Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

Flow uncertainty is commonly encountered in turbomachinery. To mitigate the negative
effects caused by the flow uncertainty, a framework coupled with adaptive polynomial chaos …

In this paper, a novel random forest (RF)-based multifidelity machine learning (ML) algorithm
to predict the high-fidelity Reynolds-averaged Navier-Stokes (RANS) flow field is proposed …

The stochastic variation of inlet flow is one common uncertainty in turbomachines, the
performance impact of which requires to be quantified and taken into account in the design …

Uncertainty is common in the life cycle of an aircraft, and has great influence on flight quality.
Among the approaches of uncertainty quantification, the polynomial chaos expansion (PCE) …

The possibility-based design optimization (PBDO) model under the fuzzy uncertainty can
provide the optimal design parameters by taking a trade-off between the performance and …

A improved efficient uncertainty quantification analysis framework is proposed by the
combination of sparse Polynomial Chaos Expansion (PCE) and Universal Kriging (UK) …

In the present context, the use of fossil fuels is rising rapidly, leading to a further rise in the
level of air pollution. Due to all these environmental challenges, there is a need for some …

Failure credibility can quantify safety degree of structure under fuzzy uncertainty, and fuzzy
first-order and second-moment (FFOSM) method is an efficiently and accurately analytical …

Purpose This study aims to get a better understanding of the impact of streamlined high-
speed trains (HSTs) with geometric uncertainty on aerodynamic performance, as well as the …