Results for “methods and demonstration” — Page 12

DOE OSTI2024

Modeling PAH Mixture Interactions in a Human In Vitro Organotypic Respiratory Model

One of the most significant challenges in human health risk assessment is to evaluate hazards from exposure to environmental chemical mixtures. Polycyclic aromatic hydrocarbons (PAHs) are a class of ubiquitous contaminants typically found as mixtures in gaseous and particulate phases in ambient air pollution associated with petrochemicals from Superfund sites and the burning of fossil fuels. However, little is understood about how PAHs in mixtures contribute to toxicity in lung cells. To investigate mixture interactions and component additivity from environmentally relevant PAHs, two synthetic mixtures were created from PAHs identified in passive air samplers at a legacy creosote site impacted by wildfires. The primary human bronchial epithelial cells differentiated at the air–liquid interface were treated with PAH mixtures at environmentally relevant proportions and evaluated for the differential expression of transcriptional biomarkers related to xenobiotic metabolism, oxidative stress response, barrier integrity, and DNA damage response. Component additivity was evaluated across all endpoints using two independent action (IA) models with and without the scaling of components by toxic equivalence factors. Both IA models exhibited trends that were unlike the observed mixture response and generally underestimated the toxicity across dose suggesting the potential for non-additive interactions of components. Overall, this study provides an example of the usefulness of mixture toxicity assessment with the currently available methods while demonstrating the need for more complex yet interpretable mixture response evaluation methods for environmental samples.

3D in vitro models↗

DOE OSTI2026

Advancing density functional tight-binding method for large organic molecules through equivariant neural networks

Semi-empirical quantum-mechanical (QM) methods have become valuable tools for studying complex (bio)molecular systems due to their balance between computational efficiency and accuracy. A key aspect of these methods is their parameterization, which not only governs the reliability of the results but also provides an opportunity to enhance their overall performance. In our previous work [J. Phys. Chem. Lett., 2021, 11, 16], we advanced the third-order semi-empirical density functional tight-binding (DFTB3) method for computing multiple properties of small molecules by developing the machine learning (ML) potential NN rep to bridge the gap between DFTB3 electronic components and those of the hybrid DFT-PBE0 functional. To overcome the limitations of NN rep , we introduce the EquiDTB framework, which leverages physics-inspired equivariant neural networks (NN) to parameterize scalable and transferable many-body Δ TB potentials, replacing the standard pairwise DFTB repulsive potential. This advancement extends the applicability of our ML-corrected DFTB approach to larger molecules and non-covalent systems (including only C, N, O, and H atoms), going beyond the chemical space represented in the training QM datasets. The enhanced performance of EquiDTB over the standard TB methods is demonstrated by the accurate computation of the atomic forces of S66x8 molecular dimers, as well as their interaction energies. Moreover, EquiDTB can be effectively employed to explore the potential energy surfaces of large and flexible drug-like molecules—for example, to determine the minimum energy path between isomers, analyze structural transitions during dynamical simulations, compute vibrational modes, and investigate energetic rankings. The performance for single molecules slightly decreases when the DFTB electronic energy is reduced to first-order but remains superior to standard TB methods. Our work thus demonstrates that an optimal integration of an equivariant NN with QM datasets can advance the DFTB method while maintaining high efficiency, paving the way for reliable (bio)molecular simulations.

Medrano Sandonas, Leonardo [Technische Universität↗

DOE OSTI2020

Improved sampling technique to collect natural gas from hydrate-bearing pressure cores

High quality gas compositional data are an important factor in interpreting the genetic source of natural gas hosted in hydrate-bearing sediments and other subsurface systems. In order to accurately characterize the composition of gas samples degassed from hydrate-bearing pressure cores, one must use a reproducible sampling technique that minimizes artifacts of the sampling process. Herein, we review sediment core degassing techniques and compare data obtained from a commonly used degassing approach, which we term the standard quantitative degassing (SQD) technique, to our newly developed modified quantitative degassing (MQD) method designed to minimize atmospheric contamination and gas-water interactions. The SQD method allows sample gas to interact with water in a bubbling chamber, which we hypothesize could alter the gas composition following mixing with water or dissolved gases in the bubbling chamber. Whereas, the MQD method allows for the collection of sample gas prior to the bubbling chamber. To compare the SQD and MQD methods, we performed a side-by-side comparison of noble (He, Ne, Ar, Kr, and Xe), major (H 2 , N 2 , O 2 , and CO 2 ), and hydrocarbon (CH 4 , C 2 H 6 , C 3 H 8 , i-C 4 H 10 , C 4 H 10 , i-C 5 H 12 , C 5 H 12 ) gas concentrations and select isotopic compositions obtained using both sample collection techniques. Gas samples were collected from hydrate-bearing pressure cores recovered and maintained under hydrate stable conditions from the northern Gulf of Mexico during the UT-GOM 2 -1 Expedition. The MQD method displayed significantly lower concentrations of atmospheric gases, higher proportions of hydrocarbon gases, lower ratios of C 1 /C 2 + , and heavier stable carbon and hydrogen isotopes of methane than the SQD method. These results demonstrate that the MQD method reduced air contamination and minimized alteration of the hydrocarbon gases. Finally, we conclude this method may be important for future work that seeks to determine the composition of natural gas from pressure cores using quantitative degassing experiments, especially those seeking to measure major (e.g., N 2 ) and noble gases.

58 GEOSCIENCES↗

DOE OSTI2024

A Jacobian-free pseudo-arclength continuation method for phase transitions in inhomogeneous thermodynamic systems

Developing phase diagrams for inhomogeneous systems in thermodynamics is difficult, in part, due to the large phase space and the possibility of unstable and metastable solutions arising from first-order phase transitions. Pseudo-arclength continuation (PAC) is a method that allows one to trace out stable and unstable solutions of nonlinear systems. Typically, PAC utilizes the Jacobian in order to implement Newton (or quasi-Newton) steps. In this work, we present a Jacobian-free PAC method that is amenable to the usual workflows in inhomogeneous thermodynamics. We demonstrate our method in systems that have first-order phase transitions, including a novel example of polyelectrolyte complex coacervation in confinement, where multiple surface phase transitions occur and can overlap with one another.

Chemistry↗

DOE OSTI2026

A novel method for thermal noise reduction, enabling measurements of broadband, low-amplitude electron temperature fluctuations using individual radiometer channels

A new analysis method has been developed for measurements of broadband, low-amplitude turbulent electron temperature fluctuations in fusion plasmas using individual radiometer channels of a correlation electron cyclotron emission diagnostic. This method takes advantage of differences in the correlation time of thermal noise compared to the correlation time of plasma fluctuations in fusion reactors. The validation of this single-channel method is demonstrated using comparisons with the standard dual-channel radiometer spectral decorrelation method for measurements of turbulent electron temperature fluctuations in the core and edge of low confinement (L), improved confinement (I), and high confinement (H)-mode plasmas at the ASDEX Upgrade tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Rigid registration algorithm based on the minimization of the total variation of the difference map

Image registration is broadly used in various scenarios in which similar scenes in different images are to be aligned. However, image registration becomes challenging when the contrasts and backgrounds in the images are vastly different. This work proposes using the total variation of the difference map between two images (TVDM) as a dissimilarity metric in rigid registration. A method based on TVDM minimization is implemented for image rigid registration. The method is tested with both synthesized and real experimental data that have various noise and background conditions. The performance of the proposed method is compared with the results of other rigid registration methods. It is demonstrated that the proposed method is highly accurate and robust and outperforms other methods in all of the tests. The new algorithm provides a robust option for image registrations that are critical to many nano-scale X-ray imaging and microscopy applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Evaluation of Radiography for TRISO Buffer Layer Density Measurement

Tristructural isotropic (TRISO) fuel particles consist of a central uranium-bearing kernel and a series of coating layers designed to retain fission products and to ensure fuel performance. Several parameters such as thickness and density must be measured for these coating layers to show that they conform with fuel specifications. Current methods for measuring the density of pyrolytic carbon and silicon carbide layers (liquid gradient density column) and the buffer layer (mercury porosimetry) generate Resource Conservation and Recovery Act (RCRA) radiological-mixed waste. In addition, measurement of buffer and inner pyrolytic carbon layer densities require hot sampling or interrupted coating runs and the mercury porosimetry method used for buffer density measurement only measures the mean buffer density, not the interparticle distribution. A new approach has been evaluated to measure the density of coating layers in TRISO particles based on the dependence of x-ray attenuation in radiographs on material density. This method does not generate RCRA mixed waste, measures density on a particle-by-particle basis, and in principle is capable of measuring the density of all coating layers in a single process. Initial results using thinned TRISO particle sections to evaluate radiography measurement of density as a quality control characterization method are reported herein. In this work, the primary focus is on measurement of the density of the buffer layer; however, with appropriate calibration the method should be applicable to other coating layers. Improvements to the initial method and a full demonstration of the method on the remaining coating layers may be pursued as a future effort.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

A Geo-Data Science Method for Assessing Unconventional Rare-Earth Element Resources in Sedimentary Systems

Abstract Rare-earth elements (REEs) supply raw materials that constitute many of our modern critical infrastructure, defense, technology, and electrification needs. Despite REE accumulations occurring in conventional bedrock and ion-adsorption deposits sourced from weathering of igneous rocks, unconventional host materials such as coal and related sedimentary strata have been identified as promising sources of REEs to meet growing demand. To maximize the potential of unconventional resources such as REE-coal systems, new approaches are needed overcome challenges from mineral systems with no known deposits and areas with sparse geochemical data. This article presents a systematic knowledge-data resource assessment method for predicting and identifying REE resource potential and occurrence in these unconventional systems. The method utilizes a geologic and geospatial knowledge-data approach informed and guided by REE accumulation mechanisms to systematically assess and identify areas of higher enrichment. An assessment of the Powder River Basin is presented as a test case to demonstrate the method workflow and results. The key output is a potential enrichment score map reported with varying confidence levels based on the amount of supporting evidence. Results from the test case indicate several locations with promising potential for different types of coal-REE deposits, demonstrating the viability of the method for exploration and assessment of unconventional REE resources. The method is flexible by design and, with sufficient applicable knowledge and data, can be adapted for assessing critical mineral systems in other sedimentary systems as well.

58 GEOSCIENCES↗

DOE OSTI2022

Modified Energy Span Analysis of Catalytic Parallel Pathways and Selectivity

Mechanistic modeling provides vital insights into catalytic reactions. To analyze complex reaction networks with parallel pathways, we leverage the graph theory approach of the Energy Span Model (ESM) to develop a modified energy span analysis (MESA). A new method of cycle plots is proposed to perform reaction pathways analysis visually. We demonstrate this method on two published models: one describing carbon monoxide oxidation and the other simulating ethylene conversion to propanal via hydroformylation or ethane via hydrogenation. Fundamental insights explain kinetic observables, such as a reactant’s negative reaction order. General principles are revealed, such as rate-determining surface species being outside the primary reaction flux cycle and pathway selectivity being a purely kinetic property when reaction conditions are not near equilibrium. Lastly, we demonstrate MESA’s consistency with published microkinetic modeling results, highlighting this technique’s extension of the ESM to heterogeneous catalysts using collision theory to describe adsorption steps and concentration effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Demonstrating Computational Equivalence Between Continuous and Discrete Adjoint Methods by Calculating Time-Dependent Adjoint Solutions with Neutron Diffusion Models

The continuous adjoint method and the discrete adjoint method are two alternative approaches used to calculate adjoint solutions for adjoint systems. The continuous adjoint method derives adjoint equations analytically from continuous forward equations and then solves the adjoint equations either analytically or numerically in a discretized form whereas the discrete adjoint method calculates the adjoint solutions directly from the discretized forward equations. With regard to the methodology development and calculation procedure, distinct differences are well recognized between the two methods. For certain reasons, both methods are exclusively preferred and commonly used by different computational communities, but limited studies clarify the connections between the two adjoint methods from either of the communities. Herein, this paper demonstrates the computational equivalence between the continuous and discrete adjoint methods by investigating time-dependent adjoint solutions to the two-group neutron diffusion model in nuclear reactor analysis problems using both methods. Adjoint solutions can be used to estimate system parameters for reactor safety analysis. Appropriate final state conditions for the adjoint systems are specified in both of the methods, and the conditions are clarified with proper physical explanations. With the help of an event-based case study on neutron diffusion models, the accuracy of the time-dependent adjoint fluxes obtained from both methods is verified, and the pros and cons of both adjoint methods are examined. More importantly, the computational equivalence of both methods is demonstrated when they are applied to multigroup neutron diffusion systems. The advantage of calculating time-dependent adjoint fluxes by directly solving time-dependent adjoint systems rather than taking steady-state approximations as in common practice is also demonstrated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

All-optical reconfiguration of single silicon-vacancy centers in diamond for non-volatile memories

Strain engineering is vital for tuning the optical and spin properties of solid-state color centers, enhancing spin coherence and compensating emission wavelength shift. Here, we develop an all-optical approach to directly modify the local strain of color centers at the nanoscale by migrating the nearby defect. High-power pulsed optical irradiation triggers defect migration, which subsequently leads to the redistribution of the local crystal lattice of the host material. This redistribution alters the strain experienced by nearby color centers. Using silicon-vacancy centers in diamond, we validate this method and demonstrate a ground state splitting enhancement of up to 1.8 THz. Unlike conventional methods, our approach requires no external fields or nanostructure modifications, enabling non-volatile strain control and optical memory functionality across wide temperature ranges. Its local, permanent nature offers a scalable path for enhancing spin coherence in large-scale quantum systems and has potential applications in photonic machine learning.

Nanoscale devices↗

DOE OSTI2022

Fast Solution of Fully Implicit Runge--Kutta and Discontinuous Galerkin in Time for Numerical PDEs, Part I: the Linear Setting

Fully implicit Runge--Kutta (IRK) methods have many desirable properties as time integration schemes in terms of accuracy and stability, but high-order IRK methods are not commonly used in practice with numerical PDEs due to the difficulty of solving the stage equations. This paper introduces a theoretical and algorithmic preconditioning framework for solving the systems of equations that arise from IRK methods applied to linear numerical PDEs (without algebraic constraints). Additionally, this framework also naturally applies to discontinuous Galerkin discretizations in time. Under quite general assumptions on the spatial discretization that yield stable time integration, the preconditioned operator is proven to have condition number bounded by a small, order-one constant, independent of the spatial mesh and time-step size, and with only weak dependence on number of stages/polynomial order; for example, the preconditioned operator for 10th-order Gauss IRK has condition number less than two, independent of the spatial discretization and time step. The new method can be used with arbitrary existing preconditioners for backward Euler-type time-stepping schemes and is amenable to the use of three-term recursion Krylov methods when the underlying spatial discretization is symmetric. The new method is demonstrated to be effective on various high-order finite-difference and finite element discretizations of linear parabolic and hyperbolic problems, demonstrating fast, scalable solution of up to 10th-order accuracy. The new method consistently outperforms existing block preconditioning approaches, and in several cases, the new method can achieve 4th-order accuracy using Gauss integration with roughly half the number of preconditioner applications and wallclock time as required using standard diagonally IRK methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Analysis of Rig Parameter Data Using Drilling Process Modeling Constraints, Volume 5: Utah FORGE Well 16B(78)-32

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Analysis of Rig Parameter Data Using Drilling Process Modeling Constraints, Volume 4: Utah FORGE Well 78B-32

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Analysis of Rig Parameter Data Using Drilling Process Modeling Constraints, Volume 3: Utah FORGE Well 56-32

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Analysis of Rig Parameter Data Using Drilling Process Modeling Constraints, Volume 1: Summary of Utah FORGE Wells 16A(78)-32, 56-32, 78B-32 and 16B(78)-32

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Physics-Informed Deep Learning for Reconstruction of Spatial Missing Climate Information in the Antarctic

Understanding the influence of the Antarctic on the global climate is crucial for the prediction of global warming. However, due to very few observation sites, it is difficult to reconstruct the rational spatial pattern by filling in the missing values from the limited site observations. To tackle this challenge, regional spatial gap-filling methods, such as Kriging and inverse distance weighted (IDW), are regularly used in geoscience. Nevertheless, the reconstructing credibility of these methods is undesirable when the spatial structure has massive missing pieces. Inspired by image inpainting, we propose a novel deep learning method that demonstrates a good effect by embedding the physics-aware initialization of deep learning methods for rapid learning and capturing the spatial dependence for the high-fidelity imputation of missing areas. We create the benchmark dataset that artificially masks the Antarctic region with ratios of 30%, 50% and 70%. The reconstructing monthly mean surface temperature using the deep learning image inpainting method RFR (Recurrent Feature Reasoning) exhibits an average of 63% and 71% improvement of accuracy over Kriging and IDW under different missing rates. With regard to wind speed, there are still 36% and 50% improvements. In particular, the achieved improvement is even better for the larger missing ratio, such as under the 70% missing rate, where the accuracy of RFR is 68% and 74% higher than Kriging and IDW for temperature and also 38% and 46% higher for wind speed. In addition, the PI-RFR (Physics-Informed Recurrent Feature Reasoning) method we proposed is initialized using the spatial pattern data simulated by the numerical climate model instead of the unified average. Compared with RFR, PI-RFR has an average accuracy improvement of 10% for temperature and 9% for wind speed. When applied to reconstruct the spatial pattern based on the Antarctic site observations, where the missing rate is over 90%, the proposed method exhibits more spatial characteristics than Kriging and IDW.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

A second-order-in-time, explicit approach addressing the redundancy in the low-Mach, variable-density Navier-Stokes equations

A novel algorithm for explicit temporal discretization of the variable-density, low-Mach Navier-Stokes equations is presented here in this study. Recognizing there is a redundancy between the mass conservation equation, the equation of state, and the transport equation(s) for the scalar(s) which characterize the thermochemical state, and that it destabilizes explicit methods, we demonstrate how to analytically eliminate the redundancy and propose an iterative scheme to solve the resulting transformed scalar equations. The method obtains second-order accuracy in time regardless of the number of iterations, so one can terminate this subproblem once stability is achieved. Hence, flows with larger density ratios can be simulated while still retaining the efficiency, low cost, and parallelizability of an explicit scheme. The temporal discretization algorithm is used within a pseudospectral direct numerical simulation which extends the method of Kim, Moin, and Moser for incompressible flow to the variable-density, low-Mach setting, where we demonstrate stability for density ratios up to ~25.7.

97 MATHEMATICS AND COMPUTING↗