On the use of nonlinear normal modes for nonlinear reduced order modelling

In many areas of engineering, nonlinear numerical analysis is playing an increasingly important role in supporting the design and monitoring of structures. Whilst increasing computer resources have made such formerly prohibitive analyses possible, certain use cases such as uncertainty quantification and real time high-precision simulation remain computationally challenging. This motivates the development of reduced order modelling methods, which can reduce the computational toll of simulations relying on mechanistic principles. The majority of existing reduced order modelling techniques involve projection onto linear bases. Such methods are well established for linear systems but when considering nonlinear systems their application becomes more difficult. Targeted schemes for nonlinear systems are available, which involve the use of multiple linear reduction bases or the enrichment of traditional bases. These methods are however generally limited to weakly nonlinear systems. In this work, nonlinear normal modes (NNMs) are demonstrated as a possible invertible reduction basis for nonlinear systems. The extraction of NNMs from output only data using machine learning methods is demonstrated and a novel NNM-based reduced order modelling scheme introduced. The method is demonstrated on a simulated example of a nonlinear 20 degree-of-freedom (DOF) system

Reduced order modeling of non-linear monopile dynamics via an AE-LSTM scheme

Non-linear analysis is of increasing importance in wind energy engineering as a result of their exposure in extreme conditions and the ever-increasing size and slenderness of wind turbines. Whilst modern computing capabilities facilitate execution of complex analyses, certain applications which require multiple or real-time analyses remain a challenge, motivating adoption of accelerated computing schemes, such as reduced order modelling (ROM) methods. Soil structure interaction (SSI) simulations fall in this class of problems, with the non-linear restoring force significantly affecting the dynamic behaviour of the turbine. In this work, we propose a ROM approach to the SSI problem using a recently developed ROM methodology. We exploit a data-driven non-linear ROM methodology coupling an autoencoder with long short-term memory (LSTM) neural networks. The ROM is trained to emulate a steel monopile foundation constrained by non-linear soil and subject to forces and moments at the top of the foundation, which represent the equivalent loading of an operating turbine under wind and wave forcing. The ROM well approximates the time domain and frequency domain response of the Full Order Model (FOM) over a range of different wind and wave loading regimes, whilst reducing the computational toll by a factor of 300. We further propose an error metric for capturing isolated failure instances of the ROM

A hysteretic multiscale formulation for validating computational models of heterogeneous structures

A framework for the development of accurate yet computationally efficient numerical models is proposed in this work, within the context of computational model validation. The accelerated computation achieved herein relies on the implementation of a recently derived multiscale finite element formulation, able to alternate between scales of different complexity. In such a scheme, the micro-scale is modelled using a hysteretic finite elements formulation. In the micro-level, nonlinearity is captured via a set of additional hysteretic degrees of freedom compactly described by an appropriate hysteric law, which gravely simplifies the dynamic analysis task. The computational efficiency of the scheme is rooted in the interaction between the micro- and a macro-mesh level, defined through suitable interpolation fields that map the finer mesh displacement field to the coarser mesh displacement field. Furthermore, damage related phenomena that are manifested at the micro-level are accounted for, using a set of additional evolution equations corresponding to the stiffness degradation and strength deterioration of the underlying material. The developed modelling approach is utilized for the purpose of model validation; firstly, in the context of reliability analysis; and secondly, within an inverse problem formulation where the identification of constitutive parameters via availability of acceleration response data is sought

An application of generative adversarial networks in structural health monitoring

In the current work, the use of generative adversarial networks (GANs) in a simulated structural health monitoring (SHM) application is studied. A specific type of GAN is considered, aiming at a disentangled representation of underlying features and clusters of data through some latent variables. This idea could prove useful in SHM, since explanation of how damage mechanisms or environmental conditions affect a structure may be exploited in order to monitor structures more effectively. In a simulated mass-spring example, different damage cases are introduced by reducing the stiffness of specific springs and different damage levels by applying different extents of stiffness reduction. The GAN implementation proves able to capture different damage cases through its categorical latent variables, as well as the damage extent within its continuous latent variables. The results demonstrate that the latent variables are indeed capturing the effect of damage in the structure and can be exploited for the purpose of condition assessment

Glucocorticoid-Induced Attenuation of the Inflammatory Response in Zebrafish.

Glucocorticoids are steroid hormones that are secreted upon stress. Their effects are mediated by the glucocorticoid receptor, which acts as a transcription factor. Because the antiinflammatory activity of glucocorticoids has been well established, they are widely used clinically to treat many inflammatory and immune-related diseases. However, the exact specificity, mechanisms, and level of regulation of different inflammatory pathways have not been fully elucidated. In the present study, a tail fin amputation assay was used in 3-day-old zebrafish larvae to study the immunomodulatory effects of the synthetic glucocorticoid beclomethasone. First, a transcriptome analysis was performed, which showed that upon amputation mainly immune-related genes are regulated. This regulation was inhibited by beclomethasone for 86% of regulated genes. For two immune-related genes, tlr4bb and alox5ap, the amputation-induced increase was not attenuated by beclomethasone. Alox5ap is involved in eicosanoid biosynthesis, but the increase in leukotriene B4 concentration upon amputation was abolished, and lipoxin A4 levels were unaffected by beclomethasone. Furthermore, we studied the migration of neutrophils and macrophages toward the wound site. Our results show that amputation induced migration of both types of leukocytes and that this migration was dependent on de novo protein synthesis. Beclomethasone treatment attenuated the migratory behavior of neutrophils in a glucocorticoid receptor-dependent manner but left the migration of macrophages unaffected. In conclusion, beclomethasone has a dramatic inhibitory effect on the amputation-induced proinflammatory gene regulation, and this is reflected in an inhibition of the neutrophil migration but not the migration of macrophages, which are likely to be involved in inflammation resolution.Animal science

A hysteretic multiscale formulation for nonlinear dynamic analysis of composite materials

This article has been made available through the Brunel Open Access Publishing Fund.A new multiscale finite element formulation is presented for nonlinear dynamic analysis of heterogeneous structures. The proposed multiscale approach utilizes the hysteretic finite element method to model the microstructure. Using the proposed computational scheme, the micro-basis functions, that are used to map the microdisplacement components to the coarse mesh, are only evaluated once and remain constant throughout the analysis procedure. This is accomplished by treating inelasticity at the micro-elemental level through properly defined hysteretic evolution equations. Two types of imposed boundary conditions are considered for the derivation of the multiscale basis functions, namely the linear and periodic boundary conditions. The validity of the proposed formulation as well as its computational efficiency are verified through illustrative numerical experiments

Retinoic Acid Functions as a Key GABAergic Differentiation Signal in the Basal Ganglia

Retinoic acid (RA) is essential for the generation of GABAergic inhibitory neurons in the mouse forebrain, and RA treatment of embryonic stem cells induces the production of GABAergic neurons