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At least 271 records · Page 15

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DOE OSTI2016

Fracture Network and Fluid Flow Imaging for Enhanced Geothermal Systems Applications from Multi-Dimensional Electrical Resistivity Structure

We have developed an algorithm for the inversion of magnetotelluric (MT) data to a 3D earth resistivity model based upon the finite element method. Hexahedral edge finite elements are implemented to accommodate discontinuities in the electric field across resistivity boundaries, and to accurately simulate topographic variations. All matrices are reduced and solved using direct solution modules which avoids ill-conditioning endemic to iterative solvers such as conjugate gradients, principally PARDISO for the finite element system and PLASMA for the parameter step estimate. Large model parameterizations can be handled by transforming the Gauss-Newton estimator to data-space form. Accuracy of the forward problem and jacobians has been checked by comparison to integral equations results and by limiting asymptotes. Inverse accuracy and performance has been verified against the public Dublin Secret Test Model 2 and the well-known Mount St Helens 3D MT data set. This algorithm we believe is the most capable yet for forming 3D images of earth resistivity structure and their implications for geothermal fluids and pathways.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Time-resolved atomic-resolution Brownian tomography of single nanocrystals reveals size-dependent dynamics

Atomic-resolution structure identification of nanocrystals by graphene liquid cell electron microscopy (GLC-EM) has revealed that small, solubilized platinum nanocrystals consist of an ordered crystalline core surrounded by mobile surface atoms, which dissociate during oxidative etching, resulting in distinct temporal structural states. Requirements imposed by the 3D reconstruction algorithm limit the number of structural states that can be resolved. We introduce a regularized 3D reconstruction algorithm that exploits the redundancy inherent in the experimental data, allowing us to improve the time resolution. Our developments provide a comprehensive molecular picture at unprecedented spatial and temporal resolution of the nonlinear, linear, and fluctuating dynamic phenomena that single nanocrystals undergo during the GLC-EM experiment. We determined atomic structures of 66 temporal structural states, extracted from 15 time trajectories of individual nanocrystals. Large (478 to 698 atoms) and small (<300 atoms) nanocrystals show etching that preserves a stable core, whereas mid-sized (351 to 571 atoms) nanocrystals present dynamics that change the coordination of the core.

Meana-Pañeda, Rubén↗

DOE OSTI2022

Joint Inversion of Body Wave Arrival Times, Surface Wave Dispersion Data and Receiver Functions: Method and Application to South China

In this work, we have developed a new joint inversion method that incorporates body wave arrival times, surface wave dispersion and receiver functions to simultaneously update earthquake locations and constrain three-dimensional P-wave (Vp) and S-wave velocity (Vs) models. Due to complementary sensitivities of the three types of data, the proposed joint inversion algorithm can reduce the intrinsic non-uniqueness of inversions using fewer types of data and better determine smooth velocity variations and velocity discontinuities. Synthetic tests demonstrate the advantages of this new joint inversion algorithm in resolving velocity structures, especially in constraining velocity gradients across the Moho interface. We have applied the proposed joint inversion algorithm to image the lithosphere velocity structure of south China. The inverted Vp and Vs models fit body wave arrival times, surface wave dispersion and receiver functions well. We further analyzed the distribution of Vs gradients across the Moho interface in detail, which helps us better understand tectonics in south China.

58 GEOSCIENCES↗

DOE OSTI2025

Direct structural retrieval from gas-phase ultrafast diffraction data using a genetic algorithm

Ultrafast scattering techniques such as ultrafast electron diffraction and ultrafast x-ray diffraction have been utilized to elucidate the structural dynamics, reaction intermediates, and final products in molecular reactions following photoexcitation. The time-dependent structures are typically not directly retrieved from the experimental data, but they rely on comparison with calculations. The genetic algorithm (GA), a global optimization strategy, can be used to retrieve the molecular structures directly from diffraction patterns without any theoretical input. However, the robustness of the GA with respect to real experimental conditions such as a limited momentum transfer range, noise, and artifacts has not been studied in detail. In this work, we characterize the performance of the GA with simulated data that mimic realistic experimental conditions. We have developed and implemented a variant of the GA specific to diffraction measurements which performs better in the presence of imperfect data compared to the standard implementation of the GA. We demonstrate this method with both synthetic data and experimental ultrafast electron diffraction data on the UV-induced photodissociation of trifluoroiodomethane (C⁢F 3⁡ I) molecules.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2016

Fast Parallel Stochastic Subspace Algorithms for Large-Scale Ambient Oscillation Monitoring

With the installation of synchrophasors widely across the power grid, measurement-based oscillation monitoring algorithms are becoming increasingly useful in identifying the real-time oscillatory modal properties in power systems. When the number of phasor measurement unit (PMU) channels grows, the computational time of many PMU data based algorithms is dominated by the computational burden in processing large-scale dense matrices. In order to overcome this limitation, this paper presents new formulations and computational strategies for speeding up an ambient oscillation monitoring algorithm, namely, stochastic subspace identification (SSI). Based on previous work, two fast singular value decomposition (SVD) approaches are first applied to the SVD evaluation within the SSI algorithm. Next, block structures are exploited so that the large-scale dense matrix computations can be processed in parallel. This helps in memory savings as well as in overall computational time. Experimental results from three sets of archived data of the western interconnection demonstrate that the new approaches can provide significant speedups while retaining modal estimation accuracy. With proposed fast parallel algorithms, the real-time oscillation monitoring of the large-scale system using hundreds of PMU measurements becomes feasible.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Modeling approaches for addressing unrelaxable bound constraints with unconstrained optimization methods

We explore novel approaches for solving nonlinear optimization problems with unrelaxable bound constraints, which must be satisfied before the objective function can be evaluated. Our method reformulates the unrelaxable bound-constrained problem as an unconstrained optimization problem that is amenable to existing unconstrained optimization methods. The reformulation relies on a domain warping to form a merit function; the choice of the warping determines the level of exactness with which the unconstrained problem can be used to find solutions to the bound-constrained problem, as well as key properties of the unconstrained formulation such as smoothness. We develop theory when the domain warping is a multioutput sigmoidal warping, and we explore the practical elements of applying unconstrained optimization methods to the formulation. Here, we develop an algorithm that exploits the structure of the sigmoidal warping to guarantee that unconstrained optimization algorithms applied to the merit function will find a stationary point to the desired tolerance.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Prediction of chlorine and fluorine crystal structures at high pressure using symmetry driven structure search with geometric constraints

The high-pressure properties of fluorine and chlorine are not yet well understood because both are highly reactive and volatile elements, which have made conducting diamond anvil cell and x-ray diffraction experiments a challenge. Here, we use ab initio methods to search for stable crystal structures of both elements at megabar pressures. We demonstrate how symmetry and geometric constraints can be combined to efficiently generate crystal structures that are composed of diatomic molecules. Furthermore, our algorithm extends the symmetry driven structure search method by adding constraints for the bond length and the number of atoms in a molecule while still maintaining generality. As a method of validation, we have tested our approach for dense hydrogen and reproduced the known molecular structures of Cmca-12 and Cmca-4. We apply our algorithm to study chlorine and fluorine in the pressure range of 10 GPa–4000 GPa while considering crystal structures with up to 40 atoms per unit cell. We predict chlorine to follow the same series of phase transformations as elemental iodine from Cmca to Immm to Fm3¯m, but at substantially higher pressures. We predict fluorine to transition from a C2/c to Cmca structure at 70 GPa, to a novel orthorhombic and metallic structure with P42/mmc symmetry at 2500 GPa, and finally to its cubic analog form with Pm3¯n symmetry at 3000 GPa.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Efficient analysis of small-angle scattering curves for large biomolecular assemblies using Monte Carlo methods

Structure elucidation from small-angle scattering curves of large biomolecular assemblies is notoriously challenging. This is because the simulation of high-resolution features in the structure of large macromolecular assemblies, such as de novo protein assemblies, is computationally demanding when it needs to cover a broad range of length scales. Conventional methods, such as the numerical approximation to the Debye equation or the use of spherical harmonics, do not scale well as the size of the assembly increases, which limits their application to small structures (e.g. individual proteins). This work explores the effectiveness of a Monte Carlo method to simulate and fit scattering curves for large biomolecular assemblies spanning over ranges covering atomic and molecular detail (e.g. spacing and orientation of proteins in an assembly) as well as large-scale (hundreds of nanometres) features. Owing to its speed and scalability, it can be combined with a fitting algorithm to extract structural features from experimental small-angle scattering curves in biomolecular assemblies that are otherwise intractable for interpretation. This work first demonstrates the effectiveness of the tool using experimental small-angle X-ray scattering (SAXS) data from tile-like proteins that assemble into 1D tube-like macromolecular structures. Here, the diameter distribution of tubes is extracted from SAXS fits, and this is quantitatively compared with distributions from electron microscopy. SAXS data are also obtained from 2D sheet-like protein assemblies, and the proposed method is used to quantify structural features such as the separation distance between protein building blocks and the flexing of the sheet. An open-source implementation of the methodology is provided for use in a broad range of biological systems involving multi-scale scattering analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Impact of the plastic deformation microstructure in metals on the kinetics of recrystallization: A phase-field study

The sensitivity of recrystallization kinetics in metals to the heterogeneity of microstructure and deformation history is a widely accepted experimental fact. However, most of the available recrystallization models employ either a mean field approach or use grain-averaged parameters, and thus neglecting the mesoscopic heterogeneity induced by prior deformation. Here in the present study, we investigate the impact of deformation-induced dislocation (subgrain) structure on the kinetics of recrystallization in metals using the phase-field approach. The primary focus here is upon the role of dislocation cell boundaries. The free energy formulation of the phase-field model accounts for the heterogeneity of the microstructure by assigning localized energy to the resulting dislocation microstructure realizations generated from experimental data. These microstructure realizations are created using the universal scaling laws for the spacing and the misorientation angles of both the geometrically necessary and incidental dislocation boundaries. The resulting free energy is used into an Allen-Cahn based model of recrystallization kinetics, which are solved using the finite element method. The solutions thus obtained shed light on the critical role of the spatial heterogeneity of deformation in the non-smooth growth of recrystallization nuclei and on the final grain structure. The results showed that, in agreement with experiment, the morphology of recrystallization front exhibits protrusions and retrusions. By resolving the subgrain structure, the presented algorithm paves the way for developing predictive kinetic models that fully account for the deformed state of recrystallizing metals.

36 MATERIALS SCIENCE↗

DOE OSTI2014

SU-F-BRF-13: Investigating the Feasibility of Accurate Dose Measurement in a Deforming Radiochromic Dosimeter

Purpose: Presage-Def, a deformable radiochromic 3D dosimeter, has been previously shown to have potential for validating deformable image registration algorithms. This work extends this effort to investigate the feasibility of using Presage-Def to validate dose-accumulation algorithms in deforming structures. Methods: Two cylindrical Presage-Def dosimeters (8cm diameter, 4.5cm length) were irradiated in a water-bath with a simple 4-field box treatment. Isocentric dose was 20Gy. One dosimeter served as control (no deformation) while the other was laterally compressed during irradiation by 21%. Both dosimeters were imaged before and after irradiation with a fast (∼10 minutes for 1mm isotropic resolution), broad beam, high resolution optical-CT scanner. Measured dose distributions were compared to corresponding distributions calculated by a commissioned Eclipse planning system. Accuracy in the control was evaluated with 3D gamma (3%/3mm). The dose distribution calculated for the compressed dosimeter in the irradiation geometry cannot be directly compared via profiles or 3D gamma to the measured distribution, which deforms with release from compression. Thus, accuracy under deformation was determined by comparing integral dose within the high dose region of the deformed dosimeter distribution versus calculated dose. Dose profiles were used to study temporal stability of measured dose distributions. Results: Good dose agreement was demonstrated in the control with a 3D gamma passing rate of 96.6%. For the dosimeter irradiated under compression, the measured integral dose in the high dose region (518.0Gy*cm3) was within 6% of the Eclipse-calculated integral dose (549.4Gy*cm3). Elevated signal was noted on the dosimeter edge in the direction of compression. Change in dosimeter signal over 1.5 hours was ≤2.7%, and the relative dose distribution remained stable over this period of time. Conclusion: Presage-Def is promising as a 3D dosimeter capable of accurately measuring dose in a deforming structure, and warrants further study to quantify comprehensive accuracy at different levels of deformation. This work was supported by NIH R01CA100835. John Adamovics is the president of Heuris Inc., which commercializes PRESAGE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2018

A continuous analogue of the tensor-train decomposition

We develop new approximation algorithms and data structures for representing and computing with multivariate functions using the functional tensor-train (FT), a continuous extension of the tensor-train (TT) decomposition. The FT represents functions using a tensor-train ansatz by replacing the three-dimensional TT cores with univariate matrix-valued functions. The main contribution of this paper is a framework to compute the FT that employs adaptive approximations of univariate fibers, and that is not connected to any tensorized discretization. The algorithm can be coupled with any univariate linear or nonlinear approximation procedure. We demonstrate that this approach can generate multivariate function approximations that are several orders of magnitude more accurate, for the same cost, than those based on the conventional approach of compressing the coefficient tensor of a tensor-product basis. Our approach is in the spirit of other continuous computation packages such as Chebfun, and yields an algorithm which requires the computation of “continuous” matrix factorizations such as the LU and QR decompositions of vector-valued functions. To support these developments, we describe continuous versions of an approximate maximum-volume cross approximation algorithm and of a rounding algorithm that re-approximates an FT by one of lower ranks. We demonstrate that our technique improves accuracy and robustness, compared to TT and quantics-TT approaches with fixed parameterizations, of high-dimensional integration, differentiation, and approximation of functions with local features such as discontinuities and other nonlinearities.

42 ENGINEERING↗

DOE OSTI2018

System and method of storing and analyzing information

A system and method of storing and analyzing information is disclosed. The system includes a compiler layer to convert user queries to data parallel executable code. The system further includes a library of multithreaded algorithms, processes, and data structures. The system also includes a multithreaded runtime library for implementing compiled code at runtime. The executable code is dynamically loaded on computing elements and contains calls to the library of multithreaded algorithms, processes, and data structures and the multithreaded runtime library.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

System and method of storing and analyzing information

Feo, John T.↗

DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Open data set of live cyanobacterial cells imaged using an X-ray laser

Structural studies on living cells by conventional methods are limited to low resolution because radiation damage kills cells long before the necessary dose for high resolution can be delivered. X-ray free-electron lasers circumvent this problem by outrunning key damage processes with an ultra-short and extremely bright coherent X-ray pulse. Diffraction-before-destruction experiments provide high-resolution data from cells that are alive when the femtosecond X-ray pulse traverses the sample. This paper presents two data sets from micron-sized cyanobacteria obtained at the Linac Coherent Light Source, containing a total of 199,000 diffraction patterns. Utilizing this type of diffraction data will require the development of new analysis methods and algorithms for studying structure and structural variability in large populations of cells and to create abstract models. Such studies will allow us to understand living cells and populations of cells in new ways. New X-ray lasers, like the European XFEL, will produce billions of pulses per day, and could open new areas in structural sciences.

59 BASIC BIOLOGICAL SCIENCES↗

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 OSTI2014

Long-read, whole-genome shotgun sequence data for five model organisms

Single molecule, real-time (SMRT) sequencing from Pacific Biosciences is increasingly used in many areas of biological research including de novo genome assembly, structural-variant identification, haplotype phasing, mRNA isoform discovery, and base-modification analyses. High-quality, public datasets of SMRT sequences can spur development of analytic tools that can accommodate unique characteristics of SMRT data (long read lengths, lack of GC or amplification bias, and a random error profile leading to high consensus accuracy). In this paper, we describe eight high-coverage SMRT sequence datasets from five organisms (Escherichia coli, Saccharomyces cerevisiae, Neurospora crassa, Arabidopsis thaliana, and Drosophila melanogaster) that have been publicly released to the general scientific community (NCBI Sequence Read Archive ID SRP040522). Data were generated using two sequencing chemistries (P4C2 and P5C3) on the PacBio RS II instrument. The datasets reported here can be used without restriction by the research community to generate whole-genome assemblies, test new algorithms, investigate genome structure and evolution, and identify base modifications in some of the most widely-studied model systems in biological research.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Genetic algorithm-based optimisation of the few-group structure for lead fast reactors analysis

The optimal choice of the few-group structure for full-core transient analyses is still an open issue in reactor physics, especially for fast system like the lead fast reactor. One possible approach to select the group boundaries is represented by heuristic search algorithms, such as evolutionary ones. In this paper, a genetic algorithm coupled with the SIMMER code is employed to determine optimized six-group boundaries for the analysis of the ALFRED reactor. The Serpent Monte Carlo code is adopted to produce both the fine-group cross section library and the fine-group flux, used as a figure of merit to drive the genetic optimisation. The results show that the algorithm is indeed able to find satisfactory solutions that comply with the set objectives and can be reasonably interpreted in light of the underlying physics of the considered core. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗