This Smart Temperature Monitor with Visual Alerts was conceived as a result of frustration with plain, area-wide weather apps that provide general, macro-level temperature predictions but no hyper-local, real-time temperatures. Although these types of apps provide general temperature data for an entire city or a broad neighborhood, they don't take microclimates into account—such as the differences in temperature between a sunny balcony, a shaded backyard, or a hot garage. I yearned for something that would quantify certain conditions in my area and provide immediate visual feedback in lieu of biased estimates. From an Arduino, DHT11 sensor, and LEDs, I developed a device which displays precise temperature and humidity on an LCD while lighting color-coded notifications (cold in blue, normal in green, hot in red) with programmable thresholds. This project answers the problem of localized weather uncertainty by offering location-specific, real-time accurate information, which is especially useful for gardeners, homebrewers, or anyone who wants to monitor indoor/outdoor microenvironments. In practice, aside, it's a hands-on lesson in sensor accuracy, conditional logic, and physical IoT design—all at the cost of the reliability of mass-market weather gadgets.
This is a Visual Alert Smart Temperature Monitor that measures an accurate real-time temperature and humidity reading from a DHT11 sensor and displays it on a 16x2 LCD screen. For the purpose of offering extra functionality, the system consists of three LEDs of dissimilar colors (blue, green, and red) that are illuminated according to different temperature thresholds: the blue LED illuminates in the case of low temperatures below 10°C, the green LED at 10°C to 25°C, moderate condition, and the red LED for being hot above 25°C. The sensor values are computed by the Arduino after every 5 seconds, meaning accurate real-time readings of environmental conditions. This compact but potent system is an easy-to-use introduction to sensor projects, demonstrating how microcontrollers can capture real-world input and display visual feedback. With potential applications ranging from basic weather stations to indoor climate alerts, this project is an excellent method for exploring embedded systems, conditional programming, and real-time feedback systems in electronics.
This project investigates the phenomenon of jet refraction, wherein a fluid jet bends when entering fine mesh as it experiences a change of direction while passing through due to pressure gradients, or varying medium properties. It is necessary to know this phenomenon in several engineering applications, such as fuel injection systems, industrial spray processes, and aerodynamic control systems.
The study derives a mathematical model for explaining the deflection angle of the jet in terms of governing parameters, including jet velocity, fluid surface tension, and different mesh types. Using fluid dynamics concepts, the model derives expressions for the refraction angle in terms of these parameters. The formulation provides for conservation equations, momentum balance, and empirical relations wherever appropriate. The project further examines experimental verification through the use of high-speed imaging and optical measurement techniques in order to monitor jet trajectory and test against theory, and computational physics through computational fluid dynamics (CFD) simulation.
The current study has universal relevance in aerospace propulsion, fuel atomization, and spray cooling technology where it is vital to have very accurate control of fluid flow. Enhancing the precision of jet refraction prediction models, the study is aiming at making improvements toward enhanced efficiency in controlled fluid dispersal-reliant industrial and scientific processes.