On-the-fly Doppler broadening of cross sections is important in Monte Carlo simulations,particularly in Monte Carlo neutronics-thermal hydraulics coupling simulations.Methods such as Target Motion Sampling(TMS)and win...On-the-fly Doppler broadening of cross sections is important in Monte Carlo simulations,particularly in Monte Carlo neutronics-thermal hydraulics coupling simulations.Methods such as Target Motion Sampling(TMS)and windowed multipole as well as a method based on regression models have been developed to solve this problem.However,these methods have limitations such as the need for a cross section in an ACE format at a given temperature or a limited application energy range.In this study,a new on-the-fly Doppler broadening method based on a Back Propagation(BP)neural network,called hybrid windowed networks(HWN),is proposed to resolve the resonance energy range.In the HWN method,the resolved resonance energy range is divided into windows to guarantee an even distribution of resonance peaks.BP networks with specially designed structures and training parameters are trained to evaluate the cross section at a base temperature and the broadening coefficient.The HWN method is implemented in the Reactor Monte Carlo(RMC)code,and the microscopic cross sections and macroscopic results are compared.The results show that the HWN method can reduce the memory requirement for cross-sectional data by approximately 65%;moreover,it can generate keff,power distribution,and energy spectrum results with acceptable accuracy and a limited increase in the calculation time.The feasibility and effectiveness of the proposed HWN method are thus demonstrated.展开更多
As temperature changes constantly in nuclear reactor operation, on-the-fly Doppler broadening methods are commonly adopted for generating nuclear cross sections at various temperatures in neutron transport simulation....As temperature changes constantly in nuclear reactor operation, on-the-fly Doppler broadening methods are commonly adopted for generating nuclear cross sections at various temperatures in neutron transport simulation. Among the existing methods, the widely used SIGMA1 approach is inefficient because it involves error function and Taylor series expansion. In this paper, we present a new on-the-fly Doppler broadening with optimal double-exponential formula based on SuperMC to improve efficiency with given accuracy. In this method, doubleexponential formula in 1/16 steps is used for broadening cross section at low energy, with both accuracy and efficiency. Meanwhile, the Gauss–Hermite quadrature of different orders is used for broadening cross section at resonance energy. The method can generate neutron cross section rapidly and precisely at the desired temperature.Typical nuclide cross sections and benchmarking tests are presented in detail.展开更多
Swarming magnetic micro/nanorobots hold great promise for biomedical applications,but at present suffer from inferior capabilities to perceive and respond to chemical signals in local microenvironments.Here we demonst...Swarming magnetic micro/nanorobots hold great promise for biomedical applications,but at present suffer from inferior capabilities to perceive and respond to chemical signals in local microenvironments.Here we demonstrate swarming magnetic photonic crystal microrobots(PC-bots)capable of sponta-neously performing on-the-fly visual pH detection and self regulated drug delivery by perceiving local pH changes.The magnetic PC-bots consist of pH-responsive hydrogel microspheres with encapsulated one-dimensional periodic assemblies of Fe3O4 nanoparticles.By programming extemnal rotating magnetic fields,they can self-organize into large swarms with much-enhanced collective velocity to actively find targets while shining bright“blinking”structural colors.When approaching the target with abnormal pH conditions(e.g.an ulcerated superficial tumor lesion),the PC-bots can visualize local pH changes on the fly via pH-responsive structural colors,and realize self-regulated release of the loaded drugs by recognizing local pH.This work facilita tes the develop-ment of intelligent micro/nanorobots for active“motile-targeting”tumor diag-nosis and treatment.展开更多
Micro/nanorobots can propel and navigate in many hard-to-reach biological environments,and thus may bring revolutionary changes to biomedical research and applications.However,current MNRs lack the capability to colle...Micro/nanorobots can propel and navigate in many hard-to-reach biological environments,and thus may bring revolutionary changes to biomedical research and applications.However,current MNRs lack the capability to collectively perceive and report physicochemical changes in unknown microenvironments.Here we propose to develop swarming responsive photonic nanorobots that can map local physicochemical conditions on the fly and further guide localized photothermal treatment.The RPNRs consist of a photonic nanochain of periodically-assembled magnetic Fe_(3)O_(4)nanoparticles encapsulated in a responsive hydrogel shell,and show multiple integrated functions,including energetic magnetically-driven swarming motions,bright stimuli-responsive structural colors,and photothermal conversion.Thus,they can actively navigate in complex environments utilizing their controllable swarming motions,then visualize unknown targets(e.g.,tumor lesion)by collectively mapping out local abnormal physicochemical conditions(e.g.,pH,temperature,or glucose concentra-tion)via their responsive structural colors,and further guide external light irradiation to initiate localized photothermal treatment.This work facilitates the development of intelligent motile nanosensors and versatile multifunctional nanotheranostics for cancer and inflam-matory diseases.展开更多
In explicit-state model checking, system proper- ties are typically expressed in linear temporal logic （LTL）, and translated into a BUchi automaton （BA） to be checked. In order to improve performance of the conver...In explicit-state model checking, system proper- ties are typically expressed in linear temporal logic （LTL）, and translated into a BUchi automaton （BA） to be checked. In order to improve performance of the conversion algo- rithm, some model checkers involve the intermediate au- tomata, such as a generalized Btichi automaton （GBA）. The de-generalization is a translation from a GBA to a BA. In this paper, we present a conversion algorithm to translate an LTL formula to a BA directly. A labeling, acceptance degree, is presented to record acceptance conditions sat- isfied in each state and transition. Acceptance degree is a set of U-subformulae or F-subformulae of the given LTL formula. According to the acceptance degree, on-the-fly de- generalization algorithm, which is different from the standard de-generalization algorithm, is conceived and implemented. On-the-fly de-generalization algorithm is carried out during the expansion of the given LTL formula. It is performed in the case of the given LTL formula contains U-subformulae and F-subformulae, that is, the on-the-fly de-generalization algorithm is performed as required. In order to get a more deterministic BA, the shannon expansion is used recursively during expanding LTL formulae. Ordered binary decision diagrams are used to represent the BA and simplify LTL formulae. We compare the conversion algorithm presented in this paper to previous works, and show that it is more efficient for five families LTL formulae in common use and four setsof random formulae generated by LBTT （an LTL-to-BUchi translator testbench）.展开更多
基金supported by the Science Challenge Project(No.TZ2018001)the National Natural Science Foundation of China(Nos.11775126,11545013,11775127)+1 种基金Young Elite Scientists Sponsorship Program by CAST(No.2016QNRC001)Tsinghua University Initiative Scientific Research Program。
文摘On-the-fly Doppler broadening of cross sections is important in Monte Carlo simulations,particularly in Monte Carlo neutronics-thermal hydraulics coupling simulations.Methods such as Target Motion Sampling(TMS)and windowed multipole as well as a method based on regression models have been developed to solve this problem.However,these methods have limitations such as the need for a cross section in an ACE format at a given temperature or a limited application energy range.In this study,a new on-the-fly Doppler broadening method based on a Back Propagation(BP)neural network,called hybrid windowed networks(HWN),is proposed to resolve the resonance energy range.In the HWN method,the resolved resonance energy range is divided into windows to guarantee an even distribution of resonance peaks.BP networks with specially designed structures and training parameters are trained to evaluate the cross section at a base temperature and the broadening coefficient.The HWN method is implemented in the Reactor Monte Carlo(RMC)code,and the microscopic cross sections and macroscopic results are compared.The results show that the HWN method can reduce the memory requirement for cross-sectional data by approximately 65%;moreover,it can generate keff,power distribution,and energy spectrum results with acceptable accuracy and a limited increase in the calculation time.The feasibility and effectiveness of the proposed HWN method are thus demonstrated.
基金supported by the Strategic Priority Science and Technology Program of the Chinese Academy of Sciences(No.XDA03040000)the Innovation Foundation of the Chinese Academy of Sciences(No.CXJJ-16Q231)+7 种基金the National Natural Science Foundation of China(NSFC)(Nos.11305205,11305203,11405204and 11605233)the National Magnetic Confinement Fusion Science Program of China(No.2014GB112001)the Special Program for Informatization of the Chinese Academy of Sciences(No.XXH12504-1-09)the Anhui Provincial Special project for High Technology Industrythe Special Project of Youth Innovation Promotion Association of Chinese Academy of Sciencesthe Industrialization Fundthe Open Funds of Engineering Research Center of Nuclear Technology Application of Ministry of Education(No.HJSJYB2011-11)Jiang Xi young science foundation project(No.GJJ150558)
文摘As temperature changes constantly in nuclear reactor operation, on-the-fly Doppler broadening methods are commonly adopted for generating nuclear cross sections at various temperatures in neutron transport simulation. Among the existing methods, the widely used SIGMA1 approach is inefficient because it involves error function and Taylor series expansion. In this paper, we present a new on-the-fly Doppler broadening with optimal double-exponential formula based on SuperMC to improve efficiency with given accuracy. In this method, doubleexponential formula in 1/16 steps is used for broadening cross section at low energy, with both accuracy and efficiency. Meanwhile, the Gauss–Hermite quadrature of different orders is used for broadening cross section at resonance energy. The method can generate neutron cross section rapidly and precisely at the desired temperature.Typical nuclide cross sections and benchmarking tests are presented in detail.
基金National Key Research and Development Program,Grant/Award Numbers:2021YFA1201400,2022YF B4701700National Natural Science Foundation of China,Grant/Award Numbers:21875175,52073222,52175009+3 种基金Interdisciplinary Research Foundation of HIT,Grant/Award Number:1R20211219Natural Science Foundation of Chonging,Grant/Award Number:CSTB2022NSCQ-MSX0507Natural Science Foundation of Heilongian Province,Grant/Award Number.YQ2022E022Central University Basic Research Fund of China,Grant/Award Number:2022IVA201。
文摘Swarming magnetic micro/nanorobots hold great promise for biomedical applications,but at present suffer from inferior capabilities to perceive and respond to chemical signals in local microenvironments.Here we demonstrate swarming magnetic photonic crystal microrobots(PC-bots)capable of sponta-neously performing on-the-fly visual pH detection and self regulated drug delivery by perceiving local pH changes.The magnetic PC-bots consist of pH-responsive hydrogel microspheres with encapsulated one-dimensional periodic assemblies of Fe3O4 nanoparticles.By programming extemnal rotating magnetic fields,they can self-organize into large swarms with much-enhanced collective velocity to actively find targets while shining bright“blinking”structural colors.When approaching the target with abnormal pH conditions(e.g.an ulcerated superficial tumor lesion),the PC-bots can visualize local pH changes on the fly via pH-responsive structural colors,and realize self-regulated release of the loaded drugs by recognizing local pH.This work facilita tes the develop-ment of intelligent micro/nanorobots for active“motile-targeting”tumor diag-nosis and treatment.
基金supported by the National Key Research and Development Project(No.2021YFA1201400)National Natural Science Foundation of China(Nos.52073222,51573144 and 21474078)the Fundamental Research Funds for the Central Universities(WUT:2021IVA118 and 2022IVA201).
文摘Micro/nanorobots can propel and navigate in many hard-to-reach biological environments,and thus may bring revolutionary changes to biomedical research and applications.However,current MNRs lack the capability to collectively perceive and report physicochemical changes in unknown microenvironments.Here we propose to develop swarming responsive photonic nanorobots that can map local physicochemical conditions on the fly and further guide localized photothermal treatment.The RPNRs consist of a photonic nanochain of periodically-assembled magnetic Fe_(3)O_(4)nanoparticles encapsulated in a responsive hydrogel shell,and show multiple integrated functions,including energetic magnetically-driven swarming motions,bright stimuli-responsive structural colors,and photothermal conversion.Thus,they can actively navigate in complex environments utilizing their controllable swarming motions,then visualize unknown targets(e.g.,tumor lesion)by collectively mapping out local abnormal physicochemical conditions(e.g.,pH,temperature,or glucose concentra-tion)via their responsive structural colors,and further guide external light irradiation to initiate localized photothermal treatment.This work facilitates the development of intelligent motile nanosensors and versatile multifunctional nanotheranostics for cancer and inflam-matory diseases.
文摘In explicit-state model checking, system proper- ties are typically expressed in linear temporal logic （LTL）, and translated into a BUchi automaton （BA） to be checked. In order to improve performance of the conversion algo- rithm, some model checkers involve the intermediate au- tomata, such as a generalized Btichi automaton （GBA）. The de-generalization is a translation from a GBA to a BA. In this paper, we present a conversion algorithm to translate an LTL formula to a BA directly. A labeling, acceptance degree, is presented to record acceptance conditions sat- isfied in each state and transition. Acceptance degree is a set of U-subformulae or F-subformulae of the given LTL formula. According to the acceptance degree, on-the-fly de- generalization algorithm, which is different from the standard de-generalization algorithm, is conceived and implemented. On-the-fly de-generalization algorithm is carried out during the expansion of the given LTL formula. It is performed in the case of the given LTL formula contains U-subformulae and F-subformulae, that is, the on-the-fly de-generalization algorithm is performed as required. In order to get a more deterministic BA, the shannon expansion is used recursively during expanding LTL formulae. Ordered binary decision diagrams are used to represent the BA and simplify LTL formulae. We compare the conversion algorithm presented in this paper to previous works, and show that it is more efficient for five families LTL formulae in common use and four setsof random formulae generated by LBTT （an LTL-to-BUchi translator testbench）.
