Application of scaled boundary finite element method in static and dynamic fracture problems

The scaled boundary finite element method （SBFEM） is a recently developed numerical method combining advantages of both finite element methods （FEM） and boundary element methods （BEM） and with its own special features as well. One of the most prominent advantages is its capability of calculating stress intensity factors （SIFs） directly from the stress solutions whose singularities at crack tips are analytically represented. This advantage is taken in this study to model static and dynamic fracture problems. For static problems, a remeshing algorithm as simple as used in the BEM is developed while retaining the generality and flexibility of the FEM. Fully-automatic modelling of the mixed-mode crack propagation is then realised by combining the remeshing algorithm with a propagation criterion. For dynamic fracture problems, a newly developed series-increasing solution to the SBFEM governing equations in the frequency domain is applied to calculate dynamic SIFs. Three plane problems are modelled. The numerical results show that the SBFEM can accurately predict static and dynamic SIFs, cracking paths and load-displacement curves, using only a fraction of degrees of freedom generally needed by the traditional finite element methods.

Visualization of atomic scale reaction dynamics of supported nanocatalysts during oxidation and ammonia synthesis using in-situ environmental(scanning) transmission electron microscopy

Reaction dynamics in gases at operating temperatures at the atomic level are the basis of heterogeneous gas-solid catalyst reactions and are crucial to the catalyst function.Supported noble metal nanocatalysts such as platinum are of interest in fuel cells and as diesel oxidation catalysts for pollution control,and practical ruthenium nanocatalysts are explored for ammonia synthesis.Graphite and graphitic carbons are of interest as supports for the nanocatalysts.Despite considerable literature on the catalytic processes on graphite and graphitic supports,reaction dynamics of the nanocatalysts on the supports in different reactive gas environments and operating temperatures at the single atom level are not well understood.Here we present real time in-situ observations and analyses of reaction dynamics of Pt in oxidation,and practical Ru nanocatalysts in ammonia synthesis,on graphite and related supports under controlled reaction environments using a novel in-situ environmental(scanning) transmission electron microscope with single atom resolution.By recording snapshots of the reaction dynamics,the behaviour of the catalysts is imaged.The images reveal single metal atoms,clusters of a few atoms on the graphitic supports and the support function.These all play key roles in the mobility,sintering and growth of the catalysts.The experimental findings provide new structural insights into atomic scale reaction dynamics,morphology and stability of the nanocatalysts.

Dynamic scaling characteristics of single-phase natural circulation based on different strain transformations

To understand the dynamical system scaling(DSS)analysis theory,the applicability of DSSβ-andω-strain transformation methods for the scaling analysis of complex loops was explored.A simplified model consisting of two loops was established based on the primary and secondary sides of a nuclear reactor,andβ-andω-strain transformation methods were used to ana-lyze the single-phase natural circulation in the primary circuit.For comparison with the traditional method,simplified DSSβ-andω-strain methods were developed based on the standard scaling criterion.The strain parameters in these four methods were modified to form multiple groups of scaled-down cases.The transient process of the natural circulation was simulated using the Relap5 code,and the variation in the dynamic flow characteristics with the strain numbers was obtained using different scaling methods.The results show that both the simplified and standard DSS methods can simulate the dynamic characteristics of natural circulation in the primary circuit.The scaled-down cases in the simplified method exhibit the same geometric scaling and correspond to small core power ratios.By contrast,different scaled-down cases in the standard DSS method correspond to different geometric scaling criteria and require more power.The dynamic process of natural circula-tion can be simulated more accurately using the standard DSS method.

Dynamic scaling behaviour of late-stage phase separation in Ni75AlxV25-x alloys

The dynamic scaling behaviour of late-stage phase separation and coarsening mechanisms of L12 and D022 in Ni75AlxV25-x （3 ≤ x ≤ 10, at.%） alloys are studied using the microscopic phase-field dynamic model. The microelaso ticity field is incorporated into the diffusion dynamic model. The results show the morphology and coarsening dynamics being greatly changed by the elastic interactions among different precipitates, the particles aligning along the dominant directions, the average domain size （ADS） of L12 and D022 deviating from the exponent of temporal power-law, and the growth slowing down due to the increasing of elastic interactions. The dynamic scaling regime of late-stage coarsening of the precipitates is attained. Thus the scaling behaviour of structure function is also applicable for elastic interaction systems. It is also found that the variations of ADS and scaling function depend on the volume fraction of precipitates.

Numerical study of anomalous dynamic scaling behaviour of (1+1)-dimensional Das Sarma-Tamborenea model

In order to discuss the finite-size effect and the anomalous dynamic scaling behaviour of Das Sarma-Tamborenea growth model, the （1＋1）-dimensional Das Sarma-Tamborenea model is simulated on a large length scale by using the kinetic Monte-Carlo method. In the simulation, noise reduction technique is used in order to eliminate the crossover effect. Our results show that due to the existence of the finite-size effect, the effective global roughness exponent of the （1＋1）-dimensional Das Sarma-Tamborenea model systematically decreases with system size L increasing when L 〉 256. This finding proves the conjecture by Aarao Reis[Aarao Reis F D A 2004 Phys. Rev. E 70 031607]. In addition, our simulation results also show that the Das Sarma-Tamborenea model in 1＋1 dimensions indeed exhibits intrinsic anomalous scaling behaviour.

Two‑parameter dynamical scaling analysis of single‑phase natural circulation in a simple rectangular loop based on dilation transformation

Scaling analysis is widely used to design scaled-down experimental facilities through which the prototype phenomena can be effectively evaluated.As a new method,dynamic system scaling(DSS)must be verified as a rational and applicable method.A DSS method based on dilation transformation was evaluated using single-phase natural circulation in a simple rectangular loop.The scaled-down cases were constructed based on two parameters—length ratio and dilation number—and the corresponding transient processes were simulated using the Relap5 computational code.The results show that this DSS method can simulate the dynamic flow characteristics of scaled-down cases.The transient deviation of the temperature difference and mass flow rate of the scaled cases decrease with increases in the length ratio and dilation number.The distortion of the transient temperature difference is smaller than that of the mass flow;however,the overall deviation is within a reasonable range.

AdaptNF: Adaptive service chain scheduling with stateless migration and NF consolidation

The combination of network function virtualization and software-defined networking allows various network functions to process flows according to their characteristics and requirements.Due to the highly dynamic nature of the workload,the network infrastructure needs to properly schedule the underlying resources in order to respond to workload changes in a timely manner.However,the existing NFV platform lacks a comprehensive solution for how to scale under workload variation,which may seriously hurt the overall system performance.To improve the scalability of the NFV platform and ensure consistent high performance under dynamic workloads,we propose AdaptNF,a novel NFV platform designed to support a combination of course-grained and fine-grained resource scheduling strategies.To deal with resource imbalance,which is the essential scheduling problem that leads to insufficient NFV performance,AdaptNF adopts a novel algorithm that can efficiently balance the workload among multiple network function instances through stateless flow migration.Our controlled experiments show that the AdaptNF scheme can optimize resource allocation and ensure outstanding performance after scaling.In terms of network throughput and latency,AdaptNF significantly improves the performance of the underlying NFV platform.

A Review of the Current Task Offloading Algorithms,Strategies and Approach in Edge Computing Systems

Task offloading is an important concept for edge computing and the Internet of Things(IoT)because computationintensive tasksmust beoffloaded tomore resource-powerful remote devices.Taskoffloading has several advantages,including increased battery life,lower latency,and better application performance.A task offloading method determines whether sections of the full application should be run locally or offloaded for execution remotely.The offloading choice problem is influenced by several factors,including application properties,network conditions,hardware features,and mobility,influencing the offloading system’s operational environment.This study provides a thorough examination of current task offloading and resource allocation in edge computing,covering offloading strategies,algorithms,and factors that influence offloading.Full offloading and partial offloading strategies are the two types of offloading strategies.The algorithms for task offloading and resource allocation are then categorized into two parts:machine learning algorithms and non-machine learning algorithms.We examine and elaborate on algorithms like Supervised Learning,Unsupervised Learning,and Reinforcement Learning(RL)under machine learning.Under the non-machine learning algorithm,we elaborate on algorithms like non(convex)optimization,Lyapunov optimization,Game theory,Heuristic Algorithm,Dynamic Voltage Scaling,Gibbs Sampling,and Generalized Benders Decomposition(GBD).Finally,we highlight and discuss some research challenges and issues in edge computing.

Energy-Efficient Scheduling Based on Task Migration Policy Using DPM for Homogeneous MPSoCs

Increasing the life span and efficiency of Multiprocessor System on Chip(MPSoC)by reducing power and energy utilization has become a critical chip design challenge for multiprocessor systems.With the advancement of technology,the performance management of central processing unit(CPU)is changing.Power densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip size.When energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor(CMOS)circuits and reduces the speed by 10%–15%because excessive on-chip temperature shortens the chip’s life cycle.In this paper,we address the scheduling&energy utilization problem by introducing and evaluating an optimal energy-aware earliest deadline first scheduling(EA-EDF)based technique formultiprocessor environments with task migration that enhances the performance and efficiency in multiprocessor systemon-chip while lowering energy and power consumption.The selection of core andmigration of tasks prevents the system from reaching itsmaximumenergy utilization while effectively using the dynamic power management(DPM)policy.Increase in the execution of tasks the temperature and utilization factor(u_(i))on-chip increases that dissipate more power.The proposed approach migrates such tasks to the core that produces less heat and consumes less power by distributing the load on other cores to lower the temperature and optimizes the duration of idle and sleep times across multiple CPUs.The performance of the EA-EDF algorithm was evaluated by an extensive set of experiments,where excellent results were reported when compared to other current techniques,the efficacy of the proposed methodology reduces the power and energy consumption by 4.3%–4.7%on a utilization of 6%,36%&46%at 520&624 MHz operating frequency when particularly in comparison to other energy-aware methods for MPSoCs.Tasks are running and accurately scheduled to make an energy-efficient processor by controlling and managing the thermal effects on-chip and optimizing the energy consumption of MPSoCs.

An Optimal DPM Based Energy-Aware Task Scheduling for Performance Enhancement in Embedded MPSoC

Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip(MPSoC)has become an integral chip design issue for multiprocessor systems.The performance measurement of computational systems is changing with the advancement in technology.Due to shrinking and smaller chip size power densities onchip are increasing rapidly that increasing chip temperature in multi-core embedded technologies.The operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor(CMOS)circuits because high on-chip temperature adversely affects the life span of the chip.In this paper an energy-aware dynamic power management technique based on energy aware earliest deadline first(EA-EDF)scheduling is proposed for improving the performance and reliability by reducing energy and power consumption in the system on chip(SOC).Dynamic power management(DPM)enables MPSOC to reduce power and energy consumption by adopting a suitable core configuration for task migration.Task migration avoids peak temperature values in the multicore system.High utilization factor(ui)on central processing unit(CPU)core consumes more energy and increases the temperature on-chip.Our technique switches the core bymigrating such task to a core that has less temperature and is in a low power state.The proposed EA-EDF scheduling technique migrates load on different cores to attain stability in temperature among multiple cores of the CPU and optimized the duration of the idle and sleep periods to enable the low-temperature core.The effectiveness of the EA-EDF approach reduces the utilization and energy consumption compared to other existing methods and works.The simulation results show the improvement in performance by optimizing 4.8%on u_(i) 9%,16%,23%and 25%at 520 MHz operating frequency as compared to other energy-aware techniques for MPSoCs when the least number of tasks is in running state and can schedule more tasks to make an energy-efficient processor by controlling and managing the energy consumption of MPSoC.

Full-State-Constrained Non-Certainty-Equivalent Adaptive Control for Satellite Swarm Subject to Input Fault

Satellite swarm coordinated flight(SSCF)technology has promising applications,but its complex nature poses significant challenges for control implementation.In response,this paper proposes an easily solvable adaptive control scheme to achieve high-performance trajectory tracking of the SSCF system subject to actuator efficiency losses and external disturbances.Most existing adaptive controllers based on the certaintyequivalent(CE)principle show unpredictability and nonconvergence in their online parameter estimations.To overcome the above vulnerabilities and the difficulties caused by input failures of SSCF,this paper proposes an adaptive estimator based on scaling immersion and invariance(I&I),which reduces the computational complexity while improving the performance of the parameter estimator.Besides,a barrier Lyapunov function(BLF)is applied to satisfy both the boundedness of the system states and the singularity avoidance of the computation.It is proved that the estimator error becomes sufficiently small to converge to a specified attractive invariant manifold and the closed-loop SSCF system can obtain asymptotic stability under full-state constraints.Finally,numerical simulations are performed for comparison and analysis to verify the effectiveness and superiority of the proposed method.

Effects of grassland management on the community structure, aboveground biomass and stability of a temperate steppe in Inner Mongolia, China

Plant community structure responds strongly to anthropogenic disturbances, which greatly influence community stability. The changes in community structure, aboveground biomass（AGB）, biodiversity and community stability associated with different management practices were studied with a three-year field investigation in a temperate steppe of Inner Mongolia, China. The species richness, Shannon-Wiener index, evenness, plant functional type abundance, AGB, temporal community stability, summed covariance, scaling coefficient and dominant species stability were compared among areas subjected to long-term reservation（R）, long-term grazing（G）, mowing since enclosure in 2008（M） and grazing enclosure since 2008（E）. Site R had higher perennial grass abundance and lower species richness than sites G, M and E, although the AGB was not significantly different among the four sites. The species structure varied from a single dominant species at site R to multiple dominant species at sites G, M and E. The long-term reservation grassland had lower biodiversity but higher stability, whereas the enclosed grassland with/without mowing had higher biodiversity but lower stability. Different stability mechanisms, such as the compensatory dynamics, mean-variance scaling and dominant species stability were examined. Results showed that community stability was most closely related to the relative stability of the dominant species, which supports the biomass ratio hypothesis proposed by Grime.

THE UNIQUE PROPERTIES OF THE SOLID-LIKE CONFINED LIQUID FILMS:A LARGE SCALE MOLECULAR DYNAMICS SIMULATION APPROACH

The properties of the confined liquid are dramatically different from those of the bulk state, which were reviewed in the present work. We performed large-scale molecular dynamics simulations and full-atom nonequilibrium molecular dynamics simulations to investigate the shear response of the confined simple liquid as well as the n-hexadecane ultrathin films. The shear viscosity of the confined simple liquid increases with the decrease of the film thickness. Apart from the well-known ordered structure, the confined n-hexaxiecane exhibited a transition from 7 layers to 6 in our simulations while undergoing an increasing shear velocity. Various slip regimes of the confined n-hexadecane were obtained. Viscosity coefficients of individual layers were examined and the results revealed that the local viscosity＇coefficient varies with the distance from the wall. The individual n-hexadecane layers showed the shear-thinning behaviors which can be correlated with the occurrence of the slip. This study aimed at elucidating the detailed shear response of the confined liquid and may be used in the design and application of microand nano-devices.

Anomalous friction of graphene nanoribbons on waved graphenes

Friction plays a critical role in the function and maintenance of small-scale structures, where the conventional Coulomb friction law often fails. To probe the friction at small scales, here we present a molecular dynamics study on the process of dragging graphene nanoribbons on waved graphene substrates. The simulation shows that the induced friction on graphene with zero waviness is ultra-low and closely related to the surface energy barrier. On waved graphenes, the friction generally increases with the amplitude of the wave at a fixed period, but anomalously increases and then decreases with the period at a fixed amplitude. These findings provide the insights into the ultraqow friction at small scales, as well as some guidelines into the fabrication ofgraphene-based nano-composites with high performance.

Multi-time scale dynamics in power electronics-dominated power systems

Electric power infrastructure has recently undergone a comprehensive transformation from electromagnetics to semiconductors. Such a development is attributed to the rapid growth of power electronic converter applications in the load side to realize energy conservation and on the supply side for renewable generations and power transmissions using high voltage direct current transmission. This transformation has altered the fundamental mechanism of power system dynamics, which demands the establishment of a new theory for power system control and protection. This paper presents thoughts on a theoretical framework for the coming semiconducting power systems.

A Hybrid Model for Reliability Aware and Energy-Efficiency in Multicore Systems

Recently,Multicore systems use Dynamic Voltage/Frequency Scaling(DV/FS)technology to allow the cores to operate with various voltage and/or frequencies than other cores to save power and enhance the performance.In this paper,an effective and reliable hybridmodel to reduce the energy and makespan in multicore systems is proposed.The proposed hybrid model enhances and integrates the greedy approach with dynamic programming to achieve optimal Voltage/Frequency(Vmin/F)levels.Then,the allocation process is applied based on the availableworkloads.The hybrid model consists of three stages.The first stage gets the optimum safe voltage while the second stage sets the level of energy efficiency,and finally,the third is the allocation stage.Experimental results on various benchmarks show that the proposed model can generate optimal solutions to save energy while minimizing the makespan penalty.Comparisons with other competitive algorithms show that the proposed model provides on average 48%improvements in energy-saving and achieves an 18%reduction in computation time while ensuring a high degree of system reliability.

FSM Based DFS Link for Network on Chip

As low power consumption is the main design issue involved in a network on chip (NoC), researchers are concentrating more on both algorithms and architectural approaches. The conventional Dynamic Frequency Scaling (DFS) and history based Frequency Scaling (HDFS) algorithms are utilized to process the energy constrained data traffic. However, these conventional algorithms achieve higher energy efficiencies, and they result in performance degradation due to the auxiliary latency between clock domains. In this paper, we present a variable power optimization interface for NoC using a Finite State Machine (FSM) approach to attain better performance improvement. The parameters are estimated using 45 nm TSMCCMOS technology. In comparison with DFS system, the evaluation results show that FSM-DFS link achieves 81.55% dynamic power savings on the links in the on-chip network, and 37.5% leakage power savings of the link. Also, this proposed work is evaluated for various performance parameters and compared with conventional work. The simulation results are superior to conventional work.

Influence of Dodecyl Trimethylammonium Chloride on the Structure and Properties of Konjac Glucanmannan

This study presents the interaction between konjac glucanmannan（KGM） and cationic surfactant dodecyl trimethylammonium chloride（DTAC） to provide theoretical guidance and prediction for the experimental design and application of this composite system. Dissipative particle dynamics（DPD） method was used to simulate the interaction between KGM and the cationic surfactant. Influences of concentration, temperature and shear process on the structure and properties of aggregates were mainly examined. The results revealed that the density peak increased with the increase of concentration of KGM. With increasing the temperature, density peak moved to the right and increased, and then decreased when the temperature rose to a certain value. The density peak moved to the right at the low shear rate while decreased at the high one. During simulation, the high viscosity related to the low diffusion rate, which made it difficult to form a large continuous phase.

The transmission mechanism theory of disease dynamics:Its aims,assumptions and limitations

Most of the progress in the development of single scale mathematical and computational models for the study of infectious disease dynamics which now span over a century is build on a body of knowledge that has been developed to address particular single scale descriptions of infectious disease dynamics based on understanding disease transmission process.Although this single scale understanding of infectious disease dynamics is now founded on a body of knowledge with a long history,dating back to over a century now,that knowledge has not yet been formalized into a scientific theory.In this article,we formalize this accumulated body of knowledge into a scientific theory called the transmission mechanism theory of disease dynamics which states that at every scale of organization of an infectious disease system,disease dynamics is determined by transmission as the main dynamic disease process.Therefore,the transmission mechanism theory of disease dynamics can be seen as formalizing knowledge that has been inherent in the study of infectious disease dynamics using single scale mathematical and computational models for over a century now.The objective of this article is to summarize this existing knowledge about single scale modelling of infectious dynamics by means of a scientific theory called the transmission mechanism theory of disease dynamics and highlight its aims,assumptions and limitations.

Energy-efficient reconfigurable processor for QC-LDPC via adaptive coding-voltage-frequency tuning

To apply a quasi-cyclic low density parity check(QC-LDPC)to different scenarios,a data-stream driven pipelined macro instruction set and a reconfigurable processor architecture are proposed for the typical QC-LDPC algorithm.The data-level parallelism is improved by instructions to dynamically configure the multi-core computing units.Simultaneously,an intelligent adjustment strategy based on a programmable wake-up controller(WuC)is designed so that the computing mode,operating voltage,and frequency of the QC-LDPC algorithm can be adjusted.This adjustment can improve the computing efficiency of the processor.The QC-LDPC processors are verified on the Xilinx ZCU102 field programmable gate array(FPGA)board and the computing efficiency is measured.The experimental results indicate that the QC-LDPC processor can support two encoding lengths of three typical QC-LDPC algorithms and 20 adaptive operating modes of operating voltage and frequency.The maximum efficiency can reach up to 12.18 Gbit/(s·W),which is more flexible than existing state-of-the-art processors for QC-LDPC.