•Digital twin for multi-timescale dynamical system is proposed.•The approach uses fuses system physics with data-based approach.•ME-GP is proposed for tracking time-evolution of system parameters•The ...proposed approach can accurately predict future responses
Digital twin technology has a huge potential for widespread applications in different industrial sectors such as infrastructure, aerospace, and automotive. However, practical adoptions of this technology have been slower, mainly due to a lack of application-specific details. Here we focus on a digital twin framework for linear single-degree-of-freedom structural dynamic systems evolving in two different operational time scales in addition to its intrinsic dynamic time-scale. Our approach strategically separates into two components – (a) a physics-based nominal model for data processing and response predictions, and (b) a data-driven machine learning model for the time-evolution of the system parameters. The physics-based nominal model is system-specific and selected based on the problem under consideration. On the other hand, the data-driven machine learning model is generic. For tracking the multi-timescale evolution of the system parameters, we propose to exploit a mixture of experts as the data-driven model. Within the mixture of experts model, Gaussian Process (GP) is used as the expert model. The primary idea is to let each expert track the evolution of the system parameters at a single time-scale. For learning the hyperparameters of the ‘mixture of experts using GP’, an efficient framework that exploits expectation-maximization and sequential Monte Carlo sampler is used. Performance of the digital twin is illustrated on a multi-timescale dynamical system with stiffness and/or mass variations. The digital twin is found to be robust and yields reasonably accurate results. One exciting feature of the proposed digital twin is its capability to provide reasonable predictions at future time-steps. Aspects related to the data quality and data quantity are also investigated.
Global food production must increase by 50% to meet the projected demand of the world's population by 2050. Meeting this difficult challenge will be made even harder if climate change melts portions ...of the Himalayan glaciers to affect 25% of world cereal production in Asia by influencing water availability. Pest and disease management has played its role in doubling food production in the last 40 years, but pathogens still claim 10-16% of the global harvest. We consider the effect of climate change on the many complex biological interactions affecting pests and pathogen impacts and how they might be manipulated to mitigate these effects. Integrated solutions and international co-ordination in their implementation are considered essential. Providing a background on key constraints to food security, this overview uses fusarium head blight as a case study to illustrate key influences of climate change on production and quality of wheat, outlines key links between plant diseases, climate change and food security, and highlights key disease management issues to be addressed in improving food security in a changing climate.
Creation of novel functionality in materials is always fascinating for researchers/scientists. Bismuth ferrite (BFO), a well known multiferroics material with simultaneous magnetic and electric ...order, is promising for spintronics and memory devices. Herein, we were able to demonstrate for the first time very good carbon monoxide (CO) sensing capability of bismuth ferrite (BFO) nanoparticles prepared through a simple sol–gel technique. Morphological/structural characterization was performed using various standard sophisticated modern probes, which confirms the formation of single phase BFO nanoparticles. The sensing properties of BFO nanoparticles towards CO was tested for concentrations in the range of 5 ppm–30 ppm at different temperatures (between 270 °C to 450 °C). Very good response ( R g / R a ) of around 2.12 towards 30 ppm CO at an operating temperature of 350 °C was observed. The as-prepared sensors exhibited rapid response and recovery time of around 25 s and 13 s, respectively, for sensing 30 ppm CO. In addition, the sensors are selective, highly reproducible and remain quite stable for almost 150 days, which is required for device application. A plausible mechanism has been proposed to explain the CO sensing behaviour of the BFO nanoparticles. On the basis of the observed sensing properties we believe that a BFO nanoparticle-based sensor could be a suitable alternative to conventional oxide-based sensors having various difficulties.
The worldwide spread of the novel coronavirus originating from Wuhan, China led to an ongoing pandemic as COVID-19. The disease being a contagion transmitted rapidly in India through the people ...having travel histories to the affected countries, and their contacts that tested positive. Millions of people across all states and union territories (UT) were affected leading to serious respiratory illness and deaths. In the present study, two unsupervised clustering algorithms namely k-means clustering and hierarchical agglomerative clustering are applied on the COVID-19 dataset in order to group the Indian states/UTs based on the pandemic effect and the vaccination program from the period of March, 2020 to early June, 2021. The aim of the study is to observe the plight of each state and UT of India combating the novel coronavirus infection and to monitor their vaccination status. The research study will be helpful to the government and to the frontline workers coping to restrict the transmission of the virus in India. Also, the results of the study will provide a source of information for future research regarding the COVID-19 pandemic in India.
•A Gaussian process surrogate of a digital twin of a discrete damped dynamic system model is proposed.•The concept of “slow time” is proposed to differentiate the evolution of the digital twin from ...its “real time” dynamics.•Functional variation of stiffness and mass in the discrete system is considered.•Sensor errors are modeled in the update process of the digital twin surrogate.•Effect of data quality and sampling rate on the Gaussian process surrogate is evaluated.
Digital twin technology has significant promise, relevance and potential of widespread applicability in various industrial sectors such as aerospace, infrastructure and automotive. However, the adoption of this technology has been slower due to the lack of clarity for specific applications. A discrete damped dynamic system is used in this paper to explore the concept of a digital twin. As digital twins are also expected to exploit data and computational methods, there is a compelling case for the use of surrogate models in this context. Motivated by this synergy, we have explored the possibility of using surrogate models within the digital twin technology. In particular, the use of Gaussian process (GP) emulator within the digital twin technology is explored. GP has the inherent capability of addressing noisy and sparse data and hence, makes a compelling case to be used within the digital twin framework. Cases involving stiffness variation and mass variation are considered, individually and jointly, along with different levels of noise and sparsity in data. Our numerical simulation results clearly demonstrate that surrogate models, such as GP emulators, have the potential to be an effective tool for the development of digital twins. Aspects related to data quality and sampling rate are analysed. Key concepts introduced in this paper are summarised and ideas for urgent future research needs are proposed.
This investigation is directed to understand the asymmetry in ΔX variations caused due to the relative roles played by Interplanetary Magnetic Field (IMF) Bz and IMF By in a particular interval ...(22:22–22:55 UT), during the main phase of a strong geomagnetic storm event of 06 April 2000 (Ap = 236). Two pairs of antipodal stations, being part of the SuperMAG network, are considered here. Ionospheric convection maps from SuperDARN network are used to understand spatio‐temporal evolution of the DP2 ionospheric convection patterns over high‐latitudes. The two‐dimensional maps of equivalent currents are used to show signatures of global DP2 currents associated with the interplay effect between the two IMF components. Observations show increases in the difference in ΔX variations between nearly antipodal stations from the Japanese‐European/African sector with respect to the same between the nearly antipodal stations from the Pacific/American‐Indian sector. This asymmetry is observed during the period when the absolute magnitude of IMF By is larger than that of IMF Bz resulting in a significant and conspicuous enhancement in IMF |By/Bz|. It is suggested that the distortions in DP2 cells and associated rotation of electrodynamic day‐night divider, bring one pair of stations under the same DP2 cell and one station of the other pair under a different DP2 cell and throat flow region leading to the asymmetry in ΔX variations between the antipodal stations. Therefore, the work highlights the importance of the interplay between IMF Bz and IMF By in determining the ionospheric impact over low latitudes during strong geomagnetic conditions.
Key Points
First observations of asymmetry in ΔX variations between two pairs of nearly antipodal locations over low latitudes
Increase in the ratio of Interplanetary Magnetic Field By to Bz is suggested to cause differences of ΔX variations in one pair with respect to the other
Distortions in DP2 cells, rotation of electrodynamical divider, and associated throat flows are believed to determine the asymmetry observed
Abstract
In the background of a homogeneous and isotropic space-time with zero spatial curvature, we consider interacting scenarios between two barotropic fluids, one is the pressureless dark matter ...and the other one is dark energy (DE), in which the equation of state (EoS) in DE is either constant or time-dependent. In particular, for constant EoS in DE, we show that the evolution equations for both fluids can be analytically solved. For all these scenarios, the model parameters have been constrained using the current astronomical observations from Type Ia supernovae, Hubble parameter measurements and baryon acoustic oscillation distance measurements. Our analysis shows that both for constant and variable EoS in DE, a very small but non-zero interaction in the dark sector is favoured while the EoS in DE can predict a slight phantom nature, i.e. the EoS in DE can cross the phantom divide line ‘−1’. On the other hand, although the models with variable EoS describe the observations better, the Akaike Information Criterion supports models with minimal number of parameters. However, it is found that all the models are very close to the Λ cold dark matter cosmology.
The unsteady laminar magnetohydrodynamics （MHD） boundary layer flow and heat transfer of nanofluids over an accelerating convectively heated stretching sheet are numerically studied in the presence ...of a transverse magnetic field with heat source/sink The unsteady governing equations are solved by a shooting method with the Runge-Kutta- Fehlberg scheme. Three different types of water based nanofluids, containing copper, aluminium oxide, and titanium dioxide, are taken into consideration. The effects of the pertinent parameters on the fluid velocity, the temperature, the entropy generation num- ber, the Bejan number, the shear stress, and the heat transfer rate at the sheet surface are graphically and quantitatively discussed in detail. A comparison of the entropy generation due to the heat transfer and the fluid friction is made with the help of the Bejan number. It is observed that the presence of the metallic nanoparticles creates more entropy in the nanofluid flow than in the regular fluid flow.
In this paper, we develop a method of analyzing long transient dynamics in a class of predator–prey models with two species of predators competing explicitly for their common prey, where the prey ...evolves on a faster timescale than the predators. In a parameter regime near a
singular zero-Hopf bifurcation
of the coexistence equilibrium state, we assume that the system under study exhibits bistability between a periodic attractor that bifurcates from the singular Hopf point and another attractor, which could be a periodic attractor or a point attractor, such that the invariant manifolds of the coexistence equilibrium point play central roles in organizing the dynamics. To find whether a solution that starts in a vicinity of the coexistence equilibrium approaches the periodic attractor or the other attractor, we reduce the equations to a suitable normal form, and examine the basin boundary near the singular Hopf point. A key component of our study includes an analysis of the long transient dynamics, characterized by their rapid oscillations with a slow variation in amplitude, by applying a moving average technique. We obtain a set of necessary and sufficient conditions on the initial values of a solution near the coexistence equilibrium to determine whether it lies in the basin of attraction of the periodic attractor. As a result of our analysis, we devise a method of identifying early warning signals, significantly in advance, of a future crisis that could lead to extinction of one of the predators. The analysis is applied to the predator–prey model considered in Sadhu (Discrete Contin Dyn Syst B 26:5251–5279, 2021) and we find that our theory is in good agreement with the numerical simulations carried out for this model.
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