasification-based energy projects in Bangladesh. It pioneers a path toward eco-friendly waste management, reduced greenhouse gas emissions, and the adoption of sustainable energy solutions for the country.
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    A protocol for developing and evaluating neural network-based surrogate models and its application to building energy prediction
    (Renewable and Sustainable Energy Reviews, 2024) Hou, D.; Evins, R.
    Because of their low computational costs, surrogate models (SMs), also known as meta-models, have attracted attention as simplified approximations of detailed simulations. Besides conventional statistical approaches, machine-learning techniques, such as neural networks (NNs), have been used to develop surrogate models. However, surrogate models based on NNs are currently not developed in a consistent manner. The development process of the models is not adequately described in most studies. There may be some doubt regarding the abilities of such models due to a lack of documented validation. In order to address these issues, this paper presents a protocol for the systematic development of NN-based surrogate models and how the procedure should be reported and justified. The protocol covers the model development procedure sample generation, data processing, SM training and validation, how to report the implementation, and how to justify the modeling choices. The protocol is used to critically review the quality of NN-based SMs in the prediction of building energy consumption. Sixty-eight papers are reviewed, and details of the developed surrogate models are summarized. The reported developing procedures were evaluated using the criteria proposed in the protocol. The results show that the selection of the number of neurons is the best-implemented step with a justification, followed by the determination of model architecture, mostly justified in a discussion way. While greater focus should be given to sample dataset generation, especially input variables selection, considering independence check and clear report of model validation on training and test data. Also, data preprocessing is strongly recommended.
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    Scapular morphology variation affects reverse total shoulder arthroplasty biomechanics. A predictive simulation study using statistical and musculoskeletal shoulder models
    (Journal of Orthopaedic Research, 2024) Silvestros, Pavlos; Athwal, George S.; Giles, Joshua
    Reverse total shoulder arthroplasty (RTSA) accounts for over half of shoulder replacement surgeries. At present, the optimal position of RTSA components is unknown. Previous biomechanical studies have investigated the effect of construct placement to quantify mobility, stability and functionality postoperatively. While studies have provided valuable information on construct design and surgical placement, they have not systematically evaluated the importance of scapular morphology on biomechanical outcomes. The aim of this study was to assess the influence of scapular morphology variation on RTSA biomechanics using statistical models, musculoskeletal modeling and predictive simulation. The scapular geometry of a musculoskeletal model was altered across six modes of variation at four levels (±1 and ±3 SD) from a clinically derived statistical shape model. For each model, a standardized virtual surgery was performed to place RTSA components in the same relative position on each model then implemented in 50 predictive simulations of upward and lateral reaching tasks. Results showed morphology affected functional changes in the deltoid moment arms and recruitment for the two tasks. Variation of the anatomy that reduced the efficiency of the deltoids showed increased levels of muscle force production, joint load magnitude and shear. These findings suggest that scapular morphology plays an important role in postoperative biomechanical function of the shoulder with an implanted RTSA. Furthermore a “one-size-fits-all” approach for construct surgical placement may lead to suboptimal patient outcomes across a clinical population. Patient glenoid as well as scapular anatomy may need to be carefully considered when planning RTSA to optimize postoperative success.
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    High-throughput exploration of triple-cation perovskites via all-in-one compositionally-graded films
    (Small, 2023) Moradi, Shahram; Kundu, Soumya; Awais, Muhammad; Haruta, Yuki; Nguyen, Hai-Dang; Zhang, Dongyang; Tan, Furui; Saidaminov, Makhsud I.
    Many devices heavily rely on combinatorial material optimization. However, new material alloys are classically developed by studying only a fraction of giant chemical space, while many intermediate compositions remain unmade in light of the lack of methods to synthesize gapless material libraries. Here report a high-throughput all-in-one material platform to obtain and study compositionally-tunable alloys from solution is reported. This strategy is applied to make all CsxMAyFAzPbI3 perovskite alloys (MA and FA stand for methylammonium and formamidinium, respectively), in less than 10 min, on a single film, on which 520 unique alloys are then studied. Through stability mapping of all these alloys in air supersaturated with moisture, a range of targeted perovskites are found, which are then chosen to make efficient and stable solar cells in relaxed fabrication conditions, in ambient air. This all-in-one platform provides access to an unprecedented library of compositional space with no unmade alloys, and hence aids in a comprehensive accelerated discovery of efficient energy materials.
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    Stackelberg–Nash game approach for price-based demand response in retail electricity trading
    (International Journal of Electrical Power and Energy Systems, 2024) Wan, Yanni; Qin, Jiahu; Shi, Yang; Fu, Weiming; Xiao, Feng
    This paper studies the price-based demand response problem in a deregulated retail electricity trading, aiming to coordinate the energy consumption behavior of end-users under dynamic retail prices. The challenge here is that in addition to the hierarchical decision-making process between utility company and end-users considered in existing works, the non-cooperative and competitive interdependence among end-users cannot be ignored. To address this issue, we first construct a novel Stackelberg–Nash game, in which the Stackelberg game is used to capture the hierarchical decision-making process between utility company and end-users, while the Nash game is dedicated to describing the interdependence among end-users. Then the existence and uniqueness of the Stackelberg–Nash equilibrium is provided along with theoretical analysis. On the basis of the analysis of equilibrium, we propose a distributed iterative algorithm with an adaptive step size, which is benchmarked with a fixed step-size algorithm. The comparison results on a real-life residential retail electricity market show that our proposed algorithm has better performance in terms of effectiveness and scalability.
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    Ensuring secure platooning of constrained intelligent and connected vehicles against Byzantine attacks: A distributed MPC framework
    (Engineering, 2023) Wei, Henglai; Zhang, Hui; AI-Haddad, Kamal; Shi, Yang
    This study investigates resilient platoon control for constrained intelligent and connected vehicles (ICVs) against F-local Byzantine attacks. We introduce a resilient distributed model-predictive platooning control framework for such ICVs. This framework seamlessly integrates the predesigned optimal control with distributed model predictive control (DMPC) optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles. Notably, our strategy uses previously broadcasted information and a specialized convex set, termed the “resilience set”, to identify unreliable data. This approach significantly eases graph robustness prerequisites, requiring only an (F + 1)-robust graph, in contrast to the established mean sequence reduced algorithms, which require a minimum (2F + 1)-robust graph. Additionally, we introduce a verification algorithm to restore trust in vehicles under minor attacks, further reducing communication network robustness. Our analysis demonstrates the recursive feasibility of the DMPC optimization. Furthermore, the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs, while ensuring constraint compliance and cybersecurity. Simulation results verify the effectiveness of our theoretical findings.
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    Solvi: A visual constraint modeling tool
    (Journal of Computer Languages, 2024) Zhu, Xu; Nacenta, Miguel; Akgün, Özgür; Zenkovitch, Daniel
    Discrete constraint problems surface often in everyday life. Teachers might group students with complex considerations and hospital administrators need to produce staff rosters. Constraint programming (CP) provides techniques to efficiently find solutions. However, there remains a key challenge: these techniques are still largely inaccessible because expressing constraint problems requires sophisticated programming and logic skills. In this work we contribute a language and tool that leverage knowledge of how non-experts conceptualize problems to facilitate the expression of constraint models. Additionally, we report the results of a study surveying the advantages and remaining challenges towards making CP accessible to the wider public.
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    All-scalable CH₃NH₃PbI₃ perovskite solar cells fabricated in ambient air
    (Solar RRL, 2023) Ahmed, Yameen; Gangadharan, Deepak Thrithamarassery; Kokaba, Mohammad Reza; Yeddu, Vishal; Awais, Muhammad; Zhang, Dongyang; Kamraninejad, Vahid; Saidaminov, Makhsud I.
    Perovskite solar cells (PSCs) are an attractive emerging photovoltaic technology due to their high-performance while being made by low-cost fabrication processes. The most efficient PSCs are small area and made by nonscalable coating method in an inert atmosphere, but these sizes and fabrication conditions are commercially irrelevant. Herein, fabrication of PSCs is reported using only scalable methods, that is, slot-die coating and blade coating methods, all in ambient air. The tolerance to relaxed fabrication conditions is enabled by the use of hydrated nonhalogenated lead source. Resurfacing strategy is then introduced to suppress charge carrier nonradiative recombination and obtained an efficiency of 19.91% for rigid and 17.4% for flexible PSCs by all-scalable fabrication. To the best of our knowledge, these are the highest efficiencies for n–i–p structured MAPbI3-based PSCs in ambient air using all-scalable method to date. The devices showed excellent tolerance to oxygen and moisture (ISOS-D-1) as well as stable maximum power point operation following burn in a dry air glove box (relative humidity ≈ 20%) without encapsulation.
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    Building energy simulation and its application for building performance optimization: A review of methods, tools, and case studies
    (Advances in Applied Energy, 2023) Pan, Yiqun; Zhu, Mingya; Lv, Yan; Yang, Yikun; Liang, Yumin; Yin, Ruxin; Yang, Yiting; Jia, Xiaoyu; Wang, Xi; Zeng, Fei; Huang, Seng; Hou, Danlin; Xu, Lei; Yin, Rongxin; Yuan, Xiolei
    As one of the most important and advanced technology for carbon-mitigation in the building sector, building performance simulation (BPS) has played an increasingly important role with the powerful support of building energy modelling (BEM) technology for energy-efficient designs, operations, and retrofitting of buildings. Owing to its deep integration of multi-disciplinary approaches, the researchers, as well as tool developers and practitioners, are facing opportunities and challenges during the application of BEM at multiple scales and stages, e.g., building/system/community levels and planning/design/operation stages. By reviewing recent studies, this paper aims to provide a clear picture of how BEM performs in solving different research questions on varied scales of building phase and spatial resolution, with a focus on the objectives and frameworks, modelling methods and tools, applicability and transferability. To guide future applications of BEM for performance-driven building energy management, we classified the current research trends and future research opportunities into five topics that span through different stages and levels: (1) Simulation for performance-driven design for new building and retrofit design, (2) Model-based operational performance optimization, (3) Integrated simulation using data measurements for digital twin, (4) Building simulation supporting urban energy planning, and (5) Modelling of building-to-grid interaction for demand response. Additionally, future research recommendations are discussed, covering potential applications of BEM through integration with occupancy and behaviour modelling, integration with machine learning, quantification of model uncertainties, and linking to building monitoring systems.
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    Datasets of disrupted transportation networks on Canada's West Coast in a plausible M9.0 Cascadia Subduction Zone earthquake scenario
    (Data in Brief, 2023) Souza Almeida, Luana; Rodrigues, Lauryne; Goerlandt, Floris; Ancona-Segovia, Jose; Pelot, Ronald; Bristow, David; Chang, Stephanie
    This article presents a database with geographical and demographic information characterizing the impacts to road and maritime networks, and coastal communities, of a plausible magnitude M9.0 megathrust Cascadia Subduction Zone earthquake scenario near Vancouver Island in British Columbia, Canada. The database consists of a medium and a high impact case associated with the earthquake scenario. The data include the geographical location of communities, ports, and airports/helipads/heliports, the structure of the roads network and their expected damage levels, the resilience level and population size of the communities on Vancouver Island, and the trajectories, expected delays and capacities of ferries and barges. The data originates from government and carriers’ open available reports and external datasets, and several impact models. The primary purpose of this database is to support disaster management researchers working to develop and test network models that focus on road repair and restoration, and on the multi-modal distribution of relief supplies to victims. In addition, the data can be used to test heuristic and metaheuristic approaches applied to network models in the context of natural disasters.
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    Ensemble Siamese Network (ESN) using ECG signals for human authentication in smart healthcare system
    (Sensors, 2023) Hazratifard, Mehdi; Agrawal, Vibhav; Gebali, Fayez; Elmiligi, Haytham; Mamun, Mohammad
    Advancements in digital communications that permit remote patient visits and condition monitoring can be attributed to a revolution in digital healthcare systems. Continuous authentication based on contextual information offers a number of advantages over traditional authentication, including the ability to estimate the likelihood that the users are who they claim to be on an ongoing basis over the course of an entire session, making it a much more effective security measure for proactively regulating authorized access to sensitive data. Current authentication models that rely on machine learning have their shortcomings, such as the difficulty in enrolling new users to the system or model training sensitivity to imbalanced datasets. To address these issues, we propose using ECG signals, which are easily accessible in digital healthcare systems, for authentication through an Ensemble Siamese Network (ESN) that can handle small changes in ECG signals. Adding preprocessing for feature extraction to this model can result in superior results. We trained this model on ECG-ID and PTB benchmark datasets, achieving 93.6% and 96.8% accuracy and 1.76% and 1.69% equal error rates, respectively. The combination of data availability, simplicity, and robustness makes it an ideal choice for smart healthcare and telehealth.
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    Heat transfer and evaporation processes from the Enskog-Vlasov equation and its moment equations
    (2024-02-14) Struchtrup, Henning; Jahandideh, Hamidreza; Couteau, Arthur; Frezzotti, Aldo
    Nonequilibrium heat and mass transfer processes through liquid-vapor interfaces are studied through solutions of the Enskog-Vlasov (EV) and the corresponding system of EV26 moment equations. These models fully resolve liquid and vapor bulk regions and the diffuse interface connecting both. With that, evaporation and heat transfer processes can be studied without the need of modelled interface relations. Comparison of numerical results shows qualitative agreement of moment simulations with DSMC solutions of the EV equation, but quantitative differences. Interface resistivities for jump interface conditions are determined from the simulations, which show marked differences to those found from classical kinetic theory, where dimensionless resistivities are constants. In contrast, the EV models give temperature dependent resistivities, some negative off-diagonal resistivities, and indicate non-linear behavior where resistivities depend on mass and heat fluxes through the interface. In summary, the results point to the urgent need for systematic evaluation of resistivities over a wide range of conditions between weak and strong nonequilibrium, close to and far from the critical point.
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    A new macroscopic traffic flow characterization incorporating traffic emissions
    (Applied Sciences, 2023) Qaiser, Tanveer; Altamimi, Ahmed B.; Khan, Fayaz A.; Alsaffar, Mohammad; Alreshidi, Abdulrahman; Khattak, Khurram S.; Khan, Zawar H.; Khan, Wilayat
    Densely populated cities have led to increased traffic congestion and, consequently, increased greenhouse gas emissions from vehicles. Thus, it is important to develop traffic models to overcome congestion and increased air pollution. In the literature, traffic model characterizations rely predominantly on traffic dynamics and ignore traffic emissions. In this study, a new macroscopic model targeting traffic emissions and drivers’ presumption based on traffic emissions is proposed to overcome traffic congestion and pollution. The traffic emissions characterization was based on the CO₂ data employed in the second traffic system. For the performance analysis, the results of the proposed and Zhang’s traffic models were compared. The results were obtained using the ROE technique to predict traffic evolution. The scheme was implemented in MATLAB. Compared with Zhang’s traffic model, the suggested traffic model based on emissions reflected traffic behavior more realistically.
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    Knowledge-based features for speech analysis and classification: pronunciation diagnosis
    (Electronics, 2023) Liu, Lichuan; Li, Wei; Morris, Sherrill; Zhuang, Mutian
    Accurate pronunciation of speech sounds is essential in communication. As children learn their native language, they refine the movements necessary for intelligible speech. While there is variability in the order of acquisition of speech sounds, there are some sounds that are more complex and are later developing. The rhotic /r/ is a later-developing sound in English, and some children require intervention to achieve accurate production. Additionally, individuals learning English as a second language may have difficulty learning accurate /r/ production, especially if their native language does not have an /r/, or the /r/ they produce is at a different place of articulation. The goal of this research is to provide a novel approach on how a knowledge-based intelligence program can provide immediate feedback on the accuracy of productions. In the proposed approach, the audio signals will first be detected, after which features of audio signals will be extracted, and finally, knowledge-based intelligent classification will be performed. Based on the obtained knowledge and application scenarios, novel features are proposed and used to classify various speaker scenarios.
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    DOPESLAM: High-precision ROS-based semantic 3D SLAM in a dynamic environment
    (Sensors, 2023) Roch, Jesse; Jamil, Fayyad; Homayoun, Najjaran
    Recent advancements in deep learning techniques have accelerated the growth of robotic vision systems. One way this technology can be applied is to use a mobile robot to automatically generate a 3D map and identify objects within it. This paper addresses the important challenge of labeling objects and generating 3D maps in a dynamic environment. It explores a solution to this problem by combining Deep Object Pose Estimation (DOPE) with Real-Time Appearance-Based Mapping (RTAB-Map) through means of loose-coupled parallel fusion. DOPE’s abilities are enhanced by leveraging its belief map system to filter uncertain key points, which increases precision to ensure that only the best object labels end up on the map. Additionally, DOPE’s pipeline is modified to enable shape-based object recognition using depth maps, allowing it to identify objects in complete darkness. Three experiments are performed to find the ideal training dataset, quantify the increased precision, and evaluate the overall performance of the system. The results show that the proposed solution outperforms existing methods in most intended scenarios, such as in unilluminated scenes. The proposed key point filtering technique has demonstrated an improvement in the average inference speed, achieving a speedup of 2.6× and improving the average distance to the ground truth compared to the original DOPE algorithm.
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    Macroscopic traffic characterization based on driver memory and traffic stimuli
    (Transportation Engineering, 2023) Khan, Zawar H.; Imran, Waheed; Gulliver, T. Aaron; Khattak, Khurram S.; Din, Ghayas Ud; Minallah, Nasru; Khan, Mushtaq A.
    A new macroscopic traffic flow model is proposed which incorporates traffic alignment behavior at transitions. In this model, velocity is a function of the distance headway and driver response time. It can be used to characterize the traffic flow for both uniform and non uniform headways. The well-known Zhang model characterizes this flow based on driver memory which can produce unrealistic results. The performance of the proposed Khan-Imran-Gulliver (KIG) and Zhang models is evaluated for an inactive bottleneck on a 2000 m circular road. The results obtained show that the traffic behavior with the KIG model is more realistic.
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    Cavitation hydrodynamic performance of 3-D printed highly skewed stainless steel tidal turbine rotors
    (Energies, 2023) Pitsikoulis, Stylianos Argyrios; Tekumalla, Sravya; Sharma, Anurag; Wong, Wai Leong Eugene; Turkmen, Serkan; Liu, Pengfei
    Hydraulic turbines contribute to 60% of renewable energy in the world; however, they also entail some adverse effects on the aquatic ecology system. One such effect is their excessive noise and vibration. To minimize this effect, one of the most effective and feasible solutions is to modify the design of the turbine rotor blade by introducing a skew. In this study, two 0.3-meter tidal turbines with 0-degree (no-skewness) and positive 90-degree skewness made of stainless steel 316L were designed and printed using a 3-D printing powder bed fusion technique. These rotors were then tested at the Emerson Cavitation Tunnel (ECT) at Newcastle University, UK, and the variation in the skewness of the blades of the turbines as a function of the power coefficient on a given tip speed ratio (TSR) value was ascertained. Results showed that the highly skewed rotor had significantly lower drag and torque fluctuations, with a slight decrease in efficiency compared to the non-skewed one, which warrants further investigation on the effect of added skew to reduce vibration and noise. Numerical simulations were also performed for verification and validation of the experimental tests, using the H45 dynamometer at the ECT. A comprehensive software code for propellers and tidal turbines, ROTORYSICS, was used to examine the cavitation effect of the two rotors; a comparison was made for both, with and without cavitation. The results indicate that for a high immersion depth of tidal turbine rotors, cavitation rarely occurs, but for hydrokinetic turbines that are installed on dams in rivers and falls, cavitation could be a serious concern. It was concluded that the 0-degree skewed rotor is more hydrodynamically efficient than the 90-degree skewed rotor.
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