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Detonative Pressure Gain Combustion has the potential to increase the propulsion efficiency of aero-engines and the thermal efficiency of stationary gas turbines. Important advances were made in this field, especially in the case of Rotating Detonation Combustion (RDC). Although experimental and numerical studies reported in the literature have significantly increased in number, the major open problem is a lack of efficient turbomachinery to transform the fluctuating potential energy from an RDC into power output. For this problem to be properly addressed, time resolved data at the outlet of an RDC needs to be collected. As a first step, numerical data can be used to generate a geometry for the turbine, which must be validated experimentally. To determine the performance of a turbine vane row, total pressure losses need to be measured. There are several challenges in measuring the total pressure between the outlet of an RDC and the inlet of a turbine vane row. The high temperature values, the distance of the pressure transducer from the outlet of the combustor lead to a lower time resolution of the pressure signal. The confined space is also an issue, allowing for very few options in measuring the total pressure. Another major problem is the shock wave that may form as a detached shock wave with respect to the body of the pressure probe at certain moments in the flow cycle, which leads to measuring a different value rather than the actual value of the flow field. To address these issues, the current study presents a numerical investigation of a guide vane row that was experimentally tested at the outlet of an RDC working on hydrogen and air under stoichiometric conditions. One of the vane rows was 3D printed with a geometry allowing the measurement of total pressure. Static pressure at the outlet of the RDC was also measured. It was observed that the measured pressures are average values in time. Based on these averages, the total inlet pressure and velocity variations in time were reconstructed in an exponential trend, according to the ones reported in the literature and the aforementioned experiments. These variations were set as inlet conditions for transient numerical simulations. Results show that the total pressure amplitude decreases significantly when the flow passes the annulus and the vanes as well. By looking in to the flow field detail, the presence of shock wave in front of the blade is investigated. Additionally, it is calculated that the average total pressure decreases 7.9% by the vane row.

Designing a gas turbine from scratch has always been an extremely laborious task in terms of obtaining the desired power output and efficiency. Theoretical prediction of the performances of a gas turbine has proven in time to be a compromise between accuracy and simplicity of the calculus. Methods such as the Smith chart are very easy to apply, but to make an exact prediction of the flow in a turbine would lead to an almost infinite number of variables to be considered. A quite precise method of determining total-loss coefficients for a gas turbine, based on a large number of turbine tests, was developed by D.G. Ainley and G.C.R. Mathieson, with an error of the calculated efficiency within 2%. The accuracy of the method has been validated by Computational Fluid Dynamics simulations, included in the paper. Even if it is not a novel approach, the method provides accurate numerical results, and thus it is still widely used in turbine blade design. Its difficulty consists of the large number of man-hours of work required for estimating the performances at each working regime due to the many interdependent variables involved. Since this calculus must be conducted only once the geometry of the turbine is determined, if the results are not satisfactory one must go back to the preliminary design and repeat the entire process. Taking into account all the above, this paper aims at optimizing the efficiency of a newly design turbine, while maintaining the required power output. Considering the gas-dynamic parameters used for determining the preliminary geometry of a turbine, and the influence of the geometry upon the turbine efficiency, according to the procedure stated above, a Monte Carlo optimizing method is proposed. The optimization method consists in a novel genetic algorithm, presented in the paper. The algorithm defines a population of turbine stage geometries using a binary description of their geometrical configuration as the chromosomes. The turbine efficiency is the fitness function and also acts as the mating probability criterion. The turbine energy output is verified for each member of the population in order to verify that the desired turbine power is still within acceptable limits. Random mutations carried on by chromosome string reversal are included to avoid local optima. Hard limits are imposed on optimization parameter variation in order to avoid ill defined candidate solutions. The approach presented here significantly reduces the time between design goal definition and the prototype.

Adjoint aerodynamic optimisation has recently gained increased popularity for turbomachinery applications due to the large number of parameters that can be used without incurring additional major computational costs. This work presents an adjoint based aero-structural optimisation method having efficiency as the objective function and maximum von Mises stress set as a constraint. The full optimisation loop was set up with free-form deformation for geometry parametrisation. A response surface was created beforehand for computing the maximum von Mises stress using a meshless method. A discrete adjoint approach was used to obtain the gradients of the objective function with respect to each design parameter, while the constraint gradients were computed using finite differences. A sequential least squares programming algorithm was used as the optimizer. Tests carried out on a highly loaded compressor blade showed that the method successfully increases the efficiency by more than 3% while maintaining the maximum stress under the imposed value. The results also showed that the constrained optimisation loses about 1% in potential efficiency gain compared to the same optimisation process without stress constraint. Overall, the work provides a methodology for conducting structurally constrained adjoint aerodynamic optimisation that can be applied for large number of design parameters while maintaining low computational costs. It also provides reference for constructing and selecting a response surface to be used in the optimisation process.