Showing papers in "Inventions in 2022"

A Systematic Review of Applications of Machine Learning Techniques for Wildfire Management Decision Support

Abstract: Wildfires threaten and kill people, destroy urban and rural property, degrade air quality, ravage forest ecosystems, and contribute to global warming. Wildfire management decision support models are thus important for avoiding or mitigating the effects of these events. In this context, this paper aims at providing a review of recent applications of machine learning methods for wildfire management decision support. The emphasis is on providing a summary of these applications with a classification according to the case study type, machine learning method, case study location, and performance metrics. The review considers documents published in the last four years, using a sample of 135 documents (review articles and research articles). It is concluded that the adoption of machine learning methods may contribute to enhancing support in different fire management phases.

Perspectives on the Barriers to Nuclear Power Generation in the Philippines: Prospects for Directions in Energy Research in the Global South

Abstract: This paper offers a discussion on the social dimensions of the barriers to nuclear power generation in the country. The aim of this paper is to contribute to the literature by identifying the barriers to nuclear power generation in the Philippines and offering perspectives on the social relevance of potentially adding nuclear sources to the country’s energy mix. Given the contemporary relevance of the energy transitions globally, this work builds on the available sources over the past decade concerning nuclear energy technology in the Philippines and provides further discussions on the diverse barriers to the country’s energy transition pathway. Findings present barriers related to politics, policy, infrastructure, technical capacities, environment and information. The differences in priorities and values concerning nuclear energy reflect that the barriers to nuclear energy generation in the Philippines are social as much as technical. Based on the findings and descriptions of the current discussions on Philippine energy generation, this work provides some key points for consideration in order to deploy nuclear power plants in the country. These recommendations, however, are not definitive measures and are still subject to local conditions that may arise. This study hopes to be instructive to other countries in terms of further reflecting on the social dimensions of the barriers to nuclear energy generation.

A Novel Solution for Day-Ahead Scheduling Problems Using the IoT-Based Bald Eagle Search Optimization Algorithm

Abstract: Advances in technology and population growth are two factors responsible for increasing electricity consumption, which directly increases the production of electrical energy. Additionally, due to environmental, technical and economic constraints, it is challenging to meet demand at certain hours, such as peak hours. Therefore, it is necessary to manage network consumption to modify the peak load and tackle power system constraints. One way to achieve this goal is to use a demand response program. The home energy management system (HEMS), based on advanced internet of things (IoT) technology, has attracted the special attention of engineers in the smart grid (SG) field and has the tasks of demand-side management (DSM) and helping to control equality between demand and electricity supply. The main performance of the HEMS is based on the optimal scheduling of home appliances because it manages power consumption by automatically controlling loads and transferring them from peak hours to off-peak hours. This paper presents a multi-objective version of a newly introduced metaheuristic called the bald eagle search optimization algorithm (BESOA) to discover the optimal scheduling of home appliances. Furthermore, the HEMS architecture is programmed based on MATLAB and ThingSpeak modules. The HEMS uses the BESOA algorithm to find the optimal schedule pattern to reduce daily electricity costs, reduce the PAR, and increase user comfort. The results show the suggested system’s ability to obtain optimal home energy management, decreasing the energy cost, microgrid emission cost, and PAR (peak to average ratio).

Engineering and Analytical Method for Estimating the Parametric Reliability of Products by a Low Number of Tests

Abstract: The paper provides an overview of methods for determining reliability indicators and, on the basis of the analysis, proposes a new method for assessing the parametric reliability of products based on a small number of tests. The determination of the parameters and double logistic distribution based on the test results is considered, a statistical experiment was carried out, which was based on the method of statistical modeling of Monte Carlo. An example of evaluating parametric reliability by a new method is also given, on the basis of which an engineering technique is proposed. In the conclusion, remarks are made regarding the advantages of the novel method.

A Fingerprint Matching Algorithm Using the Combination of Edge Features and Convolution Neural Networks

Abstract: This study presents an algorithm for fingerprint classification using a CNN (convolutional neural network) model and making use of full images belonging to four digital databases. The main challenge that we face in fingerprint classification is dealing with the low quality of fingerprints, which can impede the identification process. To overcome these restrictions, the proposed model consists of the following steps: a preprocessing stage which deals with edge enhancement operations, data resizing, data augmentation, and finally a post-processing stage devoted to classification tasks. Primarily, the fingerprint images are enhanced using Prewitt and Laplacian of Gaussian filters. This investigation used the fingerprint verification competition with four databases (FVC2004, DB1, DB2, DB3, and DB4) which contain 240 real fingerprint images and 80 synthetic fingerprint images. The real images were collected using various sensors. The innovation of the model is in the manner in which the number of epochs is selected, which improves the performance of the classification. The number of epochs is defined as a hyper-parameter which can influence the performance of the deep learning model. The number of epochs was set to 10, 20, 30, and 50 in order to keep the training time at an acceptable value of 1.8 s/epoch, on average. Our results indicate the overfitting of the model starting with the seventh epoch. The accuracy varies from 67.6% to 98.7% for the validation set, and between 70.2% and 75.6% for the test set. The proposed method achieved a very good performance compared to the traditional hand-crafted features despite the fact that it used raw data and it does not perform any handcrafted feature extraction operations.

Using Drones for Thermal Imaging Photography and Building 3D Images to Analyze the Defects of Solar Modules

Abstract: In this research, drones were used to capture thermal images and detect different types of failure of solar modules, and MATLAB® image analysis was also conducted to evaluate the health of the solar modules. The processes included image acquisition and transmission by drone, grayscale conversion, filtering, 3D image construction, and analysis. The analyzed targets were the solar modules installed on buildings. The results showed that the employment of drones to monitor solar module farms could significantly improve inspection efficiency. Moreover, by combining the mean and median filtering techniques, an innovative box filtering method was successfully created. Additionally, this study compared the differences between the mean, median, and box filtering techniques, and proved that the 3D image improved by box filtering is a more convenient and accurate way to check the health of solar modules than the mean and median filtering methods. In addition, this new method can simplify the maintenance process, as it helps maintenance personnel to determine whether to replace the solar modules on site, achieving the goal of power generation efficiency enhancement. It is worth noting that 3D image recognition technology can enhance the clarity of thermal images, thereby providing maintenance personnel with better defect diagnosis capability. It is also able to provide the temperature value of the defect zone, and to indicate the scale of defects through the cumulative temperature chart, so the 3D image is qualified as a quantitative and qualitative indicator. The analysis of the transmitted image is innovative that it not only can locate the defect area of the module, but also can display the temperature of the module, providing more information for maintenance personnel.

Stepped-Frequency Continuous-Wave Signal Processing Method for Human Detection Using Radars for Sensing Rooms through the Wall

Abstract: The problem of detecting moving and stationary people in a room with a specialized radar system sensing through the wall is considered in the paper. The high-range resolution of the system is achieved by effective processing of reflected ultra-wideband stepped-frequency continuous-wave signals (SFCW). The paper presents a new method which is based on normalization of complex-valued samples of the received SFCW signals and extends traditional processing steps including quadrature-phase demodulation, sampling and inverse discrete Fourier transform. The proposed method is aimed at improving the performance of the interperiodic difference and variance of sample algorithms which are briefly described in relation to the SFCW radar system. The computer modeling showed that the introduced normalization mitigates the background noise and merely decreases the artifacts commonly appearing in radar images due to the non-uniform amplitude-frequency characteristics of the radar circuits. The described algorithms were implemented in a software part of the real-time working prototype of the radar system designed and assembled at the University research center. The results of field experiments confirmed the advantage of the proposed method in typical scenarios and showed the increase of the signal-to-noise ratio to 5 dB compared to traditional radar algorithm-processing SFCW signals.

Numerical Study on Plastic Strain Distributions and Mechanical Behaviour of a Tube under Bending

Abstract: Tubular pipe structures have been used in various applications—domestic, aviation, marine, manufacturing and material testing. The applications of tubular pipes have been considered greatly in the installation of tubular pipes, marine risers and pipe bending. For the investigation of plastic strains and the mechanical behaviour of a tube under bending, considerations were made utilising an exponent model with assumptions on the plane strain. The bending moment, wall thickness effect, cross-sectional distribution, stresses during bending and neutral layer boundaries were all presented as necessary theoretical formulations on the physics of tubular pipe bending. This model was based on the analytical and numerical investigation. In principle, the application can be observed as the spooling of pipes, bending of pipes and reeling. Comparisons were made on two models developed on the finite element analysis in Simscale OpenFEA, namely the linear-elastic and the elasto-plastic models. This study presents visualization profiles using plastic strain to assess its effect on the tubular pipes. This can increase due to the limitation of plastic deformation on the composite materials selected.

Smart Emergency EV-to-EV Portable Battery Charger

Abstract: With the increase in the number of electric vehicles (EVs) and developments in their related charging infrastructures, consumers have still some concerns about some limiting factors in the EV industry such as battery life, charging station availability, electric grid capacity, limited driving range, and slow charging of batteries. Although some solutions are proposed for these limitations, they are not sufficiently efficient and cost-effective. Moreover, charging of EVs on-the-road is still a challenging issue which requires more innovation. This paper proposes a novel battery charger, known as an Emergency EV-to-EV Portable Battery Charger (EPBC), which provides a cost-effective solution for charging EVs on-the-road in emergency mode. The suggested smart charger can charge another EV based on the state of charge (SOC), capacity, and other important technical specifications of batteries in a safe and reliable manner. The smart charger can regulate the output voltage and the injected current to the EV simultaneously. To realize these features, a model free nonlinear integral backstepping control (MF-NIBC) is adopted to regulate the output voltage of the battery charger. By utilizing the actor and critic networks, a deep deterministic policy gradient (DDPG) is adopted to adjust the MF-NIBC controller. Finally, real-time tests based on the OPAL-RT setup are conducted to confirm the applicability and feasibility of the proposed EV-to-EV portable battery charger.

The Strategy of Factors Influencing Learning Satisfaction Explored by First and Second-Order Structural Equation Modeling (SEM)

Abstract: The goal of this research was to create a partial least square structural equation model (PLS-SEM) with a second-order structural model to investigate the interaction between research-based methodologies and relationship factors that significantly influence learning satisfaction among university students. The instruments used in this study were a simple random sampling technique for structural equation model (SEM) analysis, while a quantitative process of survey data collection was manipulated through SPSS and Smart-PLS. The presented study attempted to explore whether teachers’ strategies are linked with their students for the students’ learning satisfaction. Thus, it represents the demands and expectations of two statistically significant common phenomena: research-based components and relationship approach components. This set of teaching techniques encourages university students and enhances their learning satisfaction. Moreover, this study explored teaching strategies that influence factors having a directly significant influence on learning satisfaction at university level. Each factor measures the relationship’s construct, proven to be a second-order SEM reflective model that is statistically significant. Our study explored learning satisfaction as an integral part of teaching strategies, by first- and second-order structural equation modeling, supported by students’ expectations, and the study’s empirical results provide potential implications for learning satisfaction.

Improvement of the Liquefied Natural Gas Vapor Utilization System Using a Gas Ejector

Abstract: The production, transportation, and storage of liquefied natural gas (LNG) is a promising area in the gas industry due to a number of the fuel’s advantages, such as its high energy intensity indicators, its reduced storage volume compared to natural gas in the gas-air state, and it ecological efficiency. However, LNG storage systems feature a number of disadvantages, among which is the boil-off gas (BOG) recovery from an LNG tank by flaring it or discharging it to the atmosphere. Previous attempts to boil-off gas recovery using compressors, in turn, feature such disadvantages as large capital investments and operating costs, as well as low reliability rates. The authors of this article suggest a technical solution to this problem that consists in using a gas ejector for boil-off gas recovery. Natural gas from a high-pressure gas pipeline is proposed as a working fluid entraining the boil-off gas. The implementation of this method was carried out according to the developed algorithm. The proposed technical solution reduced capital costs (by approximately 170 times), metal consumption (by approximately 100 times), and power consumption (by approximately 55 kW), and improved the reliability of the system compared to a compressor unit. The sample calculation of a gas ejector for the boil-off gas recovery from an LNG tank with a capacity of 300 m3 shows that the ejector makes it possible to increase the boil-off gas pressure in the system by up to 1.13 MPa, which makes it possible to not use the first-stage compressor unit for the compression of excess vapours.

An Analysis of the Wind Parameters in the Western Side of the Black Sea

Abstract: In the present research, an overview of the wind climate on the northwestern coast of the Black Sea basin is assessed, using a total of 6 years of data (2015–2020) provided by the National Institute of Marine Geology and Geoecology (GeoEcoMar). It is well known that the enclosed/semi-enclosed basins are complex environments and to accurately represent the features of wind and wave are necessary high resolution spatial fields. For the Black Sea, which is an enclosed basin with complicated regional geography, the main weather parameters reported (wind direction, wind speed, air temperature, air pressure) give a more comprehensive picture of how energetic the area of interest is, and represent the features of the Black Sea’s diversified marine environment. Finally, the results obtained in this paper cover a broad range of applications in marine studies, being useful for future research in the area of wind climate in the Black Sea.

Introducing the Living Lab Approach in the Coastal Area of Constanta (Romania) by Using Design Thinking

Abstract: Living Labs are an innovative concept that combines research, governance, and citizens, using technology and knowledge. Using design thinking techniques as a method of approach, this innovative idea builds a bridge between decision makers and stakeholders, promoting a shared vision of growth and innovation at the community level. The coastal zone is an extremely dynamic area in terms of human and natural activities. This is a particularly sensitive area to climate change, necessitating ongoing adaptation and mitigating action. This paper aims to introduce the application of the Living lab concept in the management of the coastal area of Constanta (Romania). The concept of the Living Lab means involving citizens along with public bodies and research structures. This new coastal management model will use the design thinking approach and will consider the pressures that exist between the activities that occur in the examined coastal zone. In the study, “Multi-Criteria Analysis of the Mass Tourism Management Model Related to the Impact on the Local Community in Constanța (Romania)”, published in MDPI Inventions on 28 June 2021, a coastal management model was built that took into consideration only the data given by the government. In this paper, the authors aim to expand their research by including data from independent sources, using the concept of a Living Lab.

Numerical Investigation of Winglet Aerodynamics and Dimple Effect of NACA 0017 Airfoil for a Freight Aircraft

Abstract: Drag reduction is an ever-present challenge within the aeronautical engineering industry. This paper presents two substantial wing modifications: the addition of a winglet of a freighter aircraft and a dimpled wing on the NACA 0017 aerofoils. Studies on nine (9) different geometries of dimpled aerofoils were performed against a control model of an aerofoil without any dimple. Computational fluid dynamics (CFD) analysis was performed using two (2) commercial CFD platforms. This paper also explored two novel solutions of aircraft optimisation to mitigate the effects of drag and leading-edge pressure, while increasing the effect of lift. The optimised performance model of a freighter aircraft increased its aerodynamic efficiency. The study found that at take-off velocity of 82 m/s, winglets decreased pressure on the wing by 16.31%, through flow redirection and better flow integration into aerofoils wake. The study also analysed the separation layer and its effect through the appropriate use of the dimple effect. Increased lift effects were observed on a NACA 0017 aerofoil. Despite the low increase in drag of 6% from the modifications, the resultant L/D ratio was highly increased. This study also faced some challenges with validating the model. Hence some validation approaches were taken, and some other approaches suggested for future studies.

Investigation and Numerical Simulation of the Acoustic Target Strength of the Underwater Submarine Vehicle

Abstract: Modern weapon systems’ survival hinges on their detection capabilities more than anything else. In the active sonar equation, the acoustic target strength is crucial. Under the assumption of plane wave propagation, the standard target strength equation is used to forecast the reradiated intensity for the far field. The ability of a submarine to remain unnoticed while on patrol or accomplishing a mission is its primary defense. Sonar, sometimes known as sound navigation ranging, is a popular method for locating submarines. This is because saltwater effectively absorbs radio frequencies. Sonar technology is used in more than just the commercial fishing business; it is also used in undersea research. The submarine’s designers consider the reflection of acoustic waves to minimize the amount of space required for such reflections. The Target Strength (TS) metric is used to assess the sonar objects’ size. This manuscript explains and demystifies the Benchmark Target Echo Strength Simulation (BeTTSi) benchmark submarine’s TS analysis. This model’s Pressure Acoustic-Boundary Element Model (PA-BEM) interface has been stabilized, and the model itself is pretty huge acoustically.

Manufacturing of a Granular Fertilizer Based on Organic Slurry and Hardening Agent

Abstract: The manufacturing of a granular fertilizer based on organic slurry (OS) and sorptive materials aims to enhance the circular economy. This article describes a technology that was conceived after appraising the chemistry of the anaerobic digestate and wood ash and the synergies of combining both materials. The information available in the literature about similar materials such as cattle slurry and lime was also considered to build a better understanding of the underlying science. The processes and machinery designed were optimized from the points of view of energy and material consumption, cost of storage, transportation and land application. The system was sized to process 1 tonne of OS (97% moisture) in a 10 h batch-shift, consuming 140 kg of wood pellets and 0.55 kW of electricity for the fan blowing preheated air. The results of the market research allowed us to reach the most economically viable routes for the commercialization of granular fertilizers. Based on the financial study, an initial investment of GBP 20,000 is needed to successfully implement the value proposition and business plan. The wide adoption of the composite fertilizer improves the management of the OS and reduces the contamination of air, soil, and water derived from intensive agricultural practices.

An Experimental Investigation and Computer Modeling of Direct Tension Pullout Test of Reinforced Concrete Cylinder

Abstract: Executing the obligation of strengthened concrete is essential in investigating load exchanges from concrete to the inner reinforcing bar. The bond–displacement conduct and extreme pullout quality for pullout samples are essential information related to the durability of RC structures. The slip in the interface is basically due to a contrast in stresses between concrete and reinforcement. This distinction brings about the start of the split in encompassing concrete. This study examined the simple pullout solid 3D cylinder model strengthened by a reinforced steel bar, considered a line element for bond–slip conduct. The non-linear finite element model utilizing ANSYS software was established to concentrate on the concrete and steel reinforcement bond. Material nonlinearity because of cracking, crushing of concrete, and the steel reinforcing bar’s yielding were investigated. Test results showed that: a prediction model for early-age bond stress–slip relationship between steel bars and concrete was proposed based on modeling, which showed good agreement with test results. The precision of this model is explored by contrasting the finite element numerical analysis and that anticipated from test consequences of pullout examples. Immense homogeneity between the model and test results was found. This study could provide more accurate bond properties for structural analysis and design.

TiO2/Au/TiO2 Plasmonic Photocatalysts: The Influence of Titania Matrix and Gold Properties

Abstract: Plasmonic photocatalysts have gained more and more attention because of possible applications for solar energy conversion, environmental decontamination, and water treatment. However, the activity under visible light is usually very low, and the property-governed activity as well as the mechanisms are not fully understood yet. Accordingly, this study examines four different titania photocatalysts (anatase and rutile with fine and large crystallites) modified with gold by photodeposition. Three kinds of samples were prepared, as follows: (i) gold-modified titania (Au/TiO2), (ii) physically mixed Au/TiO2 samples (Au/TiO2(1) + Au/TiO2(2)), and (iii) Au/(TiO2(1) + Au/TiO2(2)) samples, prepared by subsequent deposition of gold on the mixture of bare and gold-modified titania. In total, twelve samples were prepared and well characterized, including diffuse reflectance spectroscopy (DRS), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and scanning transmission electron microscopy (STEM). The photocatalytic activity was examined in three reaction systems: (i) methanol dehydrogenation during gold photodeposition under UV/vis irradiation, (ii) oxidative decomposition of acetic acid (UV/vis), and (iii) oxidation of 2-propanol to acetone under visible light irradiation (λ > 450 nm). It was found that during subsequent deposition, gold is mainly formed on the surface of pre-deposited Au nanoparticles (NPs), localized on fine titania NPs, through the electrostatic attractions (negatively charged gold resulting from photogenerated electrons’ accumulation). This gold aggregation, though detrimental for UV activity (many “naked” large titania with low activity), is highly beneficial for vis activity because of efficient light harvesting and increased interface between gold and titania (gold deposits surrounded by fine titania NPs). Moreover, it was found that rutile is more active than anatase for plasmonic photocatalysis, probably due to easier electron transfer from gold via titania to adsorbed oxygen (more negative conduction band), which might hinder the back reaction (electron transfer: Au→TiO2→Au).

Sensitivity Analysis of Artificial Neural Networks Identifying JWH Synthetic Cannabinoids Built with Alternative Training Strategies and Methods

Abstract: This paper presents the alternative training strategies we tested for an Artificial Neural Network (ANN) designed to detect JWH synthetic cannabinoids. In order to increase the model performance in terms of output sensitivity, we used the Neural Designer data science and machine learning platform combined with the programming language Python. We performed a comparative analysis of several optimization algorithms, error parameters and regularization methods. Finally, we performed a new goodness-of-fit analysis between the testing samples in the data set and the corresponding ANN outputs in order to investigate their sensitivity. The effectiveness of the new methods combined with the optimization algorithms is discussed.

Mobile Visual Servoing Based Control of a Complex Autonomous System Assisting a Manufacturing Technology on a Mechatronics Line

Abstract: The main contribution of this paper is the modeling and control for a complex autonomous system (CAS). It is equipped with a visual sensor to operate precision positioning in a technology executed on a laboratory mechatronics line. The technology allows the retrieval of workpieces which do not completely pass the quality test. Another objective of this paper is the implementation of an assisting technology for a laboratory processing/reprocessing mechatronics line (P/RML) containing four workstations, assisted by the following components: a complex autonomous system that consists of an autonomous robotic system (ARS), a wheeled mobile robot (WMR) PeopleBot, a robotic manipulator (RM) Cyton 1500 with seven degrees of freedom (7 DOF), and a mobile visual servoing system (MVS) with a Logitech camera as visual sensor used in the process of picking, transporting and placing the workpieces. The purpose of the MVS is to increase the precision of the RM by utilizing the look and move principle, since the initial and final positions of the CAS can slightly deviate from their trajectory, thus increasing the possibility of errors to appear during the process of catching and releasing the pieces. If the processed piece did not pass the quality test and has been rendered as defective, it is retrieved from the last station of the P/RML and transported to the first station for reprocessing. The control of the WMR is done using the trajectory-tracking sliding-mode control (TTSMC). The RM control is based on inverse kinematics model, and the MVS control is implemented with the image moments method.

Blockchain and 6G-Enabled IoT

Abstract: Ubiquitous computing turns into a reality with the emergence of the Internet of Things (IoT) adopted to connect massive numbers of smart and autonomous devices for various applications. 6G-enabled IoT technology provides a platform for information collection and processing at high speed and with low latency. However, there are still issues that need to be addressed in an extended connectivity environment, particularly the security and privacy domain challenges. In addition, the traditional centralized architecture is often unable to address problems associated with access control management, interoperability of different devices, the possible existence of a single point of failure, and extensive computational overhead. Considering the evolution of decentralized access control mechanisms, it is necessary to provide robust security and privacy in various IoT-enabled industrial applications. The emergence of blockchain technology has changed the way information is shared. Blockchain can establish trust in a secure and distributed platform while eliminating the need for third-party authorities. We believe the coalition of 6G-enabled IoT and blockchain can potentially address many problems. This paper is dedicated to discussing the advantages, challenges, and future research directions of integrating 6G-enabled IoT and blockchain technology for various applications such as smart homes, smart cities, healthcare, supply chain, vehicle automation, etc.

A Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) Forecasting Model-Based Time Series Approach

Abstract: Time series modeling is an effective approach for studying and analyzing the future performance of the power sector based on historical data. This study proposes a forecasting framework that applies a seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model to forecast the long-term performance of the electricity sector (electricity consumption, generation, peak load, and installed capacity). In this study, the model was used to forecast the aforementioned factors in Saudi Arabia for 30 years from 2021 to 2050. The historical data that were inputted into the model were collected from Saudi Arabia at quarterly intervals across a 40-year period (1980−2020). The SARIMAX technique applies a time series approach with seasonal and exogenous influencing factors, which helps reduce the error values and improve the overall model accuracy, even in the case of close input and output dataset lengths. The experimental findings indicated that the SARIMAX model has promising performance in terms of categorization and consideration, as it has significantly improved forecasting accuracy compared with the simpler autoregressive integrated moving average-based techniques. Furthermore, the model is capable of coping with different-sized sequential datasets. Finally, the model aims to help address the issue of a lack of future planning and analyses of power performance and intermittency, and it provides a reliable forecasting technique, which is a prerequisite for modern energy systems.

Design and Stability Analysis of an Offshore Floating Multi-Wind Turbine Platform

Abstract: The multi-wind turbine platform technology has the potential to harness the significant source of offshore wind energy in deep waters. However, the wake interference between the turbines on the multi-wind turbine platform can cause a reduction in power production; hence, it is important to study the wake effects in the initial phase of the design. This paper studies the effects of wake interference between the wind turbines on three different platform configurations to find a suitable configuration for the wind turbines on a multi-turbine platform. The analytical Larsen wake model and computational fluid dynamics (CFD) simulations are used for evaluating the wake effects. The platform configuration required for the wind turbines is determined based on the results of wake effects, and then a novel platform is designed. The free-floating stability behavior of the multi-wind turbine platform is analyzed using the hydrostatic analysis of the modeled platform. The wave-body interaction between the platform and the waves is predicted using the hydrodynamic analysis. A preliminary cost analysis of the multi-turbine platform concept is evaluated and compared with a single wind turbine floating concept. The results showed that the presented design is a promising concept that can enhance the offshore wind industry.

An Investigation on the Vortex Effect of a CALM Buoy under Water Waves Using Computational Fluid Dynamics (CFD)

Abstract: Floating offshore structures (FOS) must be designed to be stable, to float, and to be able to support other structures for which they were designed. These FOS are needed for different transfer operations in oil terminals. However, water waves affect the motion response of floating buoys. Under normal sea states, the free-floating buoy presents stable periodic responses. However, when moored, they are kept in position. Mooring configurations used to moor buoys in single point mooring (SPM) terminals could require systems such as Catenary Anchor Leg Moorings (CALM) and Single Anchor Leg Moorings (SALM). The CALM buoys are one of the most commonly-utilised type of offshore loading terminal. Due to the wider application of CALM buoy systems, it is necessary to investigate the fluid structure interaction (FSI) and vortex effect on the buoy. In this study, a numerical investigation is presented on a CALM buoy model conducted using Computational Fluid Dynamics (CFD) in ANSYS Fluent version R2 2020. Some hydrodynamic definitions and governing equations were presented to introduce the model. The results presented visualize and evaluate specific motion characteristics of the CALM buoy with emphasis on the vortex effect. The results of the CFD study present a better understanding of the hydrodynamic parameters, reaction characteristics and fluid-structure interaction under random waves.

A Numerical Analysis of Fluid Flow and Torque for Hydropower Pelton Turbine Performance Using Computational Fluid Dynamics

Abstract: The difficulty of delivering electrical power to rural areas motivated the researchers to explore more accessible power sources. Hydropower is considered a desirable option due to its sustainability and lower costs. Pelton turbines have been widely used in hydropower plants because of their low installation and maintenance costs. This study provides a computational fluid dynamics (CFD) model for Pelton turbine performance under various flow conditions. The model is based on the conservation of mass principle, Newton’s second law, and the first law of thermodynamics. It is used to predict the torque produced by a turbine at different rotational speeds. Previously published experimental results for the same turbine geometry and flow parameters were used to validate the model’s predictions. Validation revealed that the model can reproduce the experimental results. This provides the required robustness for its use as a tool for turbine design and modification.

Biosafety Construction Composite Based on Iron Oxide Nanoparticles and PLGA

Abstract: Nanocomposites based on polymers and nanoparticles are used in agriculture for photoconversion of solar radiation, as a basis for covering material, as a packaging material, and as functional films. At the same time, nanocomposites are almost never used in agriculture as biosafe structural materials. In this work, we have developed a technology for obtaining a nanocomposite based on PLGA and iron oxide nanoparticles. The nanocomposite has unique physical and chemical properties and also exhibits pronounced antibacterial properties at a concentration of iron oxide nanoparticles of more than 0.01%. At the same time, the nanocomposite does not affect the growth and development of pepper and is biocompatible with mammalian cells. Nanocomposites based on PLGA and iron oxide nanoparticles can be an attractive candidate for the manufacture of structural and packaging materials in agriculture.

Image Moment-Based Features for Mass Detection in Breast US Images via Machine Learning and Neural Network Classification Models

Abstract: Differentiating between malignant and benign masses using machine learning in the recognition of breast ultrasound (BUS) images is a technique with good accuracy and precision, which helps doctors make a correct diagnosis. The method proposed in this paper integrates Hu’s moments in the analysis of the breast tumor. The extracted features feed a k-nearest neighbor (k-NN) classifier and a radial basis function neural network (RBFNN) to classify breast tumors into benign and malignant. The raw images and the tumor masks provided as ground-truth images belong to the public digital BUS images database. Certain metrics such as accuracy, sensitivity, precision, and F1-score were used to evaluate the segmentation results and to select Hu’s moments showing the best capacity to discriminate between malignant and benign breast tissues in BUS images. Regarding the selection of Hu’s moments, the k-NN classifier reached 85% accuracy for moment M1 and 80% for moment M5 whilst RBFNN reached an accuracy of 76% for M1. The proposed method might be used to assist the clinical diagnosis of breast cancer identification by providing a good combination between segmentation and Hu’s moments.

Research and Development of Trinary Power Cycles

Abstract: The most effective and environmentally safe fossil fuel power production facilities are the combined cycle gas turbine (CCGT) ones. Electric efficiency of advanced facilities is up to 58% in Russia and up to 64% abroad. The further improvement of thermal efficiency by increase of the gas turbine inlet temperature (TIT) is limited by performance of heat resistance alloys that are used for the hot gas path components and the cooling system efficiency. An alternative method for the CCGT efficiency improvement is utilization of low potential heat of the heat recovery steam generator (HRSG) exhaust gas in an additional cycle operating on a low-boiling heat carrier. This paper describes a thermodynamic analysis of the transition from binary cycles to trinary ones by integration of the organic Rankine cycle (ORC). A mathematical model of a cooled gas turbine plant (GT) has been developed to carry out calculations of high-temperature energy complexes. Based on the results of mathematical modeling, recommendations were made for the choice of the structure and parameters of the steam turbine cycle, as well as the ORC, to ensure the achievement of the maximum thermal efficiency of trinary plants. It is shown that the transition from a single pressure CCGT to a trinary plant allows the electric power increase from 213.4 MW to 222.7 MW and the net efficiency increase of 2.14%. The trinary power facility has 0.45% higher efficiency than the dual pressure CCGT.

Communication and Control of an Assembly, Disassembly and Repair Flexible Manufacturing Technology on a Mechatronics Line Assisted by an Autonomous Robotic System

Abstract: This paper aims to describe modeling and control in what concerns advanced manufacturing technology running on a flexible assembly, disassembly and repair on a mechatronic line (A/D/RML) assisted by an Autonomous Robotic System (ARS), two robotic manipulators (RM) and visual servoing system (VSS). The A/D/RML consists of a six workstations (WS) mechatronics line (ML) connected to a flexible cell (FC) equipped with a 6-DOF ABB industrial robotic manipulator (IRM) and an ARS used for manipulation and transport. A hybrid communication and control based on programmable logic controller (PLC) architecture is used, which consists of two interconnected systems that feature both distributed and centralized topology, with specific tasks for all the manufacturing stages. Profinet communication link is used to interconnect and control FC and A/D/RML. The paper also discusses how to synchronize data between different field equipment used in the industry and the control systems. Synchronization signals between the master PLC and ARS is performed by means of Modbus TCP protocol and OPC UA. The structure of the ARS consists of a wheeled mobile robot (WMR) with two driving wheels and one free wheel (2DW/1FW) equipped with a 7-DOF RM. Trajectory tracking sliding-mode control (TTSMC) is used to control WMR. The end effector of the ARS RM is equipped with a mobile eye-in-hand VSS technology for the precise positioning of RM to pick and place the workparts in the desired location. Technology operates synchronously with signals from sensors and from the VSS HD camera. If the workpiece does not pass the quality test, the process handles it by transporting back from the end storage unit to the flexible cell where it will be considered for reprocessing, repair or disassembling with the recovery of the dismantled parts. The recovered or replaced components are taken over by the ARS from disassembling location and transported back to the dedicated storage warehouses to be reused in the further assembly processes.

Determination of the Expected Value of Losses Caused by the Cargo Transportation Insurance Risks by Water Transport

Abstract: The purpose of this study was mathematical model development for assessing the cost of losses from risks in the maritime transportation of goods that are dynamic in nature, and developing a methodical approach to the dynamic costs assessment for each of the risks separately and integral costs for all risks and ensuring the fulfillment of the requirement to anticipate the insurance cost changes over the rate of change of the transportation integral risk (or its stage). The risks factor analysis in water transport, their classification and determination of the type and nature of their impact on sea transportation of goods were carried out. The groups of risk factors that lead to emergency situations for water transport in Ukraine were studied by comparing the data of 2019 and 2021 and determining their share in the total number of accidents before the start of the active phase of hostilities in Ukraine; the rates of their change were analyzed. This made it possible to develop a systematic assessment algorithm for the dependence of the expected and actual value of losses on risks and to create a mathematical approach to risks forecasting as a factor influencing the cost of expenses. As a result of the study, a methodical approach to forecasting the cost of losses from risks was formed for each of their types. However, the main attention was paid to the identification and assessment of dynamic risks, the impact of which has an absorbing nature relative to all others in their totality. Such risks in the waters of the Black and Azov seas today mainly include risks associated with the conduct of military operations, including such operations that go against international legal norms.