Engineering topics

Chen, Tianju

Publications and source records attributed to Chen, Tianju.

4 records

DOE OSTI2023

Preliminary prediction of long-term aging and creep behavior of AM 316 SS

This report describes the development of initial mechanism models for the long term behavior of additively manufactured (AM), laser powder-bed fusion 316H stainless steel under the conditions expected in future advanced nuclear reactors. These models focus on key features of the material microstructure and response that differ from the conventionally-manufactured wrought material. Specifically, the report describes the development of models to capture the unique response of the AM material focusing on irradiation creep and swelling, the effect of internal stress, for example caused by dislocation structure, on precipitation, and the effect of the AM grain and dislocation structure on the macroscale creep and thermal aging behavior. This single mechanism models represent progress towards a complete, physics-based model for the long-term material behavior as well as elucidate key differences in the AM material behavior, when compared to the better-understood, conventionally-manufactured 316H.

36 MATERIALS SCIENCE↗

DOE OSTI2023

High temperature inelastic constitutive models for the ASME Section III, Division 5 Class A materials

This report describes the exploration of a universal high temperature inelastic constitutive model for use with the ASME Boiler & Pressure Vessel Code Section III, Division 5, Class A design rules. The idea, developed based on feedback from reactor vendors, is to simplify the current bespoke material models for Grade 91, 316H, and Alloy 617 and the new model for Alloy 800H into a simple, single model form. The report describes a new parallel time integration technique implemented in the pyoptmat package which allowed us to explore a wide variety of model forms, searching for a suitable common model. The report then describes preliminary work on models for monontonic deformation and then the development of a set of models suitable for capturing high temperature cyclic deformation. These models are suitable for use with the ASME design rules, except potentially for a narrow, material-specific band of temperatures at the edge of the creep regime. An incremental improvement the current models could overcome this discrepancy and provide a new, simpler set of constitutive model for all four materials.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Training material models using gradient descent algorithms

High temperature design requires accurate constitutive models to describe material inelastic deformation and failure behavior. Oftentimes, calibrating accurate models devolves into the problem of fitting the model parameters against experimental test data. Here, we present the pyopmat package, an open source framework for calibrating constitutive models against experiment data subjected to various loading conditions using machine learning techniques. The package calculates the exact gradient of the model response with respect to the parameters using a combination of automatic differentiation and the adjoint method. Given this exact gradient, we compare the performance of several gradient-based optimization techniques in fitting realistic constitutive models against data. Here, we demonstrate the efficiency and accuracy of our package through example problems using both synthetic data, generated using known parameter sets, under monotonic and cyclic loading conditions and also with an example applying the techniques developed here to actual high temperature creep-fatigue test data.

36 MATERIALS SCIENCE↗

DOE OSTI2023

An ICME Modeling Framework for Titanium/Tungsten-Carbide Metal Matrix Composites

This report describes a collaborative project to develop a validated, predictive model for the high temperature mechanical properties of a titanium-matrix, tungsten-carbide/cobalt-reinforced metal matrix composite. The modeling approach was to first develop a detailed, microstructural model linking the material structure and the interfacial debonding properties to the effective properties of the material. The project then completed a throughput simulation campaign to generate a large number of simulations for discrete microstructures and different debonding parameters. Finally, the project trained a fast, Gaussian process surrogate model against this simulation database to provide a quick model linking the material compositions, structure, and processing parameters to the resulting material properties. This model was validated against high temperature tensile test data on a few particular composite compositions. The tests validate the model predictions for ultimate tensile strength and uniform elongation/ductility, meaning the final surrogate model can now be used to tune the material composition and processing parameters to identify optimal composite compositions for particular applications.

36 MATERIALS SCIENCE↗