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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Enabling Graph Mining in RDF Triplestores using SPARQL for Holistic In-situ Graph Analysis

The graph analysis is now considered as a promising technique to discover useful knowledge in data with a new perspective. We envi- sion that there are two dimensions of graph analysis: OnLine Graph Analytic Processing (OLGAP) and Graph Mining (GM) where each respectively focuses on subgraph pattern matching and automatic knowledge discovery in graph. Moreover, as these two dimensions aim to complementarily solve complex problems, holistic in-situ graph analysis which covers both OLGAP and GM in a single system is critical for minimizing the burdens of operating multiple graph systems and transferring intermediate result-sets between those systems. Nevertheless, most existing graph analysis systems are only capable of one dimension of graph analysis. In this work, we take an approach to enabling GM capabilities (e.g., PageRank, connected-component analysis, node eccentricity, etc.) in RDF triplestores, which are originally developed to store RDF datasets and provide OLGAP capability. More specifically, to achieve our goal, we implemented six representative graph mining algorithms using SPARQL. The approach allows a wide range of available RDF data sets directly applicable for holistic graph analysis within a system. For validation of our approach, we evaluate performance of our implementations with nine real-world datasets and three different computing environments - a laptop computer, an Amazon EC2 instance, and a shared-memory Cray XMT2 URIKA-GD graph-processing appliance. The experimen- tal results show that our implementation can provide promising and scalable performance for real world graph analysis in all tested environments. The developed software is publicly available in an open-source project that we initiated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Neural network model of neutral beam injection in the EAST tokamak to enable fast transport simulations

The neutral beam injection (NBI) system in EAST produces energetic neutral particles, which collide with electrons and ions in tokamak plasmas and heat the plasmas through Coulomb collisions. Moreover, it drives a non-inductive source of current, due to the charge-exchange collision between neutral particles and ions, and injects toroidal torque, which generates a toroidal rotation of the plasma. The effect caused by the NBI system, such as plasma heating, current drive, total neutron rate, momentum transfer, and shine-through, are modeled by a comprehensive module called NUBEAM. However, NUBEAM is computationally intensive since it relies on Monte Carlo methods. In this work, a neural network model has been developed as a surrogate model for NUBEAM in EAST. The database for neural-network model training, validation and testing is generated by running TRANSP for experimental discharges from recent EAST campaigns (after the latest NBI upgrade) while using the NUBEAM module. Simulation results illustrate that the trained neural network has the capability of replicating the predictions made by NUBEAM while demanding a significantly shorter execution time. Finally, these results indicate that surrogate models like the one proposed in this work could enable fast transport simulations for EAST after integrating them into a control-oriented predictive code such as COTSIM.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Fast model-based scenario optimization in NSTX-U enabled by analytic gradient computation

Model-based optimization offers a systematic approach to advanced scenario planning. In this case, the feedforward-control inputs (actuator trajectories) that are needed to attain and sustain a desired scenario are obtained by solving a nonlinear constrained optimization problem. This class of problems generally minimize a cost function that measures the difference between desired and actual plasma states. Several numerical optimization algorithms, such as sequential quadratic programming, require repeated calculation of the cost function gradients with respect to the input trajectories. Calculating these gradients numerically can be computationally intensive, increasing the time needed to solve the feedforward-control optimization problem. Here, this work introduces a method to analytically calculate these cost function gradients from the current profile evolution model. This can significantly reduce the computational time and allow for fast feedforward-control optimization, which would eventually enable optimal scenario planning between discharges. The performance of the feedforward optimizer with analytical gradients is compared to a traditional optimization algorithm based on numerical gradients for different NSTX-U scenarios. The plasma dynamics in the optimization algorithm are simulated using the Control Oriented Transport SIMulator (COTSIM). Results of the work show that analytical gradients consistently reduce the computation time while achieving trajectories that are comparable to those obtained by traditional optimization algorithms based on numerical gradients.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Rapid assessment of the creep rupture life of metals: A model enabling experimental design

Prediction of the creep rupture life of engineering metals is critical for qualification and design of new materials. The use of long-term creep tests and the need to quantify the performance variability in a priori similar systems hinder the rapid creep assessment of a given material. Therefore, it is essential to develop methods that can extrapolate the long-term performance of alloys and the associated variability from short-term experiments. To this end, this study introduces a new model which enables the estimation of the rupture life of a material for a given stress and temperature. This model relies on two components. First, a new relation for the minimum creep rate (MCR) of materials is introduced. It includes a stress dependent stress exponent allowing the model to capture the variation of MCR across a wide range of temperatures and stresses. Second, employing the Monkman-Grant (MG) law, we establish a relation between stress, temperature and creep rupture life. Together, these two elements yield a new closed-form mathematical expression for the Larson Miller parameter as a function of stress and temperature. This expression captures the creep rupture time for many metals (Gr91, Copper, Gr122 and 347H) and compares favorably with alternate empirical approaches. The model is then used to assess the minimum duration of creep rates necessary to qualify the material up to 100000h. Furthermore, it is found that depending on the material system, creep tests as few as five limited to 5000 h for steels (Gr91, Gr122, 347H) and 100 h for copper are sufficient to model creep lifetimes. Finally, using a Bayesian inference-based approach to calibrate the model, we demonstrate that variability in rupture life can be captured via the quantification of the uncertainty in the model parameters and extrapolated from a limited number of short to moderately short creep tests; thereby paving the way for accelerated creep testing.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Enhancing performance of PEM fuel cells: Using the Au nanoplatelet/Nafion interface to enable CO oxidation under ambient conditions

We developed a method for fabrication of Au nanoparticle platelets which can be coated onto the Nafion membranes of polymer electrolyte membrane (PEM) fuel cells simply by Langmuir–Blodgett (LB) trough lift off from the air water interface. By incorporating the coated membranes into fuel cells with one membrane electrode assembly (MEA) we enhanced the maximum power output by more than 50% when operated under ambient conditions. An enhancement of more than 200% was observed when 0.1% CO was incorporated into the H 2 input gas stream and minimal enhancement was observed when the PEM fuel cell was operated with 100% O 2 gas at the cathode, or when particles were deposited on the electrodes. Density function theory (DFT) calculations were carried out to understand the origin of improved output power. Au NPs with 3-atomic layer in height and 2 nm in size were constructed to model the experimentally synthesized Au NPs. Our results indicated that the Au NPs interacted synergistically with the SO 3 groups, attached at end of Nafion side chains, to reduce the energy barrier for the oxidation of CO occurring at the perimeter of the Au NPs, from 1.292 eV to 0.518 eV, enabling the reaction to occur at T<300 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Dual-function lignin monomers enable high-performance graphene electrodes via interface confinement and proton transfer enhancement

Graphene oxide (GO)-based energy storage faces dual bottlenecks: unsustainable reduction methods and sluggish proton transfer kinetics. Here, we introduce a groundbreaking green strategy using lignin-derived vanillyl alcohol (VA) as a dual-function monomer to simultaneously address these challenges. By thermally annealing GO/VA films at mild temperatures (<100 °C), VA triggers an interface-confined reduction of GO while self-polymerizing into redox-active oligomers (P-VA) that intercalate between graphene layers. This dual role-reducing agent and proton highway enables a 3D conductive network with minimized graphene restacking, abundant redox sites, and rapid H + transport pathways. Density Functional Theory (DFT) reveals how P-VA optimizes proton dynamics, while the resulting rGO-P-VA4-T90 electrode achieves a record volumetric capacitance of 311.1F/cm 3 (777.8F/cm 2 ) and retains 87.8 % capacity after 10,000 cycles. Flexible solid-state supercapacitors deliver 94.2 μWh/cm 2 energy density at 63.8 μW/cm 2 , rivaling state-of-the-art devices. Furthermore, this work redefines sustainable graphene engineering, merging biomass valorization with high-performance energy storage in a scalable, eco-friendly paradigm.

Graphene oxide reduction↗
DOE OSTI2022

Energy-effective and low-cost carbon capture from point-sources enabled by water-lean solvents

Aqueous amines, as the most mature carbon capture technology, are subject to high energy and cost penalties due to the large water content in their formulations. Emerging technologies are in demand to enable a transition to a low-carbon global economy. However, rigorous process modeling and techno-economic analyses are limited for emerging carbon capture technologies. Here, four CO 2 -Binding Organic Liquids (CO 2 BOLs), all water-lean solvents were presented as promising options towards energy-effective and low-cost carbon capture from point sources. Rigorous solvent property and process models were developed in Aspen Plus for a coal-fired power plant with CO 2 BOL-based carbon capture unit. Techno-economic analyses were conducted in 2018 US pricing basis. The results suggest that water-lean formulations can minimize water condensation and vaporization, leading to a 36% energy saving compared with aqueous amines. Indeed, these CO 2 BOLs can capture up to 97–99% CO 2 from coal fired plant. The estimated carbon capture cost is about $40/tonne CO 2 at 90–97% carbon capture rate, about 12–23% less expensive than the conventional aqueous amine technology. The comparison between these CO 2 BOLs showed that in addition to vapor liquid equilibrium and kinetics (key properties for aqueous solvents), viscosity, volatility, and hydrophobicity, also have strong impacts on the performance of water-lean solvents. The methods presented in this work can be used to evaluate other emerging carbon capture technologies, while the results linking costs and performance of carbon capture solvents with their properties. Additionally, this work identifies research directions and targets for further reductions in total costs of capture from either cost or energy perspectives for these leading water-lean solvents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Enabling probabilistic learning on manifolds through double diffusion maps

Here, we present a generative learning framework for probabilistic sampling that extends Probabilistic Learning on Manifolds (PLoM), which is designed to generate statistically consistent realizations of a random vector in a finite-dimensional Euclidean space, informed by a (representative) set of observations. In its original form, PLoM constructs a reduced-order probabilistic model by combining three main components: (a) kernel density estimation to approximate the underlying probability measure, (b) Diffusion Maps to characterize the manifold of the data, and (c) a reduced-order Itô Stochastic Differential Equation (ISDE) to sample from the learned distribution. However, its sampling dynamics are posed in the ambient space and the retained number of reduced coordinates is chosen by projection-reconstruction error. In practice, this often (i) requires more coordinates than the data’s intrinsic dimension to achieve stable sampling and (ii) lacks a smooth, basis-independent lifting back to the data domain; moreover, standard Diffusion Maps emphasize harmonic eigenfunctions and can miss non-harmonic latent structure. We address these limitations by decoupling geometry learning from sampling: a first Diffusion Maps pass identifies non-harmonic coordinates on which we formulate a full-order ISDE directly in the latent space, while Double Diffusion Maps captures multiscale geometric features and Geometric Harmonics (GH) learns a smooth lifting map to the ambient variables that is independent of the particular diffusion basis. This hybrid design preserves the system’s dynamical richness with a compact geometric representation and enables principled out-of-sample inference. The effectiveness and robustness of the proposed method are illustrated through two numerical studies: one based on data generated from two-dimensional Hermite polynomial functions and another based on high-fidelity simulations of a detonation wave in a reactive flow.

Double diffusion maps↗
DOE OSTI2016

On the irradiation creep by climb-enabled glide of dislocations

The plastic deformation is defined by the proportional to stress elastic deflections of pinned dislocations in climb-enabled glide models of irradiation creep. Here, we argue that this relation is incorrect; instead, as in other pinning-unpinning-type models, the dislocations move from one set of obstacles to another, so that the inter-obstacle spacing determines creep rate, whereas the dependence on the applied stress is only implicit in the unpinning time.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Cascade CO 2 electroreduction enables efficient carbonate-free production of ethylene

CO 2 electroreduction provides a route to convert waste emissions into chemicals such as ethylene (C 2 H 4 ). However, the direct transformation of CO 2 -to-C 2 H 4 suffers from CO 2 loss to carbonate, consuming up to 72% of energy input. A cascade approach—coupling a solid-oxide CO 2 -to-CO electrochemical cell (SOEC) with a CO-to-C 2 H 4 membrane electrode assembly (MEA)—would eliminate CO 2 loss to carbonate. However, this approach requires a CO-to-C 2 H 4 MEA with energy efficiency well beyond demonstrations to date. Focusing on the MEA, in this study we find that an N-tolyl substituted tetrahydro-bipyridine film improves the stabilization of key reaction intermediates, while an SSC ionomer enhances CO transport to the Cu surface, enabling a C 2 H 4 faradaic efficiency of 65% at 150 mA cm –2 for 110 h. Demonstrating a cascade SOEC-MEA approach, we achieve CO 2 -to-C 2 H 4 with a ~48% reduction in energy intensity compared with the direct route. We further reduce the energy intensity by coupling CO electroreduction (CORR) with glucose electrooxidation.

10 SYNTHETIC FUELS↗
DOE OSTI2022

Highly reversible Zn anode with a practical areal capacity enabled by a sustainable electrolyte and superacid interfacial chemistry

Aqueous zinc-metal batteries are plagued by poor Zn reversibility owing to zinc dendrite and layered double hydroxide (LDH) formation. Here, we introduce a novel additive—N,N-dimethylformamidium trifluoromethanesulfonate (DOTf)—in a low-cost aqueous electrolyte that can very effectively address these issues. The initial water-assisted dissociation of DOTf into triflic superacid creates a robust nanostructured solid-electrolyte interface (SEI)—revealed by operando spectroscopy and cryomicroscopy—which excludes water and enables dense Zn deposition. We demonstrate excellent Zn plating/stripping in a Zn||Cu asymmetric cell for more than 3,500 cycles. Furthermore, near 100% CE is realized at a combined high current density of 4 mA cm -2 and an areal capacity of 4 mAh cm -2 over long-term cycling. Zn||Zn 0.25 V 2 O 5 ·nH 2 O full cells retain ~83% of their capacity after 1,000 cycles with mass-limited Zn anodes. By restricting the depth of discharge, the cathodes exhibit less proton intercalation and LDH formation with an extended lifetime of 2,000 cycles.

25 ENERGY STORAGE↗
DOE OSTI2025

Carboxyl-functionalized perovskite enables ALD growth of a compact and uniform ion migration barrier

Mixed-halide wide-band-gap perovskites are critical components of highly efficient tandem cells, but their operating stability is limited by halide migration. Metal oxides deposited via atomic layer deposition (ALD) have been shown to block halide migration; however, previously pursued methods result in inhomogeneous nucleation and growth. We hypothesized that functionalizing the perovskite surface with ALD-active carboxyl groups could promote nucleation and enable higher-temperature metal oxide growth. We find that 5-ammonium valeric acid iodide (5-AVAI) facilitates the formation of a compact and uniform aluminum oxide (Al 2 O 3 ) layer and allows growth at 100°C compared with the previous limit of 75°C. We demonstrate that halide migration into the C 60 electron transport layer is reduced by a factor of 10 compared with the reference case. Finally, Al 2 O 3 -capped perovskite solar cells with a band gap of 1.78 eV retain 90% of their initial power conversion efficiency after 1,000 h of continuous operation under 1-sun illumination at 55°C.

Al2O3↗
DOE OSTI2025

Solid-state batteries enabled by ultra-high-frequency self-heating

Solid-state batteries (SSBs) are promising next-generation batteries due to their high energy density and enhanced thermal stability and safety. However, their sluggish kinetics and transport at room temperature result in high internal impedance and critically reduce the attainable discharge energy density. Taking advantage of their strong temperature-dependent ionic conductivity, here we introduce ultra-high-frequency (greater than 105 Hz) self-heating (UHFSH) of SSBs, which can rapidly warm up the batteries from room temperature to operating temperature (∼65°C) in less than a minute. As proof of concept, UHFSH experiments were conducted on symmetric solid-state cells with lithium aluminum germanium phosphate electrolyte in different configurations. Using an experimentally validated model, pack-level simulations predict fast heating (50 K/min) and minimized heating energy consumption (less than 4%). Without any modification of the materials or structure of the batteries, our non-intrusive self-heating strategy potentially enables SSBs to discharge more than 2-fold energy in 25°C ambient.

pulse heating↗
DOE OSTI2026

Universal Workflow Language and Software Enable Geometric Learning and FAIR Scientific Protocol Reporting

Written language and conventional data structures for representing scientific procedures suffer from low process detail, often fail to accurately represent protocols, and lack universality. New strategies for the handling of experimental data are needed to provide viable process information for both humans and machines. In this work, we present the universal workflow language (UWL) and interface (UWLi). UWL is a findable, accessible, interoperable, and reusable (FAIR)-compatible, graph-based data architecture that can capture arbitrary scientific procedures through workflow representation, and UWLi is an accompanying software package for building, manipulating, and interpreting UWL entries. The UWL format was found to be highly effective in identifying deficiencies in the reported process details of high-impact, peer-reviewed scientific journals, and in simulated scenarios, the graph format was shown to be more effective than conventional methods in predictively modeling the outcome of diverse scientific protocols. Implementation of UWL could enable more accurate scientific communication and more impactful process datasets.

14 SOLAR ENERGY↗
DOE OSTI2026

Electrochemical Reactions Under Reverse Bias Create Additional Mobile Ions That Enable Hole Tunneling in Metal Halide Perovskite Diodes

Gradual reverse-bias breakdown in metal-halide perovskite diodes and solar cells is thought to originate from hole tunneling through steep bands in an ionic depletion region near the electron-transport layer after positively charged iodine vacancies accumulate near the hole-transport layer (HTL). However, typical reported mobile-ion concentrations near 1 x 10^17 cm-3 are too small to quantitatively explain significant tunneling-current densities and (Zener) breakdown observed near -5 V. Here, we show that inferred mobile-ion concentrations increase by more than 100x, to over 1 x 10^18cm-3 , within just 3 min of reverse bias at -6.0 V in p-i-n perovskite diodes. We attribute this increase to iodide oxidation and coupled iodine vacancy creation that must be balanced by reduction reactions near the HTL. Sub-optimal HTL coverage leads to direct contact between the transparent conducting electrode and perovskite, facilitates reduction events, enables the creation of even larger inferred mobile-ion concentrations (~1 x 10^19cm-3 ), and leads to faster degradation under reverse bias. This explains previous work that showed increased breakdown voltages and improved reverse-bias stability by implementing thick, uniform HTLs.

14 SOLAR ENERGY↗
DOE OSTI2014

Kokkos: Enabling manycore performance portability through polymorphic memory access patterns

The manycore revolution can be characterized by increasing thread counts, decreasing memory per thread, and diversity of continually evolving manycore architectures. High performance computing (HPC) applications and libraries must exploit increasingly finer levels of parallelism within their codes to sustain scalability on these devices. We found that a major obstacle to performance portability is the diverse and conflicting set of constraints on memory access patterns across devices. Contemporary portable programming models address manycore parallelism (e.g., OpenMP, OpenACC, OpenCL) but fail to address memory access patterns. The Kokkos C++ library enables applications and domain libraries to achieve performance portability on diverse manycore architectures by unifying abstractions for both fine-grain data parallelism and memory access patterns. In this paper we describe Kokkos’ abstractions, summarize its application programmer interface (API), present performance results for unit-test kernels and mini-applications, and outline an incremental strategy for migrating legacy C++ codes to Kokkos. Furthermore, the Kokkos library is under active research and development to incorporate capabilities from new generations of manycore architectures, and to address a growing list of applications and domain libraries.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Optimization of Graphite–SiO blend electrodes for lithium-ion batteries: Stable cycling enabled by single-walled carbon nanotube conductive additive

Lithium-alloying materials are of great interest to improve the gravimetric and volumetric energy density of lithium-ion batteries, though their associated volume fluctuation with cycling often leads to poor cycling performance. Active-inactive alloys and blending alloys with carbon materials are common strategies to accommodate volume fluctuation. Herein we set out to optimize graphite-SiO blend electrode formulations to eliminate rapid capacity fade. Electrodes with highly stable cycling were prepared by simple planetary mixing procedures, enabled by the use of just a fraction of a weight percent of commercial SWCNTs as the only conductive additive, and by the appropriate choice of binder/stabilizing agent. In fact, the use of SWCNTs allowed for graphite-free SiO electrodes with approximately 74% higher volumetric energy density relative to traditional graphite electrodes, and superior capacity retention in coin-type full-cell testing versus NMC532 cathodes.

25 ENERGY STORAGE↗
DOE OSTI2022

Feature engineering for machine learning enabled early prediction of battery lifetime

Accurate battery lifetime estimates enable accelerated design of novel battery materials and determination of optimal use protocols for longevity in deployments. Unfortunately, traditional battery testing may take years to reach thousands of cycles. Recent studies have shown that machine learning (ML) tools can predict lithium-ion battery lifetimes from 100 or fewer preliminary cycles, representing only a few weeks of cycling. Until now, conclusions about the efficacy and broad applicability of these predictions across a variety of cathode chemistries have been limited by available experimental information. In this work, we leverage a battery cycling dataset representing six cathode chemistries (NMC111, NMC532, NMC622, NMC811, HE5050, and 5Vspinel), multiple electrolyte/anode compositions, and 300 total carefully prepared pouch batteries to explore feature selection and battery chemistry's role in ML battery lifetime predictions. Here, a mean absolute error (MAE) of 78 cycles in prediction was seen for a chemistry-spanning test set from 100 preliminary cycles. Furthermore, an MAE of 103 cycles was seen when using only the first cycle. This study represents an in-depth investigation of strategies for feature selection for battery lifetime prediction, ML models' generalization across multiple battery chemistries, and predictions beyond the training set in the chemical space.

25 ENERGY STORAGE↗