工程學

The aim of this project is to enhance the mobility of individuals with disabilities, particularly aiding them in navigating stairs and challenging terrains. Across the world, powered wheelchair employ various methods, primarily categorized into two: 1) tracked mechanisms and 2) robotic wheelchair utilizing intricate robotic systems. The design presented by our team belongs to the latter category, which is recognized for its lighter build when contrasted with the former. However, despite its lightweight structure, this wheelchair design incorporates equipment that renders it more cost-effective and practical than conventional designs within the same category. Our design integrates three distinct mechanisms to adjust the height and center of mass of the passenger during stair climbing and maintain balance and surface contact. Utilizing an array of sensors, it continuously monitors the position of the person on the wheelchair and the wheelchair on the surface. This data guides adjustments in the mechanisms, ensuring stability. This innovation harbors the potential for enhancing various functionalities, including: GPS integration for user navigation. Real-time monitoring of vital signs (e.g., heart rate, blood pressure, body temperature). In an emergency, this data can be transmitted to ambulance centers to pinpoint the individual's location and immediate assistance. A simplified ambulance request system, accessible via a single button press. Overall, this innovative wheelchair prototypes aims to revolutionize accessibility, granting enhanced mobility and independence to individuals with disabilities.

One of the main factors that contribute to fire incidents and the excessive heat people feel during a heat wave is the building materials used, and one such material that possesses durable and heat-resistant properties is sandwich panels. A possible structure that can be used to model sandwich panels is honeycomb structures; however, further research has yet to be conducted on its applications as a heat-resistant urban construction material. This study aims to design a three-dimensional model of a honeycomb sandwich panel and simulate its performance under different thermal and structural stressors. A 3D model of the honeycomb sandwich panel was generated using Autodesk Fusion 360. Then, multiple versions of the panel were generated with varying heat-resistant core materials—namely, aluminum, nickel, nickel-copper alloy 400, and copper—along with polystyrene as the core material for the control model. The following properties of every panel were assessed using finite element analysis (FEA): static deformation, stress distribution, strain distribution, total heat flux, and thermal gradient. Results showed that when subjected to varying structural loads (2 kN, 5 kN, 7 kN), the nickel-core panel demonstrated the best results in terms of static deformation and strain distribution due to its relatively lower deformation and elongation values, respectively. Meanwhile, under the same structural loads, the aluminum-core panel performed better than other core materials in terms of stress distribution due to it having the relatively highest difference between its simulated von Mises stress and its yield strength. The honeycomb sandwich panels have also shown to possess heat-resistivity when subjected to a thermal load of 90°C, with polystyrene being the most promising material overall in terms of heat-resistance due to its relatively lower heat flux and thermal gradient. The results from this study would contribute to future research on honeycomb sandwich panels and may be used in real-life applications.

Carbon capture and storage technology (CCS) has tremendous potential to enable the use of fossil fuels while reducing the emissions of CO₂ into the atmosphere, and, consequently, combating climate change. CCS faces several challenges such as energy consumption, cost, low practical applications and environmental friendliness. In this work, a new approach to carbon capture that is not energy intensive is proposed.

The theme of this project is to develop software to facilitate and innovate the design of low-voltage industrial electrical circuits. The goal is to develop a program that makes projects more efficient in terms of time, accuracy, and sustainability, automating dimensions such as calculating conductor cross-sections, protections, single-line diagrams, and analyzing with AI at which points industrial electrical circuits can be more sustainable. The 2023 Electric Energy Yearbook of the Energy Research Company describes that electricity consumption increases 2% per year in Brazil, and industrial installations represent the largest part of the national electrical sector (36.2%). As stated in standard NBR 5410/2004, when developing an installation project, an electrical professional works with several processes, depending on several criteria and calculations to present a reliable electrical installation. Minimal errors in calculations can cause damage to equipment, conductors, and individuals present in the installation. Using software to model these circuits optimizes time and brings more confidence to the project. This work aims to differentiate itself in this field by filling in the gaps in existing solutions for the industry, providing support for Brazilian standards, automatically generating single-line diagrams and presenting suggestions for sustainability in the circuits. The program is developed in Python, based on NBR 5410/2004 and engineering works. The software developed allows the user to size different distribution boards, motors and circuits, calculating the cross-section of the conductors/electrical protections, a particular transformer, and generating a single-line diagram in CAD. The program also presents suggestions aimed at sustainability to reduce material/energy costs. Tests were carried out with electrical engineering companies and students in the technical area, where the software presented high precision and very positive feedback from the interviewees, and it can be said that the work achieved its objectives.

Agro-guard aims to revolutionize disease identification and community-based projects in the field of agriculture. Integrating Machine learning, Computer vision, clustering, and community-based technology, this project helped farmers to detect their plant disease with their solution and for early warning of plant disease which was spreading in their community which helped in crop management. The research project is divided into three parts.First,Integrating Machine learning to detect and classify plant disease with their solutions.Second,Integrating Density-Based Spatial Clustering of Applications with Noise (DBSCAN),to identify disease and analyze the pattern within agricultural regions.Third,Establishing notification system to notify real-time alerts to farmers about disease spreading in particular region.The research is crucial because it solve one of the crucial problem of our community which is untimely detection of disease.The finding of the research highlight the effectiveness of Agro-Guard framework in early disease detection and community detection.The machine learning models achieved high accuracy in identifying common plant disease and clustering results the pattern in diseases that were very important for notifying the community.The significance of these findings is that it can build powerful system which will overall grow the production of crops and plants due to timely update of the disease prevailing in the community.It contributes in sustainability production of crops and plants which ultimately ensure the good livelihood of farmer.

Wind energy is one of the most promising and rapidly growing sources of renewable energy, although maximizing its efficiency while minimizing noise remains a challenge and limits its widespread adoption. The emergence of toroidal propellers, which have gained popularity for producing comparable thrust levels to traditional drone propellers while producing less noise, could mitigate this. This study aimed to develop a small-scale toroidal HAWT and compare its power and noise output to a conventional rotor design under similar wind velocity conditions. 15-centimeter diameter models of the toroidal and conventional rotors were created in Fusion 360 and simulated using Ansys Fluent to identify the significant aerodynamic characteristics that positively affect the blades’ power coefficient. The toroidal design with the greatest simulated power output at low tip speed ratios (TSRs) was then 3D printed and physically tested in a wind tunnel against the control rotor. The experimental results confirmed that the toroidal design had greater power coefficients at lower TSRs compared to the control rotor. The toroidal rotor started operating at a wind velocity of 3 m/s compared to the control rotor’s 6 m/s, which indicates superior start-up characteristics. While the toroidal rotor produced half the power output of the control at the highest tested wind speed of 7 m/s, it emitted 18 decibels less noise and showed a reduction in discernible noise between frequencies of two to five kilohertz. The results from this study show its potential in low-noise wind turbines within low-wind velocity environments.

As of 2021, there are 368 harmful algae blooms and over 6000 invasive species in the United States of America. Furthermore, it is reported that the United States spends more than 11.1 billion dollars per year on clean-up methods for marine debris. However, there currently isn’t a method to monitor aquatic problems simultaneously, autonomously, and efficiently, creating a capability in the aquatic biosecurity sector. To combat this, we have created an autonomous vehicle that can conduct long-term monitoring of freshwater bodies for up to 60 hours.

The economy around the world is changing rapidly, with new ways of industrial production being introduced all the time. This is due to the Fourth Industrial Revolution. The main objectives of Industry 4.0 [5] are digitalization and full automation of production processes, which increase productivity and worker safety.

Due to global population growth and industrialization, excessive inflow of causative nitrogen into rivers, and the increase in water temperature due to global warming, the occurrence of harmful algal blooms (HABs) is increasing. HABs can cause not only ecological destruction but also various social and economic problems. Additionally, consuming water from lakes with abundant toxic cyanobacteria can lead to liver damage, vomiting, abdominal pain, and even death if consumed over a long period. The first recorded occurrence of animal mortality due to HABs was in Australia in 1878, and since then, livestock and wildlife have suffered damages from HABs worldwide. Furthermore, the United States' Lake Erie has experienced frequent HAB occurrences since 2011, and in 2007, China faced social disruption when a massive HAB outbreak in Lake Tai, one of the freshwater lakes, resulted in a suspension of the water supply. In order to address these HAB occurrence issues and assess the severity of HAB events, several systems have already been established and potential solutions have been proposed. However, these systems have limitations such as being highly systematic and advanced in terms of equipment and configuration. They are often located only in periodically affected areas, and they involve substantial costs. Therefore, we aim to overcome these limitations and design a system that can effectively manage HABs.

This paper describes a power-profile-based approach to CanSat picosatellite energy usage estimation. We measured the power profile of individual CanSat components on a test bench. The collected data was used to create a power consumption model for a simple flight control algorithm. The power consumption model was implemented in Python programming language and was used to simulate the CanSat flight. The simulation results were compared with the measured power profile of a fully integrated CanSat running the same flight control algorithm. The model showed an error of 2.2% and proved to be suitable for use in battery capacity optimization.