Abstract: The Gaoloushan Tunnel is a control project for Longnan city and Jiuzhaigou, Sichuan Province. With the characteristics of "three high and one large," this project is a typical representative of a deeply buried, long highway tunnel under complex geological conditions. In this dissertation, two types of rockburst in the field are taken as the research objects. By impacting a rockburst experimental system and setting different stress paths, an image acquisition system recorded the entire process of impact rockburst and slab buckling rockburst in real time to analyze the characteristics of spalling and ejection. Rockburst due to static loading was performed and compared with rockburst caused by static loading + dynamic loading. Then, the quality, scale distribution, shape, and fractal dimension of slab buckling rockburst test fragments (rockburst fragments 1), impact rockburst test fragments (rockburst fragments 2), and unknown types of rockburst fragments (rockburst fragments 3) collected in the field were compared. On this basis, the image change process of the rockburst test was combined to deepen the understanding of the causes of different types of rockburst fragments and the rockburst mechanism. The results show that (1) slab-buckling rockburst and impact rockburst differ in the damage dominant mechanism, one being tension damage dominant, the other being tension-shear damage dominant. V-shaped and pan-shaped rockburst pit shapes are consistent with the on-site rockburst situation, which proves the rationality of this experiment. Slab-buckling rockburst has attenuation characteristics during the rockburst process, unlike the impact rockburst. From an analysis of debris ejection velocity and rockburst pit shape, impact rockburst is more severe than slab buckling rockburst. (2) Type 1 rockburst fragments are dominated by medium-grained slate fragments, which are more easily broken in the length direction and have a much larger mass than rockburst fragments 2, which is closely related to the incubation mechanism of slab buckling rockburst with a slabbing failure structure formed by vertical stress concentration. (3) Because of the intervention of dynamic load, type 2 rockburst fragments are obviously sheared, so they are easier and more broken in the thickness direction, forming fragments dominated by coarse-grained flake fragments, which may be more harmful because of their large mass, large volume, and long ejection distance in on-site rockburst. (4) The above comparison and analysis show that type 3 rockburst fragments probably correspond to impact rockburst, and the flaky and "V"-shaped characteristics are a unique fragment type of this rockburst.
Abstract: The frequent occurrence of core disking in boreholes under high in-situ stress in deep rock engineering leads to the difficulty in intact core sampling on sites. In this study, rock and ore blocks with irregular sizes after blasting in stopes were selected to perform a point load strength index test, and the equivalent diameter and failure load of the samples with irregular sizes were obtained through the test. Then, the point load strength index of the sample was obtained. However, the results are affected by the size effect of the samples. Two correction coefficients ($ {f_1} $ and $ {f_2} $), including an exponential type related to the failure load as well as equivalent diameter and a linear type related to the equivalent diameter, were used to modify the point load strength index. The modified point load strength indexes are $I_{{\rm{s}}50\text{-}1}$ and $I_{{\rm{s}}50\text{-}2}$. The distribution characteristics of the point load strength index before and after modification were obtained. The distribution frequency of the point load strength index before and after modification basically meets the normal distribution requirements. Before modification, it presented a normal skewness distribution, and after modification, it basically presented a standard normal distribution. The normal distribution of the point load strength index after the linear-type modification is obvious, and its strength value is larger than that after the exponential-type modification on the whole. The point load strength is basically equal for the two modification methods when the equivalent diameter is approximately 50 mm. The results obtained by the exponential correction method are greatly affected by the size effect, and the deviation of the strength value is large. By contrast, the results obtained by the linear correction method are more accurate than that of the exponential correction menthod. With a 95% confidence interval, the confidence interval of the standard point load strength index of the rock sample is 1.09–1.57 MPa, and the mean value is 1.33 MPa. The confidence interval of the ore sample is 0.37–0.45 MPa, and the mean value is 0.39 MPa. Due to the small size of the regular samples and the influence of the blasting damage, the calculated values of the point load strength index based on the uniaxial compressive strength of the rock and ore samples are approximately 1.62 and 3.67 times of the test results, respectively. Therefore, to reduce the influence of the sample size effect and excavation blasting disturbance on the test results of the mechanical properties of surrounding rocks, rock blocks with less blasting disturbance and size of approximately 50 mm in three directions should be selected to perform the point load test in the deep engineering site, where it is difficult to obtain intact rock cores.
Abstract: To deeply understand the capillary diffusion and seepage hysteresis behavior of the leaching solution in unsaturated ore heaps, this study builds a capillary seepage model suitable for an unsaturated ore heap by employing the COMSOL multiphysics finite element numerical platform to perform the capillary seepage visual simulation. A time-domain reflector is used to detect in-situ liquid holdup changes in the unsaturated heap in real-time, and multifactor response regulations of the capillary seepage process are explored based on a design expert. The potential connection mechanism among the liquid holdup, capillary suction, porosity, and irrigation rate of unsaturated ore heaps is also discussed. Research results show that the heap porosity has an obvious impact on the heap liquid holdup than the irrigation intensity. The increased convergence of the liquid holdup improves with the spraying time, and the ore heap with small porosity takes a longer time to reach a steady status of the liquid holdup. When the effect of the liquid irrigation is not considered, the heap liquid holdup is positively correlated with the porosity ratio and hydraulic conductivity. Especially in the initial stage of the irrigation period (0–20 s), the effects of the irrigation rate, hydraulic conductivity, and porosity ratio on the ore heap liquid holdup are more significant. An unsaturated ore pile solution capillary seepage model considering the gas?liquid two-phase migration is preliminarily constructed. The capillary suction is observed to be more sensitive in the ore heap with lesser porosity. The larger the irrigation rate and the smaller the porosity, the greater is the capillary suction at the bottom of the ore heap, and it is easier for the ore heap to reach a steady state of the liquid holdup.
Abstract: The composition of nonmetallic inclusions in the steel varied continuously during the solidification and cooling process of the molten steel and the heating process of the solid steel. To quantitatively evaluate this evolution of inclusion composition, this study proposes an integrated model and discusses the effect of the cooling rate during continuous casting and the holding time during the heating process on the transformation of the inclusion composition. Besides, a concept of transformation fraction of inclusion composition was put forward. Using this concept, several characteristic curves with a significant application value were raised, including the isothermal transformation curve (time-temperature-transformation, TTT), continuous cooling transformation curve (CCT), and equal diameter transformation curve (time diameter transformation, TDT). The integrated model consisted of the fluid flow, heat transfer, solidification and element segregation, thermodynamic equilibrium between the steel and inclusions, mass transfer kinetics in the steel and inclusions, and the variation of the spatial position of the calculation domain. Employing the integrated model, the spatial distribution of inclusion composition in blooms was obtained. Since the transformation of inclusion composition was mainly due to reactions between CaO and CaS, the transformation fraction was used to characterize the extent of the transformation, which was defined as the ratio of the content of CaS in inclusions at a certain time to that in thermodynamic equilibrium at room temperature. The continuous cooling transformation curve of the inclusion composition in the bearing steel was obtained to analyze the effect of the cooling rate on the inclusion composition during the solidification and cooling of liquid steel. At a fixed cooling rate, the transformation fraction of the inclusion composition increased with the reaction time. Simultaneously, the critical cooling rate of different types of steel could be obtained intuitively using these curves. The isothermal transformation curve of the inclusion composition in the heavy rail steel was also acquired to estimate the effect of the heating temperature and holding time on the inclusion composition in solid steels. With the increase of the holding time and heating temperature, the transformation fraction of the inclusion composition had an apparent increase. Moreover, the influence of the steel composition and inclusion size on the transformation of the inclusion composition could be determined using the equal diameter transformation curve in pipeline steel at 1473 K. Inclusions with a small size almost transformed completely within 60 min, while larger inclusions only exhibit a slight change even after heating for several hours. These concepts and characteristic curves can intuitively show the composition transformation of nonmetallic inclusions in steels during the solidification and cooling of liquid steel and heating of solid steel, expanding the control strategy of inclusions in steels from liquid steel to solid steel.
Abstract: Offering the advantages of easy preparation and low cost, polymer systems account for many of the high-performance materials used in industry. With the widespread application of polymer materials, higher requirements for the properties of polymer products have been proposed. Recently, polymer-based functional composites have been a worldwide research focus, and the structure of the composites directly affects their properties. The special multilayer structure of shells and trees often brings excellent performance or special functions. Discussing the strength and toughness mechanism of natural materials helps guide new functional composite preparation. In recent decades, a novel micro/nano-lamination technology has attracted the interest of academia and industry. Micro/nano-lamination technology is a layer-by-layer assembly (LbL) technology, which can combine two or more types of polymers into tens of thousands of layers alternately arranged, and each microlayer thickness can reach nanometer level to form composite materials with an alternating layered structure. Compared with solution LbL assembly methods, such as dip coating, spin coating, and spray coating, it is a continuous melt processing technique that involves no solvent, which has the advantages of flexibility, versatility, economy, and eco-friendliness. Compared with the composites prepared by blending melt extrusion or with fewer layers, the shape memory properties, electrical properties, barrier properties, and mechanical properties of composites prepared by micro/nano-lamination technology are considerably improved. As an application of biomimetic materials in the polymer research field, multilayer alternating composites prepared using micro/nano-lamination have a special multilayer structure, rich layer interface, and micro/nano-scale layer confined space. The multilayer composites prepared by micro/nano-lamination have an important positive synergistic effect on mechanical properties, and the unique multilayer structure can adjust stress distribution, stress transfer, and microcrack propagation. In this paper, according to the research status of micro/nano-lamination, the principle and process of micro/nano-lamination are briefly introduced. The mechanical property enhancement mechanism of multilayer alternating composites is reviewed, including interlayer interface interaction, layer interface-induced crystallization, regulation of polymer phase morphology, regulation of the dispersion orientation of inorganic particles, and in situ fiber formation. Micro/nano-lamination technology can coordinate the properties of different materials, integrate the excellent properties of many types of polymer materials, and make composite materials with good comprehensive properties. This model of high-performance functional materials has a broad market application prospect.
Abstract: The brake pad is the key component in the braking system of high-speed railway trains. The running speeds of commercial high-speed railway trains in China can reach higher than 350 km·h?1. The friction coefficient of brake pads is a key factor determining the safety of any vehicle brake, and a high and stable friction coefficient is ideal for ensuring the safety of the braking system. In practical applications, the friction coefficient can vary because of the changes in the working conditions, such as sliding speed, braking pressure, and temperature between contact surfaces. Under severe conditions, such as high-speed braking and overload, the friction coefficient decreases markedly, which lengthens the braking distance and braking time. Based on the friction performance collaborative regulation theory of powder metallurgy friction materials, a Cu-based friction material was designed. The performance of the brake pad was tested on the full-scale dynamometer, and the characteristics of the friction film were analyzed in detail. Results show that the brake pad exhibits high stability of the friction coefficient, low wear loss, and the capability to protect the brake disc. Both the instantaneous friction coefficient and average friction coefficient of the developed brake pad meet the requirements of the TJ/CL307—2019 technical condition. The stability of the friction coefficient is 0.0015. The recession of the friction coefficient from 250 to 380 km·h?1 is as low as 0.027. The average friction coefficient at 380 km·h?1 remains at the relatively high value of 0.35, and the average wear loss is only 0.06 cm3·MJ?1. The excellent friction and braking performance of brake pads can be attributed to the formation of friction films with high strength and toughness and low transfer rate. The friction components with large particle sizes are used as external motion obstacles to nail the friction film. The submicron wear debris in the friction film serves as the meshing point between the friction film and the dual disc to provide friction resistance, thus maintaining the friction coefficient during high-speed braking. Oxides are continuously supplied by adding easily oxidized components, and the nanosized oxides generated by the severe grinding process are used in the dispersion-strengthening phase. The multiscale particles synergistically enhance the dynamic stability of the friction film. The metal oxide layer on the friction surface reduces and stabilizes the friction coefficient and enhances the wear resistance because it prevents metal–metal contact between the brake pad and the brake disc. The fade phenomenon that occurs under high braking speed and overload conditions is effectively prevented.
Abstract: Tin-based alloy solder joints are an indispensable key part of electronic products and the basis of realizing the functionalization of electronic components. The failure of an electronic product is often caused by solder joint damage. Life prediction of the solder joint is of great significance for the reliability research of electronic products. The intermetallic compound (IMC) thickness is an important parameter to evaluate the quality of solder joints. This study takes the thickness of the IMC layer and the assembly solder joints of the 62Sn36Pb2Ag QFP device as the key performance degradation parameter and the research object, respectively. After the reflowing process, Cu6Sn5 and Cu3Sn IMC phases were observed at the copper lead side, and the (CuxNi1-x)6Sn5 phase was observed at the PCB side. The evolution of interfacial microstructures was observed by a scanning electron microscope (SEM). The thickness of the IMC layer after storage at 94, 120, and 150 °C for different periods (1, 4, 9, 16, 25, 36, 49 days) was monitored. The growth process of the IMC is controlled by diffusion. As the storage time increases, the thickness of the IMC layer gradually increases. The growth rate of the IMC layer increases with the increase of the storage temperature because of the higher diffusion coefficient. Based on the Arrhenius equation, the growth kinetics model of the IMC with a bilateral interface is established. The failure density function is obtained by fitting the initial IMC thickness with a normal distribution, and the reliability function is then obtained to predict the long-term storage failure life of QFP assembly solder joints. Finally, this work calculates the median life and characteristic life of QFP assembly solder joints to be 16092 years and 17471 years, respectively. These results are expected to provide a new way to predict the life of solder joints stored for a long time and provide experimental and data support for the reliable application of the 62Sn36Pb2Ag solder.
Abstract: Machine learning algorithms are widely used to predict the corrosion rate of materials in a specific environment. However, the interpretability of such black-box models is poor, which hinders their application in the field of material corrosion. Therefore, to increase algorithm transparency in practical applications, the causal relationship in the material corrosion phenomenon based on machine learning models needs to be further explored. To solve the aforementioned problems, this study analyzed the corrosion process of carbon steel in the atmosphere with many variables and complex mechanisms and proposed an important variable mining framework based on the comprehensive intelligent model. This framework can mine the important environmental variables that affect the early atmospheric corrosion of carbon steel and their influence on the corrosion galvanic current. This study collected the hour-level atmospheric corrosion data, including relative humidity, temperature, rainfall, and O3, SO2, NO2, PM2.5, and PM10 concentrations, of 45# carbon steel from five test sites in China using the atmospheric corrosion monitor of the China Meteorological Administration. To ensure the stability of the results, three machine learning models with different fitting strategies, namely, random forest, gradient boosted regression trees, and backpropagation neural network, are constructed. Then, the multimodel ensemble important variable selection (MEIVS) algorithm is used to quantify the importance of environmental variables and extract important environmental variables that severely affect the early atmospheric corrosion of carbon steel. Eventually, the partial dependence plot (PDP) of the environmental variables and corrosion galvanic current is drawn. Based on the simulation results, three significant conclusions are obtained: (1) Compared with Pearson’s and Spearman’s correlation coefficients, the important environmental variables mined using the MEIVS algorithm are more consistent with the prior law of early atmospheric corrosion of carbon steel. Relative humidity, temperature, and rainfall have the most significant impact on the early atmospheric corrosion of carbon steel, and O3 has a considerable influence on the early atmospheric corrosion of carbon steel in Sanya. Moreover, other pollutants in various regions have a weak impact on the early atmospheric corrosion of carbon steel. (2) PDP shows that, in most cases, the corrosion galvanic current of 45# carbon steel is negatively correlated with temperature and positively correlated with relative humidity. (3) PDP and MEIVS are well consistent. The simulation reveals that PDP corresponding to important environmental variables has a greater range of change, and the changing trend of PDP can reflect the influence of environmental variables on the corrosion galvanic current.
Abstract: In this study, the linear thermal expansion coefficient of electrolytic through pitch copper (Cu-ETP) was used as a resonator material in the single-pressure refractive-index gas thermometer and was evaluated in situ at high precision via the multi-mode microwave resonance method in the temperature range of 4.3 to 299 K. Two experimental measurement schemes, cooling method (5–299 K) and temperature control method (4.3–26 K), are employed for different temperature ranges. These methods adopt the same calculation method, wherein the relation between the length and temperature is obtained first, and then the polynomial fitting is used to obtain the linear thermal expansion coefficient of the resonator. The resonator installed in the cryostat has a quasi–spherical shape, with similar radii in the x, y, and z axes; for example, if the radius in one direction is R, then the radii in the other two directions are 1.001R and 1.0005R. The accurate radius of the quasi–sphere in low temperature can be measured by the multi-mode microwave resonance method, which is a mature method with a significant non-ideal correction to reduce the difference between the actual and ideal environments. For the cooling method, to reduce the impact of random errors, we collect five microwave modes (TM11, TE11, TM12, TE12, and TE13) and repeat four experiment runs (Run9, Run10, Run12, and Run17), assuming the average value as the final result. The max radius deviation during the different modes is 0.37 μm, indicating that the result has a good mode consistency. Then, the measurement uncertainty of the radius is analyzed, with all values within 0.27 μm and the mode consistency being the main influencing item. The linear thermal expansion coefficient can be calculated by the polynomial fitting method with the standard uncertainty of 2.2×10?7 K?1, with repeatability being the main source of uncertainty. As for the controlling method, the same analyzing procedure is implemented, the max deviation of the radius during the four modes (TM11, TE11, TM12, and TE13) is 0.12 μm, and the deviation of different runs from the average value is within 0.0056 μm, smaller than the radius uncertainty, which has good repeatability. The standard uncertainty of radius is within 0.12 μm in the entire range and the non-ideal correction and frequency stability are the two main influencing factors. The standard uncertainty of the linear thermal expansion is 2.9×10?9 K?1, and the two main sources are the microwave mode consistency and repeatability. Due to the higher stability of temperature control and lower microwave measurement noise, the results determined by the temperature control method are more accurate. Finally, equations for the linear thermal expansion coefficient of Cu-ETP are further developed to realize a high-precision correlation between the experimental data and temperature.
Abstract: A swarm intelligence optimization algorithm is an effective method to rapidly solve large-scale complex optimization problems. The JAYA algorithm is a new swarm intelligence evolutionary optimization algorithm, which was proposed in 2016. Compared with other active evolutionary algorithms, the JAYA algorithm has several advantages, such as a clear mechanism, concise structure, and ease of implementation. Further, it has guiding characteristics, obtains the best solution, and avoids the worst solution. The JAYA algorithm has an excellent optimization effect on many problems, and it is one of the most influential algorithms in the field of swarm intelligence. However, when dealing with the CEC test suite, which contains and combines shifted, rotation, hybrid, combination, and other composite characteristics, and the complex engineering constrained optimization problems with considerable difficulty and challenges, the JAYA algorithm has some flaws, that is, it easily falls into the local extremum, its optimization accuracy is sometimes low, and its solution is unstable. To better solve complex function optimization and engineering constrained optimization problems and further enhance the optimization capability of the JAYA algorithm, a global optimization-oriented hybrid evolutionary JAYA algorithm is proposed. First, opposition-based learning is introduced to calculate the current best and worst individuals, which improves the possibility of the best and worst individuals jumping out of the local extremum region. Second, the sine–cosine operator and differential disturbance mechanism are introduced and integrated into individual position updating, which not only improves the diversity of the population but also better balances and meets the different requirements of the algorithm for exploration and mining in different iteration periods. Finally, in the algorithm structure, the hybrid evolution strategy with different parity states is adopted and the advantages of different evolution mechanisms are effectively used, which further improves the convergence and accuracy of the algorithm. Then, the pseudocode of the improved algorithm is given, and the theoretical analysis proves that the time complexity of the improved algorithm is consistent with the basic JAYA algorithm. Through the simulation experiment of function extremum optimization of six representative algorithms on multiple dimensions of the CEC2017 test suite, which contains and combines 30 benchmark functions and the optimal solution of six challenging engineering design problems, such as tension/compression spring, corrugated bulkhead, tubular column, reinforced concrete beam, welded beam, and car side impact. The optimal solution of the test results shows that the improved algorithm has significantly improved the optimization accuracy, convergence performance, and solution stability, and it has obvious advantages in solving CEC complex functions and engineering constrained optimization problems.
Abstract: To meet the lightweight design requirements of the control arm, an automobile suspension control arm with a carbon fiber reinforced plastics (CFRP)–aluminum foam sandwich structure was proposed, and the structure optimization design of the CFRP panel was performed. The accuracy of the cellular pore model of aluminum foam hexahedron was verified by the quasi-static compression test of aluminum foam. The performance parameters of carbon fiber reinforced plastics were obtained by the mechanical property test of CFRP. A suspension control arm composed of a CFRP–aluminum foam sandwich structure body and an aluminum alloy connector was designed, and the adhesive-bolted hybrid joint was used to connect the two. Based on this, the finite element model of the control arm of the CFRP–aluminum foam sandwich structure was established. The porosity of aluminum foam in the sandwich was 55%. The multi-level optimization method was used to optimize the layering of the CFRP panels. Free size optimization was used to obtain the layered shape of CFRP under four classical ply angles, during which the mass of the panel was reduced while its stiffness improved. Based on the regularization of the CFRP layer, the ply thickness was discretized into manufacturing thickness by size optimization. Simultaneously, the number of layers of the panel was determined, and its mass was further reduced as the stiffness of the composite material is also dependent on the ply angle. Therefore, the arrangement order of the classical ply angle was obtained by ply stacking sequence optimization, further improving the panel stiffness. The results show that compared with the steel control arm, the mass of the optimized sandwich structure control arm was reduced by 26%. Simultaneously, the maximum stress at the foam aluminum sandwich was reduced from 225.6 MPa before optimization to 151.2 MPa. The safety factor and the failure coefficient of the CFRP panel after optimization were 1.1 and 0.81, respectively, both meeting the strength requirements. From the stiffness perspective, the longitudinal stiffness of the optimized control arm increased by 54.7% compared to the initial control arm of the sandwich structure, 103.2% compared to the steel control arm, and the lateral stiffness increased by 37% compared to the initial control arm of the sandwich structure and 56% compared to the steel control arm, respectively. Thus, the stiffness improvement effect was obvious. The first-order modal frequency of the optimized control arm was 785 Hz, 573.1 Hz higher than that of the steel control arm, and the vibration performance was significantly improved.
Abstract: With the vigorous development of material synthesis, mechanical manufacturing, and computer technology, as well as the in-depth study of control theory and bionics, robotics has undergone tremendous changes in recent decades. From rigid robots to discrete redundancy robots, from continuum robots to soft robots, the application of robots has long been beyond traditional industrial fields such as assembly, welding, and painting. It has expanded to medicine, education, agriculture, the military, etc., covering almost every aspect of people's lives. Soft manipulators have broad application prospects in medicine, aerospace engineering, and other fields due to their excellent environmental adaptability and safe human–machine interaction. However, soft robots comprise flexible materials and often have no internal support structure, so their ends have a very limited carrying capacity. To compensate for this inadequacy, soft robots usually use the bending of the entire body to grasp objects or operate underwater to partially counteract gravity. In addition, the deformation state of a soft robot is difficult to estimate when it is affected by external force or in contact with the environment, which also causes many difficulties in the modeling and control of soft robots. In the case of inaccurate modeling and poor controllability, the accessibility and accuracy of its end are bound to be greatly compromised. For a class of line-driven soft manipulators, a modeling method based on strain parameterization is proposed that can describe the motion of soft manipulators in three-dimensional space under different wiring methods. First, the entire soft manipulator is treated as a Cosserat beam and modeled by the mature Cosserat beam theory, wherein the strain field of the soft manipulator is discretized using the Ritz method to obtain a set of ordinary differential equations, and then a back propagation (BP) neural network is used to complete the drive force conversion between the shape and driver spaces. A radial basis function (RBF) neural network is used to approximate and compensate for the unknown dynamics present in the soft manipulator model. The stability of the closed-loop system after introducing the adaptive neural network controller is then demonstrated on the basis of Lyapunov's stability theory. Finally, a series of simulation experiments are performed for the model and the adaptive neural network controller to verify the effectiveness of the model and the control algorithm. Therefore, the modeling control of a type of soft manipulator is realized.
Abstract: In a low-Earth orbit (LEO) satellite network, the satellite operation speed is high, the operation cycle is short, and intersatellite links change dynamically. To sense the intersatellite link state in time and select the correct route for an intelligent routing decision, a dendritic network-based intelligent-aware routing algorithm for LEO satellites is proposed in this paper. This algorithm divides the intersatellite link routing of an LEO satellite network into situation-aware, quality-aware, and routing-decision stages and establishes a routing policy framework with real-time correction capability from the source node to the destination. This approach overcomes the problems of the limited selection of routing paths from fixed labels of existing deep learning-based routing algorithms and the long convergence time of reinforcement learning-based routing algorithms.In the intersatellite link situational awareness stage, the intersatellite visibility of the entire LEO satellite network is periodically obtained by analyzing the constraint conditions of the intersatellite link establishment. In the intersatellite link quality perception stage, the final output of the probabilistic forwarding matrix based on the ant colony algorithm is used as the label of the training set, and the corresponding intersatellite link quality is evaluated using the probability value of the current node by selecting the next hop node. By changing the weight coefficients in the path cost function under different load states, more effective training set label data can be collected, which can be consequently used to improve the performance of the trained dendritic network. Moreover, the training set can be optimized in real-time through semi-supervised learning. The trained dendritic network is used to analyze and process the link state parameters, perceive the comprehensive service quality of the link, and output the evaluation value matrix of the next hop routing. It is also used to automatically adjust the weight of the global satellite network link. Meanwhile, the traditional Dijkstra algorithm is optimized to realize the quality perception of the intersatellite link. In the routing decision stage, the reciprocal of the evaluation value matrix is used as the adjacency matrix to pass the shortest-path algorithm. Then, the initial routing path between the source and destination nodes is obtained. Finally, the initial path is corrected via periodic monitoring to cope with the failure of the satellite node. The simulation results show that the routing algorithm based on the dendritic network has low computational complexity and fast convergence. The algorithm can determine the status of the intersatellite link establishment in time, assess the quality of the intersatellite link in real-time, and automatically avoid congested satellite nodes. Accordingly, its end-to-end path delay, delay jitter, and packet loss rate are lower than those of the traditional heuristic routing algorithm and Dijkstra routing algorithm.
Abstract: With software service transactions shifting from pay-before-use to pay-as-you-go, the Software as a Service (SaaS) subscription model is facing legalization and financialization challenges. This means that it does not accept financial payment on a pay-as-you-go basis, nor does it legally regulate the rights and obligations of service providers, consumers, and platforms. To address these issues, this paper introduces a new architecture called Smart Legal Contract (SLC), which is integrated into a service computing platform (SaaSC). To begin with, a contract-type service interface scheme is intended to handle the subscription process of service registration and publication on SaaS. In this scheme, we define six types of interactions, four kinds of microservice states, and their state transition procedures, and then establish the mapping from the general service interface following the OpenAPI Specification to the contract terms in the SLC-style SPESC language. To achieve a regularized interaction approach during service registration, a new term, called Service Registration Term (SRT), is proposed. Furthermore, the legal Negotiation-Acceptance mechanism is used to grant consumer rights to obtain software services. Second, in the process of service discovery and consumption, a payment mechanism for contracting demand is proposed. Specifically, based on the service matching approach with a three-level cache, other new terms, called Service Discovery Term (SDT) and Service Customization Term (SCT), are designed to specify the requests and responses of service discovery and invocation. A billing model driven by SRT, SDT, and SCT has been developed to implement fine-grained charging on the level of service interface calls and to evidence the preservation of service transactions in the blockchain. As a result, it provides a legal guarantee for the use of pay-as-you-go mode. From the aspect of service legalization, the SaaS+SaaSC architecture supports establishing three kinds of terms, including service registration, discovery, and customization terms, in an SLC-based software subscription contract so that a complete transaction procedure can be regulated among the three above parties based on their interactions, service states, and their transition process. In terms of service financialization, the interface declaration is appended to the SLC-based contract. By automatically executing smart contracts and checking the terms, the pay-as-you-go mode is implemented through fine-grained charging every time when calling the service interface. Furthermore, we take the weather forecast service as an example to implement and analyze the acquisition, delivery, and contractual payment of software services on blockchain smart contracts. The experimental results demonstrate the feasibility and effectiveness of the proposed SaaS+SaaSC architecture, which should be a practicable approach for contracting of software services.
Abstract: Offshore wind power has been the fastest-growing form of renewable energy for the last few years, owing to its effectiveness in achieving carbon neutrality through a reasonable and efficient utilization of wind power resources. With offshore wind farms gradually developing into the deep and far sea, greater attention is paid to the bearing characteristics of foundations for offshore wind turbines. Therefore, it becomes significantly important to explore new foundations, effectively promoting the development of offshore wind power. Numerous researchers have substantially investigated novel foundations for offshore wind turbines to help with offshore wind farm construction. Compared to other foundations, the pile–bucket composite foundation has obvious advantages in terms of bearing performance. In this paper, a series of numerical calculation models for pile–bucket composite foundations in heterogeneous saturated clay are established using the finite element software ABAQUS. Additionally, the undrained shear strength changes with depth are examined via field variables to explain soil heterogeneity in the finite element model. Next, the displacement control method is adopted to apply the vertical loading V, horizontal loading H, and bending moment M at the top of the foundation. Simultaneously, the ultimate bearing capacity of the foundations is obtained by the double tangent method, and the bearing capacity factors of each load direction are obtained by normalizing the ultimate bearing capacity in different calculations. To obtain the preliminary design method for the size of pile–bucket composite foundation, the priority of influencing factors is studied through the orthogonal test. The results show that the saturated clay coefficient K has a nominal effect on the vertical bearing capacity coefficient NcV. When K is different, NcV remains almost unchanged for a certain foundation. Concerning the impact of bucket shapes on the bearing capacity coefficient in three directions, great interaction is observed between the diameter D and the buried depth L of the bucket structure. The diameter of the bucket has the greatest influence on the bearing characteristics of the pile–bucket composite foundations, wherein increasing the former can significantly improve the latter. The research results provide a reference for the design of the pile–bucket composite foundation of an offshore wind turbine.
Abstract: The most commonly used method for industrial flue gas denitrification is selective catalytic reduction (SCR). However, the catalyst preparation is complex and expensive. The iron and steel industry produces large amounts of waste containing metal oxides that can be used as active catalytic components for SCR of nitrogen oxides. In this study, a novel catalyst for SCR of nitrogen oxides was prepared by roasting, sulfuric acid, and sulfuric acid-roasting modification of steelmaking sludge, which is used as the raw material. The physical and chemical properties of the catalysts from steelmaking sludge before and after modification were analyzed using Brunauer-Emmett-Teller analysis, scanning electron microscopy, X-ray diffraction, X-ray fluorescence, and temperature-programmed desorption of ammonia. It has been revealed that Fe, Mn, V, and Ti are the main active groups of the catalyst. Calcination can transform Fe3O4 to α-Fe2O3 with better denitrification activity, thus improving the catalyst reactivity. A high calcination temperature can cause a collapse of the pore structure of the catalyst, thereby decreasing the surface area and active sites and ultimately reducing the catalytic activity. The catalyst modified at the optimum calcination temperature of 400 °C has the highest catalytic activity at 350 °C and a denitrification efficiency of 57.6%. The sulfuric acid-modified catalyst has excellent catalytic activity. Sulfuric acid impregnation changes the surface morphology of the catalyst, reduces the grain size, generates numerous sulfate species, provides more acidic sites on the catalyst surface, and promotes catalyst performance. The 9 mol·L?1 sulfuric acid-modified catalyst has the highest denitrification efficiency at 300 °C. Compared with the unmodified catalyst, the denitrification efficiency significantly increased from 22.9% to 88.5%. Conversely, a denitrification efficiency of 72.9% is measured for the catalyst modified by sulfuric acid and roasting modification, which is lower than that of the sulfuric acid-modified catalyst at 300 °C. This may be explained by the fact that sulfuric acid and roasting modification causes not only structural changes in the catalyst but also the decomposition of the generated sulfate species, thereby leading to catalytic efficiency reduction. This work shows a feasible preparation of a low-cost SCR catalyst for denitrification by roasting and acid modification using steelmaking sludge as the raw material, provides a theoretical basis for developing low-cost denitrification catalysts using metallurgical solid wastes and promotes clean production in the metallurgical industry.
Abstract: The dynamic shear modulus and damping ratio are essential parameters for the site seismic response analysis of major projects. During highway construction along the Yellow River, the feasibility of using Yellow River sediment as a subgrade filler has been verified and valued. However, the dynamic characteristics of Yellow River sediment as subgrade filler are rarely studied. In this paper, the British GDS dynamic triaxial test system was used to perform dynamic triaxial stress control tests on the sediment taken from the Zhengzhou section of the middle and lower reaches of the Yellow River. A total of 11 groups of tests were performed to explore the effects of confining pressure, relative density, and test frequency on the dynamic shear stress–dynamic shear strain relationship, dynamic shear modulus G, and damping ratio D of Yellow River sediment. The backbone curve and hysteresis curve of the dynamic shear stress–dynamic shear strain relationship were plotted. The results show that the relationship between the dynamic shear modulus, damping ratio, and shear strain of Yellow River sediment can be described by the Hardin hyperbolic model, and confining pressure has the greatest influence on the dynamic shear modulus and damping ratio of Yellow River sediment. For a given shear strain condition, the larger the confining pressure is, the larger the dynamic shear modulus. When the strain level is large, the dynamic shear modulus increases with the relative density; the damping ratio decreases with increasing confining pressure and increasing relative density. Frequency has no obvious effect on the dynamic shear modulus and damping ratio. A comprehensive comparison with the dynamic characteristics of other soils shows that the dynamic shear modulus reduction curve law and damping ratio D curve law of Yellow River sediment are consistent with those of other soils, and their dynamic characteristics are closer to silt and sand, but not completely consistent with those of other soils, with certain particularity. Finally, considering the influence of confining pressure and relative density, combined with the existing empirical formula, an empirical formula that can better describe the relationship between the maximum dynamic shear modulus Gmax and the confining pressure and void ratio of Yellow River sediment is established. Additionally, a mathematical model of the dynamic shear modulus ratio G/Gmax and D is established. The fitting results show that the established model can better describe the variation in G/Gmax and D with the shear strain of Yellow River sediment. This capability provides an important basis for the seismic design of Yellow River sediment as subgrade filler.
Monthly, started in 1955 Supervising institution：Ministry of Education Sponsoring Institution：University of Science and Technology Beijing Editorial office：Editorial Department of Chinese Journal of Engineering Publisher：Science Press Chairperson：Ren-shu Yang Editor-in-Chief：Ai-xiang Wu ISSN 2095-9389CN 2095-9389
