A computer simulation of stress transfer in carbon nanotube/polymer nanocomposites

The reinforcing efficiency or stress transfer of carbon nanotubes (CNT) on polymers in polymer/CNT composites mainly is controlled by the polymer-CNT interface. Enhancement of polymer-CNT interactions and interfacial crystallisation is as an important way for improvement of the reinforcement experimentally. However, it is not clear about the crystallisation and orientation of polymer chains on the CNT surface, and how the interfacial crystallisation layer affects the failure of the composite. In this work, poly(vinyl alcohol)/CNT nanocomposites was selected as an example and based on the molecular dynamics simulation, the crystallisation process, failure behaviour and stress transfer in poly(vinyl alcohol)/CNT nanocomposites were analysed. The crystallisation temperature of the polymer chains on the CNT surface is slightly higher than the bulk crystallisation temperature. CNT induced crystallisation can be divided into three stages: chain folding, orientating and growing on the CNT surface. A slower crack growth was observed in the interfacial crystallised polymer/CNT systems, compare to relative amorphous systems. The effect of the interfacial crystalline layer on stress transfer is similar as enhanced polymer-CNT interaction systems. The change of the polymer-CNT surficial energy to strain has been used to analyse the interfacial failure and the stress transfer

Seismic performance of horizontal swivel system of asymmetric continuous girder bridge

The bridge horizontal swivel system generally adopts a symmetrical structure and uses a spherical hinge structure that can adjust the rotation to complete rotation construction. Because of the complexity of railway lines under bridges, some asymmetrical horizontal swivel systems have been increasingly applied in practical engineering in recent years. This system is more suitable for areas with complex railway lines, reduces the bridge span, and provides better economic benefits. However, it is also extremely unstable. In addition, instability can easily occur under dynamic loads, such as earthquake action and pulsating wind effects. Therefore, it is necessary to study their mechanical behavior. Based on the horizontal swivel system of an 11,000-ton asymmetric continuous girder bridge, the dynamic response of the horizontal swivel system to seismic action was studied using the finite element simulation analysis method. Furthermore, using the Peer database, seismic waves that meet the calculation requirements are screened for time-history analysis and compared to the response spectrum method. The mechanical properties of the structural system during and after rotation were obtained through calculations. During rotation, the seismic response of the structure is greater. To reduce the calculation time cost, an optimization algorithm based on the mode shape superposition method is proposed. The calculation result is 87% that of the time-history analysis, indicating a relatively high calculation accuracy

Towards Visual Foundational Models of Physical Scenes

We describe a first step towards learning general-purpose visual representations of physical scenes using only image prediction as a training criterion. To do so, we first define "physical scene" and show that, even though different agents may maintain different representations of the same scene, the underlying physical scene that can be inferred is unique. Then, we show that NeRFs cannot represent the physical scene, as they lack extrapolation mechanisms. Those, however, could be provided by Diffusion Models, at least in theory. To test this hypothesis empirically, NeRFs can be combined with Diffusion Models, a process we refer to as NeRF Diffusion, used as unsupervised representations of the physical scene. Our analysis is limited to visual data, without external grounding mechanisms that can be provided by independent sensory modalities.Comment: TLDR: Physical scenes are equivalence classes of sufficient statistics, and can be inferred uniquely by any agent measuring the same finite data; We formalize and implement an approach to representation learning that overturns "naive realism" in favor of an analytical approach of Russell and Koenderink. NeRFs cannot capture the physical scenes, but combined with Diffusion Models they ca