A probabilistic framework for forward model-driven SHM

A challenge for many structural health monitoring (SHM) technologies is the lack of available damage state data. This problem arises due to cost implications of damaging a structure in addition to issues associated with the feasibility and safety of testing a structure in multiple damage scenarios. Many data-driven approaches to SHM are successful when the appropriate damage state data is available, however the problem of obtaining data for various damage states of interest restricts their use in industry. Forward model-driven approaches to SHM seek to aid this challenge. This methodology uses validated physical models to generate predictions of the system at different damage states, providing machine learning strategies with training data, to infer decision bounds. An ideal forward model-driven SHM framework is one in which one or more physical models are able to produce predictions that are statistically representative of data obtained from the physical structure. Validation of these physical models requires observational data. As a result, validation is performed on a component or sub-system level where damage state data can be more easily obtained. This methodology requires the combination of several low-level physical models via a multi-level uncertainty integration technique. This paper outlines such a framework using uncertainty quantification technologies and statistical methods for combining low-level probabilistic models whilst accounting of discrepancies that may occur in interactions with other low-level models. The method contains several statistical techniques for accounting for model discrepancies that may occur at any point throughout the modelling process. Model discrepancies arise due to missing physics or simplifications and result in the model deviating from the observed physics even when the ‘true’ parameters of the model are known. By accounting for model discrepancies throughout the framework the approach allows for further insight into model form errors whilst also improving the techniques ability to produce statistically representative predictions across damage states. The paper presents the key stages highlighting the relevant technologies and application considerations. Additionally, a discussion of integration with current data-driven approaches and the appropriate machine learning tools is given for a forward model-driven SHM approach

On digital twins, mirrors and virtualisations

A powerful new idea in the computational representation of structures is that of the digital twin. The concept of the digital twin emerged and developed over the last two decades, and has been identified by many industries as a highly-desired technology. The current situation is that individual companies often have their own definitions of a digital twin, and no clear consensus has emerged. In particular, there is no current mathematical formulation of a digital twin. A companion paper to the current one will attempt to present the essential components of the desired formulation. One of those components is identified as a rigorous representation theory of models, how they are validated, and how validation information can be transferred between models. The current paper will outline the basic ingredients of such a theory, based on the introduction of two new concepts: mirrors and virtualisations. The paper is not intended as a passive wish-list; it is intended as a rallying call. The new theory will require the active participation of researchers across a number of domains including: pure and applied mathematics, physics, computer science and engineering. The paper outlines the main objects of the theory and gives examples of the sort of theorems and hypotheses that might be proved in the new framework

Experimental characterisation of a novel phase change material heat storage unit for state-of-charge estimation

Energy storage methods will be a critical part of the future energy supply system, as demand is increased by electrification of domestic heat and take-up of renewable generation increases the variability of supply. Domestic storage units are likely to be key to this transition, and this paper will present investigations into a unit for storage of domestic heat. A phase change material (PCM) heat storage unit has been developed that has the potential to provide effective heat storage facility in a domestic capacity. Effective control and use of the storage units is dependent on accurate state-of-charge (SoC) estimation, tools for which are being developed and supported by data presented in this paper. A novel test bed has been developed in order to acquire data for this purpose. The rig is situated in a temperature-controlled chamber which enables charge and discharge tests at a range of ambient temperatures. Automated control of the rig is also available, which allows for repeatable tests to be carried out to gather data for model development

Digital twins: State-of-the-art future directions for modelling and simulation in engineering dynamics applications

This paper presents a review of the state-of-the-art for digital twins in the application domain of engineering dynamics. The focus on applications in dynamics, is because: (i) they offer some of the most challenging aspects of creating an effective digital twin, and (ii) they are relevant to important industrial applications such as energy generation and transport systems. The history of the digital twin is discussed first, along with a review of the associated literature; the process of synthesising a digital twin is then considered, including definition of the aims and objectives of the digital twin. An example of the asset management phase for a wind turbine is included in order to demonstrate how the synthesis process might be applied in practice. In order to illustrate modelling issues arising in the construction of a digital twin, a detailed case study is presented, based on a physical twin which is a small-scale three-storey structure. This case study shows the progression towards a digital twin highlighting key processes including: system identification, data-augmented modelling and verification and validation. Finally, a discussion of some open research problems and technological challenges is given, including: workflow, joints, uncertainty management and the quantification of trust. In a companion paper, as part of this special issue, a mathematical framework for digital twin applications is developed, and together the authors believe this represents a firm framework for developing digital twin applications in the area of engineering dynamics

On digital twins, mirrors and virtualisations: Frameworks for model verification and validation

A powerful new idea in the computational representation of structures is that of the digital twin. The concept of the digital twin emerged and developed over the last decade, and has been identified by many industries as a highly-desired technology. The current situation is that individual companies often have their own definitions of a digital twin, and no clear consensus has emerged. In particular, there is no current mathematical formulation of a digital twin. A companion paper to the current one will attempt to present the essential components of the desired formulation. One of those components is identified as a rigorous representation theory of models; most importantly, governing how they are verified and validated, and how validation information can be transferred between models. Unlike its companion, which does not attempt detailed specification of any twin components, the current paper will attempt to outline a rigorous representation theory of models, based on the introduction of two new concepts: mirrors and virtualisations. The paper is not intended as a passive wish-list; it is intended as a rallying call. The new theory will require the active participation of researchers across a number of domains including: pure and applied mathematics, physics, computer science and engineering. The paper outlines the main objects of the theory and gives examples of the sort of theorems and hypotheses that might be proved in the new framework

Combinations of PARP Inhibitors with Temozolomide Drive PARP1 Trapping and Apoptosis in Ewing's Sarcoma.

Ewing's sarcoma is a malignant pediatric bone tumor with a poor prognosis for patients with metastatic or recurrent disease. Ewing's sarcoma cells are acutely hypersensitive to poly (ADP-ribose) polymerase (PARP) inhibition and this is being evaluated in clinical trials, although the mechanism of hypersensitivity has not been directly addressed. PARP inhibitors have efficacy in tumors with BRCA1/2 mutations, which confer deficiency in DNA double-strand break (DSB) repair by homologous recombination (HR). This drives dependence on PARP1/2 due to their function in DNA single-strand break (SSB) repair. PARP inhibitors are also cytotoxic through inhibiting PARP1/2 auto-PARylation, blocking PARP1/2 release from substrate DNA. Here, we show that PARP inhibitor sensitivity in Ewing's sarcoma cells is not through an apparent defect in DNA repair by HR, but through hypersensitivity to trapped PARP1-DNA complexes. This drives accumulation of DNA damage during replication, ultimately leading to apoptosis. We also show that the activity of PARP inhibitors is potentiated by temozolomide in Ewing's sarcoma cells and is associated with enhanced trapping of PARP1-DNA complexes. Furthermore, through mining of large-scale drug sensitivity datasets, we identify a subset of glioma, neuroblastoma and melanoma cell lines as hypersensitive to the combination of temozolomide and PARP inhibition, potentially identifying new avenues for therapeutic intervention. These data provide insights into the anti-cancer activity of PARP inhibitors with implications for the design of treatment for Ewing's sarcoma patients with PARP inhibitors.Research in the M.J.G. laboratory is supported by grants from the Wellcome Trust (086357 and 102696/Z/13/Z; http://www.wellcome.ac.uk/Funding). Research in the S.P.J. laboratory is funded by Cancer Research UK Program Grant C6/A11224 (http://www.cancerresearchuk.org/funding-for-researchers/our-funding-schemes), the European Research Council (http://erc.europa.eu/funding-and-grants)and the European Community Seventh Framework Program grant agreement no. HEALTH-F2-2010-259893 (DDResponse). Core infrastructure funding was provided by Cancer Research UK Grant C6946/A14492 and Wellcome Trust Grant WT092096. S.P.J. receives a salary from the University of Cambridge, supplemented by Cancer Research UK. J.T. was funded by the European Community Seventh Framework Program grant agreement no. HEALTH-F2-2010-259893 (DDResponse). U.M. is supported by a Cancer Research UK Clinician Scientist Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the final version of the article. It first appeared from PLOS via http://dx.doi.org/10.1371/journal.pone.014098

Learning of model discrepancy for structural dynamics applications using Bayesian history matching

Calibration of computer models for structural dynamics is often an important task in creating valid predictions that match observational data. However, calibration alone will lead to biased estimates of system parameters when a mechanism for model discrepancy is not included. The definition of model discrepancy is the mismatch between observational data and the model when the 'true' parameters are known. This will occur due to the absence and/or simplification of certain physics in the computer model. Bayesian History Matching (BHM) is a 'likelihood-free' method for obtaining calibrated outputs whilst accounting for model discrepancies, typically via an additional variance term. The approach assesses the input space, using an emulator of the complex computer model, and identifies parameter sets that could have plausibly generated the target outputs. In this paper a more informative methodology is outlined where the functional form of the model discrepancy is inferred, improving predictive performance. The algorithm is applied to a case study for a representative five storey building structure with the objective of calibrating outputs of a finite element (FE) model. The results are discussed with appropriate validation metrics that consider the complete distribution

N-1 modal interactions of a three-degree-of-freedom system with cubic elastic nonlinearities

In this paper the (Formula presented.) nonlinear modal interactions that occur in a nonlinear three-degree-of-freedom lumped mass system, where (Formula presented.), are considered. The nonlinearity comes from springs with weakly nonlinear cubic terms. Here, the case where all the natural frequencies of the underlying linear system are close (i.e. (Formula presented.)) is considered. However, due to the symmetries of the system under consideration, only (Formula presented.) modes interact. Depending on the sign and magnitude of the nonlinear stiffness parameters, the subsequent responses can be classified using backbone curves that represent the resonances of the underlying undamped, unforced system. These backbone curves, which we estimate analytically, are then related to the forced response of the system around resonance in the frequency domain. The forced responses are computed using the continuation software AUTO-07p. A comparison of the results gives insights into the multi-modal interactions and shows how the frequency response of the system is related to those branches of the backbone curves that represent such interactions