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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Cosmological parameter forecasts for a CMB-HD survey

We present forecasts on cosmological parameters for a CMB-HD survey. For a $\Lambda$CDM + $N_{eff}$ + $\sum m_\nu$ model, we find $\sigma(n_s) = 0.0013$ and $\sigma(N_{eff}) = 0.014$ using CMB and CMB lensing multipoles in the range of $\ell \in [30, 20000]$, after adding anticipated residual foregrounds, delensing the acoustic peaks, and adding DESI BAO data. This is about a factor of two improvement in ability to probe inflation via $n_s$ compared to precursor CMB surveys. The $N_{eff}$ constraint can rule out light thermal particles back to the end of inflation with 95% CL; for example, it can rule out the QCD axion in a model-independent way assuming the Universe's reheating temperature was high enough that the axion thermalized. We find that delensing the acoustic peaks and adding DESI BAO tightens parameter constraints. We also find that baryonic effects can bias parameters if not marginalized over, and that uncertainties in baryonic effects can increase parameter error bars; however, the latter can be mitigated by including information about baryonic effects from kinetic and thermal Sunyaev-Zel'dovich measurements by CMB-HD. The CMB-HD likelihood and Fisher estimation codes used here are publicly available; the likelihood is integrated with Cobaya to facilitate parameter forecasting.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Impact of Molecular Design on Degradation Lifetimes of Degradable Imine-Based Semiconducting Polymers

Transient electronics are a rapidly emerging field due to their potential applications in the environment and human health. Recently, a few studies have incorporated acid-labile imine bonds into polymer semiconductors to impart transience; however, understanding of the structure–degradation property relationships of these polymers is limited. In this work, we systematically design and characterize a series of fully degradable diketopyrrolopyrrole-based polymers with engineered sidechains to investigate the impact of several molecular design parameters on the degradation lifetimes of these polymers. By monitoring degradation kinetics via ultraviolet–visible spectroscopy, we reveal that polymer degradation in solution is aggregation-dependent based on the branching point and M n , with accelerated degradation rates facilitated by decreasing aggregation. Additionally, increasing the hydrophilicity of the polymers promotes water diffusion and therefore acid hydrolysis of the imine bonds along the polymer backbone. The aggregation properties and degradation lifetimes of these polymers rely heavily on solvent, with polymers in chlorobenzene taking six times as long to degrade as in chloroform. We develop a new method for quantifying the degradation of polymers in the thin film and observe that similar factors and considerations (e.g., interchain order, crystallite size, and hydrophilicity) used for designing high-performance semiconductors impact the degradation of imine-based polymer semiconductors. We found that terpolymerization serves as an attractive approach for achieving degradable semiconductors with both good charge transport and tuned degradation properties. This study provides crucial principles for the molecular design of degradable semiconducting polymers, and we anticipate that these findings will expedite progress toward transient electronics with controlled lifetimes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

3D simulation of lower-hybrid drift waves in strong guide field asymmetric reconnection in laboratory experiments

The magnetic reconnection experiment has recently seen short wavelength ([Formula: see text]) lower-hybrid waves near the electron diffusion region in strong guide field reconnection. Based on plasma parameters from the experiment, we perform a three-dimensional fully kinetic simulation in order to investigate the generation of the lower-hybrid waves and their effects on the reconnection process. We find that the low-beta regions around the reconnection site are unstable to the lower-hybrid drift instability propagating in the outflow direction, driven by the difference between the electron and ion outflows. The waves modify the electron distributions, leading to periodic opening and closing of gaps in electron velocity space, and provide a small contribution to the anomalous resistivity. Finally, the simulation results are discussed in the context of space observations and laboratory experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

maranasgroup/KETCHUP

Repository for Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the steady-state fluxes and concentrations in wild-type and perturbed metabolic networks.

Suthers, Patrick↗
DOE OSTI2022

Study of Radiative Heat Transfer and Flow Physics from Medium-scale Methanol Pool Fire Simulations [Slides]

Analysis of methanol pool fire conducted as part of validation study for SIERRA/Fuego. Radiation model was effectively calibrated by modifying radiation model parameters for methanol. Computing integrated buoyancy flux, entrainment rate, and turbulent kinetic energy allowed for evaluation of less typical quantities in this validation study. Quantities were compared with experimental data or correlations and generally showed agreement. TKE needed fine mesh to be computed accurately. Predicted flame height less sensitive to variations in mixture fraction than temperature. Mixture fraction is a preferable threshold variable for this application.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Creation Synthetic Data to Train a Digital Twin to Predict Reactor Operations

Understanding techniques to strengthen the nuclear safeguards regime is crucial in preventing nuclear proliferation due to recent advancements in the nuclear energy industry such as Generation IV reactors and microreactors. Prior to the construction of a nuclear power plant, it is necessary to understand the proliferation potential of the plant's reactor. Digital twins serve as a unique solution to recognizing reactor behavior indicative of nuclear proliferation. A digital twin is defined as a virtual model that works in unison to represent a physical asset, with a transference of data between the virtual and physical assets [1]. This work serves as validation for training a digital twin on synthetic data fabricated via means of Serpent reactor physics and point kinetics equations simulations. In this case this work is based on parameters of Idaho State University's AGN-201 reactor. The synthetic data can then be utilized to train machine learning models in the future to further investigate the utility of these methods. The accuracy of the predicted data is measured against real operational data to verify the reliability of the synthetic data creation methods and decide whether these methods should be used in the future to inform inspectors of a reactor's proliferation potential.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Plasma Dynamics and Nonthermal Particle Acceleration in 3D Nonrelativistic Magnetic Reconnection

Understanding plasma dynamics and nonthermal particle acceleration in 3D magnetic reconnection has been a long-standing challenge. In this paper, we explore these problems by performing large-scale fully kinetic simulations of multi-X-line plasmoid reconnection with various parameters in both the weak- and strong-guide-field regimes. In each regime, we have identified its unique 3D dynamics that lead to field-line chaos and efficient acceleration, and we have achieved nonthermal acceleration of both electrons and protons into power-law spectra. The spectral indices agree well with a simple Fermi acceleration theory that includes guide-field dependence. In the low-guide-field regime, the flux rope kink instability governs the 3D dynamics for efficient acceleration. The weak dependence of the spectra on the ion-to-electron mass ratio and β ($\ll$1) implies that the particles are sufficiently magnetized for Fermi acceleration in our simulations. While both electrons and protons are injected at reconnection exhausts, protons are primarily injected by perpendicular electric fields through Fermi reflections and electrons are injected by a combination of perpendicular and parallel electric fields. The magnetic power spectra agree with in situ magnetotail observations, and the spectral index may reflect a reconnection-driven size distribution of plasmoids instead of the Goldreich–Sridhar vortex cascade. As the guide field becomes stronger, the oblique flux ropes of large sizes capture the main 3D dynamics for efficient acceleration. Intriguingly, the oblique flux ropes can also experience flux rope kink instability, to drive extra 3D dynamics. This work has broad implications for 3D reconnection dynamics and particle acceleration in heliophysics and astrophysics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Non-Gaussianity from explicit U(1)-breaking interactions

We investigate primordial non-Gaussianity (NG) arising from the explicit U(1) symmetry-breaking interactions during inflation involving a nearly massless axial component of a complex scalar field P. We analyze the induced NG parameter f NL under scenarios where the axial field functions as either a curvaton or cold dark matter (CDM). In the curvaton framework, there is a conventional contribution to the local NG of f NL ≃ -O(1). Additional positive local NG can result from either the self-interactions of axial field fluctuations, their interactions with a light radial partner, or kinetic mixing with the inflaton via U(1) symmetry-breaking terms. We identify parameter regions where the interactions lead to cancellations, suppressing the overall local NG to |f loc NL | ≲ O(0.1). In the CDM scenario, these interactions enhance the NG in the isocurvature fluctuations. Moreover, interactions between the axial field and another light scalar, such as a curvaton, can generate O(1) curvature NG signals and significant mixed curvature-isocurvature NGs that are within the reach of future experiments with σ(f loc NL ) ∼ 1. We also explore the role of a heavy radial field in generating oscillating correlation signals, noting that such signals can dominate the shape of the mixed adiabatic-isocurvature bispectrum. In certain cases, an oscillatory isocurvature bispectrum signal may be observable in the future, aiding in distinguishing between certain types of the U(1)-breaking self-interactions of the axial field.

axions↗
DOE OSTI2022

Stability analysis of a one-dimensional multiphysics model of a molten salt fast reactor

Reactor designs with a fast neutron spectrum have recently moved into the research focus for possible Generation IV reactor concepts - a representative of which is the molten salt fast reactor (MSFR). In order to assess the safety and reliability of this reactor concept, it is necessary to obtain knowledge of the system behaviour. An important part of this research concerns the dynamical stability of the MSFR. Research regarding the MSFR stability so far has either been using linearized models for stability criteria or only been examining certain transients at fixed parameter values. This work delivers a comprehensive stability analysis for a non-linearized MSFR model, and a wide range of values for all relevant parameters. For this purpose, a one-dimensional MSFR model was set up, taking neutron kinetics and thermal hydraulics in the form of a system of differential equations into account. The stability of this model was investigated by means of the numerical tool MATCONT, which was used to monitor the evolution of a so-called fixed-point solution, here referring to the steady state at operating conditions of the system, while system parameters got varied. The MATCONT analyses showed no loss of stability for any of the considered parameter variations and no solutions that might co-exist in parallel with the steady-state fixed-point solution. Therefore these results indicate a stable fixed point to which all solution transients converge, making the considered MSFR model insusceptible to deviations from the equilibrium. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

A Coupled Small and Wide-Angle X-Ray Scattering Study of Phase Transformation Mechanisms in U-6Wt Pct Nb

The elevated temperature phase transformation kinetics of U-6wt%Nb in both wrought and cast conditions were investigated using in-situ high energy synchrotron X-ray scattering. The evolution of the phase content, γ-phase composition, and microstructure morphology was determined through the analysis of simultaneously collected wide-angle diffraction and small-angle scattering (SAXS) data. The results of the wide-angle scattering analysis, especially the phase fractions and average composition of the γ-phase, provide model parameters to enable a quantitative analysis using the SAXS data. Greater clarity regarding the kinetics of different precipitation mechanisms, namely the continuous precipitation (CP) of α-U within the γ-phase matrix and the discontinuous (DP) grain boundary precipitation of a lamellar microstructure, consisting of α-U and Nb-rich γ-phase are obtained from the coupled measurement modes. These results indicate that the CP mechanism is responsible for the initial precipitation of α-U at all temperatures considered. Near the nose in the time-temperature-transformation (TTT) diagram at ~798 K, both mechanisms are active during the early stages of phase transformation. However, the DP mechanism has an increasingly long incubation period with decreasing temperature below the TTT nose. In conclusion, the average interlamellar spacing obtained from the SAXS analysis increases with time, providing further evidence of the so-called divergent DP behavior previously observed in U-6wt%Nb alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Numerical simulation of involute-plate research reactor flow behavior using RANS, LES and DNS

This paper investigates the flow behavior of involute-plate research reactors by performing Reynolds-Averaged Navier Stokes simulation (RANS), Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) of the channel flow between fuel plates. By modeling turbulence with different numerical approaches, this study provides data with three levels of fidelity. For the RANS simulation, three widely used turbulence models, i.e., k-ε, k-ω, Reynolds Stress Turbulence model (RST) are applied by using the commercial CFD code STAR-CCM +. For LES and DNS, the open-source CFD code, Nek5000, is used given its outstanding scalability on High Performance Computer (HPC) and high-order technique. The results from RANS simulations are compared with that from LES and DNS for benchmarking. Both macroscale parameters and turbulence statistics, such as velocity magnitude, lateral velocity and turbulence kinetic energy, are presented and analyzed. The results from RANS simulation achieve good agreement with LES and DNS on velocity and turbulence kinetic energy prediction. The RST turbulence model predicts the most similar flow pattern of lateral velocity as compared to LES and DNS. The Lambda-2 (λ2) criterion with a reasonable threshold is used to demonstrate the instantaneous vortices distribution in the involute channel from both LES and DNS calculation. The DNS simulation captures more detailed turbulence especially near the corner, which explains the discrepancy between LES and DNS results near the corner. The normalized RMS error are defined and calculated to assess the performance of those turbulence models. The RST model captures the anisotropic feature of turbulence, which enable it to outperform other turbulence models for predicting the flow behavior in an involute channel. Although some discrepancies are found between LES and DNS results in the corner, the overall deviations between LES and DNS are found to be small. In conclusion, given that the computational cost of DNS calculation is an order of magnitude higher, using LES data for benchmarking RANS model is a cost-effective approach.

DNS↗
DOE OSTI2022

Lamellae of α phase in a metastable β -Ti alloy studied by small-angle x-ray scattering

The shapes and orientations of lamellae of the hexagonal close-packed α phase in single crystals of the body-centred cubic β phase in a metastable β -Ti alloy (Timetal LCB) were investigated by synchrotron small-angle x-ray scattering. Several steps of thermal treatment with various annealing times and temperatures were employed. The measured scattering data were fitted to a model of ellipsoidal lamellae of random sizes, and the mean lamella shapes and orientations of their axes with respect to the β -Ti matrix were determined from the fit. The number and relative volume of the lamellae were estimated from the integrated scattered intensity. The elastic energy of the lamellae as well as the energy of defects at the α / β interface (misfit dislocations) were calculated from the parameters of the lamellae. In conclusion, the results provide information on the kinetics of the β $ \rightarrow$ α transformation in metastable β -Ti alloys and allow assessing the degree of thermodynamic equilibrium of the final α / β composite.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Development of a high temperature pyrolysis mechanism for cyclopentanone, a potential biofuel derived from biomass

Cyclopentanone (CPO) is a promising biofuel for spark-ignition engines due to its ring strain and high auto-ignition resistance. The Department of Energy has identified CPO as a potential biofuel candidate because of its unique chemical properties. Understanding CPO decomposition is crucial to building a high-temperature combustion model. In this study, we present a comprehensive kinetic model for high-temperature pyrolysis of CPO with verified results from high-pressure shock tube (HPST) measurements. The time histories of carbon monoxide (CO), ethylene (C2H4), and CPO absorbances over the temperature range of 1156-1416 K and 8.53-10.06 atm were measured. A corresponding kinetic model was generated using the Reaction Mechanism Generator (RMG) with the parameters of dominant pathways from either previous studies or quantum calculation within current work. The final model containing 821 species and 79,859 reactions exhibited a good agreement with the experimental results. In this study, the C2H4/CO ratio was used as an essential factor to validate the model and to prove that radical-involved bimolecular pathways were as significant as unimolecular decomposition of CPO. The rate of production (ROP) analysis showed that H radicals play major roles in both primary reactions and product pyrolysis. The insights from this work can be used to generate a better CPO combustion model and help evaluate CPO as a biofuel. Such understanding can also be helpful when proposing pyrolysis or combustion models for other biofuels.

cyclopentanone, reaction mechanism, pyrolysis, sho↗
DOE OSTI2023

Physics-informed neural network with transfer learning (TL-PINN) based on domain similarity measure for prediction of nuclear reactor transients

Nuclear reactor safety and efficiency can be enhanced through the development of accurate and fast methods for prediction of reactor transient (RT) states. Physics informed neural networks (PINNs) leverage deep learning methods to provide an alternative approach to RT modeling. Applications of PINNs in monitoring of RTs for operator support requires near real-time model performance. However, as with all machine learning models, development of a PINN involves time-consuming model training. Here, we show that a transfer learning (TL-PINN) approach achieves significant performance gain, as measured by reduction of the number of iterations for model training. Using point kinetic equations (PKEs) model with six neutron precursor groups, constructed with experimental parameters of the Purdue University Reactor One (PUR-1) research reactor, we generated different RTs with experimentally relevant range of variables. The RTs were characterized using Hausdorff and Fréchet distance. We have demonstrated that pre-training TL-PINN on one RT results in up to two orders of magnitude acceleration in prediction of a different RT. The mean error for conventional PINN and TL-PINN models prediction of neutron densities is smaller than 1%. We have developed a correlation between TL-PINN performance acceleration and similarity measure of RTs, which can be used as a guide for application of TL-PINNs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Experimental validation of a collision-radiation dataset for molecular hydrogen in plasmas

Quantitative spectroscopy of molecular hydrogen has generated substantial demand, leading to the accumulation of diverse elementary process data encompassing radiative transitions, electron-impact transitions, predissociations, and quenching. However, their rates currently available are still sparse, and there are inconsistencies among those proposed by different authors. In this study, we demonstrate an experimental validation of such a molecular dataset by composing a collisional-radiative model (CRM) for molecular hydrogen and comparing experimentally obtained vibronic populations across multiple levels. From the population kinetics of molecular hydrogen, the importance of each elementary process in various parameter space is studied. In low-density plasmas (electron density ne≲1017 m−3) the excitation rates from the ground states and radiative decay rates, both of which have been reported previously, determine the excited state population. The inconsistency in the excitation rates affects the population distribution the most significantly in this parameter space. However, in higher density plasmas (ne≳1018 m−3), the excitation rates from excited states become important, which have never been reported in the literature, and may need to be approximated in some way. In order to validate these molecular datasets and approximated rates, we carried out experimental observations for two different hydrogen plasmas; a low-density radio frequency heated plasma (ne≈1016 m−3) and the Large Helical Device (LHD) divertor plasma (ne≳1018 m−3). The visible emission lines from EF1Σg+, HH¯1Σg+, D1Πu±, GK1Σg+, I1Πg±, J1Δg±, h3Σg+, e3Σu+, d3Πu±,g3Σg+, i3Πg±, and j3Δg± states were observed simultaneously and their population distributions were obtained from their intensities. We compared the observed population distributions with the CRM prediction, in particular the CRM with the rates compiled by Janev et al., Miles et al., and those calculated with the molecular convergent close-coupling (MCCC) method. The MCCC prediction gives the best agreement with the experiment, particularly for the emission from the low-density plasma. However, the population distribution in the LHD divertor shows a worse agreement with the CRM than those from low-density plasma, indicating the necessity of the precise excitation rates from excited states. We also found that the rates for the electron attachment is inconsistent with experimental results. This requires further investigation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

The influence of the cloud virial parameter on the initial mass function

ABSTRACT Crucial for star formation is the interplay between gravity and turbulence. The observed cloud virial parameter, $\alpha _{\mathrm{vir}}$, which is the ratio of twice the turbulent kinetic energy to the gravitational energy, is found to vary significantly in different environments, where the scatter among individual star-forming clouds can exceed an order of magnitude. Therefore, a strong dependence of the initial mass function (IMF) on $\alpha _{\mathrm{vir}}$ may challenge the notion of a universal IMF. To determine the role of $\alpha _{\mathrm{vir}}$ on the IMF, we compare the star-particle mass functions obtained in high-resolution magnetohydrodynamical simulations including jet and heating feedback, with $\alpha _{\mathrm{vir}}=0.0625$, 0.125, and 0.5. We find that varying $\alpha _{\mathrm{vir}}$ from $\alpha _{\mathrm{vir}}\sim 0.5$ to $\alpha _{\mathrm{vir}}< 0.1$ shifts the peak of the IMF to lower masses by a factor of $\sim 2$ and increases the star formation rate by a similar factor. The dependence of the IMF and star formation rate on $\alpha _{\mathrm{vir}}$ is non-linear, with the dependence subsiding at $\alpha _{\mathrm{vir}}< 0.1$. Our study shows a systematic dependence of the IMF on $\alpha _{\mathrm{vir}}$. Yet, it may not be measurable easily in observations, considering the uncertainties, and the relatively weak dependence found in this study.

Mathew, Sajay Sunny (ORCID:0000000283818195)↗
DOE OSTI2026

Development of a deep potential model for F and CF 2 etching of Si and SiO 2

An understanding of plasma-surface interactions at increasingly smaller scales is invaluable for the development of novel technologies and processing techniques. Molecular dynamics (MD) simulations can provide insights into atomic-scale interactions, though they are restricted by the availability of interatomic potentials. Machine learning methods, such as Deep Potential Molecular Dynamics (DeepMD), provide a systematic framework for the development of accurate and flexible ab initio-based models. In this work, we develop DeepMD models for the ion-enhanced etching of Si and SiO 2 by F and CF 2 radicals. We employ an active learning process to expand the data set on which the model is trained and demonstrate its effect on the model accuracy. The DeepMD results are compared to data from classical MD simulations and experiments. Physical sputtering yields of SiO 2 by Ar + ions show good agreement with previous simulation results using conventional interatomic potentials, though the predicted depth profiles are different. Etching yields are calculated as a function of ion energy and neutral to ion flux ratio for the Ar + ion-enhanced etching of SiO 2 and Si by F atoms, as well as for etching of SiO 2 by CF 2 radicals, showing reasonable agreement with experimental data. Finally, an ion-enhanced surface kinetic model is fitted to the DeepMD etch yields, and the fitted parameters are compared to quantities computed directly from DeepMD simulations. This study illustrates how molecular dynamics simulations using machine learning potentials can provide an accurate model of etching processes relevant to device manufacturing.

Kounis-Melas, Andreas [Princeton Univ., NJ (United↗