Algorithm For Reliability Analysis Research Articles

Time-domain fatigue reliability analysis for floating offshore wind turbine substructures using coupled nonlinear aero-hydro-servo-elastic simulations

The present study aims to develop a novel methodology for the fatigue reliability analysis and design for a floating offshore wind turbine substructure using its fully coupled nonlinear dynamic responses in the time domain. The developed methodology offers a computationally efficient yet robust assessment for designing fatigue-critical welded joints on floating substructures. The methodology starts with the sea state selection based on the fatigue damage contribution of all the sea states in the scatter diagram. To this end, the dynamic response is analysed by fully coupled aero-hydro-servo-elastic simulations using OpenFAST. The resulting short-term fatigue loading is then used to estimate the hotspot stress history using the analytical solution for global structural analysis and the empirical solution for the stress concentration factor. The long-term fatigue damage is calculated as the joint probability-weighted sum of the short-term fatigue damage using Palmgren-Miner's linear damage rule. Afterwards, the selected sea states are used to perform dynamic response simulations with multiple random seeds, ensuring no significant accuracy loss. To obtain the probabilistic fatigue life prediction, a novel time-domain fatigue reliability analysis algorithm is developed based on the bootstrapping method, which creates a pool of short-term fatigue responses with different random seeds and combines them within a Monte Carlo Simulation. Finally, the fatigue reliability analysis considering the S-N and damage-tolerant approaches for design is carried out.

Decomposition and reconstruction algorithms for IoT reliability analysis utilizing 5G technology for smart cities

Internet of Things (IoT) and 5G communication technologies in smart cities deliver promising services for heterogeneous applications. The application reliability banks on uninterrupted and seamless services experienced by the users. However, the increasing smart city application demands influence the experience reliability through augmented wait times. This article therefore introduces a Coherent Reliability Service Broadcasting Technique (CRSBT) for sustaining constructive application services. This technique incorporates linear regressive and digressive learning for application service improvements and restrictions. Based on the demand, the regressive process verifies the wait time and with the reducing demands, the service broadcast ratio is verified. These two factors are verified post the demand and response through 5G resource allocations and IoT computations. Both the service-oriented features are validated for regressive service broadcast and either of the one is used for digressive response. The coherence between the computations (IoT) and resources (5G) is verified on-demand and linearly. Therefore, the proposed technique is reliable in sustaining service broadcast, less wait time, and maximum flexibility.

Aerothermal Performance Robustness and Reliability Analysis of Turbine Blade Squealer Tip With Film Cooling

Abstract The uncertainty quantification in the turbine components' aerodynamic and heat transfer performances is widely considered to be the most challenging topic due to its intricate and nonlinear characteristics. This paper first proposes an efficient uncertainty quantification method based on an original parallel framework combining Polynomial Chaos Expansions (PCE) with two forms (stochastic response surface-based and Galerkin projection-based) and the Universal Kriging method. The rigorous mathematical tests are performed to verify the reliability and computational efficiency of the proposed method, and the results support that this method can dramatically reduce computational samples compared to the conventional PCE method while maintaining computational accuracy. Then, the genetic algorithm was introduced to establish an efficient uncertainty quantification framework, and it is applied to the aerothermal performance robustness investigation of the GE-E3 rotor blade tip with and without film cooling. Based on the findings of uncertainty quantification, the injection of cooling air drastically enhances the unstable tendency of the flow and thermal fields, resulting in the actual aerothermal performance of the squealer tip being much lower than that predicted by deterministic calculations. The setting of the film cooling, although effective in reducing the heat flux around the cooling holes, also induces more chaotic flow and thermal fields, leading to sharp heat flux fluctuations around the cooling holes. Finally, our novel reliability analysis algorithm, rooted in the quantification of uncertainty, corroborates the assertion that the introduction of coolant gas, while extending the operational longevity of turbine blades, confers only marginal improvements in the mitigation of lifespan variability. The comprehensive lifespan assessment elucidates that the mean operational longevity of the conventional squealer tip design stands at an estimated 16,169.44 h, accompanied by a standard deviation of 2,750.31 h. In stark contrast, the mean operational longevity of the squealer tip integrated with film cooling measures a significantly enhanced 17,035.17 h, exhibiting a standard deviation of 2,492.73 h. Consequently, the operational lifespan of the conventional squealer tip experiences a decrement of 10.17% in comparison to the anticipated mean lifespan, whereas the reduction for the film-cooled squealer tip registers at 5.36%.

Augmented line sampling and combination algorithm for imprecise time-variant reliability analysis

Assessment of imprecise time-variant reliability in engineering is a critical task when accounting for both the variability of structural properties and loads over time and the presence of uncertainties involved in the ambiguity of parameters simultaneously. To estimate the Imprecise Time-variant Failure Probability Function (ITFPF) and derive the imprecise reliability results as a byproduct, Adaptive Combination Augmented Line Sampling (ACALS) is proposed. It consists of three integrated features: Augmented Line Sampling (ALS), adaptive strategy, and the optimal combination. ALS is adopted as an efficient analysis tool to obtain the failure probability function w.r.t. imprecise parameters. Then, the adaptive strategy iteratively applies ALS while considering both imprecise parameters and time simultaneously. Finally, the optimal combination algorithm collects all result components in an optimal manner to minimize the Coefficient of Variance (C.o.V.) of the ITFPF estimate. Overall, the proposed ACALS method outperforms the original ALS method by efficiently estimating the ITFPF while guaranteeing a minimal C.o.V. Thus, the proposed approach can serve as an effective tool for imprecise time-variant reliability analysis in real engineering applications. Several examples are presented to demonstrate the superiority of the proposed approach in addressing the challenges of estimating the ITFPF.

An enhanced learning function for bootstrap polynomial chaos expansion-based enhanced active learning algorithm for reliability analysis of structure

Sparse polynomial chaos expansion (PCE) combined with the bootstrap resampling method is a viable alternative to obtain an active learning algorithm for reliability analysis. The existing learning functions in PCE-based active learning algorithms do not consider the joint probability density function (PDF) information. The present study explores a sparse PCE-based active learning algorithm based on a newly proposed learning function that maintains a balance between the misclassification probability and the joint PDF information of sample points. In doing so, the coefficients of the sparse PCE are estimated using a Bayesian compressive sensing regressor, as it is noted to be one of the best-performing regression solvers for PCE, irrespective of sampling schemes. The proposed learning function considers the weight of the joint PDF with the local accuracy measure of bootstrap PCE (bPCE) to add new samples iteratively in the existing training set. The convergence is achieved when the ten consecutive failure estimates are within a negligible discrepancy and also checks the confidence bounds of the bPCE estimates. The effectiveness of the proposed approach is demonstrated using two structural engineering examples and one well-known analytical test function and is found to be quite efficient and accurate in estimating reliability.

Research on Networking of 10 kV Substation Management Information Area

At present, the substation network management information area, as an essential part of the substation network communication, is an important transmission channel to realize the intelligent auxiliary control of the substation network. Establishing a stable and reliable substation management information area network has become an essential part of upgrading the substation network. Firstly, this paper designs a Gigabit-Capable PON, and an industrial Ethernet networking scheme, based on certain city data. Secondly, based on the availability of single-device operation, a calculation model algorithm for reliability analysis is innovatively proposed to realize the comparison of networking solutions. Thirdly, regarding the economy, the concept of unit incremental investment reliability index is applied to two groups of networking solutions comparison for the first time. Finally, a networking scheme and suggestions for the substation network management information area are proposed.

Physics-based corrosion reliability analysis of miter gates using multi-scale simulations and adaptive surrogate modeling

Stress corrosion cracking (SCC) initiation is usually simulated at the mesoscale, and these computations are usually expensive. This is made more computationally challenging or impossible when such simulations are coupled with a macroscale structural model required for reliability analysis, due to the sources of uncertainty from both scales. This paper tackles this computational barrier to perform physics-based corrosion reliability analysis of large structures using mesoscale simulations via a novel, adaptive surrogate modeling framework. A global surrogate model of the structure is first constructed from a finite element (FE) mechanical model to propagate various sources of input uncertainty at the macroscale to the local stress responses. After that, a mesoscale surrogate model is constructed from phase-field (PF) simulations to predict the failure probability of a given location by accounting for uncertainty in both the macroscale and mesoscale models. In order to guarantee the accuracy of the mesoscale surrogate model and reduce the number of PF simulations, an adaptive surrogate modeling method is proposed using importance sampling (IS) and active learning to refine iteratively the surrogate model in critical regions. Corrosion reliability analysis of a miter gate structure is adopted to demonstrate the efficacy of the proposed method. The result shows that the proposed framework can efficiently and accurately generate a failure probability map for a large structure like a miter gate based on computationally expensive mesoscale PF simulations. In addition, the proposed method is more accurate and converges faster than existing surrogate model-based reliability analysis algorithms.

A dynamic Gaussian process surrogate model based on the grasshopper optimization algorithm for non-probabilistic reliability analysis of complex structures

In practical complex engineering structures, the performance function (PF) for reliability analysis is commonly implicit and highly nonlinear. Commonly used surrogate models are appropriate for structural reliability analysis with an implicit PF. However, these methods require hundreds of PF values, which are time-consuming to obtain by adopting numerical analysis, such as finite element analysis (FEA). Therefore, since non-probabilistic reliability analysis does not require a large number of samples, it has great development potential. In this paper, a dynamic Gaussian process (GP) surrogate model based on the grasshopper optimization algorithm (DGP-GOA) is proposed for the non-probabilistic reliability analysis. First, with the help of the scale factor of the convex set model, the non-probabilistic reliability analysis problem is transformed into an unconstrained optimization problem. Second, the DGP-GOA fits the PF by constructing a GP surrogate model with a small dataset. Third, the GOA is used to search for the global optimal solution to obtain a non-probabilistic reliability index. Then, a dynamic retraining strategy is proposed to improve the fitting accuracy and efficiency. The results demonstrate that the proposed method is highly applicable to the non-probabilistic structural reliability analysis of complex engineering structures.

Kriging-Based Performance Measure Function Approximation Method for Hybrid Reliability-Based Design Optimization

Hybrid reliability-based design optimization (HRBDO) can provide an effective way to obtain the optimum design in the presence of both random and interval variables. HRBDO is typically described as a nested optimization model. It is computationally expensive when directly solving the HRBDO problem by the nested optimization method. To address this issue, this paper develops an efficient decoupled HRBDO method that aims at performance measure function approximation by the adaptive Kriging model (KPMFA). The proposed KPMFA method includes three main blocks, namely hybrid inverse reliability analysis, performance measure function approximation, and equivalent deterministic optimization. In KPMFA, the adaptation of the adaptive chaos control (ACC) algorithm for inverse reliability analysis that accommodates interval variables is developed. Moreover, an adaptive strategy with two-stage of enrichment for the Kriging model is developed to approximate performance measure functions on the region of interest. Then, the optimization can be proceeded using the Kriging model of performance measure functions. Finally, five illustrative HRBDO problems are investigated to demonstrate the accuracy and efficiency of the proposed KPMFA method.

Reliability analysis of compressed timber studs on the buckling criterion

Introduction. Ensuring safety is a priority goal in the design, construction and operation of building and structures. A quantitative assessment of safety can be the failure probability of a structural element. The article presents algorithms for reliability analysis of timber studs by the buckling criterion under central compression force.
 
 Materials and methods. The FOSM (First Order Second Moment) method is a classic approach for solving many reliability analyses tasks in the engineering sector. This method uses the second statistical moments of random variables (mathematical expectation and variance) and the method of statistical linearization with the decomposition of the function into a Taylor series of the first order. However, it is often necessary to deal with nonlinear limit state functions, where the statistical linearization method can lead to incorrect results. In this case, it is necessary to use other algorithms for reliability analysis: the Hasofer – Lind method, SORM (Second Order Reliability Method), etc.
 
 Results. The numerical example of reliability analysis for compressed timber studs on the buckling criterion is considered. To illustrate the problem of the research, the dimensions of the cross-section of the studs and the strength of the wood are taken as random variables, and the load is considered a deterministic value. In the following, an algorithm for reliability analysis is described for a timber studs under random load. The classical FOSM approach showed an overestimated reliability index by 35 % compared to the results of the Monte Carlo numerical experiment and the analytical solution using the Hasofer – Lind algorithm.
 
 Conclusions. The use of the traditional FOSM method for reliability analysis of wooden studs according to the buckling criterion can lead to overestimated reliability index estimates. It is unacceptable from the point of view of the structural safety. The calculation and analysis of the reliability of timber studs should be carried out on the basis of the Hasofer – Lind algorithm, the SORM method or other optimization methods.

Asset Management Framework and Tools for Facing Challenges in the Adoption of Product-Service Systems

Servitization is recognized as a key business strategy for original equipment manufacturers willing to move up the value chain. However, several barriers have to be overcome in order to successfully integrate products and services. Many of these barriers are caused by the technical challenges associated with the design and management of the product-service systems (PSSs), such as life cycle service level and cost estimation, risk management, or the system design and pricing. Asset management (AM) presents itself as a key research area in order to overcome these barriers as well as to integrate PSSs within the manufacturers’ operations management. It is the scope of this article to provide theoretical and practical insights with regards to the alignment of AM and PSS research areas. To support the alignment between both areas, a management framework which gathers specific technologies, including reliability analysis, simulation modeling, and multiobjective optimization algorithms, is presented. The purpose of the framework is to provide manufacturers with a decision-support tool that facilitates the main managerial challenges faced when implementing a servitization strategy. The article contributions are successfully applied to case studies in the railway and wind energy sectors based on real field data, thereby demonstrating their suitability for both facilitating manufacturer’s decision-making process and better satisfying stakeholders’ interests.

Geotechnical RBDO: Coupling the inverse reliability algorithm with multi-objective reliability-based design optimization of geotechnical systems

The analysis and design of geotechnical engineering inevitably deal with various sources of uncertainties. To further expand the application of reliability methods in geotechnical engineering design works, the inverse reliability algorithm is modified to incorporate unknown geotechnical design parameters in this paper. Based on the established HLRF algorithm for first order reliability analysis, the proposed approach can easily realize traditional single-objective inverse reliability-based design (RBD). Further, the multi-objective inverse reliability design is achieved by combining several single-objective inverse reliability analysis modules through a coupling matrix without seeking for any other surrogate functions. The Nataf transformation is integrated into the method to deal with the correlation of random variables. In addition, the algorithm can be combined with any nonlinear optimization method when the number of design parameters is greater than the number of performance functions, at which point the RBD is extended to reliability-based design optimization (RBDO) with an objective function related to the optimum design purpose. Verifications of the proposed approach are illustrated through several examples, which show that the proposed reliability design optimization framework is effective and can be used as a basic tool for RBDO problems in geotechnical engineering.

A Novel Support-Vector-Machine-Based Grasshopper Optimization Algorithm for Structural Reliability Analysis

Aiming at the characteristics of high computational cost, implicit expression and high nonlinearity of performance functions corresponding to large and complex structures, this paper proposes a support-vector-machine- (SVM) based grasshopper optimization algorithm (GOA) for structural reliability analysis. With this method, the reliability problem is transformed into an optimization problem. On the basis of using the finite element method (FEM) to generate a small number of samples, the SVM model is used to construct a surrogate model of the performance function, and an explicit expression of the implicit nonlinear performance function under the condition of small samples is realized. Then, the GOA is used to search for the most probable point (MPP), and a reasonable iterative method is constructed. The MPP information of each iteration step is used to dynamically improve the reconstruction accuracy of the surrogate model in the region that contributes most to the failure probability. Finally, with the MPP after the iteration as the sampling center, the importance sampling method (ISM) is used to further infer the structural failure probability. The feasibility of the method is verified by four numerical cases. Then, the method is applied to a long-span bridge. The results show that the method has significant advantages in computational accuracy and computational efficiency and is suitable for solving structural reliability problems of complex engineering.

An envelope-function-based algorithm for time-dependent reliability analysis of structures with hybrid uncertainties

This paper proposes a novel envelope-function-based algorithm for time-dependent reliability analysis considering random and interval uncertainties. First, the envelope function of the bound of the limit-state function is approximated with assistance of the first-order reliability method, thereby transforming time-dependent reliability problem into a time-independent one. Here, the Kriging modeling method is utilized to construct the expansion point determination function for reducing the most probable point search, which significantly accelerates the expansion point determination in constructing the envelope function. After the envelope function is found, the time-dependent reliability in terms of random and interval uncertainties is efficiently calculated by a multivariate Gaussian integral. Finally, five case studies are used to demonstrate the effectiveness of the proposed algorithm. The results indicate it can provide accurate and efficient time-dependent reliability analysis for structures where the random and interval uncertainties coexist.

Анализ надежности топливоснабжения источников тепловой энергии на основе метода статистических испытаний

Thermal power plants highly depend on fossil fuel supply. This research is studying reliability of fuel supply systems in order to prevent fossil fuel shortages what is crucial for district heating. The basis of the conducted research is the Monte Carlo method as well as the certain data on fuel supply and demand. We have developed a reliability analysis algorithm with multiple simulation options such as fuel reserve and interchangeability between several power plants. The results of simulation tests are presented in this paper.

An adaptive subset simulation algorithm for system reliability analysis with discontinuous limit states

Many system reliability problems involve performance functions with a discontinuous distribution. Such situations occur in both connectivity- and flow-based network reliability problems, due to binary or multi-state random variables entering the definition of the system performance or due to the discontinuous nature of the system model. When solving this kind of problems, the standard subset simulation algorithm with fixed intermediate conditional probability and fixed number of samples per level can lead to substantial errors, since the discontinuity of the output can result in an ambiguous definition of the sought percentile of the samples and, hence, of the intermediate domains. In this paper, we propose an adaptive subset simulation algorithm to determine the reliability of systems whose performance function is a discontinuous random variable. The proposed algorithm chooses the number of samples and the intermediate conditional probabilities adaptively. We discuss two MCMC algorithms for generation of the samples in the intermediate domains, the adaptive conditional sampling method and a novel independent Metropolis–Hastings algorithm that efficiently samples in discrete input spaces. The accuracy and efficiency of the proposed algorithm are demonstrated by a set of numerical examples.

Support vector machine-based similarity selection method for structural transient reliability analysis

The transient reliability analysis of structures enduring complex loads from multiple sources originating from extraordinarily tangled and complicated operation situation, plays a leading role in the operational safety and design cost of system. In this work, a support vector machine-based similarity selection genetic algorithm (SVM-SSGA) for structural transient reliability analysis is developed by integrating support vector machine (SVM), similarity selection strategy and genetic algorithm (GA). The transient reliability analysis of nose landing gear (NLG) shock strut outer fitting stress is performed to verify the modeling and simulation performance of SVM-SSGA. The results show that (i) the developed SVM-SSGA method holds eminent modeling features, resulting from that average absolute error is 0.7493 × 106 Pa and modeling time is 0.1847s, and (ii) the SVM-SSGA method is superior to other methods in simulation characteristics, since simulation time is 0.2347s of 104 MC samples and precision reaches 99.99% compared to direct simulation, (iii) the reliability degree of the NLG shock strut outer fitting stress is 0.9972 when the allowable stress is σ=1.5020 × 109 Pa. The efforts of this study provide a promising method in transient structural reliability analysis, which is prospective to improve the operational safety and reliability of the system besides the NLG.

The GLO method: An efficient algorithm for time-dependent reliability analysis based on outcrossing rate

Outcrossing method has been widely applied in time-dependent reliability analysis (TRA), where the cumulative failure probability is calculated based on the numerical integration of outcrossing rate. The efficiency of outcrossing method is influenced by two factors, i.e., the number of discretized instants for numerical integration and the computational time for solving outcrossing rate at each instant. Focusing on these factors, an efficient Gauss-Legendre quadrature based outcrossing (GLO) method is proposed in this study. The main innovation of the proposed method consists of two aspects. First, the cumulative failure probability is evaluated by weighted sum of outcrossing rate at only three to five instants with the aid of Gauss-Legendre quadrature, which avoids the time-consuming numerical integration with a large number of instants discretized. Second, an efficient algorithm for computing outcrossing rate is developed from a recently proposed system reliability method based on the well-known FORM/SORM. Three examples are presented to demonstrate the application of the proposed GLO method, including explicit, implicit, and strong nonlinear limit state functions. It is shown that, reliability results by the proposed GLO method are in close agreement with those by Monte Carlo simulation, with the computational time significantly reduced.

Reliability Analysis Algorithm for Multiple Microgrids in Distribution Systems Based on Complex Network Mathematical Theory

It exists a great potential in microgrids connected to distribution systems of being taken into advantage reconfiguration possibilities with the purpose of achieving the quality of service regulatory requirements that become more demanding each day. In addition, it is possible to optimize the network operators’ income by increasing the incentives for upgrading the quality indexes. In this paper, it's proposed an evaluation algorithm of the connection points of multiple microgrids in a distribution system that upgrades the reliability of the system as a whole, being based in complex network analysis (CNA), a perspective of power systems that allows the evaluation of an electrical system as a graph. For that, a model of a trial system is made from the CNA point of view utilizing the MATLAB software and afterwards, as validation of the proposal of this work, the system's reliability is evaluated by connecting multiple microgrids into critical nodes provided by the CNA making use of the NEPLAN tool of power systems simulation.

HUT‐based method for structural reliability considering the non‐normal and unknown distributions

AbstractStructural reliability analysis involving the non‐normal random variables are commonly encountered in practical engineering. Furthermore, some random variables are often unknown probability distributions, and the probabilistic characteristic of these variables may be expressed using only statistical moments. In this contribution, an efficient algorithm for structural reliability analysis considering the random variables with non‐normal and unknown probability distributions is proposed, in which high‐order unscented transformation (HUT) and fourth‐moment methods are adopted to efficiently evaluate the structural reliability index. By introducing the Rosenblatt transformation and third‐order polynomial transformation (TPT) techniques, random variables with non‐normal or unknown distributions are transformed into standard normal distributions. Accordingly, the performance functions can be reconstructed using standard normal random variables, and HUT is then employed to efficiently estimate the first four moments (i.e., mean, standard deviation, skewness, and kurtosis) of the performance function. Based on the Hermite polynomial model, the complete expression of fourth‐moment reliability index is derived for estimating the reliability index using the first four moments of the performance function. The efficiency and accuracy of the proposed algorithm are demonstrated through six numerical examples. The results show that the proposed algorithm is applicable to estimating reliability index of both static and dynamic reliability problems involving explicit and implicit performance functions.