Publications

Abstract not provided.

On October 16-17, 2024, in Long Beach, California, the U.S. DOE Bioenergy Technologies Office (BETO), in collaboration with Sandia National Laboratories, NREL, PNNL, and LANL, convened experts from industry and academia to explore the potential for utilizing existing petroleum refinery infrastructure to expand the bioeconomy. Although refinery integration is not a new concept, recent trends have revitalized interest in this topic.

A series of high entropy AB2-type Ti2-xZrxMnCrFeNi alloys (x = 0.6, 0.7, 0.8, 0.9, 1.0, 1.1 and 1.2) were synthesized to investigate their potential for hydrogen storage and chemical compression. The influence of the Ti/Zr ratio was explored in terms of structural, microstructural and thermodynamic properties. The storage capacity together with the reaction enthalpy and entropy changes of the synthesized high entropy alloys were compared to predictions from Machine Learning (ML) to investigate changes in these properties across the explored composition space. The results revealed that a decreasing Zr content consistently lowered the hydride formation enthalpy and increased the plateau pressure from 8 to >90 bar H2 at 25 °C, in good agreement with ML predictions. Selected compositions (x = 1.0 and 1.2) demonstrated reversible hydrogen storage capability over 150 cycles, with capacities of 1.34–1.40 wt % H2 and remarkable reaction kinetics (<4 min) at ambient temperature. These experimental and computational findings highlight the potential of this Laves-HEA system as tuneable, stable, and cost-effective materials suitable for long-term operations in stationary hydrogen storage and compression applications.

Abstract not provided.

This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use the operator inference approach to model reduction that poses the problem of learning low-dimensional model terms as a regression of state space data and corresponding time derivatives by minimizing the residual of reduced system equations. Standard operator inference models perform well with accurate training data that are dense in time, but producing stable and accurate models when the state data are noisy and/or sparse in time remains a challenge. Another challenge is the lack of uncertainty estimation for the predictions from the operator inference models. Our approach addresses these challenges by incorporating Gaussian process surrogates into the operator inference framework to (1) probabilistically describe uncertainties in the state predictions and (2) procure analytical time derivative estimates with quantified uncertainties. The formulation leads to a generalized least-squares regression and, ultimately, reduced-order models that are described probabilistically with a closed-form expression for the posterior distribution of the operators. The resulting probabilistic surrogate model propagates uncertainties from the observed state data to reduced-order predictions. We demonstrate the method is effective for constructing low-dimensional models of two nonlinear partial differential equations representing a compressible flow and a nonlinear diffusion–reaction process, as well as for estimating the parameters of a low-dimensional system of nonlinear ordinary differential equations representing compartmental models in epidemiology.

See document.

We present the first fully curated, publicly accessible archive of infrasonic records from ten large bolide events documented by the U.S. Air Force Technical Applications Center’s global microbarometer network between 1960 and 1972. Captured on analog strip-chart paper, these waveforms predate modern digital arrays and space-based sensors, making them a unique window on meteoroid activity in the mid-twentieth century. Prior studies drew important scientific conclusions from the records but released only limited artifacts, chiefly period–amplitude tables and unprocessed scans, leaving the underlying data inaccessible for independent study. The present release transforms those limited excerpts into a research-ready resource. By capturing ten large events in the mid-20th century, the dataset constitutes a critical reference point for assessing bolide activity before the advent of modern space-based and digital ground-based monitoring. The multi-year coverage and worldwide distribution of events provide a valuable reference for comparing past and more recent detections, facilitating assessments of long-term flux and the dynamics of acoustic wave propagation in Earth’s atmosphere. The dataset’s availability in a consolidated format ensures straightforward access to waveforms and derived measurements, supporting a wide range of scientific inquiries into bolide physics and infrasound monitoring. By preserving these historical acoustic observations, the collection maintains a significant record of mid-20th-century meteoroid entries. It thereby establishes a basis for further refinement of impact hazard evaluations, contributes to historical continuity in atmospheric observation, and enriches the study of meteoroid-generated infrasound signals on a global scale. Dataset: DOI data identification number: 10.7910/DVN/VGSN7Q (direct URL to data: https://doi.org/10.7910/DVN/VGSN7Q). Dataset License: CC-BY-NC

Carbon fiber provides opportunity to reduce weight in structural composites, including wind turbine blades, due to the material's superior specific stiffness and specific strength compared to alternatives. Despite these advantages, cost and compressive performance are considered weaknesses for carbon fiber products available today. Studies to produce low-cost carbon fiber alternatives, including the use of textile-derived precursor systems, have shown progress and merit through the DOE/ORNL low-cost carbon fiber initiatives. This work focuses on enabling increases in compressive strength through design of the carbon fiber geometry, applicable to both textile and conventional precursor systems, while also providing opportunities to reduce carbon fiber processing costs. Fiber-resin interface and fiber alignment are among the most frequently cited factors controlling composite compressive performance. However, it is believed that there is opportunity in traditionally unexplored routes to increasing compressive strength through alteration of the carbon fiber geometry by increasing the fiber area moment of inertia and/or the fiber perimeter and interfacial area. This paper presents initial results from manufacturing carbon fiber materials to assess the impacts of carbon fiber size on tested composite compressive performance with projected neutral or even beneficial impact on fiber and composite manufacturing economics. Carbon fiber systems with increasing size illustrate a favorable correlation for compressive performance greater than predicted from a micromechanical failure model. The manufacturing and mechanical test results support the hypothesis of this work that alterations to fiber geometry can be used to produce improvements of the compressive strength of carbon fiber reinforced polymers and provide incentive for related work in designing alternative shapes to further enhance compressive performance.

We present theories and techniques to support safe, predictable, and efficient impact testing at the Sandia National Laboratories TA-III Mechanical Shock Complex (MSC), located in Building 6570. The two testing instruments used at the MSC are the 20-inch actuator, which is a launcher that propels sleds down an indoor track, and an indoor 6-in bore compressed-gas powered cannon. The theories and techniques presented derive from basic mechanical engineering principles but are significantly informe

This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use the operator inference approach to model reduction that poses the problem of learning low-dimensional model terms as a regression of state space data and corresponding time derivatives by minimizing the residual of reduced system equations. Standard operator inference models perform well with accurate training data that are dense in time, but producing stable and accurate models when the state data are noisy and/or sparse in time remains a challenge. Another challenge is the lack of uncertainty estimation for the predictions from the operator inference models. Our approach addresses these challenges by incorporating Gaussian process surrogates into the operator inference framework to (1) probabilistically describe uncertainties in the state predictions and (2) procure analytical time derivative estimates with quantified uncertainties. The formulation leads to a generalized least-squares regression and, ultimately, reduced-order models that are described probabilistically with a closed-form expression for the posterior distribution of the operators. The resulting probabilistic surrogate model propagates uncertainties from the observed state data to reduced-order predictions. We demonstrate the method is effective for constructing low-dimensional models of two nonlinear partial differential equations representing a compressible flow and a nonlinear diffusion–reaction process, as well as for estimating the parameters of a low-dimensional system of nonlinear ordinary differential equations representing compartmental models in epidemiology.

NPJ Advanced Manufacturing

Parametrized artificial neural networks (ANNs) can be very expressive ansatzes for variational algorithms, reaching state-of-the-art energies on many quantum many-body Hamiltonians. Nevertheless, the training of the ANN can be slow and stymied by the presence of local minima in the parameter landscape. One approach to mitigate this issue is to use parallel tempering methods, and in this work, we focus on the role played by the temperature distribution of the parallel tempering replicas. Using an adaptive method that adjusts the temperatures in order to equate the exchange probability between neighboring replicas, we show that this temperature optimization can significantly increase the success rate of the variational algorithm with negligible computational cost by eliminating bottlenecks in the replicas' random walk. We demonstrate this using two different neural networks, a restricted Boltzmann machine and a feedforward network, which we use to study a toy problem based on a permutation invariant Hamiltonian with a pernicious local minimum and the J1-J2 model on a rectangular lattice.

Background/Objectives: Children’s biological age does not always correspond to their chronological age. In the case of BMI trajectories, this can appear as phase variation, which can be seen as shift, stretch, or shrinking between trajectories. With maturation thought of as a process moving towards the final state - adult BMI, we assessed whether children can be divided into latent groups reflecting similar maturational age of BMI. The groups were characterised by early factors and time-related features of the trajectories. Subjects/Methods: We used data from two general population birth cohort studies, Northern Finland Birth Cohorts 1966 and 1986 (NFBC1966 and NFBC1986). Height (n = 6329) and weight (n = 6568) measurements were interpolated in 34 shared time points using B-splines, and BMI values were calculated between 3 months to 16 years. Pairwise phase distances of 2999 females and 3163 males were used as a similarity measure in k-medoids clustering. Results: We identified three clusters of trajectories in females and males (Type 1: females, n = 1566, males, n = 1669; Type 2: females, n = 1028, males, n = 973; Type 3: females, n = 405, males, n = 521). Similar distinct timing patterns were identified in males and females. The clusters did not differ by sex, or early growth determinants studied. Conclusions: Trajectory cluster Type 1 reflected to the shape of what is typically illustrated as the childhood BMI trajectory in literature. However, the other two have not been identified previously. Type 2 pattern was more common in the NFBC1966 suggesting a generational shift in BMI maturational patterns.

Abstract not provided.

Abstract not provided.

We present AMR-Wind, a verified and validated high-fidelity computational-fluid-dynamics code for wind farm flows. AMR-Wind is a block-structured, adaptive-mesh, incompressible-flow solver that enables predictive simulations of the atmospheric boundary layer and wind plants. It is a highly scalable code designed for parallel high-performance computing with a specific focus on performance portability for current and future computing architectures, including graphical processing units (GPUs). In this paper, we detail the governing equations, the numerical methods, and the turbine models. Establishing a foundation for the correctness of the code, we present the results of formal verification and validation. The verification studies, which include a novel actuator line test case, indicate that AMR-Wind is spatially and temporally second-order accurate. The validation studies demonstrate that the key physics capabilities implemented in the code, including actuator disk models, actuator line models, turbulence models, and large eddy simulation (LES) models for atmospheric boundary layers, perform well in comparison to reference data from established computational tools and theory. We conclude with a demonstration simulation of a 12-turbine wind farm operating in a turbulent atmospheric boundary layer, detailing computational performance and realistic wake interactions.

Update slides for CRADA between SNL and GA

Abstract not provided.

Sandia National Laboratories has tested and evaluated a suite of four T120 broadband seismometers designed and manufactured by Nanometrics. Specifically, two T120 Horizon V2 sensors, one T120 Horizon V1 sensor and one T120 Slim Posthole (PH) sensor were evaluated. The purpose of this seismometer evaluation is to measure performance characteristics in areas such as power consumption, sensitivity, frequency response, full scale, self-noise, dynamic range, calibration system response, and passband. The T120 model of sensors are being evaluated to explore the potential for a future seismometer Type Approval process in the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty (CTBT).

DOE maintains an up-to-date documentation of the number of available full drawdowns of each of the caverns at the Strategic Petroleum Reserve (SPR), where an available full drawdown is defined as the ability to move all the oil out from a cavern at full drawdown rates. This report covers impacts on drawdown availability due to SPR operations during Calendar Year 2024.

A structural uncertainty validation study for a large-scale, fire-engulfed, elevated object subjected to crosswind is presented to establish the credibility of a high-fidelity, low-Mach, turbulent reacting flow wall-modeled large-eddy simulation (WMLES) approach that includes multiphysics coupling to participating media radiation and conjugate heat transfer. To establish that WMLES can accurately predict surface quantities including drag and pressure coefficient in the low-Mach crosswind regime, a foundational elevated isothermal cylinder validation case is presented at a similar gap-to-diameter ratio of 0.25, spanning the subcritical to supercritical drag regime (ReD=1.1×105 and 4.3×105, respectively). This study exercised both static and dynamic coefficient LES (Smagorinsky and ksgs) with both local and exchange-based velocity sampling. Results showcase that the drag crisis (or the sudden drop in drag coefficient at increased ReD) is well captured when using an exchange-based dynamic coefficient WMLES methodology, while noting lack of mesh convergence and overall drag and pressure coefficient predictively when using a static coefficient, local velocity sampling WMLES. For the O(10) m JP-8 liquid pool fire crosswind validation study presented, two experimental crosswind configurations (2 m/s and 9.5 m/s) are showcased for a fire-engulfed mock fuselage roughly 4 m in diameter. Using the best model-form practices identified in the isothermal study, dynamic coefficient ksgs exchange-based WMLES fire validation findings demonstrate accurate peak irradiation and skin temperature predictions as a function of crosswind magnitude. Excessive yaw in the low-crosswind fuselage configuration, consistent with experimental findings, captured a significant predicted asymmetry in flame attachment and heat flux toward the downwind cylindrical cap - indicative of axial vortex structures transporting the flame along the upper and lower fuselage leeward surface. All fire mesh resolution simulations captured the experimental finding that as crosswind increased, predicted flame shape and peak irradiation magnitude onto the fuselage transitioned from a windward to a leeward cylinder location due to the migration of the upper- to lower-shear fuel/air mixing layer thereby demonstrating the novelty, significance, and credibility of this high-fidelity WMLES reacting flow framework.

We introduce the Poisson tensor completion (PTC) estimator, a non-parametric differential entropy estimator. The PTC estimator leverages inter-sample relationships to compute a low-rank Poisson tensor decomposition of the frequency histogram. Our crucial observation is that the histogram bins are an instance of a space partitioning of counts and thus can be identified with a spatial Poisson process. The Poisson tensor decomposition leads to a completion of the intensity measure over all bins—including those containing few to no samples—and leads to our proposed PTC differential entropy estimator. A Poisson tensor decomposition models the underlying distribution of the count data and guarantees non-negative estimated values and so can be safely used directly in entropy estimation. Our estimator is the first tensor-based estimator that exploits the underlying spatial Poisson process related to the histogram explicitly when estimating the probability density with low-rank tensor decompositions for the purpose of tensor completion. Furthermore, we demonstrate that our PTC estimator is a substantial improvement over standard histogram-based estimators for sub-Gaussian probability distributions because of the concentration of norm phenomenon.

Abstract not provided.

Sputter-deposited, nanocrystalline Cu-Ag thin films produced across a broad compositional and deposition-parameter space were evaluated to unravel the process-structure-property relationships important for creating hard, conductive electrical contacts and coatings. Combinatorial deposition involving pulsed direct current magnetron sputtering of elemental targets enabled swift examination of nearly the full range of alloy compositions and a relevant portion of deposition atomistics. Several high-throughput characterization modalities were employed to evaluate the chemistry, structure, and properties of the films. The resultant hardness, modulus, film density, crystal texture, and resistivity were analyzed in terms of key deposition characteristics (incident atom kinetic energy and incidence angle) predicted by binary-collision, kinematic Monte Carlo simulations. The study revealed improved hardness, parabolic resistivity dependence on composition, and compositional and process dependencies of film tarnishing. The results are discussed in the context of variations in microstructure and film density. Transmission electron microscopy and X-ray diffraction demonstrate several forms of compositional variation including solute segregation to grain boundaries as well as periodic, intragranular compositional modulations. Annealing of a Cu-rich alloy film exhibiting grain boundary segregation showed that this as-deposited, compositional variation is not stable above 100 °C. Finally, the Cu-Ag system is shown to have potential for hard, conductive, tarnish-resistant and room temperature-stable nanocrystalline thin films across the composition space.

Quantum-cascade vertical-external-cavity surface-emitting-lasers (VECSELs) based on disordered amplifying metasurfaces are demonstrated and explored as potential broadband, multi-mode THz sources. The disorder is introduced along one spatial axis of the metasurface by pseudo-randomly varying the width of its resonant ridge antennas. Compared to a quantum-cascade (QC) VECSEL based on a uniform metasurface, the disordered structure supports much more localized transverse modes with reduced spatial overlap within the QC gain material. This localization is hypothesized to facilitate the spatial hole burning of the gain material and, therefore, enable multi-mode lasing, particularly for short cavities on the order of a few wavelengths. Several devices have been fabricated and shown to differ from uniform QC-VECSELs in a few key ways, possessing highly nonlinear light-current characteristics, angle-dependent emission spectra and broadband multi-mode lasing. At most, 17 modes are simultaneously observed, spanning 680 GHz. The number of VECSEL modes is shown to have an inverse relationship with the cavity length, which is attributed to increased diffractive losses in the open plano-plano cavity. As the cavity length is tuned, the device emits over a quasi-continuous band from 3.15 to 3.97 THz or up to 4.24 THz with a longitudinal mode hop.