Compressor Blade Design Research Articles

Concurrent and sequential coupled optimization design of a transonic compressor blade with axial slot casing treatment

This paper explores the optimization of the interaction between the rotor and axial slot casing treatment (ASCT) to enhance both efficiency and stability margins. Previous studies primarily focused on rotor tip optimization, often compromising flow in non-tip regions and reducing overall rotor performance. To address these limitations, this study introduces a new optimization framework integrating full-span 3D blade design with a semicircular axial slot, utilizing both sequential and concurrent optimization strategies. The sequential approach first optimizes blade geometries, followed by ASCT adjustments, while the concurrent approach treats the blade and ASCT as a unified aerodynamic system. Compared to prior studies focusing exclusively on tip-only 3D shape optimization, results indicate that sequential coupled optimization improves the stall margin by 13.32% and efficiency by 0.05%, whereas the concurrent strategy further enhances performance, shaping the blades into an inverse S form, reducing efficiency losses from 0.12% to 0.02%, and increasing the stall margin increment from 14.88% to 21.83%. These findings demonstrate the significant performance gains achievable through coordinated rotor and ASCT design.

An efficient sparse surrogate model for aerodynamic characteristics of a supersonic compressor cascade with uncertain geometric deformations

The increasing demand for high-load compressors necessitates research and design of supersonic compressor blades. Geometric deformations of the blades resulting from manufacturing errors introduce uncertainties in their aerodynamic characteristics. For uncertainty analysis, Polynomial Chaos Expansion (PCE) has gained popularity among engineers in various disciplines. However, as the number of random variables used to describe uncertainties increases, the efficiency of the PCE method diminishes. To overcome this limitation, an Efficient Sparse Surrogate Model (ESSM) was first proposed. The ESSM combines PCE to approximate global characteristics and Gaussian process modeling to capture local variability, resulting in a highly accurate model. Additionally, by utilizing an iteratively diffeomorphic modulation under observable response preserving homotopy, important PCE basis functions are adaptively selected, leading to a sparse surrogate model that significantly reduces the required training samples. Then using the ESSM, efficient Uncertainty Quantification (UQ) and global sensitivity analysis were conducted to evaluate the impact of uncertain geometric deformations on the aerodynamic characteristics of a supersonic compressor cascade. Principal Component Analysis (PCA) was employed in conjunction with an Improved Class Shape Transformation (ICST) to characterize geometric deformations caused by manufacturing errors. The proposed ICST method exhibits enhanced accuracy in fitting near the leading and trailing edges compared to the CST method. PCA enables a 50% reduction in the number of random variables needed to describe geometric deformations. The UQ results establish a quantitative correlation between geometric deformations and supersonic aerodynamics, and the sensitivity analysis identifies some specific forms of geometric deformations that significantly impact the aerodynamics. Finally, the study investigated the flow mechanisms associated with typical geometric deformation forms, further providing recommendations for manufacturing and testing of supersonic blades to prevent performance deterioration.

Low Reynolds Number Effects in Compressor Blade Design

Low Reynolds Number Effects in Compressor Blade Design

Development of a deviation package method for low-cost robust optimization in compressor blade design

Manufacture variations can greatly increase the performance variability of compressor blades. Current robust design optimization methods have a critical role in reducing the adverse impact of the variations, but can be affected by errors if the assumptions of the deviation models and distribution parameters are inaccurate. A new approach for robust design optimization without the employment of the deviation models is proposed. The deviation package method and the interval estimation method are exploited in this new approach. Simultaneously, a stratified strategy is used to reduce the computational cost and assure the optimization accuracy. The test case employed for this study is a typical transonic compressor blade profile, which resembles most of the manufacture features of modern compressor blades. A set of 96 newly manufactured blades was measured using a coordinate measurement machine to obtain the manufacture variations and produce a deviation package. The optimization results show that the scatter of the aerodynamic performance for the optimal robust design is 20% less than the baseline value. By comparing the optimization results obtained from the deviation package method with those obtained from widely-used methods employing the deviation model, the efficiency and accuracy of the deviation package method are demonstrated. Finally, the physical mechanisms that control the robustness of different designs were further investigated, and some statistical laws of robust design were extracted.

Optimization of the two-dimensional stator

Gas turbine engine is mainly composed of compressor, inlet , tail nozzle, combustion chamber and turbine. The compressor pressurizes the air from the inlet into high-pressure air, which is used to mix and burn with fuel in the combustion chamber to form high-pressure gas. In this paper, numerical analysis of compressor stator performance and corresponding optimization were conducted using COMSOL Multiphysics 5.6. Firstly, an overview of compressor blade design is given. Then, the physical model and computational model are introduced and established by COMSOL. Afterwards, after the validation of the computational model, the numerical computations and results are generated and used to compare with NASA67 stator experimental data, with several key performance indicators including turning angle and loss coefficient. The polynomial expression is used to construct the blade shape/profile with different maximum thickness and its position, maximum bending and its position, trailing and leading edge angle and chord length are chosen as parameters for optimization. Then, I choose the maximum thickness and its position, maximum bending and its position, trailing and leading edge angle as the optimization parameter. Finally, we reach some conclusions of a better stator design possible for newer compressor requirements and further studies are recommended.

The Current Gap between Design Optimization and Experiments for Transonic Compressor Blades

The successful design of compressor blades through numerical optimization relies on accurate CFD-RANS solvers that are able to capture the general performance of a given design candidate. However, this is a difficult task to achieve in transonic flow conditions, where the flow is dominated by inherently unsteady shock effects. In order to assess the current gap between numerics and experiments, the DLR has tested the recently optimized Transonic Cascade TEAMAero at the transonic cascade wind tunnel. The tests were performed at a Mach number of 1.2 and with inflow angles between 145 and 147°. The results indicate satisfactory agreement across the expected working range, over which the cascade losses were consistently predicted within a 3–6% error. However, some key differences are observed in the details of the wake and in the performance near the endpoints of the working range. This comparison helps validate the design process but also informs its constraints based on the limitations of CFD-RANS solvers.

Dynamic particle swarm optimization-radial function extremum neural network method of HCF probability analysis for compressor blade

The high cycle fatigue (HCF) of compressor rotor seriously affects the performance and reliability of gas turbine. Probabilistic HCF evaluation is an effective measure to quantify the uncertain traits of vibration stresses and assess the reliable HCF life for compressor rotor. To improve modeling accuracy and computational efficiency in transient probability analysis of HCF life of gas turbine compressor, radial basis function neural network (RBFNN) and dynamic particle swarm optimization (DPSO) algorithm were introduced into extreme response surface method (ERSM). In this paper, we propose a HCF life probability evaluation method for compressor blades based on the dynamic particle swarm optimization-radial basis function extremum neural network (DPSO-RBFENN) model. By selecting random input variables such as aerodynamic load, centrifugal load and material parameters, a prediction model was established based on DPSO-RBFENN sample learning, and the reliability evaluation of compressor blade HCF life was carried out. Distribution characteristics, reliability and sensitivity to fatigue life were obtained, which provided a basis for the structural design of compressor blades. The Monte Carlo method, ERSM, RBFNN and DPSO-RBFENN are compared.

A data-driven robust design optimization method and its application in compressor blade

The probability-based robust optimization methods require a large amount of sample data to build probability distribution models of uncertain parameters. However, it is a common situation that only scarce sampled data are available in practice due to expensive tests. This study proposes a data-driven robust optimization framework by embedding a novel uncertainty quantification (UQ) method, which can quantify the uncertainty based on the statistical moments of scarce input data. The computational robustness and accuracy of the developed UQ methods are validated. Then, the data-driven multi-objective optimization framework is applied to improve the mean performance and aerodynamic robustness of a two-dimensional compressor blade with real stagger angle errors. Uncertainty analysis shows that there is a probability of 47.55% to deviate from the nominal total pressure loss coefficient by more than 1% for the actual performance values at high positive incidence i=7° condition. In the optimization process, the total pressure loss coefficient is selected as the objective function, while the static pressure ratio is used as a constraint. The Gaussian process regression model is trained to improve the robust optimization efficiency. The robust optimization is conducted under the most sensitive conditions. Optimized results indicate that compared with the nominal blade, the mean performance of the selected robust blades is increased by 10.9%, 8.56%, and 0.83%; the performance dispersion is decreased by 19.0%, 24.8%, and 35.3%, respectively. The optimized results can provide useful references for the robust design of compressor blades.

The New Chapter of Transonic Compressor Cascade Design at the DLR

Abstract The design of compressor blades has been transformed by the advent of optimization algorithms, allowing designers to focus on finding the best optimization strategy for a desired application. However, transonic flow conditions on compressor blades still present considerable modeling challenges, even for a 2D blade section. This paper then focuses on the design of a new state-of-the-art compressor cascade for future test campaigns at the DLR’s Transonic Cascade Wind Tunnel (TGK). For this purpose, a review of the cascades previously tested at the TGK was performed to select a main reference with good efficiency at high loading. The data gathered also informed the optimization strategy applied with the DLR’s optimizer, AutoOpti. The process chain was evaluated with Reynolds Averaged Navier–Stokes CFD simulations using the DLR’s solver, TRACE. The optimization was set to minimize two objective functions: the first one focused on the efficiency at the design point, and the second one focused on the efficiency over the working range. The result is a Pareto front of cascades with a wide variety of design features with an efficiency improvement over the working range of about 24%. This improvement was achieved with a comparable aerodynamic loading. Further analyses were performed to select the “best” cascade for future test campaigns. The significant improvement obtained with respect to the reference and the wide variety of designs observed demonstrates that there is still much to be learned about blade design through optimization; even for 2D cascades and specially in transonic flow.

Application of Machine Learning to Forced Response Predictions of an Industrial Axial Compressor Rotor Blade

Abstract Machine learning has gone way beyond a ground-breaking technology a few decades ago to now taken for granted in many day-to-day activities. It is now providing new ways for manufacturing, assembling, operating, monitoring, and maintaining products. Typical application areas include performance optimization, quality improvements, fault detection and predictive maintenance. In this paper application of machine learning algorithms to forced response prediction during the design and analysis of a typical gas turbine compressor blade is reported. The forced response prediction process typically involves utilizing harmonic or time domain computational fluid dynamics (CFD) analyses to compute the forcing and the aero damping, to calculate reserve factors that represent the high cycle fatigue life of the blade. This time-consuming process is generally limited to the later phases of the design cycle and can lead to hundreds of calculations if one must address all the resonances in a typical twin shaft running range. A neural network trained using historical data is used to directly predict the reserve factor with high confidence without the need for costlier high fidelity CFD by using just the finite elements (FE) predicted parameters. This allows to perform high-fidelity aero-mechanical assessment at an early stage in the design process. Further, application of image recognition using a convoluted neural network to aid in the identification of FE predicted Modeshapes is also demonstrated, which can also improve the quality of the reserve factor predictions.

An approach of Proper Orthogonal Decomposition-aided Free-form Deformation with application in compressor blade design

Compressor blade design influences aero-engine performance mainly through its total pressure ratio and efficiency. As a volume-based geometric parameterization, Free-form Deformation (FFD) brings three-dimensionality that is essential to blade design. However, the manipulation of control points with respect to simple numeric parametric perturbation renders the use of design space low-efficient. Therefore, an improved FFD with ‘wiser’ control point lay-out is expected to identify those more important design variables. This paper proposes novel design variables that are assigned to grouping of control points’ displacement in FFD lay-out. In short, the approach is realized as: (i) establish a library of sufficient blade shape samples; (ii) filter the database with geometric constraints; (iii) extract dominant modes via (POD) Proper Orthogonal Decomposition; (iv) construct new design variables and apply them in optimization. With geometric constraint filtering, problem-oriented information is injected. With POD, the dominance of new selected geometric parameters in problem description is assured. Perfunctory details of displacement data of each control point in the lay-out can be replaced by grouped data as new design variable candidates. As a proof-of-concept study of the new approach, compressor blade Rotor 37 is selected to be the good platform of testing the feasibility of POD-aided FFD as a global and flexible yet economic geometric parameterization. Result demonstrates the feasibility of proposed POD-aided FFD approach that helps conduct an optimization involving displacement of 6 × 4 × 3 control points with as few as five new design variables, while still being capable of bringing optimization effect in three test cases.

A flexible method for geometric design of axial compressor blades

This paper proposes a method for the geometric design of axial compressor blades. First, a novel approach for creating 2D blade sections is proposed, where each 2D blade section is obtained by imposing the defined thickness distribution along the camber line. The camber line of each 2D blade section is defined by specifying the angle change of each point on the camber line. In order to make this method applicable to a wider range of blade section design, the camber line of each section can be designed in several (usually 2 or 3) segments. It takes into account the change rule of blade section along the flow channel, which is the most concerned in the aerodynamic design, rather than the specific curve form of blade section. Then, the 2D blade sections are mapped to the corresponding arbitrary rotational flow surface. Finally, the 3D blade model is generated by lofting the blade sections in the flow surfaces. Some typical examples are presented. It shows that this method has good applicability and flexibility to realize the geometric design of multiple types of blades.

A Coupling Method for the Design of Shape-Adaptive Compressor Blades

The design of flexible and efficient aircraft engines and propulsion systems plays a crucial role in the development of future low-emission aircraft. Implementing shape-variable blades to compressor front stage rotors presents a high potential for increasing efficiency, since through adaptation, the blades are capable of optimizing their shape for different flight phases and aerodynamic conditions. Modifying the shape of the blades by using structurally integrated actuators allows this adaptation and therefore helps enhance their aerodynamic behavior for different flight regimes. Since up to now no morphing compressor or any other aircraft engine blades exist, here a multidisciplinary method for their design is introduced. This new method brings together existing structural and aerodynamic design methodologies, couples them together already at the earliest stages of the design process, while addressing the challenges that arise with a tightly coupled multidisciplinary design. As a result, first performance gain evaluations applied to the NASA 67 rotor test case are presented, showing the potential of morphing compressor blades and the potential of the introduced design methodology.

Способ расчета динамических напряжений в лопатке переменного сечения турбомашины

The problem of assessing the dynamic stresses arising from vibrations of the blades of turbo machines is an urgent and significant problem affecting the overall reliability of the turbo machine. Its solution requires a mathematical study and a physical experiment to determine the intensity of the gas flow impact and the blade reaction.However, there is relatively little information in the scientific publications on this issue. The article considers a semi-empirical method for calculating dynamic stresses at the base of a variable cross-section blade at the first tone resonant vibrations. These vibrations can be considered as the most dangerous because of the maximum amplitude. To perform the calculation a real blade was replaced with a calculated one, composed of separate portions with a constant profile, and the contribution of each part to the stress in the base section was determined. An example of calculating the dynamic stress by the proposed method with a resonant vibration of the first tone of a constant-section blade is given. The calculation showed that the solution to a complex problem can be represented as a sum of solutions to simpler problems. The calculation method can be used in the design of turbine and compressor blades.

A Parametric Blade Design Method for High-Speed Axial Compressor

The blade geometry design method is an important tool to design high performance axial compressors, expected to have large design space while limiting the quantity of design variables to a suitable level for usability. However, the large design space tends to increase the quantity of the design variables. To solve this problem, this paper utilizes the normalization and subsection techniques to develop a geometry design method featuring flexibility and local adjustability with limited design variables for usability. Firstly, the blade geometry parameters are defined by using the normalization technique. Then, the normalized camber angle f1(x) and thickness f2(x) functions are proposed with subsection techniques used to improve the design flexibility. The setting of adjustable coefficients acquires the local adjustability of blade geometry. Considering the usability, most of the design parameters have clear, intuitive meanings to make the method easy to use. To test this developed geometry design method, it is applied in the design of a transonic, two flow-path axial fan component for an aero engine. Numerical simulations indicate that the designed transonic axial fan system achieves good efficiency above 0.90 for the entire main-flow characteristic and above 0.865 for the bypass flow characteristic, while possessing a sufficiently stable operation range. This indicates that the developed design method has a large design space for containing the good performance compressor blade of different inflow Mach numbers, which is a useful platform for axial-flow compressor blade design.

New Design of Compressor Blade with Moderate Material Properties for Performance Optimization

New Design of Compressor Blade with Moderate Material Properties for Performance Optimization

Numerical Investigation of Compressor Tandem Aerofoils Featuring Near-Endwall Modification

A highly loaded 1.5-stage low-speed compressor stage is investigated numerically using fully turbulent and steady state RANS simulations. The tandem stator at the end of the stage is replaced by a tandem stator with a near-endwall modification. This unconventional compressor blade design is using a single profiling near both endwalls and a tandem profiling at midspan. Using this modification the positive behaviour of a tandem stator can be maintained. Locally the leading edge of the rear vane and as a result the gap flow near both endwalls will be removed by introducing a single-profile segment. This paper will describe the approach to set up a numerical model for the new geometry and will characterize the flow features and loss mechanisms of the near-endwall modification. The first comparison between the stators with and without near-endwall modification, showed a reduction of corner separation at the rear vane, caused by the removal of the low momentum gap-flow. On the other side, the near-endwall modification is creating higher losses at the connecting region between the tandem and single segment. The numerical results presented in this paper will be the starting point for further optimizations of the tandem blade with near-endwall modifications.

Characterization of titanium grade 5 alloy compressor blade in a jet engine using advanced materials for optimum thrust production

Abstract Machining operations on titanium and its alloys have always been a challenge in the design of compressor blade in the jet engine which undergo profound stress during impact of debris and affects the life span of both static and rotary blades. Titanium grade 5 alloy is used to resist the creep and fatigue due to the thermal expansion and sudden impact including bird strike. However, machining them is difficult. An attempt was made to mitigate the temperature during drilling of Ti-6Al-4V by adopting cryogenic coolants LN2, CO2 and a novel approach of coconut oil under varying cutting speed and feed rates. LN2 provided 17 and 55% reduction in temperature over CO2 and coconut oil condition respectively. Employing the optimum machining procedure for Titanium alloy can reduce the weight of the aircraft by mounting the titanium alloy at the leading edge of the carbon fiber reinforced plastic composite blades.

Characterization of titanium grade 5 alloy compressor blade in a jet engine using advanced materials for optimum thrust production

Abstract Machining operations on titanium and its alloys have always been a challenge in the design of compressor blade in the jet engine which undergo profound stress during impact of debris and affects the life span of both static and rotary blades. Titanium grade 5 alloy is used to resist the creep and fatigue due to the thermal expansion and sudden impact including bird strike. However, machining them is difficult. An attempt was made to mitigate the temperature during drilling of Ti-6Al-4V by adopting cryogenic coolants LN2, CO2 and a novel approach of coconut oil under varying cutting speed and feed rates. LN2 provided 17 and 55% reduction in temperature over CO2 and coconut oil condition respectively. Employing the optimum machining procedure for Titanium alloy can reduce the weight of the aircraft by mounting the titanium alloy at the leading edge of the carbon fiber reinforced plastic composite blades.

The Objective Space and the Formulation of Design Requirement in Natural Laminar Flow Optimization

Design requirement is as important in aerodynamic design as in other industries because it sets up the objective for the samples in design space to approach. Natural Laminar Flow (NLF) optimization belongs to the type of aerodynamic design problems featured by the combination of distinct aerodynamic performance, where the design requirement is often formulated in form of summation of laminar-related performance and pressure drag performance with different weight assignment according to different perspectives. However, the formulations are rather experience-oriented and are decided non-quantitatively. Inspired by data manipulation approaches in design space (spanned by design variables of geometrical representation parameters) in many aerodynamic designs, this paper proposes new formulations of design requirement in NLF optimization via consideration of objective space (projection of design space through aerodynamics) and shows the impact of the corresponding formulation of design requirement to the result of NLF optimization in cases of transonic airfoil and aero engine compressor blade design from two perspectives: Pareto front convergence and improving effect of accessory performance. The paper uses Principal Component Analysis (PCA) to obtain the eigenvectors of objective space to extract the intrinsic information about specific problem. The method is realized in two cases with satisfactory result.