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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multiple electron beam generation with different energies and comparable currents from a single cathode potential for high power traveling wave tubes (TWTs)

We present a technique for generating multiple electron beams with different energies and comparable currents from a single cathode stalk at a single potential using nested magnetically insulated coaxial diodes (MICDs). The application is for a multi-stream traveling wave tube. Particle-in-cell simulations are performed for an experimental parameter-based geometry where two thin-walled intense relativistic electron beams immersed in a strong uniform magnetic field propagate through a cylindrical vacuum channel. The analytically derived results are obtained by extending Fedosov’s solution for generating a hollow electron beam from an MICD on a cathode stalk in an infinite magnetic field. Two electron beams are generated and accelerated downstream assuming zero initial kinetic energy of the electrons from the cathodes. Results show both electron beam currents ranging from 66 to 2.8 kA with an energy difference ranging from 6% to 27% depending on voltages applied from 100 to 600 kV and the geometry of the two MICDs. An optimal geometry is a crucial factor in achieving the maximum energy difference between the electron beams for comparable currents. The analytical and numerical simulation results show good agreement. We are currently in the process of planning experiments using the electron beam accelerator (SINUS-6 at UNM) to validate the analytical and simulation results.

Islam, K. N. (ORCID:0000000214397710)↗
DOE OSTI2023

Comparing Experimentally-Measured Sand’s Times with Concentrated Solution Theory Predictions in a Polymer Electrolyte

We compare the electrochemically measured Sand’s time, the time required for the cell potential to diverge when the applied current density exceeds the limiting current, with theoretical predictions for a 0.47 M poly(ethylene oxide) (5 kg mol −1 )/LiTFSI electrolyte. The theoretical predictions are made using concentrated solution theory which accounts for both concentration polarization and polymer motion, using independently measured parameters that depend on concentration, c : conductivity ( κ ), salt diffusion coefficient ( D ), cationic transference number with respect to the solvent velocity ( t + 0 ), thermodynamic factor 1 + dln f ± dln c , and partial molar volume of the salt ( V ̅ ); f ± is the mean molar activity coefficient of the salt. We find quantitative agreement between experimental data and theoretical predictions. We derive a generalized analytical expression for Sand’s time for electrolytes based on dilute solution theory. This expression correctly predicts the divergence of the Sand’s time at the limiting current, in agreement with experimental data and concentrated solution theory predictions. When the applied current is large compared to the limiting current, the analytical expression approaches the standard expression for Sand’s time used in the literature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Influence of Carbon-Nitride Dot-Emitting Species and Evolution on Fluorescence-Based Sensing and Differentiation

Carbon dots have attracted widespread interest for sensing applications based on their low cost, ease of synthesis, and robust optical properties. We investigate structure–function evolution on multiemitter fluorescence patterns for model carbon-nitride dots (CNDs) and their implications on trace-level sensing. Hydrothermally synthesized CNDs with different reaction times were used to determine how specific functionalities and their corresponding fluorescence signatures respond upon the addition of trace-level analytes. Archetype explosives molecules were chosen as a testbed due to similarities in substituent groups or inductive properties (i.e., electron withdrawing), and solution-based assays were performed using ratiometric fluorescence excitation–emission mapping (EEM). Analyte-specific quenching and enhancement responses were observed in EEM landscapes that varied with the CND reaction time. We then used self-organizing map models to examine EEM feature clustering with specific analytes. Finally, the results reveal that interactions between carbon-nitride frameworks and molecular-like species dictate response characteristics that may be harnessed to tailor sensor development for specific applications.

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

Exploring Approaches for Accelerated Mass Spectrometric Detection of Fluorochemicals from Aqueous Solutions Using Indirect, Adsorbent-Facilitated Desorption Ionization

There is a growing need for rapid, low-cost analytical tools to screen water supplies for persistent contaminants. Due to their ubiquitous presence in the environment and their detrimental effects on human health, there is a public interest in widespread testing for perfluoroalkyl substances (PFAS). However, the most common methods for PFAS analysis can be time-intensive, requiring a preconcentration step followed by liquid chromatography–mass spectrometry (LC–MS), and are cost-prohibitive for most members of the public. To address these challenges, this study explores a novel method for rapid analysis of aqueous analytes by concentrating analytes on solid adsorbents and detecting them using mass spectrometric analysis via desorption ionization directly from the adsorbent surface. We demonstrate this general approach of indirect solution analysis (ISA) with desorption electrospray ionization (ISA-DESI) and laser desorption ionization (ISA-LDI). Various adsorbent materials were screened, including commercial mesoporous carbons, alumina, and metal–organic frameworks (MOFs). Using ISA-DESI, we consistently detect concentrations as low as 5–100 parts per trillion (ppt) for perfluorooctanoic acid (PFOA) and 10–100 ppt for perfluoroundecanoic acid (PFUnDA). This approach enables rapid detection in under 5 min, making it suitable for applications requiring trace detection with fast response times. We further demonstrate the use of ISA-DESI on a range of environmentally relevant contaminants. Advantages of this indirect solution analysis approach include simplified sample preparation, accelerated analysis, and low cost with readily available materials, making it a promising tool to accelerate mass spectrometric PFAS detection for identifying contaminated sites, performing laboratory studies, and monitoring industrial waste streams.

Bays, Nathan Robert [Sandia National Laboratories ↗
DOE OSTI2020

Dissolution of a cylindrical disk in Hele-Shaw flow: a conformal-mapping approach

Here we apply conformal mapping to find the evolving shapes of a dissolving cylinder in a potential flow. Similar equations can be used to describe melting in a flowing liquid phase. Results are compared with microfluidic experiments and numerical simulations. Shapes predicted by conformal mapping agree almost perfectly with experimental observations, after a modest (20 %) rescaling of the time. Finite-volume simulations show that the differences with experiment are connected to the underlying assumptions of the analytical model: potential flow and diffusion-limited dissolution. Approximate solutions of the equations describing the evolution of the shape of the undissolved solid can be derived from a Laurent expansion of the mapping function from the unit circle. Asymptotic expressions for the evolution of the area of the disk and the shift in its centre of mass have been derived at low and high Péclet number. Analytic approximations to the leading-order Laurent coefficients provide additional insight into the mechanisms underlying pore-scale dissolution.

58 GEOSCIENCES↗
DOE OSTI2024

Transitioning from Simulation to Reality: Applying Chatter Detection Models to Real-World Machining Data

Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due to discrepancies between simulated and actual machining environments. The primary goal of this study is to bridge the gap between simulation-based machine learning models and real-world applications by developing and validating a Random Forest-based chatter detection system. This research focuses on improving manufacturing efficiency through reliable chatter detection by integrating Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL). The study applies a Random Forest classification model trained on over 140,000 simulated machining datasets, incorporating techniques like Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL) to adapt the model for real-world operational data. The model is validated against 1600 real-world machining datasets, achieving an accuracy of 86.1%, with strong precision and recall scores. The results demonstrate the model’s robustness and potential for practical implementation in industrial settings, highlighting challenges such as sensor noise and variability in machining conditions. This work advances the use of predictive analytics in machining processes, offering a data-driven solution to improve manufacturing efficiency through more reliable chatter detection.

42 ENGINEERING↗
DOE OSTI2023

A projection-based analytical Jacobian framework for chemical kinetics applications

A major challenge in simulating complex combustion systems with detailed chemical kinetic models is the cost of integrating the chemical source terms, often done using stiff ODE solvers that require frequent Jacobian evaluations. Using analytically derived Jacobian matrices instead of divided-difference-based numerical Jacobian approximations can significantly reduce the associated computational cost. However, ambiguities arise in the formulation of analytical Jacobians because the chemical state of the system, or state vector, can be expressed in multiple ways, involving variables that are typically not independent from one another. Here, in this work, the consequences of those ambiguities on practical calculations are characterized in detail, and a generalized, projection-based framework is proposed as a mitigation strategy. Performances are assessed in a series of test cases involving a variety of configurations and numerical solution approaches. Results show that with proper treatment, commonly used analytical Jacobian formulations can be considered as equivalent for practical purposes, thereby alleviating concerns that the state vector chosen to express the governing equations and corresponding analytical Jacobian may significantly impact the accuracy of the simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Amphiphile conformation impacts aggregate morphology and solution structure across multiple lengthscales

Although the self-assembly of amphiphiles is well-studied in aqueous solutions, much less is understood about the fundamental driving forces and structure property relationships in non-polar media. In recent work [Journal of Physical Chemistry B, 2020, 124, 10822.] the authors have studied a series of malonomide-based amphiphiles that are relevant to liquid-liquid extraction. That work demonstrated that aggregation is largely driven by local dipole-dipole interactions between molecules. Here, we build upon this observation to develop a more detailed understanding of how the balance of dipole-dipole interactions (controlled by conformation) and molecular architecture influences the morphology of the aggregates across lengthscales. Using constrained molecular dynamics about key degrees of freedom, we demonstrate that the conformation of N,N-dimethyl,N,N-dioctylhexylethoxy malonamide (DMDOHEMA) and N,N-dimethyl,N,N-dibutyltetradecyl malonamide (DMDBTDMA) has a significant impact upon self-association - where appropriate conformational sampling is essential. To quantify the aggregate morphology, several graph theoretic and persistent homology based properties are determined. The former examines the patterns of intermolecular interactions within clusters, while the latter examines the 3-dimensional spatial distribution. Based upon these analyses, we find that the morphology of aggregates, particularly at higher malonamide concentration, depends on a balance of dipole alignment and alkyl tail sterics. Dipole alignment encourages linear patterns of the intermolecular interactions within aggregates, while the alkyl tail steric interactions between the malonamide result in noticeably less linear aggregates for DMDOHEMA than DMDBTDMA. This is reflected in the spatial distribution, where more holes or voids exist between extractants within the DMDOHEMA that distribute within the solution in more of a "swiss cheese" arrangement as opposed to the more filamentous distribution of DMBDTDMA. This study links conformation and molecular structure to the morphology of amphiphile assemblies, and serves as a basis for ongoing study of multicomponent amphiphile solutions with polar and other solutes, and how these impact aggregation phenomena.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

An Integrated Platform for Collaborative Data Analytics

While collaboration among data scientists is a key to organizational productivity, data analysts face significant barriers to achieving this end, including data sharing, accessing and configuring the required computational environment, and a unified method of sharing knowledge. Each of these barriers to collaboration is related to the fundamental question of knowledge management “how can organizations use knowledge more effectively?”. In this paper, we consider the problem of knowledge management in collaborative data analytics and present ShareAL, an integrated knowledge management platform, as a solution to that problem. The ShareAL platform consists of three core components: a full stack web application, a dashboard for analyzing streaming data and a High Performance Computing (HPC) cluster for performing real time analysis. Prior research has not applied knowledge management to collaborative analytics or developed a platform with the same capabilities as ShareAL. ShareAL overcomes the barriers data scientists face to collaboration by providing intuitive sharing of data and analytics via the web application, a shared computing environment via the HPC cluster and knowledge sharing and collaboration via a real time messaging application.

Oesch, T↗
DOE OSTI2020

Description of Sensor Assignment Optimization Method as Deployed on a Multi-Node Cluster

Data analytic methods are being developed to address the problem of how to assign a sensor set in a nuclear facility such that a requisite level of process monitoring capability is realized and that the sensor set is sufficiently rich to determine the status of the individual sensors with respect to need for calibration. There is an awareness in the nuclear industry that data analytics combined with rich sensor sets represent a means to improve operations and reduce costs. In the industry the calibration problem has been previously approached as an empirical data-driven problem with several methods having been developed. However, the experience of the utilities over the past ten years with these methods indicates that the absence of physics-based information renders the data-driven approach less reliable. Complicating factors such as the inherent variability of operation (both equipment alignment and operating condition) can confound a pure data-driven approach while there are no rigorous guidelines for determining what constitutes an adequate sensor set. The solution under development to overcome these shortcomings supplements the data analytic method with process information in a so-called process-constrained data-analytic approach. Simple balance equations are written for generic components (e.g., mechanical pump, valve, and heat exchanger). These do not require a priori knowledge of process parameters, such as heat transfer coefficients or friction factors. All that is needed on the part of the utility user is to identify the components and how they are connected. This report describes the development of a parallel computing capability for determining the optimal sensor set. The optimal sensor set problem suffers from the curse of dimensionality. Computation time increases exponentially as the size of the system grows. To overcome this difficulty a pre-conditioner algorithm is developed to find an approximate solution close the actual solution. This serves as a seed for the full-blown algorithm and acts to constrain the space that must searched. The optimization algorithms are described and the implementation on a parallel computing platform is described. The application of the method to a use case we are solving in collaboration with our utility partner served to illustrate how the default sensor set in a nuclear plant may not provide sufficient coverage to infer sensor calibration status.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Joint Optimization of Multimodal Transit Frequency and Shared Autonomous Vehicle Fleet Size with Hybrid Metaheuristic and Nonlinear Programming

Shared autonomous vehicles (SAVs) bring competition to traditional transit services but redesigning multimodal transit network can utilize SAVs as feeders to enhance service efficiency and coverage. This paper presents an optimization framework for the joint multimodal transit frequency and SAV fleet size problem, a variant of the transit network frequency setting problem. The objective is to maximize total transit ridership (including SAV-fed trips and subtracting boarding rejections) across multiple time periods under budget constraints, considering endogenous mode choice (transit, point-to-point SAVs, driving) and route selection, while allowing for strategic route removal by setting frequencies to zero. Due to the problem’s non-linear, non-convex nature and the computational challenges of large-scale networks, we develop a hybrid solution approach that combines a metaheuristic approach (particle swarm optimization) with nonlinear programming for local solution refinement. To ensure computational tractability, the framework integrates analytical approximation models for SAV waiting times based on fleet utilization, multimodal network assignment for route choice, and multinomial logit mode choice behavior, bypassing the need for computationally intensive simulations within the main optimization loop. Applied to the Chicago metropolitan area’s multimodal network, our method illustrates a 33.3% increase in transit ridership through optimized transit route frequencies and SAV integration, particularly enhancing off-peak service accessibility and strategically reallocating resources.

Ng, Max↗
DOE OSTI2023

Ion Specificity Influences on the Structure of Zwitterionic Brushes

Zwitterionic brushes have a wide range of applications, including use as lubricating surfaces and antifouling membranes. In this study, densely end-tethered poly(cysteine methacrylate) (PCysMA) brushes were synthesized using surfaceinitiated activators regenerated by electron transfer atom transfer radical polymerization (SI-ARGET-ATRP). The structure of the PCysMA brushes was investigated using ellipsometry, X-ray reflectivity (XRR), attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR), and atomic force microscopy (AFM). The results of these characterization techniques were used to study the effect of SO 4 2– , Cl – , NO 3 – , Br – , and SCN – anions, divalent Ca 2+ and Ba 2+ cations, and trivalent Y 3+ cations on the structure of the PCysMA brushes. The results showed that the PCysMA brushes in solution exhibit an “antipolyelectrolyte” effect to a certain degree, which inversely follows the Hofmeister series of anions. The introduction of divalent cations Ca 2+ and Ba 2+ had a modest impact on the dimensions of the PCysMA brushes, indicating that chelating interactions between the cations and zwitterion units work against the “antipolyelectrolyte” effect. The complexation was even stronger in the presence of trivalent Y 3+ cations, which caused the PCysMA brushes to shrink. These findings highlight the importance of ion specificity to the structure of zwitterionic brushes in aqueous solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Effect of lithium incorporation on tweaking the electrocatalytic behavior of tantalum-based oxides

To study the effect of lithium incorporation on the electrochemical properties and catalytic behavior of tantalum-based oxides, a series of mesoporous lithium-contained tantalum oxides was developed adopting an inverse micelle-based sol gel synthesis method. Four various phases of amorphous Ta 2 O 5 , polycrystalline LiTaO 3 , Li 5.04 Ta 6.16 O 17.92 , and Li 3 TaO 4 were determined for samples synthesized at Li/Ta ratios of 0.5 (LTO-0.5), 1 (LTO-1), 2 (LTO-2), and 3 (LTO-3) respectively. The surface analysis revealed that LTO-2 sample is featured with lower amount of lithium atoms at the surface rather than the bulk of the sample. The electrochemical analysis of LTO samples showed that LTO-2 possesses lower charge transfer resistance extracted from impedance spectroscopy that consequently leads to a better electrocatalytic performance. The LTO-2 electrode also showed a low open circuit potential (OCP) combined with a relatively higher electroactive surface area, thus capable of running an electrooxidation reaction at a potential lower than all other LTO samples. The more negative OCP value of LTO-2 electrode in AA solution states a more facile electron transfer between the analyte and electrode compared to other synthesized samples. Mott–Schottky plots of LTO electrodes are characterized by positive slopes which indicate n-type semiconductor behavior. The charge carrier density (5.5 × 10 19 cm -3 ) in LTO-2 sample is the lowest among all other samples. The charge carrier density is dependent on the amount of applied lithium in the synthesis confirming various lithium diffusion behaviors in LTO samples. The lower carrier concentration in LTO-2 justifies its higher activity in electrooxidation providing less populated lower unoccupied molecular orbital (LUMO) for the accommodation of electrons extracted from the analyte higher occupied molecular orbital (HOMO). Therefore, LTO-2 showed an improved electrocatalytic performance due to lower charge transfer resistance (higher conductivity) offered by lower amount of lithium atom at its surface and higher oxygen vacancies in the bulk.

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

Data Assimilation for Robust UQ Within Agent-Based Simulation on HPC Systems

Agent-based simulation provides a powerful tool for in silico system modeling. However, these simulations do not provide built-in methods for uncertainty quantification (UQ). Within these types of models a typical approach to UQ is to run multiple realizations of the model then compute aggregate statistics. This approach is limited due to the compute time required for a solution. When faced with an emerging biothreat, public health decisions need to be made quickly and solutions for integrating near real-time data with analytic tools are needed. We propose an integrated Bayesian UQ framework for agent-based models based on sequential Monte Carlo sampling. Given streaming or static data about the evolution of an emerging pathogen this Bayesian framework provides a distribution over the parameters governing the spread of a disease through a population. These estimates of the spread of a disease may be provided to public health agencies seeking to abate the spread. By coupling agent-based simulations with Bayesian modeling in a data assimilation, our proposed framework provides a powerful tool for modeling dynamical systems in silico. We propose a method which reduces model error and provides a range of realistic possible outcomes. Moreover, our method addresses two primary limitations of ABMs: the lack of UQ and an inability to assimilate data. Our proposed framework combines the flexibility of an agent-based model with UQ provided by the Bayesian paradigm in a workflow which scales well to HPC systems. We provide algorithmic details and results on a simulated outbreak with both static and streaming data.

Spannaus, Adam [ORNL] (ORCID:0000000225213657)↗
DOE OSTI2020

Cylinder Test

In 1941 G.I. Taylor proposed the cylinder test as an experiment to determine the properties of explosive products. The test consists of a hollow metal cylinder containing an unreacted cylindrical charge of explosives. The charge is detonated at one end of the cylinder and, as the detonation wave propagates, the product gas expands behind it. As the product gas is produced, the pressure acting on the metal increases and begins to push the walls outward. Consider the time at which the detonation wave has reached the halfway point between the two ends of the cylinder. In front of the wave nothing has changed and the metal tube has the same initial radius. Behind the wave, the radius of the tube has expanded. The farther behind the wave a section of tube is, the larger its radius has become. The longer the wall is exposed to the pressurized gas, the more it expands, so that the radius is largest where the detonation was initiated. The metal most commonly used for this experiment is soft copper. Its material properties are well known and soft copper is also easy to shape with precision machining. During the cylinder test the radial expansion of the cylinder is measured over time and this data is used to determine the amount of work applied to the cylinder wall by the product gas as a function of time. Since volume increases in this problem, the time dependence can be seen as a volume dependence. The volume dependent work yields a path in thermodynamic space. The cylinder test experiment can be modeled using simulation. The simulation gives you the answer, but it cannot tell you why. The goal of the proposed work is to use analytic analysis methods to give insight into the simulation solution.

36 MATERIALS SCIENCE↗
DOE OSTI2024

LandScan Global 2023: Silver Edition

For a quarter of a century, the LandScan Global (LSG) project has annually released a global, high-resolution gridded population dataset representing the ambient or unwarned population at a 30 arcsecond resolution. LSG supports a range of applications such as emergency management, disaster response, and human health and security for understanding populations at risk. The 2023 release of LSG, the LandScan Silver Edition, represents a major methodological leap forward while also leveraging previous knowledge—the previous year was the baseline for the current annual update carrying forward valuable knowledge of the built environment for the past quarter century—to train the machine learning models. Compared with annual releases over the past 24years, multiple advancements were made to different aspects of the methodology to achieve reproducibility, transparency, and consistent global propagation of solutions to modeling or population distribution issues identified during the review process. These novel changes include incorporation of the latest available geospatial inputs across the globe, machine learning models instead of manual modifications, population feature importance analysis, open-source solutions vs. proprietary software, generation of multiple global versions, analytic validations, and human-in-the-loop revisions to produce the final version. Additionally, algorithms—such as anomaly detection—were introduced to quickly identify areas of focus to develop a new and robust systematic review. Significant changes in modeled population distributions were observed between the 2022 and 2023 releases, largely attributable to improvements in data and methods and discussed thoroughly within this report. In summation, the LandScan Silver Edition leverages the best of the past quarter century of LSG legacy knowledge and continues a tradition of applying cutting-edge enhancements to serve as a new benchmark for accurate, actionable gridded population data

Lebakula, Viswadeep↗