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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Performance of Different Water-Based Binder Formulations for Ni-Rich Cathodes Evaluated in LiNi 0.8 Mn 0.1 Co 0.1 O 2 //Graphite Pouch Cells

Water-based processing for lithium-ion battery electrodes is attractive due to its lower manufacturing cost and smaller environmental impact. However, multiple challenges associated with aqueous cathode processing have hindered commercial adoption. Polymer binders are an important component of the electrode, and thus the choice of binders can alter electrode cycling performance significantly. In this work, four different water-based binder combinations are investigated for Ni-rich LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811)-based cathodes, with a focus on the long-term electrochemical performance in practical-format full pouch cells. No additional pH-modulating additives were added to the aqueous cathode slurries, and no protective coatings were present on the cathode or aluminum current collector. Results are compared with the standard PVDF/NMP-based binder/solvent combination, used as a baseline. The influence of water-based binder type on slurry rheology and electrode microstructure are also discussed. All cells made by water-processing had worse rate performance compared to the baseline. However, the cell discharge capacity after 1000 U.S. Advanced Battery Consortium (USABC) cycles at C/3 charge/discharge rate was comparable to the baseline for two of the water-based cathode formulations (CMC & JSR, and LiPAA), demonstrating the potential viability of aqueous-processed Ni-rich cathodes at a commercial scale.

25 ENERGY STORAGE↗
DOE OSTI2025

Investigating Radiation-Induced Actinide Species in Solution

Our fundamental understanding of actinide radiation-induced redox chemistry is crucial to nuclear fuel cycle development, due to the unavoidable exposure of these elements to ionizing radiation fields, both inherent and from in-process applications. Plutonium (Pu) and americium (Am) both possess multiple oxidation states, the careful manipulation of which are essential in the study and utilization of their rich chemistry, developing new nuclear technologies, and securing the long-term sustainability of nuclear power. However, knowledge in this area is far from complete. Consequently, we have studied the radiation-induced chemistry of both Pu and Am through a variety of techniques. Temperature-controlled electron pulse radiolysis has been used to study Am for the first time, determining the feasibility of Am redox reactions under used nuclear fuel reprocessing conditions, (e.g. nitric acid, non-ambient temperature). Additionally, we have developed an experimentally evaluated multi-scale computer model for the prediction of gamma radiation-induced Pu redox chemistry due to radiolysis and disproportionation reactions in concentrated nitric acid solutions.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

PWR loading pattern optimization with reinforcement learning

The core loading pattern optimization problem belongs to the class of combinatorial optimization problem and has been studied since the dawn of commercial nuclear energy industry. It is characterized by multiple objectives and constraints, with a very high number of candidate patterns, which makes it impossible to solve explicitly. Stochastic optimization methodologies including Genetic Algorithms and Simulated Annealing are used by different nuclear utilities and vendors to perform fuel cycle reload design. Nevertheless, hand-designed solutions continue to be the prevalent method in the industry. To improve the state-of-the-art core reload patterns, we aim to create a method as scalable as possible, that agrees with the designer's goal of performance and safety. To help in this task Deep Reinforcement Learning (DRL), in particular Proximal Policy Optimization is leveraged. DRL has recently experienced a strong impetus from its successes applied to games, sometimes even reaching 'super-human' performances. This paper lays out the foundation of this method and proposes to study the behavior of several hyper-parameters that influence the DRL algorithm. The algorithm is highly dependent on multiple factors such as an exploration/exploitation trade-off that manifests through different parameters such as the number of loading patterns seen and the number of samples collected before a policy update, but also the shape of the objective function derived for the core design. Experimental results also demonstrate the effectiveness of the method in finding high-quality solutions from scratch within a reasonable amount of time. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Data-driven modeling of power generation for a coal power plant under cycling

Increased penetration of renewables for power generation has negatively impacted the dynamics of conventional fossil fuel-based power plants. The power plants operating on the base load are forced to cycle, to adjust to the fluctuating power demands. This results in an inefficient operation of the coal power plants, which leads up to higher operating losses. To overcome such operational challenge associated with cycling and to develop an optimal process control, this work analyzes a set of models for predicting power generation. Moreover, the power generation is intrinsically affected by the state of the power plant components, and therefore our model development also incorporates additional power plant process variables while forecasting the power generation. We present and compare multiple state-of-the-art forecasting data-driven methods for power generation to determine the most adequate and accurate model. We also develop an interpretable attention-based transformer model to explain the importance of process variables during training and forecasting. The trained deep neural network (DNN) LSTM model has good accuracy in predicting gross power generation under various prediction horizons with/without cycling events and outperforms the other models for long-term forecasting. The DNN memory-based models show significant superiority over other state-of-the-art machine learning models for short, medium and long range predictions. The transformer-based model with attention enhances the selection of historical data for multi-horizon forecasting, and also allows to interpret the significance of internal power plant components on the power generation. This newly gained insights can be used by operation engineers to anticipate and monitor the health of power plant equipment during high cycling periods.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Economic Dispatch Optimization of Multi-Unit SMR Site

With the increase of integration of variable renewable into the grid, grid resilience can be compromised without flexible electricity suppliers. Flexible operation of NPP can expand the scope of integration of new nuclear power plants in regions with high share of renewables. It also provides opportunity to increase profits through participation in ancillary services. In this research, NuScale modules are used as the case study. NuScale is an integrated pressurized water reactor with thermal power of 250 MWth. Having multiple units can provide higher flexibility in changing power levels and supplying the grid with electricity all year long. Additionally, load-following might perturbate the ordered sequence of refueling (every 24 months for NuScale). To maintain the 2 year refueling cycles, the loading patterns will need to be adjusted in a year-to-year basis, resulting in additional unnecessary costs. Another solution is to find the optimal outage schedule. This will also shift the focus of the planned outage to a more optimized predictive outages and maintenance. Results from short-term optimization (i.e., daily participation in energy and ancillary services markets) shows that average commitment to regulation down ancillary service markets during late night and early morning hours can increase revenues. Participating in upward reserves during pre-work commuting hours and early evening hours can also generate additional revenues due to high ramp up of electricity demand. Having multiple units at a single site can also help operate flexibly. Results from long-term optimization show that daily fluctuation in electric power is usually handled by few units while other units operate on base-load profile. It is possible to combine base-load operation with flexible operation to extend component remaining useful life in some units and find optimal outage schedule for the units.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Nutrient Dynamics in a Coupled Terrestrial Biosphere and Land Model (ELM-FATES-CNP)

We present a representation of nitrogen and phosphorus cycling in the Functionally Assembled Terrestrial Ecosystem Simulator, a demographic vegetation model within the Energy Exascale Earth System land model. This representation is modular, and designed to allow testing of multiple hypothetical approaches for carbon-nutrient coupling in plants. Novel model hypotheses introduced in this work include, (a) the controls on plant acquisition of aqueous mineralized nutrients in the soil and (b) fairly straight forward methods of allocating nutrients to specific plant organs and their losses through live plant turnover as well as litter fluxes generated through plant mortality. This combines the new with pre-existing hypotheses (such as nitrogen fixation and soil decomposition) into a system that can accommodate plant-soil dynamics for a large number of size- and functional-type-resolved plant cohorts within a time-since-disturbance-resolved ecosystem. Root uptake of nutrients is governed by fine root biomass, and plants vary in their fine root biomass allocation in order to balance carbon and nutrient limitations to growth. We test the sensitivity of the model to a wide range of parameter variations and structural representations, and in the context of observations at Barro Colorado Island, Panama. A key model prediction is that plants in the high-light-availability canopy positions allocate more carbon to fine roots than plants in low-light understory environments, given the widely different carbon versus nutrient constraints of these two niches within a given ecosystem. This model provides a basis for exploring carbon-nutrient coupling with vegetation demography within Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Bidirectional Three-phase Current Source Converter based Buck-boost AC/DC System using Bidirectional Switches

The 1.2 kV 4H-SiC BiDirectional Field Effect Transistor (BiDFET) is the first monolithic SiC bidirectional switch, which offers a lower voltage drop and semiconductor devices count alternative to the reverse-voltage-blocking (RB) switch used in the current-source converters (CSC). The bidirectional switch based CSC also allows DC-link current reversal for bidirectional power flow and provides multiple system-level benefits in a buck-boost AC/DC system consisting of buck-type DC/DC converter and CSC. This paper discusses the selection of buck converter duty cycle and CSC modulation index for the system’s buck-boost operation with a wide variation in DC voltage. CSC modulation schemes categorized based on the number of hard-turn-on transitions per switching cycle are also analyzed along with the three-step and four-step commutation schemes that are essential for the CSC commutation cells. Finally, the different schemes are evaluated and compared through the experimental results of a 10 kW, 480 VRMS, LL/ (400 − 800) V AC/DC system.

Bidirectional switches, four-quadrant switches, cu↗
DOE OSTI2021

Assessment of Turbulent Combustion Models for Simulating Prechamber Ignition in a Natural Gas Engine

Lean combustion is a promising strategy to increase thermal efficiency in an internal combustion engine, by exploiting a favorable specific heat ratio of the fresh mixture while simultaneously suppressing the heat losses to the cylinder wall. However, unstable ignition and slow flame propagation at fuel-lean conditions lead to large cycle-to-cycle variability and limit the high-efficiency engine operating range. Prechamber ignition is considered an effective concept to extend the lean operating limit, by providing spatially distributed ignition with multiple turbulent flame-jets and enabling a faster combustion rate compared to the conventional spark ignition approach. From a numerical modeling standpoint to date science base and available simulation tools are inadequate to properly understand and predict the combustion processes in prechamber ignited engines. In this paper, conceptually different Reynolds-averaged Navier–Stokes (RANS) combustion models widely adopted in the engine modeling community are used to simulate the ignition and combustion processes in a medium-duty natural gas engine with a prechamber spark-ignition system. Here, a flamelet-based turbulent combustion model, i.e., G-equation, and a multizone well-stirred reactor model are employed for this modeling study. Simulation results are compared with experimental data in terms of in-cylinder pressure and heat release rate. Finally, the analysis of the performance of the two models is carried out to highlight the strengths and limitations of the two evaluated approaches.

42 ENGINEERING↗
DOE OSTI2025

High-voltage water-scarce hydrogel electrolytes enable mechanically safe stretchable Li-ion batteries

Soft Li-ion batteries, based on conventional organic electrolytes, face performance degradation challenges due to moisture penetration and safety concerns due to possible leakage of toxic fluorine compounds and flammable solvents under mechanical damage. We design a water-scarce hydrogel electrolyte with fluorine-free lithium salt to achieve wide electrochemical stability window (up to 3.11 volts) in ambient air without hermetic packaging while balancing high stretchability (1348%), ion conductivity (41 millisiemens per centimeter), and self-healing capabilities for mechanically and chemically safe stretchable Li-ion batteries. Molecular synergy between hydrophilicity and lithiophilicity of zwitterionic polymer backbone is revealed by molecular dynamics simulations. The battery exhibits capacity retention under harsh mechanical stresses—enduring stretching, twisting, folding, and multiple through-punctures by a needle—while self-healing from repeated through cuts by a razor blade. Stable ambient operation for 1 month over 500 charge-discharge cycles (average coulomb efficiency, 95%) is achieved. A prototype self-healing electronic system with embedded soft batteries demonstrates practical application as a durable embodied energy source.

Science & Technology - Other Topics↗
DOE OSTI2020

The Local Seismoacoustic Wavefield of a Research Nuclear Reactor and Its Response to Reactor Power Level

In this article, we describe the seismoacoustic wavefield recorded outdoors but inside the facility fence of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (Tennessee). HFIR is a research nuclear reactor that generates neutrons for scattering, irradiation research, and isotope production. This reactor operates at a nominal power of 85 MW, with a full-power period between 24 and 26 days. This study uses data from a single seismoacoustic station that operated for 60 days and sampled a full operating reactor cycle, that is, full-power operation and end-of-cycle outage. The analysis presented here is based on identifying signals that characterize the steady, that is, full-power operation and end-of-cycle outage, and transitional, that is, start-up and shutdown, states of the reactor. We found that the overall seismoacoustic energy closely follows the main power cycle of the reactor and identified spectral regions excited by specific reactor operational conditions. In particular, we identified a tonal noise sequence with a fundamental frequency around 21.4 Hz and multiple harmonics that emerge as the reactor reaches 90% of nominal power in both seismic and acoustic channels. We also utilized temperature measurements from the monitoring system of the reactor to suggest links between the operation of reactor’s subsystems and seismoacoustic signals. We demonstrate that seismoacoustic monitoring of an industrial facility can identify and track some industrial processes and detect events related to operations that involve energy transport.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Total Measurement Uncertainty in Neutron Coincidence Multiplicity Analysis

Neutron multiplicity counting is the most commonly used nondestructive assay technique for determining the plutonium mass within containers of scrap PuO 2 or mixed oxide (MOX). In multiplicity analysis, the 240 Pu eff mass, leakage multiplication, and alpha ratio (the ratio of [α, n]-to-spontaneous fission neutron production) are the three primary unknown sample properties. They must be determined simultaneously. To solve for these three unknowns in a multiplicity assay, three measured values are needed: the singles, doubles, and triples neutron count rates. While the analysis is limited to solving for three unknowns, there are many additional factors that impact the observed count rates and contribute to the measurement uncertainty. In this study we investigate the various uncertainty contributors for the multiplicity analysis through a combination of traditional uncertainty propagation techniques supplemented by Monte Carlo simulations to address the dependences not explicitly expressed by the point source model. Uncertainties arising from counting statistics, calibration parameters, calibration method, nuclear data, and various material characteristics (isotopic abundances, chemical form, density, and impurities) are considered. A Total Measurement Uncertainty (TMU) estimate is then developed from these uncertainty contributors. This study is confined to multiplicity analysis of items commonly encountered in international safeguards applications. That is, the study focused on Pu oxides and MOX materials for the masses ranging up to 4000 grams total Pu. Multiplicity measurements were simulated using MCNP V6 based on the Plutonium Scrap Multiplicity Counter (PSMC), Epithermal Multiplicity Counter (ENMC), Pyrochemical Multiplicity Counter, and Large Epithermal Multiplicity Counter (LEMC) for this study; however, this report focuses on the parameterization of the uncertainties for the PSMC. The performance differences between the PSMC and the other multiplicity counting systems are relatively small, primarily manifesting in the impact on measurement precision so that the evaluation developed for the PSMC can be applied to the other multiplicity counting systems. Finally an analysis tool, the Multiplicity TMU Estimator, was developed from this study to serve as an aid for evaluation of the total measurement uncertainty of multiplicity assay results obtained from the commonly used INCC acquisition and analysis software.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Convective Boundary-Layer Spatial Heterogeneity Experiment Field Campaign Report

Earth system models (ESMs) require accurate heat, mass, and momentum exchanges between components, which requires accurate observations and modeling of the atmospheric boundary layer. Over the land, the daytime convective atmospheric boundary layer (CBL, also called the convective mixing layer) develops and evolves daily, driven by solar surface heating. Doppler lidar (DL) measurements of vertical velocities provide an effective way to document the diurnal cycle of CBL (Chu et al. 2023). With the support of the U.S. Department of Energy (DOE) Atmospheric Research (ASR) program, we studied convective mixing-layer heights (MLH) across multiple DOE Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory sites based on DL measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Battery Charge Curve Prediction via Feature Extraction and Supervised Machine Learning

Real-time onboard state monitoring and estimation of a battery over its lifetime is indispensable for the safe and durable operation of battery-powered devices. In this study, a methodology to predict the entire constant-current cycling curve with limited input information that can be collected in a short period of time is developed. A total of 10 066 charge curves of LiNiO 2 -based batteries at a constant C-rate are collected. With the combination of a feature extraction step and a multiple linear regression step, the method can accurately predict an entire battery charge curve with an error of < 2% using only 10% of the charge curve as the input information. The method is further validated across other battery chemistries (LiCoO 2 -based) using open-access datasets. The prediction error of the charge curves for the LiCoO 2 -based battery is around 2% with only 5% of the charge curve as the input information, indicating the generalization of the developed methodology for predicting battery cycling curves. The developed method paves the way for fast onboard health status monitoring and estimation for batteries during practical applications.

25 ENERGY STORAGE↗
DOE OSTI2021

RAN and YBX1 are required for cell proliferation and IL-4 expression and linked to poor prognosis in oral squamous cell carcinoma

Highlights: • RAN and YBX1 are co-expressed and required for cell proliferation in OSCC cells. • RAN and YBX1 are positively associated with the immune infiltrates of CD4{sup +} Th2 cells. • RAN and YBX1 can promote IL-4 expression. • RAN is a direct target of YBX1. • RAN and YBX1 can serve as potential co-biomarkers for poor prognosis in OSCC. Oral squamous cell carcinoma (OSCC) is one of the most common malignancies in the world, with a high mortality rate. RAN is a member of the Ras GTPase family and is overexpressed in a range of cancers, however, the relationship between RAN and OSCC is rarely reported. In this study, we found that RAN is overexpressed in OSCC tissues. RAN inhibition retarded OSCC cell proliferation and led to apoptosis and cell cycle arrest. Knockdown of RAN inhibited tumor growth in vivo. Strikingly, we found that RAN and oncogene Y-box binding protein-1 (YBX1) are positively associated with the immune infiltrates of CD4{sup +} Th2 cells in multiple types of cancer, and can promote IL-4 expression. IL-4 treatment can partially rescue RAN knockdown-induced cell apoptosis in OSCC cells. Moreover, overexpression of RAN could rescue cell growth inhibition caused by knockdown of YBX1. Furthermore, patients with low expression of both RAN and YBX1 had better overall survival than others. Collectively, these findings indicate that RAN is a target of YBX1. RAN and YBX1 are required for cell proliferation and IL-4 expression. RAN and YBX1 are co-expressed and can serve as potential co-biomarkers for poor prognosis in OSCC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Designing Moderately‐Solvating Electrolytes for High‐Performance Lithium–Sulfur Batteries

New electrolytes are critical for high‐energy lithium (Li)–sulfur (S) batteries (LSBs) to ensure their stability against Li metal anode and polysulfides (PSs) shuttling which hinder the large‐scale application of LSBs. In this study, the design principle of moderately solvating electrolytes (MSEs) for LSBs is demonstrated by using a multiple‐solvent system comprising of a highly solvating solvent, a weakly solvating solvent, and a non‐solvating solvent to create a well‐balanced electrolyte system. This resulting electrolyte significantly improves the cycle life of LSBs, achieving 300 cycles, which is twice as long as that of similar cells with the conventional electrolyte and it also ensures stable calendar life for at least seven months. The optimal MSE forms robust passivation layers enhancing the structural integrity of both S and Li metal electrodes after cycling. These virtues effectively hinder parasitic side reactions and self‐discharge behavior of LSBs. This electrolyte design principle is versatile and can be applied to other battery chemistries, providing a potential path toward the development of a more efficient and stable battery system. By addressing key challenges such as the instability of electrodes and shuttling of polysulfides, this electrolyte approach offers promising solutions for advancing LSB technology.

25 ENERGY STORAGE↗
DOE OSTI2021

Nonlinear dynamics of combustor azimuthal modes: Experiments and modeling

This paper provides an experimental and theoretical investigation of azimuthal instabilities in a lean, premixed combustor. It presents data from a high Reynolds number, industrial-scale facility with multiple fuel stages that enable variation of radial and azimuthal symmetries. The combustor exhibits a variety of behaviors – including standing, spinning, mixed waves, and intermittent behaviors, as well as periodic limit cycles and quasi-periodic oscillations – depending upon varying power and azimuthal symmetry. A second key objective of this paper is to present a weakly nonlinear framework that is used to understand and interpret these results, showing the influence of eigenmode degeneracy, background noise, and azimuthal non-uniformities in mean quantities such as temperature and flame-acoustic coupling. In particular, this analysis shows the importance of the linear instability growth/damping rate and azimuthal non-uniformities in controlling the relative prominence of standing, spinning, or mixed waves at the limit cycle, as well as the preference in the spinning direction. It also shows the conditions under which single frequency or quasi-periodic oscillations are dominant, and how these are more fundamentally influenced by azimuthal non-uniformities and background noise.

42 ENGINEERING↗
DOE OSTI2021

Feedback density and causal complexity of simulation model structure

Measures of simulation model complexity generally focus on outputs; we propose measuring the complexity of a model’s causal structure to gain insight into its fundamental character. This article introduces tools for measuring causal complexity. First, we introduce a method for developing a model’s causal structure diagram, which characterises the causal interactions present in the code. Causal structure diagrams facilitate comparison of simulation models, including those from different paradigms. Next, we develop metrics for evaluating a model’s causal complexity using its causal structure diagram. We discuss cyclomatic complexity as a measure of the intricacy of causal structure and introduce two new metrics that incorporate the concept of feedback, a fundamental component of causal structure. The first new metric introduced here is feedback density, a measure of the cycle-based interconnectedness of causal structure. The second metric combines cyclomatic complexity and feedback density into a comprehensive causal complexity measure. Finally, we demonstrate these complexity metrics on simulation models from multiple paradigms and discuss potential uses and interpretations. These tools enable direct comparison of models across paradigms and provide a mechanism for measuring and discussing complexity based on a model’s fundamental assumptions and design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Continental Scale Hydrostratigraphy: Basin‐Scale Testing of Alternative Data‐Driven Approaches

Abstract Integrated hydrological modeling is an effective method for understanding interactions between parts of the hydrologic cycle, quantifying water resources, and furthering knowledge of hydrologic processes. However, these models are dependent on robust and accurate datasets that physically represent spatial characteristics as model inputs. This study evaluates multiple data‐driven approaches for estimating hydraulic conductivity and subsurface properties at the continental‐scale, constructed from existing subsurface dataset components. Each subsurface configuration represents upper (unconfined) hydrogeology, lower (confined) hydrogeology, and the presence of a vertical flow barrier. Configurations are tested in two large‐scale U.S. watersheds using an integrated model. Model results are compared to observed streamflow and steady state water table depth (WTD). We provide model results for a range of configurations and show that both WTD and surface water partitioning are important indicators of performance. We also show that geology data source, total subsurface depth, anisotropy, and inclusion of a vertical flow barrier are the most important considerations for subsurface configurations. While a range of configurations proved viable, we provide a recommended Selected National Configuration 1 km resolution subsurface dataset for use in distributed large‐and continental‐scale hydrologic modeling.

54 ENVIRONMENTAL SCIENCES↗