Development of a predictive model for the preliminary design of Heat Exchangers in Electric Aviation

Kurzfassung

Fuel cells are a promising and viable solution for electrified aircraft engines, offering high energy efficiency and environmental benefits. Carbon-free sustainable aviation fuels such as hydrogen can be used in fuel cells, which convert their chemical energy into electricity, thus reducing the aircraft’s emission footprint. A byproduct is heat, which must be effectively removed. It is an engineering challenge to design and implement a heat rejection system suitable for optimized performance, high reliability, and life cycle, while minimizing its weight and drag. Meeting this challenge requires the efficient integration of heat exchangers into the fuel cell-powered aircraft engines. This paper presents an approach to heat exchange design using a genetic optimization algorithm based on the Python programming language. This algorithm calculates the minimum volume required for the heat exchanger, taking into account the desired pressure drop and heat flow conditions. The accuracy and reliability of the calculations are improved by incorporating the experimental results from the literature and the 3D geometry of heat exchangers, such as offset strip fins. The successful validation of the proposed algorithm highlights its potential for significant time savings during the preliminary design phase of the heat exchangers. Moreover, the genetic optimization algorithm offers a promising solution for achieving optimal heat exchanger performance, minimizing weight and drag, and thereby enhancing overall aircraft efficiency.