Achieving optimal design is a crucial aspect of any design process for safe and efficient operation. Such tasks typically require numerous simulations over many iterations, which can become computationally expensive. This paper proposes a novel method that combines Physics-informed Neural Networks (PINNs) with a Genetic Algorithm to optimize the parameters of an airfoil that aims to achieve favourable aerodynamic conditions. Traditional solvers are computationally expensive for performing such tasks, but using PINNs can significantly reduce this while keeping accuracy high. The proposed approach shows the advantage of using PINNs in optimizing complex engineering problems.

The proposed multi-objective optimized hybrid renewable energy system consists of solar panels, wind turbines, a proton exchange membrane (PEM) electrolyzer for hydrogen production, and an absorption cooling system for the summer season. This study is conducted in two locations in Egypt and Saudi Arabia as the case studies. The study presents a thermodynamic analysis to investigate the system performance. In addition, an optimization-based analysis is conducted using NSGA-II algorithm to determine optimal values of the decision variables. The hybrid renewable system can operate in a significant performance with water mass flow rate of 1.8 kg/s to produce hydrogen with a mass flow rate of 0.2 kg/s, and ammonia mass flow rate of about 0.2 kg/s to produce cooling load between 40 and 120 kW with energy and exergy efficiency of more than 65%.