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At least 109 records · Page 6

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DOE OSTI2020

High-Temperature Heat Transfer Fluid Circulator for Concentrating Solar Power Systems

A high temperature Heat Transfer Fluid circulator compressor with wide turn down ratio aerodynamic performance was designed, fabricated and tested for application with high pressure super Critical CO2 fluid capable of temperatures and pressures to >750°C and 25 MPa respectively. The modular system decoupled the drive motor system from an independent compressor shaft to provide thermal isolation between the high temperature compressor shaft and the lower temperature capable motor elements. While the thermal management approach appears to be viable there are potential losses in the secondary cooling flows that need to be taken into account and fine-tuned during operation in order to achieve desired overall system efficiencies. The system was also shown to be viable and dynamically stable throughout the entire operating speed range, thereby validating the overall system configuration and structural design approach used. Finally, while it was not possible to achieve specific PTC-10 test points, the compressor performance tests provide sufficient data to indicate that the compressor has the ability to achieve the required performance in practice. The two key factors affecting compressor design and cost are the maximum expected temperature and pressure. The maximum allowable temperatures for current state-of-the-art (SOA) motor driven compressor technology is limited to of approximately 150-200°C, but inlet temperatures >750 °C may be encountered. While the optimal efficiency of compression is in the transcritical region, some concepts require inlet pressures to 225 bar.

14 SOLAR ENERGY↗

DOE OSTI2021

High-Temperature Heat Transfer Fluid Circulator for Concentrating Solar Power Systems How Mohawk and Velo3Dare Printing the Future of Energy

14 SOLAR ENERGY↗

DOE OSTI2026

Robust dynamic operation of high temperature electrolysis solid oxide cells

Here, this study evaluates the durability of Ni/YSZ-supported SOECs under dynamic operating conditions relevant to real-world applications. Systematic tests were conducted to assess cell performance under steam cycling (3 to 75% humidified H 2 ), mode cycling between SOFC and SOEC operation, thermal cycling (150 to 750 °C at OCV, and 600 to 800 °C at 1.3V), and redox cycling (between 50% humidified H 2 and 50% humidified N 2 ). Steam cycling, mode cycling, and thermal cycling at OCV do not significantly accelerate performance degradation. Thermal cycling at 1.3V caused minimal damage within 600 to 800 °C. Full redox cycling (multi-hour oxidation holds) induced cell structural failure, while partial redox cycling (0.5 h holds) was tolerated. Extensive characterization revealed some material evolution, namely Sr and Co secondary phase formation, within the oxygen electrode due to La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3-δ instability, especially for steam cycling and mode cycling. Minimal changes were identified within the Ni-YSZ fuel electrodes. These findings provide critical insights into SOEC reliability under dynamic conditions, supporting their application in dynamic or intermittent energy systems.

08 HYDROGEN↗

DOE OSTI2024

ELECTRONIC STRUCTURE METHODS AND PROTOCOLS WITH APPLICATION TO DYNAMICS, KINETICS AND THERMOCHEMISTRY

Hydrocarbon combustion involves the reaction dynamics of a tremendous number of species beginning with many-component fuel mixtures and proceeding via a complex system of intermediates to form primary and secondary products. Combustion conditions corresponding to new advanced engines and/or alternative fuels rely increasingly on autoignition and low-temperature-combustion chemistry. In these regimes various transient radical species such as HO2, ROO·, ·QOOH, HCO, NO2, HOCO, and Criegee intermediates play important roles in determining the detailed as well as more general dynamics. A clear understanding and accurate representation of these processes is needed for effective modeling. Given the difficulties associated with making reliable experimental measurements of these systems, computation can play an important role in developing these energy technologies. Accurate calculations have their own challenges since even within the simplest dynamical approximations such as transition state theory, the rates depend exponentially on critical barrier heights and these may be sensitive to the level of quantum chemistry. Moreover, it is well-known that in many cases it is necessary to go beyond statistical theories and consider the dynamics. Quantum tunneling, resonances, radiative transitions, and non-adiabatic effects governed by spin-orbit or derivative coupling can be determining factors in those dynamics. Building upon progress made during a period of prior support through the DOE Early Career Program, this project combines developments in the areas of potential energy surface (PES) fitting and multistate multireference quantum chemistry to allow spectroscopically and dynamically/kinetically accurate investigations of key molecular systems (such as those mentioned above), many of which are radicals with strong multireference character and have the possibility of multiple electronic states contributing to the observed dynamics. An ongoing area of investigation is to develop general strategies for robustly convergent electronic structure theory for global multichannel reactive surfaces including diabatization of energy and other relevant surfaces such as dipole transition. Combining advances in ab initio methods with automated interpolative PES fitting allows the construction of high-quality PESs (incorporating thousands of high-level data) to be done rapidly through parallel processing on high-performance computing (HPC) clusters. In addition, new methods and approaches to electronic structure theory will be developed and tested through applications. This project will explore limitations in traditional multireference calculations (e.g., MRCI) such as those imposed by internal contraction, lack of high-order correlation treatment and poor scaling. Methods such as DMRG-based extended active-space CASSCF and various Quantum Monte Carlo (QMC) methods will be applied (including VMC/DMC and FCIQMC). Insight into the relative significance of different orbital spaces and the robustness of application of these approaches on leadership class computing architectures will be gained. Synergy with other components of this research program such as automated PES fitting and multireference quantum chemistry will be used to address challenges encountered by the standard approaches to computational thermochemistry (those being single-reference quantum chemistry and perturbative treatments of the anharmonic vibrational energy, which break down for some cases of electronic structure or floppy strongly coupled vibrational modes).

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2024

Data Visualization and Analytics for Optimal Process Parameter Selection in Turning

The objective of this project is to research physics-guided machine learning methods to recommend optimal tools and machining process parameters for turning applications using the MSC test database. For a given turning application, the MSC metalworking specialist needs to make decisions on tools and the associated process parameters for the MSC customer. For a given material, there are many alternatives for tools and a wide range of process parameters to consider. MSC has built a database of tools and parameters for different applications from the historical turning tests completed at various customer sites. The research project aims to use machine learning methods to predict optimal tools and process parameters for the MSC metalworking specialists using the MSC test database. This enables continuous learning of optimal tool and process parameters for different applications as new information is collected from testing. Through MSC, this information can be shared with machining shops across the US leading to improved productivity and efficiency.

42 ENGINEERING↗

DOE OSTI2023

Assessing mechanisms for microbial taxa and community dynamics using process models

Disentangling the assembly mechanisms controlling community composition, structure, distribution, functions, and dynamics is a central issue in ecology. Although various approaches have been proposed to examine community assembly mechanisms, quantitative characterization is challenging, particularly in microbial ecology. Here, we present a novel approach for quantitatively delineating community assembly mechanisms by combining the consumer–resource model with a neutral model in stochastic differential equations. Using time-series data from anaerobic bioreactors that target microbial 16S rRNA genes, we tested the applicability of three ecological models: the consumer–resource model, the neutral model, and the combined model. Our results revealed that model performances varied substantially as a function of population abundance and/or process conditions. The combined model performed best for abundant taxa in the treatment bioreactors where process conditions were manipulated. In contrast, the neutral model showed the best performance for rare taxa. Our analysis further indicated that immigration rates decreased with taxa abundance and competitions between taxa were strongly correlated with phylogeny, but within a certain phylogenetic distance only. The determinism underlying taxa and community dynamics were quantitatively assessed, showing greater determinism in the treatment bioreactors that aligned with the subsequent abnormal system functioning. Given its mechanistic basis, the framework developed here is expected to be potentially applicable beyond microbial ecology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Acylation as a successful derivatization strategy for the analysis of pinacolyl alcohol in a glycerol-rich matrix by GC-MS: application during an OPCW Proficiency Test

A derivatization protocol based on the acylation of pinacolyl alcohol (PA), an important marker for the nerve agent soman, is presented. The procedure provides a convenient means of detecting, by gas chromatography-mass spectrometry (GC-MS), PA when present at a low concentration in a complex glycerol/alcohol-rich matrix. While there are only two reports describing the specific analysis of PA in matrices at low concentrations, the protocol described herein represents the first of its kind in the analysis of PA in a highly reactive matrix. Two alternative paths for the protocol’s execution are presented. The first involves the direct derivatization of the PA with either acetyl or benzoyl chloride; both reactions yield ester products with significantly different retention times than those of the interferences of the reactive glycerol-rich matrix and in areas of the GC-chromatogram featuring lower levels of matrix interferences. A second procedure involved an initial diethyl ether/aqueous extraction of the matrix; while the extraction was found to substantially remove many of the hydrophilic matrix components and improve the overall derivatization, it also led to some loss of PA available for the derivatization. Both protocols were applied to the successful derivatization and analysis of PA by GC-MS when present at a 5 μg.mL -1 concentration in a glycerol-rich matrix sample administered during the 48th Proficiency Test administered by the Organisation for the Prohibition of Chemical Weapons (OPCW).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Impurity Levels in Cerium Oxide Microspheres Prepared by Internal Gelation Sol-Gel Methods

Impurity levels were measured in cerium oxide microspheres fabricated by modified internal gelation sol-gel methods. A combination of glow discharge mass spectrometry, electron impact mass spectrometry, combustion gas analysis and instrumental gas analysis were used to assess a wide range of potential elemental impurities. Low concentrations of carbon, nitrogen, and hydrogen impurities in microspheres showed a dependence on how microspheres were washed. Silicon impurities are believed to derive from silicone oil used during sol-gel processing. Spheres washed by the preferred approach had impurity levels below 100 ppm for all elements tested. For applications such as nuclear fuels, sol-gel methods could meet purity specifications as long as metal nitrate feed solutions of sufficient purity are used and microspheres are washed appropriately. In this study, cerium was used as a surrogate for plutonium-238, which is used in radioisotope power systems as a heat source, to determine whether carbon or other impurities were concentrated during internal gelation processing and remained after heat treatments. Analyses indicated low concentrations of impurities in cerium oxide microspheres after sintering steps that were well below documented limits for plutonium-238 oxide fuels. Modified washing methods, combined with a pressurized water treatment, resulted in sintered cerium oxide sol-gel microspheres with low impurity levels.

cerium oxide, sol-gel, internal gelation, microsph↗

DOE OSTI2022

The Oxidation of the HiSiMo Cast Irons Alloyed with Cr/Al at 800 °C

To improve the oxidation resistance of cast iron containing Si and Mo (HiSiMo), 1wt.% Cr and 3wt.% Al were added into the base alloy to improve high temperature oxidation resistance for exhaust manifold applications. Oxidation tests were performed in both dry and wet air (air + 10% H 2 O) at 800 °C for 1, 10, 24, and 50 h. The mass change and oxide thickness were measured to evaluate the oxidation resistance. Scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM/EDS) and transmission electron microscopy (TEM) were employed to characterize the oxidized samples. Here, the results showed that the additions of Al or Cr selectively oxidized to form a more oxidation resistant surface oxide layer, which reduced the mass gain during these experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Single-atom materials boosting wearable orthogonal uric acid detection

Abstract Uric acid (UA) is a vital biomarker for the diagnosis and management of various health conditions, including cardiovascular diseases, gout, kidney disorders, metabolic syndrome, and wound healing. Despite significant advances in wearable sensor technology, challenges persist in developing wearable sensors that are capable of maintaining high sensitivity, selectivity, and stability. In this study, we present an epidermal sensing platform enhanced with single-atom materials (SAMs) designed for flexible and orthogonal electrochemical detection of UA. We designed and synthesized an SAM with Fe-N 5 active sites to boost the electrochemical sensing signals, integrating it with laser-engraved graphene (LEG) to fabricate a wearable SAM-based UA patch sensor. This design provides superior UA detection performance compared to sensors based on conventional nanomaterials. In addition, we enhanced the detection accuracy and range by using an orthogonal approach that combines direct oxidation through differential pulse voltammetry (DPV) along with parallel biocatalytic amperometric detection. The resulting SAM-based UA orthogonal sensor patch demonstrated exceptional performance in wearable applications through tests measuring sweat UA levels in subjects before and after consuming a purine-rich diet. Graphical Abstract

Ding, Shichao↗

DOE OSTI2022

Time-explicit Darwin PIC algorithm

A new approach to Darwin particle-in-cell plasma simulation is described. Using a finite-element approach, the vector potential is assured to be exactly solenoidal. This allows writing the system of multi-species particles as an action-at-a-distance Hamiltonian system. Applying recently-developed explicit symplectic methods to this gives a time-explicit algorithm with all desired properties. Elliptic systems for the electrostatic and magneto-static portions of the problem are inverted efficiently by an algebraic multi-grid algorithm. The algorithm is implemented in a two-dimensional Cartesian-geometry code and tested by application to Weibel instability and to Alfvén wave dynamics. Results show the effectiveness of this approach. Extensions to arbitrary meshes and to a partially time-implicit scheme are briefly discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Efficient high-fidelity TRISO statistical failure analysis using Bison: Applications to AGR-2 irradiation testing

The ability of tri-structural isotropic (TRISO) fuel to contain fission products is largely dictated by the quality of the manufacturing process, since most of the fission product release is expected to occur due to coating layer failure in a small number of particles containing defects. The Bison fuel performance code has capabilities to predict failure in individual particles, accounting for the presence of defects, and to apply statistical analysis methods to compute the probability of failure in a set of fuel particles. Bison has recently undergone significant development both to improve its physical representations of fuel particle behavior and to improve the efficiency of its statistical failure calculations. Physical model improvements include new capabilities to account for the pressure generated by fission gases on inner pyrolytic carbon (IPyC) crack surfaces and to use local material coordinate orientation to accurately incorporate the anisotropy in the material properties in aspherical particles. To improve statistical modeling efficiency, a direct integration approach which involves directly integrating the failure probability function associated with statistically varying parameters has been developed. The direct integration approach is much more efficient than the Monte Carlo (MC) schemes commonly employed, and allows Bison to directly run high-dimensional fuel performance models, which improves the accuracy of failure probability calculations. Finally, a set of benchmark problems is considered here to compare the MC and direct integration approaches, and a statistical failure analysis of compacts in the Advanced Gas Reactor (AGR)-2 experiments is performed using the direct integration approach.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Ecologically inspired metrics for transitioning to a sustainable and resilient circular economy with application to multilayer plastic films

Current Circular Economy (CE) frameworks applied to product chains exhibit notable shortcomings. These include neglecting the resilience and robustness of design, requiring detailed economic and environmental data for impact assessment, and relying on qualitative rather than quantitative metrics capturing certain CE design aspects. In the current contribution, we addressed these shortcomings by developing an Ecologically inspired (Eco-inspired) Framework using the mathematical foundations of Ecological Network Analysis (ENA). While ENA metrics have previously found application in designing circular economies, particularly in Industrial Symbiosis (IS) networks, our adaptation tailors these metrics for use in product-level CE, recognizing the inherent distinctions between product-level CE and IS. Our Eco-inspired Framework comprises three key categories to provide holistic and granular-level metrics for designing product-level CE. The first set of metrics assesses circularity and resource efficiency. The second set gauges network intensity and robustness as complementary indicators ensuring a CE is both sustainable and resilient. The third group of metrics evaluates enhancement potential of CE strategies through introducing quantitative metrics for measuring the degree of closed-loop strategies and average circularity level of a CE design. The three comprehensive set of indicators within the Eco-inspired Framework uniquely captures various facets of circular design, whether originating from technological innovations and recovery improvement at the end of life (EoL), shifts in human consumption patterns, alterations in product design, or changes in business models. The framework’s application is tested in designing a CE for multilayer Polyethylene-Polyamide (PE-PA) films. Using the Eco-inspired Framework, we identified the best strategy for designing a resilient and sustainable CE for PE-PA films. A diverse set of EoL strategies along with a reduction in product consumption can improve circularity and resilience by 650% and 255%, respectively, and mitigate greenhouse gas emissions by 90%. The framework minimizes trade-offs between sustainability and circularity goals and offers insights on how to enhance each strategy for achieving a resilient and sustainable CE for products.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Deriving iceberg ablation rates using an on-iceberg autonomous phase-sensitive radar (ApRES)

Abstract The increase in iceberg discharge into the polar oceans highlights the importance of understanding how quickly icebergs are deteriorating and where the resulting freshwater injection is occurring. Recent advances in quantifying iceberg deterioration through combinations of modeling, remote sensing and direct in situ measurements have successfully calculated overall ablation rates, and surface and sidewall ablation; however, in situ measurements of basal melt rates have been difficult to obtain. Radar has successfully measured iceberg thickness, but repeat measurements, which would capture a change in iceberg thickness with time, have not yet been collected. Here we test the applicability of using an on-iceberg autonomous phase-sensitive radar (ApRES) to quantify basal ablation rates of a large (~800 m long) non-tabular Arctic iceberg during an intensive 2019 summer field campaign in Sermilik Fjord, southeast Greenland. We find that ApRES can be used to measure basal ablation even over a short deployment period (10 d), and also provide a lower bound on sidewall melt. This study fills a critical gap in iceberg research and pushes the limits of field instrumentation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Physics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface

Abstract Inverse analysis has been utilized to understand unknown underground geological properties by matching the observational data with simulators. To overcome the underconstrained nature of inverse problems and achieve good performance, an approach is presented with embedded physics and a technique known as algorithmic differentiation. We use a physics-embedded generative model, which takes statistically simple parameters as input and outputs subsurface properties (e.g., permeability or P-wave velocity), that embeds physical knowledge of the subsurface properties into inverse analysis and improves its performance. We tested the application of this approach on four geologic problems: two heterogeneous hydraulic conductivity fields, a hydraulic fracture network, and a seismic inversion for P-wave velocity. This physics-embedded inverse analysis approach consistently characterizes these geological problems accurately. Furthermore, the excellent performance in matching the observational data demonstrates the reliability of the proposed method. Moreover, the application of algorithmic differentiation makes this an easy and fast approach to inverse analysis when dealing with complicated geological structures.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Rapid Quenching of Molten Salts as an Approach for the Coordination Characterization of Corrosion Products

A new apparatus was built to rapidly cool molten salts in liquid argon to prevent contamination during quenching and enable new insight into the structure in the liquid state. To test the applicability of the apparatus, several industrially relevant chloride salt compositions were first melted, rapidly solidified, and then characterized. The design proved applicable for the rapid quenching of molten salt. Furthermore, the structure of the apparatus prevented exposure of the rapidly quenched salt to impurities (humidity, oxygen, etc.). X-ray diffraction of salt specimens cooled with and without liquid argon showed differences including a structure further from the expected stoichiometric equilibrium with rapid cooling. Of particular interest is the chemical state of metallic impurities, and this may be probed using electron paramagnetic resonance.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Emission factors of industrial boilers burning biomass-derived fuels

Boilers are combustion devices that provide process heat and are integral to many industrial facilities. Historically, outside of the pulp and paper industry, most boilers burned fossil fuels, although interest in decarbonization has been leading to an increased use of renewable fuels in boilers. These boilers, including those in the biorefineries, are often large sources of air pollutant emissions, and the characterization of these emissions is critical to obtaining air permits and ensuring protection of the surrounding air quality. Several industrial boilers and new biorefineries allow utilization of biomass-derived fuels (e.g. wastewater sludge, lignin, etc.) produced during their operation as a fuel for the boiler to meet process energy needs. However, there is limited empirical data on emission factors for the burning of unconventional fuels, such as solid-gas mixtures containing biomass residues. To fill this gap, we carry out a comprehensive data survey, collecting information on emission factors for boilers burning either a single or a mixture of solid and gaseous biomass-derived fuels. We review multiple hard-to-obtain and unconventional data sources, such as air permit applications, stack test data, and industry-sponsored data collection efforts, to compile emission factors for biomass-derived fuels. We then compare this data with wood residue emission factors from the U.S. Environmental Protection Agency’s AP-42 emission factor database. Our results indicate that the emission factors for boilers burning unconventional fuels vary widely depending on the fuel composition, boiler type, and fuel characteristics. Overall, we find that median emission factors of selected biomass-derived fuels are typically lower than those of wood residue boilers in AP-42. The information collected herein could be useful to permitting agencies and industries utilizing boilers who may want to reduce the carbon impact of their facilities by combusting biomass-derived wastes for process energy needs, for more accurate emission estimation for permitting.

09 BIOMASS FUELS↗

DOE OSTI2025

Criticality analysis of nuclear binding energy neural networks

Machine learning methods, in particular deep learning methods such as artificial neural networks (ANNs) with many layers, have become widespread and useful tools in nuclear physics. However, these ANNs are typically treated as ‘black boxes’, with their architecture (width, depth, and weight/bias initialization) and the training algorithm and parameters chosen empirically by optimizing learning based on limited exploration. We test a non-empirical approach to understanding and optimizing nuclear physics ANNs by adapting a criticality analysis based on renormalization group flows in terms of the hyperparameters for weight/bias initialization, training rates, and the ratio of depth to width. This treatment utilizes the statistical properties of neural network initialization to find a generating functional for network outputs at any layer, allowing for a path integral formulation of the ANN outputs as a Euclidean statistical field theory. We use a prototypical example to test the applicability of this approach: a simple ANN for nuclear binding energies. We find that with training using a stochastic gradient descent optimizer, the predicted criticality behavior is realized, and optimal performance is found with critical tuning. However, the use of an adaptive learning algorithm leads to somewhat superior results without concern for tuning and thus obscures the analysis. Nevertheless, the criticality analysis offers a way to look within the black box of ANNs, which is a first step towards potential improvements in network performance beyond using adaptive optimizers.

artificial neural network↗