Stochastic axial compressor variable geometry schedule optimisation

The design of axial compressors is dictated by the maximisation of flow efficiency at on design conditions whereas at part speed the requirement for operation stability prevails. Among other stability aids, compressor variable geometry is employed to rise the surge line for the provision of an adequate surge margin. The schedule of the variable vanes is in turn typically obtained from expensive and time consuming rig tests that go through a vast combination of possible settings. The present paper explores the suitability of stochastic approaches to derive the most flow efficient schedule of an axial compressor for a minimum variable user defined value of the surge margin. A genetic algorithm has been purposely developed and its satisfactory performance validated against four representative benchmark functions. The work carries on with the necessary thorough investigation of the impact of the different genetic operators employed on the ability of the algorithm to find the global extremities in an effective and efficient manner. This deems fundamental to guarantee that the algorithm is not trapped in local extremities. The algorithm is then coupled with a compressor performance prediction tool that evaluates each individual's performance through a user defined fitness function. The most flow efficient schedule that conforms to a prescribed surge margin can be obtained thereby fast and inexpensively. Results are produced for a modern eight stage high bypass ratio compressor and compared with experimental data available to the research. The study concludes with the analysis of the existent relationship between surge margin and flow efficiency for the particular compressor under scrutiny. The study concludes with the analysis of the existent relationship between surge margin and flow efficiency for the particular compressor under scrutiny

Time-domain harmonic balance method for aerodynamic and aeroelastic simulations of turbomachinery flows

A time-domain Harmonic Balance method is applied to simulate the blade row interactions and vibrations of state- of-the-art industrial turbomachinery configurations. The present harmonic balance approach is a time-integration scheme that turns a periodic or almost-periodic flow problem into the coupled resolution of several steady computations at different time samples of the period of interest. The coupling is performed by a spectral time-derivative operator that appears as a source term of all the steady problems. These are converged simultaneously making the method parallel in time. In this paper, a non-uniform time sampling is used to improve the robustness and accuracy regardless of the considered frequency set. Blade row interactions are studied within a 3.5-stage high-pressure axial compressor representative of the high-pressure core of modern turbofan engines. Comparisons with reference time-accurate computations show that four frequencies allow a fair match of the compressor performance, with a reduction of the computational time up to a factor 30. Finally, an aeroelastic study is performed for a counter-rotating fan stage, where the rear blade is submitted to a prescribed harmonic vibration along its first torsion mode. The aerodynamic damping is analysed, showing possible flutter

Insight into High-quality Aerodynamic Design Spaces through Multi-objective Optimization

An approach to support the computational aerodynamic design process is presented and demonstrated through the application of a novel multi-objective variant of the Tabu Search optimization algorithm for continuous problems to the aerodynamic design optimization of turbomachinery blades. The aim is to improve the performance of a specific stage and ultimately of the whole engine. The integrated system developed for this purpose is described. This combines the optimizer with an existing geometry parameterization scheme and a well- established CFD package. The system’s performance is illustrated through case studies – one two-dimensional, one three-dimensional – in which flow characteristics important to the overall performance of turbomachinery blades are optimized. By showing the designer the trade-off surfaces between the competing objectives, this approach provides considerable insight into the design space under consideration and presents the designer with a range of different Pareto-optimal designs for further consideration. Special emphasis is given to the dimensionality in objective function space of the optimization problem, which seeks designs that perform well for a range of flow performance metrics. The resulting compressor blades achieve their high performance by exploiting complicated physical mechanisms successfully identified through the design process. The system can readily be run on parallel computers, substantially reducing wall-clock run times – a significant benefit when tackling computationally demanding design problems. Overall optimal performance is offered by compromise designs on the Pareto trade-off surface revealed through a true multi-objective design optimization test case. Bearing in mind the continuing rapid advances in computing power and the benefits discussed, this approach brings the adoption of such techniques in real-world engineering design practice a ste

Unsteady end-wall pressure measurements using near-field diy sensors on fouled fan rotor blade

The fouling is identifiable by the presence of dust on rotor and stator blades, and its main origin, in industrial turbomachinery, is the presence of a film of moist or lubricant driven to the trailing edge by the near-wall flow, or centrifuged toward the casing by impeller rotation. Solid particles pile up on them, leading to eccentricity and load unbalance. The formation of build-up results in performance reduction, and the chance of a deposit detachment while the impeller spun, may cause damages due to the impact on the machine parts. In industrial fans, the presence of fouling influences the characteristic curve and could anticipate stall when the flow rate is throttled. Rotating stall is an aerodynamic instability with a typical frequency about half the rotor frequency, acoustically identifiable from the changes in the emitted rotor noise, due to displacement from the stability. This work investigates rotating stall dynamics on an axial fan with fouled blades. The stall is identified with time-resolved pseudo-sound measurements in the end-wall region using DIY sensors. The signals have been analysed in frequency domain, and time domain using a phase space reconstruction technique. It is demonstrated a modification of the dynamic to stall and are identified diverse stall precursors

Aerodynamics of advanced axial-flow turbomachinery

A multi-task research program on aerodynamic problems in advanced axial-flow turbomachine configurations was carried out at Iowa State University. The elements of this program were intended to contribute directly to the improvement of compressor, fan, and turbine design methods. Experimental efforts in intra-passage flow pattern measurements, unsteady blade row interaction, and control of secondary flow are included, along with computational work on inviscid-viscous interaction blade passage flow techniques. This final report summarizes the results of this program and indicates directions which might be taken in following up these results in future work. In a separate task a study was made of existing turbomachinery research programs and facilities in universities located in the United States. Some potentially significant research topics are discussed which might be successfully attacked in the university atmosphere

Non-Uniform Time Sampling for Multiple-Frequency Harmonic Balance Computations

A time-domain harmonic balance method for the analysis of almost-periodic (multi-harmonics) flows is presented. This method relies on Fourier analysis to derive an efficient alternative to classical time marching schemes for such flows. It has recently received significant attention, especially in the turbomachinery field where the flow spectrum is essentially a combination of the blade passing frequencies. Up to now, harmonic balance methods have used a uniform time sampling of the period of interest, but in the case of several frequencies, non-necessarily multiple of each other, harmonic balance methods can face stability issues due to a bad condition number of the Fourier operator. Two algorithms are derived to find a non-uniform time sampling in order to minimize this condition number. Their behavior is studied on a wide range of frequencies, and a model problem of a 1D flow with pulsating outlet pressure, which enables to prove their efficiency. Finally, the flow in a multi-stage axial compressor is analyzed with different frequency sets. It demonstrates the stability and robustness of the present non-uniform harmonic balance method regardless of the frequency set

C(NN)FD -- a deep learning framework for turbomachinery CFD analysis

Deep Learning methods have seen a wide range of successful applications across different industries. Up until now, applications to physical simulations such as CFD (Computational Fluid Dynamics), have been limited to simple test-cases of minor industrial relevance. This paper demonstrates the development of a novel deep learning framework for real-time predictions of the impact of manufacturing and build variations on the overall performance of axial compressors in gas turbines, with a focus on tip clearance variations. The associated scatter in efficiency can significantly increase the $CO_2$ emissions, thus being of great industrial and environmental relevance. The proposed \textit{C(NN)FD} architecture achieves in real-time accuracy comparable to the CFD benchmark. Predicting the flow field and using it to calculate the corresponding overall performance renders the methodology generalisable, while filtering only relevant parts of the CFD solution makes the methodology scalable to industrial applications