Reliability-based Multidisciplinary Optimization Research Articles

Systematic Reliability-Based Multidisciplinary Optimization by Parallel Adaptive Importance Candidate Region

Reliability-based design optimization (RBDO) has become a prevalent design for aeronautical and aerospace engineering. The main problem is that it is impractical in complex cases with multi-failure regions, especially in multi-objective optimization. The active learning method can obtain an adaptive size of samples to get a relatively acceptable accuracy. The problem of RBDO using the traditional active learning Kriging (ALK) method is that the design space is generally still and only one training point is selected, which is not reasonable based on the concept of importance sampling and parallel calculation. As a consequence, the accuracy improvement is limited. In this paper, we investigate the method of obtaining an optimal size of design and reliability to assess space in parallel, simultaneously. A strategy of parallel adaptive candidate (PAIC) region with ALK is proposed and a sequential optimization and reliability assessment (SORA) method is modified to efficiently improve the accuracy. Importance sampling is used as a demonstration for the modified SORA with more accuracy. The method is then verified using mathematical cases and a scooping system of an amphibious aircraft.

Reliability modeling and optimization of die-casting existing epistemic uncertainty

Die-casting is a typical multidisciplinary system, in which there are many disciplines involved such as hydrodynamics, heat transfer and elastic-plastic mechanics and their coupled relations are intricate. Moreover, there are inherently epistemic uncertainties in die-casting. This paper aims at reliability modeling and optimization of die-casting. Firstly, finite element analysis is applied to simulate the process of die-casting, and the quadratic response surface is used to represent the multidisciplinary optimization model of die-casting, in which the evidence theory is used to represent the epistemic uncertainty. Then, a reliability-based multidisciplinary optimization (RBMDO) procedure for die-casting is proposed. The results show the RBMDO procedure possesses high computational efficiency in optimizing multidisciplinary system with epistemic uncertainty.

Reliability Based Multidisciplinary Optimization of Aeroelastic Systems with Structural and Aerodynamic Uncertainties

A reliability-based multidisciplinary optimization framework is constructed by coupling high-fidelity commercial solvers for aeroelastic analysis and an in-house code developed for reliability analysis. The finite volume-based flow solver Fluent is used to solve inviscid three-dimensional Euler equations, whereas three-dimensional solid models are updated using Catia parametrically. A mesh-based parallel code coupling interface (MPCCI), is used to exchange the pressure and displacement information between Fluent and Abaqus to perform a loosely coupled aeroelastic analysis. The optimization criteria include both deterministic and probabilistic constraints with both structural and aerodynamic uncertainties, such as in yield strength, Mach number, and angle of attack. To evaluate the probability of failure for the probabilistic constraints a first-order reliability analysis method, the Hasofer–Lind iteration method, is implemented in MATLAB to compute the most probable failure point solution. The integrated framework is validated with structural problems and then extended to more realistic wing configurations with aeroelastic criteria. The presented reliability-based multidisciplinary optimization process is proven to be fully automatic, modular, and practical, which could find potential applications in industrial problems.

Reliability-based multidisciplinary optimization for aircraft wing design

Aircraft wing design typically involves multiple disciplines such as aerodynamics and structure. Multidisciplinary design optimization (MDO) has been recently used to deal with the multidisciplinary efforts in wing design. When reliability is considered, MDO for the wing design becomes much more computationally intensive. To improve the efficiency, the strategy of using Sequential Optimization and Reliability Assessment (SORA) in MDO is proposed with the application of a simplified wing design problem. The overall reliability-based MDO is decomposed into sequential cycles of deterministic MDO and reliability analysis. The reliability analysis is therefore decoupled from the MDO loop, and the number of reliability analyses is then reduced significantly. It is shown that the use of the SORA method is efficient through the demonstration of a light aircraft wing design problem.

Integrating System-Level and Component-Level Designs Under Uncertainty

A probabilistic optimization methodology for aerospace vehicle design is developed that takes into account coupling between system-level and component-level design requirements. The proposed methodology represents the system-component design as a reliability-based multidisciplinary optimization problem. The system design optimizes the geometry of a reusable launch vehicle for minimum weight while satisfying aerodynamic constraints. The component design relates to the structural sizing of vehicle components, in this case a liquid-hydrogen tank. The optimization formulation includes probabilistic constraints, which are evaluated using the limit state-based reliability analysis methodology. The system and component designs are linked through stochastic state variables relating to vehicle geometry and component weight, and the optimization at both levels is pursued. Finally, the reliability-based multidisciplinary optimization is solved using an efficient decoupled approach.

Reliability-Based Optimization Considering Manufacturing and Operational Uncertainties

This paper presents approaches for integrating multidisciplinary optimization and probabilistic methods to perform reliability-based multidisciplinary optimization. The approaches are built into a framework that allows solution of optimization problems, wherein system parameters including dimensional tolerances, material properties, boundary conditions, loads, and model predictions are uncertain or variable. This approach directly supports quality engineering because it allows engineers to specify manufacturing tolerances required to achieve the desired product reliability, and it results in robust designs that are optimal over the range of variable conditions because it considers uncertainties during the optimization process. The basic reliability-based multidisciplinary optimization methodology has been demonstrated to design engine components, aircraft lap joints, and transport aircraft wings. Herein this methodology is reviewed and then the focus is on demonstrating a new framework that makes it possible to use these methods with commercial CAD/CAE tools and support commercial shape parameterization to enable shape optimization and consideration of manufacturing uncertainties.