Porosity is an important attribute for evaluating the petrophysical properties of reservoirs, and has guiding significance for the exploration and development of oil and gas. The seismic inversion is a key method for ...Porosity is an important attribute for evaluating the petrophysical properties of reservoirs, and has guiding significance for the exploration and development of oil and gas. The seismic inversion is a key method for comprehensively obtaining the porosity. Deep learning methods provide an intelligent approach to suppress the ambiguity of the conventional inversion method. However, under the trace-bytrace inversion strategy, there is a lack of constraints from geological structural information, resulting in poor lateral continuity of prediction results. In addition, the heterogeneity and the sedimentary variability of subsurface media also lead to uncertainty in intelligent prediction. To achieve fine prediction of porosity, we consider the lateral continuity and variability and propose an improved structural modeling deep learning porosity prediction method. First, we combine well data, waveform attributes, and structural information as constraints to model geophysical parameters, constructing a high-quality training dataset with sedimentary facies-controlled significance. Subsequently, we introduce a gated axial attention mechanism to enhance the features of dataset and design a bidirectional closed-loop network system constrained by inversion and forward processes. The constraint coefficient is adaptively adjusted by the petrophysical information contained between the porosity and impedance in the study area. We demonstrate the effectiveness of the adaptive coefficient through numerical experiments.Finally, we compare the performance differences between the proposed method and conventional deep learning methods using data from two study areas. The proposed method achieves better consistency with the logging porosity, demonstrating the superiority of the proposed method.展开更多
The current method for inspecting microholes in printed circuit boards(PCBs)involves preparing slices followed by optical microscope measurements.However,this approach suffers from low detection efficiency,poor reliab...The current method for inspecting microholes in printed circuit boards(PCBs)involves preparing slices followed by optical microscope measurements.However,this approach suffers from low detection efficiency,poor reliability,and insufficient measurement stability.Micro-CT enables the observation of the internal structures of the sample without the need for slicing,thereby presenting a promising new method for assessing the quality of microholes in PCBs.This study integrates computer vision technology with computed tomography(CT)to propose a method for detecting microhole wall roughness using a U-Net model and image processing algorithms.This study established an unplated copper PCB CT image dataset and trained an improved U-Net model.Validation of the test set demonstrated that the improved model effectively segmented microholes in the PCB CT images.Subsequently,the roughness of the holes’walls was assessed using a customized image-processing algorithm.Comparative analysis between CT detection based on various edge detection algorithms and slice detection revealed that CT detection employing the Canny algorithm closely approximates slice detection,yielding range and average errors of 2.92 and 1.64μm,respectively.Hence,the detection method proposed in this paper offers a novel approach for nondestructive testing of hole wall roughness in the PCB industry.展开更多
Based on the uniaxial compression creep experiments conducted on bauxite sandstone obtained from Sanmenxia,typical creep experiment curves were obtained.From the characteristics of strain component of creep curves,the...Based on the uniaxial compression creep experiments conducted on bauxite sandstone obtained from Sanmenxia,typical creep experiment curves were obtained.From the characteristics of strain component of creep curves,the creep strain is composed of instantaneous elastic strain,ε（me）,instantaneous plastic strain,ε（mp）,viscoelastic strain,ε（ce）,and viscoplastic strain,ε（cp）.Based on the characteristics of instantaneous plastic strain,a new element of instantaneous plastic rheology was introduced,instantaneous plastic modulus was defined,and the modified Burgers model was established.Then identification of direct screening method in this model was completed.According to the mechanical properties of rheological elements,one- and three-dimensional creep equations in different stress levels were obtained.One-dimensional model parameters were identified by the method of least squares,and in the process of computation,Gauss-Newton iteration method was applied.Finally,by fitting the experimental curves,the correctness of direct method model was verified,then the examination of posterior exclusive method of the model was accomplished.The results showed that in the improved Burgers models,the rheological characteristics of sandstone are embodied properly,microscopic analysis of creep curves is also achieved,and the correctness of comprehensive identification method of rheological model is verified.展开更多
Too high grinding force will lead to a large increase in specific grinding energy, resulting in high temperature in grinding zone, especially for the aerospace difficult cutting metal materials,seriously affecting the...Too high grinding force will lead to a large increase in specific grinding energy, resulting in high temperature in grinding zone, especially for the aerospace difficult cutting metal materials,seriously affecting the surface quality and accuracy. At present, the theoretical models of grinding force are mostly based on the assumption of uniform or simplified morphological characteristics of grains, which is inconsistent with the actual grains. Especially for non-engineering grinding wheel,most geometric characteristics of grains are ignored, resulting in the calculation accuracy that cannot guide practical production. Based on this, an improved grinding force model based on random grain geometric characteristics is proposed in this paper. Firstly, the surface topography model of CBN grinding wheel is established, and the effective grain determination mechanism in grinding zone is revealed. Based on the known grinding force model and mechanical behavior of interaction between grains and workpiece in different stages, the concept of grain effective action area is proposed. The variation mechanism of effective action area under the influence of grain geometric and spatial characteristics is deeply analyzed, and the calculation method under random combination of five influencing parameters is obtained. The numerical simulation is carried out to reveal the dynamic variation process of grinding force in grinding zone. In order to verify the theoretical model, the experiments of dry grinding Ti-6Al-4 V are designed. The experimental results show that under different machining parameters, the results of numerical calculation and experimental measurement are in good agreement, and the minimum error value is only 2.1 %, which indicates that the calculation accuracy of grinding force model meets the requirements and is feasible. This study will provide a theoretical basis for optimizing the wheel structure, effectively controlling the grinding force range, adjusting the grinding zone temperature and improving the workpiece machining quality in the industrial grinding process.展开更多
Based on catch and effort data of tuna longline fishery operating in the South Pacific Ocean, the South Pacific albacore stock was assessed by an improved Schaefer model. The results revealed that the intrinsic growth...Based on catch and effort data of tuna longline fishery operating in the South Pacific Ocean, the South Pacific albacore stock was assessed by an improved Schaefer model. The results revealed that the intrinsic growth rate was about 1.283 74 and carrying capacities vareied in the range from 73 734 to 266 732 metric tons. The growth ability of this species is remarkable. Stock dynamics mainly depends on environmental conditions. The stock is still in good condition. However, the continuous decreasing of biomass in recent years should be noticed.展开更多
On the basis of the existing originally modified calculation models of theoretical combustion temperature（TCT）,some factors,such as the combustion ratio of pulverized coal injection（PCI）,the decomposition heat of ...On the basis of the existing originally modified calculation models of theoretical combustion temperature（TCT）,some factors,such as the combustion ratio of pulverized coal injection（PCI）,the decomposition heat of PCI and the heat consumption of SiO2 in ash reduced in high temperature environment,were amended and improved to put forward a more comprehensive model for calculating TCT.The influences of each improvement on TCT were studied and the results were analyzed compared with those of traditional model and originally modified model,which showed that the present model could reflect the thermal state of a hearth more effectively.展开更多
A comprehensive and objective risk evaluation model of oil and gas pipelines based on an improved analytic hierarchy process(AHP)and technique for order preference by similarity to an ideal solution(TOPSIS)is establis...A comprehensive and objective risk evaluation model of oil and gas pipelines based on an improved analytic hierarchy process(AHP)and technique for order preference by similarity to an ideal solution(TOPSIS)is established to identify potential hazards in time.First,a barrier model and fault tree analysis are used to establish an index system for oil and gas pipeline risk evaluation on the basis of five important factors:corrosion,external interference,material/construction,natural disasters,and function and operation.Next,the index weight for oil and gas pipeline risk evaluation is computed by applying the improved AHP based on the five-scale method.Then,the TOPSIS of a multi-attribute decision-making theory is studied.The method for determining positive/negative ideal solutions and the normalized equation for benefit/cost indexes is improved to render TOPSIS applicable for the comprehensive risk evaluation of pipelines.The closeness coefficient of oil and gas pipelines is calculated by applying the improved TOPSIS.Finally,the weight and the closeness coefficient are combined to determine the risk level of pipelines.Empirical research using a long-distance pipeline as an example is conducted,and adjustment factors are used to verify the model.Results show that the risk evaluation model of oil and gas pipelines based on the improved AHP–TOPSIS is valuable and feasible.The model comprehensively considers the risk factors of oil and gas pipelines and provides comprehensive,rational,and scientific evaluation results.It represents a new decision-making method for systems engineering in pipeline enterprises and provides a comprehensive understanding of the safety status of oil and gas pipelines.The new system engineering decision-making method is important for preventing oil and gas pipeline accidents.展开更多
The translation quality of neural machine translation(NMT)systems depends largely on the quality of large-scale bilingual parallel corpora available.Research shows that under the condition of limited resources,the per...The translation quality of neural machine translation(NMT)systems depends largely on the quality of large-scale bilingual parallel corpora available.Research shows that under the condition of limited resources,the performance of NMT is greatly reduced,and a large amount of high-quality bilingual parallel data is needed to train a competitive translation model.However,not all languages have large-scale and high-quality bilingual corpus resources available.In these cases,improving the quality of the corpora has become the main focus to increase the accuracy of the NMT results.This paper proposes a new method to improve the quality of data by using data cleaning,data expansion,and other measures to expand the data at the word and sentence-level,thus improving the richness of the bilingual data.The long short-term memory(LSTM)language model is also used to ensure the smoothness of sentence construction in the process of sentence construction.At the same time,it uses a variety of processing methods to improve the quality of the bilingual data.Experiments using three standard test sets are conducted to validate the proposed method;the most advanced fairseq-transformer NMT system is used in the training.The results show that the proposed method has worked well on improving the translation results.Compared with the state-of-the-art methods,the BLEU value of our method is increased by 2.34 compared with that of the baseline.展开更多
The dynamic characteristics of a single liquid-filled pipe have been broadly studied in the previous literature.The parallel liquid-filled pipe(PLFP)system is also widely used in engineering,and its structure is more ...The dynamic characteristics of a single liquid-filled pipe have been broadly studied in the previous literature.The parallel liquid-filled pipe(PLFP)system is also widely used in engineering,and its structure is more complex than that of a single pipe.However,there are few reports about the dynamic characteristics of the PLFPs.Therefore,this paper proposes improved frequency modeling and solution for the PLFPs,involving the logical alignment principle and coupled matrix processing.The established model incorporates both the fluid-structure interaction(FSI)and the structural coupling of the PLFPs.The validity of the established model is verified by modal experiments.The effects of some unique parameters on the dynamic characteristics of the PLFPs are discussed.This work provides a feasible method for solving the FSI of multiple pipes in parallel and potential theoretical guidance for the dynamic analysis of the PLFPs in engineering.展开更多
The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that th...The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.展开更多
Developing supersonic combustion models with efficiency,accuracy and practicality is important foundation to deeply understand the complex combustion processes in scramjet engines.Characterized by efficiency and intui...Developing supersonic combustion models with efficiency,accuracy and practicality is important foundation to deeply understand the complex combustion processes in scramjet engines.Characterized by efficiency and intuition,the flamelet-like models are widely used models in computational combustion methods.However,the supersonic combustion flow field has the nature of strong compressibility,multiple modality,and multiple scales,which poses a great challenge to the traditional flamelet-like models with fixed boundary conditions,and then the complex chemical reaction mechanisms that may face will impose additional computational burden.In this context,this paper reviews the flamelet-like models used in scramjet engines,and summarizes prominent issues in the application practice,including modeling partially premixed combustion,defining progress variable,solving temperature efficiently,evaluating assumed Probability Density Function(PDF)models,and treating mixture fraction variance.Furthermore,possible prospects and directions of improvements are proposed and highlighted for the flamelet-like models.To fully describe the physicochemical scenario and address the raised challenges,these improvements are dedicated to dealing with the compressibility,temperature rise,time-scales,species of interest,multi-inlet combustion,the progress variable definition,and the higher Mach number flight condition.展开更多
The weak intercalated soils in redbed soft rocks of Badong formation have obvious creep characters. In order to predict the unsaturated creep behaviors of weak intercalated soils, an unsaturated creep model was establ...The weak intercalated soils in redbed soft rocks of Badong formation have obvious creep characters. In order to predict the unsaturated creep behaviors of weak intercalated soils, an unsaturated creep model was established based on the unsaturated creep tests of weak intercalated soils by using GDS triaxial apparatus. The results show that the creep behaviors of intercalated soils are apparent and significantly affected by matric suction. Based on this, an empirical Mesri creep model for intercalated soils under varying matric suctions was built. The fitting results show that the parameters Ed and m of this model are in good power relations with matric suction s and stress level Dr, respectively. An improved Mesri creep model was established involving stress-matric suction-strain-time, which is more precise than the Mesri creep model in predicting the unsaturated creep behaviors of weak intercalated soils.展开更多
According to the relationships among state transition probability matrixes with different step lengths, an improved Markov chain model based on autocorrelation and entropy techniques was introduced. In the improved Ma...According to the relationships among state transition probability matrixes with different step lengths, an improved Markov chain model based on autocorrelation and entropy techniques was introduced. In the improved Markov chain model, the state transition probability matrixes can be adjusted. The steps of the historical state of the event, which was significantly related to the future state of the event, were determined by the autocorrelation technique, and the impact weights of the event historical state on the event future state were determined by the entropy technique. The presented model was applied to predicting annual precipitation and annual runoff states, showing that the improved model is of higher precision than those existing Markov chain models, and the determination of the state transition probability matrixes and the weights is more reasonable. The physical concepts of the improved model are distinct, and its computation process is simple and direct, thus, the presented model is sufficiently general to be applicable to the prediction problems in hydrology and water resources.展开更多
The strain rate sensitivity（SRS）and temperature sensitivity（TS）of 316L austenitic stainless steel were investigated by constant strain rate test（CSRT）and strain rate jump test（SRJT）under four temperatures（293...The strain rate sensitivity（SRS）and temperature sensitivity（TS）of 316L austenitic stainless steel were investigated by constant strain rate test（CSRT）and strain rate jump test（SRJT）under four temperatures（293,373,473 and 573 K）and four strain rates（5 ×10^-4/s,1 × 10^-3/s,5 × 10^-3/s and 1 × 10^-2/s）.The results show that temperature sensitivity（TS）indexes at different strain rates are coincidence to be negative,related to temperature softening.On the contrary,SRS indexes change from positive to negative with the increase in temperature associated with dynamic strain aging（DSA）.Moreover,based on the comparison between CSRT and SRJT,SRS and TS indexes obtained by two methods agree well.It proves that the SRJT can describe the SRS and TS phenomenon of 316L efficiently.Furthermore,the effects of tem-perature and strain rate on fracture mechanism were discussed.At last,an improved Johnson-Cook model was proposed to consider the temperature-dependent SRS behavior of 316L.展开更多
In this paper,under the assumption that the labor force function increases strictly and is bounded and the labor force growth rate function decreases monotonically from a positive value to zero,we obtain an improved S...In this paper,under the assumption that the labor force function increases strictly and is bounded and the labor force growth rate function decreases monotonically from a positive value to zero,we obtain an improved Solow Swan model. We prove that the per capita capital trends stabilitily to the steady state of the classical Solow Swan model with zero the labor force growth rate. Two comparison theorems,a limited theorem and a stability theorem are given. At the end of this paper,we give an example and discuss the economic meaning of this model and the theorems.展开更多
It is essential to precisely predict the crack growth,especially the near-threshold regime crack growth under different stress ratios,for most engineering structures consume their fatigue lives in this regime under ra...It is essential to precisely predict the crack growth,especially the near-threshold regime crack growth under different stress ratios,for most engineering structures consume their fatigue lives in this regime under random loading.In this paper,an improved unique curve model is proposed based on the unique curve model,and the determination of the shape exponents of this model is provided.The crack growth rate curves of some materials taken from the literature are evaluated using the improved model,and the results indicate that the improved model can accurately predict the crack growth rate in the nearthreshold and Paris regimes.The improved unique curve model can solve the problems about the shape exponents determination and weak ability around the near-threshold regime meet in the unique curve model.In addition,the shape exponents in the improved model at negative stress ratios are discussed,which can directly adopt that in the unique curve model.展开更多
In this paper, a new theoretical expression of dissipation term is presented on the basis of statistical model of breaking wave, which is an improvement to LAGFD-WAM wave model. The computational results in three typi...In this paper, a new theoretical expression of dissipation term is presented on the basis of statistical model of breaking wave, which is an improvement to LAGFD-WAM wave model. The computational results in three typical wind fields show a good improvement to LAGFD-WAM model and a better accuracy in comparison with the observed data in the South China Sea.展开更多
The mixed model of improved exponential and power function and unequal interval gray GM(1,1)model have poor accuracy in predicting the maximum pull-out load of anchor bolts.An optimal combination model was derived usi...The mixed model of improved exponential and power function and unequal interval gray GM(1,1)model have poor accuracy in predicting the maximum pull-out load of anchor bolts.An optimal combination model was derived using the optimally weighted combination theory and the minimum sum of logarithmic squared errors as the objective function.Two typical anchor bolt pull-out engineering cases were selected to compare the performance of the proposed model with those of existing ones.Results showed that the optimal combination model was suitable not only for the slow P-s curve but also for the steep P-s curve.Its accuracy and stable reliability,as well as its prediction capability classification,were better than those of the other prediction models.Therefore,the optimal combination model is an effective processing method for predicting the maximum pull-out load of anchor bolts according to measured data.展开更多
The timely and effective investment risk assessment and forecasting are of great significance to ensure the investment safety and sustainable development of wind energy along the Belt and Road.In order to obtain the s...The timely and effective investment risk assessment and forecasting are of great significance to ensure the investment safety and sustainable development of wind energy along the Belt and Road.In order to obtain the scientific and real-time forecasting result,this paper constructs a novel hybrid intelligent model based on improved cloud model combined with GRA-TOPSIS and MBA-WLSSVM.Firstly,the factors influencing investment risk of wind energy along the Belt and Road are identified fromthree dimensions:endogenous risk,exogenous risk and process risk.Through the fuzzy threshold method,the final input index system is selected.Secondly,the risk evaluation method based on improved cloud model andGRA-TOPSIS is proposed.Thirdly,a modern intelligent model based on MBA-WLSSVMis designed.In modified bat algorithm(MBA),tent chaotic map is utilized to improve the basic bat algorithm,while weighted least squares support vector machine(WLSSVM)adopts wavelet kernel function to replace the traditional radial basis function to complete the model improvement.Finally,an example is given to verify the scientificity and accuracy of themodel,which is helpful for investors tomake fast and effective investment risk forecasting of wind energy along the Belt and Road.The example analysis proves that the proposedmodel can provide reference and basis for investment corpus to formulate the investment strategy in wind energy along the Belt and Road.展开更多
Accurate identification of influential nodes facilitates the control of rumor propagation and interrupts the spread of computer viruses.Many classical approaches have been proposed by researchers regarding different a...Accurate identification of influential nodes facilitates the control of rumor propagation and interrupts the spread of computer viruses.Many classical approaches have been proposed by researchers regarding different aspects.To explore the impact of location information in depth,this paper proposes an improved global structure model to characterize the influence of nodes.The method considers both the node’s self-information and the role of the location information of neighboring nodes.First,degree centrality of each node is calculated,and then degree value of each node is used to represent self-influence,and degree values of the neighbor layer nodes are divided by the power of the path length,which is path attenuation used to represent global influence.Finally,an extended improved global structure model that considers the nearest neighbor information after combining self-influence and global influence is proposed to identify influential nodes.In this paper,the propagation process of a real network is obtained by simulation with the SIR model,and the effectiveness of the proposed method is verified from two aspects of discrimination and accuracy.The experimental results show that the proposed method is more accurate in identifying influential nodes than other comparative methods with multiple networks.展开更多
基金the support of Research Program of Fine Exploration and Surrounding Rock Classification Technology for Deep Buried Long Tunnels Driven by Horizontal Directional Drilling and Magnetotelluric Methods Based on Deep Learning under Grant E202408010the Sichuan Science and Technology Program under Grant 2024NSFSC1984 and Grant 2024NSFSC1990。
文摘Porosity is an important attribute for evaluating the petrophysical properties of reservoirs, and has guiding significance for the exploration and development of oil and gas. The seismic inversion is a key method for comprehensively obtaining the porosity. Deep learning methods provide an intelligent approach to suppress the ambiguity of the conventional inversion method. However, under the trace-bytrace inversion strategy, there is a lack of constraints from geological structural information, resulting in poor lateral continuity of prediction results. In addition, the heterogeneity and the sedimentary variability of subsurface media also lead to uncertainty in intelligent prediction. To achieve fine prediction of porosity, we consider the lateral continuity and variability and propose an improved structural modeling deep learning porosity prediction method. First, we combine well data, waveform attributes, and structural information as constraints to model geophysical parameters, constructing a high-quality training dataset with sedimentary facies-controlled significance. Subsequently, we introduce a gated axial attention mechanism to enhance the features of dataset and design a bidirectional closed-loop network system constrained by inversion and forward processes. The constraint coefficient is adaptively adjusted by the petrophysical information contained between the porosity and impedance in the study area. We demonstrate the effectiveness of the adaptive coefficient through numerical experiments.Finally, we compare the performance differences between the proposed method and conventional deep learning methods using data from two study areas. The proposed method achieves better consistency with the logging porosity, demonstrating the superiority of the proposed method.
基金Supported by National Natural Science Foundation of China(Grant Nos.52122510 and 52375415).
文摘The current method for inspecting microholes in printed circuit boards(PCBs)involves preparing slices followed by optical microscope measurements.However,this approach suffers from low detection efficiency,poor reliability,and insufficient measurement stability.Micro-CT enables the observation of the internal structures of the sample without the need for slicing,thereby presenting a promising new method for assessing the quality of microholes in PCBs.This study integrates computer vision technology with computed tomography(CT)to propose a method for detecting microhole wall roughness using a U-Net model and image processing algorithms.This study established an unplated copper PCB CT image dataset and trained an improved U-Net model.Validation of the test set demonstrated that the improved model effectively segmented microholes in the PCB CT images.Subsequently,the roughness of the holes’walls was assessed using a customized image-processing algorithm.Comparative analysis between CT detection based on various edge detection algorithms and slice detection revealed that CT detection employing the Canny algorithm closely approximates slice detection,yielding range and average errors of 2.92 and 1.64μm,respectively.Hence,the detection method proposed in this paper offers a novel approach for nondestructive testing of hole wall roughness in the PCB industry.
基金Projects (51174228,51274249) supported by the National Natural Science Foundation of China
文摘Based on the uniaxial compression creep experiments conducted on bauxite sandstone obtained from Sanmenxia,typical creep experiment curves were obtained.From the characteristics of strain component of creep curves,the creep strain is composed of instantaneous elastic strain,ε（me）,instantaneous plastic strain,ε（mp）,viscoelastic strain,ε（ce）,and viscoplastic strain,ε（cp）.Based on the characteristics of instantaneous plastic strain,a new element of instantaneous plastic rheology was introduced,instantaneous plastic modulus was defined,and the modified Burgers model was established.Then identification of direct screening method in this model was completed.According to the mechanical properties of rheological elements,one- and three-dimensional creep equations in different stress levels were obtained.One-dimensional model parameters were identified by the method of least squares,and in the process of computation,Gauss-Newton iteration method was applied.Finally,by fitting the experimental curves,the correctness of direct method model was verified,then the examination of posterior exclusive method of the model was accomplished.The results showed that in the improved Burgers models,the rheological characteristics of sandstone are embodied properly,microscopic analysis of creep curves is also achieved,and the correctness of comprehensive identification method of rheological model is verified.
基金supported by the National Natural Science Foundation of China(Nos.51975305,51905289,52105264)the Key Project of Shandong Province,China(No.ZR2020KE027)+1 种基金the Major Research Project of Shandong Province,China(Nos.2019GGX104040 and 2019GSF108236)the Natural Science Foundation of Shandong Province,China(No.ZR2021QE116).
文摘Too high grinding force will lead to a large increase in specific grinding energy, resulting in high temperature in grinding zone, especially for the aerospace difficult cutting metal materials,seriously affecting the surface quality and accuracy. At present, the theoretical models of grinding force are mostly based on the assumption of uniform or simplified morphological characteristics of grains, which is inconsistent with the actual grains. Especially for non-engineering grinding wheel,most geometric characteristics of grains are ignored, resulting in the calculation accuracy that cannot guide practical production. Based on this, an improved grinding force model based on random grain geometric characteristics is proposed in this paper. Firstly, the surface topography model of CBN grinding wheel is established, and the effective grain determination mechanism in grinding zone is revealed. Based on the known grinding force model and mechanical behavior of interaction between grains and workpiece in different stages, the concept of grain effective action area is proposed. The variation mechanism of effective action area under the influence of grain geometric and spatial characteristics is deeply analyzed, and the calculation method under random combination of five influencing parameters is obtained. The numerical simulation is carried out to reveal the dynamic variation process of grinding force in grinding zone. In order to verify the theoretical model, the experiments of dry grinding Ti-6Al-4 V are designed. The experimental results show that under different machining parameters, the results of numerical calculation and experimental measurement are in good agreement, and the minimum error value is only 2.1 %, which indicates that the calculation accuracy of grinding force model meets the requirements and is feasible. This study will provide a theoretical basis for optimizing the wheel structure, effectively controlling the grinding force range, adjusting the grinding zone temperature and improving the workpiece machining quality in the industrial grinding process.
文摘Based on catch and effort data of tuna longline fishery operating in the South Pacific Ocean, the South Pacific albacore stock was assessed by an improved Schaefer model. The results revealed that the intrinsic growth rate was about 1.283 74 and carrying capacities vareied in the range from 73 734 to 266 732 metric tons. The growth ability of this species is remarkable. Stock dynamics mainly depends on environmental conditions. The stock is still in good condition. However, the continuous decreasing of biomass in recent years should be noticed.
基金Item Sponsored by National Natural Science Foundation of China(50974143)
文摘On the basis of the existing originally modified calculation models of theoretical combustion temperature（TCT）,some factors,such as the combustion ratio of pulverized coal injection（PCI）,the decomposition heat of PCI and the heat consumption of SiO2 in ash reduced in high temperature environment,were amended and improved to put forward a more comprehensive model for calculating TCT.The influences of each improvement on TCT were studied and the results were analyzed compared with those of traditional model and originally modified model,which showed that the present model could reflect the thermal state of a hearth more effectively.
基金supported by the National Key Research and Development Program of China(Grant Nos.2017YFC0805804,2017YFC0805801)
文摘A comprehensive and objective risk evaluation model of oil and gas pipelines based on an improved analytic hierarchy process(AHP)and technique for order preference by similarity to an ideal solution(TOPSIS)is established to identify potential hazards in time.First,a barrier model and fault tree analysis are used to establish an index system for oil and gas pipeline risk evaluation on the basis of five important factors:corrosion,external interference,material/construction,natural disasters,and function and operation.Next,the index weight for oil and gas pipeline risk evaluation is computed by applying the improved AHP based on the five-scale method.Then,the TOPSIS of a multi-attribute decision-making theory is studied.The method for determining positive/negative ideal solutions and the normalized equation for benefit/cost indexes is improved to render TOPSIS applicable for the comprehensive risk evaluation of pipelines.The closeness coefficient of oil and gas pipelines is calculated by applying the improved TOPSIS.Finally,the weight and the closeness coefficient are combined to determine the risk level of pipelines.Empirical research using a long-distance pipeline as an example is conducted,and adjustment factors are used to verify the model.Results show that the risk evaluation model of oil and gas pipelines based on the improved AHP–TOPSIS is valuable and feasible.The model comprehensively considers the risk factors of oil and gas pipelines and provides comprehensive,rational,and scientific evaluation results.It represents a new decision-making method for systems engineering in pipeline enterprises and provides a comprehensive understanding of the safety status of oil and gas pipelines.The new system engineering decision-making method is important for preventing oil and gas pipeline accidents.
基金This research was supported by the National Natural Science Foundation of China(NSFC)under the grant(No.61672138).
文摘The translation quality of neural machine translation(NMT)systems depends largely on the quality of large-scale bilingual parallel corpora available.Research shows that under the condition of limited resources,the performance of NMT is greatly reduced,and a large amount of high-quality bilingual parallel data is needed to train a competitive translation model.However,not all languages have large-scale and high-quality bilingual corpus resources available.In these cases,improving the quality of the corpora has become the main focus to increase the accuracy of the NMT results.This paper proposes a new method to improve the quality of data by using data cleaning,data expansion,and other measures to expand the data at the word and sentence-level,thus improving the richness of the bilingual data.The long short-term memory(LSTM)language model is also used to ensure the smoothness of sentence construction in the process of sentence construction.At the same time,it uses a variety of processing methods to improve the quality of the bilingual data.Experiments using three standard test sets are conducted to validate the proposed method;the most advanced fairseq-transformer NMT system is used in the training.The results show that the proposed method has worked well on improving the translation results.Compared with the state-of-the-art methods,the BLEU value of our method is increased by 2.34 compared with that of the baseline.
基金Project supported by the National Natural Science Foundation of China(No.11972112)the Fundamental Research Funds for the Central Universities of China(Nos.N2103024 and N2103002)the Major Projects of Aero-Engines and Gasturbines(No.J2019-I-0008-0008)。
文摘The dynamic characteristics of a single liquid-filled pipe have been broadly studied in the previous literature.The parallel liquid-filled pipe(PLFP)system is also widely used in engineering,and its structure is more complex than that of a single pipe.However,there are few reports about the dynamic characteristics of the PLFPs.Therefore,this paper proposes improved frequency modeling and solution for the PLFPs,involving the logical alignment principle and coupled matrix processing.The established model incorporates both the fluid-structure interaction(FSI)and the structural coupling of the PLFPs.The validity of the established model is verified by modal experiments.The effects of some unique parameters on the dynamic characteristics of the PLFPs are discussed.This work provides a feasible method for solving the FSI of multiple pipes in parallel and potential theoretical guidance for the dynamic analysis of the PLFPs in engineering.
基金The authors would like to acknowledge the funding support of the National Natural Science Foundation of China （50579009, 70471090） the National 10 th Five Year Scientific Project of China for Tackling the Key Problems （2004BA608B-02 - 02） and the Excellence Youth Teacher Sustentation Fund Program of the Ministry of Education of China （Department of Education and Personnel [2002] 350）.
文摘The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.
基金the National Natural Science Foundation of China(Nos.12002381 and 11925207)the Science and Technology Foundation of State Key Laboratory,China(No.6142703200311)the Scientific Research Plan of National University of Defense Technology in 2019,China(No.ZK19-02).
文摘Developing supersonic combustion models with efficiency,accuracy and practicality is important foundation to deeply understand the complex combustion processes in scramjet engines.Characterized by efficiency and intuition,the flamelet-like models are widely used models in computational combustion methods.However,the supersonic combustion flow field has the nature of strong compressibility,multiple modality,and multiple scales,which poses a great challenge to the traditional flamelet-like models with fixed boundary conditions,and then the complex chemical reaction mechanisms that may face will impose additional computational burden.In this context,this paper reviews the flamelet-like models used in scramjet engines,and summarizes prominent issues in the application practice,including modeling partially premixed combustion,defining progress variable,solving temperature efficiently,evaluating assumed Probability Density Function(PDF)models,and treating mixture fraction variance.Furthermore,possible prospects and directions of improvements are proposed and highlighted for the flamelet-like models.To fully describe the physicochemical scenario and address the raised challenges,these improvements are dedicated to dealing with the compressibility,temperature rise,time-scales,species of interest,multi-inlet combustion,the progress variable definition,and the higher Mach number flight condition.
基金Project supported by Science&Technology Program of Hubei Traffic and Transport Office,ChinaProject(41272377)supported by the National Natural Science Foundation of China
文摘The weak intercalated soils in redbed soft rocks of Badong formation have obvious creep characters. In order to predict the unsaturated creep behaviors of weak intercalated soils, an unsaturated creep model was established based on the unsaturated creep tests of weak intercalated soils by using GDS triaxial apparatus. The results show that the creep behaviors of intercalated soils are apparent and significantly affected by matric suction. Based on this, an empirical Mesri creep model for intercalated soils under varying matric suctions was built. The fitting results show that the parameters Ed and m of this model are in good power relations with matric suction s and stress level Dr, respectively. An improved Mesri creep model was established involving stress-matric suction-strain-time, which is more precise than the Mesri creep model in predicting the unsaturated creep behaviors of weak intercalated soils.
基金Under the auspices of Major Special Technological Program of Water Pollution Control and Management (No.2009ZX07106-001)National Natural Science Foundation of China (No. 51079037, 50909063)
文摘According to the relationships among state transition probability matrixes with different step lengths, an improved Markov chain model based on autocorrelation and entropy techniques was introduced. In the improved Markov chain model, the state transition probability matrixes can be adjusted. The steps of the historical state of the event, which was significantly related to the future state of the event, were determined by the autocorrelation technique, and the impact weights of the event historical state on the event future state were determined by the entropy technique. The presented model was applied to predicting annual precipitation and annual runoff states, showing that the improved model is of higher precision than those existing Markov chain models, and the determination of the state transition probability matrixes and the weights is more reasonable. The physical concepts of the improved model are distinct, and its computation process is simple and direct, thus, the presented model is sufficiently general to be applicable to the prediction problems in hydrology and water resources.
基金financially supported by the National Natural Science Foundation of China (Grant No. 51505041)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 16KJB460002)
文摘The strain rate sensitivity（SRS）and temperature sensitivity（TS）of 316L austenitic stainless steel were investigated by constant strain rate test（CSRT）and strain rate jump test（SRJT）under four temperatures（293,373,473 and 573 K）and four strain rates（5 ×10^-4/s,1 × 10^-3/s,5 × 10^-3/s and 1 × 10^-2/s）.The results show that temperature sensitivity（TS）indexes at different strain rates are coincidence to be negative,related to temperature softening.On the contrary,SRS indexes change from positive to negative with the increase in temperature associated with dynamic strain aging（DSA）.Moreover,based on the comparison between CSRT and SRJT,SRS and TS indexes obtained by two methods agree well.It proves that the SRJT can describe the SRS and TS phenomenon of 316L efficiently.Furthermore,the effects of tem-perature and strain rate on fracture mechanism were discussed.At last,an improved Johnson-Cook model was proposed to consider the temperature-dependent SRS behavior of 316L.
文摘In this paper,under the assumption that the labor force function increases strictly and is bounded and the labor force growth rate function decreases monotonically from a positive value to zero,we obtain an improved Solow Swan model. We prove that the per capita capital trends stabilitily to the steady state of the classical Solow Swan model with zero the labor force growth rate. Two comparison theorems,a limited theorem and a stability theorem are given. At the end of this paper,we give an example and discuss the economic meaning of this model and the theorems.
文摘It is essential to precisely predict the crack growth,especially the near-threshold regime crack growth under different stress ratios,for most engineering structures consume their fatigue lives in this regime under random loading.In this paper,an improved unique curve model is proposed based on the unique curve model,and the determination of the shape exponents of this model is provided.The crack growth rate curves of some materials taken from the literature are evaluated using the improved model,and the results indicate that the improved model can accurately predict the crack growth rate in the nearthreshold and Paris regimes.The improved unique curve model can solve the problems about the shape exponents determination and weak ability around the near-threshold regime meet in the unique curve model.In addition,the shape exponents in the improved model at negative stress ratios are discussed,which can directly adopt that in the unique curve model.
文摘In this paper, a new theoretical expression of dissipation term is presented on the basis of statistical model of breaking wave, which is an improvement to LAGFD-WAM wave model. The computational results in three typical wind fields show a good improvement to LAGFD-WAM model and a better accuracy in comparison with the observed data in the South China Sea.
基金The National Natural Science Foundation of China(No.51778485).
文摘The mixed model of improved exponential and power function and unequal interval gray GM(1,1)model have poor accuracy in predicting the maximum pull-out load of anchor bolts.An optimal combination model was derived using the optimally weighted combination theory and the minimum sum of logarithmic squared errors as the objective function.Two typical anchor bolt pull-out engineering cases were selected to compare the performance of the proposed model with those of existing ones.Results showed that the optimal combination model was suitable not only for the slow P-s curve but also for the steep P-s curve.Its accuracy and stable reliability,as well as its prediction capability classification,were better than those of the other prediction models.Therefore,the optimal combination model is an effective processing method for predicting the maximum pull-out load of anchor bolts according to measured data.
基金This work is supported by the Fundamental Research Funds for the Central Universities,China(Project No.2018MS148).
文摘The timely and effective investment risk assessment and forecasting are of great significance to ensure the investment safety and sustainable development of wind energy along the Belt and Road.In order to obtain the scientific and real-time forecasting result,this paper constructs a novel hybrid intelligent model based on improved cloud model combined with GRA-TOPSIS and MBA-WLSSVM.Firstly,the factors influencing investment risk of wind energy along the Belt and Road are identified fromthree dimensions:endogenous risk,exogenous risk and process risk.Through the fuzzy threshold method,the final input index system is selected.Secondly,the risk evaluation method based on improved cloud model andGRA-TOPSIS is proposed.Thirdly,a modern intelligent model based on MBA-WLSSVMis designed.In modified bat algorithm(MBA),tent chaotic map is utilized to improve the basic bat algorithm,while weighted least squares support vector machine(WLSSVM)adopts wavelet kernel function to replace the traditional radial basis function to complete the model improvement.Finally,an example is given to verify the scientificity and accuracy of themodel,which is helpful for investors tomake fast and effective investment risk forecasting of wind energy along the Belt and Road.The example analysis proves that the proposedmodel can provide reference and basis for investment corpus to formulate the investment strategy in wind energy along the Belt and Road.
基金supported by the National Natural Science Foundation of China(Grant No.11975307).
文摘Accurate identification of influential nodes facilitates the control of rumor propagation and interrupts the spread of computer viruses.Many classical approaches have been proposed by researchers regarding different aspects.To explore the impact of location information in depth,this paper proposes an improved global structure model to characterize the influence of nodes.The method considers both the node’s self-information and the role of the location information of neighboring nodes.First,degree centrality of each node is calculated,and then degree value of each node is used to represent self-influence,and degree values of the neighbor layer nodes are divided by the power of the path length,which is path attenuation used to represent global influence.Finally,an extended improved global structure model that considers the nearest neighbor information after combining self-influence and global influence is proposed to identify influential nodes.In this paper,the propagation process of a real network is obtained by simulation with the SIR model,and the effectiveness of the proposed method is verified from two aspects of discrimination and accuracy.The experimental results show that the proposed method is more accurate in identifying influential nodes than other comparative methods with multiple networks.
