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DOE OSTI2020

On the peridynamic effective force state and multiphase constitutive correspondence principle

This article concerns modeling unsaturated deformable porous media as an equivalent single-phase and single-force state peridynamic material through the effective force state. The balance equations of linear momentum and mass of unsaturated porous media are presented by defining relevant peridynamic states. The energy balance of unsaturated porous media is utilized to derive the effective force state for the solid skeleton that is an energy conjugate to the nonlocal deformation state of the solid, and the suction force state. Through an energy equivalence, a multiphase constitutive correspondence principle is built between classical unsaturated poromechanics and peridynamic unsaturated poromechanics. The multiphase correspondence principle provides a means to incorporate advanced constitutive models in classical unsaturated porous theory directly into unsaturated peridynamic poromechanics. Finally, numerical simulations of localized failure in unsaturated porous media under different matric suctions are presented to demonstrate the feasibility of modeling the mechanical behavior of such three-phase materials as an equivalent single-phase peridynamic material through the effective force state concept.

42 ENGINEERING↗

DOE OSTI2021

Equilibrium distribution of point defects in Fe-Y-O as a typical representative of nanocluster-strengthened alloys

The statistical mechanic approach has been used to estimate the equilibrium defect distribution in a multiphase system by minimizing its free energy using microstructural characteristics and a finite set of defects obtained by first-principles calculations. Recently the approach was extended to take in consideration defects at the interface of the precipitate and matrix. Herein we apply the developed approach to the investigation of the stability of bcc Fe containing yttria, Y 2 O 3 , nanoclusters as a prototype of nanostructured ferritic alloys. It has been obtained that (100)Fe-O interface is unstable with respect to vacancies production. In a contrast to (100)Fe-O interface, the so called Klim interface is stable, i.e. local vacancy concentration at this interface at 600 K is below 10 -12 . It has been demonstrated that due to large defect formation energies the ODS particles are extremely stable and the main defect corresponds to Fe atoms substitute Y in Y 2 O 3 precipitate. Moreover, under thermodynamic equilibrium condition, the preexist vacancies in bulk Fe do not accumulate oxygen atom. The later observation not necessarily forbid the existence of a large amount of preexisting Fe vacancy - oxygen atom clusters at the initial stages of alloy formation far from equilibrium.

36 MATERIALS SCIENCE↗

DOE OSTI2023

MACHINE LEARNING-ENABLED PREDICTION OF TRANSIENT INJECTION MAP IN AUTOMOTIVE INJECTORS WITH UNCERTAINTY QUANTIFICATION

Accurate prediction of injection profiles is a critical aspect of linking injector operation with engine performance and emissions. However, highly resolved injector simulations can take one to two weeks of wall-clock time, which is incompatible with engine design cycles with desired turnaround times of less than a day. Hence, it is important to reduce the time-to-solution of the internal flow simulations by several orders of magnitude to make it compatible with engine simulations. This work demonstrates a data-driven approach for tackling the computational overhead of injector simulations, whereby the transient injection profiles are emulated for a side-oriented, single-hole diesel injector using a Bayesian machine-learning framework. First, an interpretable Bayesian learning strategy was employed to understand the effect of design parameters on the total void fraction field. Then, autoencoders are utilized for efficient dimensionality reduction of the flowfields. Gaussian process models are finally used to predict the spatiotemporal void fraction field at the injector exit for unknown operating conditions. The Gaussian process models produce principled uncertainty estimates associated with the emulated flowfields, which provide the engine designer with valuable information of where the data-driven predictions can be trusted in the design space. The Bayesian flowfield predictions are compared with the corresponding predictions from a deep neural network, which has been transfer-learned from static needle simulations from a previous work by the authors. The emulation framework can predict the void fraction field at the exit of the orifice within a few seconds, thus achieving a speed-up factor of up to 38 x 10(6) over the traditional simulation-based approach of generating transient injection maps.

machine learning↗