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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Neural-network quantum states for the nuclear many-body problem

A long-standing goal of nuclear theory is to explain how the structure and dynamics of atomic nuclei and neutron-star matter emerge from the underlying interactions among protons and neutrons. Achieving this goal requires solving the nuclear quantum many-body problem with high accuracy across a wide range of length scales and density regimes. In this review, we discuss how artificial neural network representations of the nuclear many-body wave function have significantly extended the capabilities of continuum quantum Monte Carlo methods. In particular, neural network quantum states enable calculations of larger systems than were previously accessible and provide a flexible framework for capturing phenomena that challenge conventional approaches, including the emergence of nuclear clusters and superfluid phases in dense matter. We highlight recent applications to finite nuclei, infinite nuclear and neutron matter, and dynamical processes relevant to lepton-nucleus and nucleus-nucleus scattering. We also discuss conceptual and methodological connections with condensed matter physics, emphasizing developments in neural network quantum states that bridge strongly correlated systems across disciplines. Together, these developments demonstrate how neural-network methods open new avenues toward unified and accurate descriptions of nuclear structure, matter, and reactions.

Lovato, Alessandro [Argonne; TIFPA-INFN, Trento; V↗
DOE OSTI2023

Development of HELIOS/BIPR/PARCS/MCNP6 Computation Route for WWER RPV Neutron Fluence Analysis and Validation Against Ex-Vessel Detector Measurement Data

Here, a computational route was developed for precise calculation of fast neutron fluence on a WWER-type reactor pressure vessel (RPV). The method is based on the transfer of neutronics data from HELIOS-2 lattice calculations and nodal diffusion neutronics data (power, density, and temperature) from BIPR7.1 and PARCS 3.36/PATHS core calculations into a three-dimensional (pinwise axially distributed) fixed neutron source for modeling of transport of fast neutrons from the reactor core to the outer surface of the RPV using MCNP6.2. Validation of the proposed computational method was carried out based on comparative analysis of MCNP6.2-predicted and neutron dosimetry–measured reaction rates [ 54 Fe(n,p) 54 Мn, 93 Nb(n,n') 93 mNb, and 58 Ni(n,р) 58 Со] on the outer surface of the Armenian Nuclear Power Plant (ANPP) Unit 2 RPV. Validation revealed that the MCNP6.2-predicted fast neutron fluence results are very sensitive to the ENDF-B neutron data. Particularly, MCNP6.2 with ENDF/B-VIII.0 significantly underpredicts (20% to 30%) fast neutron fluence while using ENDF/B-VII.1 data overpredicts it. Adding revised beta-released evaluations of 54 Fe, 56 Fe, 57 Fe, and 16 O from the International Nuclear Data Evaluation Network (INDEN) to ENDF/B-VIII.0 allows one to obtain reasonable agreement with measurement results for all types of measured reaction rates.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Polyester-based epoxy vitrimer integrating spent coffee ground as a natural filler

Coffee is a widely consumed beverage, but approximately 80% mass of coffee cherry is discarded as waste. Here, in this study, spent coffee grounds (SCG) were recycled and used as a natural filler in a polyester-based epoxy matrix made from reactions of diglycidyl ethers of Bisphenol A and polyethylene glycol with methyl nadic anhydride. The properties of SCG/epoxy composites with various SCG contents up to 40% w/w were evaluated using various techniques such as FT-IR spectroscopy, tensile testing, optical microscopy, differential scanning calorimetry, and rheology. The SCG filled polyester-based epoxy matrix demonstrated excellent dimensional stability and retained a permanently crosslinked network, but re-shaping was possible at ~150 °C through transesterification, which occurred due to a dynamic chain exchange reaction between the ester group of the epoxy matrix and the hydroxyl group of the SCG fillers. The transesterification also improved the interfacial adhesion between the matrix and SCG fillers, resulting in tensile strength of over 20 MPa and a modulus of around 2.5 GPa, even with high SCG contents up to 40% w/w. While neat, cured epoxy shows thermoset behavior, filling the matrix with SCG enables reforming and origami-inspired spontaneous change in shape.

42 ENGINEERING↗
DOE OSTI2025

Structure of the D1-Val185Asn mutated photosystem II complex with slow O–O bond formation reveals changes in the Cl1 water channel

During photosynthesis, the photosystem II (PSII) enzyme catalyzes the light-driven oxidation of water, fueling life on Earth by storing light energy and releasing O 2 as a byproduct. Determining the molecular mechanism for this water oxidation reaction has been of significant interest for the development of synthetic catalysts, but many details remain elusive. One of the open questions is how protons are strategically removed from the active site, a Mn 4 CaO 5 cluster called the oxygen-evolving complex (OEC), during the reaction cycle via conserved water channels, and how proton transfer contributes to O–O bond formation energetics. Site-directed mutagenesis studies have investigated the role of conserved amino acid side chains in facilitating proton transfer. One of the most influential mutations is the Val185Asn substitution on the D1 subunit, which substantially slows O 2 release kinetics without abolishing catalytic activity. Forming a molecular understanding of how this mutation affects the active site will provide insight into the water oxidation reaction mechanism. Here, we investigated the structural basis of the Val185Asn substitution by determining a 1.99 Å-resolution cryo-EM structure. We observed that Asn185 orients away from the OEC and donates a H-bond to Cl1, a conserved chloride ion. We furthermore observed an alternative D2-Glu312-facing conformation of the D2-Lys317 side chain in the Cl1 water channel, a conformation that is consistent with recent models for proton transfer. These changes also produce perturbations to the hydrogen-bonding network. Overall, these finding provide insight into proton transfer in the Cl1 channel and its effect on the water oxidation reaction mechanism.

cryo-EM↗
DOE OSTI2023

Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model

In this paper, we present a physics-informed neural network (PINN) approach for predicting the performance of an all-vanadium redox flow battery, with its physics constraints enforced by a two-dimensional (2D) mathematical model. The 2D model, which includes 6 governing equations and 24 boundary conditions, provides a detailed representation of the electrochemical reactions, mass transport and hydrodynamics occurring inside the redox flow battery. To solve the 2D model with the PINN approach, a composite neural network is employed to approximate species concentration and potentials; the input and output are normalized according to prior knowledge of the battery system; the governing equations and boundary conditions are first scaled to an order of magnitude around 1, and then further balanced with a self-weighting method. Our numerical results show that the PINN is able to predict cell voltage correctly, but the prediction of potentials shows a constant-like shift. To fix the shift, the PINN is enhanced by further constrains derived from the current collector boundary. Finally, we show that the enhanced PINN can be even further improved if a small number of labeled data is available.

25 ENERGY STORAGE↗
DOE OSTI2025

Learning nuclear cross sections across the chart of nuclides with graph neural networks

We explore the use of deep learning techniques to learn how nuclear cross sections change as we add or remove protons and neutrons. As a proof of principle, we focus on the neutron-induced reactions in the fast energy regime. Our approach follows a two-stage learning framework. First, we apply representation learning to encode cross section data into a latent space using either variational autoencoders (VAEs) or implicit neural representations (INRs). Then, we train graph neural networks (GNNs) on the resulting embeddings to predict missing values across the nuclear chart by leveraging the topological structure of neighboring isotopes. We demonstrate accurate cross section predictions within a 9 × 9 block of missing nuclei. We also find that the optimal GNN training strategy depends on the type of latent representation used, with VAE embeddings performing best under end-to-end optimization in the original space, while INR embeddings achieve better results when the GNN is trained only in the latent space. Furthermore, using clustering algorithms, we map groups of latent vectors into regions of the nuclear chart and show that VAEs and INRs can discover some of the neutron magic numbers. These findings suggest that deep-learning models based on the representation encoding of cross sections combined with graph neural networks hold significant potential in augmenting nuclear theory models, e.g., by providing reliable estimates of covariances of cross sections, including cross-material covariances.

Machine learning↗
DOE OSTI2024

CMLM (Co-Optimized Machine-Learned Manifolds) [SWR-23-41]

Co-optimized Machine-Learned Manifolds (CMLM) is a data-driven approach for developing reduced-order manifold models for high-dimensional chemically reacting systems. It involves a specially designed neural network, the training of which simultaneously optimizes linear combinations of species that define the manifold, nonlinear mapping to outputs of interest such as reaction rates, and (optionally) subfilter closure for large eddy simulation. This software package provides an implementation of the CMLM approach in Python using the PyTorch machine learning library. A few example cases are included, showing how the tool can be applied to different types of data from 0D and 1D reacting simulations performed using Cantera. The neural networks can be saved in a format that is readable by the Pele suite of combustion solvers for use in reacting computational fluid dynamics simulations. This software repository contains several python scripts to perform various tasks associated with the Co-optimized Machine Learned Manifolds (CMLM) model, which is described in Perry, Henry de Frahan, and Yellapantula, CNF, 2022 (https://doi.org/10.1016/j.combustflame.2022.112286). This includes not only the code that defines the CMLM model, but also scripts to generate suitable training data, scripts to pre-process the data, scripts to train the CMLM model, and scripts to plot the output, as well as various other helper files. The scripts depend on several commonly used python libraries for data analysis and chemical reaction computations. The trained models that result from this tool are designed to work with the an interface being implemented in the Pele suite of reacting flow solvers (https://github.com/AMReX-Combustion).

Perry, Bruce↗
DOE OSTI2022

Developing a heterogeneous ensemble learning framework to evaluate Alkali-silica reaction damage in concrete using acoustic emission signals

The monitoring and evaluation of Alkali-silica reaction (ASR) damage in concrete structures are required to ensure the serviceability and integrity of concrete infrastructures such as bridges and dams. The innovation of this paper lies in the development of an automatic ASR monitoring and evaluation approach by leveraging acoustic emission (AE) and a heterogeneous ensemble learning framework. Here, in this paper, ASR was monitored by AE sensors attached to a concrete specimen, which was placed in a chamber with high humidity and temperature. The recorded AE signals were filtered and divided by four ASR phases according to signal strength, crack width and expansion strain. A heterogeneous ensemble network including convolutional neural networks (CNN) and random forest models was employed to learn different features from AE signals and classify the AE signals into their corresponding phases. The results suggest that the proposed model has a high performance and classifies the signals into the assigned phases with high accuracy.

42 ENGINEERING↗
DOE OSTI2023

Tailoring On-Surface Molecular Reactions and Assembly through Hydrogen-Modified Synthesis: From Triarylamine Monomer to 2D Covalent Organic Framework

Relative to conventional wet-chemical synthesis techniques, on-surface synthesis of organic networks in ultrahigh vacuum has few control parameters. The molecular deposition rate and substrate temperature are typically the only synthesis variables to be adjusted dynamically. Here we demonstrate that reducing conditions in the vacuum environment can be created and controlled without dedicated sources-relying only on backfilled hydrogen gas and ion gauge filaments-and can dramatically influence the Ullmann-like on-surface reaction used for synthesizing two-dimensional covalent organic frameworks (2D COFs). Using tribromo dimethylmethylene-bridged triphenylamine ((Br 3 )DTPA) as monomer precursors, we find that atomic hydrogen (H*) blocks aryl-aryl bond formation to such an extent that we suspect this reaction may be a factor in limiting the ultimate size of 2D COFs created through on-surface synthesis. Conversely, we show that control of the relative monomer and hydrogen fluxes can be used to produce large self-assembled islands of monomers, dimers, or macrocycle hexamers, which are of interest in their own right. On-surface synthesis of oligomers, from a single precursor, circumvents potential challenges with their protracted wet-chemical synthesis and with multiple deposition sources. Using scanning tunneling microscopy and spectroscopy (STM/STS), we show that changes in the electronic states through this oligomer sequence provide an insightful view of the 2D COF (synthesized in the absence of atomic hydrogen) as the end point in an evolution of electronic structures from the monomer.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Interfacial bond characterization of epoxy adhesives to aluminum alloy and carbon fiber-reinforced polyamide by vibrational spectroscopy

Vibrational spectroscopic technique has been utilized to investigate interfacial bonding chemistry of two epoxy adhesive products, XP0012 and XP5005F, on plasma-treated AA6061 and carbon fiber-reinforced polyamide 66 (CFRP-PA66) surfaces. The change in vibrational peak ratios was measured by attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy to deduce bonding mechanisms. Both adhesives showed strong crosslinking polymerization of hydroxyl- and amine-initiated epoxy ring opening on AA6061 surface, but on CFRP surface XP0012 formed a simple amide linkage by the reaction of surface hydroxyl groups and nitrile groups of curing agent, and XP5005F formed a crosslinked network by hydroxyl-initiated epoxy ring opening polymerization. The different interfacial bonding formation of two adhesives on CFRP-PA66 surface is attributed to additive effect. Addition of additives to epoxy adhesives (XP5005F) changed the interfacial bonding mechanism on CFRP-PA66 surface, rather forming hydroxyl-initiated epoxy opening crosslinking polymerization than a simple amide bond formation (XP0012). The interfacial bonding chemistry was also proved by addition of bisphenol A (BA) to a simplified model diglycidyl ether of bisphenol A/dicyandiamide (DGEBA/DICY) epoxy system. When BA was added to the model DGEBA/DICY system, epoxy ring gradually decreased on CFRP-PA66 surface, while without BA, DGEBA/DICY showed only decrease in a nitrile peak intensity in ATR-FTIR. In conclusion, the foregoing different types of interfacial chemical bonds at the adhesive/CFRP-PA66 interfaces can affect the lap shear behavior of the joint.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Synthesis of Bio-Based Repairable Polyimines with Tailored Properties by Lignin Fractionation

Developing sustainable polymers with low-value lignin remains a challenge. Herein, lignin-containing repairable polyimines were synthesized with tailored properties using lignin fractionation. First, softwood Kraft lignin is fractionated into a more homogeneous fraction with a lower molecular weight and a higher OH content. Next, Kraft lignin and its fraction are esterified by levulinic acid to introduce active ketone groups and subsequently condensed with oleylamine (OAm) and bis(3-aminopropyl)-terminated polydimethylsiloxane (PDMS) via a catalyst-free Schiff-base reaction to form grafted lignin-OAm copolymers and cross-linked lignin-PDMS polymer networks (MKL-P and MFL-P), respectively. Results show that lignin-OAm polyimines can be self-repaired and hot reprocessed under pressure, while lignin-PDMS polyimines can be repaired with the assistance of a healing agent, heat, and pressure. Dynamic mechanical analyses demonstrate that the stress–relaxation behaviors of the polyimines follow the Arrhenius law under thermal-stress activation, indicating the occurrence of transimination. Moreover, compared with Kraft lignin, the lignin fraction ameliorates the grafting density of ketones and enhances the cross-linking density of lignin-PDMS polyimine networks. The higher cross-linking density of MFL-P leads to superior stress–relaxation activation energy, thermal stability, hydrophobicity, and light-shielding ability but inferior repairability and translucency. Finally, this work provides insights into the polymerization of lignin-based polymer networks and the potential application of lignin-PDMS polyimines for repairable, translucent, anti-UV, and hydrophobic coatings.

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

The γ -process nucleosynthesis in core-collapse supernovae

The γ-process nucleosynthesis in core-collapse supernovae is generally accepted as a feasible process for the synthesis of neutron-deficient isotopes beyond iron. However, crucial discrepancies between theory and observations still exist: the average yields of γ-process nucleosynthesis from massive stars are still insufficient to reproduce the solar distribution in galactic chemical evolution calculations, and the yields of the Mo and Ru isotopes are a factor of ten lower than the yields of the other γ-process nuclei. We investigate the γ-process in five sets of core-collapse supernova models published in the literature with initial masses of 15, 20, and 25 M ⊙ at solar metallicity. We compared the γ-process overproduction factors from the different models. To highlight the possible effect of nuclear physics input, we also considered 23 ratios of two isotopes close to each other in mass relative to their solar values. Further, we investigated the contribution of C–O shell mergers in the supernova progenitors as an additional site of the γ-process. Our analysis shows that a large scatter among the different models exists for both the γ-process integrated yields and the isotopic ratios. We find only ten ratios that agree with their solar values, all the others differ by at least a factor of three from the solar values in all the considered sets of models. The γ-process within C–O shell mergers mostly influences the isotopic ratios that involve intermediate and heavy proton-rich isotopes with A > 100. We conclude that there are large discrepancies both among the different data sets and between the model predictions and the solar abundance distribution. More calculations are needed; particularly updating the nuclear network, because the majority of the models considered in this work do not use the latest reaction rates for the γ-process nucleosynthesis. Moreover, the role of C–O shell mergers requires further investigation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Filled Elastomers: Mechanistic and Physics-Driven Modeling and Applications as Smart Materials

Elastomers are made of chain-like molecules to form networks that can sustain large deformation. Rubbers are thermosetting elastomers that are obtained from irreversible curing reactions. Curing reactions create permanent bonds between the molecular chains. On the other hand, thermoplastic elastomers do not need curing reactions. Incorporation of appropriated filler particles, as has been practiced for decades, can significantly enhance mechanical properties of elastomers. However, there are fundamental questions about polymer matrix composites (PMCs) that still elude complete understanding. This is because the macroscopic properties of PMCs depend not only on the overall volume fraction (ϕ) of the filler particles, but also on their spatial distribution (i.e., primary, secondary, and tertiary structure). This work aims at reviewing how the mechanical properties of PMCs are related to the microstructure of filler particles and to the interaction between filler particles and polymer matrices. Overall, soft rubbery matrices dictate the elasticity/hyperelasticity of the PMCs while the reinforcement involves polymer–particle interactions that can significantly influence the mechanical properties of the polymer matrix interface. For ϕ values higher than a threshold, percolation of the filler particles can lead to significant reinforcement. While viscoelastic behavior may be attributed to the soft rubbery component, inelastic behaviors like the Mullins and Payne effects are highly correlated to the microstructures of the polymer matrix and the filler particles, as well as that of the polymer–particle interface. Additionally, the incorporation of specific filler particles within intelligently designed polymer systems has been shown to yield a variety of functional and responsive materials, commonly termed smart materials. We review three types of smart PMCs, i.e., magnetoelastic (M-), shape-memory (SM-), and self-healing (SH-) PMCs, and discuss the constitutive models for these smart materials.

36 MATERIALS SCIENCE↗
NASA NTRS1984

The structure of extruded NiAl

The deformation structure of Ni-rich NiAl extruded at 550 C has been characterized by transmission electron microscopy and by optical microscopy. Dislocations having a(100) Burgers vectors were found as complex networks, tangles, and prismatic loops. a(110) dislocations, which were rare, were concluded to arise from reactions of a(100) dislocations. Evidence of recovery and recrystallization was obtained. Extrusion was deemed to have been possible by the operation of (hko)(001) slip systems (often in plane strain flow) plus diffusion-assisted processes.

Baker, I.↗
NASA NTRS1987

Ketimine modifications as a route to novel amorphous and derived semicrystalline poly(arylene ether ketone) homo- and copolymers

A series of amine terminal amorphous poly(arylene ether ketone) oligomers of controlled molecular weights (2-15 K) were synthesized. These oligomers have been found to undergo 'self-crosslinking' reactions upon heating above 220 C, via the reaction of the terminal amine groups with the in-chain keto carbonyl functionalities. The resulting networks are ductile, chemically resistant, and nonporous. The networks obtained via generated ketimine functionality were characterized by solid state NMR. They have also been found to be remarkably stable toward hydrolysis. Ketimine functional bishalide monomers have also been synthesized. Such monomers have been utilized to synthesize a wide variety of amorphous poly(arylene ether) ketimine polymers. A high molecular weight hydroquinone functional poly(arylene ether) ketimine has been acid treated to regenerate a poly(arylene ether ketone) backbone in solution. This novel procedure thus allows for the synthesis of important matrix resins under relatively mild conditions.

Mohanty, D. K.↗
NASA NTRS1989

Analysis of reactively loaded microstrip disk antenna

The moment method solution to the problem of a reactively loaded circular patch is presented. Using the reaction integral equation in conjuction with the method of moments, parameters of the Thevenin's equivalent network for the loaded patch are obtained. From the equivalent network parameters an expression for the imput impedance of the loaded patch is derived. A design procedure for a circularly polarized disk antenna is presented. Computed results are compared with the experimental data.

Deshpande, M. D.↗
NASA NTRS2001

Porosity and Variations in Microgravity Aerogel Nano-Structures: New Laser Speckle Characterization Methods - 2

The transition from sol to gel is a process that is critical to the properties of engineered nanomaterials, but one with few available techniques for observing the dynamic processes occurring during the evolution of the gel network. Specifically, the observation of various cluster aggregation models, such as diffusion-limited and reaction-limited cluster growth can be quite difficult. This can be rather important as the actual aggregation model can dramatically influence the mechanical properties of gels, and is significantly affected by the presence of convective flows, or their absence in microgravity. We have developed two new non-intrusive optical methods for observing the aggregation processes within gels in real time. These make use of the dynamic behavior of laser speckle patterns produced when an intense laser source is passed through a gelling sol. The first method is a simplified time-correlation measurement, where the speckle pattern is observed using a CCD camera and information on the movement of the scattering objects is readily apparent. This approach is extremely sensitive to minute variations in the flow field as the observed speckle pattern is a diffraction-based image, and is therefore sensitive to motions within the sol on the order of the wavelength of the probing light. Additionally, this method has proven useful in determining a precise time for the gel-point, an event often difficult to measure. Monitoring the evolution of contrast within the speckle field is another method that has proven useful for studying aeration. In this case, speckle contrast is dependent upon the size (correlation length) and number of scattering centers, increasing with increasing size, and decreasing with increasing numbers. The dynamic behavior of cluster growth in gels causes both of these to change simultaneously with time, the exact rate of which is determined by the specific aggregation model involved. Actual growth processes can now be observed, and the effects of varying gravity fields on the growth processes qualitatively described. Results on preliminary ground-based measurements have been obtained.

Hunt, A. J.↗
NASA NTRS2003

Cellerator: extending a computer algebra system to include biochemical arrows for signal transduction simulations

Cellerator describes single and multi-cellular signal transduction networks (STN) with a compact, optionally palette-driven, arrow-based notation to represent biochemical reactions and transcriptional activation. Multi-compartment systems are represented as graphs with STNs embedded in each node. Interactions include mass-action, enzymatic, allosteric and connectionist models. Reactions are translated into differential equations and can be solved numerically to generate predictive time courses or output as systems of equations that can be read by other programs. Cellerator simulations are fully extensible and portable to any operating system that supports Mathematica, and can be indefinitely nested within larger data structures to produce highly scaleable models.

Non-NASA Center↗