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DOE OSTI2025

Enhanced MPM framework with multipatch isogeometric analysis for geotechnical applications

Achieving stable stress solutions at large strains using the Material Point Method (MPM) is challenging due to the accumulation of errors associated with geometry discretization, cell-crossing noise, and volumetric locking. Several simplified attempts exist in the literature to mitigate these errors, including higher-order frameworks. However, the stability of the MPM solution in such frameworks has been limited to simple geometries and the single-phase formulation (i.e., neglecting pore fluid). Although never explored, multipatch isogeometric analysis offers desirable qualities to simulate complex geometries while mitigating errors in the MPM. The degree of required high-order spatial integration has also never been investigated to infer a minimum limit for the stability of the stress solution in MPM. This paper presents a general-purpose numerical framework for simulating stable stresses in porous media, capturing both near incompressibility and multiphase interactions. First, the numerical framework is presented considering Non-Uniform Rational B-splines (NURBS) to perform isogeometric analysis (IGA) in MPM. Additionally, a volumetric strain smoothing algorithm is used to alleviate errors associated with volumetric locking. Second, the manifestation of cell-crossing errors is assessed via a series of problems with orders ranging from linear to cubic interpolation functions. Third, the use of NURBS is investigated and verified for problems with circular geometries. Finally, multipatch analysis is deployed to simulate plane strain and 3D penetration in soils, considering nearly incompressible elastoplastic (total stress) analysis and fully-coupled hydro-mechanical (effective stress) analysis. The stability of the solution is also analyzed for different constitutive models. From the results, it can be concluded that the framework using cubic interpolation functions with strain smoothing is the most convenient, presenting stable stress solutions for a broad range of multiphase geotechnical applications.

58 GEOSCIENCES↗

DOE OSTI2023

Multiscale spatial analysis of fracture nodes in two dimensions

Spatial arrangement of fractures as a function of scale is an important component of fracture quantification for inferential and predictive modeling. Available methods that analyze fracture spatial arrangement are based on one-dimensional spacing data; therefore, they are limited to semi-parallel fractures. Such methods cannot be applied to fracture networks in higher dimensions, particularly when fractures have different orientations. Here to characterize fracture arrangements in two dimensions, we propose using Ripley’s K-function, as a method of point pattern analysis, to quantify spatial arrangement of fracture nodes. Fracture nodes, such as barycenters, intersection points, and tips, are point-based representations of fracture locations and connectivity within the fracture network. We introduce formulations for isotropic as well as directional analyses of spatial arrangement. In addition, we derive formulations for edge correction in circular and rectangular study domains. Finally, we demonstrate applications of Ripley’s K-function on two natural fracture datasets. Our proposed method supports quantification and characterization of fracture spatial arrangements that allow practitioners to build representative models of fractures in the subsurface.

02 PETROLEUM↗

DOE OSTI2020

Powering the Blue Economy - Ocean Observing Use Cases Report

The "Powering the Blue Economy" (PBE) Report identified ocean observing and autonomous underwater vehicle (AUV) recharge as near-term markets with significant potential for marine energy integration when specified qualitative and quantitative assessment criteria were used. The ocean observing market (including AUVs) scored high relative to other markets analyzed in the PBE Report with respect to market size, marine renewable energy (MRE) compatibility with functional requirements, competitive positioning (i.e., MRE versus other power sources), path to market, and alignment to the Department of Energy Water Power Technologies Office (WPTO) and other agencies' objectives. Based on this scoring, Pacific Northwest National Laboratory (PNNL) and the National Renewable Energy Laboratory (NREL) pursued the development of five use cases that represent a spectrum of ocean observation platforms, fixed and mobile, to explore the present and future power needs that might be satisfied by marine energy. The use cases were described and analyzed for this potential by examining current missions and future needs, evaluating the mix of renewable energy sources that might satisfy those needs at sea, and understanding the overall cost drivers that might enable this application.

14 SOLAR ENERGY↗

DOE OSTI2024

Learning Functions Varying along a Central Subspace

Many functions of interest are in a high-dimensional space but exhibit low-dimensional structures. This paper studies regression of an s-Hölder function in $R^D$ which varies along a central subspace of dimension $d$ while $d \ll D$. A direct approximation of $f$ in $R^D$ with an accuracy $\varepsilon$ requires the number of samples in the order of $\varepsilon^{-(2s+D)/s}$. In this paper, we analyze the generalized contour regression (GCR) algorithm for the estimation of the central subspace and use piecewise polynomials for function approximation. GCR is among the best estimators for the central subspace, but its sample complexity is an open question. In this paper, we partially answer this questions by proving that if a variance quantity is exactly known, GCR leads to a mean squared estimation error of $O(n^{-1})$ for the central subspace. The estimation error of this variance quantity is also given in this paper. The mean squared regression error of $f$ is proved to be in the order of $(n/\log n)^{-\frac{2s}{2s+d}}$, where the exponent depends on the dimension of the central subspace instead of the ambient space . This result demonstrates that GCR is effective in learning the low-dimensional central subspace. We also propose a modified GCR with improved efficiency. Here, the convergence rate is validated through several numerical experiments.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Asymmetric devices based on carbon nanotubes for terahertz-range radiation detection

Various asymmetric detecting devices based on carbon nanotubes (CNTs) are studied. The asymmetry is understood as inhomogeneous properties along the conducting channel. In the first type of devices, an inhomogeneous morphology of the CNT grid is used. In the second type of devices, metals with highly varying work functions are used as the contact material. The relation between the sensitivity and detector configuration is analyzed. Based on the data obtained, approaches to the development of an efficient detector of terahertz radiation, based on carbon nanotubes are proposed.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Ambient Mass Spectrometry Rapid Analysis Methods for Trace Inorganics

The ability to rapidly detect and characterize the chemistry of trace quantities of inorganic species has multiple potential applications, including nuclear safeguards, nuclear forensics, and environmental monitoring. Some mass spectrometric methods currently exist, but they are costly and time-intensive, often destroying chemical information in the process of ionization. Ambient mass spectrometry is a relatively new analytical method in which samples are introduced to the mass spectrometer from atmospheric pressure, rather than from high vacuum. Ambient MS can operate with gentler ionization methods such as electrospray ionization (ESI) and paper-spray ionization (PSI), reducing fragmentation and preserving the chemical information of the analyte. This may be useful for determining the intended purpose of material in a sample. Strontium, part of nuclear fallout, is analyzed here to test these new analytical methods for viability. Electrospray ionization mass spectrometry functions by spraying the sample as a solution through a needle charged to a few kV. A nebulizing gas flows out along with the sample to create the spray before droplets enter a drying chamber with heated drying gas to remove the solvent molecules. From there bare analyte ions enter the mass spectrometer. This method has the advantage of relatively fast sample preparation and stable ionization. More stable ionization produces higher-quality data. Paper-spray ionization mass spectrometry operates by applying sample to a paper triangle. The paper triangle is then electrified in a metal clamp to a few kV before spray solvent is applied. The spray solvent transports analyte in the sample to the tip, where it is ionized and enters the mass spectrometer. One advantage of this method is reduced sample preparation - an analyst can even swipe a surface and cut a triangle directly from the swipe. The data analysis serves to rapidly process tens of samples all at once. The mass spectrometer outputs a file containing a few hundred spectra for each sample. The pipeline processes each one to identify peaks of interest. A full palette of data analysis metrics are generated, including plots of the raw data and plots of calculated values such as isotopic ratios. The pipeline filters the raw data to remove noise and background peaks, as well as peaks representing detector saturation. A heat map is generated to show the overall spectrum in mass-time- intensity space. Post-processing, we obtained the desired calibration curves, demonstrating that both ESI-MS and PSI-MS are viable methods for quantifying trace amounts of inorganics while retaining their chemical speciation information. ESI-MS demonstrated a linear response region of 100 ppb to 20 ppm, with a limit-of-detection of around 100 ppb. At top left is an example spectrum with the {sup 88}SrNO{sub 3} peak identified to show the retention of chemical speciation. PSI-MS showed a linear response region of around 1 ppb to 1 ppm, with a limit-of-detection of near 1 ppb. PSI-MS produced more erratic results than ESI-MS, likely due to the variability in ionization resulting from manual positioning of the paper triangle near the MS inlet. Future efforts will focus on improving the repeatability of PSI-MS as well as analyzing other species of interest such as cerium and uranium. Improvements will also be made to create more reliable data extraction and intelligent data filtering.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

Assessment of strontium oxide functionalized graphene nanoflakes for enhanced photocatalytic activity: A density functional theory approach

A series of strontium oxide functionalized graphene nanoflakes were designed and their optoelectronic properties were studied for enhanced photocatalytic activity. The efficiency of designed molecules was studied using various parameters such as HOMO-LUMO energy gap, light harvesting efficiency and exciton binding energy. The computed results show that by increasing the degree of functionalization of strontium oxide leads to lowering the band gap of hydrogen terminated graphene nanoflakes. Furthermore, the study explores the role of strontium oxide functionalization in Frontier Molecular Orbitals, ionization potential, electron affinity, exciton binding energy and light harvesting efficiency of designed molecules. The infrared and Raman spectra were simulated for pure and SrO functionalized graphene nanoflakes. The electron rich and electron deficient regions which are favorable for electrophilic and nucleophilic attacks respectively were analyzed using molecular electrostatic potential surface analysis.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

Metagenomic strategies identify diverse integron–integrase and antibiotic resistance genes in the Antarctic environment

The objective of this study is to identify and analyze integrons and antibiotic resistance genes (ARGs) in samples collected from diverse sites in terrestrial Antarctica. Integrons were studied using two independent methods. One involved the construction and analysis of intI gene amplicon libraries. In addition, we sequenced 17 metagenomes of microbial mats and soil by high-throughput sequencing and analyzed these data using the IntegronFinder program. As expected, the metagenomic analysis allowed for the identification of novel predicted intI integrases and gene cassettes (GCs), which mostly encode unknown functions. However, some intI genes are similar to sequences previously identified by amplicon library analysis in soil samples collected from non-Antarctic sites. ARGs were analyzed in the metagenomes using ABRIcate with CARD database and verified if these genes could be classified as GCs by IntegronFinder. We identified 53 ARGs in 15 metagenomes, but only four were classified as GCs, one in MTG12 metagenome (Continental Antarctica), encoding an aminoglycoside-modifying enzyme (AAC(6´)acetyltransferase) and the other three in CS1 metagenome (Maritime Antarctica). One of these genes encodes a class D β-lactamase (blaOXA-205) and the other two are located in the same contig. One is part of a gene encoding the first 76 amino acids of aminoglycoside adenyltransferase (aadA6), and the other is a qacG2 gene.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Ab initio electronic density in solids by many-body plane-wave auxiliary-field quantum Monte Carlo calculations

We present accurate many-body results of the electronic densities in several solid materials, including Si, NaCl, and Cu. These results are obtained using the ab initio auxiliary-field quantum Monte Carlo (AFQMC) method working in a plane-wave basis with norm-conserving, multiple-projector pseudopotentials. AFQMC has been shown to be an excellent many-body total energy method. Computation of observables and correlation functions other than the ground-state energy requires back-propagation, whose adaption and implementation in the plane-wave basis AFQMC framework are discussed in the present paper. This development allows us to compute correlation functions, electronic densities, and interatomic forces, paving the way for geometry optimizations and calculations of thermodynamic properties in solids. Finite supercell size effects are considerably more subtle in the many-body framework than in independent-electron calculations. We analyze the convergence of the electronic density, and obtain best estimates for the thermodynamic limit. The densities from several typical density functionals are benchmarked against our near-exact results. The electronic densities we obtained can also be used to help construct improved density functionals.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Safety and Efficacy of Percutaneous Biliary Covered Stent Placement in Patients with Malignant Biliary Hilar Obstruction; Correlation with Liver Function

PurposeTo estimate the safety and efficacy of percutaneous ePTFE-covered biliary stent placement and the relationship between underlying liver function and stent patency in patients with malignant hilar obstruction.Materials and MethodsFrom March 2012 to June 2015, 41 patients [22 females, 19 males; mean age 69.8 (range 34–94) years] with malignant biliary obstruction underwent percutaneous biliary stent placement (31 patients with unilateral, 10 patients with bilateral side-by-side). Cumulative patient survival and stent patency rate curves were derived using the Kaplan–Meier method. A Cox model was used to explore the relationship between liver function and patient survival, and also biliary stent patency. Pearson correlation coefficient was used to analyze the relationship between patient survival and stent patency.ResultsTechnical success rate was 100 % and clinical success rate was 95 %. During follow-up, four complications occurred (two bilomas and two cases of acute cholecystitis) and were treated successfully with percutaneous drainage. No other complication occurred. Mean serum bilirubin level was 11.34 ± 7.35 mg/dL before drainage and 5.00 ± 4.83 mg/dL 2 weeks after stent placement. The median patent survival duration was 147 days (95 % CI, 69.6–224.4 days). The median stent patency duration was 101 days (95 % CI, 70.0–132.0 days). The cumulative stent patency rates at 1, 3, 6, and 12 months were 97, 57.6, 30.3, and 17.0 %, respectively. Child–Pugh score was correlated significantly with patient survival (P = 0.011) and stent patency (P = 0.007). MELD score was correlated significantly with stent patency (P = 0.044). There was a correlation between patient survival and stent patency (r = 0.778, P < 0.001).ConclusionPercutaneous placement of ePTFE-covered biliary stent was a safe and an effective method for malignant biliary obstruction. Underlying liver function seemed to be one of the important factors affecting patient survival and stent patency, and stent patency showed statistically significant correlation with patient survival.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2018

Spectral and Structural Characteristics for Cluster Systems of Charged Brownian Particles

We report on the results of analytic and numerical investigations of the dynamics for bounded ensembles of charged Brownian particles in the potential field of an electrostatic trap. Simulation has been performed for cluster systems consisting of (approximately up to one thousand) particles with the Coulomb interaction in a wide range of their parameters. The spectral structures and structural characteristics of the systems being simulated have been compared. The dependence of the form of the pair correlation function and the size of cluster systems on the temperature and the number of particles has been analyzed. The relation between the spectral density of displacements of the center of mass and of individual particles and the structural characteristics and nonideality parameter of the ensembles being modeled has been investigated.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

Assessment of CO 2 Storage Potential in Naturally Fractured Reservoirs With Dual–Porosity Models

Naturally Fractured Reservoirs (NFR’s) have received little attention as potential CO 2 storage sites. Two main facts deter from storage projects in fractured reservoirs: (1) CO 2 tends to be nonwetting in target formations and capillary forces will keep CO 2 in the fractures, which typically have low pore volume; and (2) the high conductivity of the fractures may lead to increased spatial spreading of the CO 2 plume. Numerical simulations are a powerful tool to understand the physics behind brine-CO 2 flow in NFR’s. Dual-porosity models are typically used to simulate multiphase flow in fractured formations. However, existing dual-porosity models are based on crude approximations of the matrix-fracture fluid transfer processes and often fail to capture the dynamics of fluid exchange accurately. Therefore, more accurate transfer functions are needed in order to evaluate the CO 2 transfer to the matrix. This work presents an assessment of CO 2 storage potential in NFR’s using dual-porosity models. We investigate the impact of a system of fractures on storage in a saline aquifer, by analyzing the time scales of brine drainage by CO 2 in the matrix blocks and the maximum CO 2 that can be stored in the rock matrix. A new model to estimate drainage time scales is developed and used in a transfer function for dual-porosity simulations. Furthermore, we then analyze how injection rates should be limited in order to avoid early spill of CO 2 (lost control of the plume) on a conceptual anticline model. Numerical simulations on the anticline show that naturally fractured reservoirs may be used to store CO 2 .

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Heterogeneous Mixtures of Dictionary Functions to Approximate Subspace Invariance in Koopman Operators: Why Deep Koopman Operators Work

Abstract Koopman operators model nonlinear dynamics as a linear dynamic system acting on a nonlinear function as the state. This nonstandard state is often called a Koopman observable and is usually approximated numerically by a superposition of functions drawn from a dictionary . In a widely used algorithm, extended dynamic mode decomposition (EDMD), the dictionary functions are drawn from a fixed class of functions. Deep learning combined with EDMD has been used to learn novel dictionary functions in an algorithm called deep dynamic mode decomposition (deepDMD). The learned representation both (1) accurately models and (2) scales well with the dimension of the original nonlinear system. In this paper, we analyze the learned dictionaries from deepDMD and explore the theoretical basis for their strong performance. We explore State-Inclusive Logistic Lifting (SILL) dictionary functions to approximate Koopman observables. Error analysis of these dictionary functions show they satisfy a property of subspace approximation, which we define as uniform finite approximate closure. Typically, a Koopman dictionary’s nonlinear functions are homogeneous. In this paper, we discover that structured mixing of heterogeneous dictionary functions drawn from different classes of nonlinear functions achieve the same accuracy and dimensional scaling as the deep-learning-based deepDMD algorithm Yeung et al. ( In: 2019 American Control Conference (ACC), 2019). We specifically show this by building a heterogeneous dictionary comprised of SILL functions and conjunctive radial basis functions (RBFs). This mixed dictionary achieves similar accuracy and dimensional scaling to deepDMD with an order of magnitude reduction in parameters, while maintaining geometric interpretability. These results strengthen the viability of dictionary-based Koopman models to solving high-dimensional nonlinear learning problems.

Johnson, Charles A.↗

DOE OSTI2023

Assisted migration in a warmer and drier climate: less climate buffering capacity, less facilitation and more fires at temperate latitudes?

Assisted tree migration has been proposed as a conceptual solution to mitigate lags in biotic responses to anthropogenic climate change. The rationale behind this concept is that tree species currently growing under warmer and drier climates will be more resistant and resilient to the new climatic conditions than tree species naturally growing in currently wetter and colder climates. However, we hypothesize that, by being more stress‐tolerant to warmer and drier conditions, translocated species should exhibit different functional attributes, which could induce important ecological and societal costs and overcome the desired benefits of maintaining wood production and other ecosystem services. We used principal component analysis (PCA) to analyze variation in seven traits of 106 tree and tall shrub species from contrasting latitudinal distributions in western North America and Europe to predict the potential functional changes of forest ecosystems due to the translocation of tree species from low to high latitudes. We show that species from both continents differed primarily by their position on the leaf economy spectrum (LES) and their size traits. Even though, in Europe, differences in LES were significantly correlated to species southern latitudinal positions, in both continents differences in size traits were significantly correlated to latitude. These results suggest that assisted migration by translocating more conservative species of shorter stature in currently cooler climates should decrease the buffering capacity of forest canopies, decrease facilitation for understory species, and increase wildfire risks, whose effects have the potential to accelerate climate warming through negative atmospheric feedback processes. As an alternative solution to assisted migration that may accelerate rather than mitigate climate change, we recommend that foresters gradually diversify the vertical structure and layering of the existing forest canopy to maintain a sustainable water cycle and energy balance between the soil, the tree and the atmosphere without increasing the wildfire risk.

Environmental Sciences & Ecology↗

DOE OSTI2018

Fluorescent resonance energy transfer -based biosensor for detecting conformational changes of Pin1

Highlights: • Pin1 contains two domains: WW domain and catalytic PPIase domain. • Interaction of both domains is required for Pin1 functions. • Molecular movement of Pin1 after it binds each substrate is still unclear. • FRET biosensor for detecting conformational changes of Pin1 was created. • This FRET biosensor is useful for Pin1-targeting drug screening. Pin1, a peptidyl prolyl cis/trans isomerase (PPIase), regulates the activity and stability of various phosphorylated proteins. Pin1 consists of a PPIase domain and WW domain, both of which are required for the function of Pin1. However, how the behavior of these domains changes upon binding to phosphorylated proteins has not been analyzed. We created a Fluorescent Resonance Energy Transfer (FRET)-based biosensor “CPinY”, which is composed of Pin1 flanked by CFP and YFP, and analyzed the interaction between Pin1 and c-Myc. Our results indicated that the dual phosphorylation of c-Myc at Thr58 and Ser62 is essential for tight interaction with Pin1. Additionally, this interaction caused a significant conformational change in Pin1. Our CPinY biosensor also detected a novel type of inhibitor of Pin1 function. We believe that his biosensor will be a novel drug screening technology targeting Pin1.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Numerical analysis of the unintegrated double gluon distribution

We present detailed numerical analysis of the unintegrated double gluon distribution which includes the dependence on the transverse momenta of partons. The unintegrated double gluon distribution was obtained following the Kimber-Martin-Ryskin method as a convolution of the perturbative gluon splitting function with the collinear integrated double gluon distribution and the Sudakov form factors. We analyze the dependence on the transverse momenta, longitudinal momentum fractions and hard scales. We find that the unintegrated gluon distribution factorizes into a product of two single unintegrated gluon distributions in the region of small values of x, provided the splitting contribution is included and the momentum sum rule is satisfied.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

Probing universalities in d > 2 CFTs: from black holes to shockwaves

Gravitational shockwaves are insensitive to higher-curvature corrections in the action. Recent work found that the OPE coefficients of lowest-twist multi-stress-tensor operators, computed holographically in a planar black hole background, are insensitive as well. In this paper, we analyze the relation between these two limits. We explicitly evaluate the two-point function on a shockwave background to all orders in a large central charge expansion. In the geodesic limit, we find that the ANEC exponentiates in the multi-stress-tensor sector. To compare with the black hole limit, we obtain a recursion relation for the lowest-twist products of two stress tensors in a spherical black hole background, letting us efficiently compute their OPE coefficients and prove their insensitivity to higher curvature terms. After resumming the lowest-twist stress-tensors and analytically continuing their contributions to the Regge limit, we find a perfect agreement with the shockwave computation. We also discuss the role of double-trace operators, global degenerate states, and multi-stress-tensor conformal blocks. These holographic results suggest the existence of a larger universal structure in higher-dimensional CFTs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learn- ing to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

machine learning, multiscale modeling, physics-bas↗