Computational Fluid Dynamics is one of the most relied upon tools in the design and analysis of components in turbomachines. From the propulsion fan at the inlet, through the compressor and combustion sections, to the turbines at the outlet, CFD is used to perform fluid flow and heat transfer analyses to help designers extract the highest performance out of each component. In some cases, such as the design point performance of the axial compressor, current methods are capable of delivering good predictive accuracy. However, many areas require improved methods to give reliable predictions in order for the relevant design spaces to be further explored with confidence. This paper illustrates recent developments in CFD for turbomachinery which make use of machine learning techniques to augment prediction accuracy, speed up prediction times, analyse and manage uncertainty and reconcile simulations with available data. Such techniques facilitate faster and more robust searches of the design space, with or without the help of optimization methods, and enable innovative designs which keep pace with the demand for improved efficiency and sustainability as well as parts and asset operation cost reduction.

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.

The waste heat recovery from the gas turbine (GT) exhaust is typical for increasing performance and reducing CO₂ emissions in industrial facilities. Nowadays, numerous already operating gas turbine plants could be retrofitted and upgraded with a bottoming cycle powered by the exhaust gasses. In this case, the standard solution would be to use a water Steam Rankine Cycle. However, even if this technology usually yields the best efficiency, other alternatives are often preferred on the lower size scale. Organic Rankine Cycles (ORCs) are the commercial alternatives to Steam Rankine Cycles, but many other alternative cycles exist or can be developed, with potential benefits from safety, technical or economic points of view. This study compares several alternative technologies suited to recover gas turbine waste heat, and a detailed cost analysis for each is presented. On this basis, a guideline is proposed for the technology choice considering a wide range of application sizes and temperature levels typical for waste heat recovery from GTs gas turbines. The compared technologies are ORCs, Rankine Cycles (RCs) with water and ammonia mixtures at constant composition, supercritical CO₂ cycles (sCO₂), sCO₂ cycles with mixtures of CO₂, and other gasses. As it resulted, ORCs can achieve the lowest levelized cost of energy (32 $/MWh - 46 $/MWh) if flammable fluids can be employed. Otherwise, Rankine cycles with a constant composition mixture of water and ammonia are a promising alternative, reaching a Levelised Cost Of Energy (LCOE) of 36-58 $/MWh.