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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

In-situ ion irradiation of fission products in a spent UO 2 fuel

This study investigates the behavior of fission gas bubbles and five metal precipitates (5MPs) (Mo, Ru, Rh, Tc, Pd) in spent uranium dioxide (UO 2 ) fuel under various ion irradiation doses, temperatures, and flux conditions. Utilizing in-situ ion irradiation and advanced transmission electron microscopy, we analyzed the evolution of fission gas bubbles and 5MPs in UO 2 samples from the Belgium Reactor 3 (BR-3). Our findings reveal significant shrinkage of fission gas bubbles and 5MPs with increasing irradiation dose, accompanied by a decrease in pair density. We demonstrate that ion irradiation induces a homogeneous re-solution process where individual atoms are ejected from bubbles and precipitates, leading to their dissolution and subsequent re-precipitation in the matrix. In conclusion, this study provides critical insights into the dynamic behavior of fission products under irradiation, facilitating the development of predictive models and contributing to the optimization of nuclear fuel performance and safety.

Fission products↗
DOE OSTI2025

High-resolution characterization of ceramic-metal interface of TiN coating on ferritic-steels for nuclear application

Advanced fuel cladding is critical for fast reactors, offering sufficient thermal conductivity, mechanical and dimensional stability and radiation tolerance of the cladding base material. Additionally, it must provide corrosion resistance and high temperature coolant compatibility on the cladding outer surface, as well as chemical stability on the cladding inner wall against fuel cladding chemical interaction (FCCI). TiN ceramic coating has been considered an effective diffusion barrier for inner and outer cladding-walls for enhanced performance. The TiN-metal interface microstructure and chemistry play a critical role in coating bond strength and integrity under harsh conditions. High-resolution transmission electron microscopy characterization of ceramic-metal interface at atomic resolution in unirradiated, irradiated and thermal cycled conditions were performed. The interface remained intact after irradiation up to 200 dpa or thermal cycling five times up to 550 °C. In conclusion, this work discusses the potential impact of these results on coating performance and design for advanced claddings.

36 - MATERIALS SCIENCE↗
DOE OSTI2022

Low rank approximation in simulations of quantum algorithms

Simulating quantum algorithms on classical computers is challenging when the system size, i.e., the number of qubits used in the quantum algorithm, is moderately large. However, some quantum algorithms and the corresponding quantum circuits can be simulated efficiently on a classical computer if the input quantum state is a low rank tensor and all intermediate states of the quantum algorithm can be represented or approximated by low rank tensors. Here, in this paper, we examine the possibility of simulating a few quantum algorithms by using low-rank canonical polyadic (CP) decomposition to represent the input and all intermediate states of these algorithms. Two rank reduction algorithms are used to enable efficient simulation. We show that some of the algorithms preserve the low rank structure of the input state and can thus be efficiently simulated on a classical computer. However, the rank of the intermediate states in other quantum algorithms can increase rapidly, making efficient simulation more difficult. To some extent, such difficulty reflects the advantage or superiority of a quantum computer over a classical computer. As a result, understanding the low rank structure of a quantum algorithm allows us to identify algorithms that can benefit significantly from quantum computers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Using dynamic mode decomposition to predict the dynamics of a two-time non-equilibrium Green’s function

Computing the numerical solution of the Kadanoff–Baym equations, a set of nonlinear integral differential equations satisfied by the two-time Green's functions derived from many-body perturbation theory for a quantum many-body system away from equilibrium, is a challenging task. Recently, we have successfully applied dynamic mode decomposition (DMD) to construct a data driven reduced order model that can be used to extrapolate the time-diagonal of a two-time Green's function from numerical solutions of the KBE within a small time window. In this paper, we extend the previous work and use DMD to predict off-diagonal elements of the two-time Green's function. We partition the two-time Green's function into a number of one-time functions along the diagonal and subdiagonals of the two-time window as well as in horizontal and vertical directions. We use DMD to construct separate reduced order models to predict the dynamics of these one-time functions in a two-step procedure. We extrapolate along diagonal and several subdiagonals within a subdiagonal band of a two-time window in the first step. In the second step, we use DMD to extrapolate the Green's function outside of the sub-diagonal band. In conclusion, we demonstrate the efficiency and accuracy of this approach by applying it to a two-band Hubbard model problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A machine learning pipeline for membrane segmentation of cryo-electron tomograms

We describe how to use several machine learning techniques organized in a learning pipeline to segment and identify cell membrane structures from cryo electron tomograms. These tomograms are difficult to analyze with traditional segmentation tools. The learning pipeline in our approach starts from supervised learning via a special convolutional neural network trained with simulated data. It continues with semi-supervised reinforcement learning and/or a region merging technique that tries to piece together disconnected components belonging to the same membrane structure. A parametric or non-parametric fitting procedure is then used to enhance the segmentation results and quantify uncertainties in the fitting. Domain knowledge is used in generating the training data for the neural network and in guiding the fitting procedure through the use of appropriately chosen priors and constraints. We demonstrate that the approach proposed here works well for extracting membrane surfaces in two real tomogram datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

An efficient quantum circuit for block encoding a pairing Hamiltonian

We present an efficient quantum circuit for block encoding a pairing Hamiltonian often studied in nuclear physics. Our block encoding scheme does not require mapping the creation and annihilation operators to the Pauli operators and representing the Hamiltonian as a linear combination of unitaries. Instead, we show how to encode the Hamiltonian directly using controlled swap operations. We analyze the gate complexity of the block encoding circuit and show that it scales polynomially with respect to the number of qubits required to represent a quantum state associated with the pairing Hamiltonian. We also show how the block encoding circuit can be combined with the quantum singular value transformation to construct an efficient quantum circuit for approximating the density of states of a pairing Hamiltonian. The techniques presented can be extended to encode more general second-quantized Hamiltonians.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Electrical edge effect induced photocurrent overestimation in low-light organic photovoltaics

In many literatures, the short-circuit current (J SC ) of indoor organic photovoltaics is overestimated, leading to severely wrong device performance evaluation and analysis. In this work, based on the equivalent circuit model, we demonstrate that electrical edge effect is sensitive to both transverse surface resistance and light intensity. At low light intensity, the electrical edge effect could lead to J SC being significantly overestimated, i.e., by 100% and even more. We show that for a PM6:Y6 device capped with a MoOX layer, when measured under 0.01 sun, the usually overlooked interface doping mechanism would lead to J SC and PCE overestimation by 51% and 15%, respectively. Besides, we show that the magnitude of J SC overestimation drastically increases with high photoactive-layer surface roughness. This work emphasized the significant electrical edge effect on J SC evaluations for low-light solar cells and is conducive to understanding the intrinsic mechanism of edge effect, promoting a healthier development of organic photovoltaics.

14 SOLAR ENERGY↗
DOE OSTI2021

Analytical modeling for redox flow battery design

Deeper market penetration of redox flow batteries requires optimization of the cell performance. Though important for optimization, detailed analytical solutions have not been developed for electrolyte flow, mass and charge transport, and reaction kinetics within redox flow batteries. To this end, here we present analytical solutions to active species concentration and over-potential based on advection-diffusion transport for ions and Bulter-Volmer model for interface reaction kinetics. The solutions were validated with results from a finite element model. These solutions were then applied to investigate the relationship between over-potential and state of charge, current density, reaction rate constant, flow velocity, diffusivity, total active species concentration, and electrode structure. Explicit formulas were identified for minimum activation over-potential and limiting current density as well as their dependence on electrolyte properties, operation conditions, and electrode structure. With our new mathematical formulas, this work provides a theoretical framework for flow battery design.

25 ENERGY STORAGE↗
DOE OSTI2020

Correlating capacity fade with film resistance loss in fast charging of lithium-ion battery

Fast charging of lithium-ion (Li-ion) batteries makes it susceptible to lithium plating. Here, we report the correlation between capacity loss and cell impedance changes in the battery due to lithium electrodeposition. Li-ion pouch cells with nickel-manganese-cobalt cathode and graphite anode were charged at rates varying from 0.5C to 6C. The cell voltage evolution immediately after charging was monitored to identify the C-rates that result in lithium plating. Electrochemical impedance spectroscopy (EIS) was used to monitor the cell impedance evolution. The impedance of battery cells at C-rates lower than 1C and no lithium plating showed minimal changes from their baseline values in the period immediately after charging. However, the impedance of battery cells undergoing lithium plating and C-rates higher than 3C showed a substantial reduction immediately after charging, with recovery back to baseline values after 30 min of relaxation. These observations suggest that lithium plating causes damage to the solid electrolyte interface (SEI) layer on the anode particles during the charging period, followed by the reformation of the SEI during the relaxation period. The measured capacity loss has a linear correlation with observed impedance change. The linear relationship suggests that impedance monitoring may be used for prognostication of the state-of-health of Li-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2021

Effect of thermal environments on fast charging Li-ion batteries

Battery thermal management systems (BTMSs) are expected to keep the battery temperature at a moderate level (~30 °C) to minimize the thermally exacerbated degradation. However, during fast charging, a strong cooling system is required to restrict the temperature rise of Li-ion batteries (LiBs), which significantly increases the cost and weight of battery packs, and induces a large temperature variation inside the battery. Here, in this work, we find that all these drawbacks could be relieved by allowing LiBs to charge at higher temperatures. Since the fast charging of a LiB only takes a tiny fraction of its lifetime, the aging rate is limited even at a charging temperature of 60 °C. Three types of thermal environments are proposed: kept constant at 30 °C, preheated to 60 °C, and adiabatic fast charging. With an experimentally validated electrochemical-thermal (ECT) coupled model, we explore the interplay between thermal management and the fast-charging performance. It is found that a gradually increasing temperature profile is the best option to balance the lithium plating and thermal management of the battery. Combining adiabatic fast charging with a preheating step, we can achieve minimal cooling need, perfect temperature uniformity within a battery, and fast-charging capability simultaneously.

25 ENERGY STORAGE↗
DOE OSTI2022

Establishing substitution rules of functional groups for high-capacity organic anode materials in Na-ion batteries

Tailoring molecular structures of organic electrode materials (OEMs) can enhance their performance in Na-ion batteries, however, the substitution rules and the consequent effect on the specific capacity and working potential remain elusive. Herein, by examining three sodium carboxylates with selective N substitution or extended conjugation structure, we exploited the correlation between structure and performance to establish substitution rules for high-capacity OEMs. Our results show that substitution position and types of functional groups are essential to create active centers for uptake/removal of Na+ and thermodynamically stabilize organic structures. Furthermore, rational host design and electrolytes modulation were performed to extend the cycle life to 500 cycles. A full cell based on the optimal 2,2’-bipyridine-4,4’-dicarboxylic acid disodium salt anode and the polyaniline cathode is demonstrated to confirm the feasibility of achieving all-organic batteries. Lastly, this work provides a valuable guideline for the design principle of high-capacity and stable OEMs for sustainable energy storage.

25 ENERGY STORAGE↗
DOE OSTI2022

A three-dimensional pore-scale model for redox flow battery electrode design analysis

A three-dimensional (3-D) pore-scale model has been developed to construct multiscale fibrous electrodes for redox flow batteries (RFB). New designs, such as biporous electrodes modify electrode structures by creating secondary pores on single carbon fiber to reduce internal battery resistance. Existing pore-scale models only resolve electrodes to the single carbon fiber scale and cannot incorporate recent multiscale electrode designs into numerical models. Our new model aims to bridge this gap and provide a tool to rapidly screen new electrode configurations. Two multiscale electrodes, laser-perforated and biporous electrodes, were investigated at varying operation conditions with the proposed 3-D pore-scale models. The laser-perforated electrode exhibits a reduced pressure drop, but follows the same permeability correlation compared to the corresponding pristine electrode. For the biporous electrode, the added specific surface area and faster reaction kinetics from the secondary pores are the most influential factors leading to improved battery efficiency. However, operating the biporous electrode in limiting current density conditions should be avoided due to the decreased mass transfer efficiency and a more significant voltage loss. Finally, we believe that our 3-D pore-scale model can accelerate the flow battery electrode design process and provide new insights into electrode geometry optimizations.

25 ENERGY STORAGE↗
DOE OSTI2025

A hybrid numerical and machine learning framework for evaluating the performance of a 780 cm 2 aqueous organic redox flow battery

Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low capacity degradation in 10 cm2 cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier for commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm 2 DHP-based AORFB by combining physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical quantities and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. These combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks the first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.

25 ENERGY STORAGE↗