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DOE OSTI2020

Ptychographic phase retrieval by proximal algorithms

We derive a set of ptychography phase-retrieval iterative engines based on proximal algorithms originally developed in convex optimization theory, and discuss their connections with existing ones. The use of proximal operator creates a simple frame work that allows us to incorporate the effect of noise from a maximum-likelihood (ML) principle. We focus on three particular algorithms, namely proximal minimization, alternating direction method of multiplier and accelerated proximal gradient (APG). We benchmark their performance with numerical simulations, and discuss their optimal conditions for convergence and accuracy. An experimental dataset is used to demonstrate their effectiveness as well, in which case an array of cubic Au nanoparticles with a size of 50 nm is imaged. We show that with the presence of Poisson noise, a dataset with photon counts up to 10 4 at one detector pixel already requires ML-based methods to achieve a stable convergence. Among the three algorithms derived in this work, APG method is reported first time for its application in ptychographic reconstruction and shows superior performance in terms of both accuracy and convergence rate with a noisy dataset.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Adaptive X-ray imaging with reinforcement learning

X-ray imaging is a powerful technique to scan samples in a variety of contexts including biological, environmental and materials science, but commonly requires a synchrotron light source to produce X-rays at sufficient intensity. As these facilities are expensive to operate, the available beam time is limited and always in high demand. Particularly if the illuminated samples are sparse, standard raster scanning methods can be time-consuming, with a majority of that time being spent on areas of the image that carry little information. To increase the efficiency and maximize the information gain for a given time budget, we split the scanning process into a series of steps where previous measurements are used to inform the decision making and adapt the exposure distribution at later stages of the sequence. We formulate this task as a reinforcement learning problem where the goal is to produce a sequence of exposure maps that maximize a predefined scalar metric. We demonstrate the potential of this approach in simulations where the adaptive illumination can accelerate the measurement process by up to an order of magnitude compared with standard raster scanning. Finally, we present the first results from deploying the trained agents on an X-ray fluorescence beamline at the Stanford Synchrotron Radiation Lightsource.

Reinforcement Learning↗

DOE OSTI2023

Understanding structure-processing relationships in metal additive manufacturing via featurization of microstructural images

Understanding and predicting accurate property-structure-processing relationships for additively manufactured components is important for both forward and inverse design of robust, reliable parts and assemblies. While direct mapping of process parameters to properties is sometimes plausible, it is often rendered difficult due to poor microstructural control. Exploring the direct relationship between processing conditions and microstructural features can thus provide significant physical insights and aid the overall design process. Here, in this study, we develop an automated high-throughput framework to simulate an uncertainty-aware additive manufacturing (AM) process, characterize microstructural images, and extract meaningful features/descriptors. A kinetic Monte Carlo (KMC) based model of the AM process is used to simulate microstructural evolution for a diverse set of experimentally relevant processing conditions. We perform a parametric study to explore the relationship between microstructural features and processing conditions. Our results indicate that a many-to-one mapping can exist between processing conditions and typical descriptors; therefore, multiple descriptors are thus necessary to unambiguously represent microstructural images. Our work provides crucial quantitative and qualitative in-formation that would aid in the selection of features for microstructural images. Featurized microstructures could then be utilized to build data-driven models for predictive control of microstructures and thereby properties of additively manufactured components.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Synthesis of precipitation-strengthened Al-Sc, Al-Zr and Al-Sc-Zr alloys via selective laser melting of elemental powder blends

Selective laser melting is used to create Al-1.5Sc, Al-1.5Zr and Al-0.75Sc-0.75Zr (at.%) alloys from blends of elemental Al, Sc, and Zr powders. This study investigates elemental alloying elements (Sc and Zr) which are high-melting and highly reactive, unlike previous work which focused on more concentrated elemental additions of lower-melting, lower-reactivity Cu and Si to aluminum. High-speed in situ synchrotron x-ray imaging and diffraction show that the 20–30 μm Al, Sc, and Zr powders fully melt and sufficiently mix in the molten state to create, on solidification, a homogeneous distribution of primary, micron-size L12 precipitates (Al 3 Sc, Al 3 Zr, and Al 3 (Sc,Zr), respectively) and nucleate micron-size Al matrix grains, as confirmed by SEM imaging of cross-sections. Here, a second laser pass, simulating a realistic additive-manufacturing build condition, fully remelts the initial volume which shows, after solidification, the same Al 3 (Sc,Zr) L1 2 primary micro-precipitates and very fine Al grains. After aging at 300–400°C, the alloys show large increases in hardness, consistent with an exceptionally high number density (1.4 × 10 24 m –3 ) and volume fraction (2.5%) of secondary Al 3 (Sc,Zr) nano-precipitates with a Sc-rich core and Zr-rich shell, as measured via atom-probe tomography.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning

Proton exchange membrane fuel cells, consuming hydrogen and oxygen to generate clean electricity and water, suffer acute liquid water challenges. Accurate liquid water modelling is inherently challenging due to the multi-phase, multi-component, reactive dynamics within multi-scale, multi-layered porous media. In addition, currently inadequate imaging and modelling capabilities are limiting simulations to small areas (<1 mm 2 ) or simplified architectures. Herein, an advancement in water modelling is achieved using X-ray micro-computed tomography, deep learned super-resolution, multi-label segmentation, and direct multi-phase simulation. The resulting image is the most resolved domain (16 mm 2 with 700 nm voxel resolution) and the largest direct multi-phase flow simulation of a fuel cell. This generalisable approach unveils multi-scale water clustering and transport mechanisms over large dry and flooded areas in the gas diffusion layer and flow fields, paving the way for next generation proton exchange membrane fuel cells with optimised structures and wettabilities.

25 ENERGY STORAGE↗

DOE OSTI2022

Efficiency Studies of Fast Neutron Tracking Using MCNP

Fast neutron identification and spectroscopy is of great interest to nuclear physics experiments. Using the neutron elastic scattering, the fast neutron momentum can be measured. Wang and Morris introduced the theoretical concept that the initial fast neutron momentum can be derived from up to three consecutive elastic collisions between the neutron and the target, including the information of two consecutive recoil ion tracks and the vertex position of the third collision or two consecutive elastic collisions with the timing information. Here, we also include the additional possibility of measuring the deposited energies from the recoil ions. In this paper, we simulate the neutron elastic scattering using the Monte Carlo N-Particle Transport Code (MCNP) and study the corresponding neutron detection and tracking efficiency. The corresponding efficiency and the scattering distances are simulated with different target materials, especially natural silicon (92.23%28Si, 4.67%29Si, and 3.1%30Si) and helium-4 (4He). The timing of collision and the recoil ion energy are also investigated, which are important characters for the detector design. We also calculate the ion traveling range for different energies using the software, “The Stopping and Range of Ions in Matter (SRIM)”, showing that the ion track can be most conveniently observed in 4He unless sub-micron spatial resolution can be obtained in silicon.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2026

Modeling Study of Hatch Spacing’s Effect on Grain Morphology in Repairing Damaged Metal Parts with Welding and Hatch Spacing’s Potential Use in Lunar Exploration

The welding process is a potential way of repairing a damaged metal component, especially cavity damage caused by a harsh environment like the lunar environment, which is characterized by large temperature differences and reduced gravity. The adjustment of welding parameter (e.g., hatch spacing) can improve production efficiency in the repair process. Seen from the microstructural level, hatch spacing sensitivity affects the metallic grain evolution and morphology in the welding process, which can further influence a repaired part’s mechanical properties; however, the study of hatch spacing’s effect on microstructure is challenging. Traditional experimental procedures are costly and time-consuming, and any change in hatch spacing value needs roll-back of experimental procedure. A modeling study can address the above challenges in experimental observation. In this research, a modeling approach based on the Kinetic Monte Carlo (KMC) Potts theory was used to simulate grain evolution and morphology with three hatch spacings. Through quantifying and analyzing the predicted grain morphologies, the effect of hatch spacing on microstructure in a welding-fabricated part was investigated. The predicted grain morphologies were validated with an EBSD image of welding microstructure, which has been published before. The primary grain morphologies were columnar grains with a small amount of fine equiaxed grains formed in the scanning path centerline. When increasing the hatch spacing, the columnar grains become larger and more lengthy, while the effect of hatch spacing on the equiaxed grains is not obvious.

Welding for repairing↗

DOE OSTI2025

A Robust Data-Driven Approach for Mechanical Serial Sectioning

Mechanical serial sectioning (MSS) provides detailed microstructural information across large length scales. By repeatedly removing thin layers of material and imaging the exposed surface, a 3D representation of a specimen’s internal structure can be constructed, enabling failure analysis and feature identification that are otherwise inaccessible via conventional 2D or nondestructive evaluation techniques. Achieving consistent and accurate material removal can be challenging due to system variability, requiring an experienced operator to manually adjust parameters, prolonging data collection times and necessitating post-processing routines to standardize the data. Here, to address these challenges, this paper presents the employment of a one-step model predictive control (MPC) framework tailored to a run-to-run (R2R) controller. The R2R-MPC controller automates the parameter selection process, improving the consistency of material removal through iterative feedback for disturbance rejection and accurate tracking of the target removal rate. Using a data-driven approach, the controller robustly adapts to changing material characteristics. The effectiveness of the R2R-MPC controller is demonstrated through simulation and experimental results and compared to previous data collection procedures.

3D Materials Science↗

NASA NTRS2007

Development of Natural Flaw Samples for Evaluating Nondestructive Testing Methods for Foam Thermal Protection Systems

Low density polyurethane foam has been an important insulation material for space launch vehicles for several decades. The potential for damage from foam breaking away from the NASA External Tank was not realized until the foam impacts on the Columbia Orbiter vehicle caused damage to its Leading Edge thermal protection systems (TPS). Development of improved inspection techniques on the foam TPS is necessary to prevent similar occurrences in the future. Foamed panels with drilled holes for volumetric flaws and Teflon inserts to simulate debonded conditions have been used to evaluate and calibrate nondestructive testing (NDT) methods. Unfortunately the symmetric edges and dissimilar materials used in the preparation of these simulated flaws provide an artificially large signal while very little signal is generated from the actual defects themselves. In other words, the same signal are not generated from the artificial defects in the foam test panels as produced when inspecting natural defect in the ET foam TPS. A project to create more realistic voids similar to what actually occurs during manufacturing operations was began in order to improve detection of critical voids during inspections. This presentation describes approaches taken to create more natural voids in foam TPS in order to provide a more realistic evaluation of what the NDT methods can detect. These flaw creation techniques were developed with both sprayed foam and poured foam used for insulation on the External Tank. Test panels with simulated defects have been used to evaluate NDT methods for the inspection of the External Tank. A comparison of images between natural flaws and machined flaws generated from backscatter x-ray radiography, x-ray laminography, terahertz imaging and millimeter wave imaging show significant differences in identifying defect regions.

Workman, Gary L.↗

DOE OSTI2020

Amorphous Ag 2-x Cu x S quantum dots: “all-in-one” theranostic nanomedicines for near-infrared fluorescence/photoacoustics dual-modal-imaging-guided photothermal therapy

Integrating dual-modal imaging and photothermal effect within a single nanosystem is an enormous challenge for current nanomedicine application. In this study, non-toxic amorphous Ag 2-x Cu x S quantum dots (QDs) possessing near-infrared fluorescence (NIRF), enhanced photothermal and photoacoustic (PA) performance, as “one-in-all” nanomedicines, are designed and obtained. Cu doping in amorphous Ag 2 S QDs causes red-shift of NIRF to the lowest tissue absorption wavelength in so called “first biowindow” (~820 nm) and improves photothermal conversion efficiency to 44.0% simultaneously. Density functional theory simulation results suggest the fluorescence red-shift and enhanced photothermal/photoacoustics properties can be attributed to the generation of intragap states introduced by Cu doping. The amorphous Ag 2-x Cu x S QDs exhibit high long-term biocompatibility. Under a single laser irradiation with relatively low power density, an effective tumor ablation induced by high photothermal effect of amorphous Ag 2-x Cu x S QDs is achieved in vivo. Simultaneously, the PTT process can be guided by high quality NIRF/PA dual-modal-imaging.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Microscopic Origin of Twist-Dependent Electron Transfer Rate in Bilayer Graphene

Using molecular simulation and continuum dielectric theory, we consider how electrochemical kinetics are modulated by the twist angle in bilayer graphene electrodes. By establishing a connection between the twist angle and the screening length of charge carriers within the electrode, we investigate how tunable metallicity modifies the statistics of the electron transfer energy gap. Constant potential molecular simulations show that the activation free energy for electron transfer increases with screening length, leading to a non-monotonic dependence on the twist angle. Here, the twist angle alters the density of states, tuning the number of thermally accessible channels for electron transfer and the reorganization energy by affecting the stability of the vertically excited state through attenuated image charge interactions. Understanding these effects allows us to express the Marcus rate of interfacial electron transfer as a function of the twist angle in a manner consistent with experimental observations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

DEM simulation of the compression of crushable sand: does the initial particle shape matter?

Advances in DEM modeling, combined with high-resolution X-ray tomography, opened the way for computer models based on virtual replicas of the particles which preserve nearly all facets of their geometry. This leads to simulation advantages, but also high computational costs. Here we tackle a question stemming from this trend: how accurate should particle models be to ensure accuracy? We address this question for the case of the compression of crushable sand. LS-DEM was used to generate three models of Ottawa sand (exact replicas, ellipsoids, and spheres) from digital images of its grains. Compression-induced crushing was simulated for all sets by tracking evolving size and shape distribution. The results confirm that exact replicas provide the closest match of the measurements. However, intermediate degrees of rendering (e.g. ellipsoids preserving volume and aspect ratio of the real grains) led to satisfactory results only marginally different from those of exact replicas. In conclusion, these findings provide an example of the protocols that may be followed to identify the optimal degree of particle approximation which should be regarded as mandatory to achieve a conscious, sustainable use of computational resources.

58 GEOSCIENCES↗

DOE OSTI2025

Spatial description of dislocation nucleation in the shock response of single-crystal aluminum

Nonequilibrium molecular dynamics simulations of shock loaded single-crystal Al in the $\langle$100$\rangle$, $\langle$110$\rangle$, $\langle$111$\rangle$, and $\langle$123$\rangle$ orientations are conducted to study elastic and plastic shockwave formation and details associated with dislocation activity. A computer vision-based approach is implemented to capture the presence of dislocations and describe their spatial characteristics in the zone of nucleation behind the propagating shockwave. The methodology developed relies on the sequences of images extracted during shock loading that show dislocation activity within a cross section of the sample. Results reveal that the spacing between activated slip systems is orientation dependent and exhibits a modest reduction for the $\langle$100$\rangle$ and $\langle$111$\rangle$ orientations as shock pressure increases. Comparisons are made to existing theoretical models. Such relationships between shock pressure and dislocation activity, extracted from molecular dynamics simulations, can be used to inform higher length scale simulations or modeling of dislocation-based plasticity during shock.

36 MATERIALS SCIENCE↗

DOE OSTI2020

The influence of packing structure and interparticle forces on ultrasound transmission in granular media

Ultrasound propagation through externally stressed, disordered granular materials was experimentally and numerically investigated. Experiments employed piezoelectric transducers to excite and detect longitudinal ultrasound waves of various frequencies traveling through randomly packed sapphire spheres subjected to uniaxial compression. The experiments featured in situ X-ray tomography and diffraction measurements of contact fabric, particle kinematics, average per-particle stress tensors, and interparticle forces. The experimentally measured packing configuration and inferred interparticle forces at different sample stresses were used to construct spring networks characterized by Hessian and damping matrices. The ultrasound responses of these network were simulated to investigate the origins of wave velocity, acoustic paths, dispersion, and attenuation. Results revealed that both packing structure and interparticle force heterogeneity played an important role in controlling wave velocity and dispersion, while packing structure alone quantitatively explained most of the observed wave attenuation. This research provides insight into time- and frequency-domain features of wave propagation in randomly packed granular materials, shedding light on the fundamental mechanisms controlling wave velocities, dispersion, and attenuation in such systems.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Use of computer vision for analysis of image datasets from high temperature plasma experiments

Great strides have been made in improving the quality of x-ray radiographs in high energy density plasma experiments, enabled in part by innovations in engineering and manufacturing of integrated circuits and materials. As a consequence, the radiographs of today are filled with a great deal of detail, but few of these features are extracted in a systematic way. Analysis techniques familiar to plasma physicists tend toward brittle 1D lineout or Fourier transform type analyses. The techniques applied to process our data have not kept pace with improvements in the quality of our data. Fortunately, the field of computer vision has a wealth of tools to offer, which have been widely used in industrial imaging and, more recently, adopted in biological imaging. We demonstrate the application of computer vision techniques to the analysis of x-ray radiographs from high energy density plasma experiments, as well as give a brief tutorial on the computer vision techniques themselves. These tools robustly extract 2D contours of shocks, boundaries of inhomogeneities, and secondary flows, thereby allowing for increased automation of analysis, as well as direct and quantitative comparisons with simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2007

Fluorescence Visualization of Hypersonic Flow Past Triangular and Rectangular Boundary-layer Trips

Planar laser-induced fluorescence (PLIF) flow visualization has been used to investigate the hypersonic flow of air over surface protrusions that are sized to force laminar-to-turbulent boundary layer transition. These trips were selected to simulate protruding Space Shuttle Orbiter heat shield gap-filler material. Experiments were performed in the NASA Langley Research Center 31-Inch Mach 10 Air Wind Tunnel, which is an electrically-heated, blowdown facility. Two-mm high by 8-mm wide triangular and rectangular trips were attached to a flat plate and were oriented at an angle of 45 degrees with respect to the oncoming flow. Upstream of these trips, nitric oxide (NO) was seeded into the boundary layer. PLIF visualization of this NO allowed observation of both laminar and turbulent boundary layer flow downstream of the trips for varying flow conditions as the flat plate angle of attack was varied. By varying the angle of attack, the Mach number above the boundary layer was varied between 4.2 and 9.8, according to analytical oblique-shock calculations. Computational Fluid Dynamics (CFD) simulations of the flowfield with a laminar boundary layer were also performed to better understand the flow environment. The PLIF images of the tripped boundary layer flow were compared to a case with no trip for which the flow remained laminar over the entire angle-of-attack range studied. Qualitative agreement is found between the present observed transition measurements and a previous experimental roughness-induced transition database determined by other means, which is used by the shuttle return-to-flight program.

Danehy, Paul M.↗

NASA NTRS2022

Design of Rigid-Flex PCB Robotics Leveraging Validated Finite Element Simulations

The use of rigid-flex printed circuit board (PCB) as primary structure has the potential to reduce the weight and volume of robotic systems. In the case of robotics for interplanetary exploration, these systems can leverage origamiinspired folding for increased mobility options and reduced storage volume. Folding rigid-flex PCB robotics can be constructed with rigid PCB connected by short Nomex fabric hinges coupled with flex PCB ribbon cables that permits enhanced system flexibility and energy dissipation to promote impact survivability. This paper presents a design methodology of rigidflex PCB systems with an emphasis on impact resistance. The design process considers solder joint adequacy, panel bending, and fracture using a finite element (FE) model. The proposed design methodology is developed using a case study with NASA JPL’s Pop-Up Folding Flat Explorer Robot (PUFFER). First, the finite-element (FE) modeling methodology is presented with consideration to both frequency and time-domain modeling applications, which include operational self-contact analysis and high impact scenarios. The time-domain impact modeling methodology utilizes hyperelastic material properties for the Nomex hinges. This modeling method is validated using image correlation of PUFFER drop tests. A flowchart is presented to guide users through a validated Abaqus modeling procedure for highly flexible rigid-flex systems. Next, a case study is presented in which PUFFER is subject to drop heights representative of falls into Lunar pits and then the design is refined for a more optimum impact performance. Finally, the results of the case study are used to inform a generalized design methodology for rigid-flex PCB robotics subject to high-impact loads with the considerations presented.

de la Croix, Jean-Pierre↗

DOE OSTI2026

Regional surrogates for predictive control of digital twins

Digital twins of complex systems must involve a model that is fast, generalizable, and usable for real-time control. For example, high-fidelity nonlinear multiphysics simulations can capture laser-material interactions, but are too slow for optimization or model predictive control (MPC). Reduced-order models, used to accelerate such computation, frequently fail to generalize to unseen inputs or control states. We show theoretically that this failure is intrinsic, i.e., that a learned model is non-unique outside the sampled subspace when its low-rank structure arises from limited excitation and clustered eigenvalues, rather than from a user-imposed truncation alone. Motivated by this result, we propose a control-ready regional surrogate-construction framework for both autonomous and nonautonomous dynamics; it employs Koopman lifting to represent nonlinearities, while preserving spatial locality. We illustrate our approach by constructing a control-ready surrogate for the digital twin of a thermal component of additive-manufacturing process. Our surrogate, localized in space through a von Neumann stencil, is learned from noisy high-fidelity simulations that emulate thermal-camera images collected during the manufacturing. It is linear in thermo-physically augmented states so that MPC reduces to a convex quadratic program. The surrogate requires no online correction, generalizes to unseen scan paths and power profiles of the laser, and is more than three orders of magnitude faster than a finite-difference solver. Furthermore, when the MPC sequence computed on the digital twin is applied to this solver, closed-loop temperature regulation is recovered, showing that the surrogate preserves control-relevant input-output behavior.

Data-driven model↗