Introduction to Computational Thermodynamics

In this work, the stabilities of secondary phases, including carbides, brittle phases, and inclusions, were simulated by computational thermodynamics.

They are used in various areas involving the statistics of a large number of interacting random variables, such as computational thermodynamics [5, 10], artificial intelligence [11, 21, 15], computer vision [18] and communications processing [3, 4].

The coupling of computational thermodynamics and kinetics has been the central research theme in Integrated Computational Material Engineering (ICME).

The experimental results are discussed in the light of previous thin foil data and with respect to predictions from computational thermodynamics.

Firstly, computational thermodynamics has been used to provide a simple graphical means of predicting these additions; this method has been used to explore additions of Ni and Fe to the alloy Ti–6Al–4V (Ti64).

The composition of the compound layer is predicted using computational thermodynamics to develop alloy specific nitriding potential KN and carburizing potential KC phase diagrams.

The microstructure of a hot-work tool steel additively manufactured using laser powder-bed fusion (L-PBF), and its response to post heat treatment, is studied in detail by microstructure characterization and computational thermodynamics and kinetics.

In this work, three new variants of ferritic-martensitic steels have been designed with computational thermodynamics to meet the goals of mitigating residual tensile stresses by lowering martensite start temperatures and of enhancing mechanical strength and irradiation sink strength by increasing the number density of nanoprecipitates.

A Cu precipitation-mediated austenitic transformation during ageing treatment of a 15–5 PH stainless steel is revealed through atom probe tomography, in situ synchrotron X-ray diffraction and computational thermodynamics and kinetics.

The results from the laboratory test were compared with chemical reactions with sodium predicted by computational thermodynamics and discussed with respect to relevant ternary phase diagrams.

In addition, computational thermodynamics (and related multiscale, multiphysics modeling) can also be considered characterization tools since they enable the study of the thermodynamically and kinetically mediated mechanisms (influenced by the irradiation environment) that affect microstructural evolution.

Computational thermodynamics was used to support experimental findings.

The study of coupled convective heat transfer and deformation behavior in porous media is still a major scientific and engineering challenge, despite major technological advances in both theoretical and computational thermodynamics in the past two decades.

This model is coupled with computational thermodynamics for the prediction of microstructural features, and a model for the estimation of density.

The model is built by combining the computational thermodynamics and effective medium theory.

Modern approaches to alloy design increasingly exploit the framework of computational thermodynamics and kinetics to guide the selection of alloy compositions and processing strategies, to achieve desired microstructures, and yield tailored properties.

A new low-temperature Pb-free Sn-Bi-In-Ga quaternary solder with good mechanical properties is proposed based on computational thermodynamics and validated in experiments.

To improve the microstructural stability, a heat-resistant ferritic steel was designed via computational thermodynamics to exclusively contain metal carbonitride (MX) precipitates as the primary means for strengthening at elevated temperatures.

The model parameters that determine quantitatively the driving forces for each transformation were obtained by means of first principles calculations and computational thermodynamics.

A mesoscopic simulation method based on the integration of dissipative particle dynamics (DPD), smoothed particle hydrodynamics (SPH) and computational thermodynamics (CT) has been developed.

For this, a combination of computational thermodynamics (Thermo-Calc 2015b, TCNi6 database) validated by dilatometry and X-ray diffraction was used.

The design methodology uses a multi-objective genetic algorithm (GA) integrated with computational thermodynamics and physics-based predictive models to optimize the composition of each alloy in the FGM.