This paper presents an analytical scheme for predicting the collapse strength of a flexible pipe, which considers the structural interaction between relevant layers. The analytical results were compared with a FEA mod...This paper presents an analytical scheme for predicting the collapse strength of a flexible pipe, which considers the structural interaction between relevant layers. The analytical results were compared with a FEA model and a number of test data, and showed reasonably good agreement. The theoretical analysis showed that the pressure armor layer enhanced the strength of the carcass against buckling, though the barrier weakened this effect. The collapse strength of pipe was influenced by many factors such as the inner radius of the pipe, the thickness of the layers and the mechanical properties of the materials. For example, an increase in the thickness of the barrier will increase contact pressure and in turn reduce the critical pressure.展开更多
On the basis of software testing tools we developed for programming languages, we firstly present a new control flowgraph model based on block. In view of the notion of block, we extend the traditional program\|based ...On the basis of software testing tools we developed for programming languages, we firstly present a new control flowgraph model based on block. In view of the notion of block, we extend the traditional program\|based software test data adequacy measurement criteria, and empirically analyze the subsume relation between these measurement criteria. Then, we define four test complexity metrics based on block. They are J\|complexity 0; J\|complexity 1; J\|complexity \{1+\}; J\|complexity 2. Finally, we show the Kiviat diagram that makes software quality visible.展开更多
In this paper we revise the moment theory for pattern recognition designed, to extract patterns from the noisy character datas, and develop unconstrained handwritten. Amazigh character recognition method based upon or...In this paper we revise the moment theory for pattern recognition designed, to extract patterns from the noisy character datas, and develop unconstrained handwritten. Amazigh character recognition method based upon orthogonal moments and neural networks classifier. We argue that, given the natural flexibility of neural network models and the extent of parallel processing that they allow, our algorithm is a step forward in character recognition. More importantly, following the approach proposed, we apply our system to two different databases, to examine the ability to recognize patterns under noise. We discover overwhelming support for different style of writing. Moreover, this basic conclusion appears to remain valid across different levels of smoothing and insensitive to the nuances of character patterns. Experiments tested the effect of set size on recognition accuracy which can reach 97.46%. The novelty of the proposed method is independence of size, slant, orientation, and translation. The performance of the proposed method is experimentally evaluated and the promising results and findings are presented. Our method is compared to K-NN （k-nearest neighbors） classifier algorithm; results show performances of our method.展开更多
The present work consists of dynamic detection of damages in reinforced concrete bridges by using a MMUM （mathematical model updating method） from incomplete test data. A well suited finite element model of a repair...The present work consists of dynamic detection of damages in reinforced concrete bridges by using a MMUM （mathematical model updating method） from incomplete test data. A well suited finite element model of a repaired bridge is carried out. The diagnosis enables us to locate and detect the damage in a reinforced concrete bridge. Thus, developments of analytical predictions have been checked by modal testing techniques. Besides, the FTCS （finite time centered space） scheme is developed to solve the set of equations which can easily handle finite element matrices of a bridge model. It is shown in this study that the method is applied to detect damages as well as existing cracks in real time of a repaired bridge. To check the efficiency of the method, the repaired bridge of OuedOumazer in Algeria has been selected. It is proven that identification methods have been able to detect the exact location of damage areas to be corrected avoiding the inaccuracy from the finite element model for the mass, stiffness and loading.展开更多
The Sandage-Loeb （SL） test is a promising method for probing dark energy because it measures the redshift drift in the spectra of Lyman-o： forest of distant quasars, covering the ＂redshift desert＂ of 2 ≤ z ≤ 5,...The Sandage-Loeb （SL） test is a promising method for probing dark energy because it measures the redshift drift in the spectra of Lyman-o： forest of distant quasars, covering the ＂redshift desert＂ of 2 ≤ z ≤ 5, which is not covered by existing cosmological observations. Therefore, it could provide an important supplement to current cosmological observations. In this paper, we explore the impact of SL test on the precision of cosmological constraints for two typical holographic dark energy models, i.e., the original holographic dark energy （HDE） model and the Ricci holographic dark energy （RDE） model. To avoid data inconsistency, we use the best-fit models based on current combined observational data as the fiducial models to simulate 30 mock SL test data. The results show that SL test can effectively break the existing strong degeneracy between the present-day matter density Ωm0 and the Hubble constant 1-1o in other cosmological observations. For the considered two typical dark energy models, not only can a 30-year observation of SL test improve the constraint precision of Ωm0 and h dramatically, but can also enhance the constraint precision of the model parameters c and α significantly.展开更多
With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbul...With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbulence in aerodynamics,our previous work built a data-driven model applicable to subsonic airfoil flows with different free stream conditions.The results calculated by the proposed model are encouraging.In this work,we aim to model the turbulence of transonic wing flows with fully connected deep neural networks,where there is less research at present.The proposed model is driven by two flow cases of the ONERA(Office National d'Etudes et de Recherches Aerospatiales)wing and coupled with the Navier-Stokes equation solver.Four subcritical and transonic benchmark cases of different wings are used to evaluate the model performance.The iteration process is stable,and final convergence is achieved.The proposed model can be used to surrogate the traditional Reynolds averaged Navier-Stokes turbulence model.Compared with the data calculated by the Spallart-Allmaras model,the results show that the proposed model can be well generalized to the test cases.The mean relative error of the drag coefficient at different sections is below 4%for each case.This work demonstrates that modeling turbulence by data-driven methods is feasible and that our modeling pattern is effective.展开更多
文摘This paper presents an analytical scheme for predicting the collapse strength of a flexible pipe, which considers the structural interaction between relevant layers. The analytical results were compared with a FEA model and a number of test data, and showed reasonably good agreement. The theoretical analysis showed that the pressure armor layer enhanced the strength of the carcass against buckling, though the barrier weakened this effect. The collapse strength of pipe was influenced by many factors such as the inner radius of the pipe, the thickness of the layers and the mechanical properties of the materials. For example, an increase in the thickness of the barrier will increase contact pressure and in turn reduce the critical pressure.
文摘On the basis of software testing tools we developed for programming languages, we firstly present a new control flowgraph model based on block. In view of the notion of block, we extend the traditional program\|based software test data adequacy measurement criteria, and empirically analyze the subsume relation between these measurement criteria. Then, we define four test complexity metrics based on block. They are J\|complexity 0; J\|complexity 1; J\|complexity \{1+\}; J\|complexity 2. Finally, we show the Kiviat diagram that makes software quality visible.
文摘In this paper we revise the moment theory for pattern recognition designed, to extract patterns from the noisy character datas, and develop unconstrained handwritten. Amazigh character recognition method based upon orthogonal moments and neural networks classifier. We argue that, given the natural flexibility of neural network models and the extent of parallel processing that they allow, our algorithm is a step forward in character recognition. More importantly, following the approach proposed, we apply our system to two different databases, to examine the ability to recognize patterns under noise. We discover overwhelming support for different style of writing. Moreover, this basic conclusion appears to remain valid across different levels of smoothing and insensitive to the nuances of character patterns. Experiments tested the effect of set size on recognition accuracy which can reach 97.46%. The novelty of the proposed method is independence of size, slant, orientation, and translation. The performance of the proposed method is experimentally evaluated and the promising results and findings are presented. Our method is compared to K-NN （k-nearest neighbors） classifier algorithm; results show performances of our method.
文摘The present work consists of dynamic detection of damages in reinforced concrete bridges by using a MMUM （mathematical model updating method） from incomplete test data. A well suited finite element model of a repaired bridge is carried out. The diagnosis enables us to locate and detect the damage in a reinforced concrete bridge. Thus, developments of analytical predictions have been checked by modal testing techniques. Besides, the FTCS （finite time centered space） scheme is developed to solve the set of equations which can easily handle finite element matrices of a bridge model. It is shown in this study that the method is applied to detect damages as well as existing cracks in real time of a repaired bridge. To check the efficiency of the method, the repaired bridge of OuedOumazer in Algeria has been selected. It is proven that identification methods have been able to detect the exact location of damage areas to be corrected avoiding the inaccuracy from the finite element model for the mass, stiffness and loading.
基金supported by the Top-Notch Young Talents Program of Chinathe National Natural Science Foundation of China(Grant Nos.11175042,and 11522540)+1 种基金the Provincial Department of Education of Liaoning(Grant No.L2012087)the Fundamental Research Funds for the Central Universities(Grant Nos.N140505002,N140506002,and N140504007)
文摘The Sandage-Loeb （SL） test is a promising method for probing dark energy because it measures the redshift drift in the spectra of Lyman-o： forest of distant quasars, covering the ＂redshift desert＂ of 2 ≤ z ≤ 5, which is not covered by existing cosmological observations. Therefore, it could provide an important supplement to current cosmological observations. In this paper, we explore the impact of SL test on the precision of cosmological constraints for two typical holographic dark energy models, i.e., the original holographic dark energy （HDE） model and the Ricci holographic dark energy （RDE） model. To avoid data inconsistency, we use the best-fit models based on current combined observational data as the fiducial models to simulate 30 mock SL test data. The results show that SL test can effectively break the existing strong degeneracy between the present-day matter density Ωm0 and the Hubble constant 1-1o in other cosmological observations. For the considered two typical dark energy models, not only can a 30-year observation of SL test improve the constraint precision of Ωm0 and h dramatically, but can also enhance the constraint precision of the model parameters c and α significantly.
基金supported by the National Natural Science Foundation of China(Grant Nos.92152301,and 91852115)the National Numerical Wind tunnel Project(Grand No.NNW2018-ZT1B01).
文摘With the rapid development of artificial intelligence techniques such as neural networks,data-driven machine learning methods are popular in improving and constructing turbulence models.For high Reynolds number turbulence in aerodynamics,our previous work built a data-driven model applicable to subsonic airfoil flows with different free stream conditions.The results calculated by the proposed model are encouraging.In this work,we aim to model the turbulence of transonic wing flows with fully connected deep neural networks,where there is less research at present.The proposed model is driven by two flow cases of the ONERA(Office National d'Etudes et de Recherches Aerospatiales)wing and coupled with the Navier-Stokes equation solver.Four subcritical and transonic benchmark cases of different wings are used to evaluate the model performance.The iteration process is stable,and final convergence is achieved.The proposed model can be used to surrogate the traditional Reynolds averaged Navier-Stokes turbulence model.Compared with the data calculated by the Spallart-Allmaras model,the results show that the proposed model can be well generalized to the test cases.The mean relative error of the drag coefficient at different sections is below 4%for each case.This work demonstrates that modeling turbulence by data-driven methods is feasible and that our modeling pattern is effective.
