AI Neural Emulator Revolutionizes Thermoelectric Generator Design with Enhanced Efficiency
A new neural-network emulator, TEGNet, has been developed to significantly enhance the design process of thermoelectric generators (TEGs). This AI-driven model predicts TEG performance with over 99% accuracy while using only 0.01% of the computation time required by traditional methods. Thermoelectric generators, which convert heat into electricity, are crucial for waste-heat recovery. The performance of these devices depends on material properties and design factors such as leg geometry and boundary conditions. Traditionally, optimizing these variables required extensive finite-element simulations, which are computationally expensive. TEGNet simplifies this process by learning the relationship between device inputs and outputs, allowing for rapid and accurate predictions. The model was trained using high-fidelity simulations and has shown strong agreement with traditional methods across various conditions, making it a valuable tool for engineers. 
