Reliability Design Theory Research Articles

Optimization of reliability and sensitivity analysis for flange structures

The study delves into the reliability-driven optimization design of the flanges, drawing from reliability design theory, advanced optimization methodologies, and comprehensive sensitivity analysis. Flanges, integral to mechanical systems, demand precise design. Reliability design theory emphasizes consistent operation under diverse conditions. A novel approach to the flange-specific reliability sensitivity analysis method is central to this research. It promises to revolutionize flange design. By understanding the first two moments of core random parameters, we expedite reliability-driven optimization designs for mechanical connections. The programs enhance our approach, streamlining the pursuit of reliability-driven designs and providing rapid, accurate sensitivity analysis data for mechanical connections. This data offers critical insights into parameter influence, enabling targeted design adjustments. In summary, this research represents a significant advancement in mechanical engineering. By integrating reliability design theory with cutting-edge methodologies, it promises to enhance the quality and reliability of mechanical connections, meeting the rigorous demands of contemporary engineering applications. This holistic approach will play a pivotal role in the future of mechanical component design.

Generative adversarial surrogate modeling framework for aerospace engineering structural system reliability design

To effectively realize the reliability design of engineering structural system, a generative adversarial surrogate modeling (GASM) concept is proposed by innovating generative adversarial theory into surrogate modeling methods, which realize the aerospace engineering structural system reliability design adversarial modeling. In this concept, the surrogate model is adopted as a basis function to describe the functional relationship between input variables and output response; the generative adversarial theory is employed to obtain hyper-parameters of basis functions by continuous confrontation and evolution between the generation model and discrimination model. Under the GASM concept, the generative adversarial polynomial chaos expansion (GAPCE) method is developed to achieve aerospace engineering structural system reliability design. The effectiveness of presented GAPCE is verified by three examples including the nonlinear functions approximation, reliability design of flap deflection angle, and reliability estimation for turbine blisk radial deformation. The reliability level of flap deflection angle and turbine blisk radial deformation are 0.9998 and 0.9984, when the allowable values are 9° and 1.9222 × 10−3 m. Besides, the introduced approach possesses advantages of modeling performance (i.e., modeling accuracy and efficiency) and simulation properties (i.e., simulation precision and efficiency) by comparing with various methods. The efforts of this paper can provide a valuable reference for the operational safety of flap systems and engine structures and the development of reliability design theory.

Strength design values of high-performance bamboo-based composite materials based on reliability requirements

Novel high-performance bamboo-based composite materials (HPBBC) being used instead of other building materials is a direction that cannot be ignored in the carbon peaking and carbon–neutral process. A series of mechanical property tests is carried out in this study, and statistics and structural reliability design theory are introduced to calculate the design strength of HPBBC materials. Based on material testing damage characteristics, the design strength reliability indicator in compression is 3.2, while this value is 3.7 in bending and tension. The results show that the design strength satisfying reliability requirements in the compression, tensile, and bending of the HPBBC material are 74.6 MPa, 138.4 MPa, and 146.9 MPa respectively. In addition, the design strength-to-weight ratio of HPBBC in compression and tension is 1 and 2 times that of A615-Grade 80 steel, respectively. The HPBBC material is a renewable, high-strength, and lightweight green material. The results of this study can be used as a reference for the design and use of HPBBC materials.

Failure correlation evaluation for complex structural systems with cascaded synchronous regression

Failure correlation evaluation of complex structural systems is a complex analysis problem involving multiple disciplinary analyses and multiple response objectives, whose high-nonlinearity performance function makes the traditional methods are difficult to acquire acceptable computing accuracy and efficiency. In this case, a cascaded synchronous regression (CSR) method is proposed by combining the multi-level decomposition cooperation strategy and the random regression tree modeling algorithm. In CSR modeling, the multi-level and multi-stage complex structural system is first decomposed into several correlated single-level and single-stage distributed subsystems; moreover, based on the random regression tree algorithm, the corresponding nonlinear mapping model for each distributed subsystem are established; finally, by performing the CSR-based system reliability simulation and Copula functions, the failure correlation relationships can be quantified. The effectiveness of proposed method is validated by the multi-site and multi-mode failure correlation evaluation of aeroengine turbine disc. The current efforts open up an effective way to realize the high-accuracy failure correlation evaluation of complex structural systems, and shed a light on the structural reliability design theory.

Reliability evaluation for colliery machine based on fuzzy interval model

The mechanical reliability design method is a common method, and it is the most direct and effective method to carry out the mechanical reliability design at present. The reliability optimization design is an effective optimization design method which is developed in combination with the reliability design theory on the basis of the conventional optimization design. Taking the antifriction bearing as an example, this paper systematically expounds all kinds of mechanical reliability design methods. Through comprehensive analysis of various factors that affect the reliability of colliery machine, the index system of colliery machine reliability evaluation is established. Because of the complexity and diversity of the use of colliery machine in the evaluation system, and the fuzziness of human thinking, it is difficult to give the deterministic evaluation information in numerical form, so this paper also analyzes the design method of colliery mechanical reliability, and puts forward an interval fuzzy evaluation method for colliery mechanical reliability evaluation to avoid overload operation of colliery machine and ensure safety production and safety of workers. The simulation results show that the inherent law of reliability is effectively characterized by this method, and it provides a evidence for security produce and scientific decision-making of colliery mechine.

Intelligent Extremum Surrogate Modeling Framework for Dynamic Probabilistic Analysis of Complex Mechanism

The reliability analysis of complex mechanisms involves time-varying, high-nonlinearity, and multiparameters. The traditional way is to employ Monte Carlo (MC) simulation to achieve the reliability level, but this method consumes too much computing resources and is even computationally intractable. To improve the efficiency and accuracy of dynamic probabilistic analysis of complex mechanisms, an intelligent extremum surrogate modeling framework (IESMF, short for) is proposed based on extremum response surface method (ERSM), combined with artificial neural network (ANN) method and an improved optimize particle swarm optimization (PSO) method. Hereinto, the ERSM is used to simplify the dynamic process of output response to the extremum value of transient analysis; ANN is applied to establish a mathematical model between input variables and response, and the improved PSO method is utilized in search of initial weights and thresholds of the model. The effectiveness of the IESMF is demonstrated to perform the Rack-and-pinion steering mechanism (RPSM) reliability analysis. The results show that when the allowable value of gear root stress is equal to 850 MPa, the RPSM has a reliability degree of 0.9971. Through the validation process, it is illustrated that IESMF is accurate and efficient in dynamic probabilistic analysis of complex mechanisms, and its comprehensive performance is better than the MC method and ERSM. The research effort offers new ideas for the reliability estimation of a complex mechanism, thus enriching the method and theory of mechanical reliability design.

A Study on Aeroengine Conceptual Design Considering Multi-Mission Performance Reliability

Owing to the realization of multi-mission adaptability requires more complex mechanical structure, the candidates of future aviation propulsion are confronted with more overall reliability problems than that of the conventional gas turbine engine. This situation is challenging to a traditional aeroengine deterministic design method. To overcome this challenge, the Reliability-based Multi-Design Point Methodology is proposed for aeroengine conceptual design. The presented methodology adopted an unconventional approach of engaging the reliability prediction by artificial neural network (ANN) surrogate models rather than the time-consuming Monte Carlo (MC) simulation. Based on the Adaptive Particle swarm optimization, the utilization of the pre-training technique optimizes the initial network parameters to acquire better-conditioned initial network, which is sited closer to designated optimum so that contributes to the convergence property. Moreover, a new hybrid algorithm is presented to integrate the pre-training technique into neural network training procedure in order to enhance the ANN performance. The proposed methodology is applied to the cycle design of a turbofan engine with uncertainty component performance. The testing results certify that the prediction accuracy of pre-trained ANN is improved with negligible computational cost, which only spent nearly one-millionth as much time as the MC-based probabilistic analysis (0.1267 s vs. 95,262 s, for 20 testing samples). The MC simulation results substantiate that optimal cycle parameters precisely improve the engine overall performance to simultaneously reach expected reliability (≥98.9%) in multiple operating conditions without unnecessary performance redundancy, which verifies the efficiency of the presented methodology. The presented efforts provide a novel approach for aeroengine cycle design, and enrich reliability design theory as well.

Graphic illustration for mechanical reliability design (2): theory and method

In this paper, the connotation and an extension of the mechanical reliability design theory and method are presented via charts and texts. The abstruse mechanical reliability theories and methods are illustrated intuitively and scientifically via easy-to-understand language and simple and clear illustrations. Topics such as mechanical strength design, mechanical reliability design, dynamic and gradual reliability design, reliability optimization design, reliability sensitivity design and reliability robust design are covered to help the readers understand the wonder and practicality of mechanical reliability engineering. Diagrams of reliability design, dynamic and gradual reliability theory and technology, reliability optimization design, reliability sensitivity design and reliability robust design are illustrated for the first time. Additionally, the relationship between mechanical product reliability design and conventional safety factor design is elaborated in a table. This paper provides a solid mechanical reliability engineering foundation for design, manufacturing, usage and evaluation of mechanical products.

Fuzzy Multi-SVR Learning Model for Reliability-Based Design Optimization of Turbine Blades.

The effectiveness of a model is the key factor of influencing the reliability-based design optimization (RBDO) of multi-failure turbine blades in the power system. A machine learning-based RBDO approach, called fuzzy multi-SVR learning method, was proposed by absorbing the strengths of fuzzy theory, support vector machine of regression (SVR), and multi-response surface method. The model of fuzzy multi-SVR learning method was established by adopting artificial bee colony algorithm to optimize the parameters of SVR models and considering the fuzziness of constraints based on fuzzy theory, in respect of the basic thought of multi-response surface method. The RBDO model and procedure with fuzzy multi-SVR learning method were then resolved and designed by multi-objective genetic algorithm. Lastly, the fuzzy RBDO of a turbine blade with multi-failure modes was performed regarding the design parameters of rotor speed, temperature, and aerodynamic pressure, and the design objectives of blade stress, strain, and deformation, and the fuzzy constraints of reliability degree and boundary conditions, as well. It is revealed (1) the stress and deformation of turbine blade are reduced by 92.38 MPa and 0.09838 mm, respectively. (2) The comprehensive reliability degree of the blade was improved by 3.45% from 95.4% to 98.85%. (3) It is verified that the fuzzy multi-SVR learning method is workable for the fuzzy RBDO of complex structures just like a multi-failure blade with high modeling precision, as well as high optimization, efficiency, and accuracy. The efforts of this study open a new research way, i.e., machine learning-based RBDO, for the RBDO of multi-failure structures, which expands the application of machine learning methods, and enriches the mechanical reliability design method and theory as well.

Cutting the double loop: Theory and algorithms for reliability‐based design optimization with parametric uncertainty

SummaryParametric uncertainties complicate engineering design—confounding regulated design approaches and degrading the performance of reliability efforts. The simplest means to tackle this uncertainty is double‐loop simulation, a nested Monte Carlo method that, for practical problems, is intractable. In this work, we introduce a flexible, general approximation technique that obviates the double loop. This approximation is constructed in the context of a novel theory of reliability design under parametric uncertainty: we introduce metrics for measuring the efficacy of reliability‐based design optimization strategies (epistemic design gap and effective reliability), minimal conditions for controlling uncertain reliability (precision margin), and stricter conditions that guarantee the desired reliability at a designed confidence level. We provide a number of examples with open‐source code to demonstrate our approaches in a reproducible fashion.

Probabilistic sensitivity analysis for the frame structure of missiles

In this study, the high-performance and high-reliability of missiles were achieved under the background of the probabilistic analysis for frame structure of missiles. On the basis of deep investigation of the artificial neural network method and mathematical model of multiorder moment method, artificial neural network based higher order moment approach was proposed for the probabilistic analysis of complex structure. As illustrated in frame structure probabilistic analysis with multiphysics fields based on artificial neural network based higher order moment, the reliability and failure probability of frame structure were studied. Furthermore, it was shown that the radial concentrated force P and the outer radius of the frame R2 are the most important factors, and the inside radius of the frame R1 and the ultimate tensile strength of materials σ b also have an important influence on the frame structure. Through comparison with Monte Carlo simulation, second-order fourth-moment approach and the artificial neural network based first-order second-moment approach, it was demonstrated that artificial neural network based higher order moment reshapes the possibility of complex aero-missile probabilistic analysis and improves computing efficiency while preserving the accuracy. Artificial neural network based higher order moment offers a useful insight for missile reliability design and optimization with multi-discipline. The efforts of this study also enrich the theory and method of mechanical reliability design.

Gradual Change Reliability Sensitivity Design for Spindle System on Fully Stochastic Process

Gradual change reliability design of mechanical components is considered by semi-stochastic process model of loading with change of time and fully stochastic process model of known degradation function of strength at present, yet it can’t obtain reasonable reliability design by existing information of components for these models. Thus the influence of existing information and time-dependent parameters of components on reliability should be taken into account in order to access reliability correctly. By studying effect of loading action and time-dependent character of strength, strength of components is taken as a process of independent increments and autocorrelation coefficient of strength is also calculated, meanwhile, we present a method for computing time-dependent reliability. Combining the reliability design theory with sensitivity analysis method, a numerical method for time-dependent reliability sensitivity design of components based on fully stochastic process is proposed, the variation rules of reliability sensitivity of parameters are given at any moment and the effects of design parameters on reliability of components are also studied. The method presented in this paper provides the theoretical basis for structural design and life prediction of mechanical components.

Distributed collaborative probabilistic design of multi-failure structure with fluid-structure interaction using fuzzy neural network of regression

Abstract To improve the computing efficiency and precision of probabilistic design for multi-failure structure, a distributed collaborative probabilistic design method-based fuzzy neural network of regression (FR) (called as DCFRM) is proposed with the integration of distributed collaborative response surface method and fuzzy neural network regression model. The mathematical model of DCFRM is established and the probabilistic design idea with DCFRM is introduced. The probabilistic analysis of turbine blisk involving multi-failure modes (deformation failure, stress failure and strain failure) was investigated by considering fluid–structure interaction with the proposed method. The distribution characteristics, reliability degree, and sensitivity degree of each failure mode and overall failure mode on turbine blisk are obtained, which provides a useful reference for improving the performance and reliability of aeroengine. Through the comparison of methods shows that the DCFRM reshapes the probability of probabilistic analysis for multi-failure structure and improves the computing efficiency while keeping acceptable computational precision. Moreover, the proposed method offers a useful insight for reliability-based design optimization of multi-failure structure and thereby also enriches the theory and method of mechanical reliability design.

Dynamic neural network method-based improved PSO and BR algorithms for transient probabilistic analysis of flexible mechanism

To improve the computing efficiency and precision of transient probabilistic analysis of flexible mechanism, dynamic neural network method (DNNM)-based improved particle swarm optimization (PSO)/Bayesian regularization (BR) (called as PSO/BR-DNNM) is proposed based on the developed DNNM with the integration of extremum response surface method (ERSM) and artificial neural network (ANN). The mathematical model of DNNM is established based on ANN on the foundation of investigating ERSM. Aiming at the high nonlinearity and strong coupling characteristics of limit state function of flexible mechanism, accurate weights and thresholds of PSO/BR-DNNM function are discussed by searching initial weights and thresholds based on the improved PSO and training final weights and thresholds by the BR-based training performance function. The probabilistic analysis of two-link flexible robot manipulator (TFRM) was investigated with the proposed method. Reliability degree, distribution characteristics and major factors (section sizes of link-2) of TFRM are obtained, which provides a useful reference for a more effective TFRM design. Through the comparison of three methods (Monte Carlo method, DNNM, PSO/BR-DNNM), it is demonstrated that PSO/BR-DNNM reshapes the probability of flexible mechanism probabilistic analysis and improves the computing efficiency while keeping acceptable computational precision. Moreover, the proposed method offers a useful insight for reliability-based design optimization of flexible mechanism and thereby also enriches the theory and method of mechanical reliability design.

Multi-objective reliability-based design optimization approach of complex structure with multi-failure modes

To make multi-objective reliability-based design optimization (MORBDO) more effective for complex structure with multi-failure modes and multi-physics coupling, multiple response surface method (MRSM)-based artificial neural network (ANN) (ANN-MRSM) and dynamic multi-objective particle swarm optimization (DMOPSO) algorithm are proposed based on MRSM and MOPSO algorithm. The mathematical model of ANN-MRSM is established by using artificial neural network to fit the multiple response surface function. The DMOPSO algorithm is proposed by designing dynamic inertia weight and dynamic learning factors. The proposed approach is verified by the MORBDO of turbine blisk deformation and stress with respect to fluid–thermal-structure interaction from probabilistic analysis. The optimization design results show that the proposed approach has the promising potentials to improve computational efficiency with acceptable computational precision for the MORBDO of turbine blisk deformation and stress. Moreover, Pareto front curve and a set of viable design values of turbine blisk are obtained for the high-reliability high-performance design of turbine blisk. The presented efforts provide an effective approach for MORBDO of complex structures, and enrich mechanical reliability design theory as well.

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.

Reliability and Efficiency Calculation for Drive System of Multi-Bush Roller Ironing Machine

In this paper, formula for calculating the reliability of single-chain drive system and formula for calculating the reliability and efficiency of drive system of multi-roller ironing machine are presented based on reliability design theory and the theory of mechanical efficiency calculation. The results illustrate that the reliability and efficiency of existing drive system are lower. Therefore, improvement measures are proposed: use high-quality bush roller chain that shall be regularly maintained and changed; the chain drive shall be replaced with gear drive.

Marine Engineering Steels – Properties Requirements and Evaluation

With the implementation of China's maritime strategy, the demand for marine engineering steel is increasing sharply. Due to the harsh service conditions of marine engineering steel, there are strict requirements in corrosion resistance, structural toughness, welding property, high strength and cold tolerance. This paper introduces the corrosion resistance, structural toughness and welding property of marine engineering steel in detail. Marine corrosion could be divided into uniform corrosion, pitting, crevice corrosion, impact corrosion, cavitation corrosion, galvanic corrosion, corrosion fatigue, etc. The conventional means to improve the corrosion resistance of marine engineering steel are coating, cathodic protection and improving the corrosion resistance of the steel itself. Toughness is a comprehensive embodiment of strength and plasticity. When the toughness is too low, the safety and service life decrease, while cost increase when the toughness is too high. The actual structures toughness can be reflected from the CTOD test. Welding is the most important process of marine engineering steel. The excellent design of fracture mechanics of welded joints and the reliability design theory can greatly enhance the reliability of marine engineering steel.

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.