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 aeroelastic optimization of a composite aircraft wing via fluid-structure interaction of high fidelity solvers Abstract. We consider reliability based aeroelastic optimization of a AGARD 445.6 composite aircraft wing with stochastic parameters. Both commercial engineering software and an inhouse reliability analysis code are employed in this high-fidelity computational framework. Finite volume based flow solver Fluent is used to solve 3D Euler equations, while Gambit is the fluid domain mesh generator and Catia-V5-R16 is used as a parametric 3D solid modeler. Abaqus, a structural finite element solver, is used to compute the structural response of the aeroelastic system. Mesh based parallel code coupling interface MPCCI-3.0.6 is used to exchange the pressure and displacement information between Fluent and Abaqus to perform a loosely coupled fluid-structure interaction by employing a staggered algorithm. To compute the probability of failure for the probabilistic constraints, one of the well known MPP (Most Probable Point) based reliability analysis methods, FORM (First Order Reliability Method) is implemented in Matlab. This in-house developed Matlab code is embedded in the multidisciplinary optimization workflow which is driven by Modefrontier. Modefrontier 4.1, is used for its gradient based optimization algorithm called NBI-NLPQLP which is based on sequential quadratic programming method. A pareto optimal solution for the stochastic aeroelastic optimization is obtained for a specified reliability index and results are compared with the results of deterministic aeroelastic optimization. IntroductionThe challenging task of aircraft design requires a systematic approach which can couple several engineering disciplines in the design process. Today, this task can be more efficiently and accurately performed by employing high fidelity tools in collaboration with multidisciplinary optimization techniques. The improvement of multidisciplinary optimal design depends on the fidelity level of the individual analysis used inside each discipline, how effectively the coupling method handles interaction between the disciplines and also the computational efficiency and accuracy of the algorithm used to solve the optimization problem. As being one of the most important criteria of aircraft design, aeroelasticity requires high fidelity level solutions for the fluid-structure interaction phenomena for real complex geometries. Besides, the reliability level of the proposed design is an important criteria in decision making for production of an aircraft. Thus, recently, reliability based aeroelastic optimization of aircraft structures has
Electronic devices must be effectively cooled for long-term reliability and safe operation. It is essential to determine operating conditions and optimum dimensions of cooling devices in terms of device weight, space, cost, and sound limits. Plate Fin Heat Sinks (PFHSs) are frequently used for cooling electronic devices and optimum thermal designs of PFHSs are explored in this study using teaching-learning-based-optimization algorithm where entropy generation minimization, profit factor maximization, base plate temperature excess minimization, total mass minimization, and total volume minimization are the objective functions of the constrained single-objective optimization problems. For further investigations of the entropy generation minimization method, three different optimization problems are also studied: minimization of thermal resistance, minimization of pressure drop, and minimization of pumping power. Each optimization problem is subjected to a constraint, namely temperature excess of base plate temperature should be lower than 10K. Four optimization variables are considered which are the number of plate fins, free-stream velocity, the thickness of the fin and height of the fin. Optimum configurations belonging to the different optimization problems are compared and the effect of each optimization variable on the objective functions is discussed in detail. It is found that one can obtain optimum operating conditions and geometrical dimensions of the PFHSs according to the design objective i.e. minimum mass requirement, space limitation, minimum base plate requirement, etc. As a result, the optimum designs of the studied cases are different which are superior to each other in terms of design targets.
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