Numerical Optimization Of Heat Exchanger Design Using Newton-Raphson And Genetic Algorithms

Abstract

Optimization of heat exchanger performance continues to be an essential component of thermal engineering, with applications that span a broad range of sectors, including the energy sector, aerospace, and the process industry. It is common for traditional design techniques to have difficulty striking a balance between opposing goals, such as the efficiency of heat transmission, the pressure drop, and the cost. For the purpose of improving the design of shell-and-tube heat exchangers, this research proposes a comparative numerical optimization framework that integrates the Newton-Raphson method, which is a deterministic iterative methodology, with Genetic Algorithms (GAs), which are a probabilistic, evolutionary-based heuristic. Maximizing the rate of heat transmission while simultaneously minimizing pressure drop and material consumption are the goal functions that are taken into consideration. In order to solve the nonlinear governing equations of the exchanger's thermal performance, the Newton-Raphson approach was used. On the other hand, the GA was utilized for the purpose of conducting all-encompassing searches inside the intricate, multi-dimensional design space. In order to guarantee the dependability of the results, we used benchmark datasets and empirical correlations that are customary in the industry. MATLAB was used to develop each of the approaches, and then this software was used to test them against real-world data that was gathered from the Heat Exchanger Design Handbook (HEDH). Genetic algorithms outperform the Newton-Raphson technique when it comes to handling highly nonlinear and limited optimization situations, as shown by numerical findings. This is despite the fact that the Newton-Raphson approach provides quick convergence given appropriate beginning circumstances. The dual-strategy method is a compelling improvement for industrial thermal system designers, since it guarantees optimization that is both durable and efficient.