Processing and Microstructural Characterization of Novel Invar Syntactic Foams
UNKNOWN
Chawla, Nikhilesh↗
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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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Demonstrating the Promise and Problems for Open Science in Heliophysics
The recent push towards open science comes with a new set of problems to address. The additional work required to align a project with even the concepts of open science is at best problematic and typically not reasonably possible. The issue is further complicated by the lack of supporting infrastructure for the requirements of open science. We present the first executable paper in heliophysics as an example of how a collaboration between scientists, software developers, and software engineers can streamline the research process. This novel approach to science enables the science result to more easily adhere to the concepts of open science through the application of technology from inception to publication. We also use this paper to demonstrate gaps in our infrastructure that currently prevent progress in our pursuit of open science. This presentation is meant to initiate discussions of the methods and gaps presented, and we hope the discussion will be productive.
infrastructure↗
Photochemical Evolution of the 2013 California Rim Fire: Synergistic Impacts of Reactive Hydrocarbons and Enhanced Oxidants
Large wildfires markedly alter regional atmospheric composition, but chemical complexity challenges model predictions of downwind impacts. Here, we elucidate key facets of gas-phase photochemistry and assess novel chemical processes via a case study of the 2013 California Rim Fire plume. Airborne in situ observations, acquired during the NASA Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) mission, illustrate the evolution of volatile organic compounds (VOC), oxidants, and reactive nitrogen over 12 hours of atmospheric aging. Measurements show rapid formation of ozone and peroxyacyl nitrates (PNs), sustained peroxide production, and prolonged enhancements in oxygenated VOC and nitrogen oxides (NOX). Measurements and Lagrangian trajectories constrain a 0-D puff model that approximates plume photochemical history and provides a framework for evaluating key processes. Simulations examine the effects of 1) previously-unmeasured reactive VOC identified in recent laboratory studies, and 2) emissions and secondary production of nitrous acid (HONO). Inclusion of estimated unmeasured VOC leads to a 250% increase in OH reactivity and a 70% increase in radical production via oxygenated VOC photolysis. HONO amplifies radical cycling and serves as a downwind NOX source, although two different HONO production mechanisms (particulate nitrate photolysis and heterogeneous NO2 conversion) exhibit markedly different effects on ozone, NOX, and PNs. Analysis of radical initiation rates suggests that oxygenated VOC photolysis is a major radical source, exceeding HONO photolysis when averaged over the first 2 hours of aging. Ozone production chemistry transitions from VOC-sensitive to NOX-sensitive within the first hour of plume aging, with both peroxide and organic nitrate formation contributing significantly to radical termination. To simulate smoke plume chemistry accurately, models should simultaneously account for the full reactive VOC pool and all relevant oxidant sources.
SEAC4RS↗
Rapid Laser Reactive Sintering of Garnet Li 6.1 La 3 Zr 2 Al 0.3 O 12 Solid-State Electrolyte
All solid-state lithium-ion batteries (ASSLIBs) have gained significant interest in recent years due to their wide range of applications, including mobile devices, electric vehicles, and grid storage. Garnet-type Li 7 La 3 Zr 2 O 12 (LLZO) solid-state electrolyte (SSE) continues to be a significant player in the fabrication of ASSLIBs with excellent ionic conductivity on the order of 10 -3 S•cm -1 with the integration of dopants and sintering aids to assist conductivity and structural evolution during the sintering procedure. Traditional sintering techniques, such as solid-state sintering (SSS), have been proven to yield desirable crystal structures. However, the low ionic conductivity results from inferior microstructure, and lithium loss is a significant challenge. Recently, a relatively novel sintering process called rapid laser reactive sintering (RLRS) has shown great potential for achieving fully dense solid oxide electrolytes with less surface element loss, resulted in high ionic conductivity. Here, we applied the same RLRS method to fabricate LLZO dense electrolyte with different amounts of aluminum dopants. We hope the rapid high-temperature sintering can densify the electrolyte and avoid the lithium loss for achieving high lithium-ion conductivity. In this work, we optimized multiple combinations of laser parameters (speed, power, and defocus distance), initial precursor composition, and controlled atmospheres to discover the optimal conditions for reaching the efficacious crystal structure, microstructure, and ionic conductivity.
25 ENERGY STORAGE↗
Regulatory Development for Advanced Manufacturing
Advanced Methods of Manufacturing (AMM) are new, novel manufacturing processes that have the potential to bring significant cost and schedule reductions to many industries, including the nuclear industry. These methods produce components faster and cheaper than many traditional methods. This includes replacing obsolete components at currently operating plants as well as manufacturing components of all sizes for advanced reactors.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Toward the Advanced Manufacturing of Land-Based Wind Turbine Blades: Preprint
The National Renewable Energy Laboratory (NREL) and General Electric (GE) are partners within the Additive and Modular Enabled Rotor Blades and Integrated Composites Assembly (AMERICA) project. AMERICA aims to develop advanced manufacturing solutions to reduce labor and cycle time while increasing recyclability of wind turbine blades. The project is funded by the Advanced Manufacturing Office (AMO) of the U.S. Department of Energy (DOE). This paper describes the techno-economic analysis of the novel manufacturing process applied to the tip of the blade of a representative land-based wind turbine. A comparison to a standard manufacturing process is established, highlighting challenges and opportunities. Several uncertainties affect the analysis, but with the current set of assumptions, the tip adopting advanced manufacturing is predicted to lower labor by 21%, cycle time by 39%, and total blade tip costs by 15% while simultaneously increasing production quality and adopting recyclable thermoplastic resin.
3D printing↗
TopTemp: Parsing Precipitate Structure from Temper Topology
Technological advances are in part enabled by the development of novel manufacturing processes that give rise to new materials or material property improvements. Development and evaluation of new manufacturing methodologies is labor-, time-, and resource-intensive expensive due to complex, poorly defined relationships between advanced manufacturing process parameters and the resulting microstructures. In this work, we present a topological representation of temper (heat-treatment) dependent material micro-structure, as captured by scanning electron microscopy, called TopTemp. We show that this topological representation is able to support temper classification of microstructures in a data limited setting, generalizes well to previously unseen samples, is robust to image perturbations, and captures domain interpretable features. The presented work outperforms conventional deep learning baselines and is a first step towards improving understanding of process parameters and resulting material properties.
Kassab, Lara↗
Recovery of valuable metals from electronic waste using a novel ammonia-based hydrometallurgical process
The growing quantity of waste electrical and electronic equipment (WEEE), also known as electronic waste (E-waste), has been an area of growing public concern. The abundance of valuable metals contained in waste printed circuit boards (WPCBs) has made it a promising secondary resource, especially for Cu and Au. Although the recovery of metals from WPCBs via hydrometallurgical routes has been studied extensively over the past 20 years, most of the research has been limited in the laboratory. In current hydrometallurgical processes, strong acids and expensive oxidizers are often used to ensure a high recovery of metals without considering the sustainability aspects of the environment and economics. To improve upon current hydrometallurgical offerings, the current study seeks to develop an energy-saving, environmentally friendly, economic and sustainable process to efficiently recover the valuable metals from real-world end-of-life WPCBs. The new contributions presented in this study are 1) design and evaluation of a comprehensive hydrometallurgical flowsheet; 2) employment of real end-of-life PCBs as feed materials in an investigation on Cu-NH3 leaching kinetics; 3) further application of kinetic model on a counter flow process simulation; and 4) evaluation of the influences by co-existing metals in Au-S2O3 leaching and recommendation for favorable leaching conditions.
01 COAL, LIGNITE, AND PEAT↗
Dark Photon Search at the Short-Baseline Near Detector
Neutrino physics has long been a key field in elementary particle physics, both enhancing our understanding of the Standard Model (SM) and raising new questions. Among these are the so-called "Short-Baseline Anomalies" observed by neutrino experiments, particularly the MiniBooNE experiment at Fermilab, which detected an excess of low-energy electron-like events. In recent years, beyond Standard Model (BSM) explanations have been proposed to address this anomaly, with a focus on neutrino beam-related processes. A novel interpretation involving a dark-sector explanation was recently suggested, introducing a vector portal that connects the SM and dark sectors through a new interaction mediated by a bosonic particle, the Dark Photon. This work investigates the production of dark-sector particles, specifically Dark Photons, in the Booster Neutrino Beam (BNB) at Fermilab and their potential detection at the Short-Baseline Near Detector (SBND). The BNB produces mesons which decay into Dark Photons, detectable via their decay into electron-positron pairs at SBND. By exploiting the temporal structure of the neutrino beam, we propose a method to isolate Dark Photon signals from neutrino backgrounds using time-delayed event detection. In this thesis, the sensitivity of SBND to Dark Photons is assessed using a three-year exposure, demonstrating that SBND has the potential to significantly improve current experimental constraints on Dark Photons. This analysis provides a promising avenue for future dark sector searches in neutrino experiments.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
The Profiled Feldman-Cousins Method for Confidence Interval Construction for the Nova 3-Flavor Oscillation Analysis
The small interaction cross-section of neutrinos makes experimental neutrino physics particularly responsive to technological advancements. A significant development leveraged by the NOvA experiment is large-scale parallel processing, enabling novel computational approaches to longstanding experimental challenges. Central to managing the resulting high-throughput data is NOvA’s implementation of the Freight Train model, designed for efficient data production and handling.This dissertation details the methodology and execution of the NOvA 2024 3-Flavor Oscillation Analysis, supported by a comprehensive dataset spanning ten years. It emphasizes frequentist results refined through the Feldman-Cousins (FC) technique, specifically addressing confidence interval corrections in parameter estimation. The computational intensity associated with Feldman-Cousins arises from extensive Monte Carlo simulations, which were substantially mitigated through parallel computing on the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC), employing the MPI framework.To further enhance computational efficiency, an Importance Sampling method is introduced and evaluated, demonstrating significant potential to reduce complexity, particularly in exploring extreme parameter space regions. This thesis presents both the successful application of advanced computational resources and the development of sophisticated statistical techniques, aiming to enhance the precision and scope of neutrino oscillation analyses.
Dye ajdye11190@gmail.com, Andrew Joseph [Mississip↗
Current Status on the Manufacturing of Nanomaterials for Proton Exchange Membrane Energy Systems by Vapor-Based Processes
Development of novel technologies for catalyst synthesis and membrane electrode assembly (MEA) fabrication is of primary importance for further improvement of the performance and economics of proton exchange membrane fuel cells (PEMFCs) and proton exchange membrane water electrolyzers (PEMWEs). While the traditional manufacturing methods are time-consuming, energy intensive, and require many processing steps, the new vapor-based methods provide many benefits including the development of improved catalysts and catalyst supports, deposition of uniform thin films, reduction of catalyst loading, and minimizing the number of manufacturing steps. Recent publications in the field identified spray pyrolysis, reactive spray deposition technology, chemical vapor deposition, and atomic layer deposition as advanced vapor-based catalyst synthesis and deposition methods used for fabrication of MEAs for PEMFCs and PEMWEs. Here, the MEAs fabricated via vapor-based processes have shown significant performance improvements in comparison to the state-of-the-art MEAs, which are attributed to better catalyst distribution, improved catalyst supports, and controlled, uniform catalyst layer microstructures. This review provides an overview of the vapor-based synthesis and deposition methods currently being used for the development of PEM-based devices. The advantages and disadvantages of these methods are critically compared and discussed while the outlook for future development is provided.
25 ENERGY STORAGE↗
Real-time infrared spectroscopy coupled with blind source separation for nuclear waste process monitoring
On-line infrared absorbance spectroscopy enables rapid measurement of solution-phase molecular species. Many spectra-to-concentration models exist for spectral data, with some models able to handle overlapping spectral bands and nonlinearities. However, model accuracy is limited by the quality of training data used in model fitting. The process spectra of nuclear waste simulants at the Savannah River Site display incongruity between training and process spectra; the glycolate spectral signature in the training data does not match the glycolate signature in Savannah River National Laboratory process data. A novel blind source separation algorithm is proposed that preprocesses spectral data so that process spectra more closely resemble training spectra, thereby improving model quantification accuracy when unexpected sources of variation appear in process spectra. The novel blind source separation preprocessing algorithm is shown to improve nitrate quantification from an R 2 of 0.934 to 0.988 and from 0.267 to 0.978 in two instances analyzing nuclear waste simulants from the Slurry Receipt Adjustment Tank and Slurry Mix Evaporator cycle at the Savannah River Site.
Crouse, Steven H.↗
A Novel 2-D Programmable Photonic Time Delay Device for MM-Wave Signal Processing Applications
We describe a novel programmable photonic true time delay device that has the properties of low loss, inherent two dimensionality with a packing density exceeding 25 lines/cm super 2, virtually infinite bandwidth, and is easy to manufacture.
time delay devices↗
A combination of deep eutectic solvent and ethanol pretreatment for synergistic delignification and enhanced enzymatic hydrolysis for biorefinary process
Here, a novel pretreatment system containing deep eutectic solvents and ethanol (DES-E) for synergistic carbohydrate conversion and delignification was reported in this study. The DES-E pretreatment resulted in an enhanced glucose yield compared to individual DES and ethanol pretreatment for the three tested biomass, including Broussonetia papyrifera, corn stover and pine. To further explore the delignification mechanism, the solubilized lignin and residual lignin from Broussonetia papyrifera was recovered and extracted, then thoroughly characterized. The highest total OH content was found in the DES-E solubilized lignin, which could be used as antioxidant. The presence of ethanol in pretreatment liquor could protect the β-O-4 substructure from breakage and reduce lignin condensation, which favors the subsequent enzymatic hydrolysis. Comparable glucose yield and delignification performance was achieved by recycled DES. DES-E pretreatment offers a promising method for lignin isolation and cellulose digestibility improvement simultaneously.
09 BIOMASS FUELS↗
Electric-vehicle battery second-life and recycling pathways: How economics depend on chemistry, processing, and application
We assess the economics of repurposing and recycling electric vehicle (EV) batteries by estimating the maximum acquisition price repurposers and recyclers could pay for used EV packs across cathode chemistries, first-life conditions, second-life applications, and recycling processes. We develop a novel open-source process-based cost model of a UL-1974-certified repurposing facility and leverage battery degradation models to estimate the maximum acquisition price repurposers could pay for used EV batteries while producing second-life battery energy storage systems with life-adjusted costs equivalent to new systems. We compare these maximum price estimates to maximum prices for recyclers based on cost and revenue estimates from the EverBatt model. We find that repurposing is more economical than recycling for lithium iron phosphate (LFP) batteries, due to their relatively long life and low value materials; recycling is generally more economical than repurposing for lithium nickel cobalt aluminum oxide (NCA) batteries, due to their shorter life and higher value materials; and the economics for lithium nickel manganese cobalt oxide (NMC) batteries depend more heavily on first life retirement conditions and second life application intensity. These results suggest an overall strategy: reuse LFP, recycle NCA, and sort NMC into recycling or repurposing pathways based on state of health and second-life application.
25 ENERGY STORAGE↗
Process Modeling and Analysis of a Novel Sorbent Material for Direct Air Capture Applications
This poster presents results of Task 9.0, Advanced Modeling Support of CDR Pilot Projects, of the Carbon Dioxide Removal program (CDR FWP23). Specifically, the poster presents the multi-scale modelling framework to investigate alternate processes configuration for DAC applications including Vacuum-Assisted Temperature Swing Adsorption (TVSA) process and sweep gas for regeneration.
Caballero, Daison↗
A Novel Model of Thyristor in Reverse Recovery Process for Current Interruption Test of HVDC Circuit Breakers
The high voltage thyristor is widely used in high voltage DC (HVDC) grids due to its large current capability. Recently, the high voltage thyristor is implemented to produce a high current in the HVDC circuit breaker (CB) testing process. Unfortunately, the reverse recovery process (RRP) of a thyristor could induce significant turn-off overvoltage and power losses, jeopardizing the safety of the devices. The existing models of a thyristor cannot describe such hard-switching transient accurately. Therefore, this paper divides this process into four stages based on the physical mechanism and proposes a novel cosine exponential (CE), simulating the voltage and current of the thyristor with analytical equations, which are derived from the equivalent circuit. Moreover, the extraction procedure of the proposed model is illustrated in this paper. Finally, we develop an HVDC CB test platform to validate the proposed model. Experimental results show that the proposed CE model can accurately predict the overvoltage with a relative error of less than 8% at various current varying rates.
Dongye, Zhonghao↗
A Bayesian Learning Approach to Wireless Outdoor Heatmap Construction using Deep Gaussian Process
We present a novel Bayesian learning approach to outdoor radio heatmap construction utilizing deep Gaussian process (GP). The proposed approach employs a two-layer hierarchy which consists of two cascaded Gaussian processes that are capable of modeling more complex input-output relations than standard single-layer Gaussian processes. Since deriving the exact model likelihood is challenging, a lower bound is optimized instead so that gradient descent-based methods can be performed to find out the optimal model parameters. Typically, inducing points are used in GPs to facilitate low-rank approximation of covariance (kernel) matrices for computation speedup. However, the inaccuracy induced by inducing points can accumulate when stacking multiple layers of GP which may hinder the performance of deep GP. Moreover, since inducing points need to be learned, having them at all layers of deep GP also incurs computational burden. To overcome the above challenges, in contrast to the canonical deep GP model, we use a modified architecture where a full standard GP resides in the first layer and inducing points are only introduced for the second layer. This modified architecture strikes a balance between model accuracy and training complexity. In the proposed model, the noise parameter of the first GP layer is also eliminated to improve the training efficiency as the noise parameter at the output of the second layer suffices to model the uncertainty in the output. The proposed approach is evaluated on real-world datasets, in the form of location-Received Signal Strength (RSS) pairs, collected from the Platform for Open Wireless Data-driven Experimental Research (POWDER) located at the campus of the University of Utah. Experiment results show that the proposed approach can achieve smaller prediction errors on various training and testing data configurations than DNN-based and GP-based methods.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗