Piezoelectric Energy Harvesting Controlled with an IGBT H-Bridge and Bidirectional Buck-Boost for Low-Cost 4G Devices.

In this work, a semi-submersible piezoelectric energy harvester was used to provide power to a low-cost 4G Arduino shield. Initially, unsteady Reynolds averaged Navier-Stokes (URANS)-based simulations were conducted to investigate the dynamic forces under different conditions. An adaptive differential evolution (JADE) multivariable optimization algorithm was used for the power calculations. After JADE optimization, a communication cycle was designed. The shield works in two modes: communication and power saving. The power-saving mode is active for 285 s and the communication mode for 15 s. This cycle consumes a determinate amount of power, which requires a specific piezoelectric material and, in some situations, an extra power device, such as a battery or supercapacitor. The piezoelectric device is able to work at the maximum power point using a specific Insulated Gate Bipolar Transistor (IGBT) H-bridge controlled with a relay action. For the extra power supply, a bidirectional buck-boost converter was implemented to flow the energy in both directions. This electronic circuit was simulated to compare the extra power supply and the piezoelectric energy harvester behavior. Promising results were obtained in terms of power production and energy storage. We used 0.59, 0.67 and 1.69 W piezoelectric devices to provide the energy for the 4G shield and extra power supply device.

Adaptive mesh refinement (AMR) criteria comparison for the DrivAer model.

Aerodynamics is one of the main areas of development in vehicle design. One of the most efficient ways of testing the aerodynamic design of a vehicle is to use Computational Fluid Dynamics (CFD), which allows for faster and more accurate aerodynamic simulations, which in turn helps increase the fuel economy and electric vehicle's range. Resource optimization is one of the most important aspects of CFD, and one of its main aspects is the spatial discretization of the fluid domain. This study discusses the use of Adaptive Mesh Refinement (AMR) for the aerodynamic design of private vehicles. This paper compares the results obtained with the use of AMR based on different fluid dynamic criteria for the DrivAer model and correlates the results with experimental data and computational results provided by various authors in previous publications. Four different optimization functions are defined and compared. The results for the drag coefficient, pressure coefficient, and total pressure wake have been correlated, showing great accuracy. This study has proven that the use of AMR highly optimizes computational resources by optimizing the mesh in the desired areas, thereby reducing the number of cells needed elsewhere. The use of these criteria has proven useful for drag coefficient prediction simulations because these criteria make use of the AMR to optimize the wake region.

Numerical performance of CO2 accumulation and droplet dispersion from a cough inside a hospital lift under different ventilation strategies.

The impact of mechanical ventilation on airborne diseases is not completely known. The recent pandemic of COVID-19 clearly showed that additional investigations are necessary. The use of computational tools is an advantage that needs to be included in the study of designing safe places. The current study focused on a hospital lift where two subjects were included: a healthy passenger and an infected one. The elevator was modelled with a fan placed on the middle of the ceiling and racks for supplying air at the bottom of the lateral wall. Three ventilation strategies were evaluated: a without ventilation case, an upwards-blowing exhausting fan case and a downwards-blowing fan case. Five seconds after the elevator journey began, the infected person coughed. For the risk assessment, the CO2 concentration, droplet removal performance and dispersion were examined and compared among the three cases. The results revealed some discrepancies in the selection of an optimal ventilation strategy. Depending on the evaluated parameter, downward-ventilation fan or no ventilation strategy could be the most appropriate approach.

Tobacco smoking model containing snuffing class.

In recent years, the world has faced many destructive diseases that have taken many lives across the globe. To resist these diseases, humankind needs medicine to control, cure, and predict the behaviour of such problems. Recently, the Corona virus, which primarily affects the lungs, has threatened the globe. Similarly, tobacco-related illnesses impair the immune system, and this reduces the ability to fight against Covid-19. This tobacco-smoking version is vital for the researchers to reap the solution by using the q-homotopy analysis transform method with the useful resource of the Atangana-Baleanu-Caputo impression. Hence, the graphical illustrations have been discussed to achieve a solution for this mathematical model. This work applies the q-homotopy analysis transform method to the preeminent fractional operator Atangana-Baleanu-Caputo to better comprehend the infectious model of tobacco snuffing and smoking. Figures and tables are used to display the outcomes. The paper also aids in the analysis of the practical theory by predicting how it would behave when compared to the rules when considering the replica. It offers accurate grid point outcomes and fixes. The system's accuracy in the convergent zone is shown by the curves. The smoking model has been illustrated using graphical findings and fractional derivatives for easier comprehension. It's feasible that applications in the real world will make use of fractional derivatives.

Numerical study of different ventilation schemes in a classroom for efficient aerosol control.

The air quality is a parameter to be controlled in order to live in a comfortable place. This paper analyzes the trajectory of aerosols exhaled into the environment in a classroom. Three scenarios are investigated; without ventilation, with natural and with mechanical ventilation. A multi-phase computational fluid study based on Eulerian-Lagrangian techniques is defined. Temperature and ambient relative humidity, as well as air velocity, direction and pressure is taken into account. For droplets evaporation, mass transfer and turbulent dispersion have been added. This work tends to be of great help in various areas, such as the field of medicine and energy engineering, aiming to show the path of aerosols dispersed in the air. The results show that the classroom with a mechanical ventilation scheme offers good results when it comes to an efficient control of aerosols. In all three cases, aerosols exhaled into the environment impregnate the front row student in the first 0.5 s. Reaching the time of 4, 2 and 1 s, in the class without ventilation, mechanical and natural ventilation, respectively, the aerosols have been already deposited on the table of the person in the first row, being exposed for longer in the case of no ventilation. Particles with a diameter of less than 20 µm are distributed throughout the classroom over a long period. The air jet injected into the interior space offers a practically constant relative humidity and a drop in temperature, slowing down the process of evaporation of the particles. In the first second, it can be seen that a mass of 0.0025 mg formed by 9 million droplets accumulates, in cases without ventilation and natural ventilation. The room with a mechanical installation accumulated 5.5 million particles of mass 0.0028 mg in the first second. The energy losses generated by natural ventilation are high compared to the other scenarios, exactly forty and twenty times more in the scenario with mechanical ventilation and without ventilation, respectively.

Numerical modeling of a sneeze, a cough and a continuum speech inside a hospital lift.

The global COVID-19 and its variants put us on notice of the importance of studying the spread of respiratory diseases. The most common means of propagation was the emission of droplets due to different respiration activities. This study modeled by computational fluid dynamics (CFD) techniques a high risk scenario like a hospital elevator. The cabin was provided with an extraction fan and a rack for air renewal. Inside, a sneeze, a cough and a continuum speech were simulated. Inside the lift, two occupants were introduced to observe the risk of propagation of emitted droplets and the impact in diseases spreading risk. The fan effectivity over the droplets ejection was analyzed, as well as environmental condition of a clinical setting. For this purpose the amount of droplets inside were counted during whole time of simulations. The effect of the fan was concluded as able to eject the 60% of small droplets, but also a high performance in spreading particles inside. Among the three cases, the riskiest scenario was the continuum speech due to the saturation of droplets in airborne.

Performance of magnetic dipole contribution on ferromagnetic non-Newtonian radiative MHD blood flow: An application of biotechnology and medical sciences.

Casson flow ferromagnetic liquid blood flow over stretching region is studied numerically. The domain is influence by radiation and blood flow velocity and thermal slip conditions. Blood acts an impenetrable magneto-dynamic liquid yields governing equations. The conservative governing nonlinear partial differential equations, reduced to ODEs by the help of similarity translation technique. The transport equations were transformed into first order ODEs and the resultant system are solved with help of 4th order R-K scheme. Performing a magnetic dipole with a Casson flow across a stretched region with Brownian motion and Thermophoresis is novelty of the problem. Significant applications of the study in some spheres are metallurgy, extrusion of polymers, production in papers and rubber manufactured sheets. Electronics, analytical instruments, medicine, friction reduction, angular momentum shift, heat transmission, etc. are only few of the many uses for ferromagnetic fluids. As ferromagnetic interaction parameter value improves, the skin-friction, Sherwood and Nusselt numbers depreciates. A comparative study of the present numerical scheme for specific situations reveals a splendid correlation with earlier published work. A change in blood flow velocity magnitude has been noted due to Casson parameter. Increasing change in blood flow temperature noted due to Casson parameter. Skin-friction strengthened and Nusselt number is declined with Casson parameter. The limitation of current work is a non-invasive magnetic blood flow collection system using commercially available magnetic sensors instead of SQUID or electrodes.

CNN-based flow field prediction for bus aerodynamics analysis.

The aim of this article is to evaluate the ability of a convolutional neural network (CNN) to predict velocity and pressure aerodynamic fields in heavy vehicles. For training and testing the developed CNN, various CFD simulations of three different vehicle geometries have been conducted, considering the RANS-based k-ω SST turbulent model. Two geometries correspond to the SC7 and SC5 coach models of the bus manufacturer SUNSUNDEGUI and the third one corresponds to Ahmed body. By generating different variants of these three geometries, a large number of representations of the velocity and pressure fields are obtained that will be used to train, verify, and evaluate the convolutional neural network. To improve the accuracy of the CNN, the field representations obtained are discretized as a function of the expected velocity gradient, so that in the areas where there is a greater variation in velocity, the corresponding neuron is smaller. The results show good agreement between numerical results and CNN predictions, being the CNN able to accurately represent the velocity and pressure fields with very low errors. Additionally, a substantial improvement in the computational time needed for each simulation is appreciated, reducing it by four orders of magnitude.

Effects of globalization, foreign direct investment and economic growth on renewable electricity consumption.

Renewable energy has been seen as a viable solution to the problems of environmental degradation and the energy crisis. This study examines the long - and short-run linkages between economic globalization, foreign direct investment (FDI), economic growth, and renewable electricity consumption in China's Belt and Road Initiative (BRI) countries. Therefore, this study uses the Pooled Mean Group (PMG) autoregressive distributed lag (ARDL) technique to measure the relationship between constructs based on data collected from 2000 to 2020. The overall results show the collaborative integration of Belt and Road (BRI) countries in terms of globalization, economic growth, and renewable electricity utilization. The results show that there is a long-term positive relationship between FDI and renewable electricity consumption, but a negative relationship in the short term. Furthermore, economic growth is positively correlated with renewable electricity consumption in the long run and negatively correlated in the short run. This study suggests that the governments of BRI countries should encourage globalization by improving technology and knowledge related to renewable electricity consumption in all areas.

Numerical Analysis of Unsteady Hybrid Nanofluid Flow Comprising CNTs-Ferrousoxide/Water with Variable Magnetic Field.

The introduction of hybrid nanofluids is an important concept in various engineering and industrial applications. It is used prominently in various engineering applications, such as wider absorption range, low-pressure drop, generator cooling, nuclear system cooling, good thermal conductivity, heat exchangers, etc. In this article, the impact of variable magnetic field on the flow field of hybrid nano-fluid for the improvement of heat and mass transmission is investigated. The main objective of this study is to see the impact of hybrid nano-fluid (ferrous oxide water and carbon nanotubes) CNTs-Fe3O4, H2O between two parallel plates with variable magnetic field. The governing momentum equation, energy equation, and the magnetic field equation have been reduced into a system of highly nonlinear ODEs by using similarity transformations. The parametric continuation method (PCM) has been utilized for the solution of the derived system of equations. For the validity of the model by PCM, the proposed model has also been solved via the shooting method. The numerical outcomes of the important flow properties such as velocity profile, temperature profile and variable magnetic field for the hybrid nanofluid are displayed quantitatively through various graphs and tables. It has been noticed that the increase in the volume friction of the nano-material significantly fluctuates the velocity profile near the channel wall due to an increase in the fluid density. In addition, single-wall nanotubes have a greater effect on temperature than multi-wall carbon nanotubes. Statistical analysis shows that the thermal flow rate of (Fe3O4-SWCNTs-water) and (Fe3O4-MWCNTs-water) rises from 1.6336 percent to 6.9519 percent, and 1.7614 percent to 7.4413 percent, respectively when the volume fraction of nanomaterial increases from 0.01 to 0.04. Furthermore, the body force accelerates near the wall of boundary layer because Lorentz force is small near the squeezing plate, as the current being almost parallel to the magnetic field.

Modified SIR model for COVID-19 transmission dynamics: Simulation with case study of UK, US and India.

Corona virus disease 2019 (COVID-19) is an infectious disease and has spread over more than 200 countries since its outbreak in December 2019. This pandemic has posed the greatest threat to global public health and seems to have changing characteristics with altering variants, hence various epidemiological and statistical models are getting developed to predict the infection spread, mortality rate and calibrating various impacting factors. But the aysmptomatic patient counts and demographical factors needs to be considered in model evaluation. Here we have proposed a new seven compartmental model, Susceptible- Exposed- Infected-Asymptomatic-Quarantined-Fatal-Recovered (SEIAQFR) which is based on classical Susceptible-Infected-Recovered (SIR) model dynamic of infectious disease, and considered factors like asymptomatic transmission and quarantine of patients. We have taken UK, US and India as a case study for model evaluation purpose. In our analysis, it is found that the Reproductive Rate ( R 0 ) of the disease is dynamic over a long period and provides better results in model performance ( > 0 . 98 R-square score) when model is fitted across smaller time period. On an average 40 % - 50 % cases are asymptomatic and have contributed to model accuracy. The model is employed to show accuracy in correspondence with different geographic data in both wave of disease spread. Different disease spreading factors like infection rate, recovery rate and mortality rate are well analyzed with best fit of real world data. Performance evaluation of this model has achieved good R-Square score which is 0 . 95 - 0 . 99 for infection prediction and 0 . 90 - 0 . 99 for death prediction and an average 1 % - 5 % MAPE in different wave of the disease in UK, US and India.

Thermal enhancement and numerical solution of blood nanofluid flow through stenotic artery.

The blood flow through stenotic artery is one of the important research area in computational fluid mechanics due to its application in biomedicine. Aim of this research work is to investigate the impact of nanoparticles on the characteristics of human blood flow in a stenosed blood artery. In under consideration problem Newtonian fluid is assumed as human blood. Newtonian fluid flows through large blood vessels (more than 300 µm). The constitutive equations together with the boundary conditions are diminished to non-dimensional form by using boundary layer approximation and similarity transfiguration to attain the solution of velocity and temperature distribution of blood flow through arterial stenosis numerically with the help of Matlab bvp4c. The results for physical quantities at cylindrical surface are calculated and their effects are also presented through tables. The heat transfer rate increases throughout the stenosed artery with the concentration of copper nanoparticle. Velocity curve decreases by increasing the values of flow parameter and nanoparticle volume fraction. Temperature curve increases due to increase in the values of nanoparticle volume fraction and decrease in Prandtl number.

Computational characterization of the behavior of a saliva droplet in a social environment.

The conduct of respiratory droplets is the basis of the study to reduce the spread of a virus in society. The pandemic suffered in early 2020 due to COVID-19 shows the lack of research on the evaporation and fate of droplets exhaled in the environment. The current study, attempts to provide solution through computational fluid dynamics techniques based on a multiphase state with the help of Eulerian-Lagrangian techniques to the activity of respiratory droplets. A numerical study has shown how the behavior of droplets of pure water exhaled in the environment after a sneeze or cough have a dynamic equal to the experimental curve of Wells. The droplets of saliva have been introduced as a saline solution. Considering the mass transferred and the turbulence created, the results has showed that the ambient temperature and relative humidity are parameters that significantly affect the evaporation process, and therefore to the fate. Evaporation time tends to be of a higher value when the temperature affecting the environment is lower. With constant parameters of particle diameter and ambient temperature, an increase in relative humidity increases the evaporation time. A larger particle diameter is consequently transported at a greater distance, since the opposite force it affects is the weight. Finally, a neural network-based model is presented to predict particle evaporation time.

CNN-based flow control device modelling on aerodynamic airfoils.

Wind energy has become an important source of electricity generation, with the aim of achieving a cleaner and more sustainable energy model. However, wind turbine performance improvement is required to compete with conventional energy resources. To achieve this improvement, flow control devices are implemented on airfoils. Computational fluid dynamics (CFD) simulations are the most popular method for analyzing this kind of devices, but in recent years, with the growth of Artificial Intelligence, predicting flow characteristics using neural networks is becoming increasingly popular. In this work, 158 different CFD simulations of a DU91W(2)250 airfoil are conducted, with two different flow control devices, rotating microtabs and Gurney flaps, added on its Trailing Edge (TE). These flow control devices are implemented by using the cell-set meshing technique. These simulations are used to train and test a Convolutional Neural Network (CNN) for velocity and pressure field prediction and another CNN for aerodynamic coefficient prediction. The results show that the proposed CNN for field prediction is able to accurately predict the main characteristics of the flow around the flow control device, showing very slight errors. Regarding the aerodynamic coefficients, the proposed CNN is also capable to predict them reliably, being able to properly predict both the trend and the values. In comparison with CFD simulations, the use of the CNNs reduces the computational time in four orders of magnitude.

Numerical Simulation of a Time-Dependent Electroviscous and Hybrid Nanofluid with Darcy-Forchheimer Effect between Squeezing Plates.

In this article, the behavior of transient electroviscous fluid flow is investigated through squeezing plates containing hybrid nanoparticles. A hybrid nanofluid MoS2+Au/C2H6O2-H2O was formulated by dissolving the components of an inorganic substance such as molybdenum disulfide (MoS2) and gold (Au) in a base fluid of ethylene glycol/water. This hybrid non-liquid flow was modeled by various nonlinear mathematical fluid flow models and subsequently solved by numerical as well as analytical methods. For the numerical solution of nonlinear ODEs, a built-in function BVP4C was used in MATLAB, and the same problem was solved in MATHEMATICA by HAM. The result of the present problem related to the results obtained from the existing literature under certain conditions. The outcomes revealed that the concentration profiles were more sensitive to homogeneity diversity parameters. The simulation of the various physical parameters of the model indicated that the heat transfer through a mixture of hybrid nanofluids was greater than a simple nanofluid. In addition, the phenomenon of mixed convection was considered to improve the velocity of simple nanofluids and hybrid nanofluids, when both cases have low permeability. A rise in the volume fraction of the nanomaterials, Φ, was associated with an increase in the heat transfer rate. It was observed that the heat transfer rate of the hybrid nanofluids MoS2+Au/C2H6O2-H2O was higher than that of the single nanofluids MoS2/C2H6O2-H2O.

Theoretical analysis of unsteady squeezing nanofluid flow with physical properties.

Theoretical analysis of physical characteristics of unsteady, squeezing nanofluid flow is studied. The flow of nanofluid between two plates that placed parallel in a rotating system by keeping the variable physical properties: viscosity and thermal conductivity. It is analyzed by using Navier Stokes Equation, Energy Equation and Concentration equation. The prominent equations are transformed by virtue of suitable similarity transformation. Nanofluid model includes the important effects of Thermophoresis and Brownian motion. For analysis graphical results are drawn for verity parameters of our interest i.e., Injection parameter, Squeezing number, Prandtle number and Schmidt number are investigated for the Velocity field, Temperature variation and Concentration profile numerically. The findings underline that the parameter of skin friction increases when the Squeezing Reynolds number, Injection parameter and Prandtle number increases. However, it shows inverse relationship with Schmidt number and Rotation parameter. Furthermore, direct relationship of Nusselt number with injection parameter and Reynolds number is observed while its relation with Schmidt number, Rotation parameter, Brownian parameter and Thermophoretic parameter shows an opposite trend. The results are thus obtained through Parametric Continuation Method (PCM) which is further validated through BVP4c. Moreover, the results are tabulated and set forth for comparison of findings through PCM and BVP4c which shows that the obtained results correspond to each other.

Natural Ventilation Characterization in a Classroom under Different Scenarios.

The COVID-19 pandemic has pointed to the need to increase our knowledge in fields related to human breathing. In the present study, temperature, relative humidity, carbon dioxide (CO2) concentration, and median particle size diameter measurements were taken into account. These parameters were analyzed in a computer classroom with 15 subjects during a normal 90-minute class; all the subjects wore surgical masks. For measurements, Arduino YUN, Arduino UNO, and APS-3321 devices were used. Natural ventilation efficiency was checked in two different ventilation scenarios: only windows open and windows and doors open. The results show how ventilation affects the temperature, CO2 concentration, and median particle diameter size parameters. By contrast, the relative humidity depends more on the outdoor meteorological conditions. Both ventilation scenarios tend to create the same room conditions in terms of temperature, humidity, CO2 concentration, and particle size. Additionally, the evolution of CO2 concentration as well as the particle size distribution along the time was studied. Finally, the particulate matter (PM2.5) was investigated together with particle concentration. Both parameters showed a similar trend during the time of the experiments.

Delamination Fracture Behavior of Unidirectional Carbon Reinforced Composites Applied to Wind Turbine Blades.

One of the materials that is used widely for wind turbine blade manufacturing are fiber-reinforced composites. Although glass fiber reinforcement is the most used in wind turbine blades, the use of carbon fiber allows larger blades to be manufactured due to their better mechanical characteristics. Some turbine manufacturers are using carbon fiber in the most critical parts of the blade design. The larger rotors are exposed to complex loading conditions in service. One of the most relevant structures on a wind turbine blade is the spar cap. It is usually manufactured by means of unidirectional laminates, and one of its major failures is the delamination. The determination of material features that influence delamination initiation and advance by appropriate testing is a fundamental topic for the study of composite delamination. The fracture behavior is studied across coupons of carbon fiber reinforcement epoxy laminates. Fifteen different test conditions have been analyzed. Fracture surfaces for different mode ratios have been explored using optical microscope and scanning electron microscope. Experimental results shown in the paper for critical fracture parameters agree with the theoretically expected values. Therefore, this experimental procedure is suitable for wind turbine blade material characterizing at the initial coupon-scale research level.

Temperature based maximum power point tracking for photovoltaic modules.

In this article authors propose a temperature based Maximum Power Point Tracking algorithm (MPPT). Authors show that there is an optimal current vs maximum power curve that depends on photovoltaic (PV) module temperature. Therefore, the maximum power point (MPP) can be achieved in very few commutation steps if the control forces the PV module to work in temperature dependent optimal curve. Authors shows how this PV module temperature based MPPT is stable and converges to MPP for each temperature. In order to proof its stability, authors propose a Lyapunov energy function. This Lyapunov energy function has positive values for all values except into MPP given the PV module temperature. This Lyapunov energy function has negative increment along each time step. Hence, the stability of temperature based MPPT can be demonstrated. The proposed MPPT algorithm proposes a current set point. This current set point is obtained with instantaneous PV module power and temperature dependent maximum power vs optimal current curve. Stability is analysed for different temperature levels. Optimal current vs maximum power curve has been modelled by a line. The lines' coefficients depend on PV module temperature. Proposed Lyapunov energy function is not symmetric about equilibrium or MPP because MPPT algorithm and PV module dynamic have no symmetric behaviour about this equilibrium point.

Oscillating U-Shaped Body for Underwater Piezoelectric Energy Harvester Power Optimization.

Vibration energy harvesting (VeH) techniques by means of intentionally designed mechanisms have been used in the last decade for frequency bandwidth improvement under excitation for adequately high-vibration amplitudes. Oil, gas, and water are vital resources that are usually transported by extensive pipe networks. Therefore, wireless self-powered sensors are a sustainable choice to monitor in-pipe system applications. The mechanism, which is intended for water pipes with diameters of 2-5 inches, contains a piezoelectric beam assembled to the oscillating body. A novel U-shaped geometry of an underwater energy harvester has been designed and implemented. Then, the results have been compared with the traditional circular cylinder shape. At first, a numerical study has been carried at Reynolds numbers Re = 3000, 6000, 9000, and 12,000 in order to capture as much as kinetic energy from the water flow. Consequently, unsteady Reynolds Averaged Navier-Stokes (URANS)-based simulations are carried out to investigate the dynamic forces under different conditions. In addition, an Adaptive Differential Evolution (JADE) multivariable optimization algorithm has been implemented for the optimal design of the harvester and the maximization of the power extracted from it. The results show that the U-shaped geometry can extract more power from the kinetic energy of the fluid than the traditional circular cylinder harvester under the same conditions.