Capturing the Random Changes in Process Parameters in the Stochastic Grey-box Model of the Flow-Front Dynamics

Rishi Relan, Michael Nauheimer, Henrik Madsen

Research output: Contribution to journalConference articleResearchpeer-review

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Abstract

With the continuously increasing size of the wind turbine blades, the complexity of the casting process and the risk of failures has also increased. The IntegralBlades® vacuum assisted resin transfer moulding (VATRM) production process at the Siemens Gamesa Renewable Energy facility in Aalborg, Denmark, does not permit the visual inspection of the process. Hence a sensor system (possibly virtual) for process control and monitoring is highly prized. Furthermore, the effect of material handling, variations in permeability of the casting media and the material (epoxy) properties affect the outcome of the casting process. Therefore, it is necessary to analyse the effect of such variations at an early stage of the design process (e.g. during the simulations) of such a sensor system. Therefore, in this paper, we first describe an effective method to simulate the random changes in the permeability and viscosity in high-dimensional partial differential equations based model of the fluid flow. Next, a low-dimensional grey-box (cyber-physical) spatiotemporal model is proposed to capture the effect of random change in permeability and viscosity during the progression of the flow-front. Finally, a numerical case-study is presented demonstrating the effectiveness of the proposed methodology.

Original languageEnglish
Book seriesIFAC-PapersOnLine
Volume52
Issue number10
Pages (from-to)31-36
Number of pages6
ISSN2405-8963
DOIs
Publication statusPublished - 1 Jan 2019
Event13th IFAC Workshop on Intelligent Manufacturing Systems, IMS 2019 - Oshawa, Canada
Duration: 12 Aug 201914 Aug 2019

Conference

Conference13th IFAC Workshop on Intelligent Manufacturing Systems, IMS 2019
CountryCanada
CityOshawa
Period12/08/201914/08/2019

Keywords

  • Grey-box modelling
  • Maximum likelihood estimation
  • Numerical simulations
  • Partial differential equations
  • Stochastic differential equations
  • Wind turbines

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