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Delta winglets are an effective means for enhancing heat exchange and thus the performance of renewable energy technologies, including solar energy. A pair of 0.1 mm thick, 15 mm high (h) and 30 mm long aluminum winglets separated with transversal spacing, s, of 2 h, h and 0 were scrutinized in a closed-loop wind tunnel at a Reynolds number based on h of 6300. The turbulent flow was characterized using a 3D hotwire probe, and the heat convection augmentation was quantified in terms of the normalized Nusselt number ( Nu/ Nu 0), indicating the heat transfer improvement compared to the reference case without the winglets. The interaction of the organized counter-rotational vortices intensifies and they become indiscernible at s = 0. The peak strain rate at 10h downstream increased from 390 s –1, to 478 s –1, to 514 s –1, when the spacing decreased from 2 h to h to 0, respectively. The zero-spaced winglet pair provided the largest Nu/ Nu 0, of around 1.21, at X/ h = 10 and Y/ h = 0, approximately 21% higher than that of 2 h-spaced winglet pair, due to the strongest strain rate and the absence of upwash flow. On the other hand, the 2 h-spaced winglet pair provided the largest span-averaged Nu/ Nu 0, which is of practical significance.

The failure progression of wind turbine bearings comprises of multiple degraded health states due to applied load by varying operating conditions (VOC). Therefore, determining the VOC impact on the failure dynamics severity is an essential task for bearing failure prognostics. This article introduces a hybrid prognosis method using real-time supervisory control and data acquisition (SCADA) and vibration signals to predict remaining useful life (RUL) for wind turbine bearings. The SCADA data are utilized to define the role of environmental conditions such as wind speed and ambient temperature in bearing failure dynamics. Afterward, for each environmental condition, failure dynamics are identified by the vibration signal. Finally, RUL of the faulty bearings is forecast via an adaptive Bayesian algorithm using the failure dynamics, conditional to the VOC. The efficacy of the method is validated using experimental data, and test results indicate a higher RUL accuracy compared to the Bayesian algorithm.

Flexible pressure sensors with self-healing abilities for wearable electronics are being developed, but generally either lack autonomous self-healing properties or require sophisticated material processing methods. To address this challenge, we developed flexible, low-cost and autonomously self-healing capacitive sensors using a crosslinked poly(dimethylsiloxane) through metal-ligand interactions processed into thin films via slot-die coating. These films have excellent self-healing properties, approximately 1.34 × 105 μm3 per hour at room temperature and 2.87 × 105 μm3 per hour at body temperature (37 °C). Similarly, no significant change in capacitance under bending strain was observed on these flexible thin-films when assembled on poly(ethyleneterephthalate) (PET) substrates; capacitors showed good sensitivity at low pressure regimes. More importantly, the devices fully recovered their sensitivity after being damaged and healed, which is directly attributed to the rapid and autonomous self-healing of the dielectric materials.

As wind energy is becoming a significant utility source, minimizing the operation and maintenance (O&M) expenses has raised a crucial issue to make wind energy competitive to fossil fuels. Wind turbines (WTs) are subject to unexpected failures due to operational and environmental conditions, aging, and so on. An accurate estimation of time to failures assures reliable power production and lower maintenance costs. In recent years, a notable amount of research has been undertaken to propose prognosis techniques that can be employed to forecast the remaining useful life (RUL) of wind farm assets. This article provides a recent literature review on modeling developments for the RUL prediction of critical WT components, including physics-based, artificial intelligence (AI)-based, stochastic-based, and hybrid prognostics. In particular, the pros and cons of the prognosis models are investigated to assist researchers in selecting proper models for certain applications of WT RUL forecast. Our comprehensive review has revealed that hybrid methods are now the leading and most accurate tools for WT failure predictions over individual hybrid components. Strong candidates for future research include the consideration of variable operating environments, component interaction, physics-based prognostics, and the Bayesian application in the development of WT prognosis methods.

This paper presents a T-junction microdroplet generator equipped with a pneumatic actuation for controlling the droplet size. The multi-layer device is compatible with rapid manufacturing using a desktop-based laser cutter to fabricate the fluidic and pneumatic layers. A T-junction fluidic layer sits at the bottom and the control layer consists of a pneumatic chamber, as well as inlet and outlet channels are placed at the top. Our results with pneumatic control showed that the generated droplet size is inversely proportional to the pressure inside the pneumatic chamber. Pneumatic pressure of up to 0.4 MPa showed a 38% reduction in droplet size compared to without control. The droplet size can additionally be regulated by controlling the relative flow rates of the continuous and dispersed fluid. Pneumatic actuation is advantageous, as it does not require additional fluid usage and the flow rate does not need to be ramped up to decrease the droplet size. The device presented in this paper with pneumatic control and fabrication ease provides an attractive method for microdroplet manipulation.

This paper presents a novel MEMS based ring resonator design inspired by the rose petal structure. The proposed design has four petal springs supporting the ring structure surrounded by sense electrodes, with a stem in the middle offering simplicity in fabrication and easy control of movement in the driving and sensing modes. An equation was developed to calculate the stiffness of the support springs, and the stiffness of the proposed design was analyzed and compared to other design parameters. The key geometric elements, such as the petals and the ring dimensions of the proposed design, were selected using finite element modeling techniques to enhance the sensitivity for gyroscope application. The resonance frequencies for different mode shapes were optimized by changing the geometric parameters of the proposed structure. Simulation showed that the proposed design has degenerate mode shapes (with potentially 0 Hz frequency splitting) at resonance frequencies in the range of 25–74 kHz, controlled by increasing the structure’s width from 10 to 30 µm. Scaled-down prototypes of the selected design were fabricated using a customized surface micromachining process. The results of the experiment show that the prototype has a resonance frequency at 64.91 kHz which validates the resonance frequency of the computational model at 64.89 kHz of the same prototype. Good agreement between the simulation and experimental model showed the proof of concept of the design and successful fabrication process.

In North America, many utility-scale turbines are approaching the half-way point of their anticipated initial lifespan. Accurate remaining useful life (RUL) estimation can provide wind farm owners insight into how to optimize the life and value of their farm assets. An improved understanding of the RUL of turbine components is particularly essential as many owners consider retiring, life-extending, or repowering their farms. In this article, an integrated prognosis method based on signal processing and an adaptive Bayesian algorithm is proposed to predict the RUL of various faulty bearings in wind turbines. The signal processing leverages feature extraction, feature selection, and signal denoising to detect the dynamics of various failures. Then, RUL of the faulty bearings is forecast via the adaptive Bayesian algorithm using the extracted features. Finally, a new fusion method based on an ordered weighted averaging (OWA) operator is applied to the RUL obtained from the features to improve accuracy. The efficacy of the method is evaluated using data from historical failures across three different Canadian wind farms. Experimental test results indicate that the OWA operator delivers a higher accuracy for RUL prediction compared to the other feature-based methods and Choquet integral fusion technique.

The enhancement of the breakdown power during the spark discharge process has been proved to be beneficial for the flame kernel formation process under lean/diluted conditions. Such a strategy is realized by using a conventional transistor coil ignition system with an add-on capacitance in parallel to the spark plug gap in this paper. In practical application, the use of different ceramic material other than aluminum oxide can change the parasitic capacitance of the spark plug, achieving similar effect in terms of rescheduling the discharge energy released during the breakdown phase. Detailed research has been carried out to investigate the effect of the parallel capacitance and the cross flow velocity on the flame kernel formation and propagation process. With the increase in parallel capacitance, more spark energy is delivered during the breakdown phase, while less energy is released during the arc/glow phase. Shadowgraph images of the spark plasma reveal that the high-power spark discharge can generate a larger high-temperature area with enhanced electrically prompted turbulence under quiescent conditions, as compared with that using the conventional transistor coil ignition discharge strategy under the same condition. The breakdown enhanced turbulence of the high-power spark is proved to be beneficial for the flame kernel development, especially with the lean or exhaust gas recirculation diluted combustible mixtures, given that sufficient spark energy is available for the high-power spark strategy to successfully generate the breakdown event. The results of combustion tests under flow conditions reveal that the breakdown enhanced turbulence of the high-power spark tends to be overshadowed by the turbulence generated from the flow field, and both the increase in flow velocity and parallel capacitance contribute to the reduction in discharge duration of the arc/glow phase. Therefore, the benefits brought about by the high-power spark discharge tend to diminish with the intensification of flow velocity.

Spark discharge under flow conditions in different gas media is compared. The gases include nitrogen, pure methane, and methane–air mixtures with different volume ratios. The self-illuminated spark plasma photos, and the discharge current and voltage waveforms are recorded.

Gear faults contribute to a significant portion of failures in wind turbine system. As such, condition monitoring and fault detection of these components assist in maintenance scheduling; hence, preventing catastrophic failures of the gearbox. This paper introduces a new hybrid fault detection approach to detect gear faults in wind turbines. to accomplish this task, vibration signals are collected and used to extract various time-domain features. Next, a Dynamic Principle Component Analysis (DPCA) is adaptively employed to identify failure dynamics by reducing the time-domain feature dimension. Following that, a Support Vector Machine (SVM) is implemented to detect and isolate gear faults. Experimental test studies with ten-year historical data of three wind farms in Canada are conducted. Test results indicate that the proposed hybrid approach performs superior compared to DPCA using Multilayer Perceptron (MLP) Neural Networks (NNs).

This book presents pioneering insights into advancing a sustainable tomorrow using innovative engineering and architecture techniques. It provides useful discussions on all aspects of sustainable engineering—from elevator technology and hydrogen energy to self-sufficient microbial fuel cell in architecture—that may help to shape future directions. The broad, comprehensive coverage serves as a sound starting point to understand current technological advances. Sustainable Engineering Technologies and Architectures provides:Overviews of renewable energy and sustainability, nature-friendly architecture, marine biodiversity, and energy securityInsights into frontier advances in the fields of sustainable urbanization, clean water, and clean airContributions by world-renowned experts in agriculture and irrigation, elevator design, architectures, biofuels, renewable energy, and hydrogen economy Seasoned engineers, architecture researchers, policy makers, and environmental enthusiasts will find this a pragmatic beacon for striving forward. It is suitable as an introductory text for graduate and senior undergraduate classes on energy, renewable energy, the environment, sustainability, and/or sustainable living.

Governments have implemented different interventions and response models to combat the spread of COVID-19. The necessary intensity and frequency of control measures require us to project the number of infected cases. Three short-term forecasting models were proposed to predict the total number of infected cases in Canada for a number of days ahead. The proposed models were evaluated on how their performance degrades with increased forecast horizon, and improves with increased historical data by which to estimate them. For the data analyzed, our results show that 7 to 10 weeks of historical data points are enough to produce good fits for a two-weeks predictive model of infected case numbers with a NRMSE of 1% to 2%. The preferred model is an important quick-deployment tool to support data-informed short-term pandemic related decision-making at all levels of governance.

Efficiency and cost-effectiveness play dominant roles in the commercialization of thermoelectric generator (TEG) technology. In this paper, the exergy analysis of a TEG module with 199 TE couples was considered. Two objective functions, the exergy efficiency and levelized cost of energy (LCOE), were established for exergoeconomic analysis. The geometric structure and working conditions involving TE couple length, base area ratio, working temperature, and load resistance were varied. The particle swarm optimization (PSO) method has excellent convergence and few parameters need to be adjusted. Mutation can increase randomization for the PSO method, making it possible to improve its search direction. Therefore, the mutation-PSO (M-PSO) algorithm was used to optimize the exergy efficiency and LCOE for the TEG. Through the M-PSO algorithm, the optimum corresponds to an exergy efficiency of 29% and LCOE of 1.93 $US/kWh·m2 under a maximum temperature difference of 40 K. In order to achieve a balance between the two exergoeconomic indices, the ξ-constraint combined with the M-PSO method was used to obtain alternatives, named Pareto solutions. Then, these alternatives were ranked to acquire an ideal solution based on a technique for order preference by similarity ideal solution (TOPSIS) method. The TOPSIS ideal solution corresponds to an exergy efficiency of 22.2% and LCOE of 3.02 $US/kWh·m2.

Air accumulator is a critical component in Underwater Compressed Air Energy Storage systems and Buoyancy Energy Storage systems. In order to bridge the gap between the CFD simulations and experiment verification of flow around air accumulator, in this study, the force characteristics and flow structures around a balloon-shaped air accumulator bluff body model have been investigated using a computational-experimental dual prong method for the first time. The three-dimensional Unsteady Reynolds Averaged Navier-Stokes (URANS) closure is achieved through the k-ω SST turbulence model in FLUENT. The force characteristics and flow structures are measured in a low-speed closed-loop wind tunnel to verify the simulation results. The free end and shape effects are investigated by comparing the flow structure of three different but interconnected bluff bodies at the equivalent Reynolds number of 7.0 × 104. The results show that the k-ω SST turbulence model can correctly predict the time-averaged force characteristics of the balloon-shaped bluff body and flow structures in the wake. Besides, the distinct vortex structures are induced due to the free end and tapered shape effects.