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DOE OSTI2021

Potential vorticity transport in weakly and strongly magnetized plasmas

Tangled magnetic fields, often coexisting with an ordered mean field, have a major impact on turbulence and momentum transport in many plasmas, including those found in the solar tachocline and magnetic confinement devices. We present a novel mean field theory of potential vorticity mixing in β-plane magnetohydrodynamic (MHD) and drift wave turbulence. Our results show that mean square stochastic fields strongly reduce Reynolds stress coherence. This decoherence of potential vorticity flux due to stochastic field scattering leads to suppression of momentum transport and zonal flow formation. A simple calculation suggests that the breaking of the shear-eddy tilting feedback loop by stochastic fields is the key underlying physics mechanism. Furthermore, a dimensionless parameter that quantifies the increment in power threshold is identified and used to assess the impact of stochastic field on the L-H transition. We discuss a model of stochastic fields as a resisto-elastic network.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

A strain-driven thermotropic phase boundary in BaTiO 3 at room temperature by cycling compression

In BaTiO 3 single crystals, we observed a strain-driven phase transition from the tetragonal phase to the tetragonal-orthorhombic phase boundary which can be introduced by slow cycling compressions (a loading of up to 0.5 GPa, strain rate of 10 -4 s -1 , and 100 cycles) at room temperature. Different from the well-known tetragonal to cubic phase transition under stress (~2 GPa), it only takes place locally around bent 90° domain walls. The inhomogeneous local stress and electrical fields as well as the mobile point defect pinning effect contribute to the phase re-entrance. Through comparison experiments by in situ synchrotron x-ray diffraction, Raman scattering, and (scanning) transmission electron microscopy, we explored the phase transition mechanism. Based on that, we developed a mechanical method to obtain well-stabilized high-density thermotropic phase boundary structures (with tetragonal, orthorhombic, and bridging monoclinic phases) in BaTiO 3 for potential applications.

36 MATERIALS SCIENCE↗

DOE OSTI2026

C‐Cracking of Brittle‐Material Spheres From Eccentric Hertzian Contact

Contact of brittle-material spheres can occur while they are manufactured, handled, or used in operation as roller elements or in deformably processed composite material. Statistically, the directional contact between two spheres will nearly always be oblique or eccentric in an unconfined and mobile sphere population. Although analyses of oblique contact exist, the portrayal of its maximum (tensile) first principal stress ( S 1 ) field is lacking. This information is needed for improved judgment of the prospect of crack initiation. Given these, the S 1 due to eccentric contact and subsequent crack initiation was analyzed using finite element analysis (FEA) and corroborative demonstration of c-crack creation. The FEA shows that an asymmetric S 1 field is created about the contact patch and is caused by superimposed shear intrinsic to eccentric contact. A crack will initiate if the maximum S 1 exceeds the material's tensile strength, and its field will cause the crack to propagate and arrest to form a C-shaped crack, or c-crack. C-crack initiation trends with lower applied forces, with greater amount of contact eccentricity, higher coefficient of friction, and smaller spherical radius. Finally, these metrics deserve recognition, especially when c-cracked brittle-material spheres are used in a system whose functionality or reliability is potentially compromised by the c-cracks.

Hertzian contact↗

DOE OSTI2025

A perspective on lessons learned and future needs for wind energy field campaigns

This perspective paper provides motivation and guidance to the wind energy community for suggested future investments and a long-term strategy for wind-energy-related field campaigns that will provide much-needed observations for improving wind energy science and model validation. We synthesize key lessons learned from past field campaigns, identify critical science gaps that we think should drive future field efforts, and provide a suggested pipeline on how future endeavors should be developed. When considering future grand field campaigns, we stress the need for international cooperation across funding bodies, research institutes, and industry partners to collect observations to overcome current and future grand challenges for wind energy.

17 WIND ENERGY↗

DOE OSTI2024

The use of digital thread for reconstruction of local fiber orientation in a compression molded pin bracket via deep learning

A deep convolutional neural network (DCNN) was used for microstructure reconstruction using artificial intelligence (MR-AI) by predicting local average fiber orientation distributions (FOD) in a 3D prepreg platelet molded composite (PPMC) pin bracket. To train the MR-AI model, surface strain fields from residual stresses simulated in PPMC plates were used as the input to the DCNN. A training dataset included PPMC plates with various degrees of global fiber alignment, based on the information obtained from high-fidelity flow simulation of a pin bracket. Further, the MR-AI model was then deployed to analyze FOD in the 3D pin bracket by conducting thermo-elastic residual stress analysis. Initially, the MR-AI model was established entirely on the synthetic simulation data. Then, a μCT scan of a physically molded pin bracket was used to create a finite element model that provided data for additional validation of the DCNN model. For the μCT scan finite element pin bracket the MR-AI model predicted the distribution of fiber orientation tensor components with MAE of 0.10 indicating a global prediction error of 10%. For the flow simulated pin bracket, the MR-AI model predicted the distribution of fiber orientation tensor components with a global prediction error of 11%. The MR-AI model showed the ability to predict regions of varying alignment in the base and flange of the pin bracket. The proposed MR-AI methodology allows for rapid prediction of FOD in geometrically complex parts and offers a promising path to detecting unique fiber orientation states in molded components.

42 ENGINEERING↗

DOE OSTI2026

The high explosives & affected targets (HEAT) dataset

Artificial Intelligence (AI) surrogate models offer a computationally efficient alternative to full-physics simulations, yet no existing datasets are publicly available for training, testing, and validation of machine learning models of the dynamics of high-explosive driven shocks through multiple materials. Shock propagation through materials is a computationally challenging problem because simulations must include material-specific equations of state (EOS) along with descriptions of other physical processes such as plastic deformation, phase change, damage processes, fluid instabilities, and multi-material interactions. Shocks are typically initiated by high-velocity impacts or explosive loading. The latter case necessitates the addition of models of reactive materials to represent high-explosive (HE) detonation. Here, to address the lack of an expansive dataset for multi-material shock propagation in the AI/ML community, we present the High-Explosives and Affected Targets (HEAT) Dataset. HEAT is a physics-rich collection of two-dimensional, cylindrically symmetric, simulations generated using an Eulerian, multi-material, shock-propagation code developed at Los Alamos National Laboratory. The dataset includes two partitions: (1) the expanding shock-cylinder (CYL) simulations, Figs. 1, and (2) the Perturbed Layered Interface (PLI) simulations, Fig. 2. Entries in both partitions consist of time series of arrays of thermodynamic fields (pressure, density, and temperature), kinematic fields (position and velocity), and additional fields that depend on thermodynamic and/or kinematic fields (e.g., material stress). Materials in the CYL partition include solids (aluminium, copper, depleted uranium, stainless steel, tantalum, and a generic polymer), a liquid (water), gases (air, nitrogen), and a generic detonating material (high explosive, HE). The PLI partition spans a highly varying geometry but consists of fixed materials across entries: Copper, aluminium, stainless steel, generic polymer, and generic HE. HEAT captures critical phenomena such as momentum transfer, shock propagation, plastic deformation, and thermal effects, making HEAT a valuable benchmark for development of AI/ML emulation of multi-material shock propagation.

36 MATERIALS SCIENCE↗

DOE OSTI2022

New large-strain FFT-based formulation and its application to model strain localization in nano-metallic laminates and other strongly anisotropic crystalline materials

This paper presents a new robust large-strain (LS) elasto-viscoplastic (EVP) formulation based on Fast Fourier Transforms (FFTs) for the prediction of the micro-mechanical response and microstructure evolution of polycrystalline and multiphase materials, with emphasis on the effect of strong crystallographic and/or morphologic anisotropy on localization of plastic deformation. In this work, the novel LS-EVPFFT formulation allows treatment of complex initial geometries and large deformations considering three grids of material points: a regular grid in the reference configuration, where FFTs can be performed; an irregular grid in the initial configuration, created by applying a stress-free displacement field to the reference regular grid; and an irregular grid in the current configuration, undergoing large strains and rotations as the material is loaded. Further numerical stability of the new formulation also required the use of a novel expression for the discrete modified Green’s operator, which reduces spurious field oscillations. After presenting and validating the new formulation by comparison with preexisting implementations and analytical solutions, LS-EVPFFT is applied to the prediction of slip and kink bands formation in polycrystalline columnar ice, and kink bands in single crystal zinc wires, showing good agreement with classic experiments. Finally, the model is used to study kink band formation during compression of Cu–Nb nano-metallic laminates (NMLs), in which accurate treatment of the complex geometry associated with the tortuosity of interfaces and large deformations become critical, showing consistency with corresponding micropillar experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Magnetic toroidicity

Abstract Directional non-reciprocity refers to the phenomenon where the motion in one direction differs from the motion in the opposite direction. This behavior is observed across various systems, such as one-way traffic and materials displaying electronic/optical directional dichroism, characterized by the symmetry of velocity vectors. Magnetic toroidal moments (MTMs), which typically arise from rotational spin arrangements, also possess the symmetry of velocity vectors, making them inherently directionally non-reciprocal. In this paper, we examine magnetic point groups (MPGs) that exhibit MTMs, subsequently leading to off-diagonal linear magnetoelectricity. Our focus is on the induction of MTMs through electric fields, magnetic fields, or shear stress, while enumerating the relevant MPGs. The findings of our study will serve as valuable guidance for future investigations on directional non-reciprocity, MTMs, and off-diagonal linear magnetoelectric effects.

Physics↗

DOE OSTI2023

Analytical Study and Experimental Verification of Shear-Thinning Ink Flow in Direct Ink Writing Process

Abstract Direct ink writing (DIW) process is a facile additive manufacturing technology to fabricate three-dimensional (3D) objects with various materials. Its versatility has attracted considerable interest in academia and industry in recent years. As such, upsurging endeavors are invested in advancing the ink flow behaviors in order to optimize the process resolution and the printing quality. However, so far, the physical phenomena during the DIW process are not revealed in detail, leaving a research gap between the physical experiments and its underlying theories. Here, we present a comprehensive analytical study of non-Newtonian ink flow behavior during the DIW process. Different syringe-nozzle geometries are modeled for the comparative case studies. By using the computational fluid dynamics (CFD) simulation method, we reveal the shear-thinning property during the ink extrusion process. Besides, we study the viscosity, shear stress, and velocity fields, and analyze the advantages and drawbacks of each syringe-nozzle model. On the basis of these investigations and analyses, we propose an improved syringe-nozzle geometry for stable extrusion and high printing quality. A set of DIW printing experiments and rheological characterizations are carried out to verify the simulation studies. The results developed in this work offer an in-depth understanding of the ink flow behavior in the DIW process, providing valuable guidelines for optimizing the physical DIW configuration toward high-resolution printing and, consequently, improving the performance of DIW-printed objects.

Engineering↗

DOE OSTI2023

A Deep Learning Filter for the Intraseasonal Variability of the Tropics

Abstract This paper presents a novel application of convolutional neural network (CNN) models for filtering the intraseasonal variability of the tropical atmosphere. In this deep learning filter, two convolutional layers are applied sequentially in a supervised machine learning framework to extract the intraseasonal signal from the total daily anomalies. The CNN-based filter can be tailored for each field similarly to fast Fourier transform filtering methods. When applied to two different fields (zonal wind stress and outgoing longwave radiation), the index of agreement between the filtered signal obtained using the CNN-based filter and a conventional weight-based filter is between 95% and 99%. The advantage of the CNN-based filter over the conventional filters is its applicability to time series with the length comparable to the period of the signal being extracted. Significance Statement This study proposes a new method for discovering hidden connections in data representative of tropical atmosphere variability. The method makes use of an artificial intelligence (AI) algorithm that combines a mathematical operation known as convolution with a mathematical model built to reflect the behavior of the human brain known as artificial neural network. Our results show that the filtered data produced by the AI-based method are consistent with the results obtained using conventional mathematical algorithms. The advantage of the AI-based method is that it can be applied to cases for which the conventional methods have limitations, such as forecast (hindcast) data or real-time monitoring of tropical variability in the 20–100-day range.

Stan, Cristiana↗

DOE OSTI2019

Big Wheel Farm: Farmland Scour Reduction

Flood irrigation benefits from low infrastructure costs and maintenance but the scour near the weirs can cause channeling of the flow preventing the water from evenly dispersing across the field. Using flow obstructions in front of the weir could reduce be a low cost solution to reduce the scour. The mitigation strategy was to virtually simulate the effects of various geometric changes to the morphology (e.g. holes and bumps) in front of the weir as a means to diffuse the high intensity flow coming from the gate. After running a parametric study for the dimensions of the shapes that included a Gaussian, semi-circle, and rectangle; a Gaussian-hole in front of the gates showed the most promise to reduce farm field shear-stresses with the added benefit of being easy to construct and implement in practice. Further the simulations showed that the closer the Gaussian-hole could be placed to the gate the sooner the high shear stress could be reduced. To realize the most benefit from this mitigation strategy, it was determined that the maximum depth of the Gaussian-hole should be 0.5 m. The width of the hole in the flow direction and the length of the Gaussian-hole normal to the flow should be 0.5 m and 3 m respectively as measured by the full width at half maximum.

42 ENGINEERING↗

DOE OSTI2023

Predicting Failure Using Deep Learning SAND Report

Accurate prediction of ductile failure is critical to Sandia’s NW mission, but the models are computationally heavy. The costs of including high-fidelity physics and mechanics that are germane to the failure mechanisms are often too burdensome for analysts either because of the person-hours it requires to input them or because of the additional computational time, or both. In an effort to deliver analysts a tool for representing these phenomena with minimal impact to their existing workflow, our project sought to develop modern data-driven methods that would add microstructural information to business-as-usual calculations and expedite failure predictions. The goal is a tool that receives as input a structural model with stress and strain fields, as well as a machine-learned model, and output predictions of structural response in time, including failure. As such, our project spent substantial time performing high-fidelity, three-dimensional experiments to elucidate materials mechanisms of void nucleation and evolution. We developed crystal-plasticity finite-element models from the experimental observations to enrich the findings with fields not readily measured. We developed engineering length-scale simulations of replicated test specimens to understand how the engineering fields evolve in the presence of fine-scale defects. Finally, we developed deep learning convolutional neural networks, and graph-based neural networks to encode the findings of the experiments and simulations and make forward predictions in time for structural performance. This project demonstrated the power of data-driven methods for model development, which have the potential to vastly increase both the accuracy and speed of failure predictions. These benefits and the methods necessary to develop them are highlighted in this report. However, many challenges remain to implementing these in real applications, and these are discussed along with potential methods for overcoming them.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

The Neutronic Engine: A Platform for Operando Neutron Diffraction in Internal Combustion Engines

Neutron diffraction is a powerful tool for noninvasive and nondestructive characterization of materials and can be applied even in large devices such as internal combustion engines thanks to neutrons’ exceptional ability to penetrate many materials. While proof-of-concept experiments have shown the ability to measure spatially and temporally resolved lattice strains in a small aluminum engine on a timescale of minutes over a limited spatial region, extending this capability to timescales on the order of a crank angle degree over the full volume of the combustion chamber requires careful design and optimization of the engine structure to minimize attenuation of the incident and diffracted neutrons to maximize count rates. Here, we present the design of a “neutronic engine,” which is analogous to an optical engine in that the materials and external geometry of a typical automotive engine have been optimized to maximize access of the diagnostic while maintaining the internal combustion chamber geometry and operability of the engine. The high transparency of aluminum to neutrons makes it the ideal window material for neutron diagnostics, which allows the neutronic engine to be a truly all-metal engine with the same load and boundary condition capabilities of a modern downsized passenger car engine. The neutronic engine will enable 3D and time-resolved measurements of strain, stress, and temperature fields as well as phase transformation, texture, and microstructure throughout the metal components of the combustion chamber.

Wissink, Martin↗

DOE OSTI2020

Comparative modeling of the disregistry and Peierls stress for dissociated edge and screw dislocations in Al

Many elementary deformation processes in metals involve the motion of dislocations. The planes of glide and specific processes dislocations prefer depend heavily on their atomic core structures. Atomistic simulations are desirable for dislocation modeling but their application to even sub-micron scale problems is in general computationally costly. Accordingly, continuum-based approaches, such as the phase-field microelasticity, phase-field dislocation dynamics (PFDD), generalized Peierls–Nabarro (GPN) models, and the concurrent atomistic–continuum (CAC) method, have attracted increasing attention in the field of dislocation modeling because they well represent both short-range cores interactions and long-range stress fields of dislocations. To better understand their similarities and differences, it is useful to compare these methods in the context of benchmark simulations and predictions. In this paper, we apply the CAC method and different PFDD variants – one of them is equivalent to a GPN model – to simulate an extended (i.e., dissociated) dislocation in Al with initially pure edge or pure screw character in terms of the disregistry. CAC and discrete forms of PFDD are also employed to calculate the Peierls stress. By conducting comprehensive convergence studies, we quantify the dependence of these measures on time/grid resolution and simulation cell size. Several important but often overlooked differences between PFDD/GPN variants are clarified. In conclusion, our work sheds light on the advantages and limitations of each method, as well as the path towards enabling them to effectively model complex dislocation processes at larger length scales.

36 MATERIALS SCIENCE↗

DOE OSTI2020

The Turbulent Stress Spectrum in the Inertial and Subinertial Ranges

For velocity and magnetic fields, the turbulent pressure and, more generally, the squared fields such as the components of the turbulent stress tensor, play important roles in astrophysics. For both one and three dimensions, we derive the equations relating the energy spectra of the fields to the spectra of their squares. We solve the resulting integrals numerically and show that for turbulent energy spectra of Kolmogorov type, the spectral slope of the stress spectrum is also of Kolmogorov type. For shallower turbulence spectra, the slope of the stress spectrum quickly approaches that of white noise, regardless of how blue the spectrum of the field is. For fully helical fields, the stress spectrum is elevated by about a factor of two in the subinertial range, while that in the inertial range remains unchanged. We discuss possible implications for understanding the spectrum of primordial gravitational waves from causally generated magnetic fields during cosmological phase transitions in the early universe. We also discuss potential diagnostic applications to the interstellar medium, where polarization and scintillation measurements characterize the square of the magnetic field.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Elimination of the bias-stress effect in ligand-free quantum dot field-effect transistors

Field-effect transistors (FETs) made from colloidal quantum dot (QD) solids commonly suffer from current–voltage hysteresis caused by the bias-stress effect (BSE), which complicates fundamental studies of charge transport in QD solids and the use of QD FETs in electronics. Here, we show that the BSE can be eliminated in n-channel PbSe QD FETs by first removing the QD ligands with a dose of H 2 S gas and then infilling the QD films with alumina by atomic layer deposition (ALD). The H 2 S-treated, alumina-infilled FETs have stable, hysteresis-free device characteristics (total short-term stability), indefinite air stability (total long-term stability), and a high electron mobility of up to 14 cm 2 V −1 s −1 , making them attractive for QD circuitry and optoelectronic devices. The BSE-free devices are utilized to conclusively establish the dependence of the electron mobility on temperature and QD diameter. We demonstrate that the BSE in these devices is caused by both electron trapping at the QD surface and proton drift within the film. The H 2 S/alumina chemistry produces ligand-free PbSe/PbS/Al 2 O 3 interfaces that lack the traps that cause the electronic part of the BSE, while full alumina infilling stops the proton motion responsible for the ionic part of the BSE. In conclusion, our matrix engineering approach should aid efforts to eliminate the BSE, boost carrier mobilities, and improve charge transport in other types of nanocrystal solids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Thermomechanical Stress and Creep-Fatigue Analysis of a High-Temperature Prototype Receiver for Heating Particles

This work presents a three-dimensional (3D) thermomechanical model of a prototype-scale enclosed light trapping solar receiver for heating particles. Results of the thermoelastic model are used to estimate receiver lifetime under maximum flux conditions. A computational fluid dynamics (CFD) model is first developed to predict the temperature fields in a multi-panel assembly under steady operating conditions. Solar flux distributions on the receiver are obtained from the software package SolTrace and applied to the 3D thermal model. The subsequent particle heating is captured through a simplified 1D energy balance. Panel reradiation is considered through a surface-to-surface radiation model and natural convection loss to the surrounding air is captured in a representative fluid domain surrounding the receiver. The resulting temperature fields from the CFD analysis are used as inputs for a thermoelastic mechanical model with representative boundary conditions. With the resultant temperature and stress fields, a creep-fatigue damage and lifetime analysis is performed using the linear damage accumulation (LDA) theory. The Manson-Coffin formula and Larson Miller correlation are used to calculate the fatigue and creep, respectively. A maximum damage (corresponding to a 30-year service life) is defined for design assessment. The model was first developed and verified in detail by comparing with published results in the literature (temperature and stress profiles and distributions, and creep/fatigue damage fractions) for tubular solar receivers with supercritical carbon dioxide as the working fluid. It was then implemented to model a planar-cavity receiver with various design parameters. Specifically, three different design geometries are considered, and the results show that a maximum temperature of approximately 1200 K could be reached for each design with the given incident solar flux, with the main difference being the distribution of these temperatures. Preliminary resulting stresses for the small-scale prototype without design optimization vary from 20 MPa to 250 MPa for each design, with the maximum stresses occurring on the front face and concave geometry on the side of the panel. In future work, the developed methodology shown here will be applied to analyze a full-scale (50-150 MWth) receiver.

concentrated solar power↗

DOE OSTI2025

Characterizing Stress Roughness at Utah FORGE Through Simulation of Hydraulic Fracture Growth During 16A Stage 3 Stimulation

In most geologic formations, the vertical gradient of the minimum horizontal stress (𝜎 h ) exceeds the hydrostatic gradient, often driving hydraulic fractures to propagate upward. However, observations indicate that the upward growth of real hydraulic fractures is less pronounced than theoretical predictions based on smoothly varying 𝜎 h fields. In fact, the layered structure of sedimentary rocks hinders fracture propagation across layers, partially explaining the limited height growth observed in practice (Zoback et al., 2022). While crystalline rocks lack the pervasive layered fabric of sedimentary formations, they still possess structural fabric formed over their geologic history, resulting in inherently "rough" in situ stress fields. Recent studies have identified another key factor: the "roughness" of in situ stress, characterized by temporal, relatively short-wavelength fluctuations superimposed on the overall stress gradient. This stress roughness leads to apparent toughness anisotropy, where the vertical toughness appears significantly larger than the horizontal toughness (P. Fu et al., 2019; Dontsov & Suarez-Rivera, 2021). Neglecting the effects of rock fabric and stress roughness can yield inaccurate predictions of hydraulic fracture geometry and growth rates at larger length scales.

58 GEOSCIENCES↗