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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Longitudinal spatial hole burning in high-power semiconductor lasers: numerical analysis

Longitudinal spatial hole burning (LSHB) in high-power semiconductor lasers is analysed by numerically solving one-dimensional (1D) rate equations. Calculations are performed for GaAs-based lasers operating at a wavelength of 1.06 μm. It is shown that the LSHB-induced decrease in output power can be accounted for by two mechanisms: build-up of spontaneous recombination and decrease in slope efficiency, equivalent to a rise in internal optical loss. We analyse the influence of different laser chip parameters on the magnitude of the LSHB effect. In particular, it is shown that to suppress LSHB it is preferable to increase the optical confinement factor Γ. We examine the relationship between LSHB and other mechanisms capable of reducing the output power. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Dynamics and chemical mode analysis of plasma thermal-chemical instability

The stability of the weakly ionized plasma and the transition from a stable homogeneous discharge to unstable filaments play an important role in gas laser physics, plasma-assisted combustion, chemical reforming, and material synthesis. Here, theoretical stability analysis and thermal-chemical mode analysis were performed to understand the mechanism of plasma thermal-chemical instability by using a zero-dimensional plasma system with both simplified and detailed chemical kinetics of H 2 /O 2 /N 2 mixtures. The plasma dynamic and kinetic models accounted for multiple physical mechanisms in the chemically-reactive weakly ionized plasma, including ionization, attachment/detachment, recombination, vibrational and electronic energy relaxation, convective and diffusive species/heat removal, Joule heating, and detailed chemical kinetics. An analytical criterion and the explosive mode species/temperature pointers were formulated while the representative active species were identified for different thermal-chemical modes. The results showed that in addition to the classical thermal-ionization mechanism, various chemical modes from chemical heat imbalance and elementary kinetics significantly modified the time dynamics and the stability of the weakly ionized plasma. The present analysis provides insights and guidance to control plasma instability using chemical kinetics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Co-optimized machine-learned manifold models for large eddy simulation of turbulent combustion

Many modeling approaches in large eddy simulation (LES) of turbulent combustion employ a projection of the thermochemical state onto a low-dimensional manifold within state space to reduce the number of transported variables and hence computational cost. Flamelet-generated manifolds (FGM) is an example of a well-established, physics-based approach, but increasingly, principal component analysis (PCA) is being used as a data-driven method for generating manifold models. For both approaches, the nonlinear relationship between the location on the predefined manifold and the outputs of interest, such as reaction rates, can be tabulated or encoded in a neural network. This work proposes a new approach for manifold modeling that extends these existing approaches. A modified neural network structure simultaneously encodes the definition of the manifold variables, the nonlinear mapping, and the subfilter closure for LES. This allows all three of these aspects of the model to be co-optimized, generating a model from any source of combustion thermochemical state data. The manifold parameterizing variables are constrained to be linear combinations of species, as in FGM and PCA-based models, to aid in interpretability and implementation. For LES, subfilter variances of the manifold variables are also included as inputs. Two types of a priori analysis are performed to evaluate the new approach. In the first, the model is trained on data from one-dimensional premixed flames. In this case, the approach recovers the behavior of flamelet-based manifold approaches, and in fact slightly improves performance by identifying an optimized progress variable. The approach is also applied to data from direct numerical simulations of spherical ignition kernels in isotropic turbulence. For any specified manifold dimensionality, the new approach provides substantially lower prediction errors than a PCA-based model developed from the same data set. Additionally, the LES formulation of the new approach can provide accurate predictions for filtered reaction rates across a variety of filter widths.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Evaluating the feasibility of LA-ICP-TOF-MS for the analysis of environmental particle collections

Laser ablation-inductively coupled plasma-time-of-flight-mass spectrometry (LA-ICP-TOF-MS) was employed to rapidly analyze environmental particle samples collected using aerosol contaminate extractors (ACE). The ACE particle collectors were placed at various distances (0.5, 1.3, and 4.5 km) from a source that released Ru-bearing particles. Samples for measurement were then generated (as sub-samples) from the ACE collection plates via particle “lift off” with gunshot residue (GSR) tabs. The LA-ICP-TOF-MS method was employed such that 10+ samples could be analyzed in a single unattended analytical session. A 3 × 1 mm area of individual GSR tab samples were analyzed in less than 30 minutes. This provided spatially resolved elemental and isotopic measurements of the particulate content and confirmed the presence of Ru-bearing particles within the complex background environmental particle loading. As anticipated, measurements showed collectors closest to the source had the highest concentration of the released Ru-bearing particles, while all collectors, regardless of distance, contained similar levels of background particles (e.g., Fe and Sr). Sequential scanning electron microscopy – automated particle analysis (SEM-APA) and LA-ICP-TOF-MS analysis was employed for method validation and a demonstration of the multi-modal approach. The same 2-dimensional region was analyzed by both methods and the particles identified via SEM-APA were also detected using LA-ICP-TOF-MS, with 100% accuracy. Overall, LA-ICP-TOF-MS demonstrated its utility for rapid elemental and isotopic particle analysis from environmental air samples.

Manard, Benjamin T. [Oak Ridge National Laboratory↗
DOE OSTI2020

Reduction of blob-filament radial propagation by parallel variation of flows: Analysis of a gyrokinetic simulation

Data from the XGC1 gyrokinetic simulation is analyzed to understand the three-dimensional spatial structure and the radial propagation of blob-filaments generated by quasisteady turbulence in the tokamak edge pedestal and scrape-off layer plasma. Spontaneous toroidal flows vary in the poloidal direction and shear the filaments within a flux surface resulting in a structure that varies in the parallel direction. Here, this parallel structure allows the curvature and grad-B induced polarization charge density to be shorted out via parallel electron motion. As a result, it is found that the blob-filament radial velocity is significantly reduced from estimates which neglect parallel electron kinetics, broadly consistent with experimental observations. Conditions for when this charge shorting effect tends to dominate blob dynamics are derived and compared with the simulation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

High-Field One-Dimensional and Two-Dimensional 27Al Magic-Angle Spinning Nuclear Magnetic Resonance Study of ?-, d-, and ?-Al2O3 Dominated Aluminum Oxides: Toward Understanding the Al Sites in ?-Al2O3

Herein, a detailed analysis was carried out using high-field (19.9 T) 27Al magic-angle spinning (MAS) nuclear magnetic resonance (NMR) on three specially prepared aluminum oxide samples where the ?-, d-, and ?-Al2O3 phases are dominantly expressed through careful control of the synthesis conditions. Specifically, two-dimensional (2D) multiquantum (MQ) MAS 27Al was used to obtain high spectral resolution, which provided a guide for analyzing quantitative 1D 27Al NMR spectra. Six aluminum sites were resolved in the 2D MQ MAS NMR spectra, and seven aluminum sites were required to fit the 1D spectra. A set of octahedral and tetrahedral peaks with well-defined quadrupolar line shapes was observed in the ?-phase dominant sample and was unambiguously assigned to the ?-Al2O3 phase. The distinct line shapes related to the ?-Al2O3 phase provided an opportunity for effectively deconvoluting the more complex spectrum obtained from the d-Al2O3 dominant sample, allowing the peaks/quadrupolar parameters related to the d-Al2O3 phase to be extracted. The results show that the d-Al2O3 phase contains three distinct AlO sites and three distinct AlT sites. This detailed Al site structural information offers a powerful way of analyzing the most complex ?-Al2O3 spectrum. It is found that the ?-Al2O3 phase consists of Al sites with local structures similar to those found in the d-Al2O3 and ?-Al2O3 phases albeit with less ordering. Spin–lattice relaxation time measurement further confirms the disordering of the lattice. Collectively, this study uniquely assigns 27Al features in transition aluminas, offering a simplified method to quantify complex mixtures of aluminum sites in transition alumina samples.

Xu, Suochang↗
DOE OSTI2024

ParaStell: parametric modeling and neutronics support for stellarator fusion power plants

The three-dimensional variation inherent to stellarator geometries and fusion sources motivates three-dimensional modeling to obtain accurate results from computational modeling in support of design and analysis of first wall, blanket, and shield (FWBS) systems. Manually constructing stellarator fusion power plant geometries in computer-aided design (CAD) and defining the corresponding fusion source can be cumbersome and challenging. The open-source parametric modeling toolset ParaStell has been developed to automate construction of such geometries in low-fidelity. Low-fidelity modeling is useful during the conceptual phase of engineering design as a means of rapidly exploring the design space of a given device. The modeling capability of ParaStell includes in-vessel components and magnets, for any given stellarator configuration, using a parametric definition and plasma equilibrium data. Furthermore, the toolset automates the generation of detailed, tetrahedral neutron source definitions and DAGMC geometries for use in neutronics modeling. ParaStell assists rapid design iteration, parametric study, and design optimization of stellarator fusion cores. As a demonstration of the design iteration capability, the effect of the three-dimensional parameter space on tritium breeding and magnet shielding is investigated, using the WISTELL-D configuration as a design basis. Blanket and shield thicknesses are varied in three dimensions, using the space available between the plasma edge and magnet coils as a constraint. The corresponding effects on tritium breeding ratio and magnet heating are tallied using the open-source Monte Carlo particle transport code OpenMC. The inclusion of additional and higher-fidelity modeling capabilities is planned for ParaStell’s future, as well as its implementation in machine-driven optimization.

Moreno, Connor↗
DOE OSTI2021

Short-term nodal load forecasting based on machine learning techniques

This paper introduces an advanced Short-term Nodal Load Forecasting (STNLF) method that forecasts nodal load profiles for the next day in power systems, based on the combined use of three machine learning techniques. Least Absolute Shrinkage and Selection Operator (LASSO) is employed to reduce the number of features for a single nodal load forecasting. Principal Component Analysis (PCA) is used to capture the features of historical loads in low-dimensional space compared to the original high-dimensional load space where features are barely possible to depict. Additionally, Bayesian Ridge Regression (BRR) is utilized to decide the parameters of the prediction model from a statistics perspective. Tests based on modified PJM load data demonstrate the effectiveness of the proposed STNLF method compared to the state-of-the-art General Regression Neural Network (GRNN) method. Moreover, the reliability of the day-ahead Unit Commitment (UC) solution is shown to have been improved, based on the forecasted load data using the proposed STNLF method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Rapid Inverse Parameter Inference Using Physics-Informed Neural Network

As Li-ion batteries become more essential in today's economy, tools need to be developed to accurately and rapidly diagnose a battery's internal state-of-health. Using a Li-ion battery's (high-rate) voltage response, it is proposed to determine a battery's internal state through Bayesian calibration. However, Bayesian calibration is notoriously slow and requires thousands of model runs. To accelerate parameter inference using Bayesian calibration, a surrogate model is developed to replace the underlying physics-based Li-ion model. Developing a surrogate model for rapid Bayesian calibration analysis is discussed for both the single particle model (SPM) and the pseudo two-dimensional (P2D) model. Surrogate models are constructed using physics-informed neural networks (PINNs) that encode the influence of internal properties on observed voltage responses. In practice, a neural network can be trained by: 1) using simulation results of the physics-based model (i.e., a data-loss approach); 2) using the residuals of the governing equations themselves (i.e., a physics-loss approach); or 3) using a combination of simulation results and governing equation residuals. In the present work, PINNs are developed using a variety of training losses and neural network architectures. In this analysis, it is shown that a PINN surrogate model can be reliably trained with only physics-informed loss. However, using a coupled data-informed and physics-loss approach produced the most accurate PINNs.

Bayesian calibration↗
DOE OSTI2020

Perfect quantum state transfer on diamond fractal graphs

In the quest for designing novel protocols for quantum information and quantum computation, an important goal is to achieve perfect quantum state transfer for systems beyond the well-known one- dimensional cases, such as 1D spin chains. Here, we use methods from fractal analysis and probability to find a new class of quantum spin chains on fractal-like graphs (known as diamond fractals) which support perfect quantum state transfer and which have a wide range of different Hausdorff and spectral dimensions. The resulting systems are spin networks combining Dyson hierarchical model structure with transverse permutation symmetries of varying order.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Quantum anomalous Hall insulator phases in Fe-doped GaBi honeycomb

We discuss electronic and magnetic properties of the Fe-doped GaBi honeycomb using first principles calculations. Our analysis shows that the pristine GaBi honeycomb transitions from being a two-dimensional quantum spin Hall (QSH) insulator to a quantum anomalous Hall (QAH) insulator when it is doped with one Fe atom in a 4 × 4 GaBi honeycomb. Additionally, the QAH phase in Fe-doped GaBi is found to be robust in that it maintains its Chern number (C = 1) under fairly large strains (~ 4%) and supports a gap as large as 112 meV at 2.21% strain. The QAH phase is also retained when the Fe-doped GaBi is placed on a CdTe substrate, suggesting that Fe-doped GaBi films could be useful for spintronics applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Machine learning elastic constants of multi-component alloys

The present manuscript explores application of machine learning methods for determining elastic constants and other derived mechanical properties of multi-component alloys. Here, a number of machine learning models, including linear regression, neural network and random forest based models, are trained and tested on a dataset of binary alloys generated using density functional theory (DFT) calculations and spanning over a large number of elemental species in the periodic table. Starting with a wide range of simple and easily accessible compositionally-averaged elemental features, a correlation-based feature selection strategy was used to systematically down-select a set of most relevant features towards the prediction of the elasticity tensor components. The true predictive performance and the associated uncertainties of the models were established by testing on unseen data and bootstrapping, respectively. A single and pair-wise feature partial dependence analysis was performed to visualize the average property trends in the multi-dimensional feature space in order to further understand the achieved predictive performance. The utility of the trained model is further demonstrated by obtaining sufficiently accurate yet highly efficient approximations for bulk modulus, Young’s modulus, shear modulus and Poisson’s ratio for alloys beyond the binary space (i.e., two-component alloys) on which the model was originally trained. More importantly, we test and validate the predictive performance of the developed model directly against the experimentally measured elastic constants of technologically relevant multi-component alloys (such as, Ni- and Ti-based alloys). Finally, utility of such a data-enabled route is demonstrated by predicting the possible range of various elastic properties for vast composition space available within the five component Ni-Cr-Fe-Mo-W alloy system in a high-throughput manner.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Performance and transport in the ARC tokamak

The ARC TM tokamak, a high-field (𝐵 𝑇 = 11.4 T) fusion power plant, under development by Commonwealth Fusion Systems, is studied using a suite of integrated modelling tools to predict its fusion power generation (𝑃𝑓⁡𝑢⁢𝑠), transport and confinement properties. Analysis is based off an ARC operational point scoped first with zero-dimensional (0-D) plasma operational contour (POPCON) modelling to produce 1.13 GW of fusion power. A suite of integrated modelling tools (TRANSP, ASTRA and TORAX) were applied to predict the performance and kinetic profiles of the ARC design point, yielding a range of predicted performance spanning from ∼900 to 1300 MW in rough quantitative agreement with POPCON predictions. The sensitivity of these results to uncertain modelling inputs was probed using scans of pedestal boundary conditions around EPED-predicted values (total pressure and temperature ratios), tungsten concentration and seperatrix density around their nominal assumptions. Pedestal pressure and pedestal top (𝑇 𝑖 /𝑇 𝑒 ) play a large role in 1.5-dimensional performance predictions, able to modify the predicted 𝑃 𝑓⁡𝑢⁢𝑠 by a factor of 2 within reasonable assumptions. High-fidelity core nonlinear gyrokinetic profile predictions, performed using CGYRO (Candy et al. 2016 J. Comput. Phys., vol. 324, pp. 73–93) coupled with the PORTALS (Rodriguez-Fernandez et al. 2024 Nucl. Fusion, vol. 64, 076034; Phys. Plasmas, vol. 31, 2024, 062501) framework, yield substantially lower performance (𝑃 𝑓⁡𝑢⁢𝑠 =677 MW) compared with 0-D and medium-fidelity modelling for nominal assumptions, showing that there is non-negligible uncertainty between models and that future work on SPARC may help resolve discrepancies. Lower overall performance results from significantly reduced volume-averaged densities and temperatures, along with reduced levels of density and temperature peaking. Turbulence and transport are largely dominated by ion temperature gradient across the profile, confirmed by both linear stability and the response of the nonlinear fluxes to changes in gradients, with some impact of kinetic ballooning modes in the deep core. This work represents one of the most complete scoping of potential fusion power plant conditions performed to date. The extensive integrated modelling provides confidence in ARC performance approaching 1 GW, while nonlinear gyrokinetic modelling results in open questions into the physics of density and temperature peaking in fusion-power-plant-relevant operational space. A discussion of results and the role that the SPARC tokamak (Creely et al. 2020 J. Plasma Phys., vol. 86, 865860502) will play in informing ARC design, performance and operation is presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Maximizing efficiency of dataset compression for machine learning potentials with information theory

Machine learning interatomic potentials (MLIPs) balance high accuracy and lower costs compared to density functional theory calculations, but their performance often depends on the size and diversity of training datasets. Large datasets improve model accuracy and generalization but are computationally expensive to produce and train on, while smaller datasets risk discarding rare but important atomic environments and compromising MLIP accuracy/reliability. Here, we develop an information-theoretical framework to quantify the efficiency of dataset compression methods and propose an algorithm that maximizes this efficiency. By framing atomistic dataset compression as an instance of the minimum set cover (MSC) problem over atom-centered environments, our method identifies the smallest subset of structures that contains as much information as possible from the original dataset while pruning redundant information. The approach is extensively demonstrated on the GAP-20 and TM23 datasets and validated on 64 varied datasets from the ColabFit repository. Across all cases, MSC consistently retains outliers, preserves dataset diversity, and reproduces the long-tail distributions of forces even at high compression rates, outperforming other subsampling methods. Furthermore, MLIPs trained on MSC-compressed datasets exhibit reduced error for out-of-distribution data even in low-data regimes. We explain these results using an outlier analysis and show that such quantitative conclusions could not be achieved with conventional dimensionality reduction methods. The algorithm is implemented in the open-source QUESTS package and can be used for several tasks in atomistic modeling, from data subsampling, outlier detection, and training improved MLIPs at a lower cost.

36 MATERIALS SCIENCE↗
DOE OSTI2020

ROM-Based Surrogate Systems Modeling of EBR-II

We report the System Analysis Module (SAM), developed and maintained by Argonne National Laboratory, is designed to provide whole-plant transient safety analysis capabilities for a number of advanced non-light water reactors, including sodium-cooled fast reactor (SFR), lead-cooled fast reactor (LFR), and molten salt reactor (MSR)/fluoride-salt-cooled high-temperature reactor (FHR) designs. SAM is primarily constructed as a systems-level analysis tool, with the potential to incorporate reduced order models from three-dimensional computational fluid dynamics (CFD) simulations to improve characterization of complex, multidimensional physics. It is recognized that the computational expense associated with CFD can be intractable for various engineering analyses, such as uncertainty quantification, inference, and design optimization. This paper explores the reducibility of a SAM model using recent advances in randomized linear algebra techniques, which attempt to find recurring patterns in the various realizations generated by a model after randomly perturbing all its input parameters. The reduction is described in terms of fewer degrees of freedom (DOFs), referred to as the active DOFs, for the model variables such as input model parameters and model responses. The results indicate that there is significant room for additional reduction that may be leveraged for additional computational gains when employing SAM for engineering-intensive analyses that require repeated model executions. Different from physics-based reduction approaches, the proposed approach allows one to estimate upper bounds on the reduction errors, which are rigorously developed in this work. Finally, different methods for surrogate model construction, such as regression and neural network-based training, are employed to correlate the input and output active DOFs, which are related back to the original variables using matrix-based linear transformations.

42 ENGINEERING↗
DOE OSTI2024

Characterizing structural features of two-dimensional particle systems through Voronoi topology

This paper introduces a new approach toward characterizing local structural features of two-dimensional particle systems. The approach can accurately identify and characterize defects in high-temperature crystals, distinguish a wide range of nominally disordered systems, and robustly describe complex structures such as grain boundaries. This paper also introduces two-dimensional functionality into the open-source software program VoroTop which automates this analysis. This software package is built on a recently-introduced multithreaded version of V ORO ++, enabling the analysis of systems with billions of particles on high-performance computer architectures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Computation of linear MHD instabilities with the multi-region relaxed MHD energy principle

Over the last decade, a variational principle based on a generalisation of Taylor's relaxation, referred to as multi-region relaxed magnetohydrodynamics (MRxMHDs) has been developed. The numerical solutions of the MRxMHD equilibria have been constructed using the Stepped Pressure Equilibrium Code (SPEC) (Hudson et al 2012 Phys. Plasmas 19 112502). In principle, SPEC could also be established to describe the MRxMHD stability of an equilibrium. In this work, a theoretical framework is developed to relate the second variation of the energy functional to the so-called Hessian matrix, enabling the prediction of MHD linear instabilities of cylindrical plasmas, and is implemented in SPEC. The negative and positive eigenvalues of the Hessian matrix predict the stability of an equilibrium. Verification studies of SPEC stability results with the M3D-C 1 code and the tearing mode $\Delta^{^{\prime}}$ criterion have been conducted for ideal and resistive MHD instabilities, respectively, in a pressureless cylindrical tokamak, and have shown good agreement. Overall, our stability analysis is a critical step towards understanding the MHD stability of three-dimensional MHDs where nested flux surfaces, magnetic islands and stochastic regions co-exist.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗