Developing constitutive models from EPR‐based self‐learning finite element analysis

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.     

中国古生教授学会微体古生物学分会举行代表大会和学术年会

中国古生教授学会微体古生物学分会举行代表大会和学术年会中国古竽物学会微体古物学分会第五次会员代表大分暨第六次不术年会于１９９６年１月２４日到３０日在福州市召开。来自全国地质、石油、煤炭的和产、科研和大学的１００多位代表参加了这次大会和学术研讨。大会共...     

Metamorphic phase relations in orthopyroxene-bearing granitoids: implication for high-pressure metamorphism and prograde melting in the continental crust

In this work, the factors controlling the formation and preservation of high-pressure mineral assemblages in the metamorphosed orthopyroxene-bearing metagranitoids of the Sandmata Complex, Aravalli-Delhi Mobile Belt (ADMB), northwestern India have been modelled. The rocks range in composition from farsundite through quartz mangerite to opdalite, and with varying K₂O, Ca/(Ca + Na)_rock and FeO_tot + MgO contents. A two stage metamorphic evolution has been recorded in these rocks.
An early hydration event stabilized biotite with or without epidote at the expense of magmatic orthopyroxene and plagioclase. Subsequent high-pressure granulite facies metamorphism (∼15 kbar, ∼800 °C) of these hydrated rocks produced two rock types with contrasting mineralogy and textures. In the non-migmatitic metagranitoids, spectacular garnet ± K-feldspar ± quartz corona was formed around reacting biotite, plagioclase, quartz and/or pyroxene. In contrast, biotite ± epidote melting produced migmatites, containing porphyroblastic garnet incongruent solids and leucosomes.
Applying NCKFMASHTO T–M (H₂O) and P–T pseudosection modelling techniques, it is demonstrated that the differential response of these magmatic rocks to high-pressure metamorphism is primarily controlled by the scale of initial hydration. Rocks, which were pervasively hydrated, produced garnetiferous migmatites, while for limited hydration, the same metamorphism formed sub-solidus garnet-bearing coronae. Based on the sequence of mineral assemblage evolution and the mineral compositional zoning features in the two metagranitoids, a clockwise metamorphic P–T path is constrained for the high-pressure metamorphic event. The finding has major implications in formulating geodynamic model of crustal amalgamation in the ADMB.