A Favré averaged transition prediction model for hypersonic flows

Song, Bo; Lee, Chun‐Hian

doi:10.1007/s11431-010-3173-7

Cited by 6 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the basis of the retrieved modeling, self-memorization principle has been recently developed based on the mechanism of time series data. As a mathematic realization on integration of deterministic and random theory, the self-memory model is a statistical-dynamical method to solve problems in nonlinear dynamic systems [41][42][43]. The core method of the model is to transform differential equations into difference-integral equations by introduction of the self-memory function.…”

mentioning

confidence: 99%

Dynamic prediction of building subsidence deformation with data-based mechanistic self-memory model

Wang

Hou

et al. 2012

Chin. Sci. Bull.

View full text Add to dashboard Cite

This paper describes a building subsidence deformation prediction model with the self-memorization principle. According to the non-linear specificity and monotonic growth characteristics of the time series of building subsidence deformation, a data-based mechanistic self-memory model considering randomness and dynamic features of building subsidence deformation is established based on the dynamic data retrieved method and the self-memorization equation. This model first deduces the differential equation of the building subsidence deformation system using the dynamic retrieved method, which treats the monitored time series data as particular solutions of the nonlinear dynamic system. Then, the differential equation is evolved into a difference-integral equation by the self-memory function to establish the self-memory model of dynamic system for predicting nonlinear building subsidence deformation. As the memory coefficients of the proposed model are calculated with historical data, which contain useful information for the prediction and overcome the shortcomings of the average prediction, the model can predict extreme values of a system and provide higher fitting precision and prediction accuracy than deterministic or random statistical prediction methods. The model was applied to subsidence deformation prediction of a building in Xi'an. It was shown that the model is valid and feasible in predicting building subsidence deformation with good accuracy. The fast development of economics promotes the urbanization of China. More and more high-rises appear in our cities [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. In order to ensure the service life and security of constructional works, it is necessary to monitor the constructional work systematically during its building and operating. Due to the diversity, complexity and non-deterministic characters of inducing factors of the building subsidence deformation, the mechanical behavior and deformation trend of building subsidence also reflect non-linear characteristics with the coexistence of the uncertainty and randomness [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Existing methods for building subsidence deformation prediction usually establish the time series analysis model by utilizing monitored data [35][36][37][38][39][40]. However, most of these models are of the "parameter model" class which may not have clear physical meaning of parameters and discard effective parts of monitoring data. On the basis of the retrieved modeling, self-memorization principle has been recently developed based on the mechanism of time series data. As a mathematic realization on integration of deterministic and random theory, the self-memory model is a statistical-dynamical method to solve problems in nonlinear dynamic systems [41][42][43]. The core method of the model is to transform differential equations into difference-integral equations by introduction of the self-memory function. For dynamic systems described by differential equations, pre...

show abstract

mentioning

confidence: 99%

Dynamic prediction of building subsidence deformation with data-based mechanistic self-memory model

Wang

Hou

et al. 2012

Chin. Sci. Bull.

View full text Add to dashboard Cite

show abstract

“…On the basis of retrieved modeling methods, the self-memory principle of dynamic system was first proposed by Cao in 1993 [38] . As a statistically dynamic method to solve problems of nonlinear dynamic systems, it successfully integrated determinism and random theories with mathematics [39] . The self-memory principle can retrieve ideal nonlinear dynamic models from practical observational data.…”

Section: Introductionmentioning

confidence: 99%

Application of a Novel Grey Self-Memory Coupling Model to Forecast the Incidence Rates of Two Notifiable Diseases in China: Dysentery and Gonorrhea

Guo

Liu

et al. 2014

PLoS ONE

View full text Add to dashboard Cite

ObjectiveIn this study, a novel grey self-memory coupling model was developed to forecast the incidence rates of two notifiable infectious diseases (dysentery and gonorrhea); the effectiveness and applicability of this model was assessed based on its ability to predict the epidemiological trend of infectious diseases in China.MethodsThe linear model, the conventional GM(1,1) model and the GM(1,1) model with self-memory principle (SMGM(1,1) model) were used to predict the incidence rates of the two notifiable infectious diseases based on statistical incidence data. Both simulation accuracy and prediction accuracy were assessed to compare the predictive performances of the three models. The best-fit model was applied to predict future incidence rates.ResultsSimulation results show that the SMGM(1,1) model can take full advantage of the systematic multi-time historical data and possesses superior predictive performance compared with the linear model and the conventional GM(1,1) model. By applying the novel SMGM(1,1) model, we obtained the possible incidence rates of the two representative notifiable infectious diseases in China.ConclusionThe disadvantages of the conventional grey prediction model, such as sensitivity to initial value, can be overcome by the self-memory principle. The novel grey self-memory coupling model can predict the incidence rates of infectious diseases more accurately than the conventional model, and may provide useful references for making decisions involving infectious disease prevention and control.

show abstract

“…On the basis of the retrieved modeling, the self-memory principle of dynamic system was developed firstly by Cao [ 18 ]. As a mathematic realization of integrating the deterministic and random theories, the principle is a statistically-dynamic method to solve problems of nonlinear dynamic systems [ 19 , 20 ]. The self-memory principle can retrieve ideal nonlinear dynamic models by means of the practical observational data.…”

Section: Introductionmentioning

confidence: 99%

A GreyNGM(1,1,k)Self-Memory Coupling Prediction Model for Energy Consumption Prediction

Guo

Liu

et al. 2014

The Scientific World Journal

View full text Add to dashboard Cite

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span.

show abstract

A Favré averaged transition prediction model for hypersonic flows

Cited by 6 publications

References 15 publications

Dynamic prediction of building subsidence deformation with data-based mechanistic self-memory model

Dynamic prediction of building subsidence deformation with data-based mechanistic self-memory model

Application of a Novel Grey Self-Memory Coupling Model to Forecast the Incidence Rates of Two Notifiable Diseases in China: Dysentery and Gonorrhea

A GreyNGM(1,1,k)Self-Memory Coupling Prediction Model for Energy Consumption Prediction

Contact Info

Product

Resources

About