Based on the optical engine,the ignition characteristics,combustion process and soot emission characteristics of diesel under different 2-Methylfuran(MF) atmospheres were investigated by high-speed photography and in-...Based on the optical engine,the ignition characteristics,combustion process and soot emission characteristics of diesel under different 2-Methylfuran(MF) atmospheres were investigated by high-speed photography and in-cylinder combustion analysis technology.The results show that at the same main injection timing,the ignition time of reactivity controlled compression ignition(RCCI) combustion mode is earlier than pure diesel combustion,and the ignition point is concentrated near the nozzle.Diesel acts as a spark plug to ignite the mixture,but the flame develops slowly in the early stages and the pressure in the cylinder rises slowly.Compared with pure diesel,RCCI combustion model has smaller peak values of in-cylinder pressure and heat release rate,shorter ignition delay period,earlier combustion phase and shorter combustion duration.At main spray time at 6℃A BTDC and 12℃A BTDC,with the increase of MF premixing ratio from 0 to 0.75,the peak cylinder pressure decreased by 19.6% and 26% respectively.In addition,with the increase of the MF heat value ratio,the area of KL factor> 1.5 in the combustion chamber decreased and the space integral natural luminescence(SINL) peak value decreased by 48.37%,and the soot formation rate and yield decreased significantly.However,when the MF heat value ratio was too large(75% of the total calorific value),the ignition delay period increased,and misfire occurred at the main injection timing of 0℃A BTDC.The RCCI mode of MF/diesel dual fuel has better stability,and better control effect can be obtained at different main inj ection timing.展开更多
The longitudinal dispersion of the projectile in shooting tests of two-dimensional trajectory corrections fused with fixed canards is extremely large that it sometimes exceeds the correction ability of the correction ...The longitudinal dispersion of the projectile in shooting tests of two-dimensional trajectory corrections fused with fixed canards is extremely large that it sometimes exceeds the correction ability of the correction fuse actuator.The impact point easily deviates from the target,and thus the correction result cannot be readily evaluated.However,the cost of shooting tests is considerably high to conduct many tests for data collection.To address this issue,this study proposes an aiming method for shooting tests based on small sample size.The proposed method uses the Bootstrap method to expand the test data;repeatedly iterates and corrects the position of the simulated theoretical impact points through an improved compatibility test method;and dynamically adjusts the weight of the prior distribution of simulation results based on Kullback-Leibler divergence,which to some extent avoids the real data being"submerged"by the simulation data and achieves the fusion Bayesian estimation of the dispersion center.The experimental results show that when the simulation accuracy is sufficiently high,the proposed method yields a smaller mean-square deviation in estimating the dispersion center and higher shooting accuracy than those of the three comparison methods,which is more conducive to reflecting the effect of the control algorithm and facilitating test personnel to iterate their proposed structures and algorithms.;in addition,this study provides a knowledge base for further comprehensive studies in the future.展开更多
In the context of global mean square error concerning the number of random variables in the representation,the Karhunen–Loève(KL)expansion is the optimal series expansion method for random field discretization.T...In the context of global mean square error concerning the number of random variables in the representation,the Karhunen–Loève(KL)expansion is the optimal series expansion method for random field discretization.The computational efficiency and accuracy of the KL expansion are contingent upon the accurate resolution of the Fredholm integral eigenvalue problem(IEVP).The paper proposes an interpolation method based on different interpolation basis functions such as moving least squares(MLS),least squares(LS),and finite element method(FEM)to solve the IEVP.Compared with the Galerkin method based on finite element or Legendre polynomials,the main advantage of the interpolation method is that,in the calculation of eigenvalues and eigenfunctions in one-dimensional random fields,the integral matrix containing covariance function only requires a single integral,which is less than a two-folded integral by the Galerkin method.The effectiveness and computational efficiency of the proposed interpolation method are verified through various one-dimensional examples.Furthermore,based on theKL expansion and polynomial chaos expansion,the stochastic analysis of two-dimensional regular and irregular domains is conducted,and the basis function of the extended finite element method(XFEM)is introduced as the interpolation basis function in two-dimensional irregular domains to solve the IEVP.展开更多
A stochastic model was developed to simulate the flow in heterogeneous media subject to random boundary conditions. Approximate partial differential equations were derived based on the Karhunen-Loeve （KL） expansion ...A stochastic model was developed to simulate the flow in heterogeneous media subject to random boundary conditions. Approximate partial differential equations were derived based on the Karhunen-Loeve （KL） expansion and perturbation expansion. The effect of random boundary conditions on the two-dimensional flow was examined. It is shown that the proposed stochastic model is efficient to include the random boundary conditions. The random boundaries lead to the increase of head variance and velocity variance. The influence of the random boundary conditions on head uncertainty is exerted over the whole simulated region, while the randomness of the boundary conditions leads to the increase of the velocity variance in the vicinity of boundaries.展开更多
The estimation of model parameters is an important subject in engineering.In this area of work,the prevailing approach is to estimate or calculate these as deterministic parameters.In this study,we consider the model ...The estimation of model parameters is an important subject in engineering.In this area of work,the prevailing approach is to estimate or calculate these as deterministic parameters.In this study,we consider the model parameters from the perspective of random variables and describe the general form of the parameter distribution inference problem.Under this framework,we propose an ensemble Bayesian method by introducing Bayesian inference and the Markov chain Monte Carlo(MCMC)method.Experiments on a finite cylindrical reactor and a 2D IAEA benchmark problem show that the proposed method converges quickly and can estimate parameters effectively,even for several correlated parameters simultaneously.Our experiments include cases of engineering software calls,demonstrating that the method can be applied to engineering,such as nuclear reactor engineering.展开更多
A graph G is {K1,4, K1,4 ＋ e}-free if G contains no induced subgraph isomorphic to K1,4 or KI,a ＋ e In this paper, we show that G has a path which is either hamiltonian or of length at least 25（G） ＋ 2 if G is a c...A graph G is {K1,4, K1,4 ＋ e}-free if G contains no induced subgraph isomorphic to K1,4 or KI,a ＋ e In this paper, we show that G has a path which is either hamiltonian or of length at least 25（G） ＋ 2 if G is a connected {K1,4, K1,4 ＋ e}-free graph on at least 7 vertices.展开更多
The Internet of Things (IoT) and Cloud computing are gaining popularity due to their numerous advantages, including the efficient utilization of internetand computing resources. In recent years, many more IoT applicat...The Internet of Things (IoT) and Cloud computing are gaining popularity due to their numerous advantages, including the efficient utilization of internetand computing resources. In recent years, many more IoT applications have beenextensively used. For instance, Healthcare applications execute computations utilizing the user’s private data stored on cloud servers. However, the main obstaclesfaced by the extensive acceptance and usage of these emerging technologies aresecurity and privacy. Moreover, many healthcare data management system applications have emerged, offering solutions for distinct circumstances. But still, theexisting system has issues with specific security issues, privacy-preserving rate,information loss, etc. Hence, the overall system performance is reduced significantly. A unique blockchain-based technique is proposed to improve anonymityin terms of data access and data privacy to overcome the above-mentioned issues.Initially, the registration phase is done for the device and the user. After that, theGeo-Location and IP Address values collected during registration are convertedinto Hash values using Adler 32 hashing algorithm, and the private and publickeys are generated using the key generation centre. Then the authentication is performed through login. The user then submits a request to the blockchain server,which redirects the request to the associated IoT device in order to obtain thesensed IoT data. The detected data is anonymized in the device and stored inthe cloud server using the Linear Scaling based Rider Optimization algorithmwith integrated KL Anonymity (LSR-KLA) approach. After that, the Time-stamp-based Public and Private Key Schnorr Signature (TSPP-SS) mechanismis used to permit the authorized user to access the data, and the blockchain servertracks the entire transaction. The experimental findings showed that the proposedLSR-KLA and TSPP-SS technique provides better performance in terms of higherprivacy-preserving rate, lower information loss, execution time, and Central Pro展开更多
Modification of KL zeolite by chemical vapour deposition (CVD) of Si(OEt)4 and (CH3)3SiOSi(CH3)3 was used to study the influence of zeoltie support on the aromatization of n-hexane over Pt/KL catalyst. The pore size a...Modification of KL zeolite by chemical vapour deposition (CVD) of Si(OEt)4 and (CH3)3SiOSi(CH3)3 was used to study the influence of zeoltie support on the aromatization of n-hexane over Pt/KL catalyst. The pore size and pore volume of zeolite were decreased by CVD. The acidic hydroxyl groups remaining on the surface of KL zeolite were removed and thus the selectivity of non-acidic catalyzed aromatization reaction was improved. Whereas the activity of aromatiztion reaction was decreased due to the destruction of some of active sites in KL zeolite for 1,6-cyclization. Becuase of the coverage of the framework of zeolite by CVD of SiO2, Pt particles became more electron-excess.Therefore, the silica-coated catalysts were easier to be poisoned by sulphur.展开更多
This study presents a Bayesian methodology for de- signing step stress accelerated degradation testing （SSADT） and its application to batteries. First, the simulation-based Bayesian de- sign framework for SSADT is p...This study presents a Bayesian methodology for de- signing step stress accelerated degradation testing （SSADT） and its application to batteries. First, the simulation-based Bayesian de- sign framework for SSADT is presented. Then, by considering his- torical data, specific optimal objectives oriented Kullback-Leibler （KL） divergence is established. A numerical example is discussed to illustrate the design approach. It is assumed that the degrada- tion model （or process） follows a drift Brownian motion; the accele- ration model follows Arrhenius equation; and the corresponding parameters follow normal and Gamma prior distributions. Using the Markov Chain Monte Carlo （MCMC） method and WinBUGS software, the comparison shows that KL divergence is better than quadratic loss for optimal criteria. Further, the effect of simulation outiiers on the optimization plan is analyzed and the preferred sur- face fitting algorithm is chosen. At the end of the paper, a NASA lithium-ion battery dataset is used as historical information and the KL divergence oriented Bayesian design is compared with maxi- mum likelihood theory oriented locally optimal design. The results show that the proposed method can provide a much better testing plan for this engineering application.展开更多
In multi-user multiple input multiple output （MU-MIMO） systems, the outdated channel state information at the transmit- ter caused by channel time variation has been shown to greatly reduce the achievable ergodic su...In multi-user multiple input multiple output （MU-MIMO） systems, the outdated channel state information at the transmit- ter caused by channel time variation has been shown to greatly reduce the achievable ergodic sum capacity. A simple yet effec- tive solution to this problem is presented by designing a channel extrapolator relying on Karhunen-Loeve （KL） expansion of time- varying channels. In this scheme, channel estimation is done at the base station （BS） rather than at the user terminal （UT）, which thereby dispenses the channel parameters feedback from the UT to the BS. Moreover, the inherent channel correlation and the parsimonious parameterization properties of the KL expan- sion are respectively exploited to reduce the channel mismatch error and the computational complexity. Simulations show that the presented scheme outperforms conventional schemes in terms of both channel estimation mean square error （MSE） and ergodic capacity.展开更多
In view of the shortage of traditional life prediction methods for machine tools,such as low accuracy of life prediction and few samples basis attributes,a life prediction model of machine tools combined with machine ...In view of the shortage of traditional life prediction methods for machine tools,such as low accuracy of life prediction and few samples basis attributes,a life prediction model of machine tools combined with machine tool attributes is proposed.The life prediction model of machine tool adopts KL dispersion distribution theory,uses modal superposition method to carry out machine tool life analysis,calculates the theoretical life of machine tool,and then carries on the simulation,obtains the machine tool life prediction value.Compared with the traditional method of machine tool life prediction,the model is based on the application life fatigue damage model,which superimposes the service times and maintenance cycle of the machine tool,derives the influence factor of machine tool life,and obtains the linear relationship between the influence factor of machine tool life and the life of machine tool.The influence factor of machine tool life is introduced as the life prediction parameter of machine tool.The data transformation relationship of HT300 parts is constructed.The original part data is enhanced.The effective training set is obtained.The life prediction model of machine tool based on deep learning is completed.The quantitative analysis of machine tool life is carried out.The experiment of machine tool life prediction using training data set proves the validity of the model.Regression test was carried out on the training data set to reflect the robustness of the model.The prediction accuracy of the model is further verified by Weibull test.展开更多
基金the financial support of the Key Scientific Research Projects of Colleges and Universities in Henan Province (No.21B470002)。
文摘Based on the optical engine,the ignition characteristics,combustion process and soot emission characteristics of diesel under different 2-Methylfuran(MF) atmospheres were investigated by high-speed photography and in-cylinder combustion analysis technology.The results show that at the same main injection timing,the ignition time of reactivity controlled compression ignition(RCCI) combustion mode is earlier than pure diesel combustion,and the ignition point is concentrated near the nozzle.Diesel acts as a spark plug to ignite the mixture,but the flame develops slowly in the early stages and the pressure in the cylinder rises slowly.Compared with pure diesel,RCCI combustion model has smaller peak values of in-cylinder pressure and heat release rate,shorter ignition delay period,earlier combustion phase and shorter combustion duration.At main spray time at 6℃A BTDC and 12℃A BTDC,with the increase of MF premixing ratio from 0 to 0.75,the peak cylinder pressure decreased by 19.6% and 26% respectively.In addition,with the increase of the MF heat value ratio,the area of KL factor> 1.5 in the combustion chamber decreased and the space integral natural luminescence(SINL) peak value decreased by 48.37%,and the soot formation rate and yield decreased significantly.However,when the MF heat value ratio was too large(75% of the total calorific value),the ignition delay period increased,and misfire occurred at the main injection timing of 0℃A BTDC.The RCCI mode of MF/diesel dual fuel has better stability,and better control effect can be obtained at different main inj ection timing.
基金the National Natural Science Foundation of China(Grant No.61973033)Preliminary Research of Equipment(Grant No.9090102010305)for funding the experiments。
文摘The longitudinal dispersion of the projectile in shooting tests of two-dimensional trajectory corrections fused with fixed canards is extremely large that it sometimes exceeds the correction ability of the correction fuse actuator.The impact point easily deviates from the target,and thus the correction result cannot be readily evaluated.However,the cost of shooting tests is considerably high to conduct many tests for data collection.To address this issue,this study proposes an aiming method for shooting tests based on small sample size.The proposed method uses the Bootstrap method to expand the test data;repeatedly iterates and corrects the position of the simulated theoretical impact points through an improved compatibility test method;and dynamically adjusts the weight of the prior distribution of simulation results based on Kullback-Leibler divergence,which to some extent avoids the real data being"submerged"by the simulation data and achieves the fusion Bayesian estimation of the dispersion center.The experimental results show that when the simulation accuracy is sufficiently high,the proposed method yields a smaller mean-square deviation in estimating the dispersion center and higher shooting accuracy than those of the three comparison methods,which is more conducive to reflecting the effect of the control algorithm and facilitating test personnel to iterate their proposed structures and algorithms.;in addition,this study provides a knowledge base for further comprehensive studies in the future.
基金The authors gratefully acknowledge the support provided by the Postgraduate Research&Practice Program of Jiangsu Province(Grant No.KYCX18_0526)the Fundamental Research Funds for the Central Universities(Grant No.2018B682X14)Guangdong Basic and Applied Basic Research Foundation(No.2021A1515110807).
文摘In the context of global mean square error concerning the number of random variables in the representation,the Karhunen–Loève(KL)expansion is the optimal series expansion method for random field discretization.The computational efficiency and accuracy of the KL expansion are contingent upon the accurate resolution of the Fredholm integral eigenvalue problem(IEVP).The paper proposes an interpolation method based on different interpolation basis functions such as moving least squares(MLS),least squares(LS),and finite element method(FEM)to solve the IEVP.Compared with the Galerkin method based on finite element or Legendre polynomials,the main advantage of the interpolation method is that,in the calculation of eigenvalues and eigenfunctions in one-dimensional random fields,the integral matrix containing covariance function only requires a single integral,which is less than a two-folded integral by the Galerkin method.The effectiveness and computational efficiency of the proposed interpolation method are verified through various one-dimensional examples.Furthermore,based on theKL expansion and polynomial chaos expansion,the stochastic analysis of two-dimensional regular and irregular domains is conducted,and the basis function of the extended finite element method(XFEM)is introduced as the interpolation basis function in two-dimensional irregular domains to solve the IEVP.
基金the National Natural Science Foundation of China ( Grant Nos. 40672164, 50379039).
文摘A stochastic model was developed to simulate the flow in heterogeneous media subject to random boundary conditions. Approximate partial differential equations were derived based on the Karhunen-Loeve （KL） expansion and perturbation expansion. The effect of random boundary conditions on the two-dimensional flow was examined. It is shown that the proposed stochastic model is efficient to include the random boundary conditions. The random boundaries lead to the increase of head variance and velocity variance. The influence of the random boundary conditions on head uncertainty is exerted over the whole simulated region, while the randomness of the boundary conditions leads to the increase of the velocity variance in the vicinity of boundaries.
基金partially sponsored by the Natural Science Foundation of Shanghai(No.23ZR1429300)the Innovation Fund of CNNC(Lingchuang Fund)。
文摘The estimation of model parameters is an important subject in engineering.In this area of work,the prevailing approach is to estimate or calculate these as deterministic parameters.In this study,we consider the model parameters from the perspective of random variables and describe the general form of the parameter distribution inference problem.Under this framework,we propose an ensemble Bayesian method by introducing Bayesian inference and the Markov chain Monte Carlo(MCMC)method.Experiments on a finite cylindrical reactor and a 2D IAEA benchmark problem show that the proposed method converges quickly and can estimate parameters effectively,even for several correlated parameters simultaneously.Our experiments include cases of engineering software calls,demonstrating that the method can be applied to engineering,such as nuclear reactor engineering.
基金Supported by Scientific Research Program of the Higher Education Institution of Xinjiang (Grant No. 2011S30)Science Foundation of Xinjiang Normal University
文摘A graph G is {K1,4, K1,4 ＋ e}-free if G contains no induced subgraph isomorphic to K1,4 or KI,a ＋ e In this paper, we show that G has a path which is either hamiltonian or of length at least 25（G） ＋ 2 if G is a connected {K1,4, K1,4 ＋ e}-free graph on at least 7 vertices.
文摘The Internet of Things (IoT) and Cloud computing are gaining popularity due to their numerous advantages, including the efficient utilization of internetand computing resources. In recent years, many more IoT applications have beenextensively used. For instance, Healthcare applications execute computations utilizing the user’s private data stored on cloud servers. However, the main obstaclesfaced by the extensive acceptance and usage of these emerging technologies aresecurity and privacy. Moreover, many healthcare data management system applications have emerged, offering solutions for distinct circumstances. But still, theexisting system has issues with specific security issues, privacy-preserving rate,information loss, etc. Hence, the overall system performance is reduced significantly. A unique blockchain-based technique is proposed to improve anonymityin terms of data access and data privacy to overcome the above-mentioned issues.Initially, the registration phase is done for the device and the user. After that, theGeo-Location and IP Address values collected during registration are convertedinto Hash values using Adler 32 hashing algorithm, and the private and publickeys are generated using the key generation centre. Then the authentication is performed through login. The user then submits a request to the blockchain server,which redirects the request to the associated IoT device in order to obtain thesensed IoT data. The detected data is anonymized in the device and stored inthe cloud server using the Linear Scaling based Rider Optimization algorithmwith integrated KL Anonymity (LSR-KLA) approach. After that, the Time-stamp-based Public and Private Key Schnorr Signature (TSPP-SS) mechanismis used to permit the authorized user to access the data, and the blockchain servertracks the entire transaction. The experimental findings showed that the proposedLSR-KLA and TSPP-SS technique provides better performance in terms of higherprivacy-preserving rate, lower information loss, execution time, and Central Pro
文摘Modification of KL zeolite by chemical vapour deposition (CVD) of Si(OEt)4 and (CH3)3SiOSi(CH3)3 was used to study the influence of zeoltie support on the aromatization of n-hexane over Pt/KL catalyst. The pore size and pore volume of zeolite were decreased by CVD. The acidic hydroxyl groups remaining on the surface of KL zeolite were removed and thus the selectivity of non-acidic catalyzed aromatization reaction was improved. Whereas the activity of aromatiztion reaction was decreased due to the destruction of some of active sites in KL zeolite for 1,6-cyclization. Becuase of the coverage of the framework of zeolite by CVD of SiO2, Pt particles became more electron-excess.Therefore, the silica-coated catalysts were easier to be poisoned by sulphur.
基金supported by the National Natural Science Foundation of China(61104182)
文摘This study presents a Bayesian methodology for de- signing step stress accelerated degradation testing （SSADT） and its application to batteries. First, the simulation-based Bayesian de- sign framework for SSADT is presented. Then, by considering his- torical data, specific optimal objectives oriented Kullback-Leibler （KL） divergence is established. A numerical example is discussed to illustrate the design approach. It is assumed that the degrada- tion model （or process） follows a drift Brownian motion; the accele- ration model follows Arrhenius equation; and the corresponding parameters follow normal and Gamma prior distributions. Using the Markov Chain Monte Carlo （MCMC） method and WinBUGS software, the comparison shows that KL divergence is better than quadratic loss for optimal criteria. Further, the effect of simulation outiiers on the optimization plan is analyzed and the preferred sur- face fitting algorithm is chosen. At the end of the paper, a NASA lithium-ion battery dataset is used as historical information and the KL divergence oriented Bayesian design is compared with maxi- mum likelihood theory oriented locally optimal design. The results show that the proposed method can provide a much better testing plan for this engineering application.
基金supported by the National Natural Science Foundation of China (6096200161071088)+2 种基金the Natural Science Foundation of Fujian Province of China (2012J05119)the Fundamental Research Funds for the Central Universities (11QZR02)the Research Fund of Guangxi Key Lab of Wireless Wideband Communication & Signal Processing (21104)
文摘In multi-user multiple input multiple output （MU-MIMO） systems, the outdated channel state information at the transmit- ter caused by channel time variation has been shown to greatly reduce the achievable ergodic sum capacity. A simple yet effec- tive solution to this problem is presented by designing a channel extrapolator relying on Karhunen-Loeve （KL） expansion of time- varying channels. In this scheme, channel estimation is done at the base station （BS） rather than at the user terminal （UT）, which thereby dispenses the channel parameters feedback from the UT to the BS. Moreover, the inherent channel correlation and the parsimonious parameterization properties of the KL expan- sion are respectively exploited to reduce the channel mismatch error and the computational complexity. Simulations show that the presented scheme outperforms conventional schemes in terms of both channel estimation mean square error （MSE） and ergodic capacity.
文摘In view of the shortage of traditional life prediction methods for machine tools,such as low accuracy of life prediction and few samples basis attributes,a life prediction model of machine tools combined with machine tool attributes is proposed.The life prediction model of machine tool adopts KL dispersion distribution theory,uses modal superposition method to carry out machine tool life analysis,calculates the theoretical life of machine tool,and then carries on the simulation,obtains the machine tool life prediction value.Compared with the traditional method of machine tool life prediction,the model is based on the application life fatigue damage model,which superimposes the service times and maintenance cycle of the machine tool,derives the influence factor of machine tool life,and obtains the linear relationship between the influence factor of machine tool life and the life of machine tool.The influence factor of machine tool life is introduced as the life prediction parameter of machine tool.The data transformation relationship of HT300 parts is constructed.The original part data is enhanced.The effective training set is obtained.The life prediction model of machine tool based on deep learning is completed.The quantitative analysis of machine tool life is carried out.The experiment of machine tool life prediction using training data set proves the validity of the model.Regression test was carried out on the training data set to reflect the robustness of the model.The prediction accuracy of the model is further verified by Weibull test.
