The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformatio...The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformation behaviors of SMAs,the concepts in classical plasticity are employed in the existing constitutive models,and a series of complex mathematical equations are involved.Such complexity brings inconvenience for the construction,implementation,and application of the constitutive models.To overcome these shortcomings,a data-driven constitutive model of SMAs is developed in this work based on the artificial neural network(ANN).In the proposed model,the components of the strain tensor in principal space,ambient temperature,and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features,and the components of the stress tensor in principal space are set as the output.The proposed ANN-based constitutive model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine.The stress-strain responses of SMA material under various loading paths and at different ambient temperatures are used to train the ANN model,which is generated from the existing constitutive model(numerical experiments).To validate the capability of the proposed model,the predicted stress-strain responses of SMA material,and the global and local responses of two typical SMA structures are compared with the corresponding numerical experiments.This work demonstrates a good potential to obtain the constitutive model of SMAs by pure data and avoid the need for vast stores of knowledge for the construction of constitutive models.展开更多
Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict...Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict key performance indicators(PIs)of pavement,namely the international roughness index(IRI)and rutting depth(RD).Subsequently,we propose a comprehensive performance indicator for the pavement quality index(PQI),which leverages the highway performance assessment standard method,entropy weight method,and fuzzy comprehensive evaluation method.This indicator can evaluate the overall performance condition of the pavement.The data used for the model development and analysis are extracted from tests on two full-scale accelerated test tracks,called MnRoad and RIOHTrack.Six variables are used as predictors,including temperature,precipitation,total traffic volume,asphalt surface layer thickness,pavement age,and maintenance condition.Furthermore,wavelet denoising is performed to analyze the impact of missing or abnormal data on the LSTM model accuracy.In comparison to a traditional autoregressive integrated moving average(ARIMAX)model,the proposed LSTM model performs better in terms of PI prediction and resiliency to noise.Finally,the overall prediction accuracy of our proposed performance indicator PQI is 93.8%.展开更多
The multi-scale modeling combined with the cohesive zone model(CZM)and the molecular dynamics(MD)method were preformed to simulate the crack propagation in NiTi shape memory alloys(SMAs).The metallographic microscope ...The multi-scale modeling combined with the cohesive zone model(CZM)and the molecular dynamics(MD)method were preformed to simulate the crack propagation in NiTi shape memory alloys(SMAs).The metallographic microscope and image processing technology were employed to achieve a quantitative grain size distribution of NiTi alloys so as to provide experimental data for molecular dynamics modeling at the atomic scale.Considering the size effect of molecular dynamics model on material properties,a reasonable modeling size was provided by taking into account three characteristic dimensions from the perspective of macro,meso,and micro scales according to the Buckinghamπtheorem.Then,the corresponding MD simulation on deformation and fracture behavior was investigated to derive a parameterized traction-separation(T-S)law,and then it was embedded into cohesive elements of finite element software.Thus,the crack propagation behavior in NiTi alloys was reproduced by the finite element method(FEM).The experimental results show that the predicted initiation fracture toughness is in good agreement with experimental data.In addition,it is found that the dynamics initiation fracture toughness increases with decreasing grain size and increasing loading velocity.展开更多
A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a force...A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.展开更多
In view of the fact that the atmospheric motion is an irreversible process, a memory function which can recall the observation data in the past is introduced, moreover, a special concept of self-memorization of the at...In view of the fact that the atmospheric motion is an irreversible process, a memory function which can recall the observation data in the past is introduced, moreover, a special concept of self-memorization of the atmospheric motion is proposed, and a so-called self-memorization equation of the atmospheric motion has been derived. Based on the self-memorization principle, a numerical model for decadal forecast is established by means of the thermodynamic equation and the precipitation equation. The verification scores of the hindcasts of the model in the period from 1 to 12 years are much higher than that of monthly weather forecasts at present.展开更多
The recovery force or recovery strain is an important indicator of NiTi-based shape memory alloy devices. However, the restoring force or recoverable strain is partially restrained due to an interaction between reorie...The recovery force or recovery strain is an important indicator of NiTi-based shape memory alloy devices. However, the restoring force or recoverable strain is partially restrained due to an interaction between reorientation and reorientation-induced plasticity. Therefore, a macroscopic multi-mechanism constitutive model was constructed to describe the degeneration of shape memory effect based on the phase diagram. The residual strain after cooling consists of reorientation strain and reorientation-induced plastic strain. An internal variable, i.e., the detwinned stress, and its evolution equation were introduced into the transformation kinetics equation to describe the nonlinear hardening characteristics induced by the combined reorien- ration and detwinning mechanisms during mechanical loading. Finally, the proposed model was numerically implemented to simulate the experiments of shape memory effect at different peak strains. Comparisons between the experimental and simulated results show that the proposed model can reasonably describe the degeneration of shape memory effect.展开更多
A macroscopic based multi-mechanism constitutive model is constructed in the framework of irreversible thermodynamics to describe the degeneration of shape memory effect occurring in the thermo-mechanical cyclic defor...A macroscopic based multi-mechanism constitutive model is constructed in the framework of irreversible thermodynamics to describe the degeneration of shape memory effect occurring in the thermo-mechanical cyclic deformation of NiTi shape memory alloys (SMAs). Three phases, austenite A, twinned martensite and detwinned martensite , as well as the phase transitions occurring between each pair of phases (, , , , and are considered in the proposed model. Meanwhile, two kinds of inelastic deformation mechanisms, martensite transformation-induced plasticity and reorientation-induced plasticity, are used to explain the degeneration of shape memory effects of NiTi SMAs. The evolution equations of internal variables are proposed by attributing the degeneration of shape memory effect to the interaction between the three phases (A, , and and plastic deformation. Finally, the capability of the proposed model is verified by comparing the predictions with the experimental results of NiTi SMAs. It is shown that the degeneration of shape memory effect and its dependence on the loading level can be reasonably described by the proposed model.展开更多
A constitutive model is developed for the transformation, reorientation and plastic deformation of shape memory alloys （SMAs）. It is based on the concept that an SMA is a mixture composed of austenite and martensite...A constitutive model is developed for the transformation, reorientation and plastic deformation of shape memory alloys （SMAs）. It is based on the concept that an SMA is a mixture composed of austenite and martensite, the volume fraction of each phase is transformable with the change of applied thermal-mechanical loading, and the constitutive behavior of the SMA is the combination of the individual behavior of its two phases. The deformation of the martensite is separated into elastic, thermal, reorientation and plastic parts, and that of the austenite is separated into elastic, thermal and plastic parts. Making use of the Tanaka＇s transformation rule modified by taking into account the effect of plastic deformation, the constitutive model of the SMA is obtained. The ferroelasticity, pseudoelastieity and shape memory effect of SMA Au-47.5 at.%Cd, and the pseudoelasticity and shape memory effect as well as plastic deformation and its effect of an NiTi SMA, are analyzed and compared with experimental results.展开更多
Based on the micromechanical method and thermodynamic theory,a constitutive model for the macroscopic mechanical behavior of porous NiTi shape memory alloy is presented.The hydrostatic stress is considered for porous ...Based on the micromechanical method and thermodynamic theory,a constitutive model for the macroscopic mechanical behavior of porous NiTi shape memory alloy is presented.The hydrostatic stress is considered for porous NiTi according to the transformation function of dense NiTi.The present model takes account of the tensile-compressive asymmetry of NiTi,and can degenerate to model dense material.Numerical calculations,which only need material parameters of dense NiTi,are conducted to investigate the nonlinear and hysteretic strain of porous NiTi,and the predicted results are in good agreement with the corresponding experiments.展开更多
The objective of this paper is to model the size-dependent thermo-mechanical behaviors of a shape memory polymer (SMP) microbeam.Size-dependent constitutive equations,which can capture the size effect of the SMP,are p...The objective of this paper is to model the size-dependent thermo-mechanical behaviors of a shape memory polymer (SMP) microbeam.Size-dependent constitutive equations,which can capture the size effect of the SMP,are proposed based on the modified couple stress theory (MCST).The deformation energy expression of the SMP microbeam is obtained by employing the proposed size-dependent constitutive equation and Bernoulli-Euler beam theory.An SMP microbeam model,which includes the formulations of deflection,strain,curvature,stress and couple stress,is developed by using the principle of minimum potential energy and the separation of variables together.The sizedependent thermo-mechanical and shape memory behaviors of the SMP microbeam and the influence of the Poisson ratio are numerically investigated according to the developed SMP microbeam model.Results show that the size effects of the SMP microbeam are significant when the dimensionless height is small enough.However,they are too slight to be necessarily considered when the dimensionless height is large enough.The bending flexibility and stress level of the SMP microbeam rise with the increasing dimensionless height,while the couple stress level declines with the increasing dimensionless height.The larger the dimensionless height is,the more obvious the viscous property and shape memory effect of the SMP microbeam are.The Poisson ratio has obvious influence on the size-dependent behaviors of the SMP microbeam.The paper provides a theoretical basis and a quantitatively analyzing tool for the design and analysis of SMP micro-structures in the field of biological medicine,microelectronic devices and micro-electro-mechanical system (MEMS) self-assembling.展开更多
The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning mode...The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.展开更多
In order to propel the development of metal magnetic memory （MMM） technique in fatigue damage detection, the Jiles-Atherton model （J-A model） was modified to describe MMM mechanism in elastic stress stage. A serie...In order to propel the development of metal magnetic memory （MMM） technique in fatigue damage detection, the Jiles-Atherton model （J-A model） was modified to describe MMM mechanism in elastic stress stage. A series of rotating bending fatigue experiments were conducted to study the stress-magnetization relationship and verify the correctness of modified J-A model. In MMM detection, the magnetization of material irreversibly approaches to the local equilibrium state Mo instead of global equilibrium state M^n under cyclic stress, and the M0-a curves are loops around the Mar,-a curve. The modified J-A model is constructed by replacing M~ in J-A model with M0, and it can describe the magnetomechanical effect well at low external magnetic field. In the rotating bending fatigue experiments, the MMM field distribution in normal direction around cylinder specimen is similar to the stress distribution, and the calculation result of model coincides with experiment result after some necessary modifications. The MMM field variation with time at a certain point in fatigue process is divided into three stages with the variation of stable stress-stain hysteresis loop, and the calculation results of model can explain not only the three stages of MMM field changes, but also the different change laws when the applied magnetic field and initial magnetic field are different. The MMM field distribution in normal direction along specimen axis reflects stress concentration effect at artificial defect, and the magnetic signal fluctuates around the defect at late fatigue stage. The calculation results coincide with the initial MMM principle and can explain signal fluctuates around the defect. The modified J-A model can explain experiment results well, and it is fit for MMM field characterization.展开更多
Metal magnetic memory（MMM） testing has been widely used to detect welded joints. However, load levels, environmental magnetic field, and measurement noises make the MMM data dispersive and bring difficulty to quanti...Metal magnetic memory（MMM） testing has been widely used to detect welded joints. However, load levels, environmental magnetic field, and measurement noises make the MMM data dispersive and bring difficulty to quantitative evaluation. In order to promote the development of quantitative MMM reliability assessment, a new MMM model is presented for welded joints. Steel Q235 welded specimens are tested along the longitudinal and horizontal lines by TSC-2M-8 instrument in the tensile fatigue experiments. The X-ray testing is carried out synchronously to verify the MMM results. It is found that MMM testing can detect the hidden crack earlier than X-ray testing. Moreover, the MMM gradient vector sum K_（vs） is sensitive to the damage degree, especially at early and hidden damage stages. Considering the dispersion of MMM data, the K_（vs） statistical law is investigated, which shows that K_（vs） obeys Gaussian distribution. So K_（vs） is the suitable MMM parameter to establish reliability model of welded joints. At last, the original quantitative MMM reliability model is first presented based on the improved stress strength interference theory. It is shown that the reliability degree R gradually decreases with the decreasing of the residual life ratio T, and the maximal error between prediction reliability degree R_1 and verification reliability degree R_2 is 9.15%. This presented method provides a novel tool of reliability testing and evaluating in practical engineering for welded joints.展开更多
This article examines some general atmospheric circulation and climate models in the context of the notion of “memory”. Two kinds of memories are defined： statistical memory and deterministic memory. The former is ...This article examines some general atmospheric circulation and climate models in the context of the notion of “memory”. Two kinds of memories are defined： statistical memory and deterministic memory. The former is defined through the autocorrelation characteristic of the process if it is random （chaotic）, while for the latter, a special memory function is introduced. Three of the numerous existing models are selected as examples. For each of the models, asymptotic （at t →∞） expressions are derived. In this way, the transients are filtered out and that which remains concerns the final behaviour of the models.展开更多
A macroscopic phenomenological constitutive model considering the martensite transformation and its reverse is constructed in this work to describe the thermo-magneto- mechanically coupled deformation of polycrystalli...A macroscopic phenomenological constitutive model considering the martensite transformation and its reverse is constructed in this work to describe the thermo-magneto- mechanically coupled deformation of polycrystalline magnetic shape memory alloys （MSMAs） by referring to the existing experimental results. The proposed model is established in the frame- work of thermodynamics by introducing internal state variables. The driving force of martensite transformation, the internal heat production and the thermodynamic constraints on constitutive equations are obtained by Clausius dissipative inequality and constructed Gibbs free energy. The spatiotemporal evolution equation of temperature is deduced from the first law of thermodynam- ics. The demagnetization effect occurring in the process of magnetization is also addressed. The proposed model is verified by comparing the predictions with the corresponding experiments. It is concluded that the thermo-magneto-mechanically coupled deformation of MSMAs including the magnetostrietive and magnetocaloric effects at various temperatures can be reasonably described by the proposed model, and the magnetocaloric effect can be significantly improved over a wide range of temperature by introducing an additional applied stress.展开更多
A thermoviscoelastic modeling approach is developed to predict the recovery behaviors of the thermally activated amorphous shape memory polymers(SMPs)based on the generalized finite deformation viscoelasticity theory....A thermoviscoelastic modeling approach is developed to predict the recovery behaviors of the thermally activated amorphous shape memory polymers(SMPs)based on the generalized finite deformation viscoelasticity theory.In this paper,a series of moduli and relaxation times of the generalized Maxwell model is estimated from the stress relaxation master curve by using the nonlinear regression(NLREG)method.Assuming that the amorphous SMPs are approximately incompressible isotropic elastomers in the rubbery state,the hyperelastic response of the materials is well modeled with a hyperelastic model in Ogden form.In addition,the Williams-Landel-Ferry(WLF)equation is used to describe the horizontal shift factor obtained with time-temperature superposition principle(TTSP).The finite element simulations show good agreement with the experimental thermomechanical behaviors.Moreover,the possibility of developing a temperature-responsive intravascular stent with the SMP studied here is investigated in terms of its thermomechanical property.Therefore,it can be concluded that the model has good prediction capabilities for the recovery behaviors of amorphous SMPs.展开更多
In this study,we propose a novel and simple exact semi-analytical model for superelastic Shape Memory Alloy(SMA)wire reinforced composites subjected to bending loads.In order to study the mechanical response of the co...In this study,we propose a novel and simple exact semi-analytical model for superelastic Shape Memory Alloy(SMA)wire reinforced composites subjected to bending loads.In order to study the mechanical response of the composite during loading/unloading,a Representative Volume Element(RVE)is extracted to examine the bending response of the composite.Analytical moment–curvature,and shear force-shear strain relations are derived based on a 3-Dimensional(3 D)thermomechanical SMA model and Timoshenko beam theory.The composite Simpson’s rule is adopted to numerically solve the exact analytical moment–curvature and shear force-shear strain relationships.Results including the moment–curvature response,axial stress distribution along the vertical and longitudinal directions,martensite volume fraction,and the tip deflection are reported and validated against 3 D finite element simulations.The influence of temperature,martensite volume fraction distribution,and matrix stiffness on the mechanical performance of the composite is also investigated.In particular,the composite is found to behave superelastically under certain conditions of temperature,SMA volume fraction,and elastic stiffness of the matrix.Such behavior is advantageous in applications requiring large recoverable strains or high energy dissipation density.展开更多
The drawing or rolling process endows polycrystal shape memory alloy with a crys- tallographic texture, which can result in macroscopic anisotropy. The main purpose of this work is to develop a constitutive model to p...The drawing or rolling process endows polycrystal shape memory alloy with a crys- tallographic texture, which can result in macroscopic anisotropy. The main purpose of this work is to develop a constitutive model to predict the thermomechanical behavior of shape memory alloy sheets, which accounts for the crystallographic texture. The total macroscopic strain is decom- posed into elastic strain and macro-transformation strain under isothermal condition. Considering the transformation strain in local grains and the orientation distribution function of crystallo- graphic texture, the macro-transformation strain and the effective elastic modulus of textured polycrystal shape memory alloy are developed by using tensor expressions. The kinetic equation is established to calculate the volume fraction of the martensite transformation under given stress. Furthermore, the Hill＇s quadratic model is developed for anisotropic transformation hardening of textured SMA sheets. All the calculation results are in good agreement with experimental data, which show that the present model can accurately describe the macro-anisotropic behaviors of textured shape memory alloy sheets.展开更多
The hysteresis characteristic is the major deficiency in the positioning control of magnetic shape memory alloy actuator. A Prandtl-Ishlinskii model was developed to characterize the hysteresis of magnetic shape memor...The hysteresis characteristic is the major deficiency in the positioning control of magnetic shape memory alloy actuator. A Prandtl-Ishlinskii model was developed to characterize the hysteresis of magnetic shape memory alloy actuator. Based on the proposed Prandtl-Ishlinskii model, the inverse Prandtl-Ishlinskii model was established as a feedforward controller to compensate the hysteresis of the magnetic shape memory alloy actuator. For further improving of the positioning precision of the magnetic shape memory alloy actuator, a hybrid control method with hysteresis nonlinear model in feedforward loop was proposed. The control method is separated into two parts: a feedforward loop with inverse Prandtl-Ishlinskii model and a feedback loop with neural network controller. To validate the validity of the proposed control method, a series of simulations and experiments were researched. The simulation and experimental results demonstrate that the maximum error rate of open loop controller based on inverse PI model is 1.72%, the maximum error rate of the hybrid controller based on inverse PI model is 1.37%.展开更多
Rust is a system-level programming language that provides thread and memory safety guarantee through a suite of static compiler checking rules and prevents segmentation errors.However,since compiler checking is too st...Rust is a system-level programming language that provides thread and memory safety guarantee through a suite of static compiler checking rules and prevents segmentation errors.However,since compiler checking is too strict to confine Rust's programmability,the developers prefer to use the keyword"unsafe"to bypass compiler checking,through which the caller could interact with OS directly.Unfortunately,the code block with"unsafe"would easily lead to some serious bugs such as memory safety violation,race condition and so on.In this paper,to verify memory and concurrency safety of Rust programs,we present RSMC(Safety Model Checker for Rust),a tool based on Smack to detect concurrency bugs and memory safety errors in Rust programs,in which we combine concurrency primitives model checking and memory boundary model checking.RSMC,with an assertion generator,can automatically insert assertions and requires no programmer annotations to verify Rust programs.We evaluate RSMC on two categories of Rust programs,and the result shows that RSMC can effectively find concurrency bugs and memory safety errors in vulnerable Rust programs,which include unsafe code.展开更多
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.12322203).
文摘The constitutive models of shape memory alloys(SMAs)play an important role in facilitating the widespread application of such types of alloys in various engineering fields.However,to accurately describe the deformation behaviors of SMAs,the concepts in classical plasticity are employed in the existing constitutive models,and a series of complex mathematical equations are involved.Such complexity brings inconvenience for the construction,implementation,and application of the constitutive models.To overcome these shortcomings,a data-driven constitutive model of SMAs is developed in this work based on the artificial neural network(ANN).In the proposed model,the components of the strain tensor in principal space,ambient temperature,and the maximum equivalent strain in the deformation history from the initial state to the current loading state are chosen as the input features,and the components of the stress tensor in principal space are set as the output.The proposed ANN-based constitutive model is implemented into the finite element program ABAQUS by deriving its consistent tangent modulus and writing a user-defined material subroutine.The stress-strain responses of SMA material under various loading paths and at different ambient temperatures are used to train the ANN model,which is generated from the existing constitutive model(numerical experiments).To validate the capability of the proposed model,the predicted stress-strain responses of SMA material,and the global and local responses of two typical SMA structures are compared with the corresponding numerical experiments.This work demonstrates a good potential to obtain the constitutive model of SMAs by pure data and avoid the need for vast stores of knowledge for the construction of constitutive models.
基金supported by the National Key Research and Development Program of China(No.2021YFB2600300).
文摘Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict key performance indicators(PIs)of pavement,namely the international roughness index(IRI)and rutting depth(RD).Subsequently,we propose a comprehensive performance indicator for the pavement quality index(PQI),which leverages the highway performance assessment standard method,entropy weight method,and fuzzy comprehensive evaluation method.This indicator can evaluate the overall performance condition of the pavement.The data used for the model development and analysis are extracted from tests on two full-scale accelerated test tracks,called MnRoad and RIOHTrack.Six variables are used as predictors,including temperature,precipitation,total traffic volume,asphalt surface layer thickness,pavement age,and maintenance condition.Furthermore,wavelet denoising is performed to analyze the impact of missing or abnormal data on the LSTM model accuracy.In comparison to a traditional autoregressive integrated moving average(ARIMAX)model,the proposed LSTM model performs better in terms of PI prediction and resiliency to noise.Finally,the overall prediction accuracy of our proposed performance indicator PQI is 93.8%.
基金Funded by the National Natural Science Foundation of China Academy of Engineering Physics and Jointly Setup"NSAF"Joint Fund(No.U1430119)。
文摘The multi-scale modeling combined with the cohesive zone model(CZM)and the molecular dynamics(MD)method were preformed to simulate the crack propagation in NiTi shape memory alloys(SMAs).The metallographic microscope and image processing technology were employed to achieve a quantitative grain size distribution of NiTi alloys so as to provide experimental data for molecular dynamics modeling at the atomic scale.Considering the size effect of molecular dynamics model on material properties,a reasonable modeling size was provided by taking into account three characteristic dimensions from the perspective of macro,meso,and micro scales according to the Buckinghamπtheorem.Then,the corresponding MD simulation on deformation and fracture behavior was investigated to derive a parameterized traction-separation(T-S)law,and then it was embedded into cohesive elements of finite element software.Thus,the crack propagation behavior in NiTi alloys was reproduced by the finite element method(FEM).The experimental results show that the predicted initiation fracture toughness is in good agreement with experimental data.In addition,it is found that the dynamics initiation fracture toughness increases with decreasing grain size and increasing loading velocity.
基金supported by the Ministry of Trade,Industry & Energy(MOTIE,Korea) under Industrial Technology Innovation Program (No.10063424,'development of distant speech recognition and multi-task dialog processing technologies for in-door conversational robots')
文摘A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.
基金The study was supported by the National Natural Science Foundation of China under GrantNo.49875025 and National Key Program fo
文摘In view of the fact that the atmospheric motion is an irreversible process, a memory function which can recall the observation data in the past is introduced, moreover, a special concept of self-memorization of the atmospheric motion is proposed, and a so-called self-memorization equation of the atmospheric motion has been derived. Based on the self-memorization principle, a numerical model for decadal forecast is established by means of the thermodynamic equation and the precipitation equation. The verification scores of the hindcasts of the model in the period from 1 to 12 years are much higher than that of monthly weather forecasts at present.
基金Financial supports by the National Natural Science Foundation of China （Nos. 11572265, 11532010）, the Excellent Youth Found of Sichuan Province （No. 2017JQ0019）
文摘The recovery force or recovery strain is an important indicator of NiTi-based shape memory alloy devices. However, the restoring force or recoverable strain is partially restrained due to an interaction between reorientation and reorientation-induced plasticity. Therefore, a macroscopic multi-mechanism constitutive model was constructed to describe the degeneration of shape memory effect based on the phase diagram. The residual strain after cooling consists of reorientation strain and reorientation-induced plastic strain. An internal variable, i.e., the detwinned stress, and its evolution equation were introduced into the transformation kinetics equation to describe the nonlinear hardening characteristics induced by the combined reorien- ration and detwinning mechanisms during mechanical loading. Finally, the proposed model was numerically implemented to simulate the experiments of shape memory effect at different peak strains. Comparisons between the experimental and simulated results show that the proposed model can reasonably describe the degeneration of shape memory effect.
基金Financial supports by the National Natural Science Foundation of China (Grant 11532010)the project for Sichuan Provincial Youth Science and Technology Innovation Team, China (Grant 2013TD0004)
文摘A macroscopic based multi-mechanism constitutive model is constructed in the framework of irreversible thermodynamics to describe the degeneration of shape memory effect occurring in the thermo-mechanical cyclic deformation of NiTi shape memory alloys (SMAs). Three phases, austenite A, twinned martensite and detwinned martensite , as well as the phase transitions occurring between each pair of phases (, , , , and are considered in the proposed model. Meanwhile, two kinds of inelastic deformation mechanisms, martensite transformation-induced plasticity and reorientation-induced plasticity, are used to explain the degeneration of shape memory effects of NiTi SMAs. The evolution equations of internal variables are proposed by attributing the degeneration of shape memory effect to the interaction between the three phases (A, , and and plastic deformation. Finally, the capability of the proposed model is verified by comparing the predictions with the experimental results of NiTi SMAs. It is shown that the degeneration of shape memory effect and its dependence on the loading level can be reasonably described by the proposed model.
基金Project supported by the National Natural Science Foundation of China (No. 10976032)
文摘A constitutive model is developed for the transformation, reorientation and plastic deformation of shape memory alloys （SMAs）. It is based on the concept that an SMA is a mixture composed of austenite and martensite, the volume fraction of each phase is transformable with the change of applied thermal-mechanical loading, and the constitutive behavior of the SMA is the combination of the individual behavior of its two phases. The deformation of the martensite is separated into elastic, thermal, reorientation and plastic parts, and that of the austenite is separated into elastic, thermal and plastic parts. Making use of the Tanaka＇s transformation rule modified by taking into account the effect of plastic deformation, the constitutive model of the SMA is obtained. The ferroelasticity, pseudoelastieity and shape memory effect of SMA Au-47.5 at.%Cd, and the pseudoelasticity and shape memory effect as well as plastic deformation and its effect of an NiTi SMA, are analyzed and compared with experimental results.
基金supported by the National Natural Science Foundation of China (Nos. 10972027 and 10772021)Foundation of Jiangsu University(No. 11JDG066)
文摘Based on the micromechanical method and thermodynamic theory,a constitutive model for the macroscopic mechanical behavior of porous NiTi shape memory alloy is presented.The hydrostatic stress is considered for porous NiTi according to the transformation function of dense NiTi.The present model takes account of the tensile-compressive asymmetry of NiTi,and can degenerate to model dense material.Numerical calculations,which only need material parameters of dense NiTi,are conducted to investigate the nonlinear and hysteretic strain of porous NiTi,and the predicted results are in good agreement with the corresponding experiments.
基金Project supported by the National Key Research and Development Program of China(No.2017YFC0307604)the Talent Foundation of China University of Petroleum(No.Y1215042)the Graduate Innovation Program of China University of Petroleum(East China)(No.YCX2019084)
文摘The objective of this paper is to model the size-dependent thermo-mechanical behaviors of a shape memory polymer (SMP) microbeam.Size-dependent constitutive equations,which can capture the size effect of the SMP,are proposed based on the modified couple stress theory (MCST).The deformation energy expression of the SMP microbeam is obtained by employing the proposed size-dependent constitutive equation and Bernoulli-Euler beam theory.An SMP microbeam model,which includes the formulations of deflection,strain,curvature,stress and couple stress,is developed by using the principle of minimum potential energy and the separation of variables together.The sizedependent thermo-mechanical and shape memory behaviors of the SMP microbeam and the influence of the Poisson ratio are numerically investigated according to the developed SMP microbeam model.Results show that the size effects of the SMP microbeam are significant when the dimensionless height is small enough.However,they are too slight to be necessarily considered when the dimensionless height is large enough.The bending flexibility and stress level of the SMP microbeam rise with the increasing dimensionless height,while the couple stress level declines with the increasing dimensionless height.The larger the dimensionless height is,the more obvious the viscous property and shape memory effect of the SMP microbeam are.The Poisson ratio has obvious influence on the size-dependent behaviors of the SMP microbeam.The paper provides a theoretical basis and a quantitatively analyzing tool for the design and analysis of SMP micro-structures in the field of biological medicine,microelectronic devices and micro-electro-mechanical system (MEMS) self-assembling.
基金funded by the National Natural Science Foundation of China (41807285)。
文摘The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.
基金Projects(11072056, 10772061) supported by the National Natural Science Foundation of ChinaProject(A200907) supported by the Natural Science Foundation of Heilongjiang Province,ChinaProject(20092322120001) supported by the PhD Programs Foundations of Ministry of Education of China
文摘In order to propel the development of metal magnetic memory （MMM） technique in fatigue damage detection, the Jiles-Atherton model （J-A model） was modified to describe MMM mechanism in elastic stress stage. A series of rotating bending fatigue experiments were conducted to study the stress-magnetization relationship and verify the correctness of modified J-A model. In MMM detection, the magnetization of material irreversibly approaches to the local equilibrium state Mo instead of global equilibrium state M^n under cyclic stress, and the M0-a curves are loops around the Mar,-a curve. The modified J-A model is constructed by replacing M~ in J-A model with M0, and it can describe the magnetomechanical effect well at low external magnetic field. In the rotating bending fatigue experiments, the MMM field distribution in normal direction around cylinder specimen is similar to the stress distribution, and the calculation result of model coincides with experiment result after some necessary modifications. The MMM field variation with time at a certain point in fatigue process is divided into three stages with the variation of stable stress-stain hysteresis loop, and the calculation results of model can explain not only the three stages of MMM field changes, but also the different change laws when the applied magnetic field and initial magnetic field are different. The MMM field distribution in normal direction along specimen axis reflects stress concentration effect at artificial defect, and the magnetic signal fluctuates around the defect at late fatigue stage. The calculation results coincide with the initial MMM principle and can explain signal fluctuates around the defect. The modified J-A model can explain experiment results well, and it is fit for MMM field characterization.
基金Supported by National Natural Science Foundation of China(Grant Nos.11272084,11472076)PetroChina Innovation Foundation(Grant No.2015D-5006-0602)Postdoctoral Science Research Developmental Foundation of Chinese Heilongjiang Province(Grant No.LBH-Q13035)
文摘Metal magnetic memory（MMM） testing has been widely used to detect welded joints. However, load levels, environmental magnetic field, and measurement noises make the MMM data dispersive and bring difficulty to quantitative evaluation. In order to promote the development of quantitative MMM reliability assessment, a new MMM model is presented for welded joints. Steel Q235 welded specimens are tested along the longitudinal and horizontal lines by TSC-2M-8 instrument in the tensile fatigue experiments. The X-ray testing is carried out synchronously to verify the MMM results. It is found that MMM testing can detect the hidden crack earlier than X-ray testing. Moreover, the MMM gradient vector sum K_（vs） is sensitive to the damage degree, especially at early and hidden damage stages. Considering the dispersion of MMM data, the K_（vs） statistical law is investigated, which shows that K_（vs） obeys Gaussian distribution. So K_（vs） is the suitable MMM parameter to establish reliability model of welded joints. At last, the original quantitative MMM reliability model is first presented based on the improved stress strength interference theory. It is shown that the reliability degree R gradually decreases with the decreasing of the residual life ratio T, and the maximal error between prediction reliability degree R_1 and verification reliability degree R_2 is 9.15%. This presented method provides a novel tool of reliability testing and evaluating in practical engineering for welded joints.
文摘This article examines some general atmospheric circulation and climate models in the context of the notion of “memory”. Two kinds of memories are defined： statistical memory and deterministic memory. The former is defined through the autocorrelation characteristic of the process if it is random （chaotic）, while for the latter, a special memory function is introduced. Three of the numerous existing models are selected as examples. For each of the models, asymptotic （at t →∞） expressions are derived. In this way, the transients are filtered out and that which remains concerns the final behaviour of the models.
基金Financial supports by the National Natural Science Foundation of China （11602203）, Young Elite Scientist Sponsorship Program by CAST （No. 2016QNRC001） and Fundamental Research Funds for the Central Universities （2682018CX43） are appreciated.
文摘A macroscopic phenomenological constitutive model considering the martensite transformation and its reverse is constructed in this work to describe the thermo-magneto- mechanically coupled deformation of polycrystalline magnetic shape memory alloys （MSMAs） by referring to the existing experimental results. The proposed model is established in the frame- work of thermodynamics by introducing internal state variables. The driving force of martensite transformation, the internal heat production and the thermodynamic constraints on constitutive equations are obtained by Clausius dissipative inequality and constructed Gibbs free energy. The spatiotemporal evolution equation of temperature is deduced from the first law of thermodynam- ics. The demagnetization effect occurring in the process of magnetization is also addressed. The proposed model is verified by comparing the predictions with the corresponding experiments. It is concluded that the thermo-magneto-mechanically coupled deformation of MSMAs including the magnetostrietive and magnetocaloric effects at various temperatures can be reasonably described by the proposed model, and the magnetocaloric effect can be significantly improved over a wide range of temperature by introducing an additional applied stress.
基金supported by the Natural Science Foundation of Jiangsu Province of China (No. BK20170759)the National Natural Science Foundation of China (No. 11572153)+3 种基金Jiangsu Government Scholarship for Overseas Studiesa project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)Outstanding Scientific and Technological Innovation Team in Colleges and Universities of Jiangsu Provincethe Doctor Special Foundation and the Research Fund of Nanjing Institute of Technology (Nos. ZKJ201603, YKJ201312)
文摘A thermoviscoelastic modeling approach is developed to predict the recovery behaviors of the thermally activated amorphous shape memory polymers(SMPs)based on the generalized finite deformation viscoelasticity theory.In this paper,a series of moduli and relaxation times of the generalized Maxwell model is estimated from the stress relaxation master curve by using the nonlinear regression(NLREG)method.Assuming that the amorphous SMPs are approximately incompressible isotropic elastomers in the rubbery state,the hyperelastic response of the materials is well modeled with a hyperelastic model in Ogden form.In addition,the Williams-Landel-Ferry(WLF)equation is used to describe the horizontal shift factor obtained with time-temperature superposition principle(TTSP).The finite element simulations show good agreement with the experimental thermomechanical behaviors.Moreover,the possibility of developing a temperature-responsive intravascular stent with the SMP studied here is investigated in terms of its thermomechanical property.Therefore,it can be concluded that the model has good prediction capabilities for the recovery behaviors of amorphous SMPs.
基金the financial support of Khalifa University through research grant No.CIRA 2019024。
文摘In this study,we propose a novel and simple exact semi-analytical model for superelastic Shape Memory Alloy(SMA)wire reinforced composites subjected to bending loads.In order to study the mechanical response of the composite during loading/unloading,a Representative Volume Element(RVE)is extracted to examine the bending response of the composite.Analytical moment–curvature,and shear force-shear strain relations are derived based on a 3-Dimensional(3 D)thermomechanical SMA model and Timoshenko beam theory.The composite Simpson’s rule is adopted to numerically solve the exact analytical moment–curvature and shear force-shear strain relationships.Results including the moment–curvature response,axial stress distribution along the vertical and longitudinal directions,martensite volume fraction,and the tip deflection are reported and validated against 3 D finite element simulations.The influence of temperature,martensite volume fraction distribution,and matrix stiffness on the mechanical performance of the composite is also investigated.In particular,the composite is found to behave superelastically under certain conditions of temperature,SMA volume fraction,and elastic stiffness of the matrix.Such behavior is advantageous in applications requiring large recoverable strains or high energy dissipation density.
基金Project supported by the National Natural Science Foundation of China(Nos.11272136,10902111,10772021 and 10972027)the Foundation of Jiangsu University(No.11JDG066)
文摘The drawing or rolling process endows polycrystal shape memory alloy with a crys- tallographic texture, which can result in macroscopic anisotropy. The main purpose of this work is to develop a constitutive model to predict the thermomechanical behavior of shape memory alloy sheets, which accounts for the crystallographic texture. The total macroscopic strain is decom- posed into elastic strain and macro-transformation strain under isothermal condition. Considering the transformation strain in local grains and the orientation distribution function of crystallo- graphic texture, the macro-transformation strain and the effective elastic modulus of textured polycrystal shape memory alloy are developed by using tensor expressions. The kinetic equation is established to calculate the volume fraction of the martensite transformation under given stress. Furthermore, the Hill＇s quadratic model is developed for anisotropic transformation hardening of textured SMA sheets. All the calculation results are in good agreement with experimental data, which show that the present model can accurately describe the macro-anisotropic behaviors of textured shape memory alloy sheets.
基金Project(51105170) supported by the National Natural Science Foundation of ChinaProject supported by the Scientific Research Foundation for the Returned Overseas Chinese Scholars,Ministry of Education,China
文摘The hysteresis characteristic is the major deficiency in the positioning control of magnetic shape memory alloy actuator. A Prandtl-Ishlinskii model was developed to characterize the hysteresis of magnetic shape memory alloy actuator. Based on the proposed Prandtl-Ishlinskii model, the inverse Prandtl-Ishlinskii model was established as a feedforward controller to compensate the hysteresis of the magnetic shape memory alloy actuator. For further improving of the positioning precision of the magnetic shape memory alloy actuator, a hybrid control method with hysteresis nonlinear model in feedforward loop was proposed. The control method is separated into two parts: a feedforward loop with inverse Prandtl-Ishlinskii model and a feedback loop with neural network controller. To validate the validity of the proposed control method, a series of simulations and experiments were researched. The simulation and experimental results demonstrate that the maximum error rate of open loop controller based on inverse PI model is 1.72%, the maximum error rate of the hybrid controller based on inverse PI model is 1.37%.
基金Supported by the National Basic Research Program of China(973 Program)(2014CB340601)。
文摘Rust is a system-level programming language that provides thread and memory safety guarantee through a suite of static compiler checking rules and prevents segmentation errors.However,since compiler checking is too strict to confine Rust's programmability,the developers prefer to use the keyword"unsafe"to bypass compiler checking,through which the caller could interact with OS directly.Unfortunately,the code block with"unsafe"would easily lead to some serious bugs such as memory safety violation,race condition and so on.In this paper,to verify memory and concurrency safety of Rust programs,we present RSMC(Safety Model Checker for Rust),a tool based on Smack to detect concurrency bugs and memory safety errors in Rust programs,in which we combine concurrency primitives model checking and memory boundary model checking.RSMC,with an assertion generator,can automatically insert assertions and requires no programmer annotations to verify Rust programs.We evaluate RSMC on two categories of Rust programs,and the result shows that RSMC can effectively find concurrency bugs and memory safety errors in vulnerable Rust programs,which include unsafe code.
