The First Five Years of a Phase Theory for Complex Systems and Networks

In this paper,we review the development of a phase theory for systems and networks in its first five years,represented by a trilogy:Matrix phases and their properties;The MIMO LTI system phase response,its physical interpretations,the small phase theorem,and the sectored real lemma;The synchronization of a multi-agent network using phase alignment.Towards the end,we also summarize a list of ongoing research on the phase theory and speculate what will happen in the next five years.

Integrating phosphorus management and cropping technology for sustainable maize production

Achieving high maize yields and efficient phosphorus(P)use with limited environmental impacts is one of the greatest challenges in sustainable maize production.Increasing plant density is considered an effective approach for achieving high maize yields.However,the low mobility of P in soils and the scarcity of natural P resources have hindered the development of methods that can simultaneously optimize P use and mitigate the P-related environmental footprint at high plant densities.In this study,meta-analysis and substance flow analysis were conducted to evaluate the effects of different types of mineral P fertilizer on maize yield at varying plant densities and assess the flow of P from rock phosphate mining to P fertilizer use for maize production in China.A significantly higher yield was obtained at higher plant densities than at lower plant densities.The application of single superphosphate,triple super-phosphate,and calcium magnesium phosphate at high plant densities resulted in higher yields and a smaller environmental footprint than the application of diammonium phosphate and monoammonium phosphate.Our scenario analyses suggest that combining the optimal P type and application rate with a high plant density could increase maize yield by 22%.Further,the P resource use efficiency throughout the P supply chain increased by 39%,whereas the P-related environmental footprint decreased by 33%.Thus,simultaneously optimizing the P type and application rate at high plant densities achieved multiple objectives during maize production,indicating that combining P management with cropping techniques is a practical approach to sustainable maize production.These findings offer strategic,synergistic options for achieving sustainable agricultural development.

Effect of gas blowing nozzle angle on multiphase flow and mass transfer during RH refining process

A three-dimensional mathematical model was developed to investigate the effect of gas blowing nozzle angles on multiphase flow,circulation flow rate,and mixing time during Ruhrstahl-Heraeus(RH) refining process.Also,a water model with a geometric scale of 1:4 from an industrial RH furnace of 260 t was built up,and measurements were carried out to validate the mathematical model.The results show that,with a conventional gas blowing nozzle and the total gas flow rate of 40 L·min^(-1),the mixing time predicted by the mathematical model agrees well with the measured values.The deviations between the model predictions and the measured values are in the range of about 1.3%–7.3% at the selected three monitoring locations,where the mixing time was defined as the required time when the dimensionless concentration is within 3% deviation from the bath averaged value.In addition,the circulation flow rate was 9 kg·s^(-1).When the gas blowing nozzle was horizontally rotated by either 30° or 45°,the circulation flow rate was found to be increased by about 15% compared to a conventional nozzle,due to the rotational flow formed in the up-snorkel.Furthermore,the mixing time at the monitoring point 1,2,and 3 was shortened by around 21.3%,28.2%,and 12.3%,respectively.With the nozzle angle of 30° and 45°,the averaged residence time of 128 bubbles in liquid was increased by around 33.3%.

Modelling erosion of a single rock block using a coupled CFD-DEM approach

Rock block removal is the prevalent physical mechanism for rock erosion and could affect the stability of dam foundations and spillways.Despite this,understanding of block removal is still inadequate because of the complex interactions among block characteristics,hydraulic forces,and erosive processes acting on the block.Herein,based on a previously conducted physical experiment of erosion of a single rock block,the removal processes of two different protruding blocks are represented by a coupled computational fluid dynamics-discrete element model(CFD-DEM)approach under varied flow conditions.Additionally,the blocks could be rotated with respect to the flow direction to consider the effect of the discontinuity orientation on the block removal process.Simulation results visualize the entire block removal process.The simulations reproduce the effects of the discontinuity orientation on the critical flow velocity inducing block incipient motion and the trajectory of the block motion observed in the physical experiments.The numerical results present a similar tendency of the critical velocities at different discontinuity orientations but have slightly lower values.The trajectory of the block in the simulations fits well with the experimental measurements.The relationship between the dimensionless critical shear stress and discontinuity orientation observed from the simulations shows that the effect of block protrusion becomes more dominant on the block incipient motion with the increase of relative protrusion height.To our knowledge,this present study is the first attempt to use the coupled finite volume method(FVM)-DEM approach for modelling the interaction behavior between the block and the flowing water so that the block removal process can be reproduced and analyzed.

A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software Systems

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple levels.Combining different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve performance.During the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these errors.Numerous studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming models.Despite the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been developed.Therefore,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot detect.For the first time,this paper presents a classification of operational errors that can result from the integration of the three models.This paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and OpenACC.This hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic technology.The hybrid technique can detect more errors because it combines two distinct technologies.The proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environment are left to the dynamic technology,which completes the validation.

Review of advanced road materials, structures, equipment, and detection technologies

As a vital and integral component of transportation infrastructure,pavement has a direct and tangible impact on socio-economic sustainability.In recent years,an influx of groundbreaking and state-of-the-art materials,structures,equipment,and detection technologies related to road engineering have continually and progressively emerged,reshaping the landscape of pavement systems.There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies.Therefore,Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of“advanced road materials,structures,equipment,and detection technologies”.This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars,all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering.It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering:advanced road materials,advanced road structures and performance evaluation,advanced road construction equipment and technology,and advanced road detection and assessment technologies.

Simulation study on the influence of longitudinal dynamic force on extreme-long heavy-haul trains

Purpose–The objective of this study is to investigate the impact of longitudinal forces on extreme-long heavy-haul trains,providing new insights and methods for their design and operation,thereby enhancing safety,operational efficiency and track system design.Design/methodology/approach–A longitudinal dynamics simulation model of the super long heavy haul train was established and verified by the braking test data of 30,000 t heavy-haul combination train on the long and steep down grade of Daqing Line.The simulation model was used to analyze the influence of factors on the longitudinal force of super long heavy haul train.Findings–Under normal conditions,the formation length of extreme-long heavy-haul combined train has a small effect on the maximum longitudinal coupler force under full service braking and emergency braking on the straight line.The slope difference of the long and steep down grade has a great impact on the maximum longitudinal coupler force of the extreme-long heavy-haul trains.Under the condition that the longitudinal force does not exceed the safety limit of 2,250 kN under full service braking at the speed of 60 km/h the maximum allowable slope difference of long and steep down grade for 40,000 t super long heavy-haul combined trains is 13‰,and that of 100,000 t is only 5‰.Originality/value–The results will provide important theoretical basis and practical guidance for further improving the transportation efficiency and safety of extreme-long heavy-haul trains.

Monitoring energy usage of heavy-haul iron ore trains with on-board energy meter for improving energy efficiency

Purpose-For billing purposes,heavy-haul locomotives in Sweden are equipped with on-board energy meters,which can record several parameters,e.g.,used energy,regenerated energy,speed and position.Since there is a strong demand for improving energy efficiency in Sweden,data from the energy meters can be used to obtain a better understanding of the detailed energy usage of heavy-haul trains and identify potential for future improvements.Design/methodology/approach-To monitor energy efficiency,the present study,therefore,develops key performance indicators(KPIs),which can be calculated with energy meter data to reflect the energy efficiency of heavy-haul trains in operation.Energy meter data of IORE class locomotives,hauling highly uniform 30-tonne axle load trains with 68 wagons,together with additional data sources,are analysed to identify significant parameters for describing driver influence on energy usage.Findings-Results show that driver behaviour varies significantly and has the single largest influence on energy usage.Furthermore,parametric studies are performed with help of simulation to identify the influence of different operational and rolling stock conditions,e.g.,axle loads and number of wagons,on energy usage.Originality/value-Based on the parametric studies,some operational parameters which have significant impact on energy efficiency are found and then the KPIs are derived.In the end,some possible measures for improving energy performance in heavy-haul operations are given.

Virtual strain loading method for low temperature cohesive failure of asphalt binder

Cohesive failure is one of the primary reasons for low-temperature cracking in asphalt pavements.Understanding the micro-level mechanism is crucial for comprehending cohesive failure behavior.However,previous literature has not fully reported on this aspect.Moreover,there has been insufficient attention given to the correlation between macroscopic and microscopic failures.To address these issues,this study employed molecular dynamics simulation to investigate the low-temperature tensile behavior of asphalt binder.By applying virtual strain,the separation work during asphalt binder tensile failure was calculated.Additionally,a correlation between macroscopic and microscopic tensile behaviors was established.Specifically,a quadrilateral asphalt binder model was generated based on SARA fractions.By applying various combinations of virtual strain loading,the separation work at tensile failure was determined.Furthermore,the impact of strain loading combinations on separation work was analyzed.Normalization was employed to establish the correlation between macroscopic and microscopic tensile behaviors.The results indicated that thermodynamic and classical mechanical indicators validated the reliability of the tetragonal asphalt binder model.The strain loading combination consists of strain rate and loading number.All strain loading combinations exhibited the similar tensile failure characteristic.The critical separation strain was hardly influenced by strain loading combination.However,increasing strain rate significantly enhanced both the maximum traction stress and separation work of the asphalt binder.An increment in the loading number led to a decrease in separation work.The virtual strain combination of 0.5%-80 provided a more accurate representation of the actual asphalt's tensile behavior trend.

High-rate metal-free MXene microsupercapacitors on paper substrates

MXene is a promising energy storage material for miniaturized microbatteries and microsupercapacitors(MSCs).Despite its superior electrochemical performance,only a few studies have reported MXene-based ultrahigh-rate(>1000 mV s^(−1))on-paper MSCs,mainly due to the reduced electrical conductance of MXene films deposited on paper.Herein,ultrahigh-rate metal-free on-paper MSCs based on heterogeneous MXene/poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)(PEDOT:PSS)-stack electrodes are fabricated through the combination of direct ink writing and femtosecond laser scribing.With a footprint area of only 20 mm^(2),the on-paper MSCs exhibit excellent high-rate capacitive behavior with an areal capacitance of 5.7 mF cm^(−2)and long cycle life(>95%capacitance retention after 10,000 cycles)at a high scan rate of 1000 mV s^(−1),outperforming most of the present on-paper MSCs.Furthermore,the heterogeneous MXene/PEDOT:PSS electrodes can interconnect individual MSCs into metal-free on-paper MSC arrays,which can also be simultaneously charged/discharged at 1000 mV s^(−1),showing scalable capacitive performance.The heterogeneous MXene/PEDOT:PSS stacks are a promising electrode structure for on-paper MSCs to serve as ultrafast miniaturized energy storage components for emerging paper electronics.

Research progress in CALPHAD assisted metal additive manufacturing

Metal additive manufacturing(MAM)technology has experienced rapid development in recent years.As both equipment and materials progress towards increased maturity and commercialization,material metallurgy technology based on high energy sources has become a key factor influencing the future development of MAM.The calculation of phase diagrams(CALPHAD)is an essential method and tool for constructing multi-component phase diagrams by employing experimental phase diagrams and Gibbs free energy models of simple systems.By combining with the element mobility data and non-equilibrium phase transition model,it has been widely used in the analysis of traditional metal materials.The development of CALPHAD application technology for MAM is focused on the compositional design of printable materials,the reduction of metallurgical imperfections,and the control of microstructural attributes.This endeavor carries considerable theoretical and practical significance.This paper summarizes the important achievements of CALPHAD in additive manufacturing(AM)technology in recent years,including material design,process parameter optimization,microstructure evolution simulation,and properties prediction.Finally,the limitations of applying CALPHAD technology to MAM technology are discussed,along with prospective research directions.

基于拍卖理论的动态多代理同类机调度算法

This paper addresses a multi-agent scheduling problem with uniform parallel machines owned by a resource agent and competing jobs with dynamic arrival times that belong to different consumer agents.All agents are self-interested and rational with the aim of maximizing their own objectives,resulting in intense resource competition among consumer agents and strategic behaviors of unwillingness to disclose private information.Within the context,a centralized scheduling approach is unfeasible,and a decentralized approach is considered to deal with the targeted problem.This study aims to generate a stable and collaborative solution with high social welfare while simultaneously accommodating consumer agents’preferences under incomplete information.For this purpose,a dynamic iterative auction-based approach based on a decentralized decision-making procedure is developed.In the proposed approach,a dynamic auction procedure is established for dynamic jobs participating in a realtime auction,and a straightforward and easy-to-implement bidding strategy without price is presented to reduce the complexity of bid determination.In addition,an adaptive Hungarian algorithm is applied to solve the winner determination problem efficiently.A theoretical analysis is conducted to prove that the proposed approach is individually rational and that the myopic bidding strategy is a weakly dominant strategy for consumer agents submitting bids.Extensive computational experiments demonstrate that the developed approach achieves high-quality solutions and exhibits considerable stability on largescale problems with numerous consumer agents and jobs.A further multi-agent scheduling problem considering multiple resource agents will be studied in future work.

Machine learning algorithm partially reconfigured on FPGA for an image edge detection system

Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)signal.However,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or spoofing.One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images.But a high effort is required for image edge extraction.Here a support vector regression(SVR)model is proposed to reduce this computational load and processing time.The dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable region.Results show that the implementation in hardware is 68 times faster than that in software.This architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without DPR.This is also the lowest power consumption in current machine learning hardware implementations.Besides,the circuitry area is 41 times smaller.SVR with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning trajectory.This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.

Flexibility Prediction of Aggregated Electric Vehicles and Domestic Hot Water Systems in Smart Grids

With the growth of intermittent renewable energy generation in power grids,there is an increasing demand for controllable resources to be deployed to guarantee power quality and frequency stability.The flexibility of demand response(DR)resources has become a valuable solution to this problem.However,existing research indicates that problems on flexibility prediction of DR resources have not been investigated.This study applied the temporal convolution network(TCN)-combined transformer,a deep learning technique to predict the aggregated flexibility of two types of DR resources,that is,electric vehicles(EVs)and domestic hot water system(DHWS).The prediction uses historical power consumption data of these DR resources and DR signals(DSs)to facilitate prediction.The prediction can generate the size and maintenance time of the aggregated flexibility.The accuracy of the flexibility prediction results was verified through simulations of case studies.The simulation results show that under different maintenance times,the size of the flexibility changed.The proposed DR resource flexibility prediction method demonstrates its application in unlocking the demand-side flexibility to provide a reserve to grids.

Spatial distribution modeling of subsurface bedrock using a developed automated intelligence deep learning procedure:A case study in Sweden

Due to associated uncertainties,modelling the spatial distribution of depth to bedrock(DTB) is an important and challenging concern in many geo-engineering applications.The association between DTB,the safety and economy of design structures implies that generating more precise predictive models can be of vital interest.In the present study,the challenge of applying an optimally predictive threedimensional(3D) spatial DTB model for an area in Stockholm,Sweden was addressed using an automated intelligent computing design procedure.The process was developed and programmed in both C++and Python to track their performance in specified tasks and also to cover a wide variety of diffe rent internal characteristics and libraries.In comparison to the ordinary Kriging(OK) geostatistical tool,the superiority of the developed automated intelligence system was demonstrated through the analysis of confusion matrices and the ranked accuracies of different statistical errors.The re sults showed that in the absence of measured data,the intelligence models as a flexible and efficient alternative approach can account for associated uncertainties,thus creating more accurate spatial 3D models and providing an appropriate prediction at any point in the subsurface of the study area.

Effects of Flue Gas Internal Recirculation on NOx and SOx Emissions in a Co-Firing Boiler

Volumetric combustion has been developed to realize a high substitution ratio of biomass in co-firing boilers, which features an intensive flue gas internal recirculation inside furnace. However, the characteristics of NOx and SOx emissions in large-scale boilers with volumetric combustion were not fully clear. In this paper, an Aspen Plus model of volumetric combustion system was built up based on a co-firing boiler. In order to characterize the reductions of NOx and SOx, three biomass substitution ratios were involved, namely, 100% biomass, 45% biomass with 55% coal, and 100% coal. The effects of flue gas recirculation ratio, air preheating temperature, oxygen concentration, and fuel types on pollutants emission in the volumetric combustion system were investigated. According to the results, it was concluded the higher substitution ratio of biomass in a co-firing boiler, the lower emissions of NOx and SOx. Moreover, flue gas internal recirculation is an effective pathway for NOx reduction and an increased recirculation ratio resulted in a significant decreasing of NOx emission;however, the SOx increased slightly. The influences of air preheating temperature and O2 concentration on NOx emission were getting weak with increasing of recirculation ratio. When 10% or even higher of flue gas was recycled, it was observed that almost no NOx formed thermodynamically under all studied conditions. Finally, to reach a low emission level of NOx, less energy would be consumed during biomass combustion than coal combustion process for internal recirculation of flue gas.

Prediction and Verification of Resistance Spot Welding Results of Ultra-High Strength Steels through FE Simulations

Resistance spot welding (RSW) is the most common welding method in automotive engineering due to its low cost and high ability of automation. However, physical weldability testing is costly, time consuming and dependent of supplies of material and equipment. Finite Element (FE) simulations have been utilized to understand, verify and optimize manufacturing processes more efficiently. The present work aims to verify the capability of FE models for the RSW process by comparing simulation results to physical experiments for materials used in automotive production, with yield strengths from approximately 280 MPa to more than 1500 MPa. Previous research has mainly focused on lower strength materials. The physical weld results were assessed using destructive testing and an analysis of expulsion limits was also carried out. Extensive new determination of material data was carried out. The material data analysis was based on physical testing of material specimens, material simulation and comparison to data from literature. The study showed good agreement between simulations and physical testing. The mean absolute error of weld nugget size was 0.68 mm and the mean absolute error of expulsion limit was 1.10 kA.

Application of Different Models for the Prediction of the Kinetics of Direct Reduction of Natural Iron Ores

Simulation of the direct reduction conditions was performed in a laboratory furnace. Lump samples from natural hematite iron ore were reduced by a gas mixture of H2 and CO (H2/CO =1.5) at temperatures of 700°C, 800°C and 900°C. The effect of reduction temperature on the reduction degree, reduction rate of samples and carbon deposition were investigated and discussed in this study. The thermo-gravimetric data obtained from the reduction experiments was run in a programme that calculates the solid conversion rate. Also, three models: 1) Grain Model (GM), 2) Volumetric Model (VM), and 3) the Random Pore Model (RPM), were used to estimate the reduction kinetics of natural iron ores. It was found that the RPM model result agreed best with the obtained experimental results. Furthermore, it gave better predictions of the natural iron oxide conversion and thereby the reduction kinetics.

Optimizing Time-Spectral Solution of Initial-Value Problems

Time-spectral solution of ordinary and partial differential equations is often regarded as an inefficient approach. The associated extension of the time domain, as compared to finite difference methods, is believed to result in uncomfortably many numerical operations and high memory requirements. It is shown in this work that performance is substantially enhanced by the introduction of algorithms for temporal and spatial subdomains in combination with sparse matrix methods. The accuracy and efficiency of the recently developed time spectral, generalized weighted residual method (GWRM) are compared to that of the explicit Lax-Wendroff and implicit Crank-Nicolson methods. Three initial-value PDEs are employed as model problems;the 1D Burger equation, a forced 1D wave equation and a coupled system of 14 linearized ideal magnetohydrodynamic (MHD) equations. It is found that the GWRM is more efficient than the time-stepping methods at high accuracies. The advantageous scalings Nt<sup style="margin-left:-6px;">1.0Ns<sup style="margin-left:-6px;">1.43 and Nt<sup style="margin-left:-6px;">0.0Ns<sup style="margin-left:-6px;">1.08 were obtained for CPU time and memory requirements, respectively, with Nt and Ns denoting the number of temporal and spatial subdomains. For time-averaged solution of the two-time-scales forced wave equation, GWRM performance exceeds that of the finite difference methods by an order of magnitude both in terms of CPU time and memory requirement. Favorable subdomain scaling is demonstrated for the MHD equations, indicating a potential for efficient solution of advanced initial-value problems in, for example, fluid mechanics and MHD.

Study on Refining Performances in Chemi-mechanical Pulping of Mixed Poplar and Eucalypt Woodchips

The refining performances of mixed poplar and eucalyptwoodchips(mixture ratio 6:4)were investigated at medium and highpulp consistency via chemi-mechanical pulping(CMP).The specificrefining energy consumption(SEC),fiber fraction proportion,andCanadian standard freeness(CSF)were determined to evaluate the effectsof pulp consistency and NaOH dosage on the refining performancesof mixed poplar and eucalypt woodchips.While the dosage of NaOHfor impregnation was maintained constant,the SEC and shive contentincreased with increasing pulp consistency.Different fractions obtainedfrom the Bauer-McNett classifier showed that higher pulp consistencycould be expected to yield more long fibers and shive in the stock.Upon increasing the NaOH dosage,the shive content and SEC reducedsignificantly.When the NaOH dosage was increased to 6%,the resultsindicated that it was difficult to reduce the shive content to less than 1%athigh pulp consistencies(25%~35%),whereas 0.18%shive fraction couldbe achieved at a medium pulp consistency(15%).