Digital Twins for Engineering Asset Management:Synthesis,Analytical Framework,and Future Directions

Effective engineering asset management(EAM)is critical to economic development and improving livability in society,but its complexity often impedes optimal asset functionalities.Digital twins(DTs)could revolutionize the EAM paradigm by bidirectionally linking the physical and digital worlds in real time.There is great industrial and academic interest in DTs for EAM.However,previous review studies have predominately focused on technical aspects using limited life-cycle perspectives,failing to holistically synthesize DTs for EAM from the managerial point of view.Based on a systematic literature review,we introduce an analytical framework for describing DTs for EAM,which encompasses three levels:DT 1.0 for technical EAM,DT 2.0 for technical-human EAM,and DT 3.0 for technical-environmental EAM.Using this framework,we identify what is known,what is unknown,and future directions at each level.DT 1.0 addresses issues of asset quality,progress,and cost management,generating technical value.It lacks multi-objective self-adaptive EAM,however,and suffers from high application cost.It is imperative to enable closed-loop EAM in order to provide various functional services with affordable DT 1.0.DT 2.0 accommodates issues of human-machine symbiosis,safety,and flexibility management,generating managerial value beyond the technical performance improvement of engineering assets.However,DT 2.0 currently lacks the automation and security of human-machine interactions and the managerial value related to humans is not prominent enough.Future research needs to align technical and managerial value with highly automated and secure DT 2.0.DT 3.0 covers issues of participatory governance,organization management,sustainable development,and resilience enhancement,generating macro social value.Yet it suffers from organizational fragmentation and can only address limited social governance issues.Numerous research opportunities exist to coordinate different stakeholders.Similarly,future research opportunities exist to develop DT 3.0 in a more open and complex system.

A multiscale material point method for impact simulation

To better simulate multi-phase interactions involving failure evolution, the material point method （MPM） has evolved for almost twenty years. Recently, a particle-based multiscale simulation procedure is being developed, within the framework of the MPM, to describe the detonation process of energetic nano-composites from molecular to continuum level so that a multiscale equation of state could be formulated. In this letter, a multiscale MPM is proposed via both hierarchical and concurrent schemes to simulate the impact response between two microrods with different nanostructures. Preliminary results are presented to illustrate that a transition region is not required between different spatial scales with the proposed approach.

Evaluation of temporal compositing algorithms for annual land cover classification using Landsat time series data

In this paper, four widely used temporal compositing algorithms, i.e.median, maximum NDVI, medoid, and weighted scoring-basedalgorithms, were evaluated for annual land cover classification usingmonthly Landsat time series data. Four study areas located in California,Texas, Kansas, and Minnesota, USA were selected for image compositingand land cover classification. Results indicated that images compositedusing weighted scoring-based algorithms have the best spatial fidelitycompared to other three algorithms. In addition, the weighted scoringbasedalgorithms have superior classification accuracy, followed bymedian, maximum NDVI, and medoid in descending order. However, themedian algorithm has a significant advantage in computational efficiencywhich was ~70 times that of weighted scoring-based algorithms, andwith overall classification accuracy just slightly lower (~0.13% onaverage) than weighted scoring-based algorithms. Therefore, werecommended the weighted scoring-based compositing algorithms forsmall area land cover mapping, and median compositing algorithm forthe land cover mapping of large area considering the balance betweencomputational complexity and classification accuracy. The findings of thisstudy provide insights into the performance difference between variouscompositing algorithms, and have potential uses for the selection ofpixel-based image compositing technique adopted for land covermapping based on Landsat time series data.

Generalization of the Hall-Petch and inverse Hall-Petch behaviors by tuning amorphous regions in 2D solids

The strengthσ_(y)(D)of a polycrystal can decrease or increase with the grain diameter D,i.e.,the famous Hall-Petch(HP)and inverse-Hall-Petch(IHP)behaviors,respectively.However,σ_(y)(D)under thick grain boundaries(GBs)(i.e.,GB thickness l>1 particle)andσ_(y)(l)have rarely been explored.Here we measure them by systematically varying D and l of two-dimensional glass-crystal composites in simulations.We demonstrate that increasing l and decreasing D have similar effects on reducing dislocation motions and promoting GB deformations.Consequently,the classical HP-IHP behaviors ofσ_(y)(D,l=1)and our generalized HP-IHP behaviors ofσ_(y)(D,l)share similar mechanisms and can be unified asσ_(y)(AGB/Atot),where AGB/Atot is the fraction of the amorphous region.The results reveal a way to exceed the maximum strength of normal polycrystals.The generalized HP-IHP behaviors ofσ_(y)(D,l)should be similar in 2D and 3D,except that the HP effect in 3D is stronger.

Understanding innovation diffusion and adoption strategies in megaproject networks through a fuzzy system dynamic model

Innovation and knowledge diffusion in megaprojects is one of the most complicated issues in project management.Compared with conventional projects,megaprojects typically entail large-scale investments,long construction periods,and conflicting stakeholder interests,which result in a distinctive pattern of innovation diffusion.However,traditional investigation of innovation diffusion relies on subjective feedback from experts and frequently neglects inter-organizational knowledge creation,which frequently emerges in megaprojects.Therefore,this study adopted project network theory and modeled innovation diffusion in megaprojects as intra-and interorganizational learning processes.In addition,system dynamics and flizzy systems were combined to interpret experts9 subject options as quantitative coefficients of the project network model.This integrated model will assist in developing an insightful understanding of the mechanisms of innovation diffusion in megaprojects.Three typical network structures,namely,a traditional megaproject procurement organization(TMO),the environ megaproject organization(EMO),and an integrated megaproject organization(IMO),were examined under six management scenarios to verify the proposed analytic paradigm.Assessment of project network productivity suggested that the projectivity of the TMO was insensitive to technical and administrative innovations,the EMO could achieve substantial improvement from technical innovations,and the 1MO trended incompatibly with administrative innovations.Thus,industry practitioners and project managers can design and reform agile project coordination by using the proposed quantitative model to encourage innovation adoption and reduce productivity loss at the start of newly established collaborations.