Challenges and opportunities for artificial intelligence and high-fidelity simulations in turbomachinery applications: A perspective

Abstract

Turbomachines are still required for the efficient conversion of renewable, chemical or potential energy into propulsion, mechanical power, or electricity. The increasing demand for efficiency, availability, reduced footprint and cost of ownership poses fundamental challenges to design methods the accuracy of which needs to be constantly upgraded to keep the pace with the availability of new materials, type of fluids and fuels, manufacturing methods and technologies. This paper discusses recent trends in design methods that take advantage of both artificial intelligence and high-fidelity simulations techniques that guide the design process by harvesting design data from multiple sources and improve the accuracy of design verification respectively. Such approach has been successfully used in aero and thermal design as well as in the critical area of materials and fluids engineering. In the future the concerted use of machine learning and high-fidelity methods may allow researchers to conduct reliable virtual tests in the framework of design loops to cut down design time and risk.

Machine Learning Methods in CFD for Turbomachinery: A Review

“…Consequently, designers are faced with the prospect of moving away from wellestablished comfort-zone designs, either by applying conventional design to uncharted applications, or by designing new units in uncharted design spaces [3]. All of these challenge the entire design process, from the early conceptual to the preliminary and detailed design stages.…”

“…In the specific areas of aerodynamics, aeromechanics (both forced response and flutter), heat transfer, and combustion, design systems are evolving from correlation-based designs, relying heavily on companies' proprietary experimental data, to more complex multidimensional and multidisciplinary approaches that require Computational Fluid Dynamics (CFD) [3,4]. Correlation-based designs are naturally bound to the investigated design space envelope and are not suited for exploring design opportunities outside this space.…”