Numerical stimulation of stress-induced crystallization of injection molded semicrystalline thermoplastics

Injection molded semicrystalline plastic products exhibit variable morphology along their thickness directions. The processing conditions have a significant effect on the crystallinity distribution in the final parts. However, because of the lack of sound theoretical models for stress-induced crystallization kinetics in thermoplastics, simulations of the injection molding process of semicrystalline plastics with the consideration of stress-induced crystallization have been scarce. A stress-induced crystallization model for semicrystalline plastics is proposed based on the theory that stress induced orientation of polymer chains increase the melting temperature of the plastics, and hence, the supercooling which is the driving force for crystallization. By assuming that the effect of stress on crystallization is only by increasing the equilibrium melting temperature, the basic quiescent state crystallization equation can be directly applied to model stress-induced crystallization kinetics. A simple experimental technique such as rotational rheometric measurement, can be used to determine the melting temperature shift. The model predicts the most prominent features of stress-induced crystallization: with the application of shear stress, crystallization rate becomes higher, the crystallization temperature range is broadened and the peak of crystallization rate shifts to higher temperatures. The main advantage of the model is that the parameters in the quiescent state crystallization model do not change and the parameters in the equilibrium melting temperature shift model are easy to determine. And the unknown constants are kept to a minimum. The injection molding process of semicrystalline plastics was simulated with the proposed stress-induced crystallization model. A pseudo-concentration method was used to track the melt front advancement. The simple Maxwell stress relaxation model in combination with WFL equation was used to investigate the importance of stress relaxation on the development of crystallinity during the injection molding. Simulations were carried out under different processing conditions to investigate the effect of processing parameters on the crystallinity of the final part. Other results such as skin layer build-up and mold pressure were also simulated. The simulation results reproduced most of the features that were obtained by the experiments reported in the literature

Enhanced Gas-Flow-Induced Voltage in Graphene

We show by systemically experimental investigation that gas-flow-induced voltage in monolayer graphene is more than twenty times of that in bulk graphite. Examination over samples with sheet resistances ranging from 307 to 1600 {\Omega}/sq shows that the induced voltage increase with the resistance and can be further improved by controlling the quality and doping level of graphene. The induced voltage is nearly independent of the substrate materials and can be well explained by the interplay of Bernoulli's principle and the carrier density dependent Seebeck coefficient. The results demonstrate that graphene has great potential for flow sensors and energy conversion devices

Intrinsic energy conversion mechanism via telescopic extension and retraction of concentric carbon nanotubes

The conversion of other forms of energy into mechanical work through the geometrical extension and retraction of nanomaterials has a wide variety of potential applications, including for mimicking biomotors. Here, using molecular dynamic simulations, we demonstrate that there exists an intrinsic energy conversion mechanism between thermal energy and mechanical work in the telescopic motions of double-walled carbon nanotubes (DWCNTs). A DWCNT can inherently convert heat into mechanical work in its telescopic extension process, while convert mechanical energy into heat in its telescopic retraction process. These two processes are thermodynamically reversible. The underlying mechanism for this reversibility is that the entropy changes with the telescopic overlapping length of concentric individual tubes. We find also that the entropy effect enlarges with the decreasing intertube space of DWCNTs. As a result, the spontaneously telescopic motion of a condensed DWCNT can be switched to extrusion by rising the system temperature above a critical value. These findings are important for fundamentally understanding the mechanical behavior of concentric nanotubes, and may have general implications in the application of DWCNTs as linear motors in nanodevices

Practical Network Acceleration with Tiny Sets: Hypothesis, Theory, and Algorithm

Due to data privacy issues, accelerating networks with tiny training sets has become a critical need in practice. Previous methods achieved promising results empirically by filter-level pruning. In this paper, we both study this problem theoretically and propose an effective algorithm aligning well with our theoretical results. First, we propose the finetune convexity hypothesis to explain why recent few-shot compression algorithms do not suffer from overfitting problems. Based on it, a theory is further established to explain these methods for the first time. Compared to naively finetuning a pruned network, feature mimicking is proved to achieve a lower variance of parameters and hence enjoys easier optimization. With our theoretical conclusions, we claim dropping blocks is a fundamentally superior few-shot compression scheme in terms of more convex optimization and a higher acceleration ratio. To choose which blocks to drop, we propose a new metric, recoverability, to effectively measure the difficulty of recovering the compressed network. Finally, we propose an algorithm named PRACTISE to accelerate networks using only tiny training sets. PRACTISE outperforms previous methods by a significant margin. For 22% latency reduction, it surpasses previous methods by on average 7 percentage points on ImageNet-1k. It also works well under data-free or out-of-domain data settings. Our code is at https://github.com/DoctorKey/PractiseComment: under review for TPAMI. arXiv admin note: substantial text overlap with arXiv:2202.0786

Repetitive Reprediction Deep Decipher for Semi-Supervised Learning

Most recent semi-supervised deep learning (deep SSL) methods used a similar paradigm: use network predictions to update pseudo-labels and use pseudo-labels to update network parameters iteratively. However, they lack theoretical support and cannot explain why predictions are good candidates for pseudo-labels. In this paper, we propose a principled end-to-end framework named deep decipher (D2) for SSL. Within the D2 framework, we prove that pseudo-labels are related to network predictions by an exponential link function, which gives a theoretical support for using predictions as pseudo-labels. Furthermore, we demonstrate that updating pseudo-labels by network predictions will make them uncertain. To mitigate this problem, we propose a training strategy called repetitive reprediction (R2). Finally, the proposed R2-D2 method is tested on the large-scale ImageNet dataset and outperforms state-of-the-art methods by 5 percentage points.Comment: Accepted by AAAI 202

Measurement framework for assessing disruptive innovations

Assessing potential disruptiveness of innovations is an important but challenging task for incumbents. However, the extant literature focuses only on technological and marketplace aspects, and most of the documented methods tend to be case specific. In this study, we present a multidimensional measurement framework to assess the disruptive potential of product innovations. The framework is designed based on the concept that the nature of disruptive innovations is multidimensional. Three aspects are considered, i.e., technological features, marketplace dynamics and external environment. Ten indicators of the three categories are proposed and then connected based on the conceptual and literature analysis. Three innovations, namely, WeChat (successful), Modularised Mobile Phone (failed) and Virtual Reality/Augmented Reality (ongoing), are selected as case studies. A panel of industrial experts with PhD degree in engineering is surveyed. The survey results are calculated and analysed according to the framework and then compared against the developments of the innovations. We also check the robustness of this framework by surveying other groups of people, and the results are nearly identical to the previous findings. This study enables a systematic assessment of disruptive potential of innovations using the framework, providing insights for decisions in product launch and resource allocation.fi=vertaisarvioitu|en=peerReviewed

The time-fractional mZK equation for gravity solitary waves and solutions using sech-tanh and radial basic function method

In recent years, we know that gravity solitary waves have gradually become the research spots and aroused extensive attention; on the other hand, the fractional calculus have been applied to the biology, optics and other fields, and it also has attracted more and more attention. In the paper, by employing multi-scale analysis and perturbation methods, we derive a new modified Zakharov–Kuznetsov (mZK) equation to describe the propagation features of gravity solitary waves. Furthermore, based on semi-inverse and Agrawal methods, the integer-order mZK equation is converted into the time-fractional mZK equation. In the past, fractional calculus was rarely used in ocean and atmosphere studies. Now, the study on nonlinear fluctuations of the gravity solitary waves is a hot area of research by using fractional calculus. It has potential value for deep understanding of the real ocean–atmosphere. Furthermore, by virtue of the sech-tanh method, the analytical solution of the time-fractional mZK equation is obtained. Next, using the above analytical solution, a numerical solution of the time-fractional mZK equation is given by using radial basis function method. Finally, the effect of time-fractional order on the wave propagation is explained. &nbsp