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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Generalized Multiscale Finite-Element Method (GMsFEM) for elastic wave propagation in heterogeneous, anisotropic media

It is important to develop fast yet accurate numerical methods for seismic wave propagation to characterize complex geological structures and oil and gas reservoirs. However, the computational cost of conventional numerical modeling methods, such as finite-difference method and finite-element method, becomes prohibitively expensive when applied to very large models. We propose a Generalized Multiscale Finite-Element Method (GMsFEM) for elastic wave propagation in heterogeneous, anisotropic media, where we construct basis functions from multiple local problems for both the boundaries and interior of a coarse node support or coarse element. The application of multiscale basis functions can capture the fine scale medium property variations, and allows us to greatly reduce the degrees of freedom that are required to implement the modeling compared with conventional finite-element method for wave equation, while restricting the error to low values. We formulate the continuous Galerkin and discontinuous Galerkin formulation of the multiscale method, both of which have pros and cons. Applications of the multiscale method to three heterogeneous models show that our multiscale method can effectively model the elastic wave propagation in anisotropic media with a significant reduction in the degrees of freedom in the modeling system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Comparing Model Representations of Physiological Limits on Transpiration at a Semi-arid Ponderosa Pine Site

Mechanistic representations of biogeochemical processes in ecosystem models are rapidly advancing, requiring advancements in model evaluation approaches. Here we quantify multiple aspects of model functional performance to evaluate improved process representations in ecosystem models. We compare semi-empirical stomatal models with hydraulic constraints against more mechanistic representations of stomatal and hydraulic functioning at a semi-arid pine site using a suite of metrics and analytical tools. We find that models generally perform similarly under unstressed conditions, but performance diverges under atmospheric and soil drought. The more empirical models better capture synergistic information flows between soil water potential and vapor pressure deficit to transpiration, while the more mechanistic models are overly deterministic. Although models can be parameterized to yield similar functional performance, alternate parameterizations could not overcome structural model constraints that underestimate the unique information contained in soil water potential about transpiration. Additionally, both multilayer canopy and big-leaf models were unable to capture the magnitude of canopy temperature divergence from air temperature, and we demonstrate that errors in leaf temperature can propagate to considerable error in simulated transpiration. This study demonstrates the value of merging underutilized observational data streams with emerging analytical tools to characterize ecosystem function and discriminate among model process representations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Assessing the utility of phase-space-localized basis functions: Exploiting direct product structure and a new basis function selection procedure

In this paper we show that it is possible to use an iterative eigensolver in conjunction with Halverson and Poirier’s symmetrized Gaussian (SG) basis [T. Halverson and B. Poirier, J. Chem. Phys. 137, 224101 (2012)] to compute accurate vibrational energy levels of molecules with as many as five atoms. This is done, without storing and manipulating large matrices, by solving a regular eigenvalue problem that makes it possible to exploit direct-product structure. These ideas are combined with a new procedure for selecting which basis functions to use. The SG basis we work with is orders of magnitude smaller than the basis made by using a classical energy criterion. We find significant convergence errors in previous calculations with SG bases. For sum-of-product Hamiltonians, SG bases large enough to compute accurate levels are orders of magnitude larger than even simple pruned bases composed of products of harmonic oscillator functions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Models for Resilience Design Patterns

Resilience plays an important role in supercomputers by providing correct and efficient operation in case of faults, errors, and failures. Resilience design patterns offer blueprints for effectively applying resilience technologies. Prior work focused on developing initial efficiency and performance models for resilience design patterns. This paper extends it by (1) describing performance, reliability, and availability models for all structural resilience design patterns, (2) providing more detailed models that include flowcharts and state diagrams, and (3) introducing the Resilience Design Pattern Modeling (RDPM) tool that calculates and plots the performance, reliability, and availability metrics of individual patterns and pattern combinations.

Kumar, Mohit↗
DOE OSTI2025

Hiperclust

This software leverages transfer learning to analyze atom probe tomography (APT) data. It is trained on synthetic data and then applies this knowledge to predict the optimal number of clusters for a given APT dataset. Initially, the software used preliminary clustering to estimate the general structure of the data. Based on this, it provides suggestions for key parameters like minimum cluster size and minimum number of points. These parameters are critical for algorithms like HDBSCAN, ensuring accurate cluster formation without the need for trial-and-error testing. The software runs on High-Performance computing (HPC) systems, enabling fast, scalable analysis of large APT datasets, ultimately saving time and improving the reliability of clustering outcomes.

Tang, Yalei [Idaho National Laboratory (INL), Idah↗
DOE OSTI2022

Comparison of the Baseline USL Calculation Methods for Loosely-Coupled and Novel Neutronic Systems [Slides]

Current work includes reconstructing 187-group ENDF B/VII.1 covariance matrix for comparison study to 44-group ENDF B/VII.1 matrix to determine how covariance matrix structures affect the USL calculations and investigating the exact domination mechanism of region-wise sensitivities in a loosely-coupled system. Future work will involve seeking to understand how bias distributions change with reactivity and how to calculate propagate distribution error into USL calculations, as well as how cross section perturbation studies can be extended to other types of calculations, such as shielding calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

A Finite Element Method for Compressible and Turbulent Multiphase Flow Instabilities with Heat Transfer

We present a new finite element framework for modeling compressible, turbulent multiphase flows with heat transfer. For two-fluid systems with a free surface, the Volume of Fluid (VOF) method is implemented without the need for interface reconstruction, while turbulence is resolved using a dynamic Vreman large eddy simulation (LES) model. Unlike most two-phase VOF studies, which neglect heat transfer, the present approach incorporates energy transport equations within the VOF formulation to account for heat exchange, an effect particularly important in turbulent flows. Conjugate heat transfer is often challenging in finite volume methods, which require explicit specification of heat fluxes at the solid–fluid interface, limiting accuracy and predictive capability. By contrast, the finite element formulation does not require heat flux inputs, allowing more accurate and robust simulation of heat transfer between solids and fluids. The method is demonstrated through three representative cases. First, a two-fluid instability with a single-mode perturbation is simulated and validated against analytical growth rates. Second, conjugate heat transfer is examined in a high-temperature flow over a cold metal cylinder, with validation performed both quantitatively—via pressure coefficient comparisons with experimental data—and qualitatively using vector field topology. Finally, compressible spray injection and breakup are modeled, demonstrating the ability of the framework to capture interfacial dynamics and atomization under turbulent, high-speed conditions. In the compressible spray injection and breakup case, the results indicate that the finite element formulation achieved higher predictive accuracy and robustness than the finite-volume method. With the same mesh resolution, the FEM reduced the root mean square error (RMSE) and mean absolute percentage error (MAPE) from 6.96 mm and 26.0% (for the FVM) to 4.85 mm and 12.7%, respectively, demonstrating improved accuracy and robustness in capturing interfacial dynamics and heat transfer. The study also introduced vector field topology to visualize and interpret coherent flow structures and instabilities, offering insights beyond conventional scalar-field analyses.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Improving Bending Moment Measurements on Wind Turbine Blades

Full-scale fatigue testing of wind turbine blades is conducted using resonance test techniques where the blade plus additional masses is excited at its first resonance frequency to achieve the target loading amplitude. Because there is not a direct relationship between the force applied by an actuator and the bending moment, the blade is instrumented with strain gauges that are calibrated under static loading conditions to determine the sensitivity or relationship between strain and applied moment. Then, during dynamic loading the applied moment is calculated using the strain response of the structure. A similar procedure is also used in the field to measure in-service loads on turbine blades. Because wind turbine blades are complex twisted structures and the deflections are large, there is often significant cross-talk coupling in the sensitivity of strain gauges placed on the structure. Recent work has shown that a sensitivity matrix with nonzero cross terms must be employed to find constant results when a blade is subjected to both flap and lead-lag loading. However, even under controlled laboratory conditions, potential for errors of 3 percent or more in the measured moment exist when using the typical cross-talk matrix approach due to neglecting the influence of large deformations and torsion. This is particularly critical when considering a biaxial load as would be applied on the turbine or during a biaxial fatigue test. This presentation describes these results demonstrating errors made when performing current loads measurement practices on wind turbine blades in the lab and evaluating potential improvements using enhanced cross-talk matrix approaches and calibration procedures.

17 WIND ENERGY↗
DOE OSTI2022

Electronic structure of intrinsic defects in c -gallium nitride: Density functional theory study without the jellium approximation

Here, we report the first nonjellium, systematic, density functional theory (DFT) study of intrinsic and extrinsic defects and defect levels in zinc-blende (cubic) gallium nitride. We use the local moment counter charge (LMCC) method, the standard Perdew-Becke-Ernzerhoff (PBE) exchange-correlation potential, and two pseudopotentials, where the Ga 3$\textit{d}$ orbitals are either in the core ($d^0$) or explicitly in the valence set ($d^{10}$). We studied 64, 216, 512, and 1000 atom supercells, and demonstrated convergence to the infinite limit, crucial for delineating deep from shallow states near band edges, and for demonstrating the elimination of finite cell-size errors. Contrary to common claims, we find that exact exchange is not required to obtain defect levels across the experimental band gap. As was true in silicon, silicon carbide, and gallium arsenide, the extremal LMCC defect levels of the aggregate of defects yield an effective LMCC defect band gap that is within 10% of the experimental gap (3.3 eV) for both pseudopotentials. We demonstrate that the gallium vacancy is more complicated than previously reported. There is dramatic metastability–a nearest-neighbor nitrogen atom shifts into the gallium site, forming an antisite, nitrogen vacancy pair, which is more stable than the simple vacancy for positive charge states. Our assessment of the $d^0$ and $d^{10}$ pseudopotentials yields minimal differences in defect structures and defect levels. The better agreement of the $d^0$ lattice constant with experiment suggests that the more computationally economical $d^0$ pseudopotentials are sufficient to achieve the fidelity possible within the physical accuracy of DFT, and thereby enable calculations in larger supercells necessary to demonstrate convergence with respect to finite size supercell errors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Corrigendum to "Predicting biomass of hyperdiverse and structurally complex central Amazonian forests — a virtual approach using extensive field data" Published in Biogeosciences, 13, 1553-1570, 2016

Old-growth forests are subject to substantial changes in structure and species composition due to the intensification of human activities, gradual climate change and extreme weather events. Trees store ca. 90% of the total aboveground biomass (AGB) in tropical forests and precise tree biomass estimation models are crucial for management and conservation. In the central Amazon, predicting AGB at large spatial scales is a challenging task due to the heterogeneity of successional stages, high tree species diversity and inherent variations in tree allometry and architecture. We parameterized generic AGB estimation models applicable across species and a wide range of structural and compositional variation related to species sorting into height layers as well as frequent natural disturbances. We used 727 trees (diameter at breast height ≥5 cm) from 101 genera and at least 135 species harvested in a contiguous forest near Manaus, Brazil. Sampling from this data set we assembled six scenarios designed to span existing gradients in floristic composition and size distribution in order to select models that best predict AGB at the landscape level across successional gradients. We found that good individual tree model fits do not necessarily translate into reliable predictions of AGB at the landscape level. We observed systematic biases ranging from -31% (pantropical) to +39 %, with root-mean-square error (RMSE) values of up to 130-Mg ha -1 (pantropical), when predicting AGB (dry mass) over scenarios using our different models and an available pantropical model. Our first and second best models had both low mean biases (0.8 and 3.9 %, respectively) and RMSE (9.4 and 18.6 Mg ha -1 ) when applied over scenarios. Predicting biomass correctly at the landscape level in hyperdiverse and structurally complex tropical forests, especially allowing good performance at the margins of data availability for model construction/calibration, requires the inclusion of predictors that express inherent variations in species architecture. Furthermore, the model of interest should comprise the floristic composition and size-distribution variability of the target forest, implying that even generic global or pantropical biomass estimation models can lead to strong biases. Reliable biomass assessments for the Amazon basin (i.e., secondary forests) still depend on the collection of allometric data at the local/regional scale and forest inventories including species-specific attributes, which are often unavailable or estimated imprecisely in most regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Localized High-Concentration Electrolytes Get More Localized

Liquid electrolytes in batteries are typically treated as macroscopically homogeneous ionic transport media, despite having complex chemical composition and atomistic solvation structures, thus leaving a knowledge gap of microstructural characteristics. A promising electrolyte system design has recently been developed with the title of a localized high-concentration electrolyte (LHCE). These electrolytes are a summation of three components – a charge-carrying salt, a solvent that solvates the salt, and a diluent that is sparingly interactive with the salt in terms of ion solvation. This creates the perception of a high-concentration electrolyte (HCE) with high anion-cation interactions that supports a salt-derived, stable solid-electrolyte interphase, while an LHCE improves bulk electrolyte properties, such as ionic conductivity and viscosity, when compared to an HCE. Much trial by error design of this LHCE concept has been supported by simulations to confirm ionic interactions, cycling data to confirm cell stability, and post-mortem analysis to provide evidence of mechanisms. However, a systematic design to extend the capabilities of LHCEs is still to be desired. Here, we analyzed a specific LHCE mixture to dictate the best viable mixture that extends the localization of solvation structures while maintaining viable cell-level capabilities. We seek to observe and understand solvent-to-salt interactions that impact electrolyte performance. These interactions range from single-ion solvation, multidentate interactions (i.e., solvation-driven clustering), to more widespread network-level interactions. With the support of both experimental and computational analysis of different electrolyte features, we have begun a methodology of formulating the most ideal LHCE mixtures for improving the cyclability of high-capacity battery chemistries.

25 ENERGY STORAGE↗
DOE OSTI2023

On the Importance of Chemical Precision in Organic Electronics: Fullerene Intercalation in Perfectly Alternating Conjugated Polymers

Abstract The true structure of alternating conjugated polymers—the state‐of‐the‐art materials for many organic electronics—often deviates from the idealized picture. Homocoupling defects are in fact inherent to the widely used cross‐coupling polymerization methods. Nevertheless, many polymers still perform excellently in the envisaged applications, which raises the question if one should really care about these imperfections. This article looks at the relevance of chemical precision (and lack thereof) in conjugated polymers covering the entire spectrum from the molecular scale, to the micro and mesostructure, up to the device level. The different types of polymerization errors for the alkoxylated variant of the benchmark (semi)crystalline polymer poly[2,5‐bis(3‐tetradecylthiophen‐2‐yl)thieno[3,2‐ b ]thiophene (PBTTT) are identified, visualized, and quantified and a general strategy to avoid homocoupling is introduced. Through a combination of experiments and supported by simulations, it is shown that these coupling defects hinder fullerene intercalation and limit device performance as compared to the homocoupling‐free analog. This clearly demonstrates that structural defects do matter and should be generally avoided, in particular when the geometrical regularity of the polymer is essential. These insights likely go beyond the specific PBTTT derivatives studied here and are of general relevance for the wider organic electronics field.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Guest-Host Interactions in Clathrate Hydrates: Benchmark MP2 and CCSD(T)/CBS Binding Energies of CH4, CO2 and H2S in (H2O)20 Cages

We present benchmark binding energies of naturally occurring gas molecules CH4, CO2, and H2S in the small cage, namely the pentagonal dodecahedron (512) (H2O)20, which is one of the constituent cages of the 3 major lattices (structures I, II and H) of clathrate hydrates. These weak interactions require higher levels of electron correlation and converge slowly with increasing basis set to the Complete Basis Set (CBS) limit, necessitating the use of large basis sets up to the augcc- pV5Z and subsequent correction for Basis Set Superposition Error (BSSE). For the host hollow (H2O)20 cages we have identified a most stable isomer with binding energy of -200.8 ± 2.1 kcal/mol at the CCSD(T)/CBS limit (-199.2 ± 0.5 kcal/mol at the MP2/CBS limit). Additionally, we report converged second order Moller-Plesset (MP2) CBS binding energies for the encapsulation of guests in the (H2O)20 cage of -4.3 ± 0.1 for CH4@(H2O)20, -6.6 ± 0.1 for CO2@(H2O)20 and -8.5 ± 0.1 kcal/mol for H2S@(H2O)20, respectively. For CH4@(H2O)20, exhibiting the weakest encapsulation affinity among the three, we report CCSD(T)/aug-cc-pVTZ binding energies and, based on them, a CCSD(T)/CBS estimate of -4.75 ± 0.1 kcal/mol. To the best of our knowledge, the CCSD(T)/aug-cc-pVTZ calculation for CH4@(H2O)20 is the largest one reported to date (168 valence electrons, 1978 basis functions and the correlation of 84 doubly occupied and 1873 virtual orbitals) and required a scalable implementation of the (T) module on 6144 nodes (350208 cores) of the “Cori” supercomputer at the National Energy Research Supercomputing Center (NERSC) for a total execution time of 195 minutes (for the (T) part). These efficient scalable implementations of highly correlated methods offer the capability to obtain long-lasting benchmarks of intermolecular interactions in complex systems. They also provide a path towards parametrizing classical potentials needed to study the dynamical and transport properties in these complex systems as well as assess the accuracy of lower scaling electronic structure methods such as Density Functional Theory (DFT) and MP2 including its spin-biased variants.

Heindel, Joseph↗
DOE OSTI2014

SU-E-T-642: Safety Procedures for Error Elimination in Cyberknife Stereotactic Radiosurgery (SRS) and Stereotactic Body Radiation Therapy (SBRT)

Purpose: Cyberknife system is used for providing stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT) hypofractionation scheme. The whole treatment delivery is based on live imaging of the patient. The minor error made at any stage may bring severe radiation injury to the patient or damage to the system itself. Several safety measures were taken to make the system safer. Methods: The radiation treatment provided thru a 6MV linac attached to Kuka robot (Cyberknife G4, Accuray Inc. Sunnyvale, CA, USA). Several possible errors were identified related to patient alignment, treatment planning, dose delivery and physics quality assurance. During dose delivery, manual and visual checks were introduced to confirm pre and intra-treatment imaging to reduce possible errors. One additional step was introduced to confirm that software tracking-tools had worked correctly with highest possible confidence level. Robotic head move in different orientations over and around the patient body, the rigidity of linac-head cover and other accessories was checked periodically. The vender was alerted when a tiny or bigger piece of equipment needed additional interlocked support. Results: As of our experience treating 525 patients on Cyberknife during the last four years, we saw on and off technical issues. During image acquisition, it was made essential to follow the site-specific imaging protocols. Adequate anatomy was contoured to document the respective doses. Followed by auto-segmentation, manual tweaking was performed on every structure. The calculation box was enclosing the whole image during the final calculation. Every plan was evaluated on slice-by slice basis. To review the whole process, a check list was maintained during the physics 2nd-check. Conclusion: The implementation of manual and visual additional checks introduced along with automated checks for confirmation was found promising in terms of reduction in systematic errors and making the system safer than before.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Quantification of modeling uncertainty in the Rayleigh damping model

Understanding and accurately characterizing energy dissipation mechanisms in civil structures during earthquakes is an important element of seismic assessment and design. The most commonly used model is attributed to Rayleigh. This paper proposes a systematic approach to quantify the uncertainty associated with Rayleigh's damping model. Bayesian calibration with embedded model error is employed to treat the coefficients of the Rayleigh model as random variables using modal damping ratios. Through a numerical example, we illustrate how this approach works and how the calibrated model can address modeling uncertainty associated with the Rayleigh damping model.

42 ENGINEERING↗
DOE OSTI2020

Revisiting the π → π* transition of the nitrite ion at the air/water interface: A combined experimental and theoretical study

Broadband deep ultraviolet electronic sum frequency generation spectroscopy allows measurement of electronic |χ (2) | 2 -spectra at aqueous interfaces, providing considerable improvement over deep ultraviolet electronic second harmonic generation spectroscopy that yields error-prone, pointwise spectra. Reexamination of the π → π* transition of nitrite at the air/water interface reveals that the interfacial |χ (2) | 2 -spectrum is strikingly similar to the bulk absorption spectrum. Molecular dynamics simulations and SOS-CIS(D 0 ) electronic structure calculations provide no evidence for the previously reported contact ion pair-induced red-shift of the π → π* transition at the air/water interface. Finally, our results thus revise the previous description for nitrite interfacial adsorption as a contact ion pair.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Data-Efficient Strategies for Probabilistic Voltage Envelopes under Network Contingencies

This work presents an efficient data-driven method to construct probabilistic voltage envelopes (PVE) using power flow learning in grids with network contingencies. First, a network-aware Gaussian process (GP) termed Vertex-Degree Kernel (VDK-GP), developed in prior work, is used to estimate voltage–power functions for a few network configurations. The paper introduces a novel multi-task vertex degree kernel (MT-VDK) that amalgamates the learned VDK-GPs to determine power flows for unseen networks, with a significant reduction in the computational complexity and hyperparameter requirements compared to alternate approaches. Simulations on the IEEE 30-Bus network demonstrate the retention and transfer of power flow knowledge in both N-1 and N-2 contingency scenarios. The MT-VDK-GP approach achieves over 50 % reduction in mean prediction error for novel N-1 contingency network configurations in low training data regimes (50–250 samples) over VDK-GP. Additionally, MT-VDK-GP outperforms a hyper-parameter based transfer learning approach in over 75 % of N-2 contingency network structures, even without historical N-2 outage data. Furthermore, the proposed method demonstrates the ability to achieve PVEs using sixteen times fewer power flow solutions compared to Monte-Carlo sampling-based methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Bandgap prediction by deep learning in configurationally hybridized graphene and boron nitride

Abstract It is well-known that the atomic-scale and nano-scale configuration of dopants can play a crucial role in determining the electronic properties of materials. However, predicting such effects is challenging due to the large range of atomic configurations that are possible. Here, we present a case study of how deep learning algorithms can enable bandgap prediction in hybridized boron–nitrogen graphene with arbitrary supercell configurations. A material descriptor that enables correlation of structure and bandgap was developed for convolutional neural networks. Bandgaps calculated by ab initio calculations, and corresponding structures, were used as training datasets. The trained networks were then used to predict bandgaps of systems with various configurations. For 4 × 4 and 5 × 5 supercells they accurately predict bandgaps, with a R 2 of >90% and root-mean-square error of ~0.1 eV. The transfer learning was performed by leveraging data generated from small supercells to improve the prediction accuracy for 6 × 6 supercells. This work will pave a route to future investigation of configurationally hybridized graphene and other 2D materials. Moreover, given the ubiquitous existence of configurations in materials, this work may stimulate interest in applying deep learning algorithms for the configurational design of materials across different length scales.

Dong, Yuan↗