Innovative Projects Advancing Sustainable Technology
Explore ReERG’s diverse portfolio of applied research and development bridging clean energy, AI, and engineering innovation.
TABESH
This topic covers essential concepts to enhance your understanding.
Single=Axis Solar Tracking System
This topic delves into advanced strategies and techniques.
EyeTrain
Explore this topic for foundational knowledge and insights.
Dual-Axis Solar Tracking System
This topic covers essential concepts to enhance your understanding.
Wind Design
This topic delves into advanced strategies and techniques.
CFD ONE
Interactive CFD platform for geometry design, meshing, flow simulation, and scientific visualization.
TABESH – Smart Solar Energy Analysis and Education Tool
Developed by ReERG, TABESH is a lightweight, desktop application that helps engineers, educators, and solar enthusiasts quickly compute and visualize key solar geometry for any location and time. With an intuitive interface and accuracy grounded in established solar‑position algorithms, TABESH streamlines routine tasks no cumbersome spreadsheets or online lookups required.
Whether you’re teaching solar geometry, designing a fixed‑tilt array, or validating hand‑calculated sun‑path charts, TABESH makes the process fast, transparent and reproducible.
TABESH is built around three core modules :
Solar Angle Calculator
Compute solar altitude, solar azimuth, hour angle, zenith and surface‑zenith angles, plus optimal panel tilt (β) using NREL’s recommended fixed‐tilt standard.
Supports manual entry or map‑based selection of latitude, longitude and local standard meridian.
Sun‑Path & 3D Visualization
Plot the sun’s daily trajectory on an interactive sun‑path diagram, highlighting the sun’s position at any specified time.
View your PV panel, sun position and resulting shadow in a real‑time 3D scene—rotate, pan or switch to standard views (S–N, E–W, top, side).
Data Export & Reporting
Produce time‑series tables (e.g. half‑hourly solar angles) and high‑resolution plots suitable for reports or further analysis.
Save to Excel or PNG directly from the app, with no external tools required.
Single-Axis Solar Tracking System
This advanced single-axis solar tracking system is designed and developed as part of a Ph.D. research initiative supported by the Department of Mechanical, Energy, Management and Transportation Engineering (DIME) at the University of Genova, under the supervision of Full Professor Marco Fossa.
Leveraging state-of-the-art solar position algorithms inspired by methodologies from the National Renewable Energy Laboratory (NREL), this tracking system dynamically adjusts its tilt throughout the day, optimizing solar energy capture from sunrise to sunset. With user-friendly command capabilities, operators can easily define and adjust tracking parameters, making the system highly adaptable and suitable for both research and industrial applications.
The core of the tracking system is powered by an advanced Raspberry Pi microcomputer, complemented by precision sensors including a high-accuracy WitMotion pitch sensor and a real-time clock module for accurate solar positioning. The system employs a robust linear actuator coupled with a precise motor driver, ensuring reliable and efficient tilt adjustments. The compact design integrates all components within a dedicated control enclosure, guaranteeing seamless operation and ease of maintenance.
This innovative solution is particularly beneficial for East-West tracking configurations where photovoltaic modules are arranged in rows, enhancing energy yield and maximizing land-use efficiency. Developed with meticulous engineering and validated through rigorous experimental testing, this solar tracking system represents a significant step forward in sustainable energy technology.
EyeTrain – Object Detection & Training Platform
EyeTrain is an interactive computer-vision platform designed to simplify the development, training, and deployment of object-detection models. The software provides an integrated environment where users can build datasets, annotate images, train machine-learning models, and perform real-time object detection within a unified interface.
The platform combines data preparation, model training, and real-time inference into a single workflow, allowing users to efficiently develop and evaluate object-recognition models based on the YOLO architecture.
Developed in Python using modern deep-learning frameworks, EyeTrain enables researchers, engineers, and developers to create custom AI vision systems without requiring complex machine-learning pipelines or external tools.
Core Capabilities
Real-Time Object Detection
EyeTrain supports real-time object recognition using camera input. Users can load pre-trained YOLO models or custom trained models to detect objects in live video streams. The detection window displays bounding boxes, object classes, and confidence scores, enabling immediate visual evaluation of model performance.
Custom Model Training
A key capability of EyeTrain is the ability to train custom object-detection models using user-generated datasets.
Users can:
• Upload training images
• Manually annotate objects using bounding boxes
• Assign object classes
• Automatically generate YOLO-compatible training datasets
• Train a custom model directly within the software interface
The training process uses the Ultralytics YOLO framework to create a new model capable of recognizing objects defined in the custom dataset.
Once training is complete, the generated model can be loaded directly into the detection system for testing and deployment.
Interactive Image Annotation
EyeTrain includes an integrated annotation environment designed for efficient dataset creation.
Users can:
• Load multiple training images
• Draw bounding boxes around target objects
• Assign class labels to each object
• Modify or clear annotations
• Automatically convert annotations into YOLO training format
The interface supports two parallel image panels to accelerate the labeling process and improve annotation productivity.
Integrated Workflow
EyeTrain provides a complete workflow for developing custom computer-vision systems:
- Upload and annotate images
- Generate a structured training dataset
- Train a YOLO-based object detection model
- Load the trained model into the detection environment
- Perform real-time object recognition using a camera
This integrated pipeline significantly reduces the complexity of building machine-learning vision systems.
Applications
EyeTrain can support a wide range of AI vision applications, including:
• Smart surveillance systems
• Industrial inspection and quality control
• Robotics and autonomous systems
• Traffic monitoring
• Research and educational machine-learning projects
• Custom object recognition systems
Development Status
EyeTrain is currently under active development as part of ReERG’s AI and intelligent systems research initiatives. Future improvements will focus on expanding dataset management capabilities, improving annotation tools, and integrating advanced model-evaluation features.
Dual-Axis Solar Tracking System
Date:
This dual-axis solar tracking system is a complementary development to the single-axis project, designed to enhance solar panel alignment with even greater precision. In addition to tracking the sun’s daily East–West movement, this system introduces a second degree of freedom to dynamically align the panel’s rotational axis with the solar azimuth throughout the day.
Equipped with two linear actuators and advanced orientation control, the system continuously adjusts both tilt and rotation to maximize solar exposure. Its robust design is ideal for research-grade or high-efficiency photovoltaic installations.
This project is a part of the ongoing innovation efforts within our solar automation line and shares its control logic and sensor integration with the single-axis platform now extended for full dual-axis motion.
AI-Driven Wind Turbine Simulation Tool (Upcoming..)
Wind Design is a practical simulation tool currently under development at ReERG, aimed at supporting early-stage planning and evaluation of wind energy systems. With a focus on usability and accuracy, the software allows users to estimate the energy output of wind turbines across various locations based on geographic and meteorological inputs.
Core Objectives
Enable comparative analysis across different turbine models and layouts.
Provide basic energy output estimations based on wind speed and location.
Support decision-making for site selection and turbine configuration.
Planned Features
Location-based wind energy potential estimation
Input customization: turbine height, rotor diameter, cut-in/cut-out speedsHourly or daily output simulation based on real or typical wind data
Visual charts of power curves and output trends
Exportable reports for offline analysis
Project Status
Development is actively in progress. Our goal is to release an initial beta version focused on single-turbine simulation and gradually expand toward more advanced modeling and AI-aided optimization tools.
CFD ONE
Computational Fluid Dynamics Platform
CFD ONE is a developing Computational Fluid Dynamics (CFD) software created to provide an interactive and modern environment for fluid flow simulation, geometry modeling, mesh generation, and scientific visualization. The software is currently in an active research and development stage. While still evolving, CFD ONE already demonstrates the foundation of a complete CFD workflow environment and represents an ongoing effort toward building a modern simulation platform for both educational and engineering applications.
Core Objectives
Using numerical methods and mathematical models, CFD allows engineers and researchers to predict flow behavior, pressure distribution, turbulence effects, thermal performance, aerodynamic forces, and many other physical phenomena before building real prototypes.
Planned Features
CFD ONE is being developed by ReERG with the vision of creating a flexible, educational, and future-oriented CFD platform that combines engineering analysis with an intuitive graphical workflow. The software is designed to help users move through the complete CFD process from geometry creation to simulation and post-processing inside a unified environment.
Project Status
The current development version of CFD ONE integrates the essential stages of a CFD workflow into one interactive environment:
Residual and mass-balance monitoring
Interactive 2D geometry design
CAD-inspired drawing, snapping & domain detection
Inlet, outlet, wall & symmetry boundary conditions
Mapped & Submap structured quad meshing
Body-fitted O-grid meshing for circular obstacles
Region-aware fluid domains and solid obstacles
Finite Volume Method (FVM) framework
Steady incompressible SIMPLE flow solver
Transient SIMPLE simulations
Euler & second-order BDF2 time integration
Courant-number monitoring & adaptive time stepping
Pressure, velocity, speed, vorticity & Cp fields
Contours, vectors & streamline visualization
Drag/lift history & cylinder-flow diagnostics