Abstract: High-concentration tailings pastes are commonly encountered in the surface deposition and underground cemented paste backfill. It is necessary to forecast the pressure loss along the pipe and the flow behavior in the mined-out area when tailings pastes are transported in a pipeline or stored up in goaf. Traditionally, thixotropy has often been overlooked while designing tailings paste transport and disposal systems due to the complexity of thixotropy characterization. However, thixotropy plays an essential role in the rheology of tailings pastes. To achieve the accurate representation of thixotropy, a suitable constitutive model has to be selected in advance. Unfortunately, there is no current available thixotropic model for tailings pastes up to date. Moreover, tailings pastes exhibit complex thixotropy that is significantly affected by the particle size of tailings, though the relevant studies are not abundant. It is important to analyze quantitively the relationship between particle size and thixotropic parameters based on an appropriate constitutive model. In this work, the constitutive thixotropic model for tailings pastes proposed by Zhang et al. was adopted. To reveal the influence of particle size on the thixotropy of tailings pastes, samples of various mean particle sizes prepared from the same tailings were applied to constant shear rate experiments. Results show that the target tailings pastes display significant thixotropy, leading to shear thinning under steady shear rates. In the steady state, the static yield stress, dynamic yield stress, and Bingham viscosity appear to correlate linearly to the reciprocal of the square of the mean particle size. As for the transient state, the corresponding fit parameters show a strong linear dependence on the mean particle size. The proposed equilibrium and thixotropic models are valid. The forecasting models for equilibrium and transient shear stress are established based on data fit, which is attributed to the quantitative characterization of steady-state and transient rheology for thixotropic tailings pastes under the effect of particle size.
Abstract: Coarse aggregate paste filling is the core direction of today’s mine development. The coarse aggregate filling can effectively reduce the discharge of the solid mine waste, which is conducive to the realization of safe, clean, and efficient mining of the deposit and can also reduce the production costs of infill mining and promote the coordinated development of green mining. To study the pipeline conveying characteristics of the tailing?waste rock paste, the rheological properties were tested by a rheometer under different tailing?waste rock ratios and solid content conditions. A resistance equation integrating the compactness, water?cement ratio, and volume concentration was constructed. This was then brought into the Comsol software for simulations and compared with the actual measurement results of the ring pipe. Errors measured by the numerical model are verified to be all within 7%, indicating that the model reasonably calculated the resistance characteristics of the tailing-waste rock paste. Variation characteristics of the pipeline conveying resistance under different solid contents, tailing?waste rock ratios, and initial velocity conditions were also simulated. Experimental results show that the plastic viscosity and yield stress increase with the solid content and tailing?waste rock ratio. Due to the friction effect between the particles, the resistance loss tends to increase and then decrease with the tailing?waste rock ratio. The increase in the solid content leads to a decrease in the water content of the paste, which consequently results in difficulty in the flow of coarse aggregate slurry and a rapid increase in the resistance loss. The initial flow rate increases, the particle motion becomes unstable, the friction increases, and the growth rate of the drag loss increases greatly after the “inflection point” of 2.2 m·s?1. It is recommended that the mine should be filled with a tailing?waste rock ratio of 5∶5 and an initial flow rate of 2.2 m·s?1. The results have certain reference significance for the design of a coarse aggregate paste pipeline conveying system, which helps the development of coarse aggregate paste conveying technology and also has a positive effect on reducing the pipeline conveying resistance and extending the conveying distance.
Abstract: Recently, with increasing mining scale, intensity, and depth, the geological and mining conditions in coal mines are becoming more complicated; therefore, it has resulted in a more difficult situation of coal mine dynamic hazards, including rockburst, coal and gas outburst etc. Dynamic hazards are now posing a serious threat to the safety of coal mining. The precise forecasting of dynamic hazards is significant to their effective control. The acoustic emission (AE) monitoring technique is an effective geophysical monitoring and early warning method which can effectively reveal the characteristics and laws of coal and rock failure under loading. It has been successfully applied in the laboratory and engineering fields. To deeply analyze the characteristics of AE signals in the process of coal-rock damage and failure, thus, to help realize the precise monitoring and early warning of coal mine dynamic hazards, this study first conducted a uniaxial compression test on coal samples in the laboratory, and at the meantime, synchronously collected the full waveform data of AE and the loading data in the entire process of coal failure. Subsequently, using the feature extraction technique in the field of automatic speech recognition, this study extracted the Mel-frequency cepstral coefficient (MFCC) of AE and used it as the sample feature; the stress state of the coal sample was defined as the ratio of the current load the sample bore to its peak load and was employed as the sample label; a model for coal failure state forecasting was established by adopting machine learning methodology. Finally, the model’s forecasting accuracy was evaluated using the five-fold cross-validation method; the influence of different MFCC combinations as sample features on the forecasting accuracy of the model was discussed. The results show that MFCC can well characterize the failure state of coal samples. This parameter behaves in regular variation with increasing loading and shows the law of an obvious sudden increase or sudden decrease or increase followed by a sudden decrease when the loading exceeds 80% of the coal sample’s peak load. The established model can be well used to forecast coal failure state. The accuracy (ACC), true positive rate (TPR), true negative rate (TNR), and area under the curve (AUC) of the model forecasting reach 88.61%, 72.34%, 93.16%, and 0.93, respectively. Machine learning methodology can fully use MFCC features of AE and can identify essential sample features that are difficult to identify with the human eyes. Significant and key features included in the samples are the keys to the high forecasting accuracy of the model. TPR, TNR, and AUC of the model forecasting would be significantly influenced if crucial features were excluded from the samples. Adding features with low importance to the samples has little influence on the forecasting result of the model. This study’s results can provide a reference for further improving the prediction and early warning of coal and rock dynamic hazards.
Abstract: The expansion evolution law of internal fractures of coal under external load is of great significance to coalbed methane production and to control coal and gas outburst disasters. The coal body is in a three-dimensional (3D) stress state under the action of original in-situ stress. It is necessary to study the fracture evolution law of a loaded coal-containing gas under triaxial compression. The industrial computed tomographic (CT) scanning test of a loaded coal-containing gas under triaxial loading was carried out using the industrial micro-CT scanning system for the loaded coal-containing gas. The CT images and stress–strain curves of coal samples were obtained at each deformation stage. The 3D digital reconstruction of CT scanning data was carried out using image analysis software. Next, 3D visualization and quantitative characterization of coal sample internal fractures were realized. Based on the gray level co-occurrence matrix (GLCM) theory, the fracture dynamic expansion characteristics and laws of the loaded coal-containing gas were analyzed. The results show that the existence of gas pressure weakens the mechanical properties of the loaded coal-containing gas and accelerates the expansion of cracks. The two-dimensional fractures of the loaded coal-containing gas first close and then expand, and then expand rapidly after the peak, forming a connected two-dimensional fracture network. The 3D fracture volume and fracture density first show a decreasing and then an increasing trend, which can be divided into three stages: fracture compaction and closure, new fracture initiation and expansion, and main fracture accelerated expansion and penetration. In the GLCM analysis, the contrast first decreases and then increases, the energy and homogeneity first increase and then decrease, and the correlation presents a monotonic decreasing trend. The analysis results accurately describe the overall development law of the internal cracks of the loaded coal-containing gas changing with stress increase.
Abstract: Yushenfu mining area, with large scale and high intensity, is an important raw coal-producing area in northern Shaanxi, but the fragile ecological environment makes the mine geological environment problems caused by coal mining particularly relevant. To grasp the development law of surface cracks and reveal the formation mechanism caused by coal mining in the Yushenfu mining area, the typical working faces of Anshan Coal Mine, Caragana Tower Coal Mine, and No. 1 Coal Mine of Xiaobaodang in the Yushenfu Mining area were chosen as the research object to conduct the study. The results show that the surface cracks can be divided into four types: step type, extrusion uplift type, sliding type, and tension type, as well as two combination modes of collapse, trough and parallel. In the Yushenfu mining area, the spatial distribution law of surface cracks is relatively unified. The performance characteristics of surface cracks are different and negatively correlated with the ratio of mining depth to mining thickness. The surface cracks induced by very shallow coal seam mining, shallow coal seam mining, and medium-deep coal seam mining have the dynamic law of lagging mining position 1.0 m, advanced mining position 8.5 m, and lagging mining position 30.14 m, respectively, and the relationship between the lag distance of surface cracks and the ratio of mining depth to mining thickness is a polynomial function. The characteristic of the width of boundary cracks and forward slope cracks in the working face was increased until stable. In contrast to the boundary cracks, the characteristic of the width of the reverse slope fractures increases and then decreases, and the width of the cracks in the flat area in the working face increases first, then declines, and then increases. The average activity time was 3.7–7.0 days. The crack with the activity of “opening first and then closing” is controlled by the dynamic evolution of overlying rock structure, and the fracture with the activity of “only opening and then closing” and “opening first and then closing” was controlled by surface dynamic evolution. However, the activation mechanism of slope fracture is closely related to slope slip. The findings of this study can provide theoretical guidance for surface crack control and ecological restoration in the Yushenfu mining area.
Abstract: Coal production will inevitably have an impact on the ecological environment. It has become the consensus of all major countries that vegetation restoration should be carried out in coal mining areas. Monitoring the vegetation in the mining area is an important part of the vegetation restoration work in the mining area, and plays an important role in the design, implementation, management and maintenance of the vegetation work in the mining area. The calculation of vegetation coverage based on NDVI is currently the most common method of coal mine ecological monitoring. It was discovered during is the process that calculating vegetation coverage based on NDVI would cause serious errors. Sentinel-2 data was used to calculate the NDVI of the study area using the remote sensing band inversion method tostudy the reasons for the formation of the error zone and provide a suitable method for ecological monitoring of grassland mining areas. Furthermore, the empirical comparison method was used to investigate the NDVI distribution characteristics of the Shengli and Pingshuo mining areas. This phenomenon has also appeared in other research results. The results show that NDVI can accurately reflect the surface vegetation coverage in areas with specific vegetation coverage, but there will be significant error areas in coal-covered areas in the mining area. The error phenomenon will appear in both study areas, with a greater impact in the Shengli mining area. This error phenomenon is caused by the inadequacy of the NDVI’s normalization algorithm, which makes distinguishingbetween coal-covered areas and low-to-medium-covered grasslands with similar characteristics in spectral curves impossible. To avoid the impact of this phenomenon, we propose to mask the relevant areas or replace the vegetation index in the mining area’s vegetation monitoring.
Abstract: As the “heart” of continuous caster, the flow field of mold directly affects the quality of the slab. For a billet caster, in-mold electromagnetic stirring (M-EMS), as its necessary configuration, can improve the flow field in the mold, homogenize the liquid steel temperature, improve segregation, and improve the slab quality. This paper utilized a 410 mm × 530 mm large billet caster in a factory, which is one of the largest section casters in China. Based on it, a three-dimensional numerical model was established using the ANSYS finite element software to study the influence of electromagnetic stirring on the flow field, liquid level fluctuation, and temperature field of the mold. After electromagnetic stirring was applied, the liquid steel was subjected to a radial electromagnetic force, and the liquid surface shows a rotating flow trend. The maximum tangential velocity of molten steel increases with the increase of current and decreases with the increase of frequency. When the current of electromagnetic stirring increases from 0 A to 500 A, the fluctuation of the liquid level increases from 1.21 mm to 4.35 mm. The maximum tangential velocity of the electromagnetic stirring center increases from 0.02 m?s?1 to 0.21 m?s?1. Electromagnetic stirring can restrain the impact of the high-temperature jet from the nozzle, move the high-temperature zone of molten steel upward, and make the temperature of molten steel more uniform. Under the action of a radial electromagnetic force, the horizontal swirl of liquid steel can inhibit the growth of the primary shell, reduce the growth rate of the shell, and reduce the thickness of the shell out of the mold by about 2.3 mm. The comprehensive analysis shows that the reasonable current of electromagnetic stirring is 400 A and the frequency is 1.5 Hz. At this time, the fluctuation of the slag level is about 2.73 mm, and the temperature field is relatively uniform.
Abstract: Coal is considered one of the largest energy sources globally, but the sulfur in coal combustion seriously affects the efficient utilization of coal. Moreover, the sulfide produced by burning high sulfur coal is one of the leading causes of several diseases and environmental pollution. Incorporating photocatalytic oxidation into the extraction desulfurization system can significantly improve the efficiency of ionic liquid reactive extraction desulfurization. To further study the mechanism of desulfurization, experiment and computer simulation were used to analyze it. The experimental results show that coupling the photocatalytic reaction process and the ionic liquid extraction process can effectively remove organic sulfur in coal. The desulfurization rate of organic sulfur in the coal treated by [HO2MMim][HSO4]–H2O–H2O2–TiO2（mass ratio 5∶5∶10∶4）can reach 12.40%. In addition, an appropriate amount of water can improve the desulfurization rate of coal, but an excessive amount of aqueous solution can reduce the concentration of hydrogen peroxide and the organic sulfur desulfurization rate in coal. Materials Studio analysis shows that the hydroxyl radical (·OH) generated by photocatalytic activity has strong oxidability, and the area near the oxygen atom of ·OH is electronegative, which is easy to form S=O double bond with the positively charged S atom in thiophene via electrostatic attraction. In addition, the addition of ionic liquid makes the original lowest vacant orbital on the thiophene ring disappear. Moreover, it lowers the energy level difference between the HOMO and the LUMO, making the reaction easier to proceed with. Using COSMO analysis, it is found that the five-member heterocyclic structure of imidazole in [HO2MMim][HSO4] formed bonds with thiophene and sulfone molecules through van der Waals forces; thus, sulfide was constantly extracted into the ionic liquid phase and that adding the oxidizer can make it easier for sulfone with higher chemical potential to enter the lower chemical potential ionic solution [HO2MMim][HSO4] than thiophene.
Abstract: The amount of zinc-containing EAF dust has increased due to the increased proportion of galvanized steel scrap used in the electric arc furnace (EAF) steelmaking process. If the zinc in the EAF dust is not recycled, it will not only lead to a waste of valuable metal resources but also results in environmental pollution. Zinc is mainly present in the EAF dust in the form of zinc ferrite (ZnFe2O4). Zinc ferrite is a kind of spinel mineral that exhibits a crystal lattice of greater stability, which increases the difficulty of recycling valuable elements such as zinc and iron from zinc-containing EAF dust. To further clarify the carbothermic reduction process of zinc ferrite, this paper studies the kinetics of the non-isothermal carbothermal reduction of zinc ferrite and its reduction reaction mechanism. The phase transition process of the zinc ferrite carbothermal reduction reaction was analyzed via the XRD results of the reduced zinc ferrite. FeO0.85·xZnO was found at 950 °C when Fe3+ was reduced to Fe2+. The relationship between the conversion and conversion rate of the zinc ferrite carbothermal reduction process is discussed. The reduction process can be divided into three stages, and the conversion of the second stage changes greatly (0.085–0.813). Finally, the kinetics of the second stage of the carbothermic reduction of the zinc ferrite at different heating rates was evaluated through the isoconversional method and the master curve fitting method. The activation energy of the second stage is between 331.01–490.04 kJ·mol?1, and the average activation energy is 362.16 kJ·mol?1. The large change in the activation energy in the second stage indicates that the reactions in this stage are more complicated, and there are obvious differences in the activation energy between the reactions. The secondary chemical reaction is the main rate-controlling link in the second stage, and the kinetics equation of the second stage is determined.
Abstract: Reducing bentonite consumption is one of the effective ways to improve the grade of pellets and realize energy saving and emission reduction. Based on the new high-efficiency composite binder, the effect of the composite binder on the quality of green pellets and structure-activity relationship with important indexes were studied by means of green pellet preparation, linear fitting analysis, and green pellet mechanical characteristics analysis. Moreover, the mechanism of the composite binder to improve the quality of the green pellets was expounded. Results show that the composite binder pellet, with a ratio of 1.2% bentonite and 0.028% organic binder, has a drop number (dropped from 0.5 m height) of 6.2, a average crushing strength of 14.5 N, and a shock temperature of 542 ℃. Compared with the pellet with 2.0% bentonite, the mass of the green pellets is similar; however, the bentonite consumption is reduced by 40%. Based on the analysis of the structure-activity relationship, the organic binder has a considerable effect on the drop number and the shock temperature of the green pellets, and the bentonite has a greater effect on the dry-crushing strength. The organic binder strengthens the drop number of the pellets by enhancing the hydrophilicity, capillary force, and viscosity, and it forms small amounts of pores on the surface layer during drying, which is beneficial for discharging water in the pellets and improving the shock temperature of the pellets. After drying, the organic binder strengthens the pellets in the form of a solid connection bridge; however, the site and the size of the pores may reduce the dry-crushing strength. Therefore, bentonite plays a decisive role in the strength of the dry pellets, and the influence of the organic binder on the strength of the dry pellets is multifaceted.
Abstract: To further meet the requirements for using pipeline steel in extreme environments and to improve its safety in service, the inclusion control level in pipeline steel urgently needs improvement. In this paper, the variation laws of inclusion type, size, and composition in the refining process of X80 pipeline steel were studied through industrial trial sampling, and the evolution mechanism of inclusions during calcium treatment and steel cooling and solidification was analyzed using thermodynamic calculations with FactSage 8.1 software. The trial results showed mainly MgO–Al2O3 and MgO–Al2O3–CaO inclusions after LF refining in proportions of 25% and 75%, respectively, with sizes mainly distributed between 1–5 μm, and the proportion of inclusions of 1–2 μm and 2–5 μm were 56.0% and 37.3%, respectively. The contents of T[O] and [N] were reduced from 0.0022% and 0.0059% after LF refining to 0.0010% and 0.0035% after RH refining, respectively, and the number density of inclusions was reduced from approximately 23.07 mm?2 after LF to 7.44 mm?2, with an inclusions removal rate of approximately 67.8%. The inclusions were mainly MgO?Al2O3–CaO and CaS–Al2O3–CaO systems during calcium treatment, the average CaS content in the inclusions increased from 8% after RH refining to 36%, and the average CaO content decreased from 24% to 12%. After soft blowing, the SiO2 content ranged from 0 to 2.5% in the inclusions smaller than 40 μm and from 6.0% to 8.0% in the inclusions larger than 40 μm, and the inclusions larger than 40 μm were mainly CaO–Al2O3–MgO–SiO2, whose chemical composition is essentially identical to that of the refining slag, whose source is the refining slag involved; thermodynamic calculations show that when the [Ca] content is between 10.5×10–6–15.8×10–6, all spinel inclusions are modified, and all the inclusions are liquid calcium aluminates; when the steel is at casting temperature, the inclusions are mainly liquid calcium aluminates, and when the temperature is lowered to 1428 ℃, the liquid inclusions completely transform into solid. As the temperature drops below 1309 ℃, the type of inclusions essentially remains constant. During the entire temperature drop, the CaO content in the inclusions decreased, and the CaS content increased.
Abstract: The development of advanced cladding material with improved service performance is a key issue in engineering applications of sodium-cooled fast reactors. At present, the cladding materials of sodium-cooled fast reactors are mainly AISI type 316 or 15-15Ti austenitic stainless steel obtained by the traditional smelting method. However, the high-temperature mechanical properties and neutron irradiation resistance of these current austenitic steels cannot meet the service performance requirements for cladding of commercial fast reactors. Oxide dispersion strengthened (ODS) austenitic steel is considered to be an important candidate material for cladding application in most Generation IV reactors because of its good high-temperature mechanical properties and excellent irradiation resistance. In this study, 15Ni?15Cr ODS austenitic steel was prepared by mechanical alloying, hot isostatic pressing, and forging processes. As the reference material, 15Ni?15Cr austenitic steel without oxide addition was also prepared by the same processes. The microstructure of the sample was characterized by high-resolution transmission electron microscopy combined with a high-angle annular dark field. The average grain size of 15Ni?15Cr ODS austenitic steels is only 0.5 μm, which is smaller than that of the reference material 15Ni?15Cr (i.e., 0.75 μm). The oxide-dispersed particles distributed in 15Ni?15Cr ODS austenitic steel are mainly δ-Y4Zr3O12 and a small amount of Al2O3. The average particle size of oxide-dispersed particles in 15Ni?15Cr ODS austenitic steel is 12.8 nm, the number density is 5.5×1022 m?3, and the interparticle spacing is 26 nm. Compared with the reference material 15Ni?15Cr, 15Ni?15Cr ODS austenitic steel exhibits higher strength, particularly at high temperature, which can be attributed to the refinement of crystal grains and the pinning effect of oxide-dispersed particles on dislocations. However, the plasticity of 15Ni?15Cr ODS austenitic steel decreases at a high temperature of 700 °C. The fracture surface of 15Ni?15Cr ODS austenitic steel at room temperature shows typically ductile fractures, whereas that at the high temperature of 700 °C shows ductile–brittle fractures.
Abstract: Rebar corrosion is the principal factor affecting the service performance of reinforced concrete (RC) structures. Corrosion reduces the effective area of rebars as well as performance, and weakens the pin bolt effect of rebar on concrete. In addition, when the rebar is severely rusted, the concrete cover breaks, and the bond behavior between reinforcement and concrete deteriorates, affecting the mechanical properties of RC structures. In this study, a three-dimensional numerical model for shear analysis incorporating the nonuniform corrosion of reinforcement was established using an RC beam as the research object. The effects of corrosion on the mechanical behavior of the RC beam were explored via a multistage analysis method (namely, corrosion-induced expansion stage and structural deterioration stage). To model and simulate the expansion of the corrosion products, nonuniform radial displacement was applied to the concrete surrounding the rebar. The cracking process and the damage patterns of concrete resulting from corrosion were obtained. Then, taking the corrosion state as the initial condition, the static load was applied to analyze the mechanical behavior of the RC beam. After verifying the rationality of the multistage numerical model, the effect of the corrosion of tensile reinforcement and the shear-span ratio on the shear behavior of concrete beams without web reinforcement was analyzed. The modeling analysis results show that the corrosion of longitudinal reinforcement causes obvious longitudinal corrosion fractures in the concrete beam. Moreover, with the development of corrosion, the cracking area of the protective layer increases, reducing the shear capacity of the beam significantly. Furthermore, the shear-span ratio has a larger effect on the shear capacity of noncorroded beams than that of corroded beams. Finally, based on the simulation results, the calculation formulas of shear capacity in relevant design codes were discussed, and a methodology for predicting the shear capacity of RC beams without web reinforcement was proposed.
Abstract: Sudden transition is a widely existing phenomenon in engineering practice. When the state of the system experiences sudden abrupt transition, calculus-based traditional mathematical modeling methods has low accuracy. Although theoretically, machine learning algorithms, such as artificial neural networks, can approximate any nonlinear function, this type of black-box method makes no reasonable explanation for the sudden transition phenomenon. The cusp catastrophe model based on the catastrophe theory can be applied to explain the discontinuous changes in the system’s state. However, the construction of traditional cusp catastrophe models is often based on large amounts of prior knowledge to select the input variables for modeling. On the condition that there is a lack of prior knowledge and comparatively large dimensions of input variables, the model has high complexity and poor accuracy. In this paper we have put forward a two-step method for constructing a cusp catastrophe model based on the selection of variables to solve the abovementioned problems. The first step was to apply multimodel ensemble important variable selection (MEIVS) to quantify the importance of the variables to be selected and extract important variables. The second step was to use the extracted important variables to construct a cusp catastrophe model based on the framework of maximum likelihood estimation (MLE). Results indicate that on a dataset with characteristics of catastrophe, the cusp catastrophe model is simple in form using the MEIVS dimensionality reduction algorithm and outperforms the unreduced cusp catastrophe model and reduced cusp catastrophe model using other dimensionality reduction algorithms in terms of evaluation indicators. This shows that the algorithm proposed in this paper have improved the accuracy and reduced the complexity of the cusp catastrophe model. At the same time, the cusp catastrophe model exhibits higher accuracy compared with the linear and logistic models. Thus, it can be used to explain the discontinuous changes of the research object, and it has a practical engineering significance.
Abstract: To meet extreme performance requirements, such as extremely low communication delay, extremely high reliability, and a higher transmission rate, for autonomous driving in the Internet of vehicles (IoV), the future IoV should be merged into a united framework that integrates communication, sensing, and computing. At the same time, as the 5G-Advanced system moves toward supporting a broader toB vertical industry, it will face a more complex and heterogeneous user environment and multidimensional digital space, which requires 5G-Advanced terminals and 5G-Advanced networks to have stronger environmental sensing, computing, and intelligence capabilities. To realize deep integration for autonomous driving in the IoV, the sensing of IoV depends on not only radar positioning, camera imaging, and various sensor detections but also communication, which can collect a variety of data to the edge node for calculation. At the same time, with the support of cloud edge and end integration efficient computing power to achieve high-precision sensing and efficient communication, the integration network further improves collaborative mobile computing robustness. Therefore, the three functions of communication, sensing, and computing for autonomous driving in the IoV are interrelated and promote each other. To break through the architectural barrier of universal sensing integration in the Internet of autonomous vehicles, it is necessary to explore how to build a universal sensing integration network architecture with decoupled resources, scalable capabilities, and reconfigurable architecture, as well as universal sensing integration resource management technology. However, communication, sensing, and computing are separated from each other in the existing IoV. Thus, we scrutinize the scientific problem of the endogenous integration of communication, sensing, and computing for autonomous driving in the IoV. First, the current research progress in integrating communication, sensing, and computing is discussed. Second, communication-sensing-computing-integrated IoV is defined, and the research progress on communication-sensing-assisted computing, communication-computing-assisted sensing, and sensing-computing-assisted communication is discussed. Aiming at the scenario of an IoV for autonomous driving, the architecture of communication-sensing-computing-integrated IoV with five layers and four planes is proposed. The horizontal five layers from bottom to top are a multiple access layer, unified network layer, multi-domain resource layer, collaborative service layer, and management and application layer. The four vertical planes are communication, sensing, computing power, and intelligent integration planes, respectively. Deeply integrating the five layers and four planes further improves the performance of the integrated IoV. Third, key performance indexes for evaluating the integrated IoV are proposed. Finally, four feasible suggestions are given for the current research problems and the future development direction.
Abstract: Orthogonal frequency division multiplexing (OFDM) technology, which can divide the frequency selective fading channel into multiple flat fading sub-channels, is widely used in wireless communication systems because of its robustness to frequency selectivity in wireless channels and the ability to mitigate multipath fading that causes inter-symbol interference. Therefore, it has become one of the key technologies of 5G mobile communication. However, it has a serious shortcoming, i.e., the high peak-to-average power ratio (PAPR), especially when the number of subcarriers is large. High PAPR will make the high-power amplifier work in its nonlinear region, leading to inter-modulation interference among subcarriers and out-of-band interference of OFDM signals. Active constellation extension (ACE) reduces the PAPR of OFDM signals effectively by extending external constellation points outwards. Most of the ACE algorithms currently used set a fixed threshold to limit the amplitude of the OFDM signal during the iteration. As the statistical characteristics of OFDM signals will change after each iteration, the same threshold will reduce the ability of the method to suppress the PAPR of OFDM systems. To solve this problem, an optimal threshold ACE (OTACE) method is proposed, which can determine an appropriate threshold according to the signal power at each iteration to enhance the performance of PAPR reduction. The appropriate number of iterations is obtained by data fitting, and on this basis, the impact of the OTACE algorithm in suppressing the PAPR is simulated and analyzed. The simulation results demonstrate that compared with POCS and SGP, OTACE can increase the performance to reduce PAPR by approximately 5 dB and 3 dB gains, respectively. Under the CDT 1, CDT 6, and Brazil A channels, the impact of the OTACE algorithm on the bit error rate (BER) is tested when the Doppler frequency shift is 20 Hz and 60 Hz, respectively. The experimental results show that the OTACE can achieve better BER performance. Compared with POCS, OTACE has about 1 dB signal-to-noise ratio (SNR) gain in BER performance. OTACE has obvious advantages over SGP at a high SNR.
Abstract: Lithium-ion batteries are widely used in energy storage and new energy electric vehicles due to their superior performance, but the internal short circuit problem of lithium-ion batteries is a safety hazard during usage for energy storage and vehicle battery packs. If it cannot be detected in time, the deepening of the internal short circuit will be accompanied by an increase in heat, which will cause thermal runaway and lead to safety accidents. Diagnosing whether the battery pack has an internal short circuit and quantitatively estimating the short circuit resistance of the battery cell that has the internal short circuit can effectively prevent the occurrence of thermal runaway. This study proposes a quantitative diagnosis algorithm of Internal short circuit (ISC) based on the remaining charge capacity based on the charging curve of the lithium-ion battery module. The simulation and experimental verification of the algorithm are carried out under the conditions of different voltage acquisition accuracies, sampling periods, temperatures, and aging degrees. The results show that the proposed algorithm can accurately and quantitatively diagnose the ISC under certain conditions: (1) For serious ISC of 10 Ω level, high diagnosis accuracy can be obtained even under the conditions of 10 mV acquisition accuracy, 10 s sampling period, and variable temperature. For early ISC of 100 Ω level, the ISC resistance is smaller than the actual value and the diagnosis time is longer. To improve the accuracy and timeliness of early ISC diagnosis, the voltage acquisition accuracy, and sampling frequency should be higher than 1 mV and 1 Hz, respectively. (2) Battery aging will reduce the accuracy of ISC diagnosis, but it has little effect on the 10 Ω level ISC, and the diagnostic error of the ISC resistance is less than 6% even at an extremely low temperature (?20 ℃). The conclusions are of great significance to improve the accuracy of quantitative diagnosis of ISC for lithium-ion batteries.
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
