The parametric surrogate models for partial differential equations(PDEs)are a necessary component for many applications in computational sciences,and the convolutional neural networks(CNNs)have proven to be an excelle...The parametric surrogate models for partial differential equations(PDEs)are a necessary component for many applications in computational sciences,and the convolutional neural networks(CNNs)have proven to be an excellent tool to generate these surrogates when parametric fields are present.CNNs are commonly trained on labeled data based on one-to-one sets of parameter-input and PDE-output fields.Recently,residual-based deep convolutional physics-informed neural network(DCPINN)solvers for parametric PDEs have been proposed to build surrogates without the need for labeled data.These allow for the generation of surrogates without an expensive offline-phase.In this work,we present an alternative formulation termed deep convolutional Ritz method(DCRM)as a parametric PDE solver.The approach is based on the minimization of energy functionals,which lowers the order of the differential operators compared to residualbased methods.Based on studies involving the Poisson equation with a spatially parameterized source term and boundary conditions,we find that CNNs trained on labeled data outperform DCPINNs in convergence speed and generalization abilities.The surrogates generated from the DCRM,however,converge significantly faster than their DCPINN counterparts,and prove to generalize faster and better than the surrogates obtained from both CNNs trained on labeled data and DCPINNs.This hints that the DCRM could make PDE solution surrogates trained without labeled data possibly.展开更多
Molecular dynamics(MD)has served as a powerful tool for designing materials with reduced reliance on laboratory testing.However,the use of MD directly to treat the deformation and failure of materials at the mesoscale...Molecular dynamics(MD)has served as a powerful tool for designing materials with reduced reliance on laboratory testing.However,the use of MD directly to treat the deformation and failure of materials at the mesoscale is still largely beyond reach.In this work,we propose a learning framework to extract a peridynamics model as a mesoscale continuum surrogate from MD simulated material fracture data sets.Firstly,we develop a novel coarse-graining method,to automatically handle the material fracture and its corresponding discontinuities in the MD displacement data sets.Inspired by the weighted essentially non-oscillatory(WENO)scheme,the key idea lies at an adaptive procedure to automatically choose the locally smoothest stencil,then reconstruct the coarse-grained material displacement field as the piecewise smooth solutions containing discontinuities.Then,based on the coarse-grained MD data,a two-phase optimizationbased learning approach is proposed to infer the optimal peridynamics model with damage criterion.In the first phase,we identify the optimal nonlocal kernel function from the data sets without material damage to capture the material stiffness properties.Then,in the second phase,the material damage criterion is learnt as a smoothed step function from the data with fractures.As a result,a peridynamics surrogate is obtained.As a continuum model,our peridynamics surrogate model can be employed in further prediction tasks with different grid resolutions from training,and hence allows for substantial reductions in computational cost compared with MD.We illustrate the efficacy of the proposed approach with several numerical tests for the dynamic crack propagation problem in a single-layer graphene.Our tests show that the proposed data-driven model is robust and generalizable,in the sense that it is capable of modeling the initialization and growth of fractures under discretization and loading settings that are different from the ones used during training.展开更多
We have developed a conceptual design of a 15-TW pulsed-power accelerator based on the linear-transformer-driver(LTD)architecture described by Stygar[W.A.Stygar et al.,Phys.Rev.ST Accel.Beams 18,110401(2015)].The driv...We have developed a conceptual design of a 15-TW pulsed-power accelerator based on the linear-transformer-driver(LTD)architecture described by Stygar[W.A.Stygar et al.,Phys.Rev.ST Accel.Beams 18,110401(2015)].The driver will allow multiple,high-energy-density experiments per day in a university environment and,at the same time,will enable both fundamental and integrated experiments that are scalable to larger facilities.In this design,many individual energy storage units(bricks),each composed of two capacitors and one switch,directly drive the target load without additional pulse compression.Ten LTD modules in parallel drive the load.Each module consists of 16 LTD cavities connected in series,where each cavity is powered by 22 bricks connected in parallel.This design stores up to 2.75 MJ and delivers up to 15 TW in 100 ns to the constant-impedance,water-insulated radial transmission lines.The transmission lines in turn deliver a peak current as high as 12.5 MA to the physics load.To maximize its experimental value and flexibility,the accelerator is coupled to a modern,multibeam laser facility(four beams with up to 5 kJ in 10 ns and one beam with up to 2.6 kJ in 100 ps or less)that can provide auxiliary heating of the physics load.The lasers also enable advanced diagnostic techniques such as X-ray Thomson scattering and multiframe and three-dimensional radiography.The coupled accelerator-laser facility will be the first of its kind and be capable of conducting unprecedented high-energy-densityephysics experiments.展开更多
Although hydrotalcite, or layered double hydroxides （LDHs）, is not a common mineral, it is an important material that can be easily synthesized in laboratory. In this study, structural evolvement and BET surface are...Although hydrotalcite, or layered double hydroxides （LDHs）, is not a common mineral, it is an important material that can be easily synthesized in laboratory. In this study, structural evolvement and BET surface area changes of heat treated Mg/AI-LDH is evaluated by XRD, TEM and N2-BET analyses. The results indicate that the magnesium-aluminum LDH with carbonate as interlayer anion, periclase-like oxides was formed at temperatures of 400-800℃. Meanwhile, 2-3 nanometer mesoporous were formed during decomposition of LDH. However, the heat treated samples still preserve the morphology of the original LDH plates. Periclase-like formed from LDH heat treatment may re-hydrolyze and recover the structure of LDH. However, crystallinity of the recovered LDH is lower than that of the original LDH. This heat treatment will result in formation of （Mg, Al）-oxide nano-crystals and nanopores among the nano-crystals. When heating temperature exceeds 1000, the periclase-like （Mg, Al）-oxide is transformed into a composite with periclase （MgO） and spinel phases. The periclase can be re-hydrolyzed and dissolved in HCl solution. After acid treatment, the sample with a high surface area is composed of spinel nano-crystals and nanopores among them. Our results will provide a new and economic way to synthesize mesoporous materials through pathways of phase transformation of precursor materials with different composition.展开更多
We use machine learning(ML)to infer stress and plastic flow rules using data from representative polycrystalline simulations.In particular,we use so-called deep(multilayer)neural networks(NN)to represent the two respo...We use machine learning(ML)to infer stress and plastic flow rules using data from representative polycrystalline simulations.In particular,we use so-called deep(multilayer)neural networks(NN)to represent the two response functions.The ML process does not choose appropriate inputs or outputs,rather it is trained on selected inputs and output.Likewise,its discrimination of features is crucially connected to the chosen inputoutput map.Hence,we draw upon classical constitutive modeling to select inputs and enforce well-accepted symmetries and other properties.In the context of the results of numerous simulations,we discuss the design,stability and accuracy of constitutive NNs trained on typical experimental data.With these developments,we enable rapid model building in real-time with experiments,and guide data collection and feature discovery.展开更多
Mechanical joints can have significant effects on the dynamics of assembled structures.However,the lack of efficacious predictive dynamic models tot joints hinders accurate prediction of their dynamic behavior.The goa...Mechanical joints can have significant effects on the dynamics of assembled structures.However,the lack of efficacious predictive dynamic models tot joints hinders accurate prediction of their dynamic behavior.The goal of our work is to develop physics-based,reduced-order,finite element models that are capable of replicating the effects of joints on vi- brating structures.The authors recently developed the so-called two-dimensional adjusted lwan beam element(2-D AIBE) to simulate the hysteretic behavior of bolted joints in 2-D beam structures.In this paper,2-D AIBE is extended to three-di- mensional cases by formulating a three-dimensional adjusted lwan beam element(3-D AIBE).hupulsive loading experi- ments are applied to a jointed frame structure and a beam structure containing the same joint.The frame is subjected to ex- citation out of plane so that the joint is under rotation and single axis bending.By assuming that the rotation in the joint is linear elastic,the parameters of the joint associated with bending in the flame are identified from acceleration responses of the jointed beam structure,using a multi-layer teed-torward neural network(MLFF).Numerieal simulation is then per- formed on the frame structure using the identified parameters.The good agreement between the simulated and experimental impulsive acceleration responses of the frame structure validates the efficacy of the presented 3-D AIBE,and indicates that the model can potentially be applied to more complex structural systems with joint parameters identified from a relatively simple structure.展开更多
Nonlinear frequency conversion is one of the most fundamental processes in nonlinear optics.It has a wide range of applications in our daily lives,including novel light sources,sensing,and information processing.It is...Nonlinear frequency conversion is one of the most fundamental processes in nonlinear optics.It has a wide range of applications in our daily lives,including novel light sources,sensing,and information processing.It is usually assumed that nonlinear frequency conversion requires large crystals that gradually accumulate a strong effect.However,the large size of nonlinear crystals is not compatible with the miniaturisation of modern photonic and optoelectronic systems.Therefore,shrinking the nonlinear structures down to the nanoscale,while keeping favourable conversion efficiencies,is of great importance for future photonics applications.In the last decade,researchers have studied the strategies for enhancing the nonlinear efficiencies at the nanoscale,e.g.by employing different nonlinear materials,resonant couplings and hybridization techniques.In this paper,we provide a compact review of the nanomaterials-based efforts,ranging from metal to dielectric and semiconductor nanostructures,including their relevant nanofabrication techniques.展开更多
This paper reviews the early development of design requirements for seismic events in USA early developing nuclear electric generating fleet. Notable safety studies,including WASH-1400,Sandia Siting Study and the NURE...This paper reviews the early development of design requirements for seismic events in USA early developing nuclear electric generating fleet. Notable safety studies,including WASH-1400,Sandia Siting Study and the NUREG1150 probabilistic risk study,are briefly reviewed in terms of their relevance to extreme accidents arising from seismic and other severe accident initiators. Specific characteristic about the nature of severe accidents in nuclear power plant (NPP) are reviewed along with present day state-of-art analysis methodologies (methods for estimation of leakages and consequences of releases (MELCOR) and MELCOR accident consequence code system (MACCS)) that are used to evaluate severe accidents and to optimize mitigative and protective actions against such accidents. It is the aim of this paper to make nuclear operating nations aware of the risks that accompany a much needed energy resource and to identify some of the tools,techniques and landmark safety studies that serve to make the technology safer and to maintain vigilance and adequate safety culture for the responsible management of this valuable but unforgiving technology.展开更多
The accidents at the Fukushima Daiichi nuclear power station stunned the world as the sequences played out over severals days and videos of hydrogen explosions were televised as they took place. The accidents all resu...The accidents at the Fukushima Daiichi nuclear power station stunned the world as the sequences played out over severals days and videos of hydrogen explosions were televised as they took place. The accidents all resulted in severe damage to the reactor cores and releases of radioactivity to the environment despite heroic measures had taken by the operating personnel. The following paper provides some background into the development of these accidents and their root causes,chief among them,the prolonged station blackout conditions that isolated the reactors from their ultimate heat sink - the ocean. The interpretations given in this paper are summarized from a recently completed report funded by the United States Department of Energy (USDOE).展开更多
To aid the United States Nuclear Regulatory Commission,Sandia National Laboratories (SNL) was contracted to investigate the seismic behavior of typical dry cask storage systems. Parametric evaluations characterized th...To aid the United States Nuclear Regulatory Commission,Sandia National Laboratories (SNL) was contracted to investigate the seismic behavior of typical dry cask storage systems. Parametric evaluations characterized the sensitivity of calculated cask response characteristics to input parameters. The parametric evaluation investigated two generic cask designs (cylindrical and rectangular),three different foundation types (soft soil,hard soil,and rock),and three different casks to pad coefficients of friction (0.2,0.55,0.8) for earthquakes with peak ground accelerations of 0.25g,0.6g,1.0g and 1.25g. A total of 1 165 analyses were completed,with regression analyses being performed on the resulting cask response data to determine relationships relating the mean (16 % and 84 % confidence intervals on the mean) to peak ground acceleration,peak ground velocity,and pseudo-spectral acceleration at 1 Hz and 5 % damping. In general,the cylindrical casks experienced significantly larger responses in comparison to the rectangular cask. The cylindrical cask experienced larger top of cask displacements,larger cask rotations (rocking),and more occurrences of cask toppling (the rectangular cask never toppled over). The cylindrical cask was also susceptible to rolling once rocking had been initiated,a behavior not observed in the rectangular cask. Cask response was not overly sensitive to foundation type,but was significantly dependent on the response spectrum employed.展开更多
The mechanical behavior of individual cells plays an important role in regulating various biological activities at the molecular and cellular levels.It can serve as a promising label-free marker of cells’physiologica...The mechanical behavior of individual cells plays an important role in regulating various biological activities at the molecular and cellular levels.It can serve as a promising label-free marker of cells’physiological states.In the past two decades,several techniques have been developed for understanding correlations between cellular mechanical changes and human diseases.However,numerous technical challenges remain with regard to realizing high-throughput,robust,and easy-to-perform measurements of single-cell mechanical properties.In this paper,we review the emerging tools for single-cell mechanical characterization that are provided by microfluidic technology.Different techniques are benchmarked by considering their advantages and limitations.Finally,the potential applications of microfluidic techniques based on cellular mechanical properties are discussed.展开更多
Black box functions, such as computer experiments, often have multiple optima over the input space of the objective function. While traditional optimization routines focus on finding a single best optimum, we sometime...Black box functions, such as computer experiments, often have multiple optima over the input space of the objective function. While traditional optimization routines focus on finding a single best optimum, we sometimes want to consider the relative merits of multiple optima. First we need a search algorithm that can identify multiple local optima. Then we consider that blindly choosing the global optimum may not always be best. In some cases, the global optimum may not be robust to small deviations in the inputs, which could lead to output values far from the optimum. In those cases, it would be better to choose a slightly less extreme optimum that allows for input deviation with small change in the output;such an optimum would be considered more robust. We use a Bayesian decision theoretic approach to develop a utility function for selecting among multiple optima.展开更多
Numerical algorithms for stiff stochastic differential equations are developed using lin-ear approximations of the fast diffusion processes,under the assumption of decoupling between fast and slow processes.Three nume...Numerical algorithms for stiff stochastic differential equations are developed using lin-ear approximations of the fast diffusion processes,under the assumption of decoupling between fast and slow processes.Three numerical schemes are proposed,all of which are based on the linearized formulation albeit with different degrees of approximation.The schemes are of comparable complexity to the classical explicit Euler-Maruyama scheme but can achieve better accuracy at larger time steps in stiff systems.Convergence analysis is conducted for one of the schemes,that shows it to have a strong convergence order of 1/2 and a weak convergence order of 1.Approximations arriving at the other two schemes are discussed.Numerical experiments are carried out to examine the convergence of the schemes proposed on model problems.展开更多
基金supported by the Laboratory Directed Research and Development Program at Sandia National Laboratories(No.218328)。
文摘The parametric surrogate models for partial differential equations(PDEs)are a necessary component for many applications in computational sciences,and the convolutional neural networks(CNNs)have proven to be an excellent tool to generate these surrogates when parametric fields are present.CNNs are commonly trained on labeled data based on one-to-one sets of parameter-input and PDE-output fields.Recently,residual-based deep convolutional physics-informed neural network(DCPINN)solvers for parametric PDEs have been proposed to build surrogates without the need for labeled data.These allow for the generation of surrogates without an expensive offline-phase.In this work,we present an alternative formulation termed deep convolutional Ritz method(DCRM)as a parametric PDE solver.The approach is based on the minimization of energy functionals,which lowers the order of the differential operators compared to residualbased methods.Based on studies involving the Poisson equation with a spatially parameterized source term and boundary conditions,we find that CNNs trained on labeled data outperform DCPINNs in convergence speed and generalization abilities.The surrogates generated from the DCRM,however,converge significantly faster than their DCPINN counterparts,and prove to generalize faster and better than the surrogates obtained from both CNNs trained on labeled data and DCPINNs.This hints that the DCRM could make PDE solution surrogates trained without labeled data possibly.
基金the projects support by the National Science Foundation(No.DMS-1753031)the Air Force Office of Scientific Research(No.FA9550-22-1-0197)+3 种基金partially supported by the National Science Foundation(No.2019035)the support of the Sandia National Laboratories(SNL)Laboratory-directed Research and Development Programthe U.S.Department of Energy(DOE)Office of Advanced Scientific Computing Research(ASCR)under the Collaboratory on Mathematics and Physics-Informed Learning Machines for Multiscale and Multiphysics Problems(PhILMs)project。
文摘Molecular dynamics(MD)has served as a powerful tool for designing materials with reduced reliance on laboratory testing.However,the use of MD directly to treat the deformation and failure of materials at the mesoscale is still largely beyond reach.In this work,we propose a learning framework to extract a peridynamics model as a mesoscale continuum surrogate from MD simulated material fracture data sets.Firstly,we develop a novel coarse-graining method,to automatically handle the material fracture and its corresponding discontinuities in the MD displacement data sets.Inspired by the weighted essentially non-oscillatory(WENO)scheme,the key idea lies at an adaptive procedure to automatically choose the locally smoothest stencil,then reconstruct the coarse-grained material displacement field as the piecewise smooth solutions containing discontinuities.Then,based on the coarse-grained MD data,a two-phase optimizationbased learning approach is proposed to infer the optimal peridynamics model with damage criterion.In the first phase,we identify the optimal nonlocal kernel function from the data sets without material damage to capture the material stiffness properties.Then,in the second phase,the material damage criterion is learnt as a smoothed step function from the data with fractures.As a result,a peridynamics surrogate is obtained.As a continuum model,our peridynamics surrogate model can be employed in further prediction tasks with different grid resolutions from training,and hence allows for substantial reductions in computational cost compared with MD.We illustrate the efficacy of the proposed approach with several numerical tests for the dynamic crack propagation problem in a single-layer graphene.Our tests show that the proposed data-driven model is robust and generalizable,in the sense that it is capable of modeling the initialization and growth of fractures under discretization and loading settings that are different from the ones used during training.
文摘We have developed a conceptual design of a 15-TW pulsed-power accelerator based on the linear-transformer-driver(LTD)architecture described by Stygar[W.A.Stygar et al.,Phys.Rev.ST Accel.Beams 18,110401(2015)].The driver will allow multiple,high-energy-density experiments per day in a university environment and,at the same time,will enable both fundamental and integrated experiments that are scalable to larger facilities.In this design,many individual energy storage units(bricks),each composed of two capacitors and one switch,directly drive the target load without additional pulse compression.Ten LTD modules in parallel drive the load.Each module consists of 16 LTD cavities connected in series,where each cavity is powered by 22 bricks connected in parallel.This design stores up to 2.75 MJ and delivers up to 15 TW in 100 ns to the constant-impedance,water-insulated radial transmission lines.The transmission lines in turn deliver a peak current as high as 12.5 MA to the physics load.To maximize its experimental value and flexibility,the accelerator is coupled to a modern,multibeam laser facility(four beams with up to 5 kJ in 10 ns and one beam with up to 2.6 kJ in 100 ps or less)that can provide auxiliary heating of the physics load.The lasers also enable advanced diagnostic techniques such as X-ray Thomson scattering and multiframe and three-dimensional radiography.The coupled accelerator-laser facility will be the first of its kind and be capable of conducting unprecedented high-energy-densityephysics experiments.
基金the National Natural Science Foundation of China （No. 40472026） the 0utstanding 0verseas Chinese Scholars Fund of Chinese Academy of Sciences （2003-1-7）.
文摘Although hydrotalcite, or layered double hydroxides （LDHs）, is not a common mineral, it is an important material that can be easily synthesized in laboratory. In this study, structural evolvement and BET surface area changes of heat treated Mg/AI-LDH is evaluated by XRD, TEM and N2-BET analyses. The results indicate that the magnesium-aluminum LDH with carbonate as interlayer anion, periclase-like oxides was formed at temperatures of 400-800℃. Meanwhile, 2-3 nanometer mesoporous were formed during decomposition of LDH. However, the heat treated samples still preserve the morphology of the original LDH plates. Periclase-like formed from LDH heat treatment may re-hydrolyze and recover the structure of LDH. However, crystallinity of the recovered LDH is lower than that of the original LDH. This heat treatment will result in formation of （Mg, Al）-oxide nano-crystals and nanopores among the nano-crystals. When heating temperature exceeds 1000, the periclase-like （Mg, Al）-oxide is transformed into a composite with periclase （MgO） and spinel phases. The periclase can be re-hydrolyzed and dissolved in HCl solution. After acid treatment, the sample with a high surface area is composed of spinel nano-crystals and nanopores among them. Our results will provide a new and economic way to synthesize mesoporous materials through pathways of phase transformation of precursor materials with different composition.
文摘We use machine learning(ML)to infer stress and plastic flow rules using data from representative polycrystalline simulations.In particular,we use so-called deep(multilayer)neural networks(NN)to represent the two response functions.The ML process does not choose appropriate inputs or outputs,rather it is trained on selected inputs and output.Likewise,its discrimination of features is crucially connected to the chosen inputoutput map.Hence,we draw upon classical constitutive modeling to select inputs and enforce well-accepted symmetries and other properties.In the context of the results of numerous simulations,we discuss the design,stability and accuracy of constitutive NNs trained on typical experimental data.With these developments,we enable rapid model building in real-time with experiments,and guide data collection and feature discovery.
文摘Mechanical joints can have significant effects on the dynamics of assembled structures.However,the lack of efficacious predictive dynamic models tot joints hinders accurate prediction of their dynamic behavior.The goal of our work is to develop physics-based,reduced-order,finite element models that are capable of replicating the effects of joints on vi- brating structures.The authors recently developed the so-called two-dimensional adjusted lwan beam element(2-D AIBE) to simulate the hysteretic behavior of bolted joints in 2-D beam structures.In this paper,2-D AIBE is extended to three-di- mensional cases by formulating a three-dimensional adjusted lwan beam element(3-D AIBE).hupulsive loading experi- ments are applied to a jointed frame structure and a beam structure containing the same joint.The frame is subjected to ex- citation out of plane so that the joint is under rotation and single axis bending.By assuming that the rotation in the joint is linear elastic,the parameters of the joint associated with bending in the flame are identified from acceleration responses of the jointed beam structure,using a multi-layer teed-torward neural network(MLFF).Numerieal simulation is then per- formed on the frame structure using the identified parameters.The good agreement between the simulated and experimental impulsive acceleration responses of the frame structure validates the efficacy of the presented 3-D AIBE,and indicates that the model can potentially be applied to more complex structural systems with joint parameters identified from a relatively simple structure.
文摘Nonlinear frequency conversion is one of the most fundamental processes in nonlinear optics.It has a wide range of applications in our daily lives,including novel light sources,sensing,and information processing.It is usually assumed that nonlinear frequency conversion requires large crystals that gradually accumulate a strong effect.However,the large size of nonlinear crystals is not compatible with the miniaturisation of modern photonic and optoelectronic systems.Therefore,shrinking the nonlinear structures down to the nanoscale,while keeping favourable conversion efficiencies,is of great importance for future photonics applications.In the last decade,researchers have studied the strategies for enhancing the nonlinear efficiencies at the nanoscale,e.g.by employing different nonlinear materials,resonant couplings and hybridization techniques.In this paper,we provide a compact review of the nanomaterials-based efforts,ranging from metal to dielectric and semiconductor nanostructures,including their relevant nanofabrication techniques.
文摘This paper reviews the early development of design requirements for seismic events in USA early developing nuclear electric generating fleet. Notable safety studies,including WASH-1400,Sandia Siting Study and the NUREG1150 probabilistic risk study,are briefly reviewed in terms of their relevance to extreme accidents arising from seismic and other severe accident initiators. Specific characteristic about the nature of severe accidents in nuclear power plant (NPP) are reviewed along with present day state-of-art analysis methodologies (methods for estimation of leakages and consequences of releases (MELCOR) and MELCOR accident consequence code system (MACCS)) that are used to evaluate severe accidents and to optimize mitigative and protective actions against such accidents. It is the aim of this paper to make nuclear operating nations aware of the risks that accompany a much needed energy resource and to identify some of the tools,techniques and landmark safety studies that serve to make the technology safer and to maintain vigilance and adequate safety culture for the responsible management of this valuable but unforgiving technology.
文摘The accidents at the Fukushima Daiichi nuclear power station stunned the world as the sequences played out over severals days and videos of hydrogen explosions were televised as they took place. The accidents all resulted in severe damage to the reactor cores and releases of radioactivity to the environment despite heroic measures had taken by the operating personnel. The following paper provides some background into the development of these accidents and their root causes,chief among them,the prolonged station blackout conditions that isolated the reactors from their ultimate heat sink - the ocean. The interpretations given in this paper are summarized from a recently completed report funded by the United States Department of Energy (USDOE).
文摘To aid the United States Nuclear Regulatory Commission,Sandia National Laboratories (SNL) was contracted to investigate the seismic behavior of typical dry cask storage systems. Parametric evaluations characterized the sensitivity of calculated cask response characteristics to input parameters. The parametric evaluation investigated two generic cask designs (cylindrical and rectangular),three different foundation types (soft soil,hard soil,and rock),and three different casks to pad coefficients of friction (0.2,0.55,0.8) for earthquakes with peak ground accelerations of 0.25g,0.6g,1.0g and 1.25g. A total of 1 165 analyses were completed,with regression analyses being performed on the resulting cask response data to determine relationships relating the mean (16 % and 84 % confidence intervals on the mean) to peak ground acceleration,peak ground velocity,and pseudo-spectral acceleration at 1 Hz and 5 % damping. In general,the cylindrical casks experienced significantly larger responses in comparison to the rectangular cask. The cylindrical cask experienced larger top of cask displacements,larger cask rotations (rocking),and more occurrences of cask toppling (the rectangular cask never toppled over). The cylindrical cask was also susceptible to rolling once rocking had been initiated,a behavior not observed in the rectangular cask. Cask response was not overly sensitive to foundation type,but was significantly dependent on the response spectrum employed.
基金This work is partially supported by the National Science Foundation under Grant Nos.1710831 and 2045169.
文摘The mechanical behavior of individual cells plays an important role in regulating various biological activities at the molecular and cellular levels.It can serve as a promising label-free marker of cells’physiological states.In the past two decades,several techniques have been developed for understanding correlations between cellular mechanical changes and human diseases.However,numerous technical challenges remain with regard to realizing high-throughput,robust,and easy-to-perform measurements of single-cell mechanical properties.In this paper,we review the emerging tools for single-cell mechanical characterization that are provided by microfluidic technology.Different techniques are benchmarked by considering their advantages and limitations.Finally,the potential applications of microfluidic techniques based on cellular mechanical properties are discussed.
文摘Black box functions, such as computer experiments, often have multiple optima over the input space of the objective function. While traditional optimization routines focus on finding a single best optimum, we sometimes want to consider the relative merits of multiple optima. First we need a search algorithm that can identify multiple local optima. Then we consider that blindly choosing the global optimum may not always be best. In some cases, the global optimum may not be robust to small deviations in the inputs, which could lead to output values far from the optimum. In those cases, it would be better to choose a slightly less extreme optimum that allows for input deviation with small change in the output;such an optimum would be considered more robust. We use a Bayesian decision theoretic approach to develop a utility function for selecting among multiple optima.
文摘Numerical algorithms for stiff stochastic differential equations are developed using lin-ear approximations of the fast diffusion processes,under the assumption of decoupling between fast and slow processes.Three numerical schemes are proposed,all of which are based on the linearized formulation albeit with different degrees of approximation.The schemes are of comparable complexity to the classical explicit Euler-Maruyama scheme but can achieve better accuracy at larger time steps in stiff systems.Convergence analysis is conducted for one of the schemes,that shows it to have a strong convergence order of 1/2 and a weak convergence order of 1.Approximations arriving at the other two schemes are discussed.Numerical experiments are carried out to examine the convergence of the schemes proposed on model problems.
