A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic response...A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.展开更多
This study aimed to explore traffic safety climate by quantifying driving conditions and driving behaviour.To achieve the objective,the random parameter structural equation model was proposed so that driver action and...This study aimed to explore traffic safety climate by quantifying driving conditions and driving behaviour.To achieve the objective,the random parameter structural equation model was proposed so that driver action and driving condition can address the safety climate by integrating crash features,vehicle profiles,roadway conditions and environment conditions.The geo-localized crash open data of Las Vegas metropolitan area were collected from 2014 to 2016,including 27 arterials with 16827 injury samples.By quantifying the driving conditions and driving actions,the random parameter structural equation model was built up with measurement variables and latent variables.Results revealed that the random parameter structural equation model can address traffic safety climate quantitatively,while driving conditions and driving actions were quantified and reflected by vehicles,road environment and crash features correspondingly.The findings provide potential insights for practitioners and policy makers to improve the driving environment and traffic safety culture.展开更多
The Brans-Dicke(BD)theory is the simplest Scalar-Tensor theory of gravity,which can be considered as a candidate of modified Einstein’s theory of general relativity.In this work,we forecast the constraints on BD theo...The Brans-Dicke(BD)theory is the simplest Scalar-Tensor theory of gravity,which can be considered as a candidate of modified Einstein’s theory of general relativity.In this work,we forecast the constraints on BD theory in the CSST galaxy clustering spectroscopic survey with a magnitude limit~23 AB mag for point-source 5σdetection.We generate mock data based on the zCOSMOS catalog and consider the observational and instrumental effects of the CSST spectroscopic survey.We predict galaxy power spectra in the BD theory from z=0 to 1.5,and the galaxy bias and other systematical parameters are also included.The Markov Chain Monte Carlo technique is employed to find the best-fits and probability distributions of the cosmological and systematical parameters.A BD parameterζis introduced,which satisfiesζ=In(1+(1/ω)).We find that the CSST spectroscopic galaxy clustering survey can give|ξ|<10^(-2),or equivalently|ω|>O(10^(2))and|■/G|<10^(-13),under the assumptionζ=0.These constraints are almost at the same order of magnitude compared to the joint constraints using the current cosmic microwave background,baryon acoustic oscillations and TypeⅠa supernova data,indicating that the CSST galaxy clustering spectroscopic survey would be powerful for constraining the BD theory and other modified gravity theories.展开更多
Negative Poisson's ratio(NPR) structure has outstanding performances in lightweight and energy absorption, and it can be widely applied in automotive industries. By combining the front anti-collision beam, crash b...Negative Poisson's ratio(NPR) structure has outstanding performances in lightweight and energy absorption, and it can be widely applied in automotive industries. By combining the front anti-collision beam, crash box and NPR structure, a novel NPR bumper system for improving the crashworthiness is first proposed in the work. The performances of the NPR bumper system are detailed studied by comparing to traditional bumper system and aluminum foam filled bumper system. To achieve the rapid design while considering perturbation induced by parameter uncertainties, a multi-objective robust design optimization method of the NPR bumper system is also proposed. The parametric model of the bumper system is constructed by combining the full parametric model of the traditional bumper system and the parametric model of the NPR structure. Optimal Latin hypercube sampling technique and dual response surface method are combined to construct the surrogate models. The multi-objective robust optimization results of the NPR bumper system are then obtained by applying the multi-objective particle swarm optimization algorithm and six sigma criteria. The results yielded from the optimizations indicate that the energy absorption capacity is improved significantly by the NPR bumper system and its performances are further optimized efficiently by the multi-objective robust design optimization method.展开更多
Machine learning and computational methods can accelerate materials discovery by accurately predicting material properties at low cost.Nevertheless,input data to algorithms and structure model parameters remains a key...Machine learning and computational methods can accelerate materials discovery by accurately predicting material properties at low cost.Nevertheless,input data to algorithms and structure model parameters remains a key obstacle.The limitations of conventional battery materials could be overcome by high-entropy materials,a unique class of special valuable materials.The knowledge of designing the crystal structure of high-entropy materials is advancing the design and fabrication of new materials for batteries and supercapacitors,even before chemical synthesis,through the use of learning algorithms and quantum computing.In this review,we first focus on quantum computing and the structure of high-entropy materials,especially high-entropy MXenes.We then discuss how to encode and decode the crystal structure of materials,which is a key factor in creating a database for high-entropy materials.We also discuss how to utilize deep learning algorithms for material discovery prior to synthesis,as well as how to employ these algorithms to identify high-entropy materials suitable for batteries and supercapacitors.Finally,we discuss the potential of new quantum computing and artificial intelligence approaches for determining the structure of high-entropy materials in the energy fields.展开更多
基金Supported by the National Natural Science Foundation of China（51079027）
文摘A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.
基金supported by National Natural Science Foundation of China(No.52072214).
文摘This study aimed to explore traffic safety climate by quantifying driving conditions and driving behaviour.To achieve the objective,the random parameter structural equation model was proposed so that driver action and driving condition can address the safety climate by integrating crash features,vehicle profiles,roadway conditions and environment conditions.The geo-localized crash open data of Las Vegas metropolitan area were collected from 2014 to 2016,including 27 arterials with 16827 injury samples.By quantifying the driving conditions and driving actions,the random parameter structural equation model was built up with measurement variables and latent variables.Results revealed that the random parameter structural equation model can address traffic safety climate quantitatively,while driving conditions and driving actions were quantified and reflected by vehicles,road environment and crash features correspondingly.The findings provide potential insights for practitioners and policy makers to improve the driving environment and traffic safety culture.
基金the support of MOST2018YFE0120800,2020SKA0110402,NSFC-11822305,NSFC11773031,NSFC-11633004CAS Interdisciplinary Innovation Team+6 种基金the Chinese Academy of Sciences(CAS)instrument grant ZDKYYQ20200008the CAS Strategic Priority Research Program XDA15020200National Natural Science Foundation of China(NSFC,Grant Nos.11773034 and 11633004)the Chinese Academy of Sciences(CAS)Strategic Priority Research Program XDA15020200the CAS Interdisciplinary Innovation Team(JCTD-2019-05)the support of NSFC(Grant Nos.11473044 and 11973047)the Chinese Academy of Science grants QYZDJ-SSW-SLH017 and XDB 23040100。
文摘The Brans-Dicke(BD)theory is the simplest Scalar-Tensor theory of gravity,which can be considered as a candidate of modified Einstein’s theory of general relativity.In this work,we forecast the constraints on BD theory in the CSST galaxy clustering spectroscopic survey with a magnitude limit~23 AB mag for point-source 5σdetection.We generate mock data based on the zCOSMOS catalog and consider the observational and instrumental effects of the CSST spectroscopic survey.We predict galaxy power spectra in the BD theory from z=0 to 1.5,and the galaxy bias and other systematical parameters are also included.The Markov Chain Monte Carlo technique is employed to find the best-fits and probability distributions of the cosmological and systematical parameters.A BD parameterζis introduced,which satisfiesζ=In(1+(1/ω)).We find that the CSST spectroscopic galaxy clustering survey can give|ξ|<10^(-2),or equivalently|ω|>O(10^(2))and|■/G|<10^(-13),under the assumptionζ=0.These constraints are almost at the same order of magnitude compared to the joint constraints using the current cosmic microwave background,baryon acoustic oscillations and TypeⅠa supernova data,indicating that the CSST galaxy clustering spectroscopic survey would be powerful for constraining the BD theory and other modified gravity theories.
基金supported by the National Natural Science Foundation of China(Grant Nos.51605219&51375007)the Natural Science Foundation of Jiangsu Province(Grant Nos.BK20160791&SBK2015022352)+1 种基金the Visiting Scholar Foundation of the State Key Lab of Mechanical Transmission in Chongqing University(Grant Nos.SKLMT-KFKT-201608,SKLMTKFKT-2014010&SKLMT-KFKT-201507)the Fundamental Research Funds for the Central Universities(Grant No.NE2016002)
文摘Negative Poisson's ratio(NPR) structure has outstanding performances in lightweight and energy absorption, and it can be widely applied in automotive industries. By combining the front anti-collision beam, crash box and NPR structure, a novel NPR bumper system for improving the crashworthiness is first proposed in the work. The performances of the NPR bumper system are detailed studied by comparing to traditional bumper system and aluminum foam filled bumper system. To achieve the rapid design while considering perturbation induced by parameter uncertainties, a multi-objective robust design optimization method of the NPR bumper system is also proposed. The parametric model of the bumper system is constructed by combining the full parametric model of the traditional bumper system and the parametric model of the NPR structure. Optimal Latin hypercube sampling technique and dual response surface method are combined to construct the surrogate models. The multi-objective robust optimization results of the NPR bumper system are then obtained by applying the multi-objective particle swarm optimization algorithm and six sigma criteria. The results yielded from the optimizations indicate that the energy absorption capacity is improved significantly by the NPR bumper system and its performances are further optimized efficiently by the multi-objective robust design optimization method.
基金supported by Khalifa University and a Natural Sciences and Engineering Research Council of Canada(NSERC)Discovery grant.This work is supported by NSERC Discovery grant.
文摘Machine learning and computational methods can accelerate materials discovery by accurately predicting material properties at low cost.Nevertheless,input data to algorithms and structure model parameters remains a key obstacle.The limitations of conventional battery materials could be overcome by high-entropy materials,a unique class of special valuable materials.The knowledge of designing the crystal structure of high-entropy materials is advancing the design and fabrication of new materials for batteries and supercapacitors,even before chemical synthesis,through the use of learning algorithms and quantum computing.In this review,we first focus on quantum computing and the structure of high-entropy materials,especially high-entropy MXenes.We then discuss how to encode and decode the crystal structure of materials,which is a key factor in creating a database for high-entropy materials.We also discuss how to utilize deep learning algorithms for material discovery prior to synthesis,as well as how to employ these algorithms to identify high-entropy materials suitable for batteries and supercapacitors.Finally,we discuss the potential of new quantum computing and artificial intelligence approaches for determining the structure of high-entropy materials in the energy fields.
