Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy...Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.展开更多
Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was...Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was developed using the6 h average bias to correct the systematic bias during model integration.The primary purpose of this study is to investigate the impact of the SBCS in the high-resolution China Meteorological Administration Meso-scale(CMA-MESO)numerical weather prediction(NWP)model to reduce the systematic bias and to improve the data assimilation and forecast results through this method.The SBCS is improved upon and applied to the CMA-MESO 3-km model in this study.Four-week sequential data assimilation and forecast experiments,driven by rapid update and cycling(RUC),were conducted for the period from 2–29 May 2022.In terms of the characteristics of systematic bias,both the background and analysis show diurnal bias,and these large biases are affected by complex underlying surfaces(e.g.,oceans,coasts,and mountains).After the application of the SBCS,the results of the data assimilation show that the SBCS can reduce the systematic bias of the background and yield a neutral to slightly positive result for the analysis fields.In addition,the SBCS can reduce forecast errors and improve forecast results,especially for surface variables.The above results indicate that this scheme has good prospects for high-resolution regional NWP models.展开更多
Accurate skin temperature is one of the critical factors in successfully assimilating satellite radiance data over land.However,model-simulated skin temperature may not be accurate enough.To address this issue,an exte...Accurate skin temperature is one of the critical factors in successfully assimilating satellite radiance data over land.However,model-simulated skin temperature may not be accurate enough.To address this issue,an extended skin temperature control variable(TSCV) approach is proposed in a variational assimilation framework,which also considers the background error correlation between skin temperature and atmospheric variables.A series of single observation tests and a 10-day cycling assimilation experiment were conducted to evaluate the impact of the TSCV approach on the assimilation of AMSU-A and ATMS(Advanced Technology Microwave Sounder) microwave temperature-sounding channels over land.The results of the single observation tests show that by applying the TSCV approach,not only the direct analysis of skin temperature is realized,but also the interaction between skin temperature and atmospheric variables can be achieved during the assimilation process.The results of the cycling experiment demonstrate that the TSCV approach improves the skin temperature analysis,which in turn reduces the RMSE of the surface variables and low-level air temperature forecasts.The TSCV approach also reduces the difference between the observed and simulated brightness temperatures of both microwave and infrared window channels over land,suggesting that the approach can facilitate the radiance simulation of these channels,thus contributing to the assimilation of window channels.展开更多
A mathematical model of principal elements of the aircraft hydraulic system is presented based on the heat transfer theory. The dynamic heat transfer process of the hydraulic oil and the pump shells within an aircraft...A mathematical model of principal elements of the aircraft hydraulic system is presented based on the heat transfer theory. The dynamic heat transfer process of the hydraulic oil and the pump shells within an aircraft hydraulic system are analyzed by the difference method. A kind of means for the prediction to variational trends of the aircraft hydraulic system temperature is provided during operation. The numerical prediction and simulation under the operational conditions are presented for ground trial running and the decelerated operation in flight. Computational results show that there is a good coincidence between the experimental data and the numerical predictions.展开更多
Waves are important physical phenomena in an ocean,and their accurate prediction is essential for ocean engineering,maritime traffic,and marine early warning systems.This study focuses on the Qinhuangdao Sea area loca...Waves are important physical phenomena in an ocean,and their accurate prediction is essential for ocean engineering,maritime traffic,and marine early warning systems.This study focuses on the Qinhuangdao Sea area located in the Bohai Sea,China.Herein,we use on-site wind data to correct the reanalysis wind data obtained from the European Centre for Medium-Range Weather Forecasts(ECMWF),improving the accuracy of boundary conditions.Then,we use the Simulating WAves Nearshore(SWAN)model to simulate the regional wave field over time.A regional wave-parameter prediction model is then developed using a limited number of sampled data(covering only 2 years,2020–2021);the model is based on the Whale Optimization Algorithm(WOA),convolutional neural networks(CNNs),and long short-term memory(LSTM)neural networks.WOA is used to optimize the CNN and LSTM framework;in this framework,CNN extracts spatial features,and the LSTM network captures temporal features,enabling accurate short and long-term predictions of wave height,period,and direction.The experimental results showed that despite the small sample size,the model achieves a goodness of fit of 0.9957 for wave height prediction,0.9973 for period,and 0.9749 for wave direction in short-term forecasting.As the prediction step size increases,the accuracy of the model decreases.When the prediction step size reaches 9 h,the root mean square error for the prediction of wave height,period,and direction increases to 0.2060 m,0.4582 s,and32.5358°,respectively.The reliability and applicability of the model are further validated by the experimental results.Our findings highlighted the potential of the developed model in operational wave forecasting,even with a limited number of sampled data.展开更多
A high-order hybrid numerical framework is developed by coupling a three-stage exponential time integrator with a Runge–Kutta scheme for the efficient solution of partial differential equations involving first-order ...A high-order hybrid numerical framework is developed by coupling a three-stage exponential time integrator with a Runge–Kutta scheme for the efficient solution of partial differential equations involving first-order time derivatives.The proposed scheme attains third-order temporal accuracy and is rigorously validated through stability and convergence analyses for both scalar and coupled systems.Its effectiveness is demonstrated by simulating unsteady Eyring-Prandtl non-Newtonian nanofluid flow over a Riga plate with coupled heat and mass transfer under electromagnetic actuation.The physical model accounts for Brownian motion and thermophoresis,and the nanofluid considered is a Prandtl-type non-Newtonian base fluid containing suspended nanoparticles,with heat and mass transport governed by coupled momentum,energy,and concentration equations.Numerical simulations are performed over practically relevant parameter ranges,with the Reynolds number fixed at Re=5 and the Prandtl number set to Pr=3 to represent moderate inertial and thermal diffusion effects typical of nanofluid transport systems.To enhance computational efficiency,an artificial neural network(ANN)-based surrogate model is developed to predict the skin friction coefficient and local Sherwood number as functions of Reynolds number,Prandtl number,Schmidt number,Brownian motion,and thermophoresis parameters.The training dataset is generated entirely from high-fidelity numerical simulations produced by the proposed hybrid scheme.The data are systematically partitioned into 70%for training,15%for validation,and 15%for testing,ensuring reliable generalization.Regression analysis yields a near-unity correlation coefficient(R≈0.99),while error histograms exhibit tightly clustered residuals around zero,confirming high predictive accuracy.Furthermore,a benchmark convergence study using Stokes’first problem demonstrates that the proposed scheme consistently achieves lower global error norms than the classical Runge–Kutta method for identical spatial and temporal resolutions.Overall,this study introduces a novel computational intelligence framework that integrates high-order numerical solvers with machine learning,offering a robust and time-efficient tool for advanced modeling and real-time prediction of non-Newtonian nanofluid transport phenomena under electromagnetic flow control.展开更多
The uncertainties caused by the errors of the initial states and the parameters in the numerical model are investigated. Three problems of predictability in numerical weather and climate prediction are proposed, which...The uncertainties caused by the errors of the initial states and the parameters in the numerical model are investigated. Three problems of predictability in numerical weather and climate prediction are proposed, which are related to the maximum predictable time, the maximum prediction error, and the maximum admissible errors of the initial values and the parameters in the model respectively. The three problems are then formulated into nonlinear optimization problems. Effective approaches to deal with these nonlinear optimization problems are provided. The Lorenz’ model is employed to demonstrate how to use these ideas in dealing with these three problems.展开更多
After decades of research and development, the WSR-88 D(NEXRAD) network in the United States was upgraded with dual-polarization capability, providing polarimetric radar data(PRD) that have the potential to improve we...After decades of research and development, the WSR-88 D(NEXRAD) network in the United States was upgraded with dual-polarization capability, providing polarimetric radar data(PRD) that have the potential to improve weather observations,quantification, forecasting, and warnings. The weather radar networks in China and other countries are also being upgraded with dual-polarization capability. Now, with radar polarimetry technology having matured, and PRD available both nationally and globally, it is important to understand the current status and future challenges and opportunities. The potential impact of PRD has been limited by their oftentimes subjective and empirical use. More importantly, the community has not begun to regularly derive from PRD the state parameters, such as water mixing ratios and number concentrations, used in numerical weather prediction(NWP) models.In this review, we summarize the current status of weather radar polarimetry, discuss the issues and limitations of PRD usage, and explore potential approaches to more efficiently use PRD for quantitative precipitation estimation and forecasting based on statistical retrieval with physical constraints where prior information is used and observation error is included. This approach aligns the observation-based retrievals favored by the radar meteorology community with the model-based analysis of the NWP community. We also examine the challenges and opportunities of polarimetric phased array radar research and development for future weather observation.展开更多
The recent progresses in the research and development of (NWP) in China are reviewed in this paper. The most impressive achievements are the development of direct assimilation of satellite irradiances with a 3DVAR (th...The recent progresses in the research and development of (NWP) in China are reviewed in this paper. The most impressive achievements are the development of direct assimilation of satellite irradiances with a 3DVAR (three-dimentional variational) data assimilation system and a non-hydrostatic modei with a semi-Lagrangian semi-implicit scheme. Progresses have also been made in modei physics and modei application to precipitation and environmental forecasts. Some scientific issues of great importance for further development are discussed.展开更多
This paper summarizes the recent progress of numerical weather prediction （NWP） research since the last review was published. The new generation NWP system named GRAPES （the Global and Regional Assimilation and Pre...This paper summarizes the recent progress of numerical weather prediction （NWP） research since the last review was published. The new generation NWP system named GRAPES （the Global and Regional Assimilation and Prediction System）, which consists of variational or sequential data assimilation and nonhydrostatic prediction model with options of configuration for either global or regional domains, is briefly introduced, with stress on their scientific design and preliminary results during pre-operational implementation. In addition to the development of GRAPES, the achievements in new methodologies of data assimilation, new improvements of model physics such as parameterization of clouds and planetary boundary layer, mesoscale ensemble prediction system and numerical prediction of air quality are presented. The scientific issues which should be emphasized for the future are discussed finally.展开更多
In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can eff...In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can effectively reduce model errors by combining the statistical analogue method with the dynamical model together in order that the information of plenty of historical data is utilized in the current complicated NWP model, Furthermore, in the ACE, the differences of the similarities between different historical analogues and the current initial state are considered as the weights for estimating model errors. The results of daily, decad and monthly prediction experiments on a complicated T63 atmospheric model show that the performance of the ACE by correcting model errors based on the estimation of the errors of 4 historical analogue predictions is not only better than that of the scheme of only introducing the correction of the errors of every single analogue prediction, but is also better than that of the T63 model.展开更多
Performance prediction for centrifugal pumps is now mainly based on numerical calculation and most of the studies merely focus on one model.Therefore,the research results are not representative.To make an improvement ...Performance prediction for centrifugal pumps is now mainly based on numerical calculation and most of the studies merely focus on one model.Therefore,the research results are not representative.To make an improvement of numerical calculation method and performance prediction for centrifugal pumps,performance of six centrifugal pump models at design flow rate and off design flow rates,whose specific speed are different,were simulated by using commercial code FLUENT.The standard k-t turbulence model and SIMPLEC algorithm were chosen in FLUENT.The simulation was steady and moving reference frame was used to consider the impeller-volute interaction.Also,how to dispose the gap between impeller and volute was presented and the effect of grid number was considered.The characteristic prediction model for centrifugal pumps is established according to the simulation results.The head and efficiency of the six models at different flow rates are predicted and the prediction results are compared with the experiment results in detail.The comparison indicates that the precision of head and efficiency prediction are all less than 5%.The flow analysis indicates that flow change has an important effect on the location and area of low pressure region behind the blade inlet and the direction of velocity at impeller inlet.The study shows that using FLUENT simulation results to predict performance of centrifugal pumps is feasible and accurate.The method can be applied in engineering practice.展开更多
The initial value error and the imperfect numerical model are usually considered as error sources of numerical weather prediction （NWP）. By using past multi-time observations and model output, this study proposes a ...The initial value error and the imperfect numerical model are usually considered as error sources of numerical weather prediction （NWP）. By using past multi-time observations and model output, this study proposes a method to estimate imperfect numerical model error. This method can be inversely estimated through expressing the model error as a Lagrange interpolation polynomial, while the coefficients of polyno- mial are determined by past model performance. However, for practical application in the full NWP model, it is necessary to determine the following criteria： （1） the length of past data sufficient for estimation of the model errors, （2） a proper method of estimating the term ＂model integration with the exact solution＂ when solving the inverse problem, and （3） the extent to which this scheme is sensitive to the observational errors. In this study, such issues are resolved using a simple linear model, and an advection diffusion model is applied to discuss the sensitivity of the method to an artificial error source. The results indicate that the forecast errors can be largely reduced using the proposed method if the proper length of past data is chosen. To address the three problems, it is determined that （1） a few data limited by the order of the corrector can be used, （2） trapezoidal approximation can be employed to estimate the ＂term＂ in this study; however, a more accurate method should be explored for an operational NWP model, and （3） the correction is sensitive to observational error.展开更多
Model error is one of the key factors restricting the accuracy of numerical weather prediction （NWP）. Considering the continuous evolution of the atmosphere, the observed data （ignoring the measurement error） can ...Model error is one of the key factors restricting the accuracy of numerical weather prediction （NWP）. Considering the continuous evolution of the atmosphere, the observed data （ignoring the measurement error） can be viewed as a series of solutions of an accurate model governing the actual atmosphere. Model error is represented as an unknown term in the accurate model, thus NWP can be considered as an inverse problem to uncover the unknown error term. The inverse problem models can absorb long periods of observed data to generate model error correction procedures. They thus resolve the deficiency and faultiness of the NWP schemes employing only the initial-time data. In this study we construct two inverse problem models to estimate and extrapolate the time-varying and spatial-varying model errors in both the historical and forecast periods by using recent observations and analogue phenomena of the atmosphere. Numerical experiment on Burgers＇ equation has illustrated the substantial forecast improvement using inverse problem algorithms. The proposed inverse problem methods of suppressing NWP errors will be useful in future high accuracy applications of NWP.展开更多
Fracture prediction is a technical issue in the field of petroleum exploration and production worldwide.Although there are many approaches to predict the distribution of cracks underground,these approaches have some l...Fracture prediction is a technical issue in the field of petroleum exploration and production worldwide.Although there are many approaches to predict the distribution of cracks underground,these approaches have some limitations.To resolve these issues,we ascertained the relation between numerical simulations of tectonic stress and the predicted distribution of fractures from the perspective of geologic genesis,based on the characteristics of the shale reservoir in the Longmaxi Formation in Dingshan;the features of fracture development in this reservoir were considered.3 D finite element method(FEM)was applied in combination with rock mechanical parameters derived from the acoustic emissions.The paleotectonic stress field of the crack formation period was simulated for the Longmaxi Formation in the Dingshan area.The splitting factor in the study area was calculated based on the rock breaking criterion.The coefficient of fracture development was selected as the quantitative prediction classification criteria for the cracks.The results show that a higher coefficient of fracture development indicates a greater degree of fracture development.On the basis of the fracture development coefficient classification,a favorable area was identified for the development of fracture prediction in the study area.The prediction results indicate that the south of the Dingshan area and the DY3 well of the central region are favorable zones for fracture development.展开更多
Retrogressive landslides are common geological phenomena in mountainous areas and on onshore and offshore slopes. The impact of retrogressive landslides is different from that of other landslide types due to the pheno...Retrogressive landslides are common geological phenomena in mountainous areas and on onshore and offshore slopes. The impact of retrogressive landslides is different from that of other landslide types due to the phenomenon of retrogression. The hazards caused by retrogressive landslides may be increased because retrogressive landslides usually affect housing, facilities, and infrastructure located far from the original slopes. Additionally, substantial geomorphic evidence shows that the abundant supply of loose sediment in the source area of a debris flow is usually provided by retrogressive landslides that are triggered by the undercutting of water. Moreover, according to historic case studies, some large landslides are the evolution result of retrogressive landslides. Hence the ability to understand and predict the evolution of retrogressive landslides is crucial for the purpose of hazard mitigation. This paper discusses the phenomenon of a retrogressive landslide by using a model experiment and suggests a reasonably simplified numerical approach for the prediction of rainfall-induced retrogressive landslides. The simplified numerical approach, which combines the finite element method for seepage analysis, the shear strength reduction finite element method, and the analysis criterion for the retrogression and accumulation effect, is presented and used to predict the characteristics of a retrogressive landslide. The results show that this numerical approach is capable of reasonably predicting the characteristics of retrogressive landslides under rainfall infiltration, particularly the magnitude of each landslide, the position of the slip surface, and the development processes of the retrogressive landslide. Therefore, this approach is expected to be a practical method for the mitigation of damage caused by rainfall-induced retrogressive landslides.展开更多
The movement of Typhoon Maggie (9903) in June 1999 is one of the rare cases ever seen in the history. At 00U on June 6 Maggie was located at about 70 km to the southwest of Taiwan. When it arrived at the coastal regio...The movement of Typhoon Maggie (9903) in June 1999 is one of the rare cases ever seen in the history. At 00U on June 6 Maggie was located at about 70 km to the southwest of Taiwan. When it arrived at the coastal region of Shanwei City (22.8N, 116.5E), it turned suddenly to move southwestward along the southern China coastal line. De June 7 Maggie finally turned to move northward, making landfall to the north of Shangchuan Island. The experimental numerical prediction system on typhoon movement that was designed based on MM5 is proved quite successful for the 48h prediction of Maggie's movement and rainfall. The mean prediction error of typhoon track is 81 km for 0-24 h and 74 km for 24-48 h. The location of typhoon center in the initial field of the model is approximately 100 km away from the actual observations. In order to modify the location of typhoon center, a bogus typhoon was intro- duced into the model and the prediction of typhoon track was improved in 0-24 h time interval. But the prediction error was enlarged in 24-36 h. We also performed a sensitivity experiment of changing the land of southern China into the ocean. It is found that the orientation of South China coastal line and the topography have no obvious effect on the movement of Typhoon Maggie.展开更多
In this paper,the unsteady flow around a high-speed train is numerically simulated by detached eddy simulation method(DES),and the far-field noise is predicted using the Ffowcs Williams-Hawkings(FW-H)acoustic model.Th...In this paper,the unsteady flow around a high-speed train is numerically simulated by detached eddy simulation method(DES),and the far-field noise is predicted using the Ffowcs Williams-Hawkings(FW-H)acoustic model.The reliability of the numerical calculation is verified by wind tunnel experiments.The superposition relationship between the far-field radiated noise of the local aerodynamic noise sources of the high-speed train and the whole noise source is analyzed.Since the aerodynamic noise of high-speed trains is derived from its different components,a stepwise calculation method is proposed to predict the aerodynamic noise of high-speed trains.The results show that the local noise sources of high-speed trains and the whole noise source conform to the principle of sound source energy superposition.Using the head,middle and tail cars of the high-speed train as noise sources,different numerical models are established to obtain the far-field radiated noise of each aerodynamic noise source.The far-field total noise of high-speed trains is predicted using sound source superposition.A step-by-step calculation of each local aerodynamic noise source is used to obtain the superimposed value of the far-field noise.This is consistent with the far-field noise of the whole train model’s aerodynamic noise.The averaged sound pressure level of the far-field longitudinal noise measurement points differs by 1.92 dBA.The step-by-step numerical prediction method of aerodynamic noise of high-speed trains can provide a reference for the numerical prediction of aerodynamic noise generated by long marshalling high-speed trains.展开更多
In this paper,both measurements and numerical simulations of railway induced vibration are discussed.A measurement campaign has been carried out along the high-speed railway track in Lincent,Belgium.The experimental d...In this paper,both measurements and numerical simulations of railway induced vibration are discussed.A measurement campaign has been carried out along the high-speed railway track in Lincent,Belgium.The experimental determination of transfer functions and vibration velocity during train passages are discussed.A numerical model is introduced to predict the transfer functions and the vibration velocity during train passages.The comparison of experimental and numerical results demonstrates the importance of accurate numerical models and input data.The results are obtained in the framework of the development of a hybrid prediction method,where numerical and experimental data can be combined to improve the prediction accuracy for railway induced vibration.展开更多
基金financially supported by the National Key Research and Development Program of China (No. 2023YFB3812601)the National Natural Science Foundation of China (No. 51925401)the Young Elite Scientists Sponsorship Program by CAST, China (No. 2022QNRC001)。
文摘Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2242213,U2142213,42305167,42175105)。
文摘Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was developed using the6 h average bias to correct the systematic bias during model integration.The primary purpose of this study is to investigate the impact of the SBCS in the high-resolution China Meteorological Administration Meso-scale(CMA-MESO)numerical weather prediction(NWP)model to reduce the systematic bias and to improve the data assimilation and forecast results through this method.The SBCS is improved upon and applied to the CMA-MESO 3-km model in this study.Four-week sequential data assimilation and forecast experiments,driven by rapid update and cycling(RUC),were conducted for the period from 2–29 May 2022.In terms of the characteristics of systematic bias,both the background and analysis show diurnal bias,and these large biases are affected by complex underlying surfaces(e.g.,oceans,coasts,and mountains).After the application of the SBCS,the results of the data assimilation show that the SBCS can reduce the systematic bias of the background and yield a neutral to slightly positive result for the analysis fields.In addition,the SBCS can reduce forecast errors and improve forecast results,especially for surface variables.The above results indicate that this scheme has good prospects for high-resolution regional NWP models.
基金sponsored by the National Natural Science Foundation of China(Grant No.42075148)the High-Performance Computing Center of Nanjing University of Information Science & Technology for supporting this work。
文摘Accurate skin temperature is one of the critical factors in successfully assimilating satellite radiance data over land.However,model-simulated skin temperature may not be accurate enough.To address this issue,an extended skin temperature control variable(TSCV) approach is proposed in a variational assimilation framework,which also considers the background error correlation between skin temperature and atmospheric variables.A series of single observation tests and a 10-day cycling assimilation experiment were conducted to evaluate the impact of the TSCV approach on the assimilation of AMSU-A and ATMS(Advanced Technology Microwave Sounder) microwave temperature-sounding channels over land.The results of the single observation tests show that by applying the TSCV approach,not only the direct analysis of skin temperature is realized,but also the interaction between skin temperature and atmospheric variables can be achieved during the assimilation process.The results of the cycling experiment demonstrate that the TSCV approach improves the skin temperature analysis,which in turn reduces the RMSE of the surface variables and low-level air temperature forecasts.The TSCV approach also reduces the difference between the observed and simulated brightness temperatures of both microwave and infrared window channels over land,suggesting that the approach can facilitate the radiance simulation of these channels,thus contributing to the assimilation of window channels.
文摘A mathematical model of principal elements of the aircraft hydraulic system is presented based on the heat transfer theory. The dynamic heat transfer process of the hydraulic oil and the pump shells within an aircraft hydraulic system are analyzed by the difference method. A kind of means for the prediction to variational trends of the aircraft hydraulic system temperature is provided during operation. The numerical prediction and simulation under the operational conditions are presented for ground trial running and the decelerated operation in flight. Computational results show that there is a good coincidence between the experimental data and the numerical predictions.
基金supported by the National Natural Science Foundation of China(Nos.52071057,52171247)the Liaoning Youth Elite Talent Program(No.XLYC220309)。
文摘Waves are important physical phenomena in an ocean,and their accurate prediction is essential for ocean engineering,maritime traffic,and marine early warning systems.This study focuses on the Qinhuangdao Sea area located in the Bohai Sea,China.Herein,we use on-site wind data to correct the reanalysis wind data obtained from the European Centre for Medium-Range Weather Forecasts(ECMWF),improving the accuracy of boundary conditions.Then,we use the Simulating WAves Nearshore(SWAN)model to simulate the regional wave field over time.A regional wave-parameter prediction model is then developed using a limited number of sampled data(covering only 2 years,2020–2021);the model is based on the Whale Optimization Algorithm(WOA),convolutional neural networks(CNNs),and long short-term memory(LSTM)neural networks.WOA is used to optimize the CNN and LSTM framework;in this framework,CNN extracts spatial features,and the LSTM network captures temporal features,enabling accurate short and long-term predictions of wave height,period,and direction.The experimental results showed that despite the small sample size,the model achieves a goodness of fit of 0.9957 for wave height prediction,0.9973 for period,and 0.9749 for wave direction in short-term forecasting.As the prediction step size increases,the accuracy of the model decreases.When the prediction step size reaches 9 h,the root mean square error for the prediction of wave height,period,and direction increases to 0.2060 m,0.4582 s,and32.5358°,respectively.The reliability and applicability of the model are further validated by the experimental results.Our findings highlighted the potential of the developed model in operational wave forecasting,even with a limited number of sampled data.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2603).
文摘A high-order hybrid numerical framework is developed by coupling a three-stage exponential time integrator with a Runge–Kutta scheme for the efficient solution of partial differential equations involving first-order time derivatives.The proposed scheme attains third-order temporal accuracy and is rigorously validated through stability and convergence analyses for both scalar and coupled systems.Its effectiveness is demonstrated by simulating unsteady Eyring-Prandtl non-Newtonian nanofluid flow over a Riga plate with coupled heat and mass transfer under electromagnetic actuation.The physical model accounts for Brownian motion and thermophoresis,and the nanofluid considered is a Prandtl-type non-Newtonian base fluid containing suspended nanoparticles,with heat and mass transport governed by coupled momentum,energy,and concentration equations.Numerical simulations are performed over practically relevant parameter ranges,with the Reynolds number fixed at Re=5 and the Prandtl number set to Pr=3 to represent moderate inertial and thermal diffusion effects typical of nanofluid transport systems.To enhance computational efficiency,an artificial neural network(ANN)-based surrogate model is developed to predict the skin friction coefficient and local Sherwood number as functions of Reynolds number,Prandtl number,Schmidt number,Brownian motion,and thermophoresis parameters.The training dataset is generated entirely from high-fidelity numerical simulations produced by the proposed hybrid scheme.The data are systematically partitioned into 70%for training,15%for validation,and 15%for testing,ensuring reliable generalization.Regression analysis yields a near-unity correlation coefficient(R≈0.99),while error histograms exhibit tightly clustered residuals around zero,confirming high predictive accuracy.Furthermore,a benchmark convergence study using Stokes’first problem demonstrates that the proposed scheme consistently achieves lower global error norms than the classical Runge–Kutta method for identical spatial and temporal resolutions.Overall,this study introduces a novel computational intelligence framework that integrates high-order numerical solvers with machine learning,offering a robust and time-efficient tool for advanced modeling and real-time prediction of non-Newtonian nanofluid transport phenomena under electromagnetic flow control.
基金the National Key Basic Research Project Research on the Formation Mechanism and Prediction Theory of Severe Synoptic Disasters i
文摘The uncertainties caused by the errors of the initial states and the parameters in the numerical model are investigated. Three problems of predictability in numerical weather and climate prediction are proposed, which are related to the maximum predictable time, the maximum prediction error, and the maximum admissible errors of the initial values and the parameters in the model respectively. The three problems are then formulated into nonlinear optimization problems. Effective approaches to deal with these nonlinear optimization problems are provided. The Lorenz’ model is employed to demonstrate how to use these ideas in dealing with these three problems.
基金supported by the NOAA (Grant Nos. NA16AOR4320115 and NA11OAR4320072)NSF (Grant No. AGS-1341878)
文摘After decades of research and development, the WSR-88 D(NEXRAD) network in the United States was upgraded with dual-polarization capability, providing polarimetric radar data(PRD) that have the potential to improve weather observations,quantification, forecasting, and warnings. The weather radar networks in China and other countries are also being upgraded with dual-polarization capability. Now, with radar polarimetry technology having matured, and PRD available both nationally and globally, it is important to understand the current status and future challenges and opportunities. The potential impact of PRD has been limited by their oftentimes subjective and empirical use. More importantly, the community has not begun to regularly derive from PRD the state parameters, such as water mixing ratios and number concentrations, used in numerical weather prediction(NWP) models.In this review, we summarize the current status of weather radar polarimetry, discuss the issues and limitations of PRD usage, and explore potential approaches to more efficiently use PRD for quantitative precipitation estimation and forecasting based on statistical retrieval with physical constraints where prior information is used and observation error is included. This approach aligns the observation-based retrievals favored by the radar meteorology community with the model-based analysis of the NWP community. We also examine the challenges and opportunities of polarimetric phased array radar research and development for future weather observation.
文摘The recent progresses in the research and development of (NWP) in China are reviewed in this paper. The most impressive achievements are the development of direct assimilation of satellite irradiances with a 3DVAR (three-dimentional variational) data assimilation system and a non-hydrostatic modei with a semi-Lagrangian semi-implicit scheme. Progresses have also been made in modei physics and modei application to precipitation and environmental forecasts. Some scientific issues of great importance for further development are discussed.
基金This work is jointly funded by the national key-research project ＂Innovative Researches on Chinese Numerical Weather Prediction System＂ （Grant No. 2004BA607B）the project of National Natural Science Foundation of China ＂Study on Weather Prediction Associated with Heavy Precipitation in China＂ （Grant No. 40233036）.
文摘This paper summarizes the recent progress of numerical weather prediction （NWP） research since the last review was published. The new generation NWP system named GRAPES （the Global and Regional Assimilation and Prediction System）, which consists of variational or sequential data assimilation and nonhydrostatic prediction model with options of configuration for either global or regional domains, is briefly introduced, with stress on their scientific design and preliminary results during pre-operational implementation. In addition to the development of GRAPES, the achievements in new methodologies of data assimilation, new improvements of model physics such as parameterization of clouds and planetary boundary layer, mesoscale ensemble prediction system and numerical prediction of air quality are presented. The scientific issues which should be emphasized for the future are discussed finally.
基金Project supported by the National Natural Science Foundation of China （Grant Nos 40575036 and 40325015）.Acknowledgement The authors thank Drs Zhang Pei-Qun and Bao Ming very much for their valuable comments on the present paper.
文摘In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can effectively reduce model errors by combining the statistical analogue method with the dynamical model together in order that the information of plenty of historical data is utilized in the current complicated NWP model, Furthermore, in the ACE, the differences of the similarities between different historical analogues and the current initial state are considered as the weights for estimating model errors. The results of daily, decad and monthly prediction experiments on a complicated T63 atmospheric model show that the performance of the ACE by correcting model errors based on the estimation of the errors of 4 historical analogue predictions is not only better than that of the scheme of only introducing the correction of the errors of every single analogue prediction, but is also better than that of the T63 model.
基金supported by National Outstanding Young Scientists Founds of China(Grant No.50825902)National Natural Science Foundation of China(Grant No.50509009)
文摘Performance prediction for centrifugal pumps is now mainly based on numerical calculation and most of the studies merely focus on one model.Therefore,the research results are not representative.To make an improvement of numerical calculation method and performance prediction for centrifugal pumps,performance of six centrifugal pump models at design flow rate and off design flow rates,whose specific speed are different,were simulated by using commercial code FLUENT.The standard k-t turbulence model and SIMPLEC algorithm were chosen in FLUENT.The simulation was steady and moving reference frame was used to consider the impeller-volute interaction.Also,how to dispose the gap between impeller and volute was presented and the effect of grid number was considered.The characteristic prediction model for centrifugal pumps is established according to the simulation results.The head and efficiency of the six models at different flow rates are predicted and the prediction results are compared with the experiment results in detail.The comparison indicates that the precision of head and efficiency prediction are all less than 5%.The flow analysis indicates that flow change has an important effect on the location and area of low pressure region behind the blade inlet and the direction of velocity at impeller inlet.The study shows that using FLUENT simulation results to predict performance of centrifugal pumps is feasible and accurate.The method can be applied in engineering practice.
基金funded by the Special Scientific Research Project for Public Interest (GYHY201206009)the National Key Technologies Research and Development Program (Grant No. 2012BAC22B02)+2 种基金the National Natural Science Foundation Science Fund for Creative Research Groups (Grant No.41221064)the Special Scientific Research Project for Public Interest (Grant No. GYHY201006013)the National Natural Science Foundation of China (Grant No. 41105070 )
文摘The initial value error and the imperfect numerical model are usually considered as error sources of numerical weather prediction （NWP）. By using past multi-time observations and model output, this study proposes a method to estimate imperfect numerical model error. This method can be inversely estimated through expressing the model error as a Lagrange interpolation polynomial, while the coefficients of polyno- mial are determined by past model performance. However, for practical application in the full NWP model, it is necessary to determine the following criteria： （1） the length of past data sufficient for estimation of the model errors, （2） a proper method of estimating the term ＂model integration with the exact solution＂ when solving the inverse problem, and （3） the extent to which this scheme is sensitive to the observational errors. In this study, such issues are resolved using a simple linear model, and an advection diffusion model is applied to discuss the sensitivity of the method to an artificial error source. The results indicate that the forecast errors can be largely reduced using the proposed method if the proper length of past data is chosen. To address the three problems, it is determined that （1） a few data limited by the order of the corrector can be used, （2） trapezoidal approximation can be employed to estimate the ＂term＂ in this study; however, a more accurate method should be explored for an operational NWP model, and （3） the correction is sensitive to observational error.
基金Project supported by the Special Scientific Research Project for Public Interest(Grant No.GYHY201206009)the Fundamental Research Funds for the Central Universities,China(Grant Nos.lzujbky-2012-13 and lzujbky-2013-11)the National Basic Research Program of China(Grant Nos.2012CB955902 and 2013CB430204)
文摘Model error is one of the key factors restricting the accuracy of numerical weather prediction （NWP）. Considering the continuous evolution of the atmosphere, the observed data （ignoring the measurement error） can be viewed as a series of solutions of an accurate model governing the actual atmosphere. Model error is represented as an unknown term in the accurate model, thus NWP can be considered as an inverse problem to uncover the unknown error term. The inverse problem models can absorb long periods of observed data to generate model error correction procedures. They thus resolve the deficiency and faultiness of the NWP schemes employing only the initial-time data. In this study we construct two inverse problem models to estimate and extrapolate the time-varying and spatial-varying model errors in both the historical and forecast periods by using recent observations and analogue phenomena of the atmosphere. Numerical experiment on Burgers＇ equation has illustrated the substantial forecast improvement using inverse problem algorithms. The proposed inverse problem methods of suppressing NWP errors will be useful in future high accuracy applications of NWP.
基金supported by the Open Fund (PLN 201718) of State Key Laboratory of Oil and Gas Reservoir Geology and ExploitationSouthwest Petroleum University and the Open Fund (SEC-2018-04) of Collaborative Innovation Center of Shale Gas Resources and EnvironmentSouthwest Petroleum University and the National Science and Technology Major Project of China (2017ZX05036003-003)
文摘Fracture prediction is a technical issue in the field of petroleum exploration and production worldwide.Although there are many approaches to predict the distribution of cracks underground,these approaches have some limitations.To resolve these issues,we ascertained the relation between numerical simulations of tectonic stress and the predicted distribution of fractures from the perspective of geologic genesis,based on the characteristics of the shale reservoir in the Longmaxi Formation in Dingshan;the features of fracture development in this reservoir were considered.3 D finite element method(FEM)was applied in combination with rock mechanical parameters derived from the acoustic emissions.The paleotectonic stress field of the crack formation period was simulated for the Longmaxi Formation in the Dingshan area.The splitting factor in the study area was calculated based on the rock breaking criterion.The coefficient of fracture development was selected as the quantitative prediction classification criteria for the cracks.The results show that a higher coefficient of fracture development indicates a greater degree of fracture development.On the basis of the fracture development coefficient classification,a favorable area was identified for the development of fracture prediction in the study area.The prediction results indicate that the south of the Dingshan area and the DY3 well of the central region are favorable zones for fracture development.
基金supported by the National Basic Research Program of China(Grant No.2014CB44701)the National Natural Science Foundation of China(NSFC)(Grants No.41272283,40902080,41130753)
文摘Retrogressive landslides are common geological phenomena in mountainous areas and on onshore and offshore slopes. The impact of retrogressive landslides is different from that of other landslide types due to the phenomenon of retrogression. The hazards caused by retrogressive landslides may be increased because retrogressive landslides usually affect housing, facilities, and infrastructure located far from the original slopes. Additionally, substantial geomorphic evidence shows that the abundant supply of loose sediment in the source area of a debris flow is usually provided by retrogressive landslides that are triggered by the undercutting of water. Moreover, according to historic case studies, some large landslides are the evolution result of retrogressive landslides. Hence the ability to understand and predict the evolution of retrogressive landslides is crucial for the purpose of hazard mitigation. This paper discusses the phenomenon of a retrogressive landslide by using a model experiment and suggests a reasonably simplified numerical approach for the prediction of rainfall-induced retrogressive landslides. The simplified numerical approach, which combines the finite element method for seepage analysis, the shear strength reduction finite element method, and the analysis criterion for the retrogression and accumulation effect, is presented and used to predict the characteristics of a retrogressive landslide. The results show that this numerical approach is capable of reasonably predicting the characteristics of retrogressive landslides under rainfall infiltration, particularly the magnitude of each landslide, the position of the slip surface, and the development processes of the retrogressive landslide. Therefore, this approach is expected to be a practical method for the mitigation of damage caused by rainfall-induced retrogressive landslides.
文摘The movement of Typhoon Maggie (9903) in June 1999 is one of the rare cases ever seen in the history. At 00U on June 6 Maggie was located at about 70 km to the southwest of Taiwan. When it arrived at the coastal region of Shanwei City (22.8N, 116.5E), it turned suddenly to move southwestward along the southern China coastal line. De June 7 Maggie finally turned to move northward, making landfall to the north of Shangchuan Island. The experimental numerical prediction system on typhoon movement that was designed based on MM5 is proved quite successful for the 48h prediction of Maggie's movement and rainfall. The mean prediction error of typhoon track is 81 km for 0-24 h and 74 km for 24-48 h. The location of typhoon center in the initial field of the model is approximately 100 km away from the actual observations. In order to modify the location of typhoon center, a bogus typhoon was intro- duced into the model and the prediction of typhoon track was improved in 0-24 h time interval. But the prediction error was enlarged in 24-36 h. We also performed a sensitivity experiment of changing the land of southern China into the ocean. It is found that the orientation of South China coastal line and the topography have no obvious effect on the movement of Typhoon Maggie.
基金Supported by National Key Research and Development Program of China(Grant No.2020YFA0710902)National Natural Science Foundation of China(Grant No.12172308).
文摘In this paper,the unsteady flow around a high-speed train is numerically simulated by detached eddy simulation method(DES),and the far-field noise is predicted using the Ffowcs Williams-Hawkings(FW-H)acoustic model.The reliability of the numerical calculation is verified by wind tunnel experiments.The superposition relationship between the far-field radiated noise of the local aerodynamic noise sources of the high-speed train and the whole noise source is analyzed.Since the aerodynamic noise of high-speed trains is derived from its different components,a stepwise calculation method is proposed to predict the aerodynamic noise of high-speed trains.The results show that the local noise sources of high-speed trains and the whole noise source conform to the principle of sound source energy superposition.Using the head,middle and tail cars of the high-speed train as noise sources,different numerical models are established to obtain the far-field radiated noise of each aerodynamic noise source.The far-field total noise of high-speed trains is predicted using sound source superposition.A step-by-step calculation of each local aerodynamic noise source is used to obtain the superimposed value of the far-field noise.This is consistent with the far-field noise of the whole train model’s aerodynamic noise.The averaged sound pressure level of the far-field longitudinal noise measurement points differs by 1.92 dBA.The step-by-step numerical prediction method of aerodynamic noise of high-speed trains can provide a reference for the numerical prediction of aerodynamic noise generated by long marshalling high-speed trains.
文摘In this paper,both measurements and numerical simulations of railway induced vibration are discussed.A measurement campaign has been carried out along the high-speed railway track in Lincent,Belgium.The experimental determination of transfer functions and vibration velocity during train passages are discussed.A numerical model is introduced to predict the transfer functions and the vibration velocity during train passages.The comparison of experimental and numerical results demonstrates the importance of accurate numerical models and input data.The results are obtained in the framework of the development of a hybrid prediction method,where numerical and experimental data can be combined to improve the prediction accuracy for railway induced vibration.
