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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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The poster presents the latest progress on utilizing a commercially scalable flat sheet sorbent for the effective enrichment of critical minerals from coal wastewater. It highlights the performance, scalability, and potential for industrial applications, addressing key challenges in critical recovery from complex wastewater streams.
The Waste Information Management System (WIMS) 2019-20 was updated to support the Department of Energy (DOE) accelerated cleanup program. The schedule compression required close coordination and a comprehensive review and prioritization of the barriers that impeded treatment and disposition of the waste streams at each site. Many issues related to waste treatment and disposal were potential critical path issues under the accelerated schedule. In order to facilitate accelerated cleanup initiatives, waste managers at DOE field sites and at DOE Headquarters in Washington, D.C., needed timely waste forecast and transportation information regarding the volumes and types of radioactive waste that would be generated by DOE sites over the next 35 years. Each local DOE site historically collected, organized, and displayed waste forecast information in separate and unique systems. In order for interested parties to understand and view the complete DOE complex-wide picture, the radioactive waste and shipment information of each DOE site needed to be entered into a common application. The WIMS application was therefore created to serve as a common application to improve stakeholder comprehension and improve DOE radioactive waste treatment and disposal planning and scheduling. WIMS allows identification of total forecasted waste volumes, material classes, disposition sites, choke points, technological or regulatory barriers to treatment and disposal, along with forecasted waste transportation information by rail, truck and intermodal shipments. The Applied Research Center (ARC) at Florida International University (FIU) in Miami, Florida, developed and deployed the web-based forecast and transportation system and is responsible for updating the radioactive waste forecast and transportation data on a regular basis to ensure the long-term viability and value of this system. (authors)
NETL developed a nanostructured Fe catalyst capable of converting syngas to olefins. Initial laboratory testing demonstrated this technology possessed high reactivity (~70% conversion), good olefin selectivity (~40 weight %), and a long-performance lifetime (greater than 500 h on stream with no degradation). Under CRADA Agreement 0988, NETL and Susteon collaborated to evaluate the commercial potential of this catalyst technology by scaling up the catalyst synthesis and collecting reactivity data to characterize the mass and energy balance associated with utilizing this catalyst in a Fischer Tropsch to Olefins (FTO) reactor. Research under CRADA Agreement 0988 illustrated there are technical barriers associated with synthesizing the catalyst that the team was not able to overcome. Reactivity testing with this material illustrated that the catalyst deactivated in less than 50 hours of time on stream. Due to this technical barrier, the team did not meet its Go/No-Go decision target of creating a FTO process that produces at least a 25% reduction in olefin production cost compared to the current state-of the-art technologies. As a result, the team has stopped all work on CRADA Agreement 0988.
Data management is a critical component of modern experimental workflows. As data generation rates increase, transferring data from acquisition servers to processing servers via conventional file-based methods is becoming increasingly impractical. The 4D Camera at the National Center for Electron Microscopy generates data at a nominal rate of 480 Gbit s -1 (87,000 frames s -1 ), producing a 700 GB dataset in 15 s. To address the challenges associated with storing and processing such quantities of data, we developed a streaming workflow that utilizes a high-speed network to connect the 4D Camera’s data acquisition system to supercomputing nodes at the National Energy Research Scientific Computing Center, bypassing intermediate file storage entirely. In this work, we demonstrate the effectiveness of our streaming pipeline in a production setting through an hour-long experiment that generated over 10 TB of raw data, yielding high-quality datasets suitable for advanced analyses. Additionally, we compare the efficacy of this streaming workflow against the conventional file-transfer workflow by conducting a postmortem analysis on historical data from experiments performed by real users. Our findings show that the streaming workflow significantly improves data turnaround time, enables real-time decision-making, and minimizes the potential for human error by eliminating manual user interactions.
Optical spectroscopy-based on-line monitoring of Hanford processing streams can enable real-time characterization of chemical composition of process streams and batches, ultimately enabling and enhancing process control. It can provide immediate feedback on process conditions and has the potential to reduce the needed number of grab sample collections, thereby reducing times and costs associated with laboratory processing. Here we discuss the utilization of Raman spectroscopy to quantify multiple target analytes that are common within Hanford tanks and waste processing streams. Analytes include: nitrate, nitrite, carbonate, chromate, sulfate, phosphate, hydroxide, oxalate, ammonia, and aluminate. Most notably in this work, Raman applications to low-concentration streams are explored and optimized. Raman instrument specifications are compared; specifically, the impact of utilizing three different Raman excitation wavelengths, 405, 532, and 671 nm, is discussed. Also, Raman data collection parameters such as collection time and spectral averaging are measured and discussed. Finally, optical libraries of chemical targets were collected using optimized collection parameters and chemometric models were built to automate quantification of chemical targets. These models were validated through application to simulants and real Hanford process samples. Chemometric models performed well on both training and validation sets, suggesting these approaches can be successfully applied to on-line and real-time monitoring of low concentration Hanford processing streams. The use of the combine Raman wave lengths with the enhanced chemometric models for the low concentration streams significantly improved the chemical detection levels and significantly reduced uncertainties of those measurements.
Environmental contamination due to human activities is a major concern, particularly for persistent chemicals. Within catchments, persistent chemicals linked to negative health outcomes such as polychlorinated biphenyls (PCBs) have great potential to be transported, through adsorption or biological uptake, with downstream locations acting as sinks for accumulation. Here we present long-term trends in PCB bioaccumulation in fish found in lower-order tributaries on the Oak Ridge Reservation, an impacted US Department of Energy property in East Tennessee, USA, and a large reservoir system adjacent to it composed of parts of the Clinch and Tennessee Rivers. Given that the reservoir system has experienced no direct PCB mitigation activities, this record offers an opportunity to explore potential natural attenuation of PCBs within a large lotic ecosystem. Additionally, attenuation rates ranged from 0% to 8% yr -1 in minnows and sunfish at stream sites and 5.4–11.3% yr -1 in catfish at reservoir sites. These rates are comparable to findings from similar studies in other regions, suggesting a consistency in responses since the banning of PCB production in 1979. Further, results suggest that PCB sources from discharge outfalls are important locally but are not primarily responsible for sustaining PCB contamination in downstream reservoirs.
Abstract Cosmic rays (CRs) are the primary driver of ionization in star-forming molecular clouds (MCs). Despite their potential impacts on gas dynamics and chemistry, no simulations of star cluster formation following the creation of individual stars have included explicit cosmic-ray transport (CRT) to date. We conduct the first numerical simulations following the collapse of a 2000 M ⊙ MC and the subsequent star formation including CRT using the STAR FORmation in Gaseous Environments framework implemented in the GIZMO code. We show that when CRT is streaming-dominated, the CR energy in the cloud is strongly attenuated due to energy losses from the streaming instability. Consequently, in a Milky Way–like environment the median CR ionization rate in the cloud is low ( ζ ≲ 2 × 10 −19 s −1 ) during the main star-forming epoch of the calculation and the impact of CRs on the star formation in the cloud is limited. However, in high-CR environments, the CR distribution in the cloud is elevated ( ζ ≲ 6 × 10 −18 ), and the relatively higher CR pressure outside the cloud causes slightly earlier cloud collapse and increases the star formation efficiency by 50% to ∼13%. The initial mass function is similar in all cases except with possible variations in a high-CR environment. Further studies are needed to explain the range of ionization rates observed in MCs and explore star formation in extreme CR environments.
In a hierarchically formed Universe, galaxies accrete smaller systems that tidally disrupt as they evolve in the host’s potential. We present a complete catalogue of disrupting galaxies accreted onto Milky Way-mass haloes from the Auriga suite of cosmological magnetohydrodynamic zoom-in simulations. We classify accretion events as intact satellites, stellar streams, or phase-mixed systems based on automated criteria calibrated to a visually classified sample, and match accretions to their counterparts in haloes re-simulated at higher resolution. Most satellites at the present day have lost substantial amounts of stellar mass – 67 per cent have $f_\text{bound} < 0.97$ (our threshold of lost stellar mass to no longer be considered intact), while 53 per cent satisfy a more stringent $f_\text{bound} < 0.8$. Streams typically outnumber intact systems, contribute a smaller fraction of overall accreted stars, and are substantial contributors at intermediate distances from the host centre ($\sim$0.1 to $\sim 0.7R_\text{200m}$, or $\sim$35 to $\sim$250 kpc for the Milky Way). We also identify accretion events that disrupt to form streams around massive intact satellites instead of the main host. Streams are more likely than intact or phase-mixed systems to have experienced pre-processing, suggesting this mechanism is important for setting disruption rates around Milky Way-mass haloes. All of these results are preserved across different simulation resolutions, though we do find some hints that satellites disrupt more readily at lower resolution. The Auriga haloes suggest that disrupting satellites surrounding Milky Way-mass galaxies are the norm and that a wealth of tidal features waits to be uncovered in upcoming surveys.
While there has been an increase in distributed energy resource (DER) adoption, particularly for solar photovoltaic (PV) DERs, distributed wind deployment is still relatively low. However, distributed wind has significant growth potential for behind-the-meter applications and can provide improved resource diversity and resilience in high-DER grid systems. In order to make optimized decisions about distributed wind deployment, it is important to identify and characterize the value streams of distributed wind in a way that will help policy makers, microgrid developers, utilities, project developers, and others understand the potential value of distributed wind. In our paper, we develop a framework to provide a consistent and comprehensive approach to finding the net benefits of distributed wind to various stakeholders in different scenarios. We do this by considering the main use cases for distributed wind, and then developing a framework which is flexible for these various use cases and the relevant stakeholders while maintaining the same overall structure for analysis. Ongoing work includes applying this framework to valuing two distributed wind power systems, one in a remote village in Alaska and one in a front-of-the-meter deployment in Iowa. Our analysis aims to understand the full value that distributed wind is providing in these systems, including the resilience benefits it is contributing. As a better understanding of distributed wind valuation is gained, it will be easier for stakeholders to make informed decisions in order to have resilient renewable energy systems.
Dataset revised on October 15, 2021. This revision adds sulfur and iron X-ray absorption near-edge structure spectra for the wetland soils and stream sediments from the field areas. It also renames the sample locations in a way that is more intuitive to readers of the companion paper that is under review. Finally, the data filenames and organization have been updated in their labeling to parallel the data sources in the associated paper. The abstract text and methods were also revised to reflect the data that was added to the dataset.Trace metals are essential for microbially-mediated biogeochemical processes occurring in anoxic wetland soils and stream bed sediments, such as denitrification, methanogenesis, and mercury methylation. Low availability of these elements may potentially inhibit key components of anaerobic carbon and nitrogen cycling and contaminant transformation. The solid-phase speciation of trace metals likely plays an important role in controlling their bioavailability. Metal speciation is well studied in contaminated soils and sediments as well as those naturally elevated in trace metals. However, less is known regarding the chemical forms of trace metals in systems having concentrations similar to geological background levels, the very settings where metal limitations may be most prevalent. We have investigated trace metal concentrations, extractability, and solid-phase speciation in three freshwater subsurface aquatic systems: marsh wetland soils, riparian wetland soils, and the sediments of a streambed.Data are provided for marsh wetland soils at Argonne National Laboratory, riparian wetland soils in the Tims Branch watershed at Savannah River National Laboratory, and stream bed sediments from East Fork Poplar Creek near Oak Ridge National Laboratory. Soil and sediment elemental abundances, mineralogy, and extractable nutrients as well as dissolved major elements, anions, trace metals, and nutrients in the overlying surface waters are provided. In addition, the results of sequential chemical extraction for the trace metals cobalt, nickel, copper, and zinc from the soils and sediment are reported as well as X-ray absorption near-edge structure (XANES) spectra in these materials are reported. To aid interpretation of these data, XANES spectra of sulfur in the soils and sediments as well as both XANES and extended X-ray absorption fine structure (EXAFS) spectra of iron in these materials are reported. The data package also includes the XANES spectra of reference standards and a potential interferent in the measurements. All data are provided in text-based CSV format with header sections indicating the data contained in each file and the corresponding units. Note that "u" is used in place of Greek lower case mu to indicate the micro prefix on units.
Here, conversion of CO 2 to CO was performed at 800-850C via reduction of calcium ferrite (CaFe 2 O 4 ) with coal followed by oxidation of reduced CaFe 2 O 4 with CO 2 to produce CO and partially oxidized Ca ferrite. The process was evaluated with several types of coals which included lignite, sub-bituminous, bituminous, anthracite and pet-coke. Reduction of CaFe 2 O 4 with sub-bituminous coal and lignite coal was more favorable than that with higher rank coals. Stable reactivity performance was demonstrated for 35-cycle test conducted with lignite coal/CaFe 2 O 4 at 800°C and with high conversion of CO 2 to CO. The rate of conversion of CO 2 to CO with reduced CaFe 2 O 4 was found to be 1st order with respect to CO 2 and had a low activation energy of 7.4 kJ/mol. Thermodynamic integrated process assessments suggest that two and three reactor process concepts can be realized and net CO 2 utilization achievable. The two reactor configuration shows the highest potential for CO 2 utilization while generating a value-added CO rich syngas stream.
Current methods of processing accumulated polyolefin waste typically require harsh conditions, precious metals or high metal loadings to achieve appreciable activities. Here, in this work, we examined supported, single-site organonickel catalysts for polyolefin upcycling. Chemisorption of Ni(COD) 2 (COD, 1,5-cyclooctadiene) onto Brønsted acidic sulfated alumina (AlS) yields a highly electrophilic Ni(I) precatalyst, AlS/Ni(COD) 2 , which is converted under H 2 to the active AlS/Ni II H catalyst. This single-site system exhibits unique hydrogenolysis selectivity that favours cleaving branched polyolefin C–C linkages, enabling the hydrogenolytic separation of polyethylene and isotactic polypropylene (iPP) mixtures. Moreover, AlS/Ni II H remains highly selective and active for hydrogenolysis of iPP admixed with polyvinyl chloride, and the spent catalyst can be repeatedly regenerated by AlEt3 treatment. Experimental mechanistic analysis and density functional theory modelling reveal a turnover-limiting C–C scission pathway featuring β-alkyl transfer and strong olefin binding. These results highlight the potential of nickel-based systems for the selective upcycling of complex plastic waste streams.
It is widely known that neural networks (NNs) are universal approximators of continuous functions. However, a less known but powerful result is that a NN with a single hidden layer can accurately approximate any nonlinear continuous operator. This universal approximation theorem of operators is suggestive of the structure and potential of deep neural networks (DNNs) in learning continuous operators or complex systems from streams of scattered data. Here, in this work, we thus extend this theorem to DNNs. We design a new network with small generalization error, the deep operator network (DeepONet), which consists of a DNN for encoding the discrete input function space (branch net) and another DNN for encoding the domain of the output functions (trunk net). We demonstrate that DeepONet can learn various explicit operators, such as integrals and fractional Laplacians, as well as implicit operators that represent deterministic and stochastic differential equations. We study different formulations of the input function space and its effect on the generalization error for 16 different diverse applications.
The properties of satellite halos provide a promising probe for dark matter (DM) physics. Observations have motivated current efforts to explain surprisingly compact DM halos. If DM is not collisionless, but has strong self-interactions, halos can undergo gravothermal collapse, leading to higher densities in the central region of the halo. However, it is challenging to model this collapse phase from first principles. To improve on this, we sought to better understand the numerical challenges and convergence properties of self-interacting dark matter (SIDM) N-body simulations in the collapse phase. Especially, our aim was to better understand the evolution of satellite halos. To do so, we ran SIDM N-body simulations of a low-mass halo in isolation and within an external gravitational potential. The simulation set-up was motivated by the perturber of the stellar stream GD-1. We find that the halo evolution is very sensitive to energy conservation errors, and a SIDM kernel size that is too large can artificially speed up the collapse. Moreover, we demonstrate that the King model can describe the density profile at small radii for the late stages that we have simulated. Furthermore, for our most highly resolved simulation (N = 5 × 10 7 ) we have made the data public. It can serve as a benchmark. Overall, we find that the current numerical methods do not suffer from convergence problems in the late collapse phase and provide guidance on how to choose numerical parameters, for example that the energy conservation error is better kept well below 1%. This allows simulations to be run of halos that become concentrated enough to explain observations of GD-1-like stellar streams or strong gravitational lensing systems.
Spent pickle liquor (SPL) is a waste stream generated in the steel industry that presents both a disposal challenge and a potential resource for metal recovery. This study investigates the electrochemical extraction of high-purity iron metal from SPL using chloride-based molten salt electrolysis (CMSE). Electrochemical characterization of diluted SPL confirmed Fe 2+ as the dominant species. Thermal dehydration of SPL under inert atmosphere yielded FeCl 2 -rich solids, which were directly employed as feedstock for electrolysis in LiCl–KCl eutectic melts at 500 °C. Cyclic voltammetry of FeCl 2 in this melt revealed well-defined Fe 2+ /Fe 0 redox behavior within the electrochemical stability window of the supporting electrolyte. High coulombic efficiency (>85%) electrodeposition was achieved demonstrating that iron metal can be produced from dehydrated SPL by CMSE. The electrodeposited iron exhibited >98 wt% purity, which was further enhanced to 99.9 wt% via arc melting. The resulting iron powder was ferromagnetic, and its size distribution was found to be suitable for powder metallurgy applications. This work demonstrates a scalable, energy-efficient pathway for valorizing SPL into high-purity iron metal, advancing circular economy strategies in the steel industry.
In FY2020, Savannah River National Laboratory (SRNL) in collaboration with the Discovery Analytics Center (DAC) at Virginia Polytechnic Institute and State University (VT) began developing a demonstration prototype system that uses multiple machine learning and data analytic methods on largescale open data sources to identify new, developing, or undeclared nuclear programs. One of the most challenging aspects of applying machine learning techniques to such a problem is the high likelihood of extremely sparse data from disparate sources. To overcome this challenge, the current work will use a strategic combination of supervised, semi-supervised, and unsupervised learning techniques to ingest and fuse data streams to make a forecast of nuclear activities in a targeted geospatial location. Identifying potential data sources and training supervised learning algorithms is dependent upon the development of a robust foundation of targeted event domains that fundamentally define the nuclear activities of interest. This report documents the definition of a hierarchical structure for both nuclear activity and event domains that will be used to guide the research team in development or use of existing semantic dictionaries that are instrumental to searching, parsing, and categorizing events for the forecasting system’s use.
Non-powered dams (NPDs) represent complex systems, situated at the intersection of natural stream environments and the built environment. The presence and operations of NPDs affect stream constituents (e.g., fish, recreational craft, sediment, water) and serve one or more engineered purposes (e.g., recreation, flood control, water supply, irrigation, tailings and debris control, navigation). By definition, NPDs do not provide hydropower generation. However, every NPD contains some untapped hydropower resource potential, represented by the pre-existing hydraulic head created by the dam and presence of flowing water. Adding power generation to an NPD requires retrofitting the NPD, which could involve the addition of any new component or function beyond that currently installed. Because this research is funded by the US Department of Energy Water Power Technologies Office, adding hydropower to NPDs is among the primary objectives for research investment. However, NPD retrofitting extends beyond the purview of hydropower additions. This report provides background information regarding NPD development in the United States, including an overview of the US NPD population, development potential, and recent development. It also summarizes challenges and opportunities facing NPD retrofit development, highlights the importance of maintaining or improving stream and dam functionality, notes key NPD characteristics, describes NPD retrofit methods, and identifies innovation areas for spurring future NPD retrofit development. Section 4 describes retrofit exemplary design principles and concepts that could apply to a wide range of NPD projects. These principles and concepts inform an NPD Retrofit Exemplary Design Specification (REDS), as included in Appendix A. The NPD REDS is heavily influenced by prior work under the standard modular hydropower technology acceleration research initiative, especially the work documented in the Exemplary Design Envelope Specification for Standard Modular Hydropower Technology (Witt et al., 2017). Whereas this prior work focuses on a particular technology class that applies most intentionally for new stream-reach development (i.e., greenfield sites), the NPD REDS contained herein applies more broadly to the NPD resource class and is technology-agnostic (i.e., the specific technologies intended to be described are not explicitly prescribed). This report serves as a part of early-phase research on NPD retrofit development, aimed toward spurring additional, follow-on efforts to specifically address development challenges, capitalize on development opportunities, and inspire innovation. As documented in the landmark “Hydropower Vision” report (DOE 2016), “transformative technical innovations able to meet the co-objectives of environmental sustainability and low-carbon energy will be critical to enabling additional hydropower growth.” This report serves as an important step in steering transformation of US non-powered dam infrastructure.
High temperature heat pumps (HTHPs) have the potential to serve two valuable functions in the U.S. industry. First, by valorizing and elevating waste heat streams, HTHPs can improve industrial energy efficiency. Second, by electrifying process heat generation, HTHPs can replace combustion technologies with less carbon-intensive sources of energy and reduce industrial greenhouse gas emissions. In certain applications, the combination of increased efficiency, cost savings, and emissions reduction makes HTHPs a promising component of strategies for industrial electrification and clean energy transition. Researchers at NREL have developed a HTHP model. This model has both a physics-based performance estimation component and an economic evaluation component designed to demonstrate the potential economic competitiveness of HTHPs. This document has three main sections. First, an overview of HTHPs is provided as a literature review of relevant information to provide the reader with state-of-the-art information. Second, a description of the HTHP model is provided along with detailed instructions about how to use the model. Third, case studies are provided to showcase the deployment of the HTHP model.