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The Study of the Dynamics Limits of Gas Flow in the Connecting Channels of Agglomeration Apparatus
No full textAmbient air quality is considered one of the indicators of a sustainable lifestyle. Traditional cleaning technologies based on gravitational, centrifugal, electrostatic, and other operating principles are not effective in removing ultra-fine solid particles, which are harmful to humans from the gas flow. Filters employing these methods can effectively capture particles larger than 1 µm, but efficiency of filtrating fine fractions from the gas stream is not sufficient. Fine particle pollutants of this type can be influenced by electric field agglomeration. After conducting thorough aerodynamic studies, a clearer understanding of flow dynamics will emerge. Specifically, the trajectories of solid particle movement and their velocities within the gas flow will be determined. These factors are crucial because the efficiency of particle agglomeration depends on them. Furthermore, the flow dynamics itself plays a significant role in increasing particle collisions. During this process, small particles coalesce into larger, singular particles that can be efficiently settled or removed from the gas stream using conventional cleaning technologies. In the study, aerodynamic drag and gas flow velocities were measured at various points of cross-section in inlet ducts at 12.4-174.3 m3/h and outlet ducts at 14.5-226.9 m3/h airflow. Additionally, the static pressure before and after the electric field agglomeration device was monitored. The static pressure at the inlet varied from 8 to 178 Pa and the maximum aerodynamic resistance was quantified at 175 Pa.Taip / Ye
Biblioteka informuoja, 2024 Nr. 4 (648)
No full textNaujai į Web of Science ir Scopus įtrauktų Vilnius Gedimino technikos darbuotojų publikacijų sąrašai ir kitos bibliotekos aktualijos.4 (648)202
Analysis of the transfer, flexural bond and anchorage lengths of pretensioned FRP reinforcement based on Eurocode
No full textCurrent design codes in North America have guidelines for assessing the anchorage zone of pretensioned fiber reinforced polymer (FRP) reinforcement. However, there is no comparison of the Eurocode approach with experimental data or suggestions for its application for prestressed FRP reinforcement. Therefore, the main objective of this article is to provide a comparison of available data in the literature on the transfer, flexural bond, and anchorage length of different types of FRP reinforcement with Eurocode and to provide insight on the adaptation of this code. The database of more than 300 and 100 specimens with the results of the transfer and flexural bond lengths is used, respectively. This database is used to derive and propose the parameters that describe the type and surface of pretensioned FRP reinforcement based on the Eurocode approach. Furthermore, the influence of shear reinforcement and the type of pretensioned reinforcement release (gradual and sudden) is taken into account. The Eurocode approach with the coefficients proposed for different FRP reinforcements on average gave the best prediction of the experimental transfer, flexural bond, and anchorage length results compared to the North American design codes for FRP reinforcement and the Eurocode for steel strands.vol. 32
Identification of Tomato Leaf Disease using YOLOv8 Detection Models on GPU and Raspberry Pi
No full textThe paper explores the automated identification of tomato leaf diseases using YOLOv8 detection models on both GPU and Raspberry Pi hardware. Through convolutional neural networks (CNNs) and transfer learning techniques, the study analyzes a dataset comprising images across 10 disease classes. Results demonstrate 0.78-0.79 precision and 0.75-0.81 recall scores for the YOLOv8 models. The Nano model processes single inference on Raspberry Pi in 0.7 second, making it suitable for real-time applications. Through experimental validation, the research underscores the practical significance of deep learning methods in agricultural practices, particularly in greenhouse monitoring and crop management, contributing to early disease detection and ensuring food security.Taip / Ye
Effects of overall satisfaction with PV Systems and subsidy policy on energy security for rooftop buildings using system dynamics
No full textA significant amount of the electricity needed to operate units of residential buildings can be generated from rooftop PV systems. This study developed system dynamics models to assess the impact of the overall satisfaction and subsidy policy with photovoltaic (PV) products for rooftops of residential buildings and households on energy security. The model was run by simulation from the year 2022 until 2035. Energy security was predicted. Results showed that at an overall satisfaction level and a subsidy proportion of 42% and 10%, the number of PV installations (generated PV power) in the year 2035 will reach 13.64 MW (9.01 GWh) and 26.99 GW (15.57 TWh), respectively. The cumulative subsidy cost reached 1.154 billion USD. In conclusion, manufacturers and suppliers of PV systems as well as the decision-makers in the energy sector can utilize the developed model as an assessment tool for improvement decisions to promote rooftop PV installations and secure sufficient renewable solar energy to meet the increasing annual energy demands
Investigation and evaluation of the influence of binder on the sound absorption properties of tyre textile fibre composite panels
No full textVykdant veiklą didelėse patalpose dažnu atveju susiduriama su nepakankamu akustiniu komfortu. Įvairūs sprendiniai yra taikomi, siekiant pagerinti akustines sąlygas. Pakabinamos lubos ir mineralinių vatų plokštės yra vieni iš būdingiausių sprendinių, kaip galima pagerinti patalpų akustinį komfortą. Tačiau, gaminant tokias plokštes, yra naudojami gamtiniai ištekliai ir eikvojami dideli energijos kiekiai. Padangų tekstilės pluoštas – tai atlieka, kuri gaunama perdirbant nebetinkamas naudoti padangas. Kartu su rišamąja medžiaga būtų sukurta kompozitinė garsą sugerianti plokštė, kuri būtų mažiau taršesnė alternatyva plačiai naudojamoms mineralinėms vatoms. Šio tyrimo tikslas yra ištirti, kokią įtaką akustinėms savybėms daro didėjantis rišamosios medžiagos kiekis kompozite. Garso sugerties koeficiento nustatymo metodas paremtas standartiniu metodu, aprašytu ISO 10534-2 standarte. Tyrimo rezultatai parodė, kad, didėjant rišamosios medžiagos kiekiui kompozite nuo 10 iki 50 %, garso sugertis visame spektre mažėja vidutiniškai nuo 4 iki 30 % (kuo didesnis rišiklio kiekis, tuo labiau mažėjo garso sugertis). Gauti rezultatai rodo, kad rišiklio kiekis kompozite lemia kompozitinės garsą sugeriančios plokštės garso sugerties gebą.When performing activities in large rooms, insufficient acoustic comfort is often encountered. Various solutions are applied to improve the acoustic conditions. Suspended ceilings and mineral wool panels are one of the most typical solutions for improving the acoustic comfort of rooms. However, the production of such panels uses natural resources and consumes large amounts of energy. Tyre textile fibre is waste obtained by recycling end-of-life tyres. Together with the binding material, a composite sound absorbing panel would be created, which would be a less polluting alternative to the widely used mineral wool. The aim of this study is to investigate the effect of increasing the amount of binder in the composite on the acoustic properties. The method for determining the sound absorption coefficient is based on the standard method described in the ISO 10534-2 standard. The results of the study showed that with an increase in the amount of binder in the composite from 10 to 50%, the sound absorption in the whole spectrum decreases on average from 4 to 30% (the higher the amount of binder, the more the sound absorption decreased). The obtained results show that the amount of binder in the composite determines the sound absorption capacity of the composite sound absorbing panel.Taip / Ye
Predicting Geographic Distribution and Potential Habitat of Marine Bivalves *
No full textUnderstanding the habitat suitability of the species has been one of the main focus of biodiversity conservation. Species Distribution Modelling (SDM) has great potential to support marine conservation planning. SDM can forecast the optimal conditions for species cultivation, aiding in the prevention of habitat loss and biodiversity degradation. Four habitat suitability models-DNN, MaxEnt, RF, and GBM-were utilized to forecast the distribution of marine bivalves. Also, a stacking ensemble-based estimation model was developed, utilizing the model performance outcomes as input to enhance estimation accuracy. Finally, the habitat suitability results based on the performance evaluation metrics such as the area under the curve (AUC), sensitivity, specificity, the kappa statistic, and the TSS were used to evaluate the predictive performance of the SDMs. The experimentation results indicate that the Ensemble model delivers superior predictive performance as evidenced by its exceptional scores and demonstrated strong predictive capabilities in forecasting the potential habitat of bivalve species. The implemented model achieves impressive AUC values of 0.98, along with Kappa statistics and Specificity scores of 0.96, and Sensitivity and TSS scores of 0.97. These findings underscore the effectiveness of the Ensemble model in accurately predicting bivalve habitat suitability, thus providing valuable insights for marine conservation planning efforts.Taip / Ye
Biblioteka informuoja, 2024 Nr. 11 (655)
No full textNaujai į Web of Science ir Scopus įtrauktų Vilnius Gedimino technikos darbuotojų publikacijų sąrašai ir kitos bibliotekos aktualijos.11 (655)202
Advanced Battery Management for Electric Vehicles: A Deep Dive into Estimation Techniques Based on Deep Learning for the State of Health and State of Charge of Lithium-Ion Batteries
No full textThe precision of state of charge (SoC) prediction prediction of the SoC is necessary to avoid deep discharging and remains an important challenge in the field of electric vehicles and overcharging, which can damage batteries and shorten their life. the energy storage industry. The SoC is the percentage of energy The degradation of lithium-ion batteries is a key area of research, available in a battery relative to its total capacity. A precise as these type of batteries are extensively employed in many sectors, in particular electric vehicles, electronic devices and renewable energies. Evaluation systems based on deep learning, like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), are becoming increasingly popular for their ability to process complex data and make accurate predictions. As lithium-ion batteries degrade over time, their reliability in storing and delivering energy is diminishing. To effectively monitor and manage this degradation, researchers are turning to evaluation systems based on deep learning. These approaches enable the prediction of battery state of health (SoH) and SoC, making it easier to optimize battery use and extend battery life. This article presents various techniques for predicting the SoH and SoC of batteries to evaluate the degradation of cells.Taip / Ye
Investigation of sentiment in the green transformation of cryptocurrencies
No full textCryptocurrencies are associated with a pressing problem for society – electricity consumption. This problem is particularly relevant when electricity is used from nonrenewable sources. Cryptocurrencies have investment potential but due to the environmental impact, sustainability-minded investors may refrain from investing in this asset. The main purpose of this paper is to identify the sentiment in the green transformation of cryptocurrencies. Cryptocurrency communities, which consist of investors, cryptocurrency developers or enthusiasts interested in this asset, often appear on the Internet or on various social media. Users share information and express their opinions on the trends of the cryptocurrency market on various social platforms. This study uses sentiment analysis to identify the sentiment of existing or prospective users in the green transformation of cryptocurrencies. The results of this study contribute to research that helps investors predict trends in the cryptocurrency market when making investment decisions. The methods of this study are the analysis of the scientific literature and the analysis of sentiment using Matlab software.Taip / Yes