Stochastic multidisciplinary improvement - Beyond optimization

The paper presents a formulation for multidisciplinary design optimization of vessels, subject to uncertain operating conditions. The formulation couples the multidisciplinary design analysis with the Bayesian approach to decision problems affected by uncertainty. In the present context, the design specifications are no longer given in terms of a single operating design point, but in terms of probability density function of the operating scenario. The optimal configuration is that which maximizes the performance expectation over the uncertain parameters variation. In this sense, the optimal solution is "robust" within the stochastic scenario assumed. Theoretical and numerical issues are addressed and numerical results in the hydroelastic optimization of a keel fin of a sailing yacht are presented.

Section: Introductionmentioning

confidence: 99%

Hydroelastic optimization of a keel fin of a sailing boat: a multidisciplinary robust formulation for ship design

Diez

Peri

Fasano

et al. 2012

“…It is well known that an optimal design can be very sensitive to small changes in the design parameters, as well as those in the operational environment (Marczyk 2000). The uncertainty in the input parameters results in the variability in the output performance parameters that may lead to performance degradation or even to failure when certain failure-related constraints are violated.…”

Section: Robustness Assessmentmentioning

confidence: 99%

“…The overall process involved 1000 optimisation steps and the total computing time was about 5 days. It has been suggested (Marczyk 2000) that one way to improve the overall computational time would be to use the method of stochastic multidisciplinary improvement. In this approach, a set of N random samples is generated around the nominal design.…”

Section: Robustness Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

CAD based shape optimization for gas turbine component design

Brujić

Ristić

Mattone

et al. 2009

In order to improve product characteristics, engineering design makes increasing use of Robust Design and Multidisciplinary Design Optimisation. Common to both methodologies is the need to vary the object's shape and to assess the resulting change in performance, both executed within an automatic loop. This shape change can be realised by modifying the parameter values of a suitably parameterised Computer Aided Design (CAD) model. This paper presents the adopted methodology and the achieved results when performing optimisation of a gas turbine disk. Our approach to hierarchical modelling employing design tables is presented, with methods to ensure satisfactory geometry variation by commercial CAD systems. The conducted studies included stochastic and probabilistic design optimisation. To solve the multi-objective optimisation problem, a Pareto optimum criterion was used. The results demonstrate that CAD centric approach enables significant progress towards automating the entire process while achieving a higher quality product with the reduced susceptibility to manufacturing imperfections.

“…Consequently, optimal designs may be non-robust and quite sensitive to parameter variabilities, and are, therefore, infeasible. Some authors even believe that optimization is actually just the opposite of robustness, see [18]. As reliability is nevertheless mandatory, engineers have to look for robust designs which avoid unexpected deviations from the nominal performance, see [23].…”

Section: Introductionmentioning

confidence: 99%

On the computation of solution spaces in high dimensions

Graff

Harbrecht

Zimmermann

2016

Abstract. A stochastic algorithm that computes box-shaped solution spaces for nonlinear, high-dimensional and noisy problems with uncertain input parameters has been proposed in [35]. This paper studies in detail the quality of the results and the e ciency of the algorithm. Appropriate benchmark problems are specified and compared with exact solutions that were derived analytically. The speed of convergence decreases as the number of dimensions increases. Relevant mechanisms are identified that explain how the number of dimensions a↵ects the performance. The optimal number of sample points per iteration is determined in dependence of the preference for fast convergence or a large volume.