Accurate prediction of coal and gas outburst(CGO)hazards is paramount in gas disaster prevention and control.This paper endeavors to overcome the constraints posed by traditional prediction indexes when dealing with C...Accurate prediction of coal and gas outburst(CGO)hazards is paramount in gas disaster prevention and control.This paper endeavors to overcome the constraints posed by traditional prediction indexes when dealing with CGO incidents under low gas pressure conditions.In pursuit of this objective,we have studied and established a mechanical model of the working face under abnormal stress and the excitation energy conditions of CGO,and proposed a method for predicting the risk of CGO under abnormal stress.On site application verification shows that when a strong outburst hazard level prediction is issued,there is a high possibility of outburst disasters occurring.In one of the three locations where we predicted strong outburst hazards,a small outburst occurred,and the accuracy of the prediction was higher than the traditional drilling cuttings index S and drilling cuttings gas desorption index q.Finally,we discuss the mechanism of CGO under the action of stress anomalies.Based on the analysis of stress distribution changes and energy accumulation characteristics of coal under abnormal stress,this article believes that the increase in outburst risk caused by high stress abnormal gradient is mainly due to two reasons:(1)The high stress abnormal gradient leads to an increase in the plastic zone of the coal seam.After the working face advances,it indirectly leads to an increase in the gas expansion energy that can be released from the coal seam before reaching a new stress equilibrium.(2)Abnormal stress leads to increased peak stress of coal body in front of working face.When coal body in elastic area transforms to plastic area,its failure speed is accelerated,which induces accelerated gas desorption and aggravates the risk of outburst.展开更多
Wellbore instability is one of the significant challenges in the drilling engineering and during the development of carbonate reservoirs,especially with open-hole completion.The problems of wellbore instability such a...Wellbore instability is one of the significant challenges in the drilling engineering and during the development of carbonate reservoirs,especially with open-hole completion.The problems of wellbore instability such as downhole collapse and silt deposit in the fractured carbonate reservoir of Tarim Basin(Ordovician)are severe.Solid destabilization and production(SDP)was proposed to describe this engineering problem of carbonate reservoirs.To clarify the mechanism and mitigate potential borehole instability problems,we conducted particle size distribution(PSD)analysis,X-ray diffraction(XRD)analysis,triaxial compression tests,and micro-scale sand production tests based on data analysis.We found that the rock fragments and silt in the wellbore came from two sources:one from the wellbore collapse in the upper unplugged layers and the other from the production of sand particles carried by the fluid in the productive layers.Based on the experimental study,a novel method combining a geomechanical model and microscopic sand production model was proposed to predict wellbore instability and analyze its influencing factors.The critical condition and failure zone predicted by the prediction model fit well with the field observations.According to the prediction results,the management and prevention measures of wellbore instability in carbonate reservoirs were proposed.It is suggested to optimize the well track in new drilling wells while upgrading the production system in old wells.This study is of great guiding significance for the optimization of carbonate solid control and it improves the understanding of the sand production problems in carbonate reservoirs.展开更多
Based on the meteorological data of Langzhong from 2010 to 2020,the human body comfort index was calculated,and tourism climate comfort was evaluated to establish the prediction equation of tourism meteorological inde...Based on the meteorological data of Langzhong from 2010 to 2020,the human body comfort index was calculated,and tourism climate comfort was evaluated to establish the prediction equation of tourism meteorological index.OLS was used to compare the correlation between actual tourist flow and tourism meteorological index and test the model effect.Average correlation coefficient R was 0.7017,so the correlation was strong,and P value was 0.The two were significantly correlated at 0.01 level(bilateral).It can be seen that the forecast equation of tourism meteorological index had a strong correlation with the actual number of tourists,and the predicted value was basically close to the actual situation,and the forecast effect is good.展开更多
In order to make a scientific pavement maintenance decision, a grey-theory-based prediction methodological framework is proposed to predict pavement performance. Based on the field pavement rutting data,analysis of va...In order to make a scientific pavement maintenance decision, a grey-theory-based prediction methodological framework is proposed to predict pavement performance. Based on the field pavement rutting data,analysis of variance （ANOVA）was first used to study the influence of different factors on pavement rutting. Cluster analysis was then employed to investigate the rutting development trend.Based on the clustering results,the grey theory was applied to build pavement rutting models for each cluster, which can effectively reduce the complexity of the predictive model.The results show that axial load and asphalt binder type play important roles in rutting development.The prediction model is capable of capturing the uncertainty in the pavement performance prediction process and can meet the requirements of highway pavement maintenance,and,therefore,has a wide application prospects.展开更多
To describe the complex phase transformation in the process of depletion exploitation of volatile oil reservoir,four fluid phases are defined,and production and remaining volume of these phases are calculated based on...To describe the complex phase transformation in the process of depletion exploitation of volatile oil reservoir,four fluid phases are defined,and production and remaining volume of these phases are calculated based on the principle of surface volume balance,then the recovery prediction method of volatile oil reservoir considering the influence of condensate content in released solution gas and the correction method of multiple degassing experiments data are established.Taking three typical kinds of crude oil(black oil,medium-weak volatile oil,strong volatile oil)as examples,the new improved method is used to simulate constant volume depletion experiments based on the corrected data of multiple degassing experiment to verify the reliability of the modified method.By using"experimental data and traditional method","corrected data and traditional method"and"corrected data and modified method",recovery factors of these three typical kinds of oil are calculated respectively.The source of parameters and the calculation methods have little effect on the recovery of typical black oil.However,with the increase of crude oil volatility,the oil recovery will be seriously underestimated by using experimental data or traditional method.The combination of"corrected data and modified method"considers the influence of condensate in gas phase in both experimental parameters and calculation method,and has good applicability to typical black oil and volatile oil.The strong shrinkage of volatile oil makes more"liquid oil"convert to"gaseous oil",so volatile oil reservoir can reach very high oil recovery by depletion drive.展开更多
By using the significance test of two-dimensional wind field anomalies and Monte Carlo simulation experiment scheme, the significance features of wind field anomalies are investigated in relation to flood/drought duri...By using the significance test of two-dimensional wind field anomalies and Monte Carlo simulation experiment scheme, the significance features of wind field anomalies are investigated in relation to flood/drought during the annually first rainy season in south China. Results show that westem Pacific subtropical high and wind anomalies over the northeast of Lake Baikal and central Indian Ocean are important factors. Wind anomalies over the northem India in January and the northwest Pacific in March may be strong prediction signals. Study also shows that rainfall in south China bears a close relation to the geopotential height filed over the northern Pacific in March.展开更多
In this paper, on the basis of the heat conduction equation without consideration of the advection and turbulence effects, one-dimensional model for describing surface sea temperature ( T1), bottom sea temperature ( T...In this paper, on the basis of the heat conduction equation without consideration of the advection and turbulence effects, one-dimensional model for describing surface sea temperature ( T1), bottom sea temperature ( Tt ) and the thickness of the upper homogeneous layer ( h ) is developed in terms of the dimensionless temperature θT and depth η and self-simulation function θT - f(η) of vertical temperature profile by means of historical temperature data.The results of trial prediction with our one-dimensional model on T, Th, h , the thickness and gradient of thermocline are satisfactory to some extent.展开更多
There are many long-term and short-term prediction methods of Total Electron Content(TEC) that need to be tested for each specific region. Recently, much attention has been paid to testing TEC models in high-, low-lat...There are many long-term and short-term prediction methods of Total Electron Content(TEC) that need to be tested for each specific region. Recently, much attention has been paid to testing TEC models in high-, low-latitude and equatorial regions. This paper compares the TEC prediction methods in the midlatitude zone according to the data of the Juliusruh, Rostov, Manzhouli stations in 2008 and 2015. For a long-term prediction, the IRI-Plas and Ne Quick models are compared with the Global Ionospheric Maps(GIM) presented by the Jet Propulsion Laboratory(JPL) and the Technical University of Catalonia(UPC).For a short-term prediction, the Standard Persistence Model(SPM) method, a 27 day median model, and the proposed short-term prediction method are compared for one day ahead. It is shown that for all stations the IRI-Plas model provides better compliance with GIM maps than the Ne Quick model irrespective of a solar activity level. An average absolute error lays in the range of 3 e3.5 TECU, relative root square mean(RMS) error in the range of 22 e27% in 2015 and 1.7 e2 TECU, 20 e25% in 2008. For the Ne Quick model, these estimates were 6.7 e8.2 TECU and 42 e45% in 2015 and 2.2 e3.6 TECU, 30 e37% in2008. For the short-term forecast, the best results were obtained by the SPM method with an average absolute error in the range of 1.95 e2.15 TECU in 2015 and 0.59 e0.98 TECU in 2008, a relative RMS error in the range of 17 e21% in 2015, 11.5 e15% in 2008. For the proposed short-term prediction method, these errors were 2.04 e2.2 TECU and 12 e14% in 2015 and 0.7 e1.0 TECU, 7 e11% in 2008. Using medians, the errors were 3.1 e3.4 TECU and 17 e21% in 2015 and 1.0 e1.3 TECU, 10 e15% in 2008. The dependence of results on the Dst-index was obtained.展开更多
This study was conducted to establish a predictable method for a heat load of an underground structure with sufficient accuracy. As the first step, our previous paper reported the measurement results of field experime...This study was conducted to establish a predictable method for a heat load of an underground structure with sufficient accuracy. As the first step, our previous paper reported the measurement results of field experiments on an underground experimental basement under internal heat generation conditions. Also, it presented the results of numerical analyses on the heat and moisture behavior and the influence of internal heat generation of the experimental basement and ground. However, it is practically impossible to utilize the model of simultaneous heat and moisture transfer at the design phase because the prediction by the model of simultaneous heat and moisture transfer requires a long calculation time. In this paper, the authors present the simple load calculation technique, using a linearized approximation indicial response of the inner surface heat flux in a basement to outdoor air temperature change. In addition, the approximation indicial responses for each part of the single-walled concrete drawn using this technique are arranged. The heat load calculation example of application to the basement of the optional size by this technique is shown.展开更多
BP neural networks is used to mid-term earthquake prediction in this paper. Some usual prediction parameters of seismology are used as the import units of neural networks. And the export units of neural networks is ca...BP neural networks is used to mid-term earthquake prediction in this paper. Some usual prediction parameters of seismology are used as the import units of neural networks. And the export units of neural networks is called as the character parameter W_0 describing enhancement of seismicity. We applied this method to space scanning of North China. The result shows that the mid-term anomalous zone of W_0-value usually appeared obviously around the future epicenter 1~3 years before earthquake. It is effective to mid-term prediction.展开更多
Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer,thereby facilitating the formulation of power outage maintenance plans and power dispatch...Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer,thereby facilitating the formulation of power outage maintenance plans and power dispatch strategies.However,existing prediction methods based on the structure of‘splicing prediction and diagnosis method’suffer from limitations such as inability to achieve global optimality,error accumulation,and low prediction accuracy.To fill this gap,a novel direct prediction method of a trans-former state based on knowledge and data fusion-driven model(K&DFDM)is pro-posed in this paper.Firstly,a state quantity data space is constructed to comprehensively reflect the changes in the health state of the transformer over time,encompassing online monitoring,offline testing,evaluation results,and actual operation data.After that,correlation knowledge between state quantities,fault diagnosis mechanism knowledge,current diagnosis experience knowledge,and uncertain fuzzy knowledge are extracted separately.The actual fault mechanism,existing expert experience,and other knowledge in the diagnosis process are quantified.Then,the attention model is sub-sequently optimised,leveraging quantitative knowledge to effectively constrain and guide the data prediction process.Incorporating fault diagnosis mechanism knowledge into the data prediction model enables the achievement of global optimisation in both diagnosis and prediction.The integration of traditional expert experience knowledge and the correlation knowledge between state quantities serves as constraints during the process of attaining the global optimum.The verification results,comprising 327 cases,demonstrate that K&DFDM effectively addresses the issue of error superposition encountered by existing state prediction methods,leading to a direct state prediction accuracy of 96.33%.展开更多
The mathematical method of ZTD(zenith tropospheric delay)spatial prediction is important for precise ZTD derivation and real-time precise point positioning(PPP)augmentation.This paper analyses the performance of the p...The mathematical method of ZTD(zenith tropospheric delay)spatial prediction is important for precise ZTD derivation and real-time precise point positioning(PPP)augmentation.This paper analyses the performance of the popular optimal function coefficient(OFC),sphere cap harmonic analysis(SCHA),kriging and inverse distance weighting(IDW)interpolation in ZTD spatial prediction and Beidou satellite navigation system(BDS)-PPP augmentation over China.For ZTD spatial prediction,the average time consumption of the OFC,kriging,and IDW methods is less than 0.1 s,which is significantly better than that of the SCHA method(63.157 s).The overall ZTD precision of the OFC is 3.44 cm,which outperforms those of the SCHA(9.65 cm),Kriging(10.6 cm),and IDW(11.8 cm)methods.We confirmed that the low performance of kriging and IDW is caused by their weakness in modelling ZTD variation in the vertical direction.To mitigate such deficiencies,an elevation normalization factor(ENF)is introduced into the kriging and IDW models(kriging-ENF and IDW-ENF).The overall ZTD spatial prediction accuracies of IDW-ENF and kriging-ENF are 2.80 cm and 2.01 cm,respectively,which are both superior to those of the OFC and the widely used empirical model GPT3(4.92 cm).For BDS-PPP enhancement,the ZTD provided by the kriging-ENF,IDW-ENF and OFC as prior constraints can effectively reduce the convergence time.Compared with unconstrained BDS-PPP,our proposed kriging-ENF outperforms IDW-ENF and OFC by reducing the horizontal and vertical convergence times by approximately 13.2%and 5.8%in Ningxia and 30.4%and 7.84%in Guangdong,respectively.These results indicate that kriging-ENF is a promising method for ZTD spatial prediction and BDS-PPP enhancement over China.展开更多
This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was...This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was established for water and mud inrush in tunnels by analyzing advanced prediction data for specifi c tunnel segments.Additionally,the indicator weights were determined using the analytic hierarchy process combined with the Huber weighting method.Subsequently,a multisource data decision-layer fusion algorithm was utilized to generate fused imaging results for tunnel water and mud inrush risk predictions.Meanwhile,risk analysis was performed for different tunnel sections to achieve spatial and temporal complementarity within the indicator system and optimize redundant information.Finally,model feasibility was validated using the CZ Project Sejila Mountain Tunnel segment as a case study,yielding favorable risk prediction results and enabling effi cient information fusion and support for construction decision-making.展开更多
Technical challenges have always been part of underground mining activities,however,some of these challenges grow in complexity as mining occurs in deeper and deeper settings.One such challenge is rock mass stability ...Technical challenges have always been part of underground mining activities,however,some of these challenges grow in complexity as mining occurs in deeper and deeper settings.One such challenge is rock mass stability and the risk of rockburst events.To overcome these challenges,and to limit the risks and impacts of events such as rockbursts,advanced solutions must be developed and best practices implemented.Rockbursts are common in underground mines and substantially threaten the safety of personnel and equipment,and can cause major disruptions in mine development and operations.Rockbursts consist of violent wall rock failures associated with high energy rock projections in response to the instantaneous stress release in rock mass under high strain conditions.Therefore,it is necessary to develop a good understanding of the conditions and mechanisms leading to a rockburst,and to improve risk assessment methods.The capacity to properly estimate the risks of rockburst occurrence is essential in underground operations.However,a limited number of studies have examined and compared yet different empirical methods of rockburst.The current understanding of this important hazard in the mining industry is summarized in this paper to provide the necessary perspective or tools to best assess the risks of rockburst occurrence in deep mines.The various classifications of rockbursts and their mechanisms are discussed.The paper also reviews the current empirical methods of rockburst prediction,which are mostly dependent on geomechanical parameters of the rock such as uniaxial compressive strength of the rock,as well as its tensile strength and elasticity modulus.At the end of this paper,some current achievements and limitations of empirical methods are discussed.展开更多
It is difficult to control the quality of Korla pear with different degrees of maturity during storage.Here,a method was proposed for predicting the effects of harvest maturity and cold storage time on the quality ind...It is difficult to control the quality of Korla pear with different degrees of maturity during storage.Here,a method was proposed for predicting the effects of harvest maturity and cold storage time on the quality indices(soluble solid content(SSC)and Vitamin C(Vc)content)of Korla pear.The generalized regression neural network(GRNN)and adaptive neuro-fuzzy inference system(ANFIS)were employed to predict the quality changes of Korla fragrant pear fruit during storage.The results demonstrated that during cold storage the SSC in pears with 10%-70%harvest maturity showed continuous increases in the first 90 d of storage and then a slight decline thereafter,while that in pears with 80%and 90%harvest maturity exhibited slow decreases throughout the storage process.With the extension of storage time,the Vc content of pears with 10%-90%harvest maturity showed continuous decreases.The harvest maturity of Korla pear was extremely positively correlated with SSC and Vc content(p<0.01)in a given storage period.Storage time showed an extremely significant negative correlation with the Vc content(p<0.01)at the 40%-90%harvest maturity and an significant negative correlation with the Vc content(p<0.05)at the 10%-30%harvest maturity.At the 10%-70%harvest maturity,storage time showed a significant positive correlation with the SSC(p<0.05).The trained model could well predict the variation trend of quality indices of pear fruit during storage.The ANFIS with the input membership function of gbellmf had the best performance in predicting the SSC(RMSE=0.175;R2=0.98),and that with the input membership function of trimf exhibited the best performance in predicting Vc content(RMSE=0.075;R2=0.99).The research findings can provide reference for predicting the fruit nutritional quality at delivery and decision-making on the storage time of Korla fragrant pear.展开更多
Currently applied prediction methods of regional freight traffic and freight ton-kilometer forecasting were analyzed using typical Chinese regional goods transportation characteristics. The review of prediction method...Currently applied prediction methods of regional freight traffic and freight ton-kilometer forecasting were analyzed using typical Chinese regional goods transportation characteristics. The review of prediction methods showes that practical planning experts tend to apply the traditional methods which are easier to implement. The comparison also demonstrates that a combination of traditional methods is more effective than the simple models for practical planning. Research using the statistical data for the Yangtze Delta, Pearl River Delta, and Bohai Rim areas shows that ignoring differences between transport modes impacts the prediction accuracy. The four main transport modes suit different methods. The results show that the power model is better for railways, and the linear model is better for highways and waterways. Thus a combined model gives better results for all modes. The results for regional systems can be generalized to national transportation systems.展开更多
The common-mode current is an important indicator with transformerless photovoltaic inverters.However,up to now,there is not an accurate method to predict common-mode current in the inverter design process,resulting f...The common-mode current is an important indicator with transformerless photovoltaic inverters.However,up to now,there is not an accurate method to predict common-mode current in the inverter design process,resulting from inappropriate device selection or exceeded the expected common-mode current.In order to solve this problem,this paper proposes an accurate common-mode current prediction method based on graph theory for transformerless photovoltaic inverters.In this paper,the mathematic model of the common-mode current is derived using graph theory analysis method in the full-bridge topology,and it is used to predict common-mode current.The validity and correctness of the proposed prediction method are validated by simulation and experiment.The oscillation frequency and amplitude can be predicted by the proposed common-mode prediction method,whereas the traditional common-mode analysis method cannot.This paper provides a novel way to predict and analyze common-mode current in the transformerless photovoltaic inverters.展开更多
This study presents the classification and prediction of severity for brittle rock failure,focusing on failure behaviors and excessive determination based on damage depth.The research utilizes extensive field survey d...This study presents the classification and prediction of severity for brittle rock failure,focusing on failure behaviors and excessive determination based on damage depth.The research utilizes extensive field survey data from the Shuangjiangkou Hydropower Station and previous research findings.Based on field surveys and previous studies,four types of brittle rock failure with different failure mechanisms are classified,and then a prediction method is proposed.This method incorporates two variables,i.e.Kv(modified rock mass integrity coefficient)and GSI(geological strength index).The prediction method is applied to the first layer excavation of the powerhouse cavern of Shuangjiangkou Hydropower Station.The results show that the predicted brittle rock failure area agrees with the actual failure area,demonstrating the method’s applicability.Next,it extends to investigate brittle rock failure in two locations.The first is the k0-890 m section of the traffic cavern,and the second one is at K0-64 m of the main powerhouse.The criterion-based prediction indicates a severity brittle rock failure in the K0-890 m section,and a moderate brittle rock failure in the K0-64 m section,which agrees with the actual occurrence of brittle rock failure in the field.The understanding and application of the prediction method using Kv and GSI are vital for implementing a comprehensive brittle rock failure prediction process in geological engineering.To validate the adaptability of this criterion across diverse tunnel projects,a rigorous verification process using statistical findings was conducted.The assessment outcomes demonstrate high accuracy for various tunnel projects,allowing establishment of the correlations that enable valuable conclusions regarding brittle rock failure occurrence.Further validation and refinement through field and laboratory testing,as well as simulations,can broaden the contribution of this method to safer and more resilient underground construction.展开更多
To achieve lower assembly delay at optical burst switching edge node, this paper proposes an approach called current weight length prediction (CWLP) to improve existing estimate mechanism in burst assembly. CWLP metho...To achieve lower assembly delay at optical burst switching edge node, this paper proposes an approach called current weight length prediction (CWLP) to improve existing estimate mechanism in burst assembly. CWLP method takes into account the arrived traffic in prediction time adequately. A parameter 'weight' is introduced to make a dynamic tradeoff between the current and past traffic under different offset time. Simulation results show that CWLP can achieve a significant improvement in terms of traffic estimation in various offset time and offered load.展开更多
A new probability function of mining overlying strata and subsidence is put forward that has a general statistical significance based on the ideal stochastic medium displacement model. It establishes a new system of p...A new probability function of mining overlying strata and subsidence is put forward that has a general statistical significance based on the ideal stochastic medium displacement model. It establishes a new system of prediction on horizontal mining subsidence and deformation, which gives a new method for prediction on mining subsidence and deformation.展开更多
基金supported by the National Natural Science Foundation of China(52174162)the Fundamental Research Funds for the Central Universities(FRF-TP-20-002A3).
文摘Accurate prediction of coal and gas outburst(CGO)hazards is paramount in gas disaster prevention and control.This paper endeavors to overcome the constraints posed by traditional prediction indexes when dealing with CGO incidents under low gas pressure conditions.In pursuit of this objective,we have studied and established a mechanical model of the working face under abnormal stress and the excitation energy conditions of CGO,and proposed a method for predicting the risk of CGO under abnormal stress.On site application verification shows that when a strong outburst hazard level prediction is issued,there is a high possibility of outburst disasters occurring.In one of the three locations where we predicted strong outburst hazards,a small outburst occurred,and the accuracy of the prediction was higher than the traditional drilling cuttings index S and drilling cuttings gas desorption index q.Finally,we discuss the mechanism of CGO under the action of stress anomalies.Based on the analysis of stress distribution changes and energy accumulation characteristics of coal under abnormal stress,this article believes that the increase in outburst risk caused by high stress abnormal gradient is mainly due to two reasons:(1)The high stress abnormal gradient leads to an increase in the plastic zone of the coal seam.After the working face advances,it indirectly leads to an increase in the gas expansion energy that can be released from the coal seam before reaching a new stress equilibrium.(2)Abnormal stress leads to increased peak stress of coal body in front of working face.When coal body in elastic area transforms to plastic area,its failure speed is accelerated,which induces accelerated gas desorption and aggravates the risk of outburst.
基金financially supported by the National Natural Science Foundation of China(Grant No.52074331).
文摘Wellbore instability is one of the significant challenges in the drilling engineering and during the development of carbonate reservoirs,especially with open-hole completion.The problems of wellbore instability such as downhole collapse and silt deposit in the fractured carbonate reservoir of Tarim Basin(Ordovician)are severe.Solid destabilization and production(SDP)was proposed to describe this engineering problem of carbonate reservoirs.To clarify the mechanism and mitigate potential borehole instability problems,we conducted particle size distribution(PSD)analysis,X-ray diffraction(XRD)analysis,triaxial compression tests,and micro-scale sand production tests based on data analysis.We found that the rock fragments and silt in the wellbore came from two sources:one from the wellbore collapse in the upper unplugged layers and the other from the production of sand particles carried by the fluid in the productive layers.Based on the experimental study,a novel method combining a geomechanical model and microscopic sand production model was proposed to predict wellbore instability and analyze its influencing factors.The critical condition and failure zone predicted by the prediction model fit well with the field observations.According to the prediction results,the management and prevention measures of wellbore instability in carbonate reservoirs were proposed.It is suggested to optimize the well track in new drilling wells while upgrading the production system in old wells.This study is of great guiding significance for the optimization of carbonate solid control and it improves the understanding of the sand production problems in carbonate reservoirs.
文摘Based on the meteorological data of Langzhong from 2010 to 2020,the human body comfort index was calculated,and tourism climate comfort was evaluated to establish the prediction equation of tourism meteorological index.OLS was used to compare the correlation between actual tourist flow and tourism meteorological index and test the model effect.Average correlation coefficient R was 0.7017,so the correlation was strong,and P value was 0.The two were significantly correlated at 0.01 level(bilateral).It can be seen that the forecast equation of tourism meteorological index had a strong correlation with the actual number of tourists,and the predicted value was basically close to the actual situation,and the forecast effect is good.
基金The Major Scientific and Technological Special Project of Jiangsu Provincial Communications Department(No.2011Y/02-G1)
文摘In order to make a scientific pavement maintenance decision, a grey-theory-based prediction methodological framework is proposed to predict pavement performance. Based on the field pavement rutting data,analysis of variance （ANOVA）was first used to study the influence of different factors on pavement rutting. Cluster analysis was then employed to investigate the rutting development trend.Based on the clustering results,the grey theory was applied to build pavement rutting models for each cluster, which can effectively reduce the complexity of the predictive model.The results show that axial load and asphalt binder type play important roles in rutting development.The prediction model is capable of capturing the uncertainty in the pavement performance prediction process and can meet the requirements of highway pavement maintenance,and,therefore,has a wide application prospects.
基金Supported by the China National Science and Technology Major Project(2016ZX05027)。
文摘To describe the complex phase transformation in the process of depletion exploitation of volatile oil reservoir,four fluid phases are defined,and production and remaining volume of these phases are calculated based on the principle of surface volume balance,then the recovery prediction method of volatile oil reservoir considering the influence of condensate content in released solution gas and the correction method of multiple degassing experiments data are established.Taking three typical kinds of crude oil(black oil,medium-weak volatile oil,strong volatile oil)as examples,the new improved method is used to simulate constant volume depletion experiments based on the corrected data of multiple degassing experiment to verify the reliability of the modified method.By using"experimental data and traditional method","corrected data and traditional method"and"corrected data and modified method",recovery factors of these three typical kinds of oil are calculated respectively.The source of parameters and the calculation methods have little effect on the recovery of typical black oil.However,with the increase of crude oil volatility,the oil recovery will be seriously underestimated by using experimental data or traditional method.The combination of"corrected data and modified method"considers the influence of condensate in gas phase in both experimental parameters and calculation method,and has good applicability to typical black oil and volatile oil.The strong shrinkage of volatile oil makes more"liquid oil"convert to"gaseous oil",so volatile oil reservoir can reach very high oil recovery by depletion drive.
基金Natural Science Foundation of China (40275028)Research Fund for the Science of Tropicaland Marine Meteorology
文摘By using the significance test of two-dimensional wind field anomalies and Monte Carlo simulation experiment scheme, the significance features of wind field anomalies are investigated in relation to flood/drought during the annually first rainy season in south China. Results show that westem Pacific subtropical high and wind anomalies over the northeast of Lake Baikal and central Indian Ocean are important factors. Wind anomalies over the northem India in January and the northwest Pacific in March may be strong prediction signals. Study also shows that rainfall in south China bears a close relation to the geopotential height filed over the northern Pacific in March.
文摘In this paper, on the basis of the heat conduction equation without consideration of the advection and turbulence effects, one-dimensional model for describing surface sea temperature ( T1), bottom sea temperature ( Tt ) and the thickness of the upper homogeneous layer ( h ) is developed in terms of the dimensionless temperature θT and depth η and self-simulation function θT - f(η) of vertical temperature profile by means of historical temperature data.The results of trial prediction with our one-dimensional model on T, Th, h , the thickness and gradient of thermocline are satisfactory to some extent.
文摘There are many long-term and short-term prediction methods of Total Electron Content(TEC) that need to be tested for each specific region. Recently, much attention has been paid to testing TEC models in high-, low-latitude and equatorial regions. This paper compares the TEC prediction methods in the midlatitude zone according to the data of the Juliusruh, Rostov, Manzhouli stations in 2008 and 2015. For a long-term prediction, the IRI-Plas and Ne Quick models are compared with the Global Ionospheric Maps(GIM) presented by the Jet Propulsion Laboratory(JPL) and the Technical University of Catalonia(UPC).For a short-term prediction, the Standard Persistence Model(SPM) method, a 27 day median model, and the proposed short-term prediction method are compared for one day ahead. It is shown that for all stations the IRI-Plas model provides better compliance with GIM maps than the Ne Quick model irrespective of a solar activity level. An average absolute error lays in the range of 3 e3.5 TECU, relative root square mean(RMS) error in the range of 22 e27% in 2015 and 1.7 e2 TECU, 20 e25% in 2008. For the Ne Quick model, these estimates were 6.7 e8.2 TECU and 42 e45% in 2015 and 2.2 e3.6 TECU, 30 e37% in2008. For the short-term forecast, the best results were obtained by the SPM method with an average absolute error in the range of 1.95 e2.15 TECU in 2015 and 0.59 e0.98 TECU in 2008, a relative RMS error in the range of 17 e21% in 2015, 11.5 e15% in 2008. For the proposed short-term prediction method, these errors were 2.04 e2.2 TECU and 12 e14% in 2015 and 0.7 e1.0 TECU, 7 e11% in 2008. Using medians, the errors were 3.1 e3.4 TECU and 17 e21% in 2015 and 1.0 e1.3 TECU, 10 e15% in 2008. The dependence of results on the Dst-index was obtained.
文摘This study was conducted to establish a predictable method for a heat load of an underground structure with sufficient accuracy. As the first step, our previous paper reported the measurement results of field experiments on an underground experimental basement under internal heat generation conditions. Also, it presented the results of numerical analyses on the heat and moisture behavior and the influence of internal heat generation of the experimental basement and ground. However, it is practically impossible to utilize the model of simultaneous heat and moisture transfer at the design phase because the prediction by the model of simultaneous heat and moisture transfer requires a long calculation time. In this paper, the authors present the simple load calculation technique, using a linearized approximation indicial response of the inner surface heat flux in a basement to outdoor air temperature change. In addition, the approximation indicial responses for each part of the single-walled concrete drawn using this technique are arranged. The heat load calculation example of application to the basement of the optional size by this technique is shown.
文摘BP neural networks is used to mid-term earthquake prediction in this paper. Some usual prediction parameters of seismology are used as the import units of neural networks. And the export units of neural networks is called as the character parameter W_0 describing enhancement of seismicity. We applied this method to space scanning of North China. The result shows that the mid-term anomalous zone of W_0-value usually appeared obviously around the future epicenter 1~3 years before earthquake. It is effective to mid-term prediction.
基金Research on Robust Decision and Full Stack Optimisation Techniques for Cloud Edge Intelligent Systems for Substation Inspection,Grant/Award Number:52550022001J。
文摘Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer,thereby facilitating the formulation of power outage maintenance plans and power dispatch strategies.However,existing prediction methods based on the structure of‘splicing prediction and diagnosis method’suffer from limitations such as inability to achieve global optimality,error accumulation,and low prediction accuracy.To fill this gap,a novel direct prediction method of a trans-former state based on knowledge and data fusion-driven model(K&DFDM)is pro-posed in this paper.Firstly,a state quantity data space is constructed to comprehensively reflect the changes in the health state of the transformer over time,encompassing online monitoring,offline testing,evaluation results,and actual operation data.After that,correlation knowledge between state quantities,fault diagnosis mechanism knowledge,current diagnosis experience knowledge,and uncertain fuzzy knowledge are extracted separately.The actual fault mechanism,existing expert experience,and other knowledge in the diagnosis process are quantified.Then,the attention model is sub-sequently optimised,leveraging quantitative knowledge to effectively constrain and guide the data prediction process.Incorporating fault diagnosis mechanism knowledge into the data prediction model enables the achievement of global optimisation in both diagnosis and prediction.The integration of traditional expert experience knowledge and the correlation knowledge between state quantities serves as constraints during the process of attaining the global optimum.The verification results,comprising 327 cases,demonstrate that K&DFDM effectively addresses the issue of error superposition encountered by existing state prediction methods,leading to a direct state prediction accuracy of 96.33%.
基金co-supported by the National Nature Science Foundation of China(No.12303071)the Shanghai Science and Technology Plan Project,China(No.23YF1455500)+1 种基金the China Postdoctoral Science Foundation(No.2023M743653)Ministry of Industry and Information Technology of China through the High Precision Timing Service Project(No.TC220A04A-80)。
文摘The mathematical method of ZTD(zenith tropospheric delay)spatial prediction is important for precise ZTD derivation and real-time precise point positioning(PPP)augmentation.This paper analyses the performance of the popular optimal function coefficient(OFC),sphere cap harmonic analysis(SCHA),kriging and inverse distance weighting(IDW)interpolation in ZTD spatial prediction and Beidou satellite navigation system(BDS)-PPP augmentation over China.For ZTD spatial prediction,the average time consumption of the OFC,kriging,and IDW methods is less than 0.1 s,which is significantly better than that of the SCHA method(63.157 s).The overall ZTD precision of the OFC is 3.44 cm,which outperforms those of the SCHA(9.65 cm),Kriging(10.6 cm),and IDW(11.8 cm)methods.We confirmed that the low performance of kriging and IDW is caused by their weakness in modelling ZTD variation in the vertical direction.To mitigate such deficiencies,an elevation normalization factor(ENF)is introduced into the kriging and IDW models(kriging-ENF and IDW-ENF).The overall ZTD spatial prediction accuracies of IDW-ENF and kriging-ENF are 2.80 cm and 2.01 cm,respectively,which are both superior to those of the OFC and the widely used empirical model GPT3(4.92 cm).For BDS-PPP enhancement,the ZTD provided by the kriging-ENF,IDW-ENF and OFC as prior constraints can effectively reduce the convergence time.Compared with unconstrained BDS-PPP,our proposed kriging-ENF outperforms IDW-ENF and OFC by reducing the horizontal and vertical convergence times by approximately 13.2%and 5.8%in Ningxia and 30.4%and 7.84%in Guangdong,respectively.These results indicate that kriging-ENF is a promising method for ZTD spatial prediction and BDS-PPP enhancement over China.
基金supported by the National Natural Science Foundation of China (grant numbers 42293351, and U2468221)。
文摘This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was established for water and mud inrush in tunnels by analyzing advanced prediction data for specifi c tunnel segments.Additionally,the indicator weights were determined using the analytic hierarchy process combined with the Huber weighting method.Subsequently,a multisource data decision-layer fusion algorithm was utilized to generate fused imaging results for tunnel water and mud inrush risk predictions.Meanwhile,risk analysis was performed for different tunnel sections to achieve spatial and temporal complementarity within the indicator system and optimize redundant information.Finally,model feasibility was validated using the CZ Project Sejila Mountain Tunnel segment as a case study,yielding favorable risk prediction results and enabling effi cient information fusion and support for construction decision-making.
基金the funding received by a grant from Natural Sciences and Engineering Research of Canada(NSERC)for this study.
文摘Technical challenges have always been part of underground mining activities,however,some of these challenges grow in complexity as mining occurs in deeper and deeper settings.One such challenge is rock mass stability and the risk of rockburst events.To overcome these challenges,and to limit the risks and impacts of events such as rockbursts,advanced solutions must be developed and best practices implemented.Rockbursts are common in underground mines and substantially threaten the safety of personnel and equipment,and can cause major disruptions in mine development and operations.Rockbursts consist of violent wall rock failures associated with high energy rock projections in response to the instantaneous stress release in rock mass under high strain conditions.Therefore,it is necessary to develop a good understanding of the conditions and mechanisms leading to a rockburst,and to improve risk assessment methods.The capacity to properly estimate the risks of rockburst occurrence is essential in underground operations.However,a limited number of studies have examined and compared yet different empirical methods of rockburst.The current understanding of this important hazard in the mining industry is summarized in this paper to provide the necessary perspective or tools to best assess the risks of rockburst occurrence in deep mines.The various classifications of rockbursts and their mechanisms are discussed.The paper also reviews the current empirical methods of rockburst prediction,which are mostly dependent on geomechanical parameters of the rock such as uniaxial compressive strength of the rock,as well as its tensile strength and elasticity modulus.At the end of this paper,some current achievements and limitations of empirical methods are discussed.
基金The authors express their acknowledgment to the Chinese Natural Science Foundation(Grant No.31660475)Innovation and Entrepreneurship Project of the Xinjiang Production and Construction Group Special Commissioner for Science and Technology(Grant No.2019CB037)+4 种基金University President Fund Project(Grant No.TDZKCQ201902)Xinjiang Production&Construction Group Key Laboratory of Agricultural Products Processing in Xinjiang South(Grant No.AP1905)Young and Middle-aged Scientific and Technological Innovation Leaders Project of Xinjiang Production and Construction Group(Grant No.2018CB014)Strong Youth Key Talents of Scientific and Technological Innovation(Grant No.2021CB039)and Research Project of Double Employment Academician Work Funds Opening in Tarim University(Grant No.SPYS202002)for financial support and all of the persons who assisted in this writing.
文摘It is difficult to control the quality of Korla pear with different degrees of maturity during storage.Here,a method was proposed for predicting the effects of harvest maturity and cold storage time on the quality indices(soluble solid content(SSC)and Vitamin C(Vc)content)of Korla pear.The generalized regression neural network(GRNN)and adaptive neuro-fuzzy inference system(ANFIS)were employed to predict the quality changes of Korla fragrant pear fruit during storage.The results demonstrated that during cold storage the SSC in pears with 10%-70%harvest maturity showed continuous increases in the first 90 d of storage and then a slight decline thereafter,while that in pears with 80%and 90%harvest maturity exhibited slow decreases throughout the storage process.With the extension of storage time,the Vc content of pears with 10%-90%harvest maturity showed continuous decreases.The harvest maturity of Korla pear was extremely positively correlated with SSC and Vc content(p<0.01)in a given storage period.Storage time showed an extremely significant negative correlation with the Vc content(p<0.01)at the 40%-90%harvest maturity and an significant negative correlation with the Vc content(p<0.05)at the 10%-30%harvest maturity.At the 10%-70%harvest maturity,storage time showed a significant positive correlation with the SSC(p<0.05).The trained model could well predict the variation trend of quality indices of pear fruit during storage.The ANFIS with the input membership function of gbellmf had the best performance in predicting the SSC(RMSE=0.175;R2=0.98),and that with the input membership function of trimf exhibited the best performance in predicting Vc content(RMSE=0.075;R2=0.99).The research findings can provide reference for predicting the fruit nutritional quality at delivery and decision-making on the storage time of Korla fragrant pear.
基金the National High-Tech Research and Development (863) Program of China (No. 2007AA11Z202)
文摘Currently applied prediction methods of regional freight traffic and freight ton-kilometer forecasting were analyzed using typical Chinese regional goods transportation characteristics. The review of prediction methods showes that practical planning experts tend to apply the traditional methods which are easier to implement. The comparison also demonstrates that a combination of traditional methods is more effective than the simple models for practical planning. Research using the statistical data for the Yangtze Delta, Pearl River Delta, and Bohai Rim areas shows that ignoring differences between transport modes impacts the prediction accuracy. The four main transport modes suit different methods. The results show that the power model is better for railways, and the linear model is better for highways and waterways. Thus a combined model gives better results for all modes. The results for regional systems can be generalized to national transportation systems.
基金This work was supported by the National Natural Science Foundation of China under Grant 51577010the Fundamental Research Funds for the Central Universities under Grant 2017JBM054the Natural Science Foundation of Guangdong Province under Grant 1714060000016.
文摘The common-mode current is an important indicator with transformerless photovoltaic inverters.However,up to now,there is not an accurate method to predict common-mode current in the inverter design process,resulting from inappropriate device selection or exceeded the expected common-mode current.In order to solve this problem,this paper proposes an accurate common-mode current prediction method based on graph theory for transformerless photovoltaic inverters.In this paper,the mathematic model of the common-mode current is derived using graph theory analysis method in the full-bridge topology,and it is used to predict common-mode current.The validity and correctness of the proposed prediction method are validated by simulation and experiment.The oscillation frequency and amplitude can be predicted by the proposed common-mode prediction method,whereas the traditional common-mode analysis method cannot.This paper provides a novel way to predict and analyze common-mode current in the transformerless photovoltaic inverters.
基金the National Natural Science Foundation of China(Nos.41825018,42141009)the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(No.2019QZKK0904).
文摘This study presents the classification and prediction of severity for brittle rock failure,focusing on failure behaviors and excessive determination based on damage depth.The research utilizes extensive field survey data from the Shuangjiangkou Hydropower Station and previous research findings.Based on field surveys and previous studies,four types of brittle rock failure with different failure mechanisms are classified,and then a prediction method is proposed.This method incorporates two variables,i.e.Kv(modified rock mass integrity coefficient)and GSI(geological strength index).The prediction method is applied to the first layer excavation of the powerhouse cavern of Shuangjiangkou Hydropower Station.The results show that the predicted brittle rock failure area agrees with the actual failure area,demonstrating the method’s applicability.Next,it extends to investigate brittle rock failure in two locations.The first is the k0-890 m section of the traffic cavern,and the second one is at K0-64 m of the main powerhouse.The criterion-based prediction indicates a severity brittle rock failure in the K0-890 m section,and a moderate brittle rock failure in the K0-64 m section,which agrees with the actual occurrence of brittle rock failure in the field.The understanding and application of the prediction method using Kv and GSI are vital for implementing a comprehensive brittle rock failure prediction process in geological engineering.To validate the adaptability of this criterion across diverse tunnel projects,a rigorous verification process using statistical findings was conducted.The assessment outcomes demonstrate high accuracy for various tunnel projects,allowing establishment of the correlations that enable valuable conclusions regarding brittle rock failure occurrence.Further validation and refinement through field and laboratory testing,as well as simulations,can broaden the contribution of this method to safer and more resilient underground construction.
基金This work was jointly supported by the National Natural Science Foundation of China (No. 69990540)the Optical Technology Plan of Shanghai.
文摘To achieve lower assembly delay at optical burst switching edge node, this paper proposes an approach called current weight length prediction (CWLP) to improve existing estimate mechanism in burst assembly. CWLP method takes into account the arrived traffic in prediction time adequately. A parameter 'weight' is introduced to make a dynamic tradeoff between the current and past traffic under different offset time. Simulation results show that CWLP can achieve a significant improvement in terms of traffic estimation in various offset time and offered load.
文摘A new probability function of mining overlying strata and subsidence is put forward that has a general statistical significance based on the ideal stochastic medium displacement model. It establishes a new system of prediction on horizontal mining subsidence and deformation, which gives a new method for prediction on mining subsidence and deformation.
