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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Important Factors for the Reliable and Reproducible Preparation of Non-aqueous Electrolyte Solutions for Lithium Batteries

Electrolyte solutions play a crucial role in rechargeable lithium batteries. Driven by an ever-increasing demand for better batteries, more and more stringent performance requirements are being asked of electrolytes. This demand to achieve electrolytes beyond the state-of-the-art has driven an increasing number of publications over the past decade. Nevertheless, a detailed description of electrolyte preparation is frequently missing when reporting new electrolytes for batteries, creating a barrier for others to reproduce important findings. In addition, false or misleading information in a paper may result from unintentional inaccuracies during the electrolyte preparation process. This article highlights important factors for the reliable and reproducible preparation of non-aqueous electrolyte solutions for lithium batteries, with the aim of encouraging researchers to follow more rigorous reporting standards.

Cao, Xia↗
DOE OSTI2021

INBOUND APPOINTEE PUBLICATION Importance of Substrate-Particle Repulsion for Protein-Templated Assembly of Metal Nanoparticles

We study the protein-directed assembly of colloidal gold nanoparticles on de novo designed protein nanofiber templates. Using sequential assembly on glass substrates, we attach of positively-charged gold nanoparticles to protein nanofibers engineered to have a high density of negatively-charged surface residues. Using a combination of electron and optical microscopy, we measure the density of particle attachment and characterize binding specificity. By varying nanoparticle size and pH of the solution, we explore the importance of charge-dependent particle-fiber and particle-substrate interactions. We find an inverse correlation between particle size and attachment density to the protein nanofibers, attributed to the balance between size-dependent electrostatic particle-fiber attraction and particle-substrate repulsion. We show pH-dependent particle attachment density and binding specificity in relation to the protonation fraction of each assembly layer. Finally, we employ hyperspectral scattering microscopy to draw conclusions about particle density and interparticle spacings of optically-observable particle assemblies.

Guye, Kathryn N.↗
DOE OSTI2021

Online data analysis and reduction: An important co-design motif for extreme-scale computers

A growing disparity between supercomputer computation speeds and I/O rates means that it is rapidly becoming infeasible to analyze supercomputer application output only after that output has been written to a file system. Instead, data-generating applications must run concurrently with data reduction and/or analysis operations, with which they exchange information via high-speed methods such as interprocess communications. The resulting parallel computing motif, online data analysis and reduction (ODAR), has important implications for both application and HPC systems design. Here we introduce the ODAR motif and its co-design concerns, describe a co-design process for identifying and addressing those concerns, present tools that assist in the co-design process, and present case studies to illustrate the use of the process and tools in practical settings.

Data Analysis↗
DOE OSTI2022

Understanding the Importance of Labile Fe(III) during Fe(II)-Catalyzed Transformation of Metastable Iron Oxyhydroxides

Transformation of metastable Fe(III) oxyhydroxides is a prominent process in natural environments and can be significantly accelerated by the coexisting aqueous Fe(II) (Fe(II) aq ). Recent evidence points to the solution mass transfer of labile Fe(III) (Fe(III) labile ) as the primary intermediate species of general importance. However, a mechanistic aspect that remains unclear is the dependence of phase outcomes on the identity of the metastable Fe(III) oxyhydroxide precursor. Here, we compared the coupled evolution of Fe(II) species, solid phases, and Fe(III) labile throughout the Fe(II)-catalyzed transformation of lepidocrocite (Lp) versus ferrihydrite (Fh) at equal Fe(III) mass loadings with 0.2–1.0 mM Fe(II) aq at pH = 7.0. Similar to Fh, the conversion of Lp to product phases occurs by a dissolution–reprecipitation mechanism mediated by Fe(III) labile that seeds the nucleation of products. Though for Fh we observed a transformation to goethite (Gt), accompanied by the transient emergence and decline of Lp, for initial Lp we observed magnetite (Mt) as the main product. Further, a linear correlation between the formation rate of Mt and the effective supersaturation in terms of Fe(III) labile concentration shows that Fe(II)-induced transformation of Lp into Mt is governed by the classical nucleation theory. When Lp is replaced by equimolar Gt, Mt formation is suppressed by opening a lower barrier pathway to Gt by heterogeneous nucleation and growth on the added Gt seeds. The collective findings add to the mechanistic understanding of factors governing phase selections that impact iron bioavailability, system redox potential, and the fate and transport of coupled elements.

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

Elucidating the Role of Hydrogen Bonding in the Optical Spectroscopy of the Solvated Green Fluorescent Protein Chromophore: Using Machine Learning to Establish the Importance of High-Level Electronic Structure

Hydrogen bonding interactions with chromophores in chemical and biological environments play a key role in determining their electronic absorption and relaxation processes, which are manifested in their linear and multidimensional optical spectra. For chromophores in the condensed phase, the large number of atoms needed to simulate the environment has traditionally prohibited the use of high-level excited-state electronic structure methods. By leveraging transfer learning, we show how to construct machine-learned models to accurately predict the high-level excitation energies of a chromophore in solution from only 400 high-level calculations. Here, we show that when the electronic excitations of the green fluorescent protein chromophore in water are treated using EOM-CCSD embedded in a DFT description of the solvent the optical spectrum is correctly captured and that this improvement arises from correctly treating the coupling of the electronic transition to electric fields, which leads to a larger response upon hydrogen bonding between the chromophore and water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Important powertrain dynamics for developing models for control of connected and automated electrified vehicles

Connected and Automated Vehicles (CAV) technology presents significant opportunities for energy saving in the transportation sector. CAV technology forecasts vehicle and powertrain power needs under various terrain, ambient, and traffic conditions. Integration of the CAV technology in Hybrid Electric Vehicles (HEVs) provides the opportunity for optimal vehicle operation. Indeed, Hybrid Electric Vehicle powertrains present high degrees of flexibility and possibility for choosing optimum powertrain modes based on the predicted traction power needs. In modeling complex CAV powertrain dynamics, the modeler needs to consider short-time scale powertrain dynamics, such as engine transients, and hysteresis of mode-switching for a multi-mode HEV. Therefore, the powertrain dynamics essential for developing powertrain controllers for a class of connected HEVs is presented. To this end, control-oriented powertrain dynamic models for a test vehicle consisting of full electric, hybrid, and conventional engine operating modes are developed. The resulting powertrain model can forecast vehicle traction torque and energy consumption for the specified prediction horizon of the test vehicle. The model considers different operating modes and associated energy penalty terms for mode switching. Thus, the vehicle controller can determine the optimum powertrain mode, torque, and speed for forecasted vehicle operation via utilizing connectivity data. The powertrain model is validated against the experimental data and shows prediction error of less than 5% for predicting vehicle energy consumption. The model is used to create energy penalty maps that can be used for CAV control, for example fuel penalty map for engine torque changes (10–40 Nm) at each engine speed. The results of model-based optimization show optimum switching delays ranging from 0.4 to 1.4 s to avoid hysteresis in mode switching.

Engineering↗
DOE OSTI2020

The Importance of Vane Configuration on Yield Stress Measurements of Cement Slurry

Vane methods combined with direct shear tests are widely applied to measure the yield stress $τ_0$ in concentrated suspensions such as cement slurries. This report reviews three different vanes with different shapes and sizes, and examines the relationship between the yield stress versus the torque and the shear rate versus the angular velocity. This research shows that vanes with conical shapes or reduced height/diameter ratio $(H/D)$ result in larger yield stress values.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Implications of Safety and Operational Features of Small, Advanced Reactors for the Evaluation of Important Human Actions

The design and operational characteristics of non-light water reactors are likely to change the role of human actions in safety function management and the types of human actions that are deemed important. The objectives of this report are to: • Identify the implications of small, advanced reactor design characteristics on human performance and the changing role of human actions in the management of safety functions. • Identify the methods that may be used to identify important human actions. • Identify how HFE safety reviewers can help ensure that the methods adequately model human actions to identify those that are important to safety. We identified the implications of small, advanced reactor characteristics on the role of personnel in safety function management. Then we addressed how designers can identify which human actions are important to safety using both probabilistic risk assessment (PRA) and deterministic analyses. PRA identifies important human actions using risk-importance criteria. Deterministically identified important human actions include those identified by analyses of situations such as transients and accidents and defense in depth. In all cases, the acceptability of the analyses is dependent on the modeling, quantification, and criterion selection to determine which human actions are important. How well the designers address these processes determines the acceptability of their methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2021

Importance of Trade Dependencies for Agricultural Inputs: A Case Study of Finland

Approximately 80% of the world's population lives in countries that are dependent on food imports to sustain an adequate food supply. Besides these food imports, further dependencies also arise due to the requirements for agricultural inputs, including energy, fertilisers, feed and agricultural machinery. While the trade of agricultural inputs is relatively well represented in economic analysis, the quantification is often in terms of monetary values, and thus the assessments of the actual input quantities are very limited. In this paper, we develop a framework for analysis of the traded input quantities at the country-level and demonstrate its utility through an in-depth analysis of Finland's dependency on agricultural inputs. Further, we assess the importance of these inputs for domestic food production from a resilience perspective. We find that Finland, a country with relatively high food self-sufficiency but also a great dependency on imported agricultural inputs, experienced an increase in its dependency over the period 1996-2016. In case of trading partners, the story is more mixed: while trading partners for soybeans increased, those decreased in case of electricity while no significant change was observed in other commodities. In Finland, the rapeseed dependency on imports (ratio of net imports and consumption) grew from around 0% to almost 50% between the years 1987-2013. Changes for electricity remained substantially smaller, with dependency only slightly growing during the study period. Crude oil and soybean supplies are totally dependent on imports as neither is produced in Finland. Interestingly, the highest dependencies for agricultural inputs were on different countries than that for foodstuff imports. Thus, when identifying and assessing approaches to increase resilience to trade-related shocks, it is essential to understand both foodstuff and agricultural input dependencies within the global food system.

trade dependencies↗
DOE OSTI2020

Processor and method of weighted feature importance estimation

A method includes determining a feature importance ranking for each pair of clusters of a plurality of clusters to generate a first plurality of feature importance rankings. The method further includes determining a feature importance ranking between a particular data element and each cluster to generate a second plurality of feature importance rankings. A distance value associated with each pair of clusters of the plurality of clusters is determined to generate a plurality of distance values, and a probability value associated with each data element is determined to generate a plurality of probability values. The method further includes weighting the first plurality of feature importance rankings based on the plurality of distance values to determine a first plurality of weighted feature importance rankings and weighting the second plurality of feature importance rankings based on the plurality of probability values to determine a second plurality of weighted feature importance rankings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Detecting Important Drivers of Gridded Population Modeling With Machine Learning

High-resolution population datasets have been lever-aged across a broad swath of domains, such as climate change, public policy, humanitarian aid, and rescue operations, among others. Machine learning methods were adopted to generate high-resolution or gridded population estimates by using various geospatial input features such as buildings, roads, and nighttime lights. In this study, we evaluate the importance of population features using Random Forest models across three levels of analysis, utilizing permutation measures. Our research aims to address key questions to enhance our understanding of high-resolution population modeling, such as: Are certain features globally (10 countries collectively) more important than others? Do optimal features vary by country? Within each country, do feature importance differ across administrative units? What similarities exist in feature importance at the global, country, and administrative unit levels? To answer these questions, we leverage the Kneedle algorithm to automate the selection of optimum features. We find that there are patterns displayed by features across spatial boundaries, evidenced by the same feature being the most important indicator of population across 7 of the 10 countries modeled. Our findings indicate that while important features may vary across geographies, certain features consistently hold greater importance than others agnostic of geography.

Lebakula, Viswadeep [ORNL] (ORCID:0000000152935914↗
DOE OSTI2022

Classification of Nuclear Reactor Operations Using Spatial Importance and Multisensor Networks

Distributed multisensor networks record multiple data streams that can be used as inputs to machine learning models designed to classify operations relevant to proliferation at nuclear reactors. The goal of this work is to demonstrate methods to assess the importance of each node (a single multisensor) and region (a group of proximate multisensors) to machine learning model performance in a reactor monitoring scenario. This, in turn, provides insight into model behavior, a critical requirement of data-driven applications in nuclear security. Using data collected at the High Flux Isotope Reactor at Oak Ridge National Laboratory via a network of Merlyn multisensors, two different models were trained to classify the reactor’s operational state: a hidden Markov model (HMM), which is simpler and more transparent, and a feed-forward neural network, which is less inherently interpretable. Traditional wrapper methods for feature importance were extended to identify nodes and regions in the multisensor network with strong positive and negative impacts on the classification problem. These spatial-importance algorithms were evaluated on the two different classifiers. The classification accuracy was then improved relative to baseline models via feature selection from 0.583 to 0.839 and from 0.811 ± 0.005 to 0.884 ± 0.004 for the HMM and feed-forward neural network, respectively. While some differences in node and region importance were observed when using different classifiers and wrapper methods, the nodes near the facility’s cooling tower were consistently identified as important—a conclusion further supported by studies on feature importance in decision trees. Node and region importance methods are model-agnostic, inform feature selection for improved model performance, and can provide insight into opaque classification models in the nuclear security domain.

Tibbetts, Jake↗
DOE OSTI2025

Emissions leakage and economic losses may undermine deforestation-linked oil crop import restrictions

Import restrictions on deforestation-linked commodities are being considered strategies to reduce global deforestation and emissions. However, limited market share of importers imposing such restrictions and the potential for emissions leakage could reduce their effectiveness. Moreover, they could result in negative economic implications for producers and consumers. We quantify future emissions and economic implications of oil palm and soybean import restrictions. Current EU restrictions will likely have minimal impact due to the EU’s otherwise small and diminishing share of global palm and soy demand. If extended beyond the EU, such import restrictions could drive reductions in cumulative LUC emissions by 2050 in key oil crop exporting regions— up to 0.9% in Indonesia, 1.5% in the rest of Southeast Asia, 3.8% in Argentina and 6.7% in Brazil, relative to a scenario with no import restrictions. However, these key exporters could also face losses ranging $\$$4.1-$\$$61 billion in cumulative agricultural production revenue by 2050.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Identification of Important Phenomena for Light Water Reactors During Heat Transport System Failure Events in Integrated Energy Systems

This work adapts historical literature and existing phenomena identification and ranking tables (PIRT) to be applicable to a novel nuclear power plant (NPP) and chemical or thermal process integrated energy system (IES), particularly focusing on the process heat and heat transport system failure events that are not a concern during normal NPP operation but become vital when an IES is considered. Nuclear energy has been suggested to go beyond base-load applications and be used for hydrogen co-generation systems, amongst other IESs. Prior to the implementation of nuclear IESs, sufficient analysis must be performed on accident events to ensure public safety. The events considered were deemed important because of their potential to damage systems, structures, and components (SSCs). Process thermal events of concern include loss of heat load and temperature transient events. Loss of heat load events were characterized as having high importance and being well understood. Temperature transient events may be further categorized by the cyclic loading and harmonics phenomena. Cyclic loading issues were classified as medium to high importance with knowledge gaps existing regarding fatigue and low power operation, while harmonics phenomena were classified as low importance and are well understood. Heat transport system failure events of concern include intermediate and process heat exchanger failures, mass addition to reactor coolant, ingress of material from thermal manifold/energy storage, and loss of intermediate fluid. Furthermore, these events tended to be of high or medium importance, with some knowledge gaps needing to be filled for individual reactor systems due to unique designs.

Integrated Energy System (IES)↗