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At least 991 records · Page 55

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DOE OSTI2020

A physics-informed operator regression framework for extracting data-driven continuum models

The application of deep learning toward discovery of data-driven models requires careful application of inductive biases to obtain a description of physics which is both accurate and robust. We present here a framework for discovering continuum models from high fidelity molecular simulation data. Our approach applies a neural network parameterization of governing physics in modal space, allowing a characterization of differential operators while providing structure which may be used to impose biases related to symmetry, isotropy, and conservation form. Here, we demonstrate the effectiveness of our framework for a variety of physics, including local and nonlocal diffusion processes and single and multiphase flows. For the flow physics we demonstrate this approach leads to a learned operator that generalizes to system characteristics not included in the training sets, such as variable particle sizes, densities, and concentration.

42 ENGINEERING↗

DOE OSTI2023

Transforming microseismic clouds into near real-time visualization of the growing hydraulic fracture

SUMMARY Microseismic observations during unconventional reservoir stimulation are typically seen as a proxy for clusters of hydraulic fractures and the extent of the stimulated reservoir. Such straightforward interpretation is often misleading and fails to provide a physically reasonable image of the fracturing process. This paper demonstrates the application of a physics-based machine learning algorithm which enables a rapid and accurate fracture mapping from the microseismic data. Our training and validation data set relies on a history-matched geomechanical modelling workflow implemented in GEOS software for the Hydraulic Fracturing Test Site 1 (HFTS-1) project. For this study we augmented the simulated fracture growth through geostatistical modelling of induced seismicity, so that the synthetic microseismic catalogue matches the main statistical properties of the field observations. We formulated the problem of mapping the actual fracture in the clutter of events to parallel common video segmentation workflows: several past video frames (microseismic density snapshots) are passed through a deep convolutional network to classify whether a given voxel is associated with a fracture or intact rock. We found that for accurate fracture mapping, the network’s input and architecture must be augmented to incorporate the fluid injection parameters (pressure, rate, concentration of proppant, and location of the perforation within the cluster). The error rate for the network reached as little as 10 per cent of the fracture area, while a conventional microseismic interpretation approach yielded ∼300 per cent. Our approach also yields must faster predictions than conventional methods (minutes instead of weeks), and could enable engineers to make rapid decisions regarding engineering parameters (pumping rate, viscosity) in real time during stimulation.

58 GEOSCIENCES↗

DOE OSTI2024

Shallow Soil Response to a Buried Chemical Explosion With Geophones and Distributed Acoustic Sensing

Abstract Shallow sediments can respond non‐linearly to large dynamic strains and undergo a subsequent healing phase as the material gradually recovers following the passing of seismic waves. This study focuses on the physical changes in the subsurface caused by the shaking from a buried chemical explosion detonated in a borehole in Nevada, USA, as a part of the Source Physics Experiment Phase II. The explosion damaged the shallow subsurface and modified the frequency content recorded by 491 geophones and 2240 Distributed Acoustic Sensing (DAS) channels within 2.5 km from surface ground zero. We observe a gradual shift of resonance frequencies in the 10–25 Hz frequency band in the hours following the explosion and develop a method to characterize the related logarithm‐type healing process of the shallow (i.e., upper ∼25 m) subsurface. We find that stronger levels of ground motion increase the relative degree of damage and duration of the subsurface healing; with the spall region exhibiting the largest degree of damage and longest healing recovery time. We observe coherent spatial patterns of damage with the region located to the southeast of the explosion exhibiting more damage than the southwest region. This study demonstrates that both DAS and co‐located geophones capture similar temporal changes associated with the physical processes occurring in the subsurface, with the high‐density sampling of DAS measurements enabling a new capability to monitor the fine‐scale changes of the Earth's shallow subsurface following the detonation of a buried explosion.

Viens, Loïc↗

DOE OSTI2022

Charge breeders: Development of diagnostic tools to probe the underlying physics

Charge breeders were developed more than 20 years ago in the context of radioactive ion beam (RIB) production. The main goal is to boost the charge state of a singly charged RIB to a higher value matching the A/Q of a post-accelerator. In that way, the RIB produced at some tens of keV can efficiently be accelerated to energies in a range of few MeV/u up to several tens of MeV/u, which is of interest for nuclear structure and nuclear astrophysics experiments. Two families of charge breeders have been developed: one based on Electron Cyclotron Resonance Ion Sources (ECRISs) suitable for Continuous Wave (CW) operation and another built around Electron Beam Ion Sources (EBISs) used for pulsed post-accelerators. For many years, experimental studies have been carried out to enhance the charge breeding process and improve the extracted beam quality, i.e., purity, intensity, emittance, and time structure. The evolution of the charge breeders is also closely related to the emergence of new facilities delivering even more exotic beams. Diagnostic tools, such as Faraday cups, beam profile monitors, and emittance scanners, are mandatory to tune the machines and evaluate their performances. However, to go beyond that, a better understanding of plasma physics (in ECRIS) and electron-ion interactions (in EBIS), as well as the 1+ beam characteristics, for instance, energy spread transverse emittance, is needed. Therefore, simulation codes modeling the physics phenomena in both type of charge breeders were developed to support the understanding of experimental outputs and to gain insights into non-observable parameters. Nevertheless, more sophisticated diagnostic tools are required to improve the charge breeding performance and to extend the potential applications toward new RIB facilities. This Review will present the two kinds of charge breeders and their technical evolutions. It will review the diagnostic tools and simulation codes employed for operation and evaluation of the internal physics processes.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

BeyondFingerprinting: AI-guided discovery of robust materials & processes

BeyondFingerprinting was a 2021-2024 Sandia Grand Challenge LDRD exploring the potential to develop new resilient materials and manufacturing processes by taking an artificial-intelligence (AI)-guided approach that integrates human-subject-matter expertise with algorithms enriched with physics-based constraints to unearth process-structure-property correlations. Such algorithms, trained on high-throughput experiments and simulations, are shown to serve as surrogate models that efficiently detect key “fingerprints” in materials data, prognose material performance, and guide effective process improvements. To accelerate broader adoption across mission areas, this AI-guided approach was demonstrated with three complex process-centric exemplars: electroplating, physical vapor deposition, and laser powder bed fusion. Together, these exemplars impact nearly every hardware component relevant to DOE and NNSA national security missions.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Multi-scale modeling of the electric field assisted sintering process

The electric field assisted sintering (EFAS) process involves tightly coupled physics that influence microstructural evolution in the particles being compacted. It is also an inherently multi-scale phenomenon, with the microstructure of the compact influencing the subsequent engineering-scale response of the sintering system. To improve understanding of how processing parameters influence microstructural evolution, we have developed a multi-scale modeling approach that couples a continuum-level model of the sintering system with a phase-field model for microstructural evolution of particles within the compact. The phase-field model couples the effect of chemical and electrical driving forces on microstructural evolution and includes the effect of charged defect segregation to surfaces and grain boundaries; this segregation leads to enhanced defect transport and heat generation at these interfaces in response to applied electric field. The effect of enhanced heat generation on particle neck growth and the influence of microstructural evolution on the engineering-scale model are demonstrated.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Multi-scale modeling of the electric field assisted sintering process

36 - MATERIALS SCIENCE↗

DOE OSTI2022

Electrochemical Hydrogen Compression Cell Design (CRADA Final Report)

Electrochemical compression and purification is a high potential technology that is likely to outcompete mechanical compression for hydrogen end use applications. However, testing and understanding what is occurring within the cell is difficult as it is not readily accessible through experimentation. Thus, the main target is to increase performance and reduce cost of electrochemical hydrogen compression (EHC) and purification (EHP) cells and stacks using multiscale mathematical modeling informed by experiment. The models can provide physical insight into limiting processes and develop mitigation methods, which can be proven out through rapid sub-scale testing of the devices.

08 HYDROGEN↗

DOE OSTI2024

Modeling Analysis of Ball-Milling Process for Battery-Electrode Synthesis

The mechanical alloying process is a promising method for synthesizing electrode materials for batteries owing to its benefits such as the ability to produce nanostructured, high-performing electrode alloys, no adverse effects on the solid electrolyte for solid-state batteries, stable production of thick electrodes, simple processing steps, and low processing costs. It is gaining intensive attention in the battery industry as one of the best methods to replace the conventional wet-slurry-solvent method, and its application is rapidly increasing these days. However, the operation is currently conducted purely based on trial-and-error methods without fully utilizing the features of its functions. Here, this may be attributed to a lack of understanding of the effect of operating parameters on the alloying process and final products. Surprisingly, there is a scarcity of the literature conducting fundamental research to comprehend the underlying physics of the entire mechanical alloying process, resulting in a significant knowledge gap. To address this knowledge gap, extensive research was conducted. The existing literature on mechanical alloying was reviewed to comprehend the current state of understanding and to discuss the direction for future research. Mathematical expressions were developed to create physics-based models capable of capturing the entire mechanical alloying process, including milling kinetics and defect-enhanced phase evolution. These methods were then applied to investigate the impact of operating parameters such as milling frequency, initial mole ratio of the alloyed materials, density of grinding balls, and energy required for the powders to become amorphous (i.e., the amorphization energy threshold). This research aimed not only to comprehend the direct effects of these operating parameters but also to unveil the physics underlying the ball-milling process. The results of our study can serve as crucial information for the battery industry in designing or operating the ball-milling process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Industrial deposition of wavelength-shifting films for liquid argon photon detection systems

The Deep Underground Neutrino Experiment (DUNE) Phase-II Far Detector is considering an approximately 2000 m 2 photon detection system to achieve a target mean light yield of 180 PE/MeV. Meeting this requirement demands scalable, cost-effective, and high-quality wavelength-shifter (WLS) coatings capable of converting 127 nm liquid-argon scintillation light into visible photons with controlled and reproducible optical performance. We report on the successful realization of an industrial physical vapor deposition (PVD) process forp-terphenyl (pTP) coatings, adapted from vacuum deposition techniques developed for OLED display manufacturing, to produce uniform WLS layers on large-area inorganic substrates, a task traditionally challenged by adhesion and uniformity issues at organic-inorganic interfaces. Surface characterization by profilometry and spectroscopic measurements demonstrates edge-region thickness variation below 10% and emission spectra consistent with high-quality pTP reference samples. The industrial process demonstrates reproducibility, scalability, and significantly reduced production time compared to laboratory-based methods, while maintaining optical characteristics consistent with established pTP reference samples. These results establish a viable pathway for mass production of high-performance pTP coatings for DUNE FD3 and future neutrino experiments, from a coating manufacturing and process standpoint. Detector-level performance validation, including quantitative VUV conversion efficiency measurements at 127 nm, is identified as future work.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

First application of a liquid argon time projection chamber for the search for intranuclear neutron-antineutron transitions and annihilation in 40 Ar using the MicroBooNE detector

We present a novel methodology to search for intranuclear neutron-antineutron transition (n→n̅) followed by n̅-nucleon annihilation within an 40 Ar nucleus, using the MicroBooNE liquid argon time projection chamber (LArTPC) detector. A discovery of n→n̅ transition or a new best limit on the lifetime of this process would either constitute physics beyond the Standard Model or greatly constrain theories of baryogenesis, respectively. The approach presented in this paper makes use of deep learning methods to select n→n̅ events based on their unique features and differentiate them from cosmogenic backgrounds. The achieved signal and background efficiencies are (70.22 ± 6.04)% and (0.0020 ± 0.0003)%, respectively. A demonstration of a search is performed with a data set corresponding to an exposure of 3.32 ×10 26 neutron-years, and where the background rate is constrained through direct measurement, assuming the presence of a negligible signal. With this approach, no excess of events over the background prediction is observed, setting a demonstrative lower bound on the n→n̅ lifetime in 40 Ar of τ m ≳ 1.1×10 26 years, and on the free n→n̅ transition time of τ n→n̅ ≳ 2.6×10 5 s, each at the 90% confidence level. This analysis represents a first-ever proof-of-principle demonstration of the ability to search for this rare process in LArTPCs with high efficiency and low background.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Raman Sideband Cooling of Molecules in an Optical Tweezer Array to the 3D Motional Ground State

Ultracold polar molecules are promising for quantum information processing and searches for physics beyond the standard model. Laser cooling to ultracold temperatures is an established technique for trapped diatomic and triatomic molecules. Further cooling of the molecules to near the motional ground state is crucial for reducing various dephasings in quantum and precision applications. In this work, we demonstrate Raman sideband cooling (RSC) of CaF molecules in optical tweezers to near their motional ground state, with average motional occupation quantum numbers of n ¯ x = 0.16 ( 12 ) , n ¯ y = 0.17 ( 17 ) (radial directions), and n ¯ z = 0.22 ( 16 ) (axial direction), and a 3-D motional-ground-state probability of 54 ± 18 % of the molecules that survive the RSC. This process paves the way to increase molecular coherence times in optical tweezers for robust quantum computation and simulation applications. Published by the American Physical Society 2024

Physics↗

DOE OSTI2022

Controlled Injection of Electrons for Improved Performance of Laser-Wakefield Acceleration

Our lab performed a series of experiments on controlled injection of LWFA with two extreme laser pulses: one laser pulse was used to drive LWFA. In contrast, the other precisely controls the injection of electrons into the wakefield plasma wave. We have demonstrated an efficient reduction of electron beam emittance by optical injection and novel spatio-temporal manipulation of the electron beam by the extreme laser-plasma waves. Moreover, the optical injection process involves unprecedented new physics, which will benefit studies in astrophysics, inertial confinement fusion, laser-driven x-ray light sources, and ultrafast science. We further improved the performance of LWFA by a better understanding and control of the extreme laser-plasma wave dynamics for optical injection. Three novel mechanisms were identified in the optical injection process: (1) collision of two laser pulses, (2) collision of a laser pulse with a wakefield plasma wave, and (3) collision of two laser wakefields. All three methods improved electron-beam energy spread, emittance, stability, and reproducibility. First, we identified, compared, and independently controlled these different injection mechanisms by controlling the polarization of the injector laser pulse. Second, we manipulated the spatiotemporal splitting of LWFA electron beams to reduce their emittance. Third, we applied the same laser-plasma-wave dynamics and the same experimental setup for improved performance of ion acceleration, which has also been predicted by the recent simulations.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Multi-fidelity modeling to predict the rheological properties of a suspension of fibers using neural networks and Gaussian processes

Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications. There are multiple factors, such as fiber aspect ratio and volume fraction, that play a significant role in altering the rheological behavior of suspensions. Three-dimensional (3D) numerical simulations of coupled differential equations of the suspension of fibers are computationally expensive and time-consuming. Machine learning algorithms can be trained on the available data and make predictions for the cases where no numerical data are available. However, some widely used machine learning surrogates, such as neural networks, require a relatively large training dataset to produce accurate predictions. Multi-fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified constitutive equations, can pave the way for more accurate predictions. Here, we focus on neural networks and the Gaussian processes with two levels of fidelity, i.e., high and low fidelity networks, to predict the steady-state rheological properties, and compare them to the single-fidelity network. High-fidelity data are obtained from direct numerical simulations based on an immersed boundary method to couple the fluid and solid motion. The low-fidelity data are produced by using constitutive equations. Multiple neural networks and the Gaussian process structures are used for the hyperparameter tuning purpose. Results indicate that with the best choice of hyperparameters, both the multi-fidelity Gaussian processes and neural networks are capable of making predictions with a high level of accuracy with neural networks demonstrating marginally better performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

The Large-Eddy Simulation (LES) Atmospheric Radiation Measurement (ARM) Symbiotic Simulation and Observation (LASSO) Activity for Continental Shallow Convection

The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility recently initiated the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) activity focused on shallow convection at ARM’s Southern Great Plains (SGP) atmospheric observatory in Oklahoma. LASSO is designed to overcome an oft-shared difficulty of bridging the gap from point-based measurements to scales relevant for model parameterization development, and it provides an approach to add value to observations through modeling. LASSO is envisioned to be useful to modelers, theoreticians, and observationalists needing information relevant to cloud processes. LASSO does so by combining a suite of observations, LES inputs and outputs, diagnostics, and skill scores into data bundles that are freely available, and by simplifying user access to the data to speed scientific inquiry. The combination of relevant observations with observationally constrained LES output provides detail that gives context to the observations by showing physically consistent connections between processes based on the simulated state. A unique approach for LASSO is the generation of a library of cases for days with shallow convection combined with an ensemble of LES for each case. The library enables researchers to move beyond the single-case-study approach typical of LES research. The ensemble members are produced using a selection of different large-scale forcing sources and spatial scales. Since large-scale forcing is one of the most uncertain aspects of generating the LES, the ensemble informs users about potential uncertainty for each date and increases the probability of having an accurate forcing for each case.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Toroidal modeling of runaway avalanche in DIII-D discharges

Abstract A toroidal modeling tool is developed to study the runaway electron (RE) avalanche production process in tokamak plasmas, by coupling the Rosenbluth–Putvinski avalanche model (Rosenbluth and Putvinski 1997 Nucl. Fusion 37 1355) with an n = 0 magneto-hydrodynamic (MHD) solver. Initial value numerical simulations are carried out for two DIII-D discharges with different plasma shapes (one near circular, and the other with high elongation). It is found that, assuming the same level of about 1% seed current level, the Rosenbluth–Putvinski model somewhat underestimates the RE plateau current for the circular-shaped plasma, as compared with that measured in DIII-D experiments. For an elongated, higher current plasma, simulations find strong runaway current avalanche production despite the lack of measured plateau RE current in experiments. A possible reason for this discrepancy is a lack of additional RE dissipation physics in the present two-dimensional model. Systematic scans of the plasma boundary shape, at fixed pre-disruption plasma current, find that the plasma elongation helps to reduce the RE avalanche production, confirming recent results obtained with an analytic model (Fülöp et al 2020 J. Plasma Phys. 86 474860101). The effect of the plasma triangularity (either positive or negative), on the other hand, has a minor effect. On the physics side, the avalanche process involves two competing mechanisms associated with the electric field. On the one hand, a stronger electric field produces a higher instantaneous avalanche growth rate. On the other hand, a fast growing RE current quickly reduces the fraction of the conduction current together with the electric field, and hence a faster dissipation of the poloidal flux. As a final result of these two dynamic processes, the runaway plateau current is not always the largest with the strongest initial electric field. These results lay the foundation for future self-consistent inclusion of the MHD dynamics and the RE amplification process.

Physics↗

DOE OSTI2020

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

14 SOLAR ENERGY↗

DOE OSTI2021

Mechanism Across Scales: A Holistic Modeling Framework Integrating Laboratory and Field Studies for Microbial Ecology

Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments.

59 BASIC BIOLOGICAL SCIENCES↗