Axial Compressor Design Research Articles

Aerodynamic optimization design and experimental verification of a high-load axial flow compressor

The performance and stable operating range of compressors are critical to the efficient operation of various turbomachinery systems. This paper proposes a multi-level optimization strategy combining the uni-uniform direct free deformation method, Linux partitioned CPU accelerated parallel computing technology, multi-objective particle swarm optimization algorithm and downhill simplex algorithm, which improves design efficiency. Numerical results show that after optimization design, the average value of peak efficiency and stall margin increases. The flow mechanism of compressor performance improvement after optimization lies in the reduction of a low-velocity separation zone in stator hub region. Moreover, the experiment study confirms the reliability and accuracy of the optimization design method and found that flow instability triggering mode, propagation characteristics of the stall cell, and surge frequency are changed after optimization design. Setting a probability distribution threshold for autocorrelation coefficient and cross-correlation coefficients can be used to predict the arrival of the surge condition.

Implementing the Streamline Curvature Method For Preliminary Design of Multi-Stage Axial Compressors

Implementing the Streamline Curvature Method For Preliminary Design of Multi-Stage Axial Compressors

Performance Analysis and Aerodynamic Design of Axial Flow Compressors

The main objective of the paper is to analyze theperformance of axial flow compressors and to generate asystematic design approach which enables to designsubsonic flow ones. In order to investigate the validity of this approach, the LP axial flow compressor of the RR SpeyMK511 turbofan engine is taken as an example. The designcalculations were based on thermodynamics, gas dynamics,fluid mechanics and empirical relations. The flow isassumed to be of two-dimensional compressible type withconstant axial and rotor blade velocities with a free-vortexswirl distribution. Design calculations includethermodynamic properties of the working fluid, number ofcompressor performance parameters such as, stagetemperature rise and number, flow and blade angles (bladetwist), velocity triangles and relative inlet Mach number atrotor blades tips as well as blades tip and hub diameters. Arepeated calculation is made to determine these parametersalong compressor stages. The variation of velocity whirlcomponents, air and blade angles, deflection and degree ofreaction from root to tip of the blades were also determined.The twist of the blades along the blade length is setaccording to the recommended values in order to obtainsmooth blade twist profile.

End-to-end design of multistage high-pressure axial compressor for a promising aviation gas turbine engine

End-to-end design of multistage high-pressure axial compressor for a promising aviation gas turbine engine

A hybrid data-driven framework for loss prediction of MCA airfoils

The Multi Circular Arc airfoils family has gained significant popularity in axial compressor design due to its capability to achieve higher pressure ratios with lower losses compared to conventional NACA airfoils. However, modeling their aerodynamic performance for transonic axial compressors remains a challenging task. This research addresses this issue by developing a data driven model for Multi Circular Arc airfoils loss correlation utilizing an automated blade-to-blade flow solver, StarCCM, under specific flow boundary conditions. Since the data built in this research includes high dimensionality, it is subjected to dimensionality reduction using Principal Component Analysis while retaining as much of its important information as possible. What sets our research apart is the comparative analysis of hybrid machine learning approaches. We specifically combine classification techniques based on shock wave formation on the airfoil’s suction side with Multi-Layer Perceptron neural network methods. This unique combination allows for a comprehensive prediction of MCA loss as the main objective of the research. The results demonstrate that this surrogate-based approach provides accurate loss model with 97% of precision error for the classification which results in 0.99 and 0.94 of R2 for loss model related to the transonic and subsonic airfoils loss prediction.

Optimization of Three-Dimensional Blade and Variable Stators for Efficiency and Stability Enhancement of Multistage Axial Flow Compressor at Variable Speeds

Abstract High efficiency and wide stability at variable speeds are equally important to the design and operation of multistage axial flow compressors. However, published research works on three-dimensional design optimization of compressors are so far mainly limited to a single blade row or stage at design speed due to the curse of dimensionality. Moreover, optimization of variable inlet guide vanes (IGV)/stators for off-design operations is carried out by using a rapid but low-fidelity prediction tool and is generally independent of design optimization of blade geometry. To tackle these issues, a three-dimensional holistic design and adjustment optimization method is developed in which both three-dimensional blade geometry and variable IGV/stators are optimized simultaneously for better efficiency and stability at design and off-design conditions. Metamodel-interpreted data mining method and adaptive infilling strategy are used respectively to enhance the capability of the metamodeling and optimization. The developed method is then applied to a modern highly loaded 3.5-stage transonic axial flow compressor at both design and part-design speeds. The results show that the stall margin is extended from 8.23% to 19.65% at 70% design speed while peak efficiency is slightly improved at design speed. The flow mechanisms responsible for the efficiency enhancement at design speed are mainly associated with the reduced total pressure loss in stators as well as inter- and intra-stage loading redistribution. The stability enhancement at 70% design speed is mainly achieved by loading the front blade rows while unloading the limiting rear blade row through variable IGV/stators adjustment. The developed holistic design and adjustment optimization method with the aid of metamodel-interpreted data mining is of great application value for the design and adjustment of advanced multistage axial flow compressors.

Patrik Kovář, Jiří Fürst

In recent years, the flow analysis by means of computational fluid dynamics (CFD) has become a useful design and optimization tool. Unfortunately, despite advances in the computational power, numerical simulations are still very time consuming. Thus, empirical correlation models keep their importance as a tool for early stages of axial compressor design and for prediction of basic performance parameters. These correlations were developed based on experimental data obtained from 2D measurements performed on cases of classical airfoils such as the NACA 65-series or C.4 profiles. There is insufficient amount of experimental data for other families of airfoils, but CFD simulations can be used instead and their results correlated using artificial neural networks (ANN), as described in this work. Unlike the classical deep learning approach using perceptrons, this work presents neural networks employing higher order neural units.

Power generation gas turbine performance enhancement in hot ambient temperature conditions through axial compressor design optimization

The output power and efficiency of gas turbines are profoundly influenced by ambient temperatures, with higher temperatures resulting in a notable decline in power output. This issue becomes particularly critical during periods of heightened power demand in hot climates. There are two common ways to solve this problem: incorporating auxiliary systems, such as inlet air cooling, or undertaking a comprehensive redesign of the gas turbine core engine. However, these methods sometimes are costly and unfeasible, particularly in regions characterized by arid conditions or constrained budgets for engine modifications. An alternative strategy has been developed to optimize axial compressor performance in order to improve gas turbine power in high-temperature environments, through an innovative approach. This approach centers on the concept of “re-staggering” the stator blades as a retrofit upgrade for existing gas turbines, enabling an increase in compressor inlet mass flow rate while maintaining peak efficiency at 45 °C. An in-house optimization tool was developed, incorporating a genetic algorithm and artificial neural network, to ascertain the optimal configuration of the re-stagger angles. This optimization process was initially coupled with a mean-line solver and subsequently refined using a through-flow model to enhance the preliminary design. The refined compressor design was then subjected to a comprehensive 3D Computational Fluid Dynamics analysis. Additionally, “end-bend” and “elliptical leading edge” techniques were employed to sustain compressor efficiency and ensure stability across various design and off-design operating conditions. Following a thorough structural analysis, the newly designed compressors were manufactured and tested at two distinct sites in Iran (Yazd and Bampur). The measured data demonstrated the commendable performance and stability of the redesigned compressors, aligning closely with numerical predictions. Notably, the results showcased a substantial increase in gas turbine power output ranging from 7.0% to 8.0% at hot ambient conditions, all while preserving efficiency and stability. Encouragingly, the upgraded compressor also exhibited superior performance even at lower temperatures, such as 15 °C. Consequently, this uprating concept presents a practical and cost-effective solution for power plant operators seeking a significant boost in power output, especially during hot weather conditions or in arid regions prone to drought.

A Correction Method for the Effects of Reynolds Number, Roughness, and Tip Clearance on Geometric Scaling of Axial Compressors

Abstract The geometric scaling method is often used in the design and development of axial compressors to reduce high costs. However, Reynolds number, surface roughness, and tip clearance are often difficult to satisfy all the similarity criteria, which breaks the similarity between the Prototype and the Model compressor. This study utilized a 1.5-stage axial compressor as the Prototype to investigate the effects of Reynolds number, surface roughness, and tip clearance on the geometric scaling process. First, the 0.5 scale Model compressor was simulated using RANS method with the scalable wall function. A test facility was constructed for the 0.5 scale Model compressor, and experiments of varying surface roughness and tip clearance were carried out to verify the reliability of the numerical method. Then, the Prototype and Models with scaling factors of 0.4 and 0.33 were simulated using the same numerical method. By analyzing the numerical results of the Prototype and the three Models, a novel correction method for the deviation between the performance of the Prototype and the Model was proposed. This method can be used to correct the deviation of compressors' performance curves caused by the change in Reynolds number, surface roughness, and tip clearance during the geometric scaling process. Meanwhile, both numerical and experimental results were used to validate the accuracy and the universal applicability of the method.

Sensitivity of Weighing Functions in Genetic Algorithm for Efficiency and Pressure Ratio Optimization in Transonic Axial Flow Compressor

Axial flow compressor design is a multi-objective problem with objectives such as maximization of efficiency, total pressure ratio, mass flow rate and minimization of weight. In this work, validated meanline design process is integrated with genetic algorithm to optimize the compressor design. Fitness function used to evaluate effectiveness of candidate design solutions is arithmetic summation of combination of objective functions and respective weighing functions. Objective functions considered are pressure ratio, efficiency and operating margin in terms of De-Haller Number and Diffusion Factor. The summation of all the weighing functions is unity in all the iterations carried out. Weighing functions of the objective functions used in fitness function dictate the performance. Sensitivity study of weighing functions for multi-objective fitness function has been conducted for the intended mass flow and total pressure ratio. Effect of sensitivity of weighing functions in multi-objective fitness function on efficiency and pressure ratio in transonic axial flow compressor design is studied and discussed. The study carried out shows maximum value of weighing function does not yield maximum value of the objective function. Design trial of weighing function combination of (0.3 pressure ratio and 0.3 efficiency) yields an optimum pressure ratio of 1.588 and 88.1% efficiency for optimized fitness function. The iterative data generated is useful for assigning weighing function value based on design requirement.

Determination of fan design parameters for light-sport aircraft

This paper is focused on the preliminary design of an electric fan for light-sport aircraft. Usage of electric motors brings some advantages compared to piston engines, especially small size and the independence of power on shaft RPM. A 1D compressible fluid flow model is used for the determination of the performace. The influence of various system parameters is analysed. Results for the case of the UL-39 ultralight aircraft are presented. Finally, input parameters for the fan design are determined according to this analysis. This can be then used as input data for the standard fan (axial compressor) design procedure.

An improved stall prediction model for axial compressor stage based on diffuser analogy

Stall represents a limit to the useful operation of the axial compressor. Despite its difficulty, a rational and effective stall prediction model is an urgent requirement for compressor designers. In this paper, a stall prediction model for determining the maximum static pressure rise capability of axial flow compressor stage is presented based on Koch's two dimensional diffuser analogy concept. Compared with the original Koch's model, where the flow angles are only used to consider the increase of inlet dynamic pressure factor caused by high stagger angle, this paper takes into account the effect of flow angles on the diffusion length, and derives the mathematical expression of the modified non-dimensional diffusion length. A semi-empirical correlation is presented to account for the effect of camber angle on the stalling static-pressure-rise coefficient of axial compressor stages. By re-evaluating the Reynolds number effect, a blockage indicator is proposed to determine the effect of boundary layer blockage on stalling capability. Good agreement is demonstrated between the predicted and test stalling pressure rise data for a wide range of axial compressor stages and a 4-stage low speed research compressor. Compared with the original Koch's model, the relative error of the modified model in predicting the stall margin is reduced from 22.5% to 3.5%. This model has a guiding significance for the selection of initial design variables in the axial compressor design process.

Mean line aerodynamic design of an axial compressor using a novel design approach based on reinforcement learning

This paper develops a novel design approach based on reinforcement learning, which can independently complete the mean line aerodynamic design process of the axial compressor. The approach combines Deep Deterministic Policy Gradient (DDPG) algorithm with mean line aerodynamic predicting program HARIKA to acquire the design experiences of the axial compressor. DDPG combines basic reinforcement learning algorithm with artificial neural networks to get continuous observation and give corresponding actions. After the specific modification of the DDPG, multi-objective optimization can be integrated into the design process. Under the guidance of this approach, the design and optimization processes of a 9-stage high-pressure axial compressor were completed without expert experiences. At the design point, the isentropic efficiency was 88.5% and the surge margin was 25%, which meets the requirement of the compressor’s efficiency and stability. And there was an increase of 13.4% and 22%, respectively, compared to the initial design. Moreover, through the analysis of the design results, the distributions of aerodynamic parameters conform to expert experiences. To verify the approach, traditional optimization methods, multi-island genetic optimization algorithm (GA), and multi-objective particle swarm optimization algorithm (MOPSO) were used to solve the same optimization problem. The DDPG optimized efficiency was 0.2% lower than the traditional optimization method, and the pressure ratio at the work point was, respectively, 0.6% and 2% higher than that of the MOPSO and GA, which proved the effectiveness of the new design approach. Furthermore, after training, this approach can give design results immediately near the specific design requirements, which is different from the traditional optimization methods. The new approach saved 93% evaluation steps compared to the GA in the −3% design point and finished the design process in 8 steps in the +3% design point, where GA failed to complete.

Robust design of compact axial compressor

The method of connection weights in neural networks was used to analyze the sensitivity of the compressor rotor, and the Back Propagation Neural Network (BPNN) was used to construct the analysis relationship between the compressor rotor's geometries and the performance based on the training and learning of the data base, and the prediction accuracy can reach more than 99.99%. Then the modified Grason Algorithm based on the neural network connect weights was used to quantify the contribution of the geometrical effects on its performance. The result shows that the tip clearance contributes 11.43% (efficiency sensitivity analysis) and 10.18% (pressure ratio sensitivity analysis) to compressor performance changes. This study focuses mainly on the robust optimization of tip clearance. Non-intrusive probability collection point method (NIPC) was adopted for the uncertainty propagation. The robust optimization method based on BPNN agent model coupled with multi-objective genetic algorithm Non-dominated sorting genetic algorithm-II (NSGA II) was used to perform the optimization. Compared to the design prototype, the variance of robust compressor rotor's efficiency could be reduced by 21.04%.

Design Optimization of a Multistage Axial Flow Compressor Based on Full-Blade Surface Parameterization and Phased Strategy

A new design optimization method is proposed for the problem of high-precision aerodynamic design of multistage axial compressors. The method mainly contains three aspects: full-blade surface parametrization can significantly reduce the number of control variables per blade row and increase the degrees of freedom of the leading edge blade angle compared with the traditional semiblade parametric method; secondly, the artificial bee colony algorithm improved initialization and food source exploration and exploitation mechanism to enhance the global optimization ability and convergence speed, and a distributed optimization system is built on the supercomputing platform based on this method; finally, a phased optimization strategy based on the “synchronous change in multirow blades” is proposed, and expert experience is introduced to achieve a better balance between exploration and exploitation. The optimization method is applied to the AL-31F four-stage low-pressure compressor. As a result, the adiabatic efficiency is improved by 0.67% and the surge margin is improved by 3.1% under the premise that the total pressure ratio and mass flow rate satisfy the constraints, which verifies the effectiveness and engineering practicality of the proposed optimization method in the field of multistage axial flow compressor aerodynamic optimization.

Aerothermodynamics optimal design of a multistage axial compressor in a gas turbine using directly manipulated free-form deformation

The compressor global geometric model including the endwall and the blade is parameterized based on a directly manipulated free-form deformation (DFFD) approach. A meridional flow streamline curvature method for solving inverse and forward problems is applied to the reverse design and performance analysis of the compressor. Combined with an improved Powell search algorithm and the multi-island genetic algorithm, a platform for the fast aerodynamic optimal design of the multistage axial compressor is established. The integrated automatic design and performance optimization of a nine-stage axial compressor in a gas turbine are performed. Meanwhile, the inlet transition section, strut and outlet expansion section of the compressor are retrofitted and optimized considering the connection with the front and rear of the test section. The result indicates that the platform can automatically and effectively realize the process of the fast design, analysis and optimization for the multistage axial compressor. Finally, the total pressure ratio and adiabatic efficiency at the design point and the surge margin at the design rotate speed are 5.057, 88.16% and 17.25%, which are 0.377%, 1.043% and 1.173% relatively higher than those before the 3D DFFD optimization respectively.

Parallel rotor/stator interaction methods and steady/unsteady flow simulations of multi-row axial compressors

Rotor/stator interaction plays a crucial role in predicting the aerodynamic performance of high-speed multi-row axial compressor. Although mixing plane method has been widely used in steady simulations of multi-row single passage flows, the exchange of spanwise non-matched profiles of flow variable between adjacent blade rows is still unknown. Besides, quite little work was devoted to sliding mesh method for unsteady simulations of multi-row full-annulus compressor flows. In this work, mixing plane method and sliding mesh method were developed and presented, respectively, in the environment of parallel computer platforms. Particular attentions were paid to the accurate exchange of spanwise non-matched profiles of fluxes in mixing plane method, and the fast search algorithms of spanwise and circumferential non-matched meshes in sliding mesh method. The developed parallel rotor/stator interaction methods were validated with two multi-row high-speed axial compressors, i.e. NASA Stage 35 and first three stages of NASA 74A, both of which were calculated with steady flow simulation of one blade passage per row and unsteady flow simulation of full-annulus passages per row. Results highlighted that both the developed mixing plane method and sliding mesh method were able to predict the aerodynamic performance curve accurately and satisfy the interface flux conservation tightly. The flow wake and shock wave were observed to transfer continuously across rotor/stator interface with sliding mesh method in the unsteady full-annulus simulation. This work is valuable for the further development of advanced aerodynamic design and accurate performance prediction of multi-row axial compressors.

Erratum: “Secondary Flows, Endwall Effects, and Stall Detection in Axial Compressor Design” [ASME J. Turbomach. 2015, 137(5), p. 051004; DOI: 10.1115/1.4028648

Erratum: “Secondary Flows, Endwall Effects, and Stall Detection in Axial Compressor Design” [ASME J. Turbomach. 2015, 137(5), p. 051004; DOI: 10.1115/1.4028648

Developing a mathematical model and a computer program for a preliminary design of a transonic axial compressor

This paper presents a mathematical model underlying the program for calculating and designing axial compressors. The process of calculating pressure loss in the elements of the axial compressor’s stage flow path is described. The loss coefficient consists of losses on the limiting surfaces, secondary losses and profile losses. The effect of roughness on the pressure loss is taken into account by introducing the corresponding empirical coefficient. An algorithm for calculating the blades’ and vanes’ angles of the impeller and the guide apparatus is presented by calculating the incidence angle and the lag angle of the flow. The flow lag angle is the sum of the lag angle of the flow on the profile and the lag angle due to viscous flow on the limiting surfaces.

Validation of steady and unsteady CFD strategies in the design of axial compressors for gas turbine engines

Steady and unsteady CFD predictions of a state-of-the-art 11-stage axial compressor performances are compared with measurements and scrutinized to determine their ability to assist in the design phase. Unsteady calculations are capable of reproducing the measured stage loads and spanwise stagnation temperature and pressure profiles with good accuracy, while front-to-back stage loads predicted by the steady calculations deviate from measurements and result in an unrealistic early stall. Numerical experiments in design and off-design conditions suggest that unsteady effects change stage loads mostly through a deformation of the spanwise flow profiles, the primary driver of the compressor operability limits. Hence, single stage steady calculations, generally used in design iterations, are demonstrated to reproduce the single stage performance extracted from full multistage unsteady calculation by simply enforcing the time averaged inlet and exit spanwise profiles extracted from the multistage run. The proposed approach may reduce the computational effort in design loops by reducing the number of unsteady runs in favor of single stage steady runs with time averaged boundary conditions.