Development of LoRaWAN-based Wireless Sensors for Monitoring Climate Changes in the Venice Lagoon

openLe aree costiere e le zone di hotspot della biodiversità litoranea, come le aree protette europee Natura 2000 e la laguna di Venezia, sono siti poco studiati a causa della loro diversità causata dalla conformazione molto eterogenea, inclusa l'intersezione di molti canali e fiumi che trasportano diversi tipi di sedimenti. I sistemi di campionamento attuali si basano su campagne periodiche di campionamento eseguite da operatori umani, che consentono solo la raccolta di un numero molto limitato di misurazioni nel tempo e nello spazio. In questo articolo presentiamo il primo prototipo di un dispositivo galleggiante a basso costo e wireless in grado di fornire misurazioni a un server a terra in tempo reale: il dispositivo, chiamato SENSWICH, è composto da un nodo LoRaWAN e un set completo di sensori di qualità dell'acqua, selezionati con l'aiuto dei ricercatori che operano presso la Stazione Idrobiologica Marina di Chioggia dell'Università di Padova, dove verrà installato il primo sensore.Coastal and littoral biodiversity hotspot areas, such as the European Natura 2000 protected areas and the Venice lagoon, are understudied sites due to their diversity caused by the very heterogeneous conformation, including the intersection of many channels and rivers carrying different types of sediments. The current sampling systems are based on periodic sampling campaigns performed by human operators, that only allow the collection of a very limited number of measurements in time and space. In this paper we present the first prototype of a low-cost wireless sensing floating device able to provide measurements to an in-land server in real time: the device, named SENSWICH, is composed of a LoRaWAN node and a complete set of water quality sensors, selected with the help of the researchers operating in the Chioggia Marine Hydrobiological Station of the University of Padova, where the first sensor will be deployed

Thermal Modeling and Optimization of Lithium-Ion Batteries for Electric Vehicles

This dissertation contributes to the modeling and optimization of Lithium-ion battery’s thermal management for electrified vehicles (EVs). EVs in automotive technology is one of the principal solutions to today’s environmental concerns such as air pollution and greenhouse impacts. Light duty and heavy duty EVs can decrease the amount of the pollution efficiently. EV’s receive their power from installed rechargeable batteries in the car. These batteries are not just utilized to power the car but used for the functioning of lights, wipers and other electrical accessories. The Lithium-ion batteries (LIBs) have attracted a lot of research interest in recent years, due to their high potential as compared to the conventional aqueous based batteries, high gravimetric and volumetric energy density, and high power capability. However, Li-ion batteries suffer from high self-heating, particularly during high power applications and fast charging, which confines their lifetime and cause safety, reliability and environmental concerns. Therefore, the first part of this study consists of the experimental investigation of the charge-discharge behavior and heat generation rate of lithium ion cells at different C-rates to monitor and record the thermal behavior of the cell. A further concern regarding LIBs is strongly dependent on the quality and efficiency of battery thermal management system. Hence, this is extremely important to identify a reliable and accurate battery management system (BMS). Here in the second part, we show that thermal management and the reliability of Li-ion batteries can be drastically improved using optimization technique. Furthermore, a LIB is a compact system including high energy materials which may undergo thermal runaway and explode the battery if overcharged due to the decomposition of battery materials within the electrolyte and electrodes that generate flammable gaseous species. The application of this kind of technology needs many laboratory experiments and simulations to identify the fundamental thermal characteristics of the system before passing it to the real use. An accurate battery model proposes a method to simulate the complex situations of the system without performing time consuming actual tests, thus a reliable scheme to identify the source of heat generation and required parameters to optimize the cell performance is necessary. For this reason, the latest phase of this research covers the development and comparison of a model based on adjustable design parameters to predict and optimize battery performances. This kind of model provides a relationship with the accuracy and simplicity to estimate the cell dynamics during charge and discharge

Contribution to the improvement of the dissolved gas analysis techniques

There is a general agreement that in service conditions the quality of mineral insulating oils gradually deteriorates under the impact of electrical, thermal and environmental stresses. It is also widely accepted that only the incipient electrical failures such as hot spots and partial discharges are responsible for the gassing of oil. Knowing that the resulting fault gases dissolve in the oil, the technique of Dissolved Gas Analysis (DGA) was developed to detect incipient failures in the transformer. DGA has now become a standard in the utility industry throughout the world and is considered to be the most important oil test for insulating liquids in electrical apparatus. More importantly, an oil sample can be taken at anytime from most equipment without having to take it out of service, allowing a "window" inside the electrical apparatus that helps with diagnosing and trouble-shooting potential problems. This thesis intends to show that the gassing of oil is a more complex phenomenon. In order to emphasize the role played by contaminants in the gassing of oil, fundamental investigations were undertaken. The amount of gases evolved under the impact of electrical stress (ASTM D6180) by a sample of new and aged oil with/without paper was accurately measured along with some physicochemical properties, to assess the relationship between the cause and the symptoms of oil or oil-paper insulation deterioration. The outcome of these investigations provided experimental evidence that the chemical composition of hydrocarbon blend, the oil born decay products and the solid insulation are also contributing factors to oil gassing. Since this finding may affect the diagnostics predicted by some DGA techniques, some thorough investigations were performed. New, aged oil and reclaimed aged oil samples were submitted to thermal and electrical stresses (considering various scenarios) and the dissolved gases analyzed by chromatography. Three of the most used DGA techniques, namely the Duval's Triangle Roger's and Domenburg's ratios were implemented in Labview based software to predict the diagnostic. The obtained results provide experimental evidence that oil born decay products may affect the diagnostics predicted by some DGA techniques. Although such a research is still in a preliminary stage, some very stimulating results have been obtained. - II est généralement admis, qu'en conditions de service, la qualité des huiles minérales isolantes se détériore progressivement sous l'effet des contraintes électriques, thermiques et environnementales. Il est également largement admis que seules les défaillances électriques naissantes telles que les points chauds et les décharges partielles sont responsables du dégazage de l'huile. Sachant que les gaz ainsi produits par les défauts se dissolvent dans l'huile, la technique d'analyse de gaz dissous (AGD) a été mise au point pour détecter les défaillances dans le transformateur. L'AGD est maintenant devenu un standard dans l'industrie à travers le monde et elle est considérée comme le test le plus important dans les appareillages électriques isolés à l'huile. Plus important encore, un échantillon d'huile peut être pris à tout moment, de la plupart des équipements, sans avoir à le mettre hors service, pour le diagnostic et le dépannage d'éventuels problèmes. Ce mémoire se propose de montrer que le gazage dans l'huile est un phénomène complexe. Afin de souligner le rôle joué par les contaminants dans le dégazage de l'huile, des investigations fondamentales ont été entreprises. La quantité de gaz qui se dégage sous l'effet de la contrainte électrique (ASTM D6180) d'un échantillon d'huile neuf ou vieilli avec/sans papier a été mesurée avec précision ainsi que certaines propriétés physico-chimiques, afin d'évaluer la relation entre la cause et les effets de la détérioration de l'isolation de l'huile ou de l'huile-papier. Le résultat de ces investigations a fourni des preuves expérimentales que la composition chimique d'un mélange d'hydrocarbures, les produits issus de la décomposition de l'huile et de l'isolation solide sont également des facteurs qui contribuent à la génération de gaz dans l'huile. Etant donné que cette découverte pourrait affecter les diagnostics prédits par certaines techniques de l'ADG, certaines investigations approfondies ont été réalisées. Des échantillons d'huile neuve, âgée et régénérée ont été soumis à des contraintes thermiques et électriques (en considérant différents scénarios) et les gaz dissous analysés par chromatographie. Trois des techniques de l'ADG les plus utilisées à savoir le Triangle de Duval, Roger et le Ratio de Dôrnenburg ont été implémentées dans le logiciel Labview pour prédire le diagnostic. Les résultats obtenus fournissent la preuve expérimentale que les produits de la décomposition de l'huile peuvent affecter les résultats de diagnostic prédis par certaines techniques de l'ADG. Bien qu'une telle recherche soit encore à un stade préliminaire, certains résultats encourageants ont été obtenus

Examining the impact of using stop-motion technique on enhancing the quality of scientific reasoning skills of secondary school students

The main purpose of this study is to determine the effectiveness of the stop motion technique in improving the scientific reasoning of third grade high school students in the chemistry course with the subject of electroplating. The study sample includes 150 students, all of whom were studying in two fields of mathematics and experimental studies in the academic year of 2011-2012. The approach of this research is quantitative and the method used is quasi-experimental, of the Salomon four-group design. Also, in terms of purpose, it is among applied researches. The sampling method is cluster. This research was done with two experimental groups and one control group. in experimental group 1; Training using stop motion technique was used in an unstructured way. in experimental group 2; Training was used in a structured way using the stop motion technique. Static model was used in the control group. The sources of data collection in this study to study the breadth and depth of vision are: audio recording, step-by-step notes of the researcher, individual interviews with students.To analyze the research data, statistical methods were used at two descriptive levels (central and dispersion indices) and inferential (t-test and analysis of variance). The results showed that the use of unstructured stop motion technique has a more effective role in improving students' scientific reasoning skills than structured stop motion technique and static model

Flood Forecasting Using Artificial Neural Networks: an Application of Multi-Model Data Fusion technique

Floods are among the natural disasters that cause human hardship and economic loss. Establishing a viable flood forecasting and warning system for communities at risk can mitigate these adverse effects. However, establishing an accurate flood forecasting system is still challenging due to the lack of knowledge about the effective variables in forecasting. The present study has indicated that the use of artificial intelligence, especially neural networks is suitable for flood forecasting systems (FFSs). In this research, mathematical modeling of flood forecasting with the application of Artificial Neural Networks (ANN) and data fusion technique were used in estimating the flood discharge. Sensitivity analysis was performed to investigate the significance of each model input and the best MLP ANN architecture. The data used in developing the model comprise discharge at different time steps, precipitation and antecedent precipitation index for a major river basin. Application of model on a case study (Karun River in Iran) indicated that rainfall-runoff process using data fusion approach produces results with higher degrees of precision

Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application

With the large-scale commercialization and growing market share of electric vehicles (EVs), many studies have been dedicated to battery systems design and development. Their focus has been on higher energy efficiency, improved thermal performance and optimized multi-material battery enclosure designs. The integration of simulation-based design optimization of the battery pack and Battery Management System (BMS) is evolving and has expanded to include novelties such as artificial intelligence/machine learning (AI/ML) to improve efficiencies in design, manufacturing, and operations for their application in electric vehicles and energy storage systems. Specific to BMS, these advanced concepts enable a more accurate prediction of battery performance such as its State of Health (SOH), State of Charge (SOC), and State of Power (SOP). This study presents a comprehensive review of the latest developments and technologies in battery design, thermal management, and the application of AI in Battery Management Systems (BMS) for Electric Vehicles (EV)

Review of the Li-Ion Battery, Thermal Management, and AI-Based Battery Management System for EV Application

With the large-scale commercialization and growing market share of electric vehicles (EVs), many studies have been dedicated to battery systems design and development. Their focus has been on higher energy efficiency, improved thermal performance and optimized multi-material battery enclosure designs. The integration of simulation-based design optimization of the battery pack and Battery Management System (BMS) is evolving and has expanded to include novelties such as artificial intelligence/machine learning (AI/ML) to improve efficiencies in design, manufacturing, and operations for their application in electric vehicles and energy storage systems. Specific to BMS, these advanced concepts enable a more accurate prediction of battery performance such as its State of Health (SOH), State of Charge (SOC), and State of Power (SOP). This study presents a comprehensive review of the latest developments and technologies in battery design, thermal management, and the application of AI in Battery Management Systems (BMS) for Electric Vehicles (EV)

The Comparison the Effect of Different Combinations of Physical, Observational and Imagery Exercise on Immediate and Delay Retention of Badminton High Serve

Cognitive teaching methods facilitate the acquisition of motor skills; among these methods, the combination of physical, observational and imagery exercises has been the focus of recent investigations. The aim of this study was to investigate the facilitative effect of the combination of physical, observational and imagery exercises on immediate and delay retention of badminton high serve. The statistical population consisted of all male Razi University students. 84 students (mean age of 20.42+1.4 yr and score of imagery ability of 48.69+6.19) voluntarily participated in this study. Pretest included immediate and delay retention of badminton high serve using Scott and Fox test. Then, participants were assigned to homogenous groups according to their pretest (each group 12 participants): physical, observation, imagery, physical-observation, physical-imagery, observation-imagery, and physical-observation-imagery. Participants accomplished three sessions of 90 trials of badminton high serve. At the end of the final training session, an immediate test of retention was administered followed by a test of delay retention after 48 hours. One-way ANOVA test indicated that in both immediate and delay retention, the physical-observation-imagery group and the physical group significantly performed high serve better than other groups (