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Performance Study Of Diesel Engine Using Pongamia Biodiesel

Rising Environmental Pollution, Ever-increasing Energy Demand, And The Depletion Of Fossil Fuel Reserves Have Driven The Search For Alternative And Sustainable Fuels For Internal Combustion Engines. Biodiesel, A Renewable, Biodegradable, And Non-toxi... Rising Environmental Pollution, Ever-increasing Energy Demand, And The Depletion Of Fossil Fuel Reserves Have Driven The Search For Alternative And Sustainable Fuels For Internal Combustion Engines. Biodiesel, A Renewable, Biodegradable, And Non-toxic Fuel Derived From Non-edible Oil Sources, Has Emerged As One Of The Most Promising Substitutes For Conventional Diesel Fuel. In This Study, Pongamia Biodiesel Was Produced From Pongamia Oil Through The Transesterification Process. The Primary Objective Of This Research Is To Investigate The Performance Of A Single-cylinder Compression Ignition Diesel Engine Fuelled With Pongamia Biodiesel Blends, With Particular Emphasis On How The Supplied Heat Energy Is Utilized And The Associated Losses. Biodiesel Blends — B20, B40, B60, B80, And B100 (20%, 40%, 60%, 80%, And 100% Biodiesel Mixed With Diesel) — Were Prepared And Tested Under Varying Load Conditions. Mechanical Efficiency, Brake Thermal Efficiency, Indicated Thermal Efficiency, And Specific Fuel Consumption Were Analysed Together With A Detailed Heat Balance Sheet For Each Fuel. The Experimental Results Indicate That Pongamia Biodiesel Blends Deliver Engine Performance Comparable To Conventional Diesel. Brake Thermal Efficiency Increases With Engine Load For All Fuel Blends, While Specific Fuel Consumption Is Marginally Higher For Biodiesel Blends Owing To Their Lower Calorific Value. Emission-related Combustion Behaviour Also Improves, With Better Utilization Of Oxygen Content Promoting More Complete Combustion. Among The Tested Blends, B60 Offers A Favourable Overall Compromise Between Performance And Fuel Economy, While B60 Shows The Most Promising Brake Thermal Efficiency. The Study Concludes That Pongamia Biodiesel Is A Viable, Renewable, And Eco-friendly Alternative To Diesel Fuel That Can Be Used In Existing Diesel Engines Without Major Modification, Contributing To Reduced Environmental Pollution And Decreased Dependence On Fossil Fuels. Read more Read less

Embedded Edge-AI Framework For Autonomous UAV-Based Human Detection And Remote Alerting Using ESP32-S3 Flight Control And ESP32-CAM Vision

Autonomous Unmanned Aerial Vehicles (UAVs) Equipped With On-board Artificial-intelligence Inference Are Increasingly Used For Surveillance And Monitoring Tasks That Require Rapid, Low-latency Decisions Without Continuous Human Supervision. This Paper... Autonomous Unmanned Aerial Vehicles (UAVs) Equipped With On-board Artificial-intelligence Inference Are Increasingly Used For Surveillance And Monitoring Tasks That Require Rapid, Low-latency Decisions Without Continuous Human Supervision. This Paper Presents The Design And Experimental Implementation Of A Compact Embedded Edge-AI UAV Platform For Autonomous Defense-oriented Human Monitoring, Built Around A Custom-developed Quadcopter Whose ESP32-S3 Microcontroller Performs Real-time Flight Control While A Separate ESP32-CAM Module Performs On-board Visual Inference. The Flight-control Subsystem Interfaces With An Inertial Measurement Unit (MPU6050) And Pulse-width-modulated Motor Drivers To Support Stabilized Takeoff, Hover, Landing And Coordinate/waypoint-based Autonomous Navigation Over A Crazyflie-compatible Wi-Fi Command Interface. For Visual Intelligence, Two Edge Impulse Models Were Trained And Evaluated On The ESP32-CAM: An Image-classification Model Distinguishing “Face” And “no_human” Classes, And An Object-detection Model Providing Spatial Localization. The Classification Model, Deployed With A 0.60 Confidence Threshold, Triggers Transmission Of The Captured JPEG Frame To A Telegram Bot For Remote Notification Whenever A Positive Detection Persists Beyond A Five-second Upload Cooldown. The Classification Model Achieved 96.0% Validation Accuracy, An F1 Score Of 0.97 For The Face Class And 0.95 For The No_human Class, And An Area Under The ROC Curve Of 0.95, While The Comparative Object-detection Model Achieved A Validation F1 Score Of 84.0% Over 60 Training Cycles. Experimental Results Confirm Functional Autonomous Navigation, Correctly Triggered Telegram Alerting And A Working End-to-end Pipeline. The Comparison Shows That Image Classification Is Best Suited To The Binary Alerting Requirement Of This Application, While Object Detection Offers Additional Spatial Information That Is Valuable For Future Localization-based Tasks. Read more Read less