Design optimization based on computational fluid dynamics (CFD) analysis has become a reliable tool for fluid flow and heat and mass transfer applications due to the rapid increase in computing power. Accurate, high-fidelity CFD simulations are used to design efficient systems that meet desired performance requirements. Unfortunately, high fidelity simulations are often computationally expensive and impractical for the entire design process. To alleviate this problem, surrogate models have been used to reduce the computational burden with a reliable representation of the simulation data. The widely used forms of surrogate models are polynomial regression, Kriging, radial basis functions, support vector regression, among others. Surrogate based optimization has been a promising tool to develop efficient designs for a wide range of applications, especially in the field of fluid flow, where the analysis using Navier-Stokes equations is quite time-consuming. Optimization methods are now recognized to be vital in the design of fluid flow equipment and processes.

The motivation of the current special issue is to achieve two comprehensive goals. Firstly, it aims to cover the recent developments and trends in the application of surrogate-based optimization technique for fluid flow and heat and mass transfer problems. Secondly, in addition to practical applications, the issue also encourages high quality research articles and innovations in surrogate development and advances in optimization methods. Review articles which describe the current state of the art are welcomed. Overall, the aim is to bring together contributions from engineers, mathematicians, and computer scientists working on basic research and practical applications in engineering optimization.

Potential topics include but are not limited to the following:

• Application of surrogate-based optimization techniques in all areas of fluid flow and heat and mass transfer applications
• Turbomachinery
• Advances in optimization methods in heat and mass transfer
• Development in metamodels and surrogates in optimization (polynomial regression, Kriging, Radial basis functions, and support vector regression)
• Improvement in global exploration and local exploitation characteristics using ensemble of surrogates
• Sampling techniques for surrogate model development
• Surrogate modeling for solving inverse optimization problems