Propagating and visualising parametric uncertainties conditioned by modelling
Lead Research Organisation:
Imperial College London
Department Name: Aeronautics
Abstract
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Publications
Pepper N
(2019)
Meta-modeling on detailed geography for accurate prediction of invasive alien species dispersal.
in Scientific reports
Pepper N
(2021)
Data fusion for Uncertainty Quantification with Non-Intrusive Polynomial Chaos
in Computer Methods in Applied Mechanics and Engineering
Pepper N
(2019)
Multiscale Uncertainty Quantification with Arbitrary Polynomial Chaos
in Computer Methods in Applied Mechanics and Engineering
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/R511961/1 | 30/09/2017 | 30/03/2023 | |||
| 2091447 | Studentship | EP/R511961/1 | 30/09/2017 | 29/09/2021 | Nicholas Pepper |
| Description | Designs of new engineering products must yield good performance in a wide variety of operating conditions. The performance of these designs are stochastic due to irreducible uncertainties present in the system. To address this, Uncertainty Quantification (UQ) techniques are used to evaluate the performance of candidate designs. As part of this award we developed several methods for assisting engineers with decision making in the early stages of the design process. At this stage, the performance of candidate designs are assessed using computational models and experimental data is typically scarce or unavailable. In particular, we address three early design challenges: how to efficiently propagate uncertainty through computational models at multiple scales; how to infer missing data from scarce experimental data using computational models; and how scarce experimental measurements of a quantity can be leveraged using computational simulations (a problem referred to as data fusion). The methods combine non-intrusive Polynomial Chaos (NIPC), probabilistic equivalence and the Maximum Entropy Principle and are applied to several test cases involving missing data in turbomachinery and composite structures. In addition to developing methods based on polynomial chaos, we have also researched how machine learning techniques may be applied to make these algorithms more scalable. For instance, we have developed a knowledge based neural network (KBaNN) method as an alternative formulation for data fusion problems. KBaNNs share the advantages of other neural networks, having good approximation properties; they can also handle scarce training sets efficiently. Finally, we are collaborating with NASA on an adaptive method for reliability analysis that uses machine learning to learn the boundary between the regions of a design space where a design is feasible and where it will suffer failure. While still in the early stages of development, our algorithm shows promising results when compared to existing methods in the literature. |
| Exploitation Route | There are several ways in which the novel methods developed as part of this award may be taken forward. The first task is to raise awareness of the outcomes of the award. From an academic standpoint, 5 papers have been published or accepted for publishing in journals and conferences, with a further 2 journal papers under review. We plan to present the results of our current collaboration with NASA at the ESREL conference in September. The second task is to apply the methods that have been developed as part of this award to more challenging industrial test cases, that can demonstrate the benefits of the algorithms to a wider audience that is perhaps not expert in Uncertainty Quantification. To this end, the results of the PhD have been presented to an internal audience at Airbus in two seminars, with discussions scheduled about possible test cases. |
| Sectors | Aerospace Defence and Marine Manufacturing including Industrial Biotechology Transport |