Reliability-based Design Optimization Strategy Research Articles

A reliability-based design optimization strategy using quantile surrogates by improved PC-kriging

In recent years, the surrogate-assisted reliability-based design optimization (RBDO) methods have been continuously developed, and numerous advanced optimization strategies have boosted efficiency and accuracy. However, ensuring sufficient accuracy and feasibility at the optimal is still a challenge. In order to achieve a well-balanced between efficiency, accuracy, and optimal feasibility, in this work, a RBDO strategy using quantile surrogates by improved PC-Kriging model is proposed. The novelty of the proposed method lies in the following main aspects: Firstly, an improved learning function has been developed to significantly enhance the convergence efficiency during the construction of the PC-Kriging model. Secondly, in the RBDO analysis process, a novel "MP+EI" combination point addition strategy is adopted to enhance the approximation of the surrogate model to the optimum of the objective function. It can further improve optimization efficiency and accuracy. On the basis of the rough probability constrained surrogate model established by the global enrichment strategy, a local refinement strategy is introduced to guarantee the accuracy of the quantile evaluation of the probability constrained surrogate model for each iteration solution during the optimization process. Finally, the proposed method is validated by three typical RBDO test examples and one engineering application example.

An effective active learning strategy for reliability-based design optimization under multiple simulation models

This paper proposes an effective active learning strategy for reliability-based design optimization (RBDO) problems in which the constraint functions are acquired from multiple simulation models. To achieve this goal, a new active learning function (ALF) is derived by estimating the increased reliability of active constraint functions after adding one point to the train points of constraint functions in each simulation model. The proposed ALF distinguishes possibly active constraint functions that seem active near the current optimum and considers how the constraint functions are active. In the proposed RBDO method, a Kriging model is iteratively updated by adding the best point to the train points of constraint functions included in the crucial simulation model until the optimum converges and the Kriging model is sufficiently accurate. The best point and the crucial simulation model are obtained by comparing the proposed ALF. The ALF is further modified to apply to problems where the cost of each simulation model is different. To verify the effectiveness of the proposed method, two numerical and one engineering examples are analyzed. The results show that the proposed method efficiently and accurately obtains the RBDO optimum involving multiple simulation models, regardless of simulation cost.

A reliability-based design and optimization strategy using a novel MPP searching method for maritime engineering structures

PurposeIn order to solve the problems faced by First Order Reliability Method (FORM) and First Order Saddlepoint Approximation (FOSA) in structural reliability optimization, this paper aims to propose a new Reliability-based Design Optimization (RBDO) strategy for offshore engineering structures based on Original Probabilistic Model (OPM) decoupling strategy. The application of this innovative technique to other maritime structures has the potential to substantially improve their design process by optimizing cost and enhancing structural reliability.Design/methodology/approachIn the strategy proposed by this paper, sequential optimization and reliability assessment method and surrogate model are used to improve the efficiency for solving RBDO. The strategy is applied to the analysis of two marine engineering structure cases of ship cargo hold structure and frame ring of underwater skirt pile gripper. The effectiveness of the method is proved by comparing the original design and the optimized results.FindingsIn this paper, the proposed new RBDO strategy is used to optimize the design of the ship cargo hold structure and the frame ring of the underwater skirt pile gripper. According to the results obtained, compared with the original design, the structure of optimization design has better reliability and stability, and reduces the risk of failure. This optimization can also better balance the relationship between performance and cost. Therefore, it is recommended for related RBDO problems in the field of marine engineering.Originality/valueIn view of the limitations of FORM and FOSA that may produce multiple MPPs for a single performance function, the new RBDO strategy proposed in this study provides valuable insights and robust methods for the optimization design of offshore engineering structures. It emphasizes the importance of combining advanced MPP search technology and integrating SORA and surrogate models to achieve more economical and reliable design.

Efficient local adaptive Kriging approximation method with single-loop strategy for reliability-based design optimization

The classical reliability-based design optimization (RBDO) methods, due to the ever increasing complexity of designs and contingent engineering situations, incur more and more intricate numerical models. The consequent intractable computational intensity has imposed great challenges for practical application of RBDO. Thus, the surrogate-based RBDO methods, and especially those using Kriging model, have received widespread attention recently for their superior computational efficiency without compromise of computational accuracy. On the other hand, the Kriging-based RBDO methods still ensue high computational demands for complex RBDO problems and especially those with implicit objective and constraint functions. To enhance the computational efficiency of the Kriging-based RBDO methods, this study proposes an efficient local adaptive Kriging approximation method with single-loop strategy (LAKAM-SLS), in which Kriging models are employed to replace both the objective and constraint functions. Two different criteria are developed to identify whether Kriging model of performance function in probability constraint is active in each iteration. If the criterion is satisfied, approximate inverse most probable point (IMPP) derived by combining sing-loop approach with Kriging will be used to refine the corresponding Kriging model. For the objective function, its Kriging model is sequentially refined through points selected by a newly proposed learning function from the sample pool generated by taking the optimal solution from each iteration as the sampling center. Four benchmark examples and an application example of head pressure shell are implemented to evaluate the computational performance of the currently proposed LAKAM-SLS against other Kriging-based RBDO methods. The result comparison shows that the currently proposed LAKAM-SLS substantially reduces the computational expense to a relatively low level.

Enhanced similarity-based metamodel updating strategy for reliability-based design optimization

Metamodels are becoming increasingly popular for handling large-scale optimization problems in product development. Metamodel-based reliability-based design optimization (RBDO) helps to improve the computational efficiency and reliability of optimal design. However, a metamodel in engineering applications is an approximation of a high-fidelity computer-aided engineering model and it frequently suffers from a significant loss of predictive accuracy. This issue must be appropriately addressed before the metamodels are ready to be applied in RBDO. In this article, an enhanced strategy with metamodel selection and bias correction is proposed to improve the predictive capability of metamodels. A similarity-based assessment for metamodel selection (SAMS) is derived from the cross-validation and similarity theories. The selected metamodel is then improved by Bayesian inference-based bias correction. The proposed strategy is illustrated through an analytical example and further demonstrated with a lightweight vehicle design problem. The results show its potential in handling real-world engineering problems.

Reliability based optimal design of a helicopter considering annual variation of atmospheric temperature

This study presents the Reliability Based Design Optimization (RBDO) of a helicopter, used in order to guarantee target performances for a large variation of annual atmospheric temperatures. To this end, analytic methods — statistical and empirical equations of aerodynamics, structure, propulsion and so on — are synthetically coupled within the multidisciplinary design analysis tool of the conceptual helicopter. Additionally, an atmospheric temperature model for annual air temperature variation is constructed in bimodal shape by considering 10 years worth of day-averaged air temperature data provided by the Korea Meteorological Administration. Based on this analysis tool and the annual atmospheric temperature model, the RBDO of a helicopter is performed to minimize maximum takeoff gross weight, with the helicopter rotor configuration parameters as a design variable. A Monte-Carlo simulation is used to accurately evaluate the reliabilities of endurance and range. This RBDO strategy is applied to a 22,000lb class medium utility helicopter, and the results are compared with those of a deterministic design optimization (DO) using constant air temperature and baseline helicopter. Through comparison of the results obtained from RBDO and those from the deterministic design optimization, it can be confirmed that the optimal design of RBDO results in greater improvements in performance over the baseline, for a wide range of operating air temperatures, than baseline helicopter and optimal shape of DO using constant air temperature. Therefore, in designing a helicopter to be operated in temperate climatic regions that show large variation in air temperature, such as Korea, China, the U.S.A, and so on, it is important to perform RBDO with reasonable annual air temperature models constructed from well-known and reliable data.

Reliability-based design optimization of an FPSO riser support using moving least squares response surface meta-models

The paper deals with the reliability-based design optimization (RBDO) of a riser support installed on a floating production storage and offloading (FPSO) unit under operation, extreme, damaged, and one line failure cases and installation loading conditions. The optimization problem is formulated such that probabilistic thickness variables described with random characteristics are determined by minimizing the weight of the riser support structure subjected to stress constraints for the given target reliability. The initial design model is generated based on actual FPSO riser support specifications. The finite element analysis is conducted using NASTRAN, and the probabilistic optimal solutions are obtained via the moving least squares method in the context of RBDO using a response surface meta-model. For the meta-modeling of the inequality constraint functions of stresses, a constraint-feasible moving least squares method (CF-MLSM) is adopted in the present study. The CF-MLSM has been shown to ensure constraint feasibility regardless of the nonlinearity of the constraint function, the feasible bounds, and the random characteristics during the meta-model-based RBDO process. The solution results from the proposed RBDO strategy present improved design performances under various riser operating conditions.

Reliability-Based Wing Design Optimization Using Trust Region-Sequential Quadratic Programming Framework

The computational efficiency of the reliability-based design optimization(RBDO) can be a critical issue when high-fidelity simulation methods are used. An efficient RBDO framework is presented that utilizes a single-loop RBDO algorithm in which reliability analysis and optimization are conducted in a sequential manner by approximating the limit-state function. A further improvement in computational efficiency is achieved by applying the fixed sensitivity of the limit-state function to the reliability analysis and the equivalent deterministic constraint calculation. To guarantee the convergence of the single loop and the fixed sensitivity algorithm, a trust regionsequential quadratic programming RBDO strategy, is developed. The proposed framework validates low-fidelity approximation models in the trust region radius. The framework is examined by solving an analytical test problem to show that the proposed strategy has the computational efficiency over existing methods while maintaining accuracy. The proposed strategy is applied to a three-dimensional aerodynamic wing design problem to get improved results satisfying probabilistic constraints.