Blade-tip Radial Running Clearance Research Articles

Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression.

To reveal the effect of high-temperature creep on the blade-tip radial running clearance of aeroengine high-pressure turbines, a distributed collaborative generalized regression extremum neural network is proposed by absorbing the heuristic thoughts of distributed collaborative response surface method and the generalized extremum neural network, in order to improve the reliability analysis of blade-tip clearance with creep behavior in terms of modeling precision and simulation efficiency. In this method, the generalized extremum neural network was used to handle the transients by simplifying the response process as one extremum and to address the strong nonlinearity by means of its nonlinear mapping ability. The distributed collaborative response surface method was applied to handle multi-object multi-discipline analysis, by decomposing one “big” model with hyperparameters and high nonlinearity into a series of “small” sub-models with few parameters and low nonlinearity. Based on the developed method, the blade-tip clearance reliability analysis of an aeroengine high-pressure turbine was performed subject to the creep behaviors of structural materials, by considering the randomness of influencing parameters such as gas temperature, rotational speed, material parameters, convective heat transfer coefficient, and so forth. It was found that the reliability degree of the clearance is 0.9909 when the allowable value is 2.2 mm, and the creep deformation of the clearance presents a normal distribution with a mean of 1.9829 mm and a standard deviation of 0.07539 mm. Based on a comparison of the methods, it is demonstrated that the proposed method requires a computing time of 1.201 s and has a computational accuracy of 99.929% over 104 simulations, which are improvements of 70.5% and 1.23%, respectively, relative to the distributed collaborative response surface method. Meanwhile, the high efficiency and high precision of the presented approach become more obvious with the increasing simulations. The efforts of this study provide a promising approach to improve the dynamic reliability analysis of complex structures.

Robust Optimization Design for Turbine Blade-Tip Radial Running Clearance using Hierarchically Response Surface Method

Considering the robust optimization computational precision and efficiency for complex mechanical assembly relationship like turbine blade-tip radial running clearance, a hierarchically response surface robust optimization algorithm is proposed. The distribute collaborative response surface method is used to generate assembly system level approximation model of overall parameters and blade-tip clearance, and then a set samples of design parameters and objective response mean and/or standard deviation is generated by using system approximation model and design of experiment method. Finally, a new response surface approximation model is constructed by using those samples, and this approximation model is used for robust optimization process. The analyses results demonstrate the proposed method can dramatic reduce the computational cost and ensure the computational precision. The presented research offers an effective way for the robust optimization design of turbine blade-tip radial running clearance.

Erratum to: Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance

Erratum to: Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance

Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance

To develop the high performance and high reliability of turbomachinery just like an aeroengine, distributed collaborative time-varying least squares support vector machine (LSSVM) (called as DC-T-LSSVM) method was proposed for the dynamic probabilistic analysis of high-pressure turbine blade-tip radial running clearance (BTRRC). For structural transient probabilistic analysis, time-varying LSSVM (called as T-LSSVM) method was developed by improving LSSVM, and the mathematical model of the T-LSSVM was established. The mathematical model of DC-T-LSSVM was built based on T-LSSVM and distributed collaborative strategy. Through the dynamic probabilistic analysis of BTRRC with respect to the nonlinearity of material property and the dynamics of thermal load and centrifugal force load, the probabilistic distributions and features of different influential parameters on BTRRC, such as rotational speed, the temperature of gas, expansion coefficients, the surface coefficients of heat transfer and the deformations of disk, blade and casing, are obtained. The deformations of turbine disk, blade and casing, the rotational speed and the temperature of gas significantly influence BTRRC. Turbine disk and blade perform the positive effects on the BTRRC, while turbine casing has the negative impact. The comparison of four methods (Monte Carlo method, T-LSSVM, DCERSM and DC-T-LSSVM) reveals that the DC-T-LSSVM reshapes the possibility of the probabilistic analysis of complex turbomachinery and improves the computational efficiency while preserving the accuracy. The efforts offer a useful insight for rapidly designing and optimizing the BTRRC dynamically from a probabilistic perspective.

Transient reliability optimization for turbine disk radial deformation

The radial deformation design of turbine disk seriously influences the control of gas turbine high pressure turbine (HPT) blade-tip radial running clearance (BTRRC). To improve the design of BTRRC under continuous operation, the nonlinear dynamic reliability optimization of disk radial deformation was implemented based on extremum response surface method (ERSM), including ERSM-based quadratic function (QF-ERSM) and ERSM-based support vector machine of regression (SR-ERSM). The mathematical models of the two methods were established and the framework of reliability-based dynamic design optimization was developed. The numerical experiments demonstrate that the proposed optimization methods have the promising potential in reducing additional design samples and improving computational efficiency with acceptable precision, in which the SR-ERSM emerges more obviously. Through the case study, we find that disk radial deformation is reduced by about 6.5×10–5 m; δ=1.31×10–3 m is optimal for turbine disk radial deformation design and the proposed methods are verified again. The presented efforts provide an effective optimization method for the nonlinear transient design of motion structures for further research, and enrich mechanical reliability design theory.

Transient probabilistic analysis for turbine blade-tip radial clearance with multi-component and multi-physics fields based on DCERSM

Against the background of the probabilistic analysis for High Pressure Turbine (HPT) Blade-tip Radial Running Clearance (BRRC) to achieve the high-performance and high-reliability of aeroengine, Distributed Collaborative Extremum Response Surface Method (DCERSM) was proposed for the dynamic probabilistic analysis of complex turbomachinery on the foundation of quadratic polynomials response surface model. On the basis of deeply investigating Extremum Response Surface Method (ERSM), the mathematical model of DCERSM was established based on quadratic polynomial function. As illustrated in BRRC transient probabilistic analysis with multiple components and multi-physics fields based on DCERSM, blade-tip radial static clearance δ=1.82 mm is advisable synthetically considering the reliability and working efficiency of gas turbine. The reliability, distribution characteristics and failure probability of BRRC are obtained. Besides, rotational speed ω and gas temperature T are the most important factors and expansivity coefficients and surface coefficients of heat transfer show also important influence on BRRC variation. Through the comparison of three methods (DCERSM, ERSM, Monte Carlo method), it is demonstrated that DCERSM reshapes the possibility of complex turbomachinery probabilistic analysis and improves computing efficiency while preserving the accuracy. DCERSM offers a useful insight for BRRC dynamic reliability design and optimization with multi-object and multi-discipline. The efforts of this study also enrich the theory and method of mechanical reliability design.

Optimum Control for Nonlinear Dynamic Radial Deformation of Turbine Casing with Time-Varying LSSVM

With the development of the high performance and high reliability of aeroengine, the blade-tip radial running clearance (BTRRC) of high pressure turbine seriously influences the reliability and performance of aeroengine, wherein the radial deformation control of turbine casing has to be concerned in BTRRC design. To improve BTRRC design, the optimum control-based probabilistic optimization of turbine casing radial deformation was implemented using time-varying least square support vector machine (T-LSSVM) by considering nonlinear material properties and dynamic thermal load. First the T-LSSVM method was proposed and its mathematical model was established. And then the nonlinear dynamic optimal control model of casing radial deformation was constructed with T-LSSVM. Thirdly, through the numerical experiments, the T-LSSVM method is demonstrated to be a promising approach in reducing additional design samples and improving computational efficiency with acceptable computational precision. Through the optimum control-based probabilistic optimization for nonlinear dynamic radial turbine casing deformation, the optimum radial deformation is 7.865 × 10−4 m with acceptable reliability degree 0.995 6, which is reduced by 7.86 × 10−5 m relative to that before optimization. These results validate the effectiveness and feasibility of the proposed T-LSSVM method, which provides a useful insight into casing radial deformation, BTRRC control, and the development of gas turbine with high performance and high reliability.

Reliability and sensitivity analyses of HPT blade-tip radial running clearance using multiply response surface model

To reasonably design the blade-tip radial running clearance (BTRRC) of high pressure turbine and improve the performance and reliability of gas turbine, the multi-object multi-discipline reliability sensitivity analysis of BTRRC was accomplished from a probabilistic prospective by considering nonlinear material attributes and dynamic loads. Firstly, multiply response surface model (MRSM) was proposed and the mathematical model of this method was established based on quadratic function. Secondly, the BTRRC was decomposed into three sub-components (turbine disk, blade and casing), and then the single response surface functions (SRSFs) of three structures were built in line with the basic idea of MRSM. Thirdly, the response surface function (MRSM) of BTRRC was reshaped by coordinating SRSFs. From the analysis, it is acquired to probabilistic distribution characteristics of input-output variables, failure probabilities of blade-tip clearance under different static blade-tip clearances δ and major factors impacting BTRRC. Considering the reliability and efficiency of gas turbine, δ=1.87 mm is an optimally acceptable option for rational BTRRC. Through the comparison of three analysis methods (Monte Carlo method, traditional response surface method and MRSM), the results show that MRSM has higher accuracy and higher efficiency in reliability sensitivity analysis of BTRRC. These strengths are likely to become more prominent with the increasing times of simulations. The present study offers an effective and promising approach for reliability sensitivity analysis and optimal design of complex dynamic assembly relationship.

A dynamic probabilistic design method for blade-tip radial running clearance of aeroengine high-pressure turbine

Around the engineering background of the probabilistic design of high-pressure turbine (HPT) blade-tip radial running clearance (BTRRC) which conduces to the high-performance and high-reliability of aeroengine, a distributed collaborative extremum response surface method (DCERSM) was proposed for the dynamic probabilistic analysis of turbomachinery. On the basis of investigating extremum response surface method (ERSM), the mathematical model of DCERSM was established. The DCERSM was applied to the dynamic probabilistic analysis of BTRRC. The results show that the blade-tip radial static clearance δ = 1.82 mm is advisable synthetically considering the reliability and efficiency of gas turbine. As revealed by the comparison of three methods (DCERSM, ERSM, and Monte Carlo method), the DCERSM reshapes the possibility of the probabilistic analysis for turbomachinery and improves the computational efficiency while preserving computational accuracy. The DCERSM offers a useful insight for BTRRC dynamic probabilistic analysis and optimization. The present study enrichs mechanical reliability analysis and design theory.

Novel method and model for dynamic reliability optimal design of turbine blade deformation

Turbine blade radial deformation seriously influences the Blade-Tip Radial Running Clearance (BTRRC) of the high pressure turbine and the performance and reliability of gas turbine engine. For blade radial deformation design under gas turbine operating conditions, Extremum Response Surface Method (ERSM)-based Support vector machine of Regression (SR) (SR-ERSM) and Importance Degree Model (IDM) were proposed for structural dynamic reliability optimal design. The mathematical model of SR-ERSM was established by taking SR model as an extremum response surface function. The IDM was developed by considering important random parameters obtained by probabilistic analysis. The proposed SR-ERSM and IDM were applied to the reliability optimal design of turbine blade radial deformation based on nonlinear material properties and time-varying loads. The optimization results show that SR-ERSM and IDM are promising to reduce additional design samples and calculated load as well as improve computational efficiency with acceptable precision for nonlinear dynamic structural optimized design. Moreover, a viable design value of blade radial deformation is gained for BTRRC control and high-performance high-reliability gas turbine design. The presented efforts provide a high-efficiency and high-accuracy method and a rapid model for dynamic optimization design of structures for further research as well as enriching mechanical reliability design theory.

Probabilistic Design of HPT Blade-Tip Radial Running Clearance with Distributed Collaborative Response Surface Method

AbstractTo develop the high-performance high-reliability of aeroengine, the probabilistic design of high-pressure turbine (HPT) blade-tip radial running clearance (BTRRC) with three objects (disk, blade, and casing) and two disciplines (heat and mechanical loads) was completed based on distributed collaborative response surface method (DCRSM) from a probabilistic perspective considering dynamic loads and nonlinear material properties. The mathematical model of DCRSM was established based on the quadratic response surface function. The basic idea of BTRRC probabilistic design based on DCRSM was introduced. The BTRRC probabilistic analysis results, consisting of the probabilistic distribution characteristics of input-output variables, the failure probability, and reliability of BTRRC under different static blade-tip clearances δ and the major factors affecting the BTRRC, reveal that the static blade-tip clearance δ=1.865×10−3 m is an optimally acceptable option for BTRRC design. The comparison of methods s...

Distributed collaborative probabilistic design for turbine blade-tip radial running clearance using support vector machine of regression

To improve the computational precision and efficiency of probabilistic design for mechanical dynamic assembly like the blade-tip radial running clearance (BTRRC) of gas turbine, a distribution collaborative probabilistic design method-based support vector machine of regression (SR)(called as DCSRM) is proposed by integrating distribution collaborative response surface method and support vector machine regression model. The mathematical model of DCSRM is established and the probabilistic design idea of DCSRM is introduced. The dynamic assembly probabilistic design of aeroengine high-pressure turbine (HPT) BTRRC is accomplished to verify the proposed DCSRM. The analysis results reveal that the optimal static blade-tip clearance of HPT is gained for designing BTRRC, and improving the performance and reliability of aeroengine. The comparison of methods shows that the DCSRM has high computational accuracy and high computational efficiency in BTRRC probabilistic analysis. The present research offers an effective way for the reliability design of mechanical dynamic assembly and enriches mechanical reliability theory and method.

Study on the theory, method and model for mechanical dynamic assembly reliability optimization

To improve the performance and reliability of gas turbine like an aeroengine, the multi-object multi-discipline (MOMD) reliability optimization design of high press turbine (HPT) blade-tip radial running clearance (BTRRC) was first accomplished based on the mechanical dynamic assembly reliability (MDAR) theory and distributed collaborative response surface method (DCRSM). Four optimization models of MDAR were developed based on the features of assembly machinery and the thought of DCRSM, which are, respectively, called as the direct reliability optimization model (denoted by M1), the multilayer reliability optimization models (denoted by M2), the direct reliability optimization model-based probabilistic analysis (denoted by M3), and the multilayer reliability optimization model-based probabilistic analysis (denoted by M4). Through the MDAR optimization design of BTRRC by the four standard optimization models, some conclusions are drawn as follows: (1) the DCRSM is proved to be effective and feasible for MOMD MDAR optimization design with high computational efficiency and precision; (2) all the reliability optimization results of BTRRC and assembly objects satisfy the requirements of optimization design, and the optimized BTTRC variations are reduced by about 10% and obey the normal distribution, which are quite promising in improving the design and control of HPT BTRRC; (3) in computational efficiency, the computing time of M1 and M3 is far less than those of M2 and M4, meanwhile M3 and M4 are superior to M1 and M2; (4) in computational accuracy, M1 and M2 are better than M3 and M4, as well as M2 and M4 are higher than M1 and M3 theoretically. The presented study does not only fulfill the HPT BTRRC dynamic assembly design from a probabilistic optimization perspective and improve the performance and reliability of gas turbine engine, but also provides a promising approach and four valuable optimization models for MDAR optimization design. Besides, the present efforts are of great significance in enriching the theory and method of mechanical reliability design.

Distributed collaborative response surface method for mechanical dynamic assembly reliability design

Because of the randomness of many impact factors influencing the dynamic assembly relationship of complex machinery, the reliability analysis of dynamic assembly relationship needs to be accomplished considering the randomness from a probabilistic perspective. To improve the accuracy and efficiency of dynamic assembly relationship reliability analysis, the mechanical dynamic assembly reliability(MDAR) theory and a distributed collaborative response surface method(DCRSM) are proposed. The mathematic model of DCRSM is established based on the quadratic response surface function, and verified by the assembly relationship reliability analysis of aeroengine high pressure turbine(HPT) blade-tip radial running clearance(BTRRC). Through the comparison of the DCRSM, traditional response surface method(RSM) and Monte Carlo Method(MCM), the results show that the DCRSM is not able to accomplish the computational task which is impossible for the other methods when the number of simulation is more than 100 000 times, but also the computational precision for the DCRSM is basically consistent with the MCM and improved by 0.40∼4.63% to the RSM, furthermore, the computational efficiency of DCRSM is up to about 188 times of the MCM and 55 times of the RSM under 10000 times simulations. The DCRSM is demonstrated to be a feasible and effective approach for markedly improving the computational efficiency and accuracy of MDAR analysis. Thus, the proposed research provides the promising theory and method for the MDAR design and optimization, and opens a novel research direction of probabilistic analysis for developing the high-performance and high-reliability of aeroengine.

Multi-Object Multi-Discipline Probabilistic Design of High-Pressure Turbine Blade-Tip Radial Running Clearance

For the probabilistic design of high-pressure turbine (HPT) blade-tip radial running clearance (BTRRC), a distributed collaborative response surface method (DCRSM) was proposed, and the mathematical model of DCRSM was established. From the BTRRC probabilistic design based on DCRSM, the static clearance δ=1.865 mm is demonstrated to be optimal for the BTRRC design considering aeroengine reliability and efficiency. Meanwhile, DCRSM is proved to be of high accuracy and efficiency in the BTRRC probabilistic design. The present study offers an effective way for HPT BTRRC dynamic probabilistic design and provides also a promising method for the further probabilistic optimal design of complex mechanical system.

Distributed collaborative extremum response surface method for mechanical dynamic assembly reliability analysis

To make the dynamic assembly reliability analysis more effective for complex machinery of multi-object multi-discipline (MOMD), distributed collaborative extremum response surface method (DCERSM) was proposed based on extremum response surface method (ERSM). Firstly, the basic theories of the ERSM and DCERSM were investigated, and the strengths of DCERSM were proved theoretically. Secondly, the mathematical model of the DCERSM was established based upon extremum response surface function (ERSF). Finally, this model was applied to the reliability analysis of blade-tip radial running clearance (BTRRC) of an aeroengine high pressure turbine (HPT) to verify its advantages. The results show that the DCERSM can not only reshape the possibility of the reliability analysis for the complex turbo machinery, but also greatly improve the computational speed, save the computational time and improve the computational efficiency while keeping the accuracy. Thus, the DCERSM is verified to be feasible and effective in the dynamic assembly reliability (DAR) analysis of complex machinery. Moreover, this method offers an useful insight for designing and optimizing the dynamic reliability of complex machinery.