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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

In vitro effects of cannabidiol and its main metabolites in mouse and human Sertoli cells

Cannabidiol (CBD) is a major cannabinoid present in extracts of the plant Cannabis sativa (marijuana). While the therapeutic effects of CBD on epilepsy have been demonstrated, less is understood regarding its potential adverse effects. Recent studies revealed that CBD induced toxicity in the male reproductive system of animal models. In this study, we used TM4, an immortalized mouse Sertoli cell line, and primary human Sertoli cells to evaluate the toxicities of CBD and its main metabolites, 7-carboxy-CBD and 7-hydroxy-CBD. CBD induced concentration- and time-dependent cytotoxicity in mouse and human Sertoli cells, which mainly resulted from the inhibition of the G1/S-phase cell cycle transition. CBD also inhibited DNA synthesis and downregulated key cell cycle proteins. Moreover, CBD reduced the mRNA and protein levels of a functional marker, Wilms’ tumor 1. Similar to CBD, 7-carboxy-CBD and 7-hydroxy-CBD inhibited cellular proliferation and decreased DNA synthesis. 7-Carboxy-CBD was less cytotoxic than CBD, while 7-hydroxy-CBD showed comparable cytotoxicity to CBD in both mouse and human Sertoli cells. Compared to mouse Sertoli cells, CBD, 7-hydroxy-CBD, and 7-carboxy-CBD were more cytotoxic in human Sertoli cells. Finally, our results indicate that CBD and its main metabolites can inhibit cell proliferation in mouse and human Sertoli cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Concurrent measurement of strain and chemical reaction rates in a calcite grain pack undergoing pressure solution: Evidence for surface-reaction controlled dissolution

Pressure solution is inferred to be a significant contributor to sediment compaction and lithification, especially in carbonate sediments. For a sediment deforming primarily by pressure solution, the compaction rate should be directly related to the rate of calcite dissolution, transport along grain contacts, and calcite reprecipitation. Previous experimental work has shown that there is evidence that deformation in wet calcite grain packs is consistent with control by pressure solution, but considerable ambiguity remains regarding the rate limiting mechanism. We present the results of laboratory compaction experiments designed to directly measure calcite dissolution and precipitation rates (recrystallization rates) concurrently with strain rate to test whether measured rates are consistent with predicted rates both in absolute magnitude and time evolution. Recrystallization rates are measured using trace element chemistry (Sr/Ca, Mg/Ca) and isotopes (87Sr/86Sr) of fluids flowing slowly through a compacting grain pack as it is being triaxially compressed. Imaging techniques are used to characterize the grain contacts and strain effects in the post-experiment grain pack. Our data show that calcite recrystallization rates calculated from all three geochemical parameters are in approximate agreement and that the rates closely track strain rate. The geochemically inferred rates are close to predicted rates in absolute magnitude. Uncertainty in grain contact dimensions makes distinguishing between surface reaction control and diffusion control difficult. Measured reaction rates decrease faster than predicted from standard pressure solution creep flow laws. This inconsistency may indicate that calcite dissolution rates at grain contacts are more complex, and more time-dependent, than suggested by geometric models designed to predict grain contact stresses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A learning-augmented approach for AC optimal power flow

Because of the high nonlinearity of AC optimal power flow (OPF), numerous efforts have been made in recent decades to find efficient methods. Machine learning (ML) has proven to significantly reduce the computational costs in many real-world problems. Thus, this paper develops a learning-augmented method for solving AC OPF, which integrates both power network equations and ML to yield near-optimal solutions. More specifically, ML models are developed to first predict bus voltage magnitudes and angles. Then, physics-based network equations are employed to calculate the power injection at different buses. Three ML algorithms, i.e., random forest, multi-target decision tree, and extreme learning machine, are explored and compared. To evaluate the efficiency of the proposed learning-augmented AC OPF solver, the MATPOWER Interior Point Solver is adopted as a baseline. Case studies on both 500-bus and 4918-bus test networks show that the proposed learning-augmented method has reduced the computational time by 15–100 times depending on the network size with a minimal loss in optimality.

42 ENGINEERING↗
DOE OSTI2020

Mechanistic modeling of CO 2 well leakage in a generic abandoned well through a bridge plug cement-casing gap

Both known and unmapped plugged and abandoned wells are potential leakage pathways for CO 2 from geologic carbon sequestration (GCS) sites. Although many abandoned wells have cement bridge plugs installed to prevent leakage, the seal between the cement and the inner casing wall is subject to failure. In this study, we carried out detailed T2Well simulations of cases of sudden non-Darcy flow of CO 2 and brine leakage up the gap between a cement plug and the inner steel casing wall that becomes a fully connected flow path during the post-injection period. The goal of our study was two-fold: (1) to understand the dynamics, rates, and the characteristic temporal signals associated with the onset of leakage through various gap-aperture sizes, and (2) to suggest potential monitoring strategies based on the findings. Simulation results show that the leakage of CO 2 and brine upward is transient with interesting phase interference behavior. Time-dependent oscillatory flows with varying pressure, temperature, and flow rates of CO 2 and brine show strong dependence on gap aperture. Phase-change and decompression lead to very low temperatures at the top of the well for gap apertures larger than 4 mm suggesting that remote thermal monitoring at the ground surface may be an effective way of monitoring even if well locations are not known a priori. Pressure in the well is also indicative of CO 2 leakage. The temporal patterns of changing temperature and pressure may be useful diagnostic signals for leakage detection. Finally, these transient leakage signals may provide information on the cause of leakage and/or characteristics of the flow path that could inform effective remediation design and execution approaches.

58 GEOSCIENCES↗
DOE OSTI2026

Representativity error scaling of models of the high temperature test facility

Error scaling is a critical step toward the validation of modeling capabilities for high-temperature gas-cooled reactors (HTGRs). Extensive effort is being made to bring HTGRs into the validation basis of numerous thermal-hydraulics codes. Here, this paper demonstrates how systems-level codes can be leveraged to perform error scaling analyses between an experimental facility and a plant-to-be facility. Specifically, we focus on two conduction cooldown experiments from the High Temperature Test Facility (HTTF) and the General Atomics 350 MW th modular high-temperature gas-cooled reactor (MHTGR-350). The error scaling methodology employed in this study is representativity, which in the context of this work was used to quantify how well experiments captured the physics of the plant facility by comparing sensitivity vectors between the two facilities. In addition, two different RELAP5–3D models of the HTTF were included in the comparison to determine if modeling methodologies notably impact the representativity results. The key figures of merit are the maximum block temperature and the coolant outlet temperature. The time-dependent maximum block temperature had a low representativity of below 0.1 for both models across both transients. The coolant outlet temperature had a higher representativity of around 0.6 for both models during the pressurized conduction cooldown, but it was below 0.2 for the depressurized conduction cooldown. Overall, the HTTF experiments were not representative of the transients in the MHTGR-350. However, these results can play a significant role in informing future potential experiments for HTGR systems that iterate on what the HTTF accomplished.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Droplet breakup by multimodal nonlinear Rayleigh Taylor instability

A droplet impacted by a shock wave will undergo a process of fragmentation due to the development of interfacial hydrodynamic instabilities. The interface experiences variable acceleration and shear that result in the development of both inertial (Rayleigh–Taylor) and shear (Kelvin–Helmholtz) instabilities. These perturbations grow in time and drive the fragmentation and breakup of the deformed droplet. Experiments are performed on nominally 0.86 mm water droplet subjected to a Mach 7.6 detonation wave, resulting in a high Weber number (~36,000) breakup event. Perturbation growth is measured from a series of high-speed (> MHz) shadowgraph images. It is proposed that, given the size of the large-scale perturbations observed in experiments, these instabilities are growing in the non-linear regime and can be described by bubble-merger models for nonlinear mixing. Calculations are performed for the growth rates and size of these instabilities using deformation and external flow models to establish the time-dependent boundary conditions. The concurrence of the measured perturbation widths and the predictions of the simple model lend credence to the theory. This novel approach serves to open a new avenue in the characterization of droplet breakup via hydrodynamic instabilities.

Droplet breakup↗
DOE OSTI2022

Creep anisotropy modeling and uncertainty quantification of an additively manufactured Ni-based superalloy

The advantages offered by additive manufacturing over traditional processes has driven a great deal of industrial and academic interest in recent years. However, the process is relatively new and requires additional investigation to become sufficiently mature for wide scale industrial adoption. Electron beam melting powder bed fusion is one technology that has shown promise for fabricating high temperature resistant materials such as nickel based superalloys. The resulting microstructures typically exhibit a strong fiber texture in the build direction giving rise to anisotropic time-dependent deformation behavior. In order to accelerate the qualification of these materials for industrial adoption accurate numerical models are needed for simulating their behavior. In this work a crystal plasticity model including non-Schmid effects is presented for capturing creep anisotropy observed in additively manufactured IN738LC. The model is calibrated via a probabilistic framework where model parameters are treated as random variables. An iterative sequential design strategy is utilized to efficiently identify the probability density of the unknown model parameters. As a case study the model is utilized to investigate the behavior of randomly oriented equiaxed grain clusters sometimes observed embedded in the additively manufactured columnar structure. A synthetic realization is simulated and uncertainty is propagated through to the full-field response. Results indicate that these features are the source of significant creep relaxation and strain accumulation which partially explains observed grain boundary decohesion at these locations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Modelling yield cap evolution in sandstone based on brittle creep experiments

The Zenifim Formation is being considered as a potential disposal formation for a deep borehole nuclear repository concept in Israel. Site selection and repository construction are intended to ensure that waste is separated from circulating groundwater, but long-term deformation of the wellbore could potentially create fluid flow pathways. To understand how time-dependent rock strength could affect wellbore stability, here we conducted creep tests under low to moderate confining pressures on retrieved core from the Zenifim formation. During creep, samples strain slowly as gradual damage accumulation progressively weakens the samples. Failure eventually occurred through the near-instantaneous formation of a shear fracture. Experimental results were used to calibrate a continuum damage poro-elastic model for sandstones. The calibrated damage-poro-elastic model successfully simulates different types of loading experiments including quasi-static and creep. The state of strain in experiments is close to yield during loading as the yield cap continuously evolves with damage accumulation. For creep tests, most damage occurs during triaxial loading. Minor damage accumulation occurs under constant load until the final stage of creep, where damage accelerates and promotes unstable fracturing.

58 GEOSCIENCES↗
DOE OSTI2022

BiCuI 4 (Pyridine) 5 a neutral ligand-supported compound of BiI 3 and CuI

Reaction of equimolar pyridine (Py) solutions BiI 3 and CuI produces the Py-supported 1:1 bimetallic complex BiCuI 4 (Py) 5 in quantitative yield. The title complex is only the second neutral complex to feature Cu–I–Bi bridging and shows an octahedral trans-[BiI 4 (Py) 2 ] unit joined to a distorted tetrahedral CuI(Py) 3 unit by an iodide bridge. The complex shows low thermal stability, decomposing under modest heating or vacuum to produce a mixture of BiI 3 and CuI. Thus, it is a potential entry/precursor to BiI 3 /CuI chemistry. Natural Localized Molecular Orbital (NLMO) calculations were performed to analyze the nature of the Cu–I–Bi bonds, revealing that the title compound lies on the cusp of being [Cu(Py) 3 ] + [BiI 4 (Py) 2 ] – . Diffuse reflectance spectroscopy measurements show a strong absorption band with an optical bandgap energy of 1.94 eV. Theoretical density of states (DOS) and time-dependent density functional theory (TD-DFT) experiments to map the electronic structure assign the primary electronic transition as a mixed halide/metal-to-ligand charge transfer between Cu–I–Bi donor orbitals and Py π* acceptor orbitals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

X-ray absorption spectroscopy of trivalent Eu, Gd, Tb, and Dy chlorides and oxychlorides

Growing interest in next generation molten salt nuclear reactors necessitates a fundamental understanding of the chemical and physico-chemical properties of fission products, including elements of the lanthanide series. In this study, we probed coordination environment of Eu, Gd, Tb, and Dy in their chloride salts with both laboratory and synchrotron scale X-ray absorption spectroscopy (XAS). Here the aerobic melting was carried out with LnCl 3 ·nH 2 O, where Ln is Eu, Gd, Tb, and Dy, and yielded predominant formation of oxychlorides (LnOCl), that was confirmed by extended X-ray absorption fine structure (EXAFS) analysis and X-ray diffraction (XRD). Density functional theory (DFT) and time-dependent density functional theory (TDDFT) computations were performed to obtain ground state structures, lattice parameters, and to simulate L 3 -edge XANES (X-ray absorption near edge structure) spectra of the LnCl 3 ·nH 2 O and LnOCl. A blue shift, initially predicted by our calculations, was also observed for the experimental Ln L 3 -edge XANES spectra of the hydrated trichlorides vs oxychlorides, highlighting the power of combining the predictive ability of theory with experiment to elucidate the properties of the compounds of interest. The blue shift is indicative of structural stabilization of LnCl 3 ·nH 2 O due to higher symmetry compared with LnOCl. To the best of knowledge, this is a first study providing the framework for fundamental insights into the structure of Ln trichlorides and oxychlorides in support of future characterization of lanthanides in the molten salt environment.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Characterization of two distinct neutrophil serine protease–binding modes within a Staphylococcus aureus innate immune evasion protein family

Extracellular adherence protein domain (EAPs) proteins are a class of innate immune evasion proteins secreted by the human pathogen Staphylococcus aureus. EAPs are potent and selective inhibitors of cathepsin-G (CG) and neutrophil elastase (NE), which are the two most abundant neutrophil serine proteases (NSPs). Previous work from our group has shown that the prototypical EAP, EapH1, relies on plasticity within a single inhibitory site to block the activities of CG and NE. However, whether other EAPs follow similar structure–function relationships is unclear. To address this question, we studied the inhibitory properties of the first (Eap1) and second (Eap2) domains of the modular extracellular adherence protein of S. aureus and determined their structures when bound to CG and NE, respectively. We observed that both Eap1 and Eap2 displayed time-dependent inhibition of CG (on the order of 10 -9 M) and of NE (on the order of 10 -10 M). We also found that whereas the structures of Eap1 and Eap2 bound to CG showed an overall inhibitory mode like that seen previously for EapH1, the structures of Eap1 and Eap2 bound to NE revealed a new inhibitory mode involving a distal region of the EAP domain. Using site-directed mutagenesis of Eap1 and Eap2, along with enzyme assays, we confirmed the roles of interfacial residues in NSP inhibition. Taken together, our work demonstrates that EAPs can form structurally divergent complexes with two closely related serine proteases and further suggests that certain EAPs may be capable of inhibiting two NSPs simultaneously.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

High-order accurate finite difference discretisations on fully unstructured dual quadrilateral meshes

Here, we present a novel approach for high-order accurate numerical differentiation on unstructured meshes of quadrilateral elements. To differentiate a given function, an auxiliary function with greater smoothness properties is defined which when differentiated provides the derivatives of the original function. The method generalises traditional finite difference methods to meshes of arbitrary topology in any number of dimensions for any order of derivative and accuracy. We demonstrate the accuracy of the numerical scheme using dual quadrilateral meshes and a refinement method based on subdivision surfaces. The scheme is applied to the solution of a range of partial differential equations, including both linear and nonlinear, second and fourth order equations, and a time-dependent first order equation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

An immersed interface method for the 2D vorticity-velocity Navier-Stokes equations with multiple bodies

We present an immersed interface method for the vorticity-velocity form of the 2D Navier Stokes equations that directly addresses challenges posed by nonconvex immersed bodies, multiply connected domains, and the calculation of force distributions on immersed surfaces. The immersed interface method is re-interpreted as a polynomial extrapolation of flow quantities and boundary conditions into the immersed solid bodies, reducing computational cost and enabling simulations with nonconvex bodies that could not be discretized with previous immersed interface methods. In the flow, the vorticity transport equation is discretized using a conservative finite difference scheme and explicit Runge-Kutta time integration. The velocity reconstruction problem is transformed to a scalar Poisson equation that is discretized with conservative finite differences, and solved using an FFT-accelerated iterative algorithm. The use of conservative differencing throughout leads to exact enforcement of a discrete Kelvin's theorem, allowing for simulations with multiply connected domains and outflow boundaries that have challenged other immersed interface vortex methods. We also explore novel methods for recovering time-dependent pressure distributions on immersed bodies within a vorticity-based method and present a novel control volume formulation for recovering aerodynamic moments from only the vorticity and velocity fields. The method achieves second order spatial accuracy and third order temporal accuracy, and is validated on a variety of 2D flows in internal and free-space domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Goal-oriented a-posteriori estimation of model error as an aid to parameter estimation

In this work, a Bayesian model calibration framework is presented that utilizes goal-oriented a-posterior error estimates in quantities of interest (QoIs) for classes of high-fidelity models characterized by PDEs. It is shown that for a large class of computational models, it is possible to develop a computationally inexpensive procedure for calibrating parameters of high-fidelity models of physical events when the parameters of low-fidelity (surrogate) models are known with acceptable accuracy. The main ingredients in the proposed model calibration scheme are goal-oriented a-posteriori estimates of error in QoIs computed using a so-called lower fidelity model compared to those of an uncalibrated higher fidelity model. The estimates of error in QoIs are used to define likelihood functions in Bayesian inversion analysis. A standard Bayesian approach is employed to compute the posterior distribution of model parameters of high-fidelity models. As applications, parameters in a quasi-linear second-order elliptic boundary-value problem (BVP) are calibrated using a second-order linear elliptic BVP. In a second application, parameters of a tumor growth model involving nonlinear time-dependent PDEs are calibrated using a lower fidelity linear tumor growth model with known parameter values.

A-posterior estimates↗
DOE OSTI2022

An efficient iterative method for dynamical Ginzburg-Landau equations

Here we propose a new finite element approach to solving the time-dependent Ginzburg-Landau equations for superconductivity under the temporal gauge and design an efficient preconditioner for the Newton iteration of the resulting discrete system. It solves the vector magnetic potential in $H$(curl) space by the lowest order of the second kind N´ed´elec element. This approach offers a simple way to deal with the boundary conditions, leading to a stable and reliable performance for superconductor geomtry with reentrant corners. The bench-marking in numerical simulations verifies the efficiency of the proposed preconditioner, which can be employed to significantly speed up in large-scale computations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

On the influence of over-parameterization in manifold based surrogates and deep neural operators

Constructing accurate and generalizable approximators (surrogate models) for complex physico-chemical processes exhibiting highly non-smooth dynamics is challenging. The main question is what type of surrogate models we should construct and should these models be under-parameterized or over-parameterized. In this work, we propose new developments and perform comparisons for two promising approaches: manifold-based polynomial chaos expansion (m-PCE) and the deep neural operator (DeepONet), and we examine the effect of over-parameterization on generalization. While m-PCE enables the construction of a mapping by first identifying low-dimensional embeddings of the input functions, parameters, and quantities of interest (QoIs), a neural operator learns the nonlinear mapping via the use of deep neural networks. Here, we demonstrate the performance of these methods in terms of generalization accuracy by solving the 2D time-dependent Brusselator reaction-diffusion system with uncertainty sources, modeling an autocatalytic chemical reaction between two species. We first propose an extension of the m-PCE by constructing a mapping between latent spaces formed by two separate embeddings of the input functions and the output QoIs. To further enhance the accuracy of the DeepONet, we introduce weight self-adaptivity in the loss function. We demonstrate that the performance of m-PCE and DeepONet is comparable for cases of relatively smooth input-output mappings. However, when highly non-smooth dynamics is considered, DeepONet shows higher approximation accuracy. We also find that for m-PCE, modest over-parameterization leads to better generalization, both within and outside of distribution, whereas aggressive over-parameterization leads to over-fitting. In contrast, an even highly over-parameterized DeepONet leads to better generalization for both smooth and non-smooth dynamics. Furthermore, we compare the performance of the above models with another recently proposed operator learning model, the Fourier Neural Operator, and show that its over-parameterization also leads to better generalization. Taken together, our studies show that m-PCE can provide very good accuracy at very low training cost, whereas a highly over-parameterized DeepONet can provide better accuracy and robustness to noise but at higher training cost. In both methods, the inference cost is negligible.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A conservative Galerkin solver for the quasilinear diffusion model in magnetized plasmas

We propose a conservative Galerkin scheme for the quasilinear model in three-dimensional momentum space and three-dimensional spectral space, with cylindrical symmetry. We construct an unconditionally conservative weak form and use a discretization that preserves conservation properties independent of the wave emission probability. The discrete operators, combined with a consistent quadrature rule, preserve all the conservation laws rigorously. The proposed scheme is quite general: it works for both relativistic and non-relativistic systems, for both magnetized and unmagnetized plasmas, and even for problems with time-dependent dispersion relations. We represent the particle distribution by continuous basis functions and use discontinuous basis functions for the wave spectral energy density, which enables the application of a positivity-preserving technique. We adopt the marching simplex algorithm, designed initially for computer graphics, for numerical integration on the resonance manifold. Furthermore, the numerical examples with a bump-on-tail initial configuration show how the unstable waves produce strong momentum space diffusion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Multi-head physics-informed neural networks for learning functional priors and uncertainty quantification

In numerous applications, the integration of prior knowledge and historical information is essential, particularly for tasks requiring the solution of ordinary or partial differential equations (ODEs/PDEs) in data-sparse or noisy environments. For instance, achieving accurate solutions to time-dependent PDEs with limited initial condition measurements necessitates an effective strategy for embedding prior knowledge. Hard-parameter sharing architectures in neural networks (NNs) have demonstrated success in both traditional and scientific machine learning domains, facilitating the learning of informative representations. Here, in this study, we introduce a novel, yet efficient, method to enhance physics-informed neural networks (PINNs) by incorporating a multi-head structure that enables the learning of functional priors from both empirical data and governing physical laws. This prior information can then be used to address data sparsity and high-level noise in solving ODE/PDE problems with uncertainty quantification (UQ). The approach, termed Multi-Head PINN (MH-PINN), consists of a shared body NN and multiple head NNs, each corresponding to an individual PINN instance. Our framework for functional prior learning is carried out in two stages: (1) training the MH-PINNs to develop a shared body NN alongside multiple head NNs, and (2) employing these trained head NNs to estimate a prior distribution through a normalizing flow-based density estimator. The learned functional prior can then be applied as a regularization mechanism in deterministic contexts or as an informative prior within a Bayesian inference framework, aiding in the resolution of subsequent ODE/PDE tasks. We evaluate the efficacy of MH-PINNs across five benchmark problems, including a high-dimensional parametric PDE, all characterized by data sparsity or substantial noise levels. Our findings reveal that MH-PINNs deliver accurate solutions and robust UQ, demonstrating adaptability across a range of complex and challenging scenarios.

Bayesian inference↗