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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Implementation of the Glycolate Destruction Process with Sodium Permanganate in the Defense Waste Processing Facility (DWPF) - 20141

The Savannah River Site (SRS) seeks to replace formic acid with glycolic acid as a chemical reductant in the Defense Waste Processing Facility (DWPF), where borosilicate glass is mixed with high-level radioactive waste, melted at high temperatures, then poured into stainless steel canisters for safe storage. Prior research and development have demonstrated the feasibility and advantages of the new nitric-glycolic acid flowsheet over the current nitric-formic acid flowsheet to chemically adjust the radioactive sludge slurry waste in the Sludge Receipt and Adjustment Tank (SRAT) prior to vitrification. Use of glycolic acid reduces hydrogen and ammonia generation during the reduction process, thus reducing flammability hazards and purge requirements in the DWPF processing vessels. In addition, glycolic acid will support the new Salt Waste Processing Facility (SWPF) by allowing for receipt of higher volumes of waste due to reduction of flammable gases, increased boil-up rates in the vessels, and easing the processing of future sludge batches containing high levels of mercury. However, during DWPF operations, trace amounts of glycolate are anticipated to be entrained in the recycle stream and sent to the Concentration, Storage, and Transfer Facilities (CSTF) via the Recycle Collection Tank (RCT). The RCT receives condensate from the Slurry Mix Evaporator Condensate Tank (SMECT) and Off-gas Condensate Tank (OGCT) that may contain unreacted glycolate. A literature review found the potential for hydrogen generation due to thermolysis of glycolate in caustic CSTF conditions. Savannah River National Laboratory (SRNL) conducted several tests on simulant and radioactive waste from various tanks at SRS and confirmed that glycolate added to waste at concentrations of approximately 1000 mg/L and higher generates hydrogen through thermolysis. There are potential operating scenarios in the future where glycolate concentration could exceed 1000 mg/L in the 2H Evaporator Drop Tank, the highest accumulation point for DWPF organics received via recycle. These findings presented a need to develop another process to mitigate the impact of glycolic acid to the CSTF. Several DWPF compatible options were evaluated for their ability to destroy glycolate in the DWPF recycle stream under various processing conditions, thereby mitigating its introduction to the CSTF. Downselection testing identified sodium permanganate as the most promising and simplest option to oxidize glycolate. It has been demonstrated to be effective in various RCT simulant compositions, operating temperatures, oxidant addition rates, solution pH, and initial glycolate concentrations. A feasibility study identified existing tanks within the facility that could be used for receiving and storing the commercially available sodium permanganate, as well as feeding it to the RCT for processing. The use of existing equipment as well as the ability to purchase the pre-mixed chemical reduces implementation and operational complexity. The downstream impacts of the sodium permanganate reaction were also evaluated and demonstrated to have minimal impact on other chemical species present in the RCT and CSTF, as well as on the materials of construction of the RCT and CSTF vessels and associated piping and instrumentation. Future work is in progress to validate the glycolate destruction process with testing in actual radioactive RCT waste before implementation in DWPF. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

access↗
DOE OSTI2025

Mitigating Cyclable Li‐Ion Inventory Loss in Full Cells with Mn‐Rich Disordered Rocksalt Cathodes

Lithium (Li)- and manganese (Mn)-rich disordered rock salt (DRX) materials are promising cathode materials for next-generation Li-ion batteries. Although these cathode materials are Li-ions rich in their pristine state, their incorporation into full cells results in challenges with maintaining Li-ion inventory during cycling. Herein, the degradation mechanisms of DRX materials in different DRX||Graphite full cells are reported. It is found that DRX electrodes contain Li impurities, primarily due to the environmental sensitivity of mechanochemically synthesized DRX materials during sample transfer and storage. In addition, the structural instability of DRX triggers Mn dissolution. Dissolved Mn ions react with exposed Li x C y compounds and induce electrolyte decomposition on the anode, further depleting Li-ion inventory. Control experiments involving the pre-addition of Mn 2+ provide clear evidence of the impact of Mn dissolution on Li-ion inventory. The electrochemical activation process can stabilize DRX, alleviate Mn dissolution and thus mitigate the loss of Li-ion inventory. These mechanistic insights inform the development of chemical pre-lithiation and electrolyte additive strategies to collectively passivate interfaces, mitigate the effects of trace dissolved Mn ions, and preserve Li-ion inventory. Ultimately, the DRX||Graphite full cell achieves highly reversible electrochemical reactions with a high capacity retention. This study fills a research gap in DRX-based full cells and provides insights into degradation mechanisms and optimization strategies for their practical use.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Real-time and post-hoc compression for data from Distributed Acoustic Sensing

Distributed Acoustic Sensing (DAS) is an emerging sensing technology that records the strain-rate along fiber optic cables at high spatial and temporal resolution. This technique is becoming a popular tool in seismology, hydrology, and other subsurface monitoring applications. However, due to the large coverage (10’s of km) and high density of measurements (1m spacing at 100’s of Hz), a DAS installation could produce terabytes of data records per day. Because many DAS instruments are deployed in remote locations, this large data size poses significant challenges to its transfer and storage. In this paper, we explore lossless compression methods to reduce the storage requirement in both real-time and post-hoc scenarios. Here we propose a two-stage compression method to improve the compression ratio and compression speed. This two-stage compression method could reduce the storage requirement by 40%, which is 20% more than other lossless methods, such as ZSTD. We demonstrate that the compression method could complete its operation well before the DAS instrument needs to output the next file, making it suitable for real-time DAS acquisition. We also implement a parallel compression method for a post-hoc scenario and demonstrate that our method could effectively utilize a parallel computer. With 256 CPU cores, our parallel compression method achieves the speed of 26GB/second.

58 GEOSCIENCES↗
DOE OSTI2023

Role of pH and Eh in geothermal systems: Thermodynamic examples and impacts on scaling and corrosion

The global demand for sustainable and clean renewable energy sources is rapidly increasing to meet growing energy demands while curtailing the CO2 emissions from fossil fuels. Harnessing the natural heat or geothermal energy of the Earth's interior can simultaneously address both issues by offering a clean and renewable energy source. Enhanced geothermal systems require the movement and cycling of fluid through a porous rock media. Further, this process requires understanding the chemistry occurring at the fluid-rock and fluid-metal interfaces both in the deep subsurface and surface, where fluid is circulated for energy transfer or storage. In this review paper, we evaluate the current state of research on how two key variables, pH and Eh, control the fluid-rock reactions controlling mineral scaling and the steel corrosion reactions associated with geothermal systems. These variables are frequently used in geochemical modeling to predict chemical reactions in geothermal systems. Prediction of the fluid-rock behavior can be utilized to formulate new strategies for extending the lifespan and efficiency of heat extraction and utilization and maximizing the safety of geothermal systems.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Recent mechanistic developments for cytochrome c nitrite reductase, the key enzyme in the dissimilatory nitrate reduction to ammonium pathway

Cytochrome c nitrite reductase, NrfA, is a soluble, periplasmic pentaheme cytochrome responsible for the reduction of nitrite to ammonium in the Dissimilatory Nitrate Reduction to Ammonium (DNRA) pathway, a vital reaction in the global nitrogen cycle. NrfA catalyzes this six-electron and eight-proton reduction of nitrite at a single active site with the help of its quinol oxidase partners. In this study, we summarize the latest progress in elucidating the reaction mechanism of ammonia production, including new findings about the active site architecture of NrfA, as well as recent results that elucidate electron transfer and storage in the pentaheme scaffold of this enzyme.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Solvent-driven aqueous separations for hypersaline brine concentration and resource recovery

Solvent-driven separation processes can extract water and high-value minerals from high salinity or contaminated brines, simultaneously reducing the environmental impact of brine disposal and enabling resource recovery. The efficient dewatering of hypersaline brines is essential for the sustainable minimal and zero liquid discharge processing of industrial wastewaters. Fractional crystallization can selectively extract ions from contaminated waste streams, allowing critical materials to be recycled, including transition and lanthanide metals required for renewable energy generation and storage. Mass transfer in solvent-driven water extraction occurs across a liquid–liquid interface, eliminating the scaling and fouling of membrane and heat exchanger surfaces and limiting the need for extensive pretreatment. Solvent-driven fractional crystallization can leverage sequential treatment and control of process conditions to rapidly recover salts without requiring evaporation of water. Despite promising applications, the principles and potential of solvent-driven aqueous separations remain poorly understood. This critical review explores the opportunities presented by solvent-based aqueous separations from the molecular to process scale, evaluating the chemistry of solvation and system design in the broader context of desalination, resource recovery, water softening, and mineral production.

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

Enhancing the Chemical Energy Flux in a High-Temperature Tubular Counterflow Solid Fuel Synthesis Reactor Using a Bypass

Redox reactions of metal oxides offer a path towards using intermittent renewable resources for high-density thermochemical energy storage. Thermochemical energy storage often involves the flow of a particulate media. We describe a novel method to increase the throughput in a gravity-driven high-temperature thermochemical storage reactor flowing pelletized MgMnO. The moving bed reactor operates under counter-flow conditions and encounters particle flowability problems at temperatures of 1500 °C leading to sintering of the bed. Inertial forces of a counter-flowing gas can overcome the gravitational forces on the particles and limit the chemical energy storage rate of the reactor. We found that the insertion of a gas bypass (a slotted tube) into the reactor results in a 100% increase of the flow rates and achieved a 50% higher chemical energy storage flux compared to the operation without a bypass tube while mitigating the effects of sintering on the particles. As a result, the higher solid flow rates require a longer heated zone to reach a comparable residence time and extent of reduction compared to the lower flow rates.

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

Atomic-Scale Imaging of Lithium Vacancies in a Battery Cathode by Multislice Electron Ptychography

Atomic-resolution imaging of battery materials is critical for identification of local defects and structural variations, which are tied to battery performance. However, since battery materials are, by design, optimized to allow ion motion in response to an applied electric field, they are also very sensitive to radiation damage by an electron beam. Image resolution is therefore severely constrained by the dose applied. Here, we show that multislice electron ptychography (MEP) can provide sub-ångström lateral resolution images of both light and heavy elements of a Li-ion battery cathode, along with nanometer-scale depth information and greater dose efficiency than conventional electron microscopy methods. Using the depth-sectioning capability of MEP, we have been able to obtain direct visualizations of Li vacancy clusters, atom column by atom column, in Li x Ni 0.33 Mn 0.33 Co 0.33 O 2 (NMC111) cathodes. This capability to track Li distributions will be valuable in understanding, informing, and optimizing electrode material design for ion storage and transfer.

batteries↗
DOE OSTI2022

Mechanistic Investigation of Enhanced Catalytic Selectivity toward Alcohol Oxidation with Ce Oxysulfate Clusters

Ceria-based materials have been highly desired in photocatalytic reactions due to their easily redox property and strong oxygen storage and transfer ability. Herein, we report the structures of one CeCe 70 oxysulfate cluster and four MCe 70 clusters (M = Cu, Ni, Co, Fe) with the same Ce 70 core. As noted, single crystal X-ray diffraction confirmed the structures of CeCe 70 and the MCe 70 series, while Raman spectroscopy indicated an increase in oxygen defects upon the introduction of Cu and Fe ions. The clusters catalyzed the oxidation of 4-methoxybenzyl alcohol under ultraviolet light. CuCe 70 and FeCe 70 exhibited enhanced reactivity compared to CeCe 70 and improved aldehyde selectivity compared to control experiments. In comparison with their homogeneous congeners, the CeCe 70 /MCe 70 clusters altered the location of radical generation from the bulk solution to the clusters’ surfaces. Mechanistic studies highlight the role of oxygen defects and specific transition metal introduction for efficient photocatalysis. Furthermore, the mechanistic pathway in this study provides insight on how to select or design a highly selective catalyst for photocatalysis.

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

The Terrestrial Biosphere Model Farm

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901–2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Iron as a driver of organic carbon fate in permafrost regions

Iron (Fe) and its biogeochemical interactions with organic carbon (OC) exert critical controls over carbon storage and water quality across Arctic and sub-Arctic landscapes. Permafrost thaw affects Fe–organic carbon interactions by exposing thawed material and driving shifts in hydrologic regimes. Here we propose a conceptual framework describing how Fe–OC interactions respond to permafrost thaw and the implications for global carbon cycling. The framework builds on current understanding of hydrologically driven processes that are altered by permafrost thaw, including microbial Fe reduction that influences OC decomposition and carbon emissions; Fe cycling through redox transformations and sulfide oxidation; dissolution and reprecipitation of Fe–OC assemblages; and Fe export to river networks. We put forward four testable hypotheses—on topics from Fe supply to its role in soil OC storage and transfer to rivers—to advance understanding of the coupled environmental controls on pore-scale reactions (that is, microscale reactions at mineral–water–microorganism interfaces) that are central to global Fe–OC cycling. Our framework highlights how soil-profile- and landscape-scale processes, such as hydrologic connectivity and redox dynamics, regulate pore-scale Fe–OC reactions.

Herndon, Elizabeth [ORNL] (ORCID:0000000291945493)↗
DOE OSTI2024

Adoption of a token-based authentication model for the CMS Submission Infrastructure

The CMS Submission Infrastructure (SI) is the main computing resource provisioning system for CMS workloads. A number of HTCondor pools are employed to manage this infrastructure, which aggregates geographically distributed resources from the WLCG and other providers. Historically, the model of authentication among the diverse components of this infrastructure has relied on the Grid Security Infrastructure (GSI), based on identities and X509 certificates. In contrast, commonly used modern authentication standards are based on capabilities and tokens. The WLCG has identified this trend and aims at a transparent replacement of GSI for all its workload management, data transfer and storage access operations, to be completed during the current LHC Run 3. As part of this effort, and within the context of CMS computing, the Submission Infrastructure group is in the process of phasing out the GSI part of its authentication layers, in favor of IDTokens and Scitokens. The use of tokens is already well integrated into the HTCondor Software Suite, which has allowed us to fully migrate the authentication between internal components of SI. Additionally, recent versions of the HTCondor-CE support tokens as well, enabling CMS resource requests to Grid sites employing this CE technology to be granted by means of token exchange. After a rollout campaign to sites, successfully completed by the third quarter of 2022, the totality of HTCondor CEs in use by CMS are already receiving Scitoken-based pilot jobs. On the ARC CE side, a parallel campaign was launched to foster the adoption of the REST interface at CMS sites (required to enable token-based job submission via HTCondor-G), which is nearing completion as well. In this contribution, the newly adopted authentication model will be described. We will then report on the migration status and final steps towards complete GSI phase out in the CMS SI.

Pérez-Calero Yzquierdo, Antonio↗
DOE OSTI2021

Enhancing ion transport in charged block copolymers by stabilizing low symmetry morphology: Electrostatic control of interfaces

Recently, the interest in charged polymers has been rapidly growing due to their uses in energy storage and transfer devices. Yet, polymer electrolyte-based devices are not on the immediate horizon because of the low ionic conductivity. In the present study, we developed a methodology to enhance the ionic conductivity of charged block copolymers comprising ionic liquids through the electrostatic control of the interfacial layers. Unprecedented reentrant phase transitions between lamellar and A15 structures were seen, which cannot be explained by well-established thermodynamic factors. X-ray scattering experiments and molecular dynamics simulations revealed the formation of fascinating, thin ionic shell layers composed of ionic complexes. The ionic liquid cations of these complexes predominantly presented near the micellar interfaces if they had strong binding affinity with the charged polymer chains. Therefore, the interfacial properties and concentration fluctuations of the A15 structures were crucially dependent on the type of tethered acid groups in the polymers. Overall, the stabilization energies of the A15 structures were greater when enriched, attractive electrostatic interactions were present at the micellar interfaces. Contrary to the conventional wisdom that block copolymer interfaces act as “dead zone” to significantly deteriorate ion transport, this study establishes a prospective avenue for advanced polymer electrolyte having tailor-made interfaces.

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

Accelerating data acquisition with FPGA-based edge machine learning: a case study with LCLS-II

New scientific experiments and instruments generate vast amounts of data that need to be transferred for storage or further processing, often overwhelming traditional systems. Edge machine learning (EdgeML) addresses this challenge by integrating machine learning (ML) algorithms with edge computing, enabling real-time data processing directly at the point of data generation. EdgeML is particularly beneficial for environments where immediate decisions are required, or where bandwidth and storage are limited. In this paper, we demonstrate a high-speed configurable ML model in a fully customizable EdgeML system using a field programmable gate array (FPGA). Our demonstration focuses on an angular array of electron spectrometers, referred to as the ‘CookieBox,’ developed for the Linac Coherent Light Source II project. The EdgeML system captures 51.2 Gbps from a 6.4 GS s −1 analog to digital converter and is designed to integrate data pre-processing and ML inside an FPGA. Our implementation achieves an inference latency of 0.2 µs for the ML model, and a total latency of 0.4 µs for the complete EdgeML system, which includes pre-processing, data transmission, digitization, and ML inference. The modular design of the system allows it to be adapted for other instrumentation applications requiring low-latency data processing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Ocelot: An Interactive, Efficient Distributed Compression-As-a-Service Platform With Optimized Data Compression Techniques

Large volumes of data generated by scientific simulations, genome sequencing, and other applications need to be moved among clusters for data collection/analysis. Data compression techniques have effectively reduced data storage and transfer costs. However, users' requirements on interactively controlling both data quality and compression ratios are non-trivial to fulfill. Here, we propose a novel Compression-as-a-Service (CaaS) platform called Ocelot with four important contributions: (1) It offers real-time visualization, interactive compression, and transfer of scientific datasets. (2) It incorporates new strategies for compressing diverse types of datasets more effectively than traditional methods. (3) It provides an effective method for estimating the compression ratio and execution time of compression tasks. (4) Experiments on multiple real-world datasets on geographically distributed computers show that Ocelot can significantly improve data transfer efficiency with a performance gain of more than 10x in computing clusters with relatively slow networks.

compression as a service (CaaS)↗
DOE OSTI2022

Material Selection and Corrosion Studies of Candidate Bearing Materials for Use in Molten Chloride Salt

Molten chloride salts are being given strong consideration for use in heat transfer and storage in concentrating solar power (CSP) systems as well as in some nuclear reactor applications. Containment of the molten salt, particularly at the highest temperatures, is a major material concern and has received considerable study. Another material issue is the pumps that will be required to move the molten salt along with the bearing materials that will be required to have sufficient corrosion resistance as well as wear resistance in the high temperature salt. A pair of coordinated studies in our laboratory has addressed the corrosion, and the wear issues of candidate bearing materials including the selection of candidate materials as well as their performance in a molten sodium chloride-potassium chloride-magnesium chloride salt environment. This work addresses the selection of candidate materials and their chemical compatibility with the molten salt. The studies have identified material pairs that have suitable properties for use as bearings that would be immersed in molten chloride salt.

14 SOLAR ENERGY↗
DOE OSTI2020

Policy Considerations When Federating Facilities for Experimental and Observational Data Analysis

Today’s computational, experimental, and observational facilities afford us tremendous opportunities to couple theory and experiment at increasingly large scales. Empirical sensing capabilities are growing dramatically with beam line and detector improvements, and with advances in our ability to deploy large-scale data gathering observations of the natural world. The coupling of computational simulations and analysis to process the data from experimental and observational facilities is giving rise to cross-facility workflows. Such federations of facilities are in fact becoming an explicit requirement for large-scale scientific discovery. As we scale up these pipelines of scientific discovery, each participating facility needs to establish and align policies so that the federation can work seamlessly in an end-to-end manner. This chapter outlines specific policy considerations in enabling the federation of facilities for data analysis. Design choices and vital policy decisions cover the areas of data acquisition and storage, data transfer, computational resource allocation and co-scheduling, seamless federated user access, and cross-cutting governance. By highlighting the explicit and implicit interdependencies between facilities, we aim to provide facility designers and policymakers the information on policy issues to address early in a facility’s operations, thus enabling successful cross-facility federation and improved experimental and observational data analysis outcomes.

Shankar, Mallikarjun (Arjun)↗