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Matrix-free aerostructural optimization of aircraft wings

机译:飞机机翼的无矩阵航空结构优化

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摘要

In structural optimization subject to failure constraints, computing the gradients of a large number of functions with respect to a large number of design variables may not be computationally practical. Often, the number of constraints in these optimization problems is reduced using constraint aggregation at the expense of a higher mass of the optimal structural design. This work presents results of structural and coupled aerodynamic and structural design optimization of aircraft wings using a novel matrix-free augmented Lagrangian optimizer. By using a matrix-free optimizer, the computation of the full constraint Jacobian at each iteration is replaced by the computation of a small number of Jacobian-vector products. The low cost of the Jacobian-vector products allows optimization problems with thousands of failure constraints to be solved directly, mitigating the effects of constraint aggregation. The results indicate that the matrix-free optimizer reduces the computational work of solving the optimization problem by an order of magnitude compared to a traditional sequential quadratic programming optimizer. Furthermore, the use of a matrix-free optimizer makes the solution of large multidisciplinary design problems, in which gradient information must be obtained through iterative methods, computationally tractable.
机译:在受故障约束的结构优化中,计算大量函数相对于大量设计变量的梯度可能在计算上不可行。通常,使用约束聚合来减少这些优化问题中的约束数量,但要以更高质量的最优结构设计为代价。这项工作介绍了使用新型无矩阵增强拉格朗日优化器对飞机机翼进行结构,空气动力学和结构设计优化的结果。通过使用无矩阵优化器,每次迭代时完全约束雅可比计算的计算将被少量雅可比矢量乘积的计算所代替。雅可比向量积的低成本可直接解决具有数千个故障约束的优化问题,从而减轻约束聚合的影响。结果表明,与传统的顺序二次规划优化器相比,无矩阵优化器将解决优化问题的计算量减少了一个数量级。此外,使用无矩阵优化器可以解决大型多学科设计问题,在这些问题中,必须通过迭代方法获得梯度信息,并且计算上很容易做到。

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