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Wind energy conversion systems have become a key technology to harvest wind energy worldwide. In permanent magnet synchronous generator-based wind turbine systems, the rotor position is needed for variable speed control and it uses an encoder or a speed sensor. However, these sensors lead to some obstacles, such as additional weight and cost, increased noise, complexity and reliability issues. For these reasons, the development of new sensorless control methods has become critically important for wind turbine generators. This paper aims to develop a new sensorless and adaptive control method for a surface-mounted permanent magnet synchronous generator. The proposed method includes a new model reference adaptive system, which is used to estimate the rotor position and speed as an observer. Adaptive control is implemented in the pulse-width modulated current source converter. In the conventional model reference adaptive system, the proportional-integral controller is used in the adaptation mechanism. Moreover, the proportional-integral controller is generally tuned by the trial and error method, which is tedious and inaccurate. In contrast, the proposed method is based on model predictive control which eliminates the use of speed and position sensors and also improves the performance of model reference adaptive control systems. In this paper, the proposed predictive controller is modelled in MATLAB/SIMULINK and validated experimentally on a 6-kW wind turbine generator. Test results prove the effectiveness of the control strategy in terms of energy efficiency and dynamical adaptation to the wind turbine operational conditions. The experimental results also show that the control method has good dynamic response to parameter variations and external disturbances. Therefore, the developed technique will help increase the uptake of permanent magnet synchronous generators and model predictive control methods in the wind power industry.

In this study a system constituted by seven double skin facades (DSF), three equipped with venetian blinds and four not equipped with venetian blinds, applied in a virtual chamber, is developed. The project will be carried out in winter conditions, using a numerical model, in transient conditions, and based on energy and mass balance linear integral equations. The energy balance linear integral equations are used to calculate the air temperature inside the DSF and the virtual chamber, the temperature on the venetian blind, the temperature on the inner and outer glass, and the temperature distribution in the surrounding structure of the DSF and virtual chamber. These equations consider the convection, conduction, and radiation phenomena. The heat transfer by convection is calculated by natural, forced, and mixed convection, with dimensionless coefficients. In the radiative exchanges, the incident solar radiation, the absorbed solar radiation, and the transmitted solar radiation are considered. The mass balance linear integral equations are used to calculate the water mass concentration and the contaminants mass concentration. These equations consider the convection and the diffusion phenomena. In this numerical work seven cases studies and three occupation levels are simulated. In each case the influence of the ventilation airflow and the occupation level is analyzed. The total number of thermal and indoor air quality uncomfortable hours are used to evaluate the DSF performance. In accordance with the obtained results, in general, the indoor air quality is acceptable, however, when the number of occupants in the virtual chamber increases, the Predicted Mean Vote index value increases. When the airflow rate increases the total of Uncomfortable Hours decreases and, after a certain value of the airflow rate, it increases. The airflow rate associated with the minimum value of total Uncomfortable Hours increases when the number of occupants increases. The energy production decreases when the airflow increases and the production of energy is higher in DSF with venetian blinds system than in DSF without venetian blinds system.

A new bi-directional circuit breaker is presented for medium-voltage dc (MVDC) systems. The Y-source impedance network topology is used to implement the breaker. The current transfer function is derived to show the frequency response and the breaker operation with the high frequencies. Mathematical analysis is achieved with different conditions of coupling among the breaker inductors. The minimum level of the magnetic coupling is determined, which is represented by the null condition. The effect of the turns-ratio on this condition is investigated as well. The breaker is designed with two types of fault conductance slope rates. The Y-source breaker is simulated, and the results verify the breaker operation during the fault condition and the load change. The results also demonstrate the effect of the coupling level on the minimum values of the source current when the fault occurs. Based on the expected fault type in the MVDC systems, the proposed breaker is developed to interrupt the overcurrent due to any of these fault types. A protection scheme is proposed for a 12-bus, two-level micro-grid, where the Y-source breakers are used in the bi-directional zones. The results verify the ability of the breaker to conduct and interrupt the current in both directions of the power flow.

This paper proposed a test bench device to emulate or simulate the electrical impulses of a wave energy converter (WEC). The objective of the study is to reconstruct under laboratory conditions the dynamics of a WEC in the form of an emulator to assess the performance, which, in this case, is the output power. The designed emulator device is programmable, which makes it possible to create under laboratory conditions the operating mode of the wave generator, identical to how the wave generator would work under real sea conditions. Any control algorithm can be executed in the designed emulator. In order to test the performance of the constructed WEC emulator, an experiment was conducted to test its power output against that of a real point-absorber WEC. The results indicate that, although the power output for that of the real WEC was higher than the WEC emulator, the emulator performed perfectly well. The relatively low power output of the emulator was because of the type of algorithm that was written for the emulator, therefore increasing the speed of the motor in the algorithm (code) would result in higher output for the proposed WEC emulator. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Accurate photovoltaic (PV) power forecasting is crucial to achieving massive PV integration in several areas, which is needed to successfully reduce or eliminate carbon dioxide from energy sources. This paper deals with short-term multi-step PV power forecasts used in model-based predictive control for home energy management systems. By employing radial basis function (RBFs) artificial neural networks (ANN), designed using a multi-objective genetic algorithm (MOGA) with data selected by an approximate convex-hull algorithm, it is shown that excellent forecasting results can be obtained. Two case studies are used: a special house located in the USA, and the other a typical residential house situated in the south of Portugal. In the latter case, one-step-ahead values for unscaled root mean square error (RMSE), mean relative error (MRE), normalized mean average error (NMAE), mean absolute percentage error (MAPE) and R2 of 0.16, 1.27%, 1.22%, 8% and 0.94 were obtained, respectively. These results compare very favorably with existing alternatives found in the literature. Programa Operacional Portugal 2020 and Operational Program CRESC Algarve 2020 grant 01/SAICT/2018. Antonio Ruano acknowledges the support of Fundação para a Ciência e Tecnologia, through IDMEC, under LAETA, grant UIDB/50022/2020. info:eu-repo/semantics/publishedVersion

Thermal power plants in different fields are regularly adapted to the state-of-the-art emissions standards, applying &ldquo, The Best Available Techniques Reference&rdquo, Since 2016 in the power plant area new, more stringent limits for power plant units with a thermal output of more than 300 MW operated with black coal are valid. Usually, in order to reach the new limits e.g., for NOX emissions, downstream reduction processes (Selective Non-Catalytic Reduction, SNCR or Selective Catalytic Reduction) are applied, which use of operating resources (essentially ammonia water) thereby increase. By the means of an experimentally validated process, by which pulverized fuel is fed by oscillation through a swirl burner into a pilot combustion chamber with a thermal output of 2.5 MW, nitrogen oxides can be reduced without further activities, for instance from 450 mg/mN3 in non-oscillation operation mode (0 Hz) to 280 mg/mN3 in oscillation operation mode (3.5 Hz), normalized to an O2&ndash, content of 6% each. These findings were patented in EP3084300. Particularly promising are the experiments which utilize oscillation of a large portion of the burn out air instead of the fuel in order to minimize the fatigue of the pulverized fuel oscillator, amongst others. Thereby, the nitrogen conversion rate, which describes the ratio of NOX to fuel nitrogen, including thermal NOX can be reduced from 26% for non-oscillation operation mode down to 16%. The present findings show that fuel oscillation alone is not sufficient to achieve nitrogen oxides concentrations below the legislative values. Therefore, a combination of different primary (and secondary) measures is required. This paper presents the experimental results for oscillating coal-dust firing. Furthermore, an expert model based on a multivariate regression is developed to evaluate the experimental results.

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The most profound requirements of fifth-generation (5G) technology implementations are the architecture design and the radio base station technology to capably run applications such as device-to-device, machine-to machine and internet of things at a reduced latency. Owing to these requirements, the implementation of 5G technology is very expensive to mobile network operators (MNO). In this study we modified the existing 4G network to form a distributed wireless network architecture (DWNA), the picocell and distributed antenna system were modified to support the enabling technology of 5G technology were a multi-edge computer (MEC), software-defined networking (SDN), massive multiple input multiple output (MIMO), ultra-dense network (UDN), Network Functions Virtualization (NFV) and device-to-device (D2D) communication at a reduced cost of ownership, improved coverage and capacity. We present a mathematical model for operational expenditure, capital expenditure and total cost of ownership (TCO) for the DWNA. A mathematical model for DWNA capacity and throughput was presented. Result shows that it is very economical for MNO to rent the space of the tower infrastructure from tower companies. The sensitivity analysis also shows a significant reduction in TCO for both the modified picocell and modified distributed antenna systems.

This paper presents the effect of grinding wheel contact stiffness on the plunge grinding cycle. First, it proposes a novel model of the generalized plunge grinding system. The model is applicable to all plunge grinding operations including cylindrical, centerless, shoe-centerless, internal, and shoe-internal grinding. The analysis of the model explicitly describes transient behaviors during the ramp infeed and the spark-out in the plunge grinding cycle. Clarification is provided regarding the premise that the system stiffness is composed of machine stiffness and wheel contact stiffness, and these stiffnesses significantly affect productivity and grinding accuracy. The elastic deflection of the grinding wheel is accurately measured and formulas for representing the deflection nature under various contact loads are derived. The deflection model allows us to find the non-linear contact stiffness with respect to the normal load. The contact stiffnesses of four kinds of grinding wheels with different grades and bond materials are presented. Both cylindrical grinding and centerless grinding tests are carried out, and it is experimentally revealed that the time constant at ramp infeed and spark-out is significantly prolonged by reducing the grinding force. It is verified that a simulation of the grinding tests using the proposed model can accurately predict critical parameters like forces and machine deflection during plunge grinding operations. Finally, this paper provides a guideline for grinding cycle design in order to achieve the required productivity and grinding accuracy.

For footwear insoles, high rebound performance is required in some instances such as for running, while softness for comfort is of higher importance during normal walking and standing to minimize high stress. Hence, materials with rebound performance in some scenarios and softness for other scenarios are desired. In this paper, we investigate rebound performance and hardness of composites made of a shear-thickening material and elastic foam. First, a hydrogel type of shear-thickening material (Slime) is characterized to investigate the influence of water content. After that, two particular shear-thickening hydrogels with better rebound performance (but not outstanding in the shear-thickening effect) are selected and integrated into the elastic foam to produce a composite insole. It is found that, as compared with the commercial elastic insole and commercial shear-thickening insole, softer and superior rebound performance can be achieved simultaneously only if the right shear-thickening material is used in the composite. Published version

This paper reviews the energy storage participation for ancillary services in a microgrid (MG) system. The MG is used as a basic empowering solution to combine renewable generators and storage systems distributed to assist several demands proficiently. However, because of unforeseen and sporadic features of renewable energy, innovative tasks rise for the consistent process of MGs. Power management in MGs that contain renewable energy sources (RES) can be improved by energy storage. The energy storage systems (ESSs) have several merits, such as supply and demand balancing, smoothing of RES power generation, enhancing power quality and reliability, and facilitating the ancillary services like voltage and frequency regulation in MG operation. The integration of ESS technology has become a solution to the challenges the power distribution networks face in achieving improved performance. By simplifying a smooth and robust energy balance within the MG, storage devices match energy generation to consumption. MG, and its multidisciplinary portrait of current MG drivers, tasks, real-world applications, and upcoming views are elucidated in this paper.

Data security of present-day power systems, such as the electricity market, has spurred global interest in both industry and academia. The electricity market can either be regulated (state-controlled entrance, policies, and pricing) or deregulated (open for competitors). While the security threats in a deregulated electricity market are commonly known and have been investigated for years, those in a regulated market still have scope for extensive research. Our current work focuses on exploring the data security of the regulated electricity market, and the regulated New Zealand Electricity Market (NZEM) has been considered for this research. Although the chances of cyberattacks on state-controlled regulated electricity market are relatively less, different layers of the current SCADA systems do pose some threats. In this context, we propose a decentralized Ethereum Blockchain-based end-to-end security prototype for a regulated electricity market such as the NZEM. This prototype aims to enhance data security between the different layers of the current SCADA systems. The detailed operation process and features of this prototype are presented in this work. The proposed prototype has prospects of offering improved data security solutions for the regulated electricity market.

In this proposed research, clear prospects of a real life marketing scenario, by analyzing a price discount policy and variable demand, are derived. The proposed study presents a production model along with time-dependent and selling price related demand for decaying items. Items deteriorate over time, therefore, considering deterioration in this model makes it more acceptable to the present marketing situation. The concept of delay-in-payments is utilized in this inventory system. In this research, a retailer buys some products, enjoys constant credit-period offers which are provided by the supplier. This model depicts a price discount strategy which is based on purchasing cost to attract more consumers in any business industry. By using this strategy, any manufacturer or business may gain more profit in comparison to methods suggested by earlier literature. The average profit function of the inventory system is maximized analytically and also finds the selling-price per unit and duration of the inventory cycle optimally. A numerical example, along with a case study and their graphical representations, are incorporated to verify the optimality of this research very clearly. The findings of this research have maximized the average profit function more than the existing literature.

Human action recognition has turned into one of the most attractive and demanding fields of research in computer vision and pattern recognition for facilitating easy, smart, and comfortable ways of human-machine interaction. With the witnessing of massive improvements to research in recent years, several methods have been suggested for the discrimination of different types of human actions using color, depth, inertial, and skeleton information. Despite having several action identification methods using different modalities, classifying human actions using skeleton joints information in 3-dimensional space is still a challenging problem. In this paper, we conceive an efficacious method for action recognition using 3D skeleton data. First, large-scale 3D skeleton joints information was analyzed and accomplished some meaningful pre-processing. Then, a simple straight-forward deep convolutional neural network (DCNN) was designed for the classification of the desired actions in order to evaluate the effectiveness and embonpoint of the proposed system. We also conducted prior DCNN models such as ResNet18 and MobileNetV2, which outperform existing systems using human skeleton joints information.

In the present scenario for the development of the unmanned aerial vehicle (UAV), artificial intelligence plays an important role in path planning and obstacle detection. Due to different environments, it is always a task to achieve the proper moment for achieving the target goal while avoiding obstacles with minimum human interference. To achieve the goal with the avoidance of obstacles, individual optimization techniques with metaheuristic algorithms such as fuzzy, particle swarm optimization (PSO), etc. were implemented in various configurations. However, the optimal solution was not attained. Thus, in order to achieve an optimal solution, a hybrid model was developed by using the firefly algorithm and the fuzzy algorithm, establishing multiple features of the individual controller. The path and time optimization were achieved by a standalone controller and a hybrid firefly&ndash, fuzzy controller in different conditions, whereby the results of the controller were validated by simulation and experimental results, highlighting the advantages of the hybrid controller over the single controller.

Metal additive manufacturing (AM) has gained much attention in recent years due to its advantages including geometric freedom and design complexity, appropriate for a wide range of potential industrial applications. However, conventional metal AM methods have high-cost barriers due to the initial cost of the capital equipment, support, and maintenance, etc. This study presents a low-cost metal material extrusion technology as a prospective alternative to the production of metallic parts in additive manufacturing. The filaments used consist of copper, bronze, stainless steel, high carbon iron, and aluminum powders in a polylactic acid matrix. Using the proposed fabrication technology, test specimens were built by extruding metal/polymer composite filaments, which were then sintered in an open-air furnace to produce solid metallic parts. In this research, the mechanical and thermal properties of the built parts are examined using tensile tests, thermogravimetric, thermomechanical and microstructural analysis.

Recurring and increasing large-scale wildfires across the globe (e.g., Southern Europe, California, Australia), as a result of worsening climate conditions with record temperatures, drought, and strong winds, present a challenge to mankind. Early fire detection is crucial for a quick reaction and effective firefighting operations, minimizing the risk to human lives as well as the destruction of assets, infrastructures, forests, and wildlife. Usually, ground firefighting relies on human intervention and dangerous exposition to high temperatures and radiation levels, proving the need for mechanisms and techniques to remotely or autonomously detect and combat fire. This paper proposes an autonomous firefighting system built with a motorized water turret, narrow beam far infrared (FIR) sensors, and a micro-controller running novel algorithms and techniques. Experimental field results validated the technical approach, indicating that when a small fire front is within the field of view of the FIR sensor and within the range of the water jet, it is possible to provide an early alarm and even autonomously extinguish or delay the approaching fire front, increasing the chance for evacuation.

This paper synthesizes the design of an infeed cylindrical grinding system into a total system composed of the grinding mechanism and the grinding machine characteristics. The causalities between the grinding parameters and the machine structures are discussed, and the infeed grinding processes are analyzed as outputs that represent responses to the inputs. These relationships are integrated into a block diagram with closed-loop feedback. A novel model exhibiting practical parameters such as grinding speed, infeed rate and MRR (Material Removal Rate) is proposed. The analysis of the grinding system derived a critical factor, the &ldquo, grinding time contact,&rdquo, which governs the transient behaviors of process parameters such as forces and machine deflection. The process parameters during the infeed cycle including spark-out grinding were investigated, and the formulas required for the cycle design are presented. Furthermore, to improve accuracy and productivity, the features of the cycle design are described and procedures for controlling size error and roundness are discussed. Finally, the model was verified with infeed grinding tests applied to both the chuck-type cylindrical and centerless grinding methods.

Owing to the expensive applications of nanoparticles in engineering sciences, an admirable attention has been intended by researchers on this topic in recent years. The utilization of nanoparticles as asource of energy is intended much attention of investigators in recent decade. This novel attempt investigates the thermal properties of Casson nanofluid containing microorganisms induced by an oscillatory moving surface. The fundamental features of heat and mass phenomenon are inspected by utilizing the temperature-dependent viscosity. Buongiorno&rsquo, s mathematical model is used to report the famous Brownian motion and thermophoretic diffusion consequences. The flow problem characterizes the partial differential equations for which analytical solution has been computed with a convincible accuracy. The insight physical features are inspected with help of various curves. The physical significances of flow parameters is studied via various graphs.

Decreasing the share of time for the technological training of new product creation is an actual and perspective task. The actuality of the given task always increases with the development of machine-building production. Changes which happen inside the organization of industrial production are connected to the development of consumer demand. Modern tendencies are such that there can be seen a constant lowering of batch products with an increase in the range of their production, and, consequently, a reduction in the production stage of the product life cycle. Under these conditions, it is necessary to react as quickly as possible to changes of external (for example, the arrival of new production orders, adjusting the number of already started production, etc.) and internal conditions (for example, stopping technological equipment for organizational reasons, correcting production routes for parts, etc.). Obviously, the effectiveness of the functioning of the production system will be determined primarily by the right decisions. Errors lead to large material costs and a serious loss of time, which can be fatal for an enterprise. The development goal described in the article is to build a digital model of a production system that allows&mdash, on a machine time scale and in the absence of material costs&mdash, the analysis of various control scenarios and finding correct solutions in in short time. The methodological basis for constructing such models is the use of simulation modeling with the combination of discrete event and agent modeling. The construction of a model on this principle allows its openness to accumulate information about successful decisions made and use them to solve similar problems in the future. The benefit of this approach is the ability to predict the production situation over time, rational distribution of technological resources, reducing equipment downtime, streamlining the routes of production tasks and determining the stages of acquiring the necessary materials and components. It increases the competitiveness of the enterprise and ensures its economic stability.

Additive manufacturing (AM) is evolving rapidly and this trend is creating a number of growth opportunities for several industries. Recent studies on AM have focused mainly on developing new machines and materials, with only a limited number of studies on the troubleshooting, maintenance, and problem-solving aspects of AM processes. Deep learning (DL) is an emerging machine learning (ML) type that has widely been used in several research studies. This research team believes that applying DL can help make AM processes smoother and make AM-printed objects more accurate. In this research, a new DL application is developed and implemented to minimize the material consumption of a failed print. The material used in this research is polylactic acid (PLA) and the DL method is the convolutional neural network (CNN). This study reports the nature of this newly developed DL application and the relationships between various algorithm parameters and the accuracy of the algorithm.

Demand-Side Generating Assets (DSGAs) relate to existing small diesel Gensets serving the critical loads of infrastructures in unexpected electrical network interruptions. This paper investigates the exploitation of DSGA as potential suppliers of ancillary services (AS), especially in the case of isolated or weak interconnected island power systems such as in Crete. In this regard, their impact on substations&rsquo, loading reductions and associated copper losses is presented, using a substation as a case study with a typical touristic load in Crete. DSGA&rsquo, s impact on such a significant part of reliability enhancement in distribution systems is evaluated, examining different scenarios of DSGA installed capacity. Subsequently, extensive research is carried out regarding the cost analysis for their access to the electrical grid providing AS, in combination with their operating cost analysis. The calculations on fuel consumption cost show that DSGA are comparable with the most expensive gas turbines of the Cretan power system. Finally, considering the capital cost for their required configurations and their fuel consumption cost, a proposal for their remuneration prices is made. The prices that arise are lower than the actual remuneration prices of other similar services such as Genset rentals for the case of the Cretan power system.

In this work, a structured design method, the Stylistic Design Engineering (SDE), isapplied for the construction of a new minivan car, in particular a new city car, which we will callFIAT 600 Omega. The SDE, or Stylistic Design Engineering, is a structured engineering method forcarrying out automotive design projects. The SDE method consists of six different phases: (1)Analysis of stylistic trends, (2) Sketches, (3) 2D Computer Aided Design (CAD) drawings, (4) 3DCAD models, (5) Rendering, (6) Solid stylistic model (also called style maquette). This project dealswith the external redesign of the Fiat 600 multiple, a small minivan which was very successful inthe 1950s and 1960s. SDE is a methodology consisting of various technologies and designmethodologies that will be further explained in detail, such as the Pininfarina method, the QualityFunction Deployment (QFD) method, Benchmarking (BM), and Top Flop Analysis (TPA). The workwas organized according to the different phases. Initially, the Fiat style was studied, in particularthe style of the FIAT 600 MULTI PURPOUSE VEHICLE (MPV). This step is essential to betterunderstand the characteristics of the brand and also the main characteristics carried out over thedecades. Then we moved on to the freehand sketching phase, based on what we learned in theprevious phase of the study. When a satisfactory shape was found for the new car, by analyzing anddiscarding the different proposals of the various types of style, we proceeded to the evaluation ofthe proportions and dimensions through two-dimensional drawings and finally we obtained thethree-dimensional shape of the new car thanks to 3D CAD software and rendering software. Manyadvantages in the industrial world SDE takes together with its development. In fact, until the early2000s, car design and styling was considered quite a craft activity, not a technical or scientific one,mostly based on the great capability of famous car designers and masters, just like Giugiaro, Zagato,Bertone, Pininfarina, Stephenson, Bangle, etc. Then, thanks to the industrial activity of Eng. LorenzoRamacciotti, former CEO of Pininfarina Spa and Mechanical Engineer, and also thanks to theacademic studies developed at ALMA MATER STUDIORUM University of Bologna, SDE becamethe object of attention, because it is able to systematize the car design process and reduce costs. WithSDE, a good design research or an industrial product development team can complete a car designproject, also without the presence of a mentor. Car Design Process finally becomes with SDE ascientific method, Car Design becomes with SDE an industrial method. Industrial needs are niceproducts made in a short time, SDE is structured to attend these issues. Industrial challenges followinnovation, in shape and functionality, SDE is able to recognize innovation. Industrial benefits canbe reached with SDE, ensuring beautiful aesthetic projects are realized systematically and with lowcosts.

This paper presents a topology of a multi-port phase shift converter for integration with a photovoltaic (PV) system, a wind turbine generator, and a battery to supply a grid-connected domestic load. The main advantage of the topology is using a multi-winding high-frequency transformer (MWHFT) to integrate the input and outputs of the system. In contrast with the case of using a common electrical bus, using MWHFT presents several advantages, such as isolating the converter ports, the easier matching of different voltage levels of the converter ports by adjusting turns ratio, simpler power flow control between the converter ports by applying leading or lagging phase shift angles, and eliminating the leakage current flow through the PV panels from the utility grid. A detailed analysis of the proposed topology and the control system is presented. A numerical simulation of the proposed system is carried out to confirm the system operation and control technique. The experimental tests have been conducted on a prototype converter to approve the proposed system performance.

Fluctuations in fossil fuel prices significantly affect the economies of countries, especially oil-importing countries, hence these countries are thoroughly investigating the increase in the utilization of renewable energy resources as it is abundant and locally available in all the countries despite challenges. Renewable energy systems (RES) such as solar and wind systems offer suitable alternatives for fossil fuels and could ensure the energy security of countries in a feasible way. Zimbabwe is one of the African countries that import a significant portion of its energy needs which endanger the energy security of the country. Several studies in the literature discussed the feasibility of different standalone and hybrid RES either with or without energy storage systems to either maximize the technical feasibility or the economic feasibility, however, none of the studies considered maximizing both feasibilities at the same time. Therefore, we present a techno-economic comparison of standalone wind and solar photovoltaic (PV) in addition to hybrid PV/wind systems based on maximizing the RES fraction with levelized cost of electricity (LCOE) being less than or equal to the local grid tariff where Gwanda, Zimbabwe, is the case study. The methodology suggested in this study could increase the utilization of renewable energy resources feasibly and at the same time increase the energy security of the country by decreasing dependency on imported energy. The results indicate that the PV/wind hybrid system does not only have the best economic benefits represented by the net present value (NPV) and the payback period (PBP), but also the best technical performance, where the maximum feasible size of the hybrid system-2 MW wind and 1 MW PV-has RES fraction of 65.07%, LCOE of 0.1 USD/kWh, PBP of 3.94 years, internal rate of return of 14.04% and NPV of 3.06 &times, 106 USD. Having similar systems for different cities in Zimbabwe will decrease the energy bill significantly and contribute toward the energy security of the country.

The mathematical modeling presented in this work concerns the conveyor-belt dryer with the tangential flow of air with respect to food. This dryer, if operating in co-current, has the advantage of well preserving the organoleptic and nutritional qualities of the dried product. In fact, it has a low air temperature in the final stretch where the product has low moisture content and is therefore more temperature sensitive. It is a bulkier dryer than the continuous through-circulation conveyor dryer with a perforated belt. The latter is therefore more frequently used and has received greater study attention from researchers and designers of the industry. With the aim to propose guidelines for a rational design of the conveyor-belt dryer with tangential flow, a mathematical model was developed here through the differentiation of the drying rate equation followed by its integration performed along the dryer belt. Consequently, and with the assumption that the final moisture content XF of the product is higher than the critical moisture content XC, the relationships between the intensive quantities (temperatures, humidity and enthalpies), the extensive quantities (air and product flow rates) and the dimensional ones (length and width of the belt), were obtained. Finally, on the basis of these relationships, the rules for an optimized design for XF &gt, XC were obtained and experimentally evaluated.

This paper proposes a novel adaptive controller based on digital twin (DT) by integrating software-in-loop (SIL) and hardware-in-loop (HIL). This work aims to reduce the difference between the SIL controller and its physical controller counterpart using the DT concept. To highlight the applicability of the suggested methodology, the regulation control of a horizontal variable speed wind turbine (WT) is considered for the design and assessment purposes. In the presented digital twin framework, the active disturbance rejection controller (ADRC) is implemented for the pitch angle control of the WT plant in both SIL and HIL environments. The design of the ADRC controllers in the DT framework is accomplished by adopting deep deterministic policy gradient (DDPG) in two stages: ( i ) by employing a fitness evaluation of wind speed error, the internal coefficients of HIL controller are adjusted based on DDPG for the regulation of WT plant, and ( ii ) the difference between the rotor speed waveforms in HIL and SIL are reduced by DDPG to obtain a similar output behavior of the system in these environments. Some examinations based on DT are conducted to validate the effectiveness, high dynamic performance, robustness and adaptability of the suggested method in comparison to the prevalent state-of-the-art techniques. The suggested controller is seen to be significantly more efficient especially in the compensation of high aerodynamic variations, unknown uncertainties and also mechanical stresses on the plant drive train.

Human gender is deemed as a prime demographic trait due to its various usage in the practical domain. Human gender classification in an unconstrained environment is a sophisticated task due to large variations in the image scenarios. Due to the multifariousness of internet images, the classification accuracy suffers from traditional machine learning methods. The aim of this research is to streamline the gender classification process using the transfer learning concept. This research proposes a framework that performs automatic gender classification in unconstrained internet images deploying Pareto frontier deep learning networks, GoogleNet, SqueezeNet, and ResNet50. We analyze the experiment with three different Pareto frontier Convolutional Neural Network (CNN) models pre-trained on ImageNet. The massive experiments demonstrate that the performance of the Pareto frontier CNN networks is remarkable in the unconstrained internet image dataset as well as in the frontal images that pave the way to developing an automatic gender classification system.

Growing energy demand is causing a significant decrease in the world&rsquo, s hydrocarbon stock in addition to the pollution of our ecosystem. Based on this observation, the search for alternative sorts of energy to fossil fuels is being increasingly explored and exploited. Wind energy is experiencing a very important development, and it offers a very profitable opportunity for exploitation since the wind is always available and inexhaustible. Several technical solutions exist to exploit wind energy, such as floating vertical axis wind turbines (F-VAWTs), which provide an attractive and cost-effective solution for exploiting higher resources of offshore wind in deep water areas. Recently, the use of the Darrieus vertical axis wind turbine (VAWT) offshore has attracted increased interest because it offers significant advantages over horizontal axis wind turbines (HAWTs). In this context, this article presents a new concept of floating Darrieus-type straight-bladed turbine with three-stage rotors. A double-multiple stream tube (DMST) model is used for aerodynamic simulations to examine several critical parameters, including, solidity turbine, number of blades, rotor radius, aspect ratio, wind velocity, and rotor height. This study also allows to identify a low solidity turbine (&sigma, = 0.3), offering the best aerodynamic performance, while a two-bladed design is recommended. Moreover, the results also indicate the interest of a variable radius rotor, as well as the variation of the height as a function of the wind speed on the aerodynamic efficiency.

Nowadays, the new concept of urban living is required as a path to struggle with urbanization challenges in order to maintain the world and make cities better settings for living through creating high quality of life and liveability. To achieve such areas, developing sustainability, urban planning based on Information Technology, Information Communication Technology infrastructure, and innovative management play important roles. So, authors try to find out new concepts of urban life concerned with these indicators through sustainability, innovation, ubiquitous, and smartness to create a sustainable and modern world through smart cities. Based on authors&rsquo, researches, Blue-Green infrastructure based on environmentally friendly, green strategies, sustainable water management, and ubiquitous services focusing on digitalization and high technologies are required to make a modern world. Fundamentally, innovation management in technology, business and marketing has important roles in designing such areas by keeping up with growing demands and low resources of energies. In this research, i-Sustainability Plus is introduced as a theory to create Blue-Green Ubiquitous cities as modern sustainable and liveable urban areas. Such areas could make the world a better place for living through sustainable development and improving quality of human life.

Graph theory (GT) concepts are potentially applicable in the field of computer science (CS) for many purposes. The unique applications of GT in the CS field such as clustering of web documents, cryptography, and analyzing an algorithm’s execution, among others, are promising applications. Furthermore, GT concepts can be employed to electronic circuit simplifications and analysis. Recently, graphs have been extensively used in social networks (SNs) for many purposes related to modelling and analysis of the SN structures, SN operation modelling, SN user analysis, and many other related aspects. Considering the widespread applications of GT in SNs, this article comprehensively summarizes GT use in the SNs. The goal of this survey paper is twofold. First, we briefly discuss the potential applications of GT in the CS field along with practical examples. Second, we explain the GT uses in the SNs with sufficient concepts and examples to demonstrate the significance of graphs in SN modeling and analysis.

Wave and tidal energies are some of the most prominent potential sources of renewable energy. Presently, these energy sources are not being utilized to their maximum extent. In this paper, we present a new conversion mechanism with an innovative electrical energy converter design that enables the use of wave energy to its maximum potential. The conventional wave energy converter comprises two stages of conversion (kinetic to mechanical and mechanical to electrical), imposing transformation loss that reduces the overall system efficiency. Additionally, the architecture and operational norms are dependent on the availability of shoreline areas, and the convertor is not suitable for all ocean weather conditions. To solve these problems, we have developed a wave energy conversion system that integrates the two stages of power with the minimum number of moving parts. This results in significant reduction of transformation losses that otherwise occur in the process. This paper presents an innovative idea of designing a DC generator that reduces the hierarchy of power conversion levels involved to improve the efficiency. The back and forth motion of the machine means it operates in a two-quadrant generation mode. The machine was constructed as a square box model with windings placed on both the top and bottom stator plates, and the rotor consisted of a field winding placed between these plates with two axes of operation. The electromagnetic field (EMF) induced in the stator plates is due to the resulting flux cutting, which is generated by a rolling object (rotor) in between them. A finite element analysis (FEA) of the machine is also listed to validate the flux linkage and operational efficiency. Additionally, a generator is fabricated to the predetermined design criteria as a proof of concept and the corresponding results are posted in the paper. Additionally, we present the material and cost limitations of this invention and outline some possible future directions.

Movement of sand particles is a complicated phenomenon that occurs in nature. In this paper, the main goal is to provide an analytic model for the deposition profile of sand particles downstream of a fence. The analytic model was derived with respect to governing equations and shear flows for upstream and downstream regions. In this approach, we obtain a new expression for the downstream velocity of the fence, which allows for the determination of potential areas of deposition particles by assuming a log-normal distribution profile. A discrete-phase flow (DPM) was used to inject particles in the simulation domain. The DPM gives capabilities to capture spatiotemporal velocities components, as we can define the probability of deposition particles in the downstream of the fence. The proposed model was validated with a numerical model and experimental results. The comparison with field data and numerical results shows that the deposition profile is in acceptable agreement. With some assumptions and modifications about the properties of particles, the results of this research can be extended to snow accumulation downstream of a fence.

Recently, hybrid multi-energy systems consisting of multiple generation, conversion, and storage technologies have been receiving great attention as a promising option to meet the multi-energy demands of residential end-users, by transforming them from passive consumers to active prosumers, who both produce and consume energy. The design problem of such systems is challenging due to the large number of degrees of freedom in the design and operation phases, so the system as a whole must be optimized. Moreover, both economic and low-carbon priorities should be considered in the design problem to foster an effective implementation and deployment. The aim of this paper was to present a methodology for the optimal design of multi-energy nanogrids (MENs) operating in grid-connected and islanded modes. Based on a pre-defined MEN superstructure, a multi-objective linear problem was established to find the types and sizes of the technologies in the MEN, with the aim to reduce the total annual cost and the fossil primary energy input, while satisfying the assigned time-varying user multi-energy demand. With reference to the latter, the thermal behavior of the building was simulated by using the dynamic simulation software TRNSYS. The Pareto frontier was found by minimizing a weighted sum of the total annual cost and fossil primary energy input, and the problem was solved by using branch-and-cut. In the numerical testing, a single-family house of 200 m2 located in Italy was considered as the residential end-user. Results show the effectiveness of the model for providing good balancing solutions for end-users based on economic and energetic priorities. Moreover, it was found that the MEN operating in grid-connected mode showed economic and environmental performances much better than those found for the configuration operating in islanded mode.

The battery State of Charge (SoC) estimation is one of the basic and significant functions for Battery Management System (BMS) in Electric Vehicles (EVs). The SoC is the key to interoperability of various modules and cannot be measured directly. An improved Extended Kalman Filter (iEKF) algorithm based on a composite battery model is proposed in this paper. The approach of the iEKF combines the open-circuit voltage (OCV) method, coulomb counting (Ah) method and EKF algorithm. The mathematical model of the iEKF is built and four groups of experiments are conducted based on LiFePO4 battery for offline parameter identification of the model. The iEKF is verified by real battery data. The simulation results with the proposed iEKF algorithm under both static and dynamic operation conditions show a considerable accuracy of SoC estimation.

The present numerical research studies the effect of nano-materials in a lid-driven cylindrical cavity with rotation of circumferential top wall. The heat is transferred from two lateral walls to the domain by constant temperature conditions while other walls are kept isolated. The non-dimensional equations are solved by Finite Volume Method (FVM) and SIMPLEC method. The effect of Reynolds (Re = 100, 400, 1000), Ryleigh (Ra = 104, 105, 106) numbers are studied. In addition, the effect of concentration of nano materials ( ϕ = 0%, 1%, 5%), the Height Ratio (HR = 1, 0.5, 2) on Nusselt number, isotherm lines and streamlines are studied. The results show that Reynolds number also can change the effect of nano particles on the heat transfer rate. It is observed that the height ratio increase can improve the Nusselt number since the number and the size of vortices inside the cavity changes. In addition, increase of Ra number can change the flow structure inside the cavity which can help in increasing of Nusselt number.

The following article summarizes United States Patent Application No. US20180052155A1, titled ‘Assay Devices and Methods’ (filed 16 August 2016, published 22 February 2018). While lateral flow assays (LFAs) have revolutionized point-of-care diagnostics by enabling accurate, inexpensive, and rapid detection of biomarkers, they typically do not provide quantitative results. Hence, there is a significant need for quantitative assays at the point of care. This patent summary describes a novel method of chronometric biomarker quantitation via enzymatic degradation of a metastable gelatin-based biomatrix, principally suited for use in paper-based microfluidic devices (microPADs). This new quantitation mechanism was designed to meet the ASSURED criteria for point-of-care diagnostic devices laid forth by the World Health Organization and may ultimately provide increased access to healthcare, at a significantly reduced cost, around the world.

Unpredicted deviations in time series data are called change points. These unexpected changes indicate transitions between states. Change point detection is a valuable technique in modeling to estimate unanticipated property changes underlying time series data. It can be applied in different areas like climate change detection, human activity analysis, medical condition monitoring and speech and image analyses. Supervised and unsupervised techniques are equally used to identify changes in time series. Even though change point detection algorithms have improved considerably in recent years, several undefended challenges exist. Previous work on change point detection was limited to specific areas, therefore, more studies are required to investigate appropriate change point detection techniques applicable to any data distribution to assess the numerical productivity of any stochastic process. This research is primarily focused on the formulation of an innovative methodology for change point detection of diversely distributed stochastic processes using a probabilistic method with variable data structures. Bayesian inference and a likelihood ratio test are used to detect a change point at an unknown time (k). The likelihood of k is determined and used in the likelihood ratio test. Parameter change must be evaluated by critically analyzing the parameters expectations before and after a change point. Real-time data of particulate matter concentrations at different locations were used for numerical verification, due to diverse features, that is, environment, population densities and transportation vehicle densities. Therefore, this study provides an understanding of how well this recommended model could perform for different data structures.

Numerical investigation for unsteady, viscous, incompressible Bingham fluid flow through parallel plates is studied. The upper plate drifts with a constant uniform velocity and the lower plate is stationary. Both plates are studied at different fixed temperatures. To obtain the dimensionless equations, the governing equations for this study have been transformed by usual transformations. The obtained dimensionless equations are solved numerically using the explicit finite difference method (FDM). The studio developer Fortran (SDF) 6.6a and MATLAB R2015a are both used for numerical simulations. The stability criteria have been established and the system is converged for Prandtl number Pr&ge, 0.08 with &Delta, Y=0.05 and &Delta, &tau, =0.0001 as constants. As a key outcome, the steady-state solutions have been occurred for the dimensionless time &tau, = 4.00. The influence of parameters on the flow phenomena and on shear stress, including Nusselt number, are explained graphically. Finally, qualitative and quantitative comparison are shown.

This paper describes a new approach that can be used to determine the mechanical properties of unknown materials and complex material systems. The approach uses inverse finite element modelling (FEM) accompanied with a designed algorithm to obtain the modulus of elasticity, yield stress and strain hardening material constants of an isotropic hardening material model, as well as the material constants of the Drucker&ndash, Prager material model (modulus of elasticity, cap yield stress and angle of friction). The algorithm automatically feeds the input material properties data to finite element software and automatically runs simulations to establish a convergence between the numerical loading&ndash, unloading curve and the target data obtained from continuous indentation tests using common indenter geometries. A further module was developed to optimise convergence using an inverse FEM analysis interfaced with a non-linear MATLAB algorithm. A sensitivity analysis determined that the dual spherical and Berkovich (S&amp, B) approach delivered better results than other dual indentation methods such as Berkovich and Vickers (B&amp, V) and Vickers and spherical (V&amp, S). It was found that better convergence values can be achieved despite a large variation in the starting parameter values and/or material constitutive model and such behaviour reflects the uniqueness of the dual S&amp, B indentation in predicting complex material systems. The study has shown that a robust optimization method based on a non-linear least-squares curve fitting function (LSQNONLIN) within MATLAB and ABAQUS can be used to accurately predict a unique set of elastic plastic material properties and Drucker&ndash, Prager material properties. This is of benefit to the scientific investigation of properties of new materials or obtaining the material properties at different locations of a part which may be not be similar because of manufacturing processes (e.g., different heating and cooling rates at different locations).

Iridoids belong to a family of monoterpenoids comprising the cyclopentan[c]-pyran system; this class of compounds offers a wide range of biological effects, namely antileishmanial, anticancer, antiplasmodial, and anti-inflammatory potency. To date, a large number of biologically active iridoid derivatives have been reported from various plant families, including Rubiaceae, Plantaginaceae, Scrophulariaceae, and Verbenaceae. Furthermore, iridoids have the potential to form conjugates with other anticancer, antidiabetic, antileishmanial, and antimalarial drugs which synergistically have the potential to increase their effects. Additionally, future research should focus on the synthesis of halo analogs as well as preparing homo dimers or heterodimers of iridoids, since these might quite conceivably possess an increased bioactivity.

The fast development of the residential sector regarding the additional integration of renewable distributed energy sources and the modern expansion usage of essential DC electrical equipment may cause severe power quality problems. For example, the integration of rooftop photovoltaic (PV) may cause unbalance, and voltage fluctuation, which can add constraints for further PV integrations to the network, and the deployment of DC native loads with their nonlinear behavior adds harmonics to the network. This paper demonstrates the smart integration of a DC microgrid to the neighborhood low-voltage distribution network (NLVDN). The DC microgrid is connected to the NLVDN through a three-phase voltage source inverter (VSI), in which the VSI works as a distribution static compensator (DSTATCOM). Unlike previous STATCOM work in the literature, the proposed controller of the VSI of the DC smart building allows for many functions: (a) it enables bidirectional active/reactive power flow between the DC building and the AC grid at point of common coupling (PCC), (b) it compensates for the legacy unbalance in the distribution network, providing harmonics elimination and power factor correction capability at PCC, and (c) it provides voltage support at PCC. The proposed controller was validated by Matlab/Simulink and by experimental implementation at the lab.

Ejectors used in refrigeration systems as entrainment and compression components or expanders, alone or in combination with other equipment devices, have gained renewed interest from the scientific community as a means of low temperature heat recovery and more efficient energy use. This paper summarizes the main findings and trends, in the area of heat-driven ejectors and ejector-based machines, using low boiling point working fluids, which were reported in the literature for a number of promising applications. An overall view of such systems is provided by discussing the ejector physics principles, as well as a review of the main developments in ejectors over the last few years. Recent achievements on thermally activated ejectors for single-phase compressible fluids are the main focus in this part of the review. Aspects related to their design, operation, theoretical and experimental approaches employed, analysis of the complex interacting phenomena taking place within the device, and performance are highlighted. Conventional and improved ejector refrigeration cycles are discussed. Some cycles of interest employing ejectors alone or boosted combinations are presented and their potential applications are indicated.

International audience; The statistical mechanics and the thermodynamics of small systems are characterized by 1 the non-equivalence of the statistical ensembles. Concerning a polymer chain or an arbitrary chain 2 of independent units, this concept leads to different force-extension responses for the isotensional 3 (Gibbs) and the isometric (Helmholtz) thermodynamic ensembles for a limited number of units 4 (far from the thermodynamic limit). While the average force-extension response has been largely 5 investigated in both Gibbs and Helmholtz ensembles, the full statistical characterization of this 6 thermo-mechanical behavior has not been approached by evaluating the corresponding probability 7 densities. Therefore, we elaborate in this paper a technique for obtaining the probability density of the 8 extension when the force is applied (Gibbs ensemble) and the probability density of the force when the 9 extension is prescribed (Helmholtz ensemble). This methodology, here developed at thermodynamic 10 equilibrium, is applied to a specific chain composed of units characterized by a bistable potential 11 energy, which is able to mimic the folding and unfolding of several macromolecules of biological 12 origin. 13

This paper concerns the health monitoring of pipelines and tubes. It proposes the k-means clustering algorithm as a simple tool to monitor the integrity of a structure (i.e., detecting defects and assessing their growth). The k-means algorithm is applied on data collected experimentally, by means of an ultrasonic guided waves technique, from healthy and damaged tubes. Damage was created by attaching magnets to a tube. The number of magnets was increased progressively to simulate an increase in the size of the defect and also, a change in its shape. To test the performance of the proposed method for damage detection, a statistical population was created for the healthy state and for each damage step. This was done by adding white Gaussian noise to each acquired signal. To optimize the number of clusters, many algorithms were run, and their results were compared. Then, a semi-supervised based method was proposed to determine an alarm threshold, triggered when a defect becomes critical.

Internet of Things (IoT) is a connection of people and things at any time, in any place, with anyone and anything, using any network and any service. Thus, IoT is a huge dynamic global network infrastructure of Internet-enabled entities with web services. One of the most important applications of IoT is the Smart Grid (SG). SG is a data communications network which is integrated with the power grid to collect and analyze data that are acquired from transmission lines, distribution substations, and consumers. In this paper, we talk about IoT and SG and their relationship. Some IoT architectures in SG, requirements for using IoT in SG, IoT applications and services in SG, and challenges and future work are discussed.

Microfluidic paper-based analytical devices (microPADs) have emerged as a promising platform for point-of-care diagnostic devices. While the inherent wicking properties of microPADs allow for fluid flow without supporting equipment, this also presents a major challenge in achieving robust fluid control, which becomes especially important when performing complex multi-step assays. Herein, we describe an ideal method of fluid control mediated by wax-printed fluidic time delays. This method relies on a simple fabrication technique, does not utilize chemicals/reagents that could affect downstream assays, is readily scalable, and has a wide temporal range of tunable fluid control. The delays are wax printed on both the top and bottom of pre-fabricated microPAD channels, without subsequent heating, to create hemi-/fully-enclosed channels. With these wax printed delays, we were able to tune the time it took aqueous solutions to wick across a 25 mm-long channel between 3.6 min and 13.4 min. We then employed these fluid delays in the sequential delivery of four dyes to a test zone. Additionally, we demonstrated the automation of two simple enzymatic assays with this fluid control modality. This method of fluid control may allow future researchers to automate more complex assays, thereby further advancing microPADs toward real-world applications.

Two-phase ejectors play a major role as refrigerant expansion devices in vapor compression systems and can find potential applications in many other industrial processes. As a result, they have become a focus of attention for the last few decades from the scientific community, not only for the expansion work recovery in a wide range of refrigeration and heat pump cycles but also in industrial processes as entrainment and mixing enhancement agents. This review provides relevant findings and trends, characterizing the design, operation and performance of the two-phase ejector as a component. Effects of geometry, operating conditions and the main developments in terms of theoretical and experimental approaches, rating methods and applications are discussed in detail. Ejector expansion refrigeration cycles (EERC) as well as the related theoretical and experimental research are reported. New and other relevant cycle combinations proposed in the recent literature are organized under theoretical and experimental headings by refrigerant types and/or by chronology whenever appropriate and systematically commented. This review brings out the fact that theoretical ejector and cycle studies outnumber experimental investigations and data generation. More emerging numerical studies of two-phase ejectors are a positive step, which has to be further supported by more validation work.

Artistic robotic painting implies creating a picture on canvas according to a brushstroke map preliminarily computed from a source image. To make the painting look closer to the human artwork, the source image should be preprocessed to render the effects usually created by artists. In this paper, we consider three preprocessing effects: aerial perspective, gamut compression and brushstroke coherence. We propose an algorithm for aerial perspective amplification based on principles of light scattering using a depth map, an algorithm for gamut compression using nonlinear hue transformation and an algorithm for image gradient filtering for obtaining a well-coherent brushstroke map with a reduced number of brushstrokes, required for practical robotic painting. The described algorithms allow interactive image correction and make the final rendering look closer to a manually painted artwork. To illustrate our proposals, we render several test images on a computer and paint a monochromatic image on canvas with a painting robot.