The accurate and efficient modeling of nuclear reactor transients is crucial for ensuring safe and optimal reactor operation.Traditional physics-based models,while valuable,can be computationally intensive and may not...The accurate and efficient modeling of nuclear reactor transients is crucial for ensuring safe and optimal reactor operation.Traditional physics-based models,while valuable,can be computationally intensive and may not fully capture the complexities of real-world reactor behavior.This paper introduces a novel hybrid digital twin-focused multi-stage deep learning framework that addresses these limitations,offering a faster and more robust solution for predicting the final steady-state power of reactor transients.By leveraging a combination of feed-forward neural networks with both classification and regression stages,and training on a unique dataset that integrates real-world measurements of reactor power and controls state from the Missouri University of Science and Technology Reactor(MSTR)with noise-enhanced simulated data,our approach achieves remarkable accuracy(96%classification,2.3%MAPE).The incorporation of simulated data with noise significantly improves the model’s generalization capabilities,mitigating the risk of overfitting.Designed as a digital twin supporting system,this framework integrates real-time,synchronized predictions of reactor state transitions,enabling dynamic operational monitoring and optimization.This innovative solution not only enables rapid and precise prediction of reactor behavior but also has the potential to revolutionize nuclear reactor operations,facilitating enhanced safety protocols,optimized performance,and streamlined making decision-processes.By aligning data-driven insights with the principles of digital twins,this work lays the groundwork for adaptable and scalable solutions for advanced reactors.展开更多
A recent review publication presented an extensive and comprehensive assessment of the phenomenological relations of Poisson’s ratios (PRs) to the behavior and responses of contemporary materials under specific loadi...A recent review publication presented an extensive and comprehensive assessment of the phenomenological relations of Poisson’s ratios (PRs) to the behavior and responses of contemporary materials under specific loading conditions. The present review and analysis paper is intended as a theoretical mechanics complement covering mathematical and physical modeling of a single original elastic and of six time and process (i.e. path and stress) dependent viscoelastic PR definitions as well as a seventh special path independent one. The implications and consequences of such models on material characterization are analyzed and summarized. Indeed, PRs based on experimentally obtained 2-D strains under distinct creep and/or relaxation processes exhibit radically different time responses for identical material specimen. These results confirm the PR’s implicit path dependence in addition to their separate intrinsic time reliance. Such non-uniqueness of viscoelastic PRs renders them unsuitable as universal material descriptors. Analytical formulations and experimental measurements also examine the physical impossibility of instantaneously achieving time independent loads or strains or their rates thus making certain PR definitions based on constant state variables, while mathematically valid, physically unrealistic and unachievable. A newly developed theoretical/experimental protocol for the determination of the time when loading patterns reach stead-state conditions based on strain accelerations demonstrates the capability to measure this time from experimental data. Due to the process dependent PRs, i.e. stress and stress history paths, the non-existence of a unique viscoelastic PR and of a universal elastic-viscoelastic correspondence principle or analogy (EVCP) in terms of PRs is demonstrated. Additionally and independently, the required double convolution integral construction of linear viscoelastic constitutive relations with the inclusion of PRs is cumbersome analytically and computationally needlessly highly CPU intensive. Furthermore, there is no theoretical fundamental hint as to what loading path is required to produce a unique universal viscoelastic PR definition necessary for formulating a PR based constitutive relation or an EVCP protocol. The analysis associated with an additional Class VII viscoelastic PR establishes it as a universal representation which is loading path and strain independent while still remaining time dependent. This Class PR can be the one used if it is desired to express constitutive relations in terms of PRs, subject to the caveat applying to all PR Classes regarding the CPU intensiveness in the time space due to triple product and double convolution integral constitutive relations. However, the use PRs is unnecessary since any set of material behavior can be uniquely and completely defined in terms of only moduli and/or compliances. The mathematical model of instantaneous initial loading paths, based on Heavi-side functions, is examined in detail and shown to lead to infinite velocities and accelerations. Additionally, even if non-instantaneous gradual loading functions are employed the resulting PRs are still load and load history dependent. Consequently, they represent specialized PR responses applicable and limited to those particular load and history combinations. Although the analyses contained herein are generalized to non-homogeneous linear viscoelastic materials, the main focus is on PR time and process dependence. The non-homogeneous material results and conclusions presented herein apply equally to homogeneous viscoelasticity and per se do not influence the results or conclusions of the analytical development regarding viscoelastic PRs. In short, these PR analyses apply to all linear viscoelastic material characterization.展开更多
In 2015,the U.S National Institute of Standards and Technology(NIST)funded the Center of Excellence for Risk-Based Community Resilience Planning(CoE),a fourteen university-based consortium of almost 100 col-laborators...In 2015,the U.S National Institute of Standards and Technology(NIST)funded the Center of Excellence for Risk-Based Community Resilience Planning(CoE),a fourteen university-based consortium of almost 100 col-laborators,including faculty,students,post-doctoral scholars,and NIST researchers.This paper highlights the scientific theory behind the state-of-the-art cloud platform being developed by the CoE-the Interdisciplinary Networked Community Resilience Modeling Environment(IN-CORE).IN-CORE enables communities,consul-tants,and researchers to set up complex interdependent models of an entire community consisting of people,businesses,social institutions,buildings,transportation networks,water networks,and electric power networks and to predict their performance and recovery to hazard scenario events,including uncertainty propagation through the chained models.The modeling environment includes a detailed building inventory,hazard scenario models,building and infrastructure damage(fragility)and recovery functions,social science data-driven house-hold and business models,and computable general equilibrium(CGE)models of local economies.An important aspect of IN-CORE is the characterization of uncertainty and its propagation throughout the chained models of the platform.Three illustrative examples of community testbeds are presented that look at hazard impacts and recovery on population,economics,physical services,and social services.An overview of the IN-CORE technology and scientific implementation is described with a focus on four key community stability areas(CSA)that encompass an array of community resilience metrics(CRM)and support community resilience informed decision-making.Each testbed within IN-CORE has been developed by a team of engineers,social scientists,urban planners,and economists.Community models,begin with a community description,i.e.,people,businesses,buildings,infras-tructure,and progresses to the damage and loss of functions caused by a hazard scenario,i.e.,a flood,tornado,hurricane,or earthquake.This process is accomplished through chaining of modular algorithms,as described.The baseline community characteristics and the hazard-induced damage sets are the initial conditions for the recovery models,which have been the least studied area of community resilience but arguably one of the most important.Communities can then test the effect of mitigation and/or policies and compare the effects of“what if”scenarios on physical,social,and economic metrics with the only requirement being that the change much be able to be numerically modeled in IN-CORE.展开更多
One of the major scientific challenges and societal concerns is to make informed decisions to ensure sustainable groundwater availability when facing deep uncertainties.A major computational requirement associated wit...One of the major scientific challenges and societal concerns is to make informed decisions to ensure sustainable groundwater availability when facing deep uncertainties.A major computational requirement associated with this is on-demand computing for risk analysis to support timely decision.This paper presents a scientific modeling service called‘ModflowOnAzure’which enables large-scale ensemble runs of groundwater flow models to be easily executed in parallel in the Windows Azure cloud.Several technical issues were addressed,including the conjunctive use of desktop tools in MATLAB to avoid license issues in the cloud,integration of Dropbox with Azure for improved usability and‘Drop-and-Compute,’and automated file exchanges between desktop and the cloud.Two scientific use cases are presented in this paper using this service with significant computational speedup.One case is from Arizona,where six plausible alternative conceptual models and a streamflow stochastic model are used to evaluate the impacts of different groundwater pumping scenarios.Another case is from Texas,where a global sensitivity analysis is performed on a regional groundwater availability model.Results of both cases show informed uncertainty analysis results that can be used to assist the groundwater planning and sustainability study.展开更多
The potential application of monolayer MS2(M?Mo,W)as thermoelectric material has been widely studied since the first report of successful fabrication.However,their performances are hindered by the considerable band ga...The potential application of monolayer MS2(M?Mo,W)as thermoelectric material has been widely studied since the first report of successful fabrication.However,their performances are hindered by the considerable band gap and the large lattice thermal conductivity in the pristine 2H phase.Recent discoveries of polymorphism in MS2s provide new opportunities for materials engineering.In this work,phonon and electron transport properties of both 2H and 1T0 phases were investigated by first-principle calculations.It is found that upon the phase transition from 2H to 1T0 in MS2,the electron transport is greatly enhanced,while the lattice thermal conductivity is reduced by several times.These features lead to a significant enhancement of power factor by one order of magnitude in MoS2 and by three times in WS2.Meanwhile,the figure of merit can reach up to 0.33 for 1T0eMoS2 and 0.68 for 1T0eWS2 at low temperature.These findings indicate that monolayer MS2 in the 1T0 phase can be promising materials for thermoelectric devices application.Meanwhile,this work demonstrates that phase engineering techniques can bring in one important control parameter in materials design.展开更多
Liquid crystal elastomer(LCE)is a type of soft active material that generates large and reversible spontaneous deformations upon temperature changes,facilitating various environmentally responsive smart applications.D...Liquid crystal elastomer(LCE)is a type of soft active material that generates large and reversible spontaneous deformations upon temperature changes,facilitating various environmentally responsive smart applications.Despite their success,most existing LCE metamaterials are designed in a forward fashion based on intuition and feature regular material patterns,which may hinder the reach of LCE’s full potential in producing complex and desired functionalities.Here,we develop a computational inverse design framework for discovering diverse sophisticated temperature-activated and-interactive nonlinear behaviors for LCE metamaterials in a fully controllable fashion.We generate intelligent LCE metamaterials with a wide range of switchable functionalities upon temperature changes.By sensing the environment,these metamaterials can realize maximized spontaneous area expansion/contraction,precisely programmable enclosed opening size change,and temperatureswitchable nonlinear stress–strain relations and deformation modes.The optimized unit cells feature irregular LCE patterns and form complex and highly nonlinear mechanisms.The inverse design computational framework,optimized material patterns,and revealed underlying mechanisms fundamentally advance the design capacity of LCE metamaterials,benefiting environment-aware and-adaptive smart materials.展开更多
Protein evolution proceeds by two distinct processes： 1） individual mutation and selection for adaptive mutations and 2） rearrangement of entire domains within proteins into novel combinations, producing new protei...Protein evolution proceeds by two distinct processes： 1） individual mutation and selection for adaptive mutations and 2） rearrangement of entire domains within proteins into novel combinations, producing new protein families that combine functional properties in ways that previously did not exist. Domain rearrangement poses a challenge to sequence alignment-based search methods, such as BLAST, in predicting homology since the methodology implicitly assumes that related proteins primarily differ from each other by individual mutations. Moreover, there is ample evidence that the evolutionary process has used （and continues to use） domains as building blocks, therefore, it seems fit to utilize computational, domain-based methods to reconstruct that process. A challenge and opportunity for computational biology is how to use knowledge of evolutionary domain recombination to characterize families of proteins whose evolutionary history includes such recombination, to discover novel proteins, and to infer protein-protein interactions. In this paper we review techniques and databases that exploit our growing knowledge of “horizontal” protein evolution, and suggest possible areas of future development. We illustrate the power of the domain-based methods and the possible directions of future development by a case history in progress aiming at facilitating a particular approach to understanding microbial pathogenicity.展开更多
基金supported by the U.S.Nuclear Regulatory Commission(NRC)in part by the National Science Foundation(NSF)under Grant No.OAC-1919789.
文摘The accurate and efficient modeling of nuclear reactor transients is crucial for ensuring safe and optimal reactor operation.Traditional physics-based models,while valuable,can be computationally intensive and may not fully capture the complexities of real-world reactor behavior.This paper introduces a novel hybrid digital twin-focused multi-stage deep learning framework that addresses these limitations,offering a faster and more robust solution for predicting the final steady-state power of reactor transients.By leveraging a combination of feed-forward neural networks with both classification and regression stages,and training on a unique dataset that integrates real-world measurements of reactor power and controls state from the Missouri University of Science and Technology Reactor(MSTR)with noise-enhanced simulated data,our approach achieves remarkable accuracy(96%classification,2.3%MAPE).The incorporation of simulated data with noise significantly improves the model’s generalization capabilities,mitigating the risk of overfitting.Designed as a digital twin supporting system,this framework integrates real-time,synchronized predictions of reactor state transitions,enabling dynamic operational monitoring and optimization.This innovative solution not only enables rapid and precise prediction of reactor behavior but also has the potential to revolutionize nuclear reactor operations,facilitating enhanced safety protocols,optimized performance,and streamlined making decision-processes.By aligning data-driven insights with the principles of digital twins,this work lays the groundwork for adaptable and scalable solutions for advanced reactors.
文摘A recent review publication presented an extensive and comprehensive assessment of the phenomenological relations of Poisson’s ratios (PRs) to the behavior and responses of contemporary materials under specific loading conditions. The present review and analysis paper is intended as a theoretical mechanics complement covering mathematical and physical modeling of a single original elastic and of six time and process (i.e. path and stress) dependent viscoelastic PR definitions as well as a seventh special path independent one. The implications and consequences of such models on material characterization are analyzed and summarized. Indeed, PRs based on experimentally obtained 2-D strains under distinct creep and/or relaxation processes exhibit radically different time responses for identical material specimen. These results confirm the PR’s implicit path dependence in addition to their separate intrinsic time reliance. Such non-uniqueness of viscoelastic PRs renders them unsuitable as universal material descriptors. Analytical formulations and experimental measurements also examine the physical impossibility of instantaneously achieving time independent loads or strains or their rates thus making certain PR definitions based on constant state variables, while mathematically valid, physically unrealistic and unachievable. A newly developed theoretical/experimental protocol for the determination of the time when loading patterns reach stead-state conditions based on strain accelerations demonstrates the capability to measure this time from experimental data. Due to the process dependent PRs, i.e. stress and stress history paths, the non-existence of a unique viscoelastic PR and of a universal elastic-viscoelastic correspondence principle or analogy (EVCP) in terms of PRs is demonstrated. Additionally and independently, the required double convolution integral construction of linear viscoelastic constitutive relations with the inclusion of PRs is cumbersome analytically and computationally needlessly highly CPU intensive. Furthermore, there is no theoretical fundamental hint as to what loading path is required to produce a unique universal viscoelastic PR definition necessary for formulating a PR based constitutive relation or an EVCP protocol. The analysis associated with an additional Class VII viscoelastic PR establishes it as a universal representation which is loading path and strain independent while still remaining time dependent. This Class PR can be the one used if it is desired to express constitutive relations in terms of PRs, subject to the caveat applying to all PR Classes regarding the CPU intensiveness in the time space due to triple product and double convolution integral constitutive relations. However, the use PRs is unnecessary since any set of material behavior can be uniquely and completely defined in terms of only moduli and/or compliances. The mathematical model of instantaneous initial loading paths, based on Heavi-side functions, is examined in detail and shown to lead to infinite velocities and accelerations. Additionally, even if non-instantaneous gradual loading functions are employed the resulting PRs are still load and load history dependent. Consequently, they represent specialized PR responses applicable and limited to those particular load and history combinations. Although the analyses contained herein are generalized to non-homogeneous linear viscoelastic materials, the main focus is on PR time and process dependence. The non-homogeneous material results and conclusions presented herein apply equally to homogeneous viscoelasticity and per se do not influence the results or conclusions of the analytical development regarding viscoelastic PRs. In short, these PR analyses apply to all linear viscoelastic material characterization.
基金The Center for Risk-Based Community Resilience Planning is a NIST-funded Center of Excellencethe Center is funded through a cooperative agreement between the U.S.National Institute of Standards and Tech-nology and Colorado State University(NIST Financial Assistance Award Numbers:70NANB15H044 and 70NANB20H008)。
文摘In 2015,the U.S National Institute of Standards and Technology(NIST)funded the Center of Excellence for Risk-Based Community Resilience Planning(CoE),a fourteen university-based consortium of almost 100 col-laborators,including faculty,students,post-doctoral scholars,and NIST researchers.This paper highlights the scientific theory behind the state-of-the-art cloud platform being developed by the CoE-the Interdisciplinary Networked Community Resilience Modeling Environment(IN-CORE).IN-CORE enables communities,consul-tants,and researchers to set up complex interdependent models of an entire community consisting of people,businesses,social institutions,buildings,transportation networks,water networks,and electric power networks and to predict their performance and recovery to hazard scenario events,including uncertainty propagation through the chained models.The modeling environment includes a detailed building inventory,hazard scenario models,building and infrastructure damage(fragility)and recovery functions,social science data-driven house-hold and business models,and computable general equilibrium(CGE)models of local economies.An important aspect of IN-CORE is the characterization of uncertainty and its propagation throughout the chained models of the platform.Three illustrative examples of community testbeds are presented that look at hazard impacts and recovery on population,economics,physical services,and social services.An overview of the IN-CORE technology and scientific implementation is described with a focus on four key community stability areas(CSA)that encompass an array of community resilience metrics(CRM)and support community resilience informed decision-making.Each testbed within IN-CORE has been developed by a team of engineers,social scientists,urban planners,and economists.Community models,begin with a community description,i.e.,people,businesses,buildings,infras-tructure,and progresses to the damage and loss of functions caused by a hazard scenario,i.e.,a flood,tornado,hurricane,or earthquake.This process is accomplished through chaining of modular algorithms,as described.The baseline community characteristics and the hazard-induced damage sets are the initial conditions for the recovery models,which have been the least studied area of community resilience but arguably one of the most important.Communities can then test the effect of mitigation and/or policies and compare the effects of“what if”scenarios on physical,social,and economic metrics with the only requirement being that the change much be able to be numerically modeled in IN-CORE.
文摘One of the major scientific challenges and societal concerns is to make informed decisions to ensure sustainable groundwater availability when facing deep uncertainties.A major computational requirement associated with this is on-demand computing for risk analysis to support timely decision.This paper presents a scientific modeling service called‘ModflowOnAzure’which enables large-scale ensemble runs of groundwater flow models to be easily executed in parallel in the Windows Azure cloud.Several technical issues were addressed,including the conjunctive use of desktop tools in MATLAB to avoid license issues in the cloud,integration of Dropbox with Azure for improved usability and‘Drop-and-Compute,’and automated file exchanges between desktop and the cloud.Two scientific use cases are presented in this paper using this service with significant computational speedup.One case is from Arizona,where six plausible alternative conceptual models and a streamflow stochastic model are used to evaluate the impacts of different groundwater pumping scenarios.Another case is from Texas,where a global sensitivity analysis is performed on a regional groundwater availability model.Results of both cases show informed uncertainty analysis results that can be used to assist the groundwater planning and sustainability study.
基金the Jiangsu provincial natural science funding Project(No.BK20160308)the NSF of Heilongjiang Province of China under Grants No.QC2015001.
文摘The potential application of monolayer MS2(M?Mo,W)as thermoelectric material has been widely studied since the first report of successful fabrication.However,their performances are hindered by the considerable band gap and the large lattice thermal conductivity in the pristine 2H phase.Recent discoveries of polymorphism in MS2s provide new opportunities for materials engineering.In this work,phonon and electron transport properties of both 2H and 1T0 phases were investigated by first-principle calculations.It is found that upon the phase transition from 2H to 1T0 in MS2,the electron transport is greatly enhanced,while the lattice thermal conductivity is reduced by several times.These features lead to a significant enhancement of power factor by one order of magnitude in MoS2 and by three times in WS2.Meanwhile,the figure of merit can reach up to 0.33 for 1T0eMoS2 and 0.68 for 1T0eWS2 at low temperature.These findings indicate that monolayer MS2 in the 1T0 phase can be promising materials for thermoelectric devices application.Meanwhile,this work demonstrates that phase engineering techniques can bring in one important control parameter in materials design.
基金Authors X.S.Z.and W.L.acknowledge the support from the U.S.National Science Foundation(NSF)CAREER Award CMMI-2047692 and NSF Award CMMI-2245251.
文摘Liquid crystal elastomer(LCE)is a type of soft active material that generates large and reversible spontaneous deformations upon temperature changes,facilitating various environmentally responsive smart applications.Despite their success,most existing LCE metamaterials are designed in a forward fashion based on intuition and feature regular material patterns,which may hinder the reach of LCE’s full potential in producing complex and desired functionalities.Here,we develop a computational inverse design framework for discovering diverse sophisticated temperature-activated and-interactive nonlinear behaviors for LCE metamaterials in a fully controllable fashion.We generate intelligent LCE metamaterials with a wide range of switchable functionalities upon temperature changes.By sensing the environment,these metamaterials can realize maximized spontaneous area expansion/contraction,precisely programmable enclosed opening size change,and temperatureswitchable nonlinear stress–strain relations and deformation modes.The optimized unit cells feature irregular LCE patterns and form complex and highly nonlinear mechanisms.The inverse design computational framework,optimized material patterns,and revealed underlying mechanisms fundamentally advance the design capacity of LCE metamaterials,benefiting environment-aware and-adaptive smart materials.
基金supported by NSF of USA under Grant Nos. 0835718 and 0235792NIH under Grant Nos. 5PN2EY016570-06 and5R01NS063405-02+2 种基金the Beckman Institute for Advanced Science and Technologythe National Center for Supercomputing Applicationsthe Renaissance Computing Institute
文摘Protein evolution proceeds by two distinct processes： 1） individual mutation and selection for adaptive mutations and 2） rearrangement of entire domains within proteins into novel combinations, producing new protein families that combine functional properties in ways that previously did not exist. Domain rearrangement poses a challenge to sequence alignment-based search methods, such as BLAST, in predicting homology since the methodology implicitly assumes that related proteins primarily differ from each other by individual mutations. Moreover, there is ample evidence that the evolutionary process has used （and continues to use） domains as building blocks, therefore, it seems fit to utilize computational, domain-based methods to reconstruct that process. A challenge and opportunity for computational biology is how to use knowledge of evolutionary domain recombination to characterize families of proteins whose evolutionary history includes such recombination, to discover novel proteins, and to infer protein-protein interactions. In this paper we review techniques and databases that exploit our growing knowledge of “horizontal” protein evolution, and suggest possible areas of future development. We illustrate the power of the domain-based methods and the possible directions of future development by a case history in progress aiming at facilitating a particular approach to understanding microbial pathogenicity.
