Turbulent gas-particle flows are studied by a kinetic description using a prob- ability density function （PDF）. Unlike other investigators deriving the particle Reynolds stress equations using the PDF equations, the...Turbulent gas-particle flows are studied by a kinetic description using a prob- ability density function （PDF）. Unlike other investigators deriving the particle Reynolds stress equations using the PDF equations, the particle PDF transport equations are di- rectly solved either using a finite-difference method for two-dimensional （2D） problems or using a Monte-Carlo （MC） method for three-dimensional （3D） problems. The proposed differential stress model together with the PDF （DSM-PDF） is used to simulate turbulent swirling gas-particle flows. The simulation results are compared with the experimental results and the second-order moment （SOM） two-phase modeling results. All of these simulation results are in agreement with the experimental results, implying that the PDF approach validates the SOM two-phase turbulence modeling. The PDF model with the SOM-MC method is used to simulate evaporating gas-droplet flows, and the simulation results are in good agreement with the experimental results.展开更多
We propose a new flame index for the transported probability density function(PDF) method. The flame index uses mixing flux projections of Lagrangian particles on mixture fraction and progress variable directions as t...We propose a new flame index for the transported probability density function(PDF) method. The flame index uses mixing flux projections of Lagrangian particles on mixture fraction and progress variable directions as the metrics to identify the combustion mode, with the Burke-Schumann solution as a reference. A priori validation of the flame index is conducted with a series of constructed turbulent partially premixed reactors. It indicates that the proposed flame index is able to identify the combustion mode based on the subgrid mixing information. The flame index is then applied the large eddy simulation/PDF datasets of turbulent partially premixed jet flames. Results show that the flame index separate different combustion modes and extinction correctly. The proposed flame index provides a promising tool to analyze and model the partially premixed flames adaptively.展开更多
The present work proposes a novel methodology for constructing coarse-grained （CG） models, which aims at minimizing the difference between CG model and the corresponding original system. The difference is defined as...The present work proposes a novel methodology for constructing coarse-grained （CG） models, which aims at minimizing the difference between CG model and the corresponding original system. The difference is defined as a functional of their equilibrium conformationaJ probability densities, then is estimated from equilibrium averages of many independent physical quantities denoted as basis functions. An orthonormalization strategy is adopted to get the independent basis functions from su^ciently preselected interesting physical quantities of the system. Thus the current method is named as probability density matching coarse-graining （PMCG） scheme, which effectively takes into account the overall cha,~acteristics of the original systems to construct CG model, and it is a rtatural improvement of the usual CG scheme wherein some physical quantities are intuitively chosen without considering their correlations. We verify the general PMCG framework in constructing a one-site CG water model from TIP3P model. Both structure of liquids and pressure of the TIP3P water system are found to be well reproduced at the same time in the constructed CG model.展开更多
This paper focuses on resolving the identification problem of a neuro-fuzzy model(NFM) applied in batch processes. A hybrid learning algorithm is introduced to identify the proposed NFM with the idea of auxiliary erro...This paper focuses on resolving the identification problem of a neuro-fuzzy model(NFM) applied in batch processes. A hybrid learning algorithm is introduced to identify the proposed NFM with the idea of auxiliary error model and the identification principle based on the probability density function(PDF). The main contribution is that the NFM parameter updating approach is transformed into the shape control for the PDF of modeling error. More specifically, a virtual adaptive control system is constructed with the aid of the auxiliary error model and then the PDF shape control idea is used to tune NFM parameters so that the PDF of modeling error is controlled to follow a targeted PDF, which is in Gaussian or uniform distribution. Examples are used to validate the applicability of the proposed method and comparisons are made with the minimum mean square error based approaches.展开更多
A new identification method of neuro-uzzy Hammerstein model based on probability density function(PDF) is presented,which is different from the idea that mean squared error(MSE) is employed as the index function in tr...A new identification method of neuro-uzzy Hammerstein model based on probability density function(PDF) is presented,which is different from the idea that mean squared error(MSE) is employed as the index function in traditional identification methods.Firstly,a neuro-fuzzy based Hammerstein model is constructed to describe the nonlinearity of Hammerstein process without any prior process knowledge.Secondly,a kind of special test signal is used to separate the link parts of the Hammerstein model.More specifically,the conception of PDF is introduced to solve the identification problem of the neuro-fuzzy Hammerstein model.The antecedent parameters are estimated by a clustering algorithm,while the consequent parameters of the model are identified by designing a virtual PDF control system in which the PDF of the modeling error is estimated and controlled to converge to the target.The proposed method not only guarantees the accuracy of the model but also dominates the spatial distribution of PDF of the model error to improve the generalization ability of the model.Simulated results show the effectiveness of the proposed method.展开更多
Predictive simulation of the combustion process in engine is crucial to understand the complex underlying physicochemical processes, improve engine performance, and reduce pollutant emissions. Key issues such as the p...Predictive simulation of the combustion process in engine is crucial to understand the complex underlying physicochemical processes, improve engine performance, and reduce pollutant emissions. Key issues such as the physical modeling of the interaction between turbulence, chemistry and droplets, and the incorporation of the detailed chemistry in high-fidelity simulations of complex flows remain essential though challenging. This paper reviews the transported probability density function method for turbulent dilute spray flames in the dual-Lagrangian framework that shows potential to address some critical modeling issues. An overview is presented for the contributions made within the last decade or so for the three key ingredients for modeling the interaction between turbulence, chemistry and droplets, i.e., micro-mixing, subgrid dispersion and two-phase coupling. Then, various methods for detailed chemistry acceleration are reviewed to address the issue of high computational cost for its use in multidimensional simulations. Finally, some applications of the dual-Lagrangian method in both laboratory-scale and device-scale configurations are provided to demonstrate its capability as well as deficiency at the current stage. Some open modeling challenges are raised and recommended for further investigation.展开更多
A hybrid LES (Large Eddy Simulation)/assumed sub-grid PDF (Probability Density Function) closure model has been devel-oped for supersonic turbulent combustion. Scalar transport equations for all species in a given che...A hybrid LES (Large Eddy Simulation)/assumed sub-grid PDF (Probability Density Function) closure model has been devel-oped for supersonic turbulent combustion. Scalar transport equations for all species in a given chemical kinetic mechanism were solved, which are necessary in the supersonic combustion where the non-equilibrium chemistry is essentially involved. The clipped Gaussian PDF of temperature and multivariate ? PDF of composition were used to close the sub-grid chemical sources that appear in the conservation equations. The sub-grid variances of temperature and composition were constructed based on scale similarity approach. A semi-implicit approach based on the PDF model was proposed to tackle the resulting numerical stiffness associated with finite rate chemistry. The model was applied to simulate a supersonic, coaxial H2-air burner, where both the mean and rms (root mean square) results were compared with the experimental data. In general, good agree-ments were achieved, which indicated that the present sub-grid PDF method could work well in simulating supersonic turbu-lent combustion. Moreover, the calculation showed that the sub-grid fluctuations of temperature and major species in the combustion region were of the order of 10%-20% of their rms, while the sub-grid fluctuation of hydroxyl might be as high as 40%-50% of its rms.展开更多
A new robust proportional-integral-derivative （PID） tracking control framework is considered for stochastic systems with non-Gaussian variable based on B-spline neural network approximation and T-S fuzzy model ident...A new robust proportional-integral-derivative （PID） tracking control framework is considered for stochastic systems with non-Gaussian variable based on B-spline neural network approximation and T-S fuzzy model identification. The tracked object is the statistical information of a given target probability density function （PDF）, rather than a deterministic signal. Following B-spline approximation to the integrated performance function, the concerned problem is transferred into the tracking of given weights. Different from the previous related works, the time delay T-S fuzzy models with the exogenous disturbances are applied to identify the nonlinear weighting dynamics. Meanwhile, the generalized PID controller structure and the improved convex linear matrix inequalities （LMI） algorithms are proposed to fulfil the tracking problem. Furthermore, in order to enhance the robust performance, the peak-to-peak measure index is applied to optimize the tracking performance. Simulations are given to demonstrate the efficiency of the proposed approach.展开更多
Soil moisture is the key link between land hydrological and ecological processes which plays an important role in the terrestrial water cycle. As extreme weather events have increased in recent years, the stochastic s...Soil moisture is the key link between land hydrological and ecological processes which plays an important role in the terrestrial water cycle. As extreme weather events have increased in recent years, the stochastic simulation of soil moisture has gradually become the focus of ecohydrology research. Based on continuous monitoring of soil moisture data from 2008 to 2011, and histor- ical precipitation data from 199l to 2011, combined with the Rodriguez-Iturbe soil moisture dynamic stochastic model, soil mois- ture dynamics and its probability density fimction in a revegetated desert area was simulated. Results show that annual soil mois- ture dynamic changes of the revegetated desert area during the growing season complied with rainfall distribution; soil moisture probability presents a single-peak distribution in the plant rhizosphere layer （0-60 cm）. The peak width in the 20 cm topsoil was wider than in other soils, and the distribution presented the strong fluctuations and multiple aggregates. The peak widths of 40 cm and 60 cm soil moisture probability distribution were small, which are in accordance with simulated results of the Rodri- guez-lturbe model. This confrms that the Rodriguez-Imrbe model has good applicability and can well simulate the statistical characteristics of soil moisture in an arid revegetated desert area.展开更多
The statistical and distribution characteristics of the responses of a floater and its mooring lines are essential in designing floating/mooring systems.In general,the dynamic responses of offshore structures obey a G...The statistical and distribution characteristics of the responses of a floater and its mooring lines are essential in designing floating/mooring systems.In general,the dynamic responses of offshore structures obey a Gaussian distribution,assuming that the structural system,and sea loads are linear or weakly nonlinear.However,mooring systems and wave loads are considerably nonlinear,and the dynamic responses of hull/mooring systems are non-Gaussian.In this study,the dynamic responses of two types of floaters,semi-submersible and spar platforms,and their mooring lines are computed using coupled dynamic analysis in the time domain.Herein,the statistical characteristics and distributions of the hull motion and mooring line tension are discussed and compared.The statistical distributions of the dynamic responses have strong non-Gaussianity and are unreasonably fitted by a Gaussian distribution for the two floating and mooring systems.Then,the effects of water depth,wave parameters,and low-frequency and wave-frequency components on the non-Gaussianity of the hull motion,and mooring line tension are investigated and discussed.A comparison of the statistical distributions of the responses with various probability density functions,including the Gamma,Gaussian,General Extreme Value,Weibull,and Gaussian Mixture Model(GMM)distributions,shows that the GMM distribution is better than the others for characterizing the statistical distributions of the hull motion,and mooring line tension responses.Furthermore,the GMM distribution has the best accuracy of response prediction.展开更多
Seasonal prediction of summer rainfall over the Yangtze River valley（YRV） is valuable for agricultural and industrial production and freshwater resource management in China, but remains a major challenge. Earlier mu...Seasonal prediction of summer rainfall over the Yangtze River valley（YRV） is valuable for agricultural and industrial production and freshwater resource management in China, but remains a major challenge. Earlier multi-model ensemble（MME） prediction schemes for summer rainfall over China focus on single-value prediction, which cannot provide the necessary uncertainty information, while commonly-used ensemble schemes for probability density function（PDF） prediction are not adapted to YRV summer rainfall prediction. In the present study, an MME PDF prediction scheme is proposed based on the ENSEMBLES hindcasts. It is similar to the earlier Bayesian ensemble prediction scheme, but with optimization of ensemble members and a revision of the variance modeling of the likelihood function. The optimized ensemble members are regressed YRV summer rainfall with factors selected from model outputs of synchronous 500-h Pa geopotential height as predictors. The revised variance modeling of the likelihood function is a simple linear regression with ensemble spread as the predictor. The cross-validation skill of 1960–2002 YRV summer rainfall prediction shows that the new scheme produces a skillful PDF prediction, and is much better-calibrated, sharper, and more accurate than the earlier Bayesian ensemble and raw ensemble.展开更多
This paper presents the Advanced Observer Model (AOM), a groundbreaking conceptual framework designed to clarify the complex and often enigmatic nature of quantum mechanics. The AOM serves as a metaphorical lens, brin...This paper presents the Advanced Observer Model (AOM), a groundbreaking conceptual framework designed to clarify the complex and often enigmatic nature of quantum mechanics. The AOM serves as a metaphorical lens, bringing the elusive quantum realm into sharper focus by transforming its inherent uncertainty into a coherent, structured ‘Frame Stream’ that aids in the understanding of quantum phenomena. While the AOM offers conceptual simplicity and clarity, it recognizes the necessity of a rigorous theoretical foundation to address the fundamental uncertainties that lie at the core of quantum mechanics. This paper seeks to illuminate those theoretical ambiguities, bridging the gap between the abstract insights of the AOM and the intricate mathematical foundations of quantum theory. By integrating the conceptual clarity of the AOM with the theoretical intricacies of quantum mechanics, this work aspires to deepen our understanding of this fascinating and elusive field.展开更多
作为电力系统中的基本量测设备,电子式电压互感器(electronic voltage transformers,EVTs)的测量精度对系统的监控、控制与安全运行至关重要。为此,提出了一种基于混合深度模型和自适应窗宽概率密度估计的互感器测量误差区间预测模型。...作为电力系统中的基本量测设备,电子式电压互感器(electronic voltage transformers,EVTs)的测量精度对系统的监控、控制与安全运行至关重要。为此,提出了一种基于混合深度模型和自适应窗宽概率密度估计的互感器测量误差区间预测模型。首先,通过改进的集合经验模态分解对历史比差特征进行数据前处理。其次,提出了基于数据驱动的双向时序卷积网络、双向门控循环单元和多头注意力机制混合深度学习模型,对分解后的不同模态分量进行预测。此外,引入自适应选择最优窗宽的核密度概率估计方法,拟合预测结果构建不同置信度下的预测区间,并比较不同核函数对于预测区间的影响。通过算例分析,验证了所提模型在提高确定性预测和概率区间预测准确度方面的有效性。展开更多
通过大涡模拟(Large Eddy Simulation,LES)湍流求解方法和概率密度函数输运方程(Transported Probability Density Function,TPDF)湍流燃烧求解方法结合,对煤油燃料双旋流燃烧室(Gas Turbine Model Combustor,GTMC)进行了模拟,并利用经...通过大涡模拟(Large Eddy Simulation,LES)湍流求解方法和概率密度函数输运方程(Transported Probability Density Function,TPDF)湍流燃烧求解方法结合,对煤油燃料双旋流燃烧室(Gas Turbine Model Combustor,GTMC)进行了模拟,并利用经验模态分解(Empirical Mode Decomposition,EMD)和快速傅里叶变换(Fast Fourier Transform,FFT)等方法分析了GTMC的温度和速度非定常特性,获得了脉动主频的空间分布。结果显示:空间坐标为(2 cm,0 cm,3 cm)的特征点的温度主频为47和761 Hz;对本征模态函数(Intrinsic Mode Function,IMF)进行显著性分析,能量密度最高的IMF的主频即原始数据的主频;温度脉动主要受湍流流动影响;根据瑞利数场,热-压力激发与抑制区域总是交替出现。展开更多
混煤燃烧存在复杂的相互影响,将混煤当成单一煤种并采用单混合分数/概率密度函数(probability density function,PDF)方法计算,意味着忽略了煤种之间的影响,结果会产生很大偏差。而双混合分数/PDF方法可以分别定义各单煤性质并跟踪各单...混煤燃烧存在复杂的相互影响,将混煤当成单一煤种并采用单混合分数/概率密度函数(probability density function,PDF)方法计算,意味着忽略了煤种之间的影响,结果会产生很大偏差。而双混合分数/PDF方法可以分别定义各单煤性质并跟踪各单煤的燃烧过程,能够体现煤种之间燃烧特性的影响。利用单、双混合分数/PDF方法对同1台300 MW四角切圆锅炉进行模拟研究,并与实测数据进行对比,结果表明:双混合分数/PDF方法模拟的结果更符合混煤在炉内实际的燃烧情况。同时采用双混合分数/PDF方法模拟某一混煤燃烧过程,得到燃烧煤粉锅炉的流动,温度和烟气分布等特性。展开更多
基金supported by the National Natural Science Foundation of China(No.51390493)
文摘Turbulent gas-particle flows are studied by a kinetic description using a prob- ability density function （PDF）. Unlike other investigators deriving the particle Reynolds stress equations using the PDF equations, the particle PDF transport equations are di- rectly solved either using a finite-difference method for two-dimensional （2D） problems or using a Monte-Carlo （MC） method for three-dimensional （3D） problems. The proposed differential stress model together with the PDF （DSM-PDF） is used to simulate turbulent swirling gas-particle flows. The simulation results are compared with the experimental results and the second-order moment （SOM） two-phase modeling results. All of these simulation results are in agreement with the experimental results, implying that the PDF approach validates the SOM two-phase turbulence modeling. The PDF model with the SOM-MC method is used to simulate evaporating gas-droplet flows, and the simulation results are in good agreement with the experimental results.
基金sponsored by King Abdullah University of Science and Technology(KAUST)the National Natural Science Foundation of China(Grant No.91841302)。
文摘We propose a new flame index for the transported probability density function(PDF) method. The flame index uses mixing flux projections of Lagrangian particles on mixture fraction and progress variable directions as the metrics to identify the combustion mode, with the Burke-Schumann solution as a reference. A priori validation of the flame index is conducted with a series of constructed turbulent partially premixed reactors. It indicates that the proposed flame index is able to identify the combustion mode based on the subgrid mixing information. The flame index is then applied the large eddy simulation/PDF datasets of turbulent partially premixed jet flames. Results show that the flame index separate different combustion modes and extinction correctly. The proposed flame index provides a promising tool to analyze and model the partially premixed flames adaptively.
基金Supported by National Natural Science Foundation of China under Grant No.11175250
文摘The present work proposes a novel methodology for constructing coarse-grained （CG） models, which aims at minimizing the difference between CG model and the corresponding original system. The difference is defined as a functional of their equilibrium conformationaJ probability densities, then is estimated from equilibrium averages of many independent physical quantities denoted as basis functions. An orthonormalization strategy is adopted to get the independent basis functions from su^ciently preselected interesting physical quantities of the system. Thus the current method is named as probability density matching coarse-graining （PMCG） scheme, which effectively takes into account the overall cha,~acteristics of the original systems to construct CG model, and it is a rtatural improvement of the usual CG scheme wherein some physical quantities are intuitively chosen without considering their correlations. We verify the general PMCG framework in constructing a one-site CG water model from TIP3P model. Both structure of liquids and pressure of the TIP3P water system are found to be well reproduced at the same time in the constructed CG model.
基金Supported by the National Natural Science Foundation of China(61374044)Shanghai Science Technology Commission(12510709400)+1 种基金Shanghai Municipal Education Commission(14ZZ088)Shanghai Talent Development Plan
文摘This paper focuses on resolving the identification problem of a neuro-fuzzy model(NFM) applied in batch processes. A hybrid learning algorithm is introduced to identify the proposed NFM with the idea of auxiliary error model and the identification principle based on the probability density function(PDF). The main contribution is that the NFM parameter updating approach is transformed into the shape control for the PDF of modeling error. More specifically, a virtual adaptive control system is constructed with the aid of the auxiliary error model and then the PDF shape control idea is used to tune NFM parameters so that the PDF of modeling error is controlled to follow a targeted PDF, which is in Gaussian or uniform distribution. Examples are used to validate the applicability of the proposed method and comparisons are made with the minimum mean square error based approaches.
基金National Natural Science Foundation of China(No.61374044)Shanghai Municipal Science and Technology Commission,China(No.15510722100)+2 种基金Shanghai Municipal Education Commission,China(No.14ZZ088)Shanghai Talent Development Plan,ChinaShanghai Baoshan Science and Technology Commission,China(No.bkw2013120)
文摘A new identification method of neuro-uzzy Hammerstein model based on probability density function(PDF) is presented,which is different from the idea that mean squared error(MSE) is employed as the index function in traditional identification methods.Firstly,a neuro-fuzzy based Hammerstein model is constructed to describe the nonlinearity of Hammerstein process without any prior process knowledge.Secondly,a kind of special test signal is used to separate the link parts of the Hammerstein model.More specifically,the conception of PDF is introduced to solve the identification problem of the neuro-fuzzy Hammerstein model.The antecedent parameters are estimated by a clustering algorithm,while the consequent parameters of the model are identified by designing a virtual PDF control system in which the PDF of the modeling error is estimated and controlled to converge to the target.The proposed method not only guarantees the accuracy of the model but also dominates the spatial distribution of PDF of the model error to improve the generalization ability of the model.Simulated results show the effectiveness of the proposed method.
基金This work was supported by the National Natural Science Foundation of China(Grants 91841302 and 52025062).
文摘Predictive simulation of the combustion process in engine is crucial to understand the complex underlying physicochemical processes, improve engine performance, and reduce pollutant emissions. Key issues such as the physical modeling of the interaction between turbulence, chemistry and droplets, and the incorporation of the detailed chemistry in high-fidelity simulations of complex flows remain essential though challenging. This paper reviews the transported probability density function method for turbulent dilute spray flames in the dual-Lagrangian framework that shows potential to address some critical modeling issues. An overview is presented for the contributions made within the last decade or so for the three key ingredients for modeling the interaction between turbulence, chemistry and droplets, i.e., micro-mixing, subgrid dispersion and two-phase coupling. Then, various methods for detailed chemistry acceleration are reviewed to address the issue of high computational cost for its use in multidimensional simulations. Finally, some applications of the dual-Lagrangian method in both laboratory-scale and device-scale configurations are provided to demonstrate its capability as well as deficiency at the current stage. Some open modeling challenges are raised and recommended for further investigation.
基金supported by the National Natural Science Foundation of China (Grant Nos. 50906098 and 91016028)
文摘A hybrid LES (Large Eddy Simulation)/assumed sub-grid PDF (Probability Density Function) closure model has been devel-oped for supersonic turbulent combustion. Scalar transport equations for all species in a given chemical kinetic mechanism were solved, which are necessary in the supersonic combustion where the non-equilibrium chemistry is essentially involved. The clipped Gaussian PDF of temperature and multivariate ? PDF of composition were used to close the sub-grid chemical sources that appear in the conservation equations. The sub-grid variances of temperature and composition were constructed based on scale similarity approach. A semi-implicit approach based on the PDF model was proposed to tackle the resulting numerical stiffness associated with finite rate chemistry. The model was applied to simulate a supersonic, coaxial H2-air burner, where both the mean and rms (root mean square) results were compared with the experimental data. In general, good agree-ments were achieved, which indicated that the present sub-grid PDF method could work well in simulating supersonic turbu-lent combustion. Moreover, the calculation showed that the sub-grid fluctuations of temperature and major species in the combustion region were of the order of 10%-20% of their rms, while the sub-grid fluctuation of hydroxyl might be as high as 40%-50% of its rms.
基金supported by National Natural Science Foundationof China (No. 60472065, No. 60774013).
文摘A new robust proportional-integral-derivative （PID） tracking control framework is considered for stochastic systems with non-Gaussian variable based on B-spline neural network approximation and T-S fuzzy model identification. The tracked object is the statistical information of a given target probability density function （PDF）, rather than a deterministic signal. Following B-spline approximation to the integrated performance function, the concerned problem is transferred into the tracking of given weights. Different from the previous related works, the time delay T-S fuzzy models with the exogenous disturbances are applied to identify the nonlinear weighting dynamics. Meanwhile, the generalized PID controller structure and the improved convex linear matrix inequalities （LMI） algorithms are proposed to fulfil the tracking problem. Furthermore, in order to enhance the robust performance, the peak-to-peak measure index is applied to optimize the tracking performance. Simulations are given to demonstrate the efficiency of the proposed approach.
基金supported by the Key Orientation Project of Chinese Academy of Sciences(KZCX2-EW-301-3)Talented Young Scientist Fund of the Cold and Arid Regions Environmental and Engineering Research Institute,CAS(51Y251971)National Natural Scientific Foundation of China(41101054,41201084)
文摘Soil moisture is the key link between land hydrological and ecological processes which plays an important role in the terrestrial water cycle. As extreme weather events have increased in recent years, the stochastic simulation of soil moisture has gradually become the focus of ecohydrology research. Based on continuous monitoring of soil moisture data from 2008 to 2011, and histor- ical precipitation data from 199l to 2011, combined with the Rodriguez-Iturbe soil moisture dynamic stochastic model, soil mois- ture dynamics and its probability density fimction in a revegetated desert area was simulated. Results show that annual soil mois- ture dynamic changes of the revegetated desert area during the growing season complied with rainfall distribution; soil moisture probability presents a single-peak distribution in the plant rhizosphere layer （0-60 cm）. The peak width in the 20 cm topsoil was wider than in other soils, and the distribution presented the strong fluctuations and multiple aggregates. The peak widths of 40 cm and 60 cm soil moisture probability distribution were small, which are in accordance with simulated results of the Rodri- guez-lturbe model. This confrms that the Rodriguez-Imrbe model has good applicability and can well simulate the statistical characteristics of soil moisture in an arid revegetated desert area.
基金the support by the National Natural Science Foundation of China(Nos.51709247 and 51490675)the National Key R&D Program of China(No.2016YFE0200100)
文摘The statistical and distribution characteristics of the responses of a floater and its mooring lines are essential in designing floating/mooring systems.In general,the dynamic responses of offshore structures obey a Gaussian distribution,assuming that the structural system,and sea loads are linear or weakly nonlinear.However,mooring systems and wave loads are considerably nonlinear,and the dynamic responses of hull/mooring systems are non-Gaussian.In this study,the dynamic responses of two types of floaters,semi-submersible and spar platforms,and their mooring lines are computed using coupled dynamic analysis in the time domain.Herein,the statistical characteristics and distributions of the hull motion and mooring line tension are discussed and compared.The statistical distributions of the dynamic responses have strong non-Gaussianity and are unreasonably fitted by a Gaussian distribution for the two floating and mooring systems.Then,the effects of water depth,wave parameters,and low-frequency and wave-frequency components on the non-Gaussianity of the hull motion,and mooring line tension are investigated and discussed.A comparison of the statistical distributions of the responses with various probability density functions,including the Gamma,Gaussian,General Extreme Value,Weibull,and Gaussian Mixture Model(GMM)distributions,shows that the GMM distribution is better than the others for characterizing the statistical distributions of the hull motion,and mooring line tension responses.Furthermore,the GMM distribution has the best accuracy of response prediction.
基金co-supported by the National Natural Science Foundation (Grant Nos. 41005052 and 41375086)the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA05110201)the National Basic Research Program of China (Grant No. 2010CB950403)
文摘Seasonal prediction of summer rainfall over the Yangtze River valley（YRV） is valuable for agricultural and industrial production and freshwater resource management in China, but remains a major challenge. Earlier multi-model ensemble（MME） prediction schemes for summer rainfall over China focus on single-value prediction, which cannot provide the necessary uncertainty information, while commonly-used ensemble schemes for probability density function（PDF） prediction are not adapted to YRV summer rainfall prediction. In the present study, an MME PDF prediction scheme is proposed based on the ENSEMBLES hindcasts. It is similar to the earlier Bayesian ensemble prediction scheme, but with optimization of ensemble members and a revision of the variance modeling of the likelihood function. The optimized ensemble members are regressed YRV summer rainfall with factors selected from model outputs of synchronous 500-h Pa geopotential height as predictors. The revised variance modeling of the likelihood function is a simple linear regression with ensemble spread as the predictor. The cross-validation skill of 1960–2002 YRV summer rainfall prediction shows that the new scheme produces a skillful PDF prediction, and is much better-calibrated, sharper, and more accurate than the earlier Bayesian ensemble and raw ensemble.
文摘This paper presents the Advanced Observer Model (AOM), a groundbreaking conceptual framework designed to clarify the complex and often enigmatic nature of quantum mechanics. The AOM serves as a metaphorical lens, bringing the elusive quantum realm into sharper focus by transforming its inherent uncertainty into a coherent, structured ‘Frame Stream’ that aids in the understanding of quantum phenomena. While the AOM offers conceptual simplicity and clarity, it recognizes the necessity of a rigorous theoretical foundation to address the fundamental uncertainties that lie at the core of quantum mechanics. This paper seeks to illuminate those theoretical ambiguities, bridging the gap between the abstract insights of the AOM and the intricate mathematical foundations of quantum theory. By integrating the conceptual clarity of the AOM with the theoretical intricacies of quantum mechanics, this work aspires to deepen our understanding of this fascinating and elusive field.
文摘作为电力系统中的基本量测设备,电子式电压互感器(electronic voltage transformers,EVTs)的测量精度对系统的监控、控制与安全运行至关重要。为此,提出了一种基于混合深度模型和自适应窗宽概率密度估计的互感器测量误差区间预测模型。首先,通过改进的集合经验模态分解对历史比差特征进行数据前处理。其次,提出了基于数据驱动的双向时序卷积网络、双向门控循环单元和多头注意力机制混合深度学习模型,对分解后的不同模态分量进行预测。此外,引入自适应选择最优窗宽的核密度概率估计方法,拟合预测结果构建不同置信度下的预测区间,并比较不同核函数对于预测区间的影响。通过算例分析,验证了所提模型在提高确定性预测和概率区间预测准确度方面的有效性。
文摘混煤燃烧存在复杂的相互影响,将混煤当成单一煤种并采用单混合分数/概率密度函数(probability density function,PDF)方法计算,意味着忽略了煤种之间的影响,结果会产生很大偏差。而双混合分数/PDF方法可以分别定义各单煤性质并跟踪各单煤的燃烧过程,能够体现煤种之间燃烧特性的影响。利用单、双混合分数/PDF方法对同1台300 MW四角切圆锅炉进行模拟研究,并与实测数据进行对比,结果表明:双混合分数/PDF方法模拟的结果更符合混煤在炉内实际的燃烧情况。同时采用双混合分数/PDF方法模拟某一混煤燃烧过程,得到燃烧煤粉锅炉的流动,温度和烟气分布等特性。
