Timothy Dodwell

The Turing Institute

Markov chain Monte CarloAlgorithmEngineeringFinite element methodConsolidation (soil)Nonlinear systemComposite materialAerospaceMaterials scienceMonte Carlo methodOverburden pressureGeometryDamage toleranceUncertainty quantificationMathematicsComputer scienceMechanicsStructural engineeringPotential energyBuckling

65Publications

13H-index

402Citations

Publications 55

Accelerating uncertainty quantification of groundwater flow modelling using a deep neural network proxy

#1Mikkel B. LykkegaardH-Index: 1

#2Timothy DodwellH-Index: 13

Last. David MoxeyH-Index: 1

view all 3 authors...

Quantifying the uncertainty in model parameters and output is a critical component in model-driven decision support systems for groundwater management. This paper presents a novel algorithmic approach which fuses Markov Chain Monte Carlo (MCMC) and Machine Learning methods to accelerate uncertainty quantification for groundwater flow models. We formulate the governing mathematical model as a Bayesian inverse problem, considering model parameters as a random process with an underlying probability...

Operational Variables for Improving Industrial Wind Turbine Yaw Misalignment Early Fault Detection Capabilities Using Data-Driven Techniques

#1Ravi Kumar Pandit (NIST: National Institute of Standards and Technology)H-Index: 8

#1Ravi Pandit (NIST: National Institute of Standards and Technology)

Last. Tim Dodwell (CU: University of Colorado Boulder)

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Offshore wind turbines are complex pieces of engineering and are, generally, exposed to harsh environmental conditions that are making them to susceptible unexpected and potentially catastrophic damage. This results in significant down time, and high maintenance costs. Therefore, early detection of major failures is important to improve availability, boost power production and reduce maintenance costs. This paper proposes a SCADA data based Gaussian Process (GP) (a data-driven, machine learning ...

Uncertainty quantification of spatially uncorrelated loads with a reduced-order stochastic isogeometric method

#1Chensen Ding (University of Exeter)

#1Chensen Ding (University of Exeter)

Last. Stéphane Bordas (Cardiff University)H-Index: 65

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This work models spatially uncorrelated (independent) load uncertainty and develops a reduced-order Monte Carlo stochastic isogeometric method to quantify the effect of the load uncertainty on the structural response of thin shells and solid structures. The approach is tested on two demonstrative applications of uncertainty, namely, spatially uncorrelated loading, with (1) Scordelis–Lo Roof shell structure, and (2) a 3D wind turbine blade. This work has three novelties. Firstly, the research mod...

#1Giles Hunt (UoB: University of Bristol)

#1Giles W HuntH-Index: 33

Last. Katia Bertoldi

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Novel design and analysis of generalized FE methods based on locally optimal spectral approximations.

#1Chupeng MaH-Index: 1

#2Robert ScheichlH-Index: 27

Last. Timothy DodwellH-Index: 13

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In this paper, the generalized finite element method (GFEM) for solving second order elliptic equations with rough coefficients is studied. New optimal local approximation spaces for GFEMs based on local eigenvalue problems involving a partition of unity are presented. These new spaces have advantages over those proposed in [I. Babuska and R. Lipton, Multiscale Model.\;\,Simul., 9 (2011), pp.~373--406]. First, in addition to a nearly exponential decay rate of the local approximation errors with ...

#1Giles W HuntH-Index: 33

#2Rainer Groh (UoB: University of Bristol)H-Index: 16

Last. Timothy Dodwell (University of Exeter)H-Index: 13

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#1Timothy Dodwell (University of Exeter)H-Index: 13

#1T.J. Dodwell (The Turing Institute)

Last. Robert Scheichl (University of Bath)H-Index: 27

view all 6 authors...

Abstract By adopting a Multilevel Monte Carlo (MLMC) framework, this paper shows that only a handful of costly fine scale computations are needed to accurately estimate statistics of the failure of a composite structure, as opposed to the many thousands typically needed in classical Monte Carlo analyses. The paper introduces the MLMC method and provides an extension called MLMC with selective refinement to efficiently calculated structural failure probabilities. Simple-to-implement, self-adaptiv...

#1Mikkel B. Lykkegaard (University of Exeter)H-Index: 1

#2Grigorios Mingas (The Turing Institute)H-Index: 7

Last. Timothy Dodwell (University of Exeter)H-Index: 13

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Uncertainty Quantification through Markov Chain Monte Carlo (MCMC) can be prohibitively expensive for target probability densities with expensive likelihood functions, for instance when the evaluation it involves solving a Partial Differential Equation (PDE), as is the case in a wide range of engineering applications. Multilevel Delayed Acceptance (MLDA) with an Adaptive Error Model (AEM) is a novel approach, which alleviates this problem by exploiting a hierarchy of models, with increasing comp...

#1Giles W HuntH-Index: 33

#2Rainer Groh (UoB: University of Bristol)H-Index: 16

Last. Timothy Dodwell (University of Exeter)H-Index: 13

view all 3 authors...

Numerical results for the axially compressed cylindrical shell demonstrate the post-buckling response snaking in both the applied load and corresponding end-shortening. Fluctuations in load, associ...

#1Amir Hosein Sakhaei (UKC: University of Kent)H-Index: 8

#2Samuel Erland (University of Exeter)H-Index: 1

Last. Timothy Dodwell (University of Exeter)H-Index: 13

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Abstract A new three-dimensional, finite deformation Cosserat continuum model for the elastic response of uncured carbon fibre composites is presented. The new composite process model captures the bending contribution of bundles of fibres at the microscale within a mesoscale continuum description of a composite ply. This is achieved by introducing higher-order, independent rotational degrees of freedom into the continuum formulation. This paper demonstrates the inclusion of such mechanics is ess...

Close Researchers

Giles W Hunt

H-index : 33

Richard Butler

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Chris Budd

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Mark A. Peletier

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Samuel Erland

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Andrew Rhead

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Anne Reinarz

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Timothy Fletcher

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Robert Scheichl

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