Developing constitutive models from EPR‐based self‐learning finite element analysis          下载免费PDF全文

Ali Nassr  Akbar Javadi  Asaad Faramarzi 《国际地质力学数值与分析法杂志》2018,42(3):401-417

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.  相似文献   

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing     

Peng Yue  Fan Gao  Boyi Shangguan  Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.  相似文献   

Muon density measurements with the KASCADE central detector     

《Astroparticle Physics》2002,16(4):183-386

Frequency distributions of local muon densities in high-energy extensive air showers (EAS) are presented as signature of the primary cosmic ray energy spectrum in the knee region. Together with the gross shower variables like shower core position, angle of incidence, and the shower sizes, the KASCADE experiment is able to measure local muon densities for two different muon energy thresholds. The spectra have been reconstructed for various core distances, as well as for particular subsamples, classified on the basis of the shower size ratio N_μ/N_e. The measured density spectra of the total sample exhibit clear kinks reflecting the knee of the primary energy spectrum. While relatively sharp changes of the slopes are observed in the spectrum of EAS with small values of the shower size ratio, no such feature is detected at EAS of large N_μ/N_e ratio in the energy range of 1–10 PeV. Comparing the spectra for various thresholds and core distances with detailed Monte Carlo simulations the validity of EAS simulations is discussed.  相似文献   

On the accretion geometry of Cyg X-1 in the low/hard state     

F. Eugenio Barrio  Chris Done  Sergei Nayakshin 《Monthly notices of the Royal Astronomical Society》2003,342(2):557-563

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Warm gas kinematics in shell galaxies     

R. Rampazzo  H. Plana  M. Longhetti  P. Amram  J. Boulesteix  J.-L. Gach  O. Hernandez 《Monthly notices of the Royal Astronomical Society》2003,343(3):819-830

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Deuterium fractionation on interstellar grains studied with the direct master equation approach     

Tatiana Stantcheva  Eric Herbst 《Monthly notices of the Royal Astronomical Society》2003,340(3):983-988

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The emission distribution in RR Pictoris     

L. Schmidtobreick  C. Tappert  I. Saviane 《Monthly notices of the Royal Astronomical Society》2003,342(1):145-150

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Laboratory kinetics of C₂H radical reactions with ethane, propane, and n-butane at T = 96-296 K: implications for Titan     

James E. Murphy  Stephen R. Leone 《Icarus》2003,163(1):175-181

The kinetics of the reactions of C₂H radical with ethane (k₁), propane (k₂), and n-butane (k₃) are studied over the temperature range of T = 96-296 K with a pulsed Laval nozzle apparatus that utilizes a pulsed laser photolysis-chemiluminescence technique. The C₂H decay profiles in the presence of both the alkane reactant and O₂ are monitored by the CH(A²Δ) chemiluminescence tracer method. The results, together with available literature data, yield the following Arrhenius expressions: k₁(T) = (0.51 ± 0.06) × 10⁻¹⁰ exp[(−76 ± 30)K/T] cm³ molecule⁻¹ s⁻¹ (T = 96-800 K), k₂(T) = (0.98 ± 0.32) × 10⁻¹⁰exp[(−71 ± 60)K/T] cm³ molecule⁻¹ s⁻¹ (T = 96-361 K), and k₃(T) = (1.23 ± 0.26) × 10⁻¹⁰ cm³ molecule⁻¹ s⁻¹ (T = 96-297 K). At T = 296 K, k₁ is measured as a function of total pressure and has little or no pressure dependence. The results from this work support a direct hydrogen abstraction mechanism for the title reactions. Implications to the atmospheric chemistry of Titan are discussed.  相似文献   

The effects of topographically-controlled thermal tides in the martian upper atmosphere as seen by the MGS accelerometer     

Paul Withers  S.W Bougher 《Icarus》2003,164(1):14-32

Mars Global Surveyor accelerometer observations of the martian upper atmosphere revealed large variations in density with longitude during northern hemisphere spring at altitudes of 130-160 km, all latitudes, and mid-afternoon local solar times (LSTs). This zonal structure is due to tides from the surface. The zonal structure is stable on timescales of weeks, decays with increasing altitude above 130 km, and is dominated by wave-3 (average amplitude 22% of mean density) and wave-2 (18%) harmonics. The phases of these harmonics are constant with both altitude and latitude, though their amplitudes change significantly with latitude. Near the South Pole, the phase of the wave-2 harmonic changes by 90° with a change of half a martian solar day while the wave-3 phase stays constant, suggesting diurnal and semidiurnal behaviour, respectively. We use a simple application of classical tidal theory to identify the dominant tidal modes and obtain results consistent with those of General Circulation Models. Our method is less rigorous, but simpler, than the General Circulation Models and hence complements them. Topography has a strong influence on the zonal structure.  相似文献   

Double–double radio galaxies: remnants of merged supermassive binary black holes     

F. K. Liu  Xue-Bing Wu  S. L. Cao 《Monthly notices of the Royal Astronomical Society》2003,340(2):411-416

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