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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 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Hybrid Improved Empirical Mode Decomposition and Artificial Neural Network Model for the Prediction of Critical Heat Flux (CHF)

Three Hybrid artificial neural network (ANN) models namely radial basis function (RBF), generalized regression neural networks (GRNN), and multi-layer perceptron (MLP) combined with empirical mode decomposition (EMD) are developed for CHF predictive modelling using CHF experimental databases.First, the original experimental inputs data series are decomposed into several intrinsic mode functions (IMFs) and one residual by EMD, whose components are divided into high, medium and low components. The performance parameters of the hybrid models indicates that the root mean square error (RMSE) are 0.8831, 0.6522, and 0.4149; the mean absolute error (MAE) are 0.6697, 0.4636, and 0.1935. The values of the R-square of the developed prediction approach utilizing EMD-RBF, EMD-GRNN, and EMD-MLP models are 0.8553, 0.9302, and 0.9818, and the index of agreement are 0.9464, 0.9700, and 0.9894., The value of the R-square and the index of agreement of the proposed models are much higher than those of the simple models .The Pearson's test results show that the association strength between the measured and the predicted values of the proposed model EMD-MLP is the strongest. These results show the following: (a) compared with other related, recent studies, the prediction accuracy of the hybrid model EMD- MLP proposed in this research is the best hybrid model; (b) the proposed hybrid model (EMD-MLP) attains superior performance compared with simple models.

Djeddou, Messaoud↗
DOE OSTI2021

Self-Healing Ceramics and Mechanical Metamaterials

We performed large scale molecular dynamics (MD) simulations and reactive & quantum molecular dynamics simulations to investigate: (1) faceting, grain growth, and crack healing in ceramics; (2) stress-release mechanism and healing in fibers; and (3) mechanical properties of two-dimensional metamaterials. We also designed highly accurate interatomic potentials and forces using artificial neural networks (ANN) and first-principles molecular dynamics simulation data for use in our MD and reactive MD simulations. We demonstrated the efficacy of modeling quantum materials atomistic simulation data using unsupervised deep learning approaches on a quantum computer. These achievements are elaborated in the following sections.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Graphene-based battery electrodes having continuous flow paths

Some batteries can exhibit greatly improved performance by utilizing electrodes having randomly arranged graphene nanosheets forming a network of channels defining continuous flow paths through the electrode. The network of channels can provide a diffusion pathway for the liquid electrolyte and/or for reactant gases. Metal-air batteries can benefit from such electrodes. In particular Li-air batteries show extremely high capacities, wherein the network of channels allow oxygen to diffuse through the electrode and mesopores in the electrode can store discharge products.

25 ENERGY STORAGE↗
DOE OSTI2019

Finding New Cell Wall Regulatory Genes in Populus trichocarpa Using Multiple Lines of Evidence

Understanding the regulatory network controlling cell wall biosynthesis is of great interest in Populus trichocarpa, both because of its status as a model woody perennial and its importance for lignocellulosic products. We searched for genes with putatively unknown roles in regulating cell wall biosynthesis using an extended network-based Lines of Evidence (LOE) pipeline to combine multiple omics data sets in P. trichocarpa, including gene coexpression, gene comethylation, population level pairwise SNP correlations, and two distinct SNP-metabolite Genome Wide Association Study (GWAS) layers. By incorporating validation, ranking, and filtering approaches we produced a list of nine high priority gene candidates for involvement in the regulation of cell wall biosynthesis. We subsequently performed a detailed investigation of candidate gene GROWTH-REGULATING FACTOR 9 (PtGRF9). To investigate the role of PtGRF9 in regulating cell wall biosynthesis, we assessed the genome-wide connections of PtGRF9 and a paralog across data layers with functional enrichment analyses, predictive transcription factor binding site analysis, and an independent comparison to eQTN data. Our findings indicate that PtGRF9 likely affects the cell wall by directly repressing genes involved in cell wall biosynthesis, such as PtCCoAOMT and PtMYB.41, and indirectly by regulating homeobox genes. Furthermore, evidence suggests that PtGRF9 paralogs may act as transcriptional co-regulators that direct the global energy usage of the plant. Using our extended pipeline, we show multiple lines of evidence implicating the involvement of these genes in cell wall regulatory functions and demonstrate the value of this method for prioritizing candidate genes for experimental validation.

network analysis↗
DOE OSTI2021

A multi-dimensional parametric study of variability in multi-phase flow dynamics during geologic CO 2 sequestration accelerated with machine learning

Successful geologic CO 2 storage projects depend on numerical simulations to predict reservoir performance during site selection, injection verification, and post-injection monitoring phases of the project. These numerical simulations solve non-linear sets of coupled partial differential equations, while accounting for multi-phase fluid dynamics on the basis of constitutive equations that are embedded into the solution scheme. As a consequence, individual simulations often require tens to hundreds of hours to complete on high-performance computing clusters. Moreover, laboratory experiments reveal that parametric functions for capillary pressure and relative permeability exhibit substantial variability, even within the same rock type. This combination of computational expense and wide-ranging parametric variability means that there remains substantial uncertainty in the behavior of multi-phase CO 2 -water systems, particularly in the context of feedbacks between relative permeability and capillary pressure. To bridge this knowledge gap, here we develop a novel workflow that utilizes physics-based numerical simulation to train an artificial neural network (ANN) emulator for interrogating the multivariate parameter space that governs both capillary pressure and relative permeability. With this approach, the ANN is trained to emulate both fluid pressure distribution and CO 2 saturation, which are then interrogated quantitatively to generate parametric response surface mappings with high-fidelity resolution. Results from this study initially show that capillary entry pressure is the dominant control on both CO 2 plume geometry and fluid pressure propagation when considering the combined effects of capillary pressure and relative permeability, particularly when phase interference is low and residual CO 2 saturation is high. Moreover, the ANN emulator provides tremendous computational speed-up by computing 2691 individual simulations in several minutes; whereas, the same simulation ensemble would have required ~3 years of simulation time using only physics-based simulation methods (25,000 times speed up).

58 GEOSCIENCES↗
DOE OSTI2021

Understanding why poly(acrylic acid) works: decarbonylation and cross-linking provide an ionically conductive passivation layer in silicon anodes

Poly(acrylic acid) (PAA) is commonly used as a binder for fabricating silicon (Si) anode active materials in lithium-ion batteries due to its useful properties including high polar solvent solubility, good rheology, and strong adhesive properties. However, the role and evolution of PAA during electrode fabrication, cycling, and calendar aging are not well understood. In this work, we reveal the evolution of PAA during electrode curing and relate its chemical change to the final electrode properties and performance. These studies are made possible using two types of in situ attenuated total reflectance-infrared Fourier transform (ATR-FTIR) spectroscopy: thermal ATR-FTIR to probe the cross-linking reaction, and ATR-FTIR spectroelectrochemistry of three-dimensional composite electrodes, a unique technique developed herein that probes the solvation dynamics of lithium ions at the silicon anode interface under electrochemical polarization. Specifically, we show that PAA undergoes a thermally-mediated, cross-linking decarbonylation reaction to form an ether-based network polymer. To show the importance of the polyether moieties, we synthesize partially esterified PAA analogues that do not undergo this cross-linking decarbonylation reaction and correlate the degree of cross-linking to half-cell performance metrics. Finally, we unveil the mechanism of the polyether binder performance through in situ ATR-FTIR spectroelectrochemistry and show that PAA acts as an interfacial material that conducts lithium-ions, limits solvent molecule access to the Si surface, and stabilizes the electrode against parasitic lithium inventory loss at high state of charge for an extended period of time.

25 ENERGY STORAGE↗
DOE OSTI2023

Synergistic Coupling Effect of Electronic Conductivity and Interphase Compatibility on High-Voltage Na 3 V 2 (PO 4 ) 2 F 3 Cathodes

Na 3 V 2 (PO 4 ) 2 F 3 (NVPF) has been considered an up-and-coming cathode material candidate for sodium (Na) ion batteries in light of its high specific capacity and working voltage. However, an erratic cathode/electrolyte interface layer is inevitably formed, accompanied by continuous electrolyte decomposition on the NVPF surface, when the voltage exceeds 4.2 V vs Na + /Na. Herein, the interphase features of NVPF are obviously enhanced owing to the ameliorated electronic conductivity obtained by combining it with carbon nanotubes (CNT). The NVPF with 3 wt % CNT (NVPF@3% CNT) reduces the Na + diffusion kinetic energy barrier and electron transport resistance. Furthermore, the conducting network formed by CNT with sturdy structure strength can promptly accommodate the volumetric changes during sequential Na + extraction/insertion and thus effectively improve the long-term cyclic performance of NVPF/hard carbon full cells. The initial discharge capacity approaches 105 mA h g –1 at 0.5C, and it retains 94% capacity retention after 200 cycles at the temperature of –10 °C. The cathode/electrolyte interphase characterization results further demonstrate that the interphase layer on the NVPF@3% CNT cathode is thinner and more compact compared with pristine samples. Here, this research provides a competitive strategy to facilitate the interfacial compatibility between the NVPF and electrolytes and accelerate the commercialization of high-performance Na-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

3D printable silicone compositions exhibiting high toughness and low durometer

3D printable silicones can be designed with varying crosslink density, network structures, and types of reinforcing additives. Within this design space, one may tailor the uncured material’s rheology and mechanical response to access a wide range of potential applications. However, printable, low modulus silicones, particularly those applicable to direct ink write, are underreported in published literature. To address the need for higher performance, low modulus silicones with demonstrated printability, a new set of ca. 20–50 Shore A hardness silicone elastomers exhibiting ca. 7 MPa ultimate tensile strength and ca. 400–1200% elongation at break is presented. Mechanical properties were analyzed, providing insight to the effects of formulation constituents on mechanical properties. Cyclic mechanical testing of the silicone formulations was also performed, and the energy loss and permanent set throughout cycling were determined. In conclusion, printed structures demonstrate the feasibility of these new silicones as durable frameworks for novel soft device applications.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Toward Chirality-Encoded Domain Wall Logic

Nonvolatile logic networks based on spintronic and nanomagnetic technologies have the possibility to create high-speed, ultralow power computational architectures. This work investigates the feasibility of “chirality-encoded domain wall logic,” a nanomagnetic logic architecture where data are encoded by the chiral structures of mobile domain walls in networks of ferromagnetic nanowires and processed by the chiral structures' interactions with geometric features of the networks. High-resolution magnetic imaging is used to test two critical functionalities: the inversion of domain wall chirality at tailored artificial defect sites (logical NOT gates) and the chirality-selective output of domain walls from 2-in-1-out nanowire junctions (common operation to AND/NAND/OR/NOR gates). The measurements demonstrate both operations can be performed to a good degree of fidelity even in the presence of complex magnetization dynamics that would normally be expected to destroy chirality-encoded information. Together, these results represent a strong indication of the feasibility of devices where chiral magnetization textures are used to directly carry, rather than merely delineate, data.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An adaptive, data-driven multiscale approach for dense granular flows

The accuracy of coarse-grained continuum models of dense granular flows is limited by the lack of high-fidelity closure models for granular rheology. One approach to addressing this issue, referred to as the hierarchical multiscale method, is to use a high-fidelity fine-grained model to compute the closure terms needed by the coarse-grained model. The difficulty with this approach is that the overall model can become computationally intractable due to the high computational cost of the high-fidelity model. In this work, we describe a multiscale modeling approach for dense granular flows that utilizes neural networks trained using high-fidelity discrete element method (DEM) simulations to approximate the constitutive granular rheology for a continuum incompressible flow model. Our approach leverages an ensemble of neural networks to estimate predictive uncertainty that allows us to determine whether the rheology at a given point is accurately represented by the neural network model. Additional DEM simulations are only performed when needed, minimizing the number of additional DEM simulations required when updating the rheology. This adaptive coupling significantly reduces the overall computational cost of the approach while controlling the error. In addition, the neural networks are customized to learn regularized rheological behavior to ensure well-posedness of the continuum solution. We first validate the approach using two-dimensional steady-state and decelerating inclined flows. We then demonstrate the efficiency of our approach by modeling three-dimensional sub-aerial granular column collapse for varying initial column aspect ratios, where our multiscale method compares well with the computationally expensive computational fluid dynamics (CFD)-DEM simulation.

Dense granular flows↗
DOE OSTI2017

A thermodynamic analysis of a novel bidirectional district heating and cooling network

In this study, we evaluate an ambient, bidirectional thermal network, which uses a single circuit for both district heating and cooling. When in net more cooling is needed than heating, the system circulates from a central plant in one direction. When more heating is needed, the system circulates in the opposite direction. A large benefit of this design is that buildings can recover waste heat from each other directly. We analyze the thermodynamic performance of the bidirectional system. Because the bidirectional system represents the state-of-the-art in design for district systems, its peak energy efficiency represents an upper bound on the thermal performance of any district heating and cooling system. However, because any network has mechanical and thermal distribution losses, we develop a diversity criterion to understand when the bidirectional system may be a more energy-efficient alternative to modern individual-building systems. We show that a simple model of a low-density, high-distribution loss network is more efficient than aggregated individual buildings if there is at least 1 unit of cooling energy per 5.7 units of simultaneous heating energy (or vice versa). We apply this criterion to reference building profiles in three cities to look for promising clusters.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Three dimensional nitrogen-doped graphene aerogels functionalized with melamine for multifunctional applications in supercapacitors and adsorption

In present work, we demonstrate an efficient and facile strategy to fabricate three-dimensional (3D) nitrogen-doped graphene aerogels (NGAs) based on melamine, which serves as reducing and functionalizing agent of graphene oxide (GO) in an aqueous medium with ammonia. Benefiting from well-defined and cross-linked 3D porous network architectures, the supercapacitor based on the NGAs exhibited a high specific capacitance of 170.5 F g{sup −1} at 0.2 A g{sup −1}, and this capacitance also showed good electrochemical stability and a high degree of reversibility in the repetitive charge/discharge cycling test. More interestingly, the prepared NGAs further exhibited high adsorption capacities and high recycling performance toward several metal ions such as Pb{sup 2+}, Cu{sup 2+} and Cd{sup 2+}. Moreover, the hydrophobic carbonized nitrogen-doped graphene aerogels (CNGAs) showed outstanding adsorption and recycling performance for the removal of various oils and organic solvents. - Graphical abstract: Three-dimensional nitrogen-doped graphene aerogels were prepared by using melamine as reducing and functionalizing agent in an aqueous medium with ammonia, which showed multifunctional applications in supercapacitors and adsorption. - Highlights: • Three-dimensional nitrogen-doped graphene aerogels (NGAs) were prepared. • Melamine was used as reducing and functionalizing agent. • NGAs exhibited relatively good electrochemical properties in supercapacitor. • NGAs exhibited high adsorption performance toward several metal ions. • CNGAs showed outstanding adsorption capacities for various oils and solvents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Direct Construction of Mesoporous Metal Sulfides via Reactive Spray Deposition Technology

In this work, a rapid one-step flame combustion deposition method was developed to fabricate the mesoporous metal sulfides directly on nickel foam without binder and template. The first-row transition metal sulfides, Co 9 S 8 , Ni 3 S 4 , NiCo 2 S 4 , and FeS, were successfully synthesized with three-dimensional architecture, large specific surface area (around 110 m 2 g –1 ), and interconnected pore networks. The binary meso-NiCo 2 S 4 exhibited a high specific capacitance (1350 F g –1 at 10 A g –1 ), excellent rate capability, and robust cycling stability in both three-electrode half-cells and asymmetric supercapacitor. This study demonstrated a new scalable strategy to obtain mesoporous metal sulfides with high electrochemical performance.

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

Deep Multimodal Networks for M-type Star Classification with Paired Spectrum and Photometric Image

Abstract Traditional stellar classification methods include spectral and photometric classification separately. Although satisfactory results can be achieved, the accuracy could be improved. In this paper, we pioneer a novel approach to deeply fuse the spectra and photometric images of the sources in an advanced multimodal network to enhance the model’s discriminatory ability. We use Transformer as the fusion module and apply a spectrum–image contrastive loss function to enhance the consistency of the spectrum and photometric image of the same source in two different feature spaces. We perform M-type stellar subtype classification on two data sets with high and low signal-to-noise ratio (S/N) spectra and corresponding photometric images, and the F1-score achieves 95.65% and 90.84%, respectively. In our experiments, we prove that our model effectively utilizes the information from photometric images and is more accurate than advanced spectrum and photometric image classifiers. Our contributions can be summarized as follows: (1) We propose an innovative idea for stellar classification that allows the model to simultaneously consider information from spectra and photometric images. (2) We discover the challenge of fusing low-S/N spectra and photometric images in the Transformer and provide a solution. (3) The effectiveness of Transformer for spectral classification is discussed for the first time and will inspire more Transformer-based spectral classification models.

Astronomy & Astrophysics↗
DOE OSTI2023

Direct prediction of inelastic neutron scattering spectra from the crystal structure*

Abstract Inelastic neutron scattering (INS) is a powerful technique to study vibrational dynamics of materials with several unique advantages. However, analysis and interpretation of INS spectra often require advanced modeling that needs specialized computing resources and relevant expertise. This difficulty is compounded by the limited experimental resources available to perform INS measurements. In this work, we develop a machine-learning based predictive framework which is capable of directly predicting both one-dimensional INS spectra and two-dimensional INS spectra with additional momentum resolution. By integrating symmetry-aware neural networks with autoencoders, and using a large scale synthetic INS database, high-dimensional spectral data are compressed into a latent-space representation, and a high-quality spectra prediction is achieved by using only atomic coordinates as input. Our work offers an efficient approach to predict complex multi-dimensional neutron spectra directly from simple input; it allows for improved efficiency in using the limited INS measurement resources, and sheds light on building structure-property relationships in a variety of on-the-fly experimental data analysis scenarios.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Predicting nonequilibrium Green’s function dynamics and photoemission spectra via nonlinear integral operator learning

Understanding the dynamics of nonequilibrium quantum many-body systems is an important research topic in a wide range of fields across condensed matter physics, quantum optics, and high-energy physics. However, numerical studies of large-scale nonequilibrium phenomena in realistic materials face serious challenges due to intrinsic high-dimensionality of quantum many-body problems and the absence of time-invariance. The nonequilibrium properties of many-body systems can be described by the dynamics of the correlator, or the Green's function of the system, whose time evolution is given by a high-dimensional system of integro-differential equations, known as the Kadanoff–Baym equations (KBEs). The time-convolution term in KBEs, which needs to be recalculated at each time step, makes it difficult to perform long-time numerical simulation. In this paper, we develop an operator-learning framework based on recurrent neural networks (RNNs) to address this challenge. We utilize RNNs to learn the nonlinear mapping between Green's functions and convolution integrals in KBEs. By using the learned operators as a surrogate model in the KBE solver, we obtain a general machine-learning scheme for predicting the dynamics of nonequilibrium Green's functions. Besides significant savings per each time step, the new methodology reduces the temporal computational complexity from $O(N_t^3)$ to $O(N_t)$ where N t is the number of steps taken in a simulation, thereby making it possible to study large many-body problems which are currently infeasible with conventional KBE solvers. Through various numerical examples, we demonstrate the effectiveness of the operator-learning based approach in providing accurate predictions of physical observables such as the reduced density matrix and time-resolved photoemission spectra. Moreover, our framework exhibits clear numerical convergence and can be easily parallelized, thereby facilitating many possible further developments and applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Finite element modeling of nanoscale-enabled microinductors for power electronics

Here, this paper focuses on the finite element modeling of toroidal microinductors, employing first-of-its-kind nanocomposite magnetic core material and superparamagnetic iron nanoparticles covalently cross-linked in an epoxy network. Energy loss mechanisms in existing inductor core materials are covered as well as discussions on how this novel core material eliminates them providing a path toward realizing these low form factor devices. Designs for both a 2 μH output and a 500 nH input microinductor are created via the model for a high-performance buck converter. Both modeled inductors have 50 wire turns, less than 1 cm 3 form factors, less than 1 Ω AC resistance, and quality factors, Q’s, of 27 at 1 MHz. In addition, the output microinductor is calculated to have an average output power of 7 W and a power density of 3.9 kW/in 3 by modeling with the 1st generation iron nanocomposite core material.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Measurements of Polarization Transfers in Real Compton Scattering by a proton target at JLAB. A new source of information on the 3D shape of the nucleon

In this thesis work, results of the analysis of the polarization transfers measured in real Compton scattering (RCS) by the Collaboration E07-002 at the Je fferson Lab Hall-C are presented. The data were collected at large scattering angle (theta_cm = 70deg) and with a polarized incident photon beam at an average energy of 3.8 GeV. Such a kind of experiments allows one to understand more deeply the reaction mechanism, that involves a real photon, by extracting both Compton form factors and Generalized Parton Distributions (GPDs) (also relevant for possibly shedding light on the total angular momentum of the nucleon). The obtained results for the longitudinal and transverse polarization transfers K_LL and K_LT, are of crucial importance, since they confirm unambiguously the disagreement between experimental data and pQCD prediction, as it was found in E99-114 experiment, and favor the Handbag mechanism. The E99-114 and E07-002 results can contribute to attract new interest on the great yield of the Compton scattering by a nucleon target, as demonstrated by the recent approval of an experimental proposal submitted to the Jefferson Lab PAC 42 for a Wide-angle Compton Scattering experiment, at 8 and 10 GeV Photon Energies. The new experiments approved to run with the updated 12 GeV electron beam at JLab, are characterized by much higher luminosities, and a new GEM tracker is under development to tackle the challenging backgrounds. Within this context, we present a new multistep tracking algorithm, based on (i) a Neural Network (NN) designed for a fast and efficient association of the hits measured by the GEM detector which allows the track identification, and (ii) the application of both a Kalman filter and Rauch-Tung-Striebel smoother to further improve the track reconstruction. The full procedure, i.e. NN and filtering, appears very promising, with high performances in terms of both association effciency and reconstruction accuracy, and these preliminary results will be discussed in detail in the last chapters.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗