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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Performance of sweeping gas membrane distillation for treating produced water: Modeling and experiments

Membrane distillation (MD) has shown promise for desalination of produced water (PW), but there are very few studies reported on the performance of MD with real PW and the characteristics of the fouling and scaling in MD. Among different MD configurations, sweeping gas MD (SGMD) is the least studied configuration. Here we report the application of SGMD for treating high-salinity PW (123 g L -1 ). The SGMD operating conditions were first carefully optimized through pure water MD experiments by varying operating conditions. The flux evaluation was accompanied by a detailed Aspen Custom Modeler simulation based on the electrolyte-NRTL thermodynamic model to better understand the heat and mass transfer in the feed and permeate channel. Supported by experiments, modeling results confirmed the partial condensation of the vapor in the permeate channel. Furthermore, it was concluded that the saturation of the sweeping gas is controlling the overall mass transfer through the membrane. Selected membranes and optimized conditions were then used to conduct short-term and long-term performance tests with PW. In long-term MD test, the concentration of the PW increased by a factor of two (50% water recovery), while the membrane exhibited small flux decline. Elemental analysis showed that strontium and sodium were the major scalant compounds.

42 ENGINEERING↗
DOE OSTI2020

A Topology Optimization Framework to Facilitate Adoption of Advanced District Thermal Energy Systems

Advanced district thermal energy systems, which circulate water at temperatures near ambient conditions, and facilitate the utilization of waste heat and renewable thermal sources, can lower the carbon-intensity of urban districts, advancing the U.N. Sustainable Development Goals. Optimization of the network topology — the selection of the best subset of buildings and the best network to connect them, to minimize life cycle cost — can increase adoption of these system in appropriate applications. The potential "solution space" of the topology optimization problem grows factorially with the number of buildings in the district, motivating the consideration of a design heuristic. In this study, a heuristic for the network selection was evaluated with an exhaustive search, for a prototypical four-building district. For the prototypical district considered, the heuristic was effective in selecting an optimal network topology. Additionally, it was found that, in this case, the selection of the subset of buildings was more influential on the life cycle cost than the selection of the network topology. This work is part of a larger effort to develop a topology optimization framework for district thermal energy systems, which is anticipated to address barriers to adoption of ambient-temperature systems.

28 EE - Advanced Manufacturing Office (EE-5A)↗
DOE OSTI2020

Silica accelerates the selective hydrogenation of CO 2 to methanol on cobalt catalysts

The reaction pathways on supported catalysts can be tuned by optimizing the catalyst structures, which helps the development of efficient catalysts. Such design is particularly desired for CO 2 hydrogenation, which is characterized by complex pathways and multiple products. Here, we report an investigation of supported cobalt, which is known for its hydrocarbon production and ability to turn into a selective catalyst for methanol synthesis in CO 2 hydrogenation which exhibits good activity and stability. The crucial technique is to use the silica, acting as a support and ligand, to modify the cobalt species via Co-O-SiOn linkages, which favor the reactivity of spectroscopically identified *CH 3 O intermediates, that more readily undergo hydrogenation to methanol than the C-O dissociation associated with hydrocarbon formation. Cobalt catalysts in this class offer appealing opportunities for optimizing selectivity in CO 2 hydrogenation and producing high-grade methanol. By identifying this function of silica, we provide support for rationally controlling these reaction pathways.

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

Exploring the impacts of conformer selection methods on ion mobility collision cross section predictions

The prediction of structure dependent molecular properties, such as collision cross sections as measured using ion mobility spectrometry, are crucially dependent on the selection of the correct population of molecular conformers. Here, we report an in-depth evaluation of multiple conformation selection techniques, including simple averaging, Boltzmann weighting, lowest energy selection, low energy threshold reductions, and similarity reduction. Generating 50,000 conformers each for 18 molecules, we used the In Silico Chemical Library Engine (ISiCLE) to calculate the collision cross sections for the entire dataset. First, we employed Monte Carlo simulations to understand the variability between conformer structures as generated using simulated annealing. Then we employed Monte Carlo simulations to the aforementioned conformer selection techniques applied on the simulated molecular property—the ion mobility collision cross section. Based on our analyses, we found Boltzmann weighting to be a good tradeoff between precision and theoretical accuracy. Combining multiple techniques revealed that energy thresholds and root-mean-squared deviation-based similarity reductions can save considerable computational expense while maintaining property prediction accuracy. Molecular dynamic conformer generation tools like AMBER can continue to generate new lowest energy conformers even after tens of thousands of generations, decreasing precision between runs. This reduced precision can be ameliorated and theoretical accuracy increased by running density functional theory geometry optimization on carefully selected conformers.

Nielson, Felicity F.↗
DOE OSTI2024

Confinement performance predictions for a high field axisymmetric tandem mirror

This paper presents Hammir tandem mirror confinement performance analysis based on Realta Fusion's first-of-a-kind model for axisymmetric magnetic mirror fusion performance. This model uses an integrated end plug simulation model including, heating, equilibrium, and transport combined with a new formulation of the plasma operation contours (POPCONs) technique for the tandem mirror central cell. Using this model in concert with machine learning optimization techniques, it is shown that an end plug utilizing high temperature superconducting magnets and modern neutral beams enables a classical tandem mirror pilot plant producing a fusion gain Q > 5. The approach here represents an important advance in tandem mirror design. The high fidelity end plug model enables calculations of heating and transport in the highly non-Maxwellian end plug to be made more accurately. The detailed end plug modelling performed in this work has highlighted the importance of classical radial transport and neutral beam absorption efficiency on end plug viability. The central cell POPCON technique allows consideration of a wide range of parameters in the relatively simple near-Maxwellian central cell, facilitating the selection of more optimal central cell plasmas. These advances make it possible to find more conservative classical tandem mirror fusion pilot plant operating points with lower temperatures, neutral beam energies, and end plug performance requirements than designs in the literature. Despite being more conservative, it is shown that these operating points have sufficient confinement performance to serve as the basis of a viable fusion pilot plant provided that they can be stabilized against MHD and trapped particle modes.

FOS: Physical sciences↗
DOE OSTI2025

Confinement performance predictions for a high field axisymmetric tandem mirror

This paper presents a Hammir tandem mirror confinement performance analysis based on Realta Fusion’s first-of-a-kind model for axisymmetric magnetic mirror fusion performance. This model uses an integrated end plug simulation model including, heating, equilibrium and transport combined with a new formulation of the plasma operation contours (POPCONs) technique for the tandem mirror central cell. Using this model in concert with machine learning optimization techniques, it is shown that an end plug utilizing high temperature superconducting magnets and modern neutral beams enables a classical tandem mirror pilot plant producing a fusion gain Q > 5. The approach here represents an important advance in tandem mirror design. The high-fidelity end plug model enables calculations of heating and transport in the highly non-Maxwellian end plug to be made more accurately. The detailed end plug modelling performed in this work has highlighted the importance of classical radial transport and neutral beam absorption efficiency on end plug viability. The central cell POPCON technique allows consideration of a wide range of parameters in the relatively simple near-Maxwellian central cell, facilitating the selection of more optimal central cell plasmas. These advances make it possible to find more conservative classical tandem mirror fusion pilot plant operating points with lower temperatures, neutral beam energies and end plug performance requirements than designs in the literature. Despite being more conservative, it is shown that these operating points have sufficient confinement performance to serve as the basis of a viable fusion pilot plant provided that they can be stabilized against magnetohydrodynamic and trapped particle modes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Evaluation of genomic selection and marker-assisted selection in Miscanthus and energycane

Abstract Although energycane ( Saccharum spp. hybrids) is widely used as a source of lignocellulosic biomass for bioethanol, breeding this crop for disease resistance is challenging due to its narrow genetic base. Therefore, efforts are underway to introgress novel sources of genetic resistance from Miscanthus into energycane. Given that disease resistance in energycane could be either qualitative or quantitative in nature, careful examination of a wide variety of genomic-enabled breeding approaches will be crucial to the success of such an undertaking. Here we examined the efficiency of both genomic selection (GS) and marker-assisted selection (MAS) for traits simulated under different genetic architectures in F 1 and BC 1 populations of Miscanthus × Miscanthus and sugarcane × sugarcane crosses. We observed that the performance of MAS was comparable and sometimes superior to GS for traits simulated with four quantitative trait nucleotides (QTNs). In contrast, as the number of simulated QTN increased, all four GS models that were evaluated tended to outperform MAS, select more phenotypically optimal F 1 individuals, and accurately predict simulated trait values in subsequent BC 1 generations. We therefore conclude that GS is preferable to MAS for introgressing genetic sources of horizontal disease resistance from Miscanthus to energycane, while MAS remains a suitable option for introgressing vertical disease resistance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Benchmarking graph neural networks for materials chemistry

Graph neural networks (GNNs) have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for materials applications. To date, a number of successful GNNs have been proposed and demonstrated for systems ranging from crystal stability to electronic property prediction and to surface chemistry and heterogeneous catalysis. However, a consistent benchmark of these models remains lacking, hindering the development and consistent evaluation of new models in the materials field. Here, we present a workflow and testing platform, MatDeepLearn, for quickly and reproducibly assessing and comparing GNNs and other machine learning models. We use this platform to optimize and evaluate a selection of top performing GNNs on several representative datasets in computational materials chemistry. From our investigations we note the importance of hyperparameter selection and find roughly similar performances for the top models once optimized. We identify several strengths in GNNs over conventional models in cases with compositionally diverse datasets and in its overall flexibility with respect to inputs, due to learned rather than defined representations. Meanwhile several weaknesses of GNNs are also observed including high data requirements, and suggestions for further improvement for applications in materials chemistry are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Balancing solvation: stabilizing lithium metal batteries via optimized cosolvents for ionic-liquid electrolytes

In this study, we examined three cosolvents with distinct solvation capabilities for ionic-liquid electrolytes based on 1-methyl-1-propyl pyrrolidinium bis(fluorosulfonyl)imide (Py13FSI). We demonstrate that 1,1,1-trifluoro-2-(2-(2-(2,2,2-trifluoroethoxy)ethoxy)ethoxy)ethane (FDG) notably enhances the cycle life of Py13FSI-based electrolytes, outperforming 1,1,2,2-tetrafluoroethyl 2,2,3,3-tetrafluoropropylether (TTE) and diglyme (DG). Electrochemical and surface analyses showed that this improvement could be attributed to the formation of a favorable cathode interphase, promoting efficient Li+ transport with reduced overpotential. Spectroscopic techniques (FTIR, Raman, and NMR spectroscopy) and molecular dynamics simulations revealed that cosolvents with varying solvation abilities can influence the solvation structures in Py13FSI-based electrolytes. The mild solvating strength and lithium stability of FDG are key contributors to its effectiveness. Conversely, DG, a strong solvating solvent, destabilized the Py13FSI-DG electrolyte at the lithium metal anode, while TTE, a non-solvating solvent, failed to enhance lithium transport or form a stable cathode interphase. Our findings highlight that balanced solvation exerted by the cosolvents is critical for forming a stable electrolyte–cathode interface, potentially through FSI decomposition. This study offers valuable insights into the development of durable ionic-liquid electrolytes, emphasizing the importance of selecting cosolvents with optimal solvation properties.

Li, Xinlin [Argonne National Laboratory (ANL), Arg↗
DOE OSTI2018

Damring Formation During Rotary Kiln Chromite Pre-reduction: Effects of Pulverized Carbonaceous Fuel Selection and Partial Pellet Melting

Electricity consumption is the largest cost contributing factor in the production of ferrochrome. Currently the pelletized chromite pre-reduction process (solid-state reduction of chromite) is the process option with the lowest specific electricity consumption (MWh/ton). In this process, pelletized chromite is fed into a rotary kiln at 1573 K (1300 °C), where partial pre-reduction takes place. Damring formation (material build-up) in the rotary kiln causes routine shutdowns, resulting in loss of revenue. The damring formation is possibly caused by melting of the ash of the pulverized coal used to fire the kiln and/or the partial melting of the chromite pellets. Ash fusion temperatures of twenty different samples were evaluated to assess the temperature at which the pulverized coal ash will start to contribute to damring formation. Sessile drop tests were used to assess the softening behavior of different ore types (e.g., UG2, MG, and LG metgrade), as well as softening of composite chromite pellets made from these ores. Actual damrings were also analyzed using scanning electron microscopy with energy dispersive X-ray spectroscopy. Results indicate that it is mainly the pulverized coal ash that will contribute to damring formation, and not ore or pellet softening. Multiple-linear regression was used to derive equations to predict the ash fusion temperatures of the pulverized coal ash, which can be used by ferrochrome producers to optimize pulverized coal selection.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Systems and Process Engineering Supporting Tool Development for Reactor Internals Decommissioning - 18552

One process of arriving at optimized design or selection of tooling for decommissioning of nuclear reactors is examined. The methodical process, as descried in this presentation is one that has been very successfully implemented on a number of CANDU{sup R} reactor internals projects to date and is still being pursued on current projects. The influencing factors and data used to define the design requirements and technical specifications of the tooling systems and individual tools are briefly described. The various stages of the process are described in some detail with examples of actual tooling systems thus built. Finally the benefits of this type of systematic approach to tool design, is described. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Parametric Optimization Problem Formulation for Connected Hybrid Electric Vehicles using Neural Network based Equivalent Model

The dynamics of powertrain control systems are complicated and involve both nonlinear plant model and control functionalities, albeit they are well defined and formulated using first principle approaches. This constitutes difficulties in exploring implementable optimal tuning rules for some selected control parameters using vehicle-to-vehicle (V2V) communications. This paper presents a way to use neural networks (NN) to represent the problem of parameter tuning for optimizing fuel consumption. For this purpose, physical modelling and validation have been firstly performed for the closed loop powertrain system of the concerned vehicle for some given driving cycles. This is then followed by the sensitivity analysis that selects most influential control parameters to optimize. Using the data generated from the obtained physical models, an equivalent NN formulation has finally been obtained that gives simple yet unified objectives and constraints ready to be used to solve the optimization problem that produces optimal tuning rules for the selected control parameters to minimize fuel consumption.

Parameter optimization, V2X, Powertrain modeling↗
DOE OSTI2019

Discovery and Lead-Optimization of 4,5-Dihydropyrazoles as Mono-Kinase Selective, Orally Bioavailable and Efficacious Inhibitors of Receptor Interacting Protein 1 (RIP1) Kinase

RIP1 kinase regulates necroptosis and inflammation and may play an important role in contributing to a variety of human pathologies, including inflammatory and neurological diseases. Currently, RIP1 kinase inhibitors have advanced into early clinical trials for evaluation in inflammatory diseases such as psoriasis, rheumatoid arthritis, and ulcerative colitis and neurological diseases such as amyotrophic lateral sclerosis and Alzheimer’s disease. Here, we report on the design of potent and highly selective dihydropyrazole (DHP) RIP1 kinase inhibitors starting from a high-throughput screen and the lead-optimization of this series from a lead with minimal rat oral exposure to the identification of dihydropyrazole 77 with good pharmacokinetic profiles in multiple species. Additionally, we identified a potent murine RIP1 kinase inhibitor 76 as a valuable in vivo tool molecule suitable for evaluating the role of RIP1 kinase in chronic models of disease. DHP 76 showed efficacy in mouse models of both multiple sclerosis and human retinitis pigmentosa.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Electric Field Tuning Molecular Packing and Electrical Properties of Solution-Shearing Coated Organic Semiconducting Thin Films

Recent improvements in solution-coated organic semiconductors (OSCs) evidence their high potential for cost-efficient organic electronics and sensors. Molecular packing structure determines the charge transport property of molecular solids. However, it remains challenging to control the molecular packing structure for a given OSC. Here, the application of alternating electric fields is reported to fine-tune the crystal packing of OSC solution-shearing coated at ambient conditions. First, a theoretical model based on dielectrophoresis is developed to guide the selection of the optimal conditions (frequency and amplitude) of the electric field applied through the solution-shearing blade during coating of OSC thin films. Next, electric field-induced polymorphism is demonstrated for OSCs with both herringbone and 2D brick-wall packing motifs in 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene and 6,13-bis(triisopropylsilylethynyl) pentacene, respectively. Favorable molecular packing can be accessible in some cases, resulting in higher charge carrier mobilities. In conclusion, this work provides a new approach to tune the properties of solution-coated OSCs in functional devices for high-performance printed electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Enhancing Molecular Alignment and Charge Transport of Solution-Sheared Semiconducting Polymer Films by the Electrical-Blade Effect

Controlling polymer chain alignment through processing is a means of tuning the charge transport of solution-based conjugated polymers. In this work, a processing strategy is proposed in which an external electric field (E-field) is applied to the coating blade (E-blade) to align polymer chain during solution-shearing, a meniscus-guided coating technique. A theoretical model based on dielectrophoresis quantitatively describes and predicts the alignment process and is used to guide the selection of the optimal conditions of the applied E-field. Using these conditions, more than two-fold increase in chain alignment is observed for E-bladed thin films of a diketopyrrolopyrrole (DPP) semiconducting polymer without affecting other morphological aspects such as film thickness, film coverage or fiber-like aggregation. Organic field effect transistors based on the E-bladed DPP polymer are fabricated at ambient conditions and over areas of a few cm2. They display a 3-fold improvement in their mobilities and a strong enhancement in charge transport anisotropy compared to films prepared without E-field. These results reveal a synergistic alignment effect from both the solution-shearing process and the applied E-field, and introduce a novel and general approach to control the morphology and the electrical properties of solutioncoated conjugated polymer thin films.

42 ENGINEERING↗
DOE OSTI2023

Quantitative Representativeness and Constituency of the Long-Term Agroecosystem Research Network and Analysis of Complementarity with Existing Ecological Networks

Abstract Studies conducted at sites across ecological research networks usually strive to scale their results to larger areas, trying to reach conclusions that are valid throughout larger enclosing regions. Network representativeness and constituency can show how well conditions at sampling locations represent conditions also found elsewhere and can be used to help scale-up results over larger regions. Multivariate statistical methods have been used to design networks and select sites that optimize regional representation, thereby maximizing the value of datasets and research. However, in networks created from already established sites, an immediate challenge is to understand how well existing sites represent the range of environments in the whole area of interest. We performed an analysis to show how well sites in the USDA Long-Term Agroecosystem Research (LTAR) Network represent all agricultural working lands within the conterminous United States (CONUS). Our analysis of 18 LTAR sites, based on 15 climatic and edaphic characteristics, produced maps of representativeness and constituency. Representativeness of the LTAR sites was quantified through an exhaustive pairwise Euclidean distance calculation in multivariate space, between the locations of experiments within each LTAR site and every 1 km cell across the CONUS. Network representativeness is from the perspective of all CONUS locations, but we also considered the perspective from each LTAR site. For every LTAR site, we identified the region that is best represented by that particular site—its constituency—as the set of 1 km grid locations best represented by the environmental drivers at that particular LTAR site. Representativeness shows how well the combination of characteristics at each CONUS location was represented by the LTAR sites’ environments, while constituency shows which LTAR site was the closest match for each location. LTAR representativeness was good across most of the CONUS. Representativeness for croplands was higher than for grazinglands, probably because croplands have more specific environmental criteria. Constituencies resemble ecoregions but have their environmental conditions “centered” on those at particular existing LTAR sites. Constituency of LTAR sites can be used to prioritize the locations of experimental research at or even within particular sites, or to identify the extents that can likely be included when generalizing knowledge across larger regions of the CONUS. Sites with a large constituency have generalist environments, while those with smaller constituency areas have more specialized environmental combinations. These “specialist” sites are the best representatives for smaller, more unusual areas. The potential of sharing complementary sites from the Long-Term Ecological Research (LTER) Network and the National Ecological Observatory Network (NEON) to boost representativeness was also explored. LTAR network representativeness would benefit from borrowing several NEON sites and the Sevilleta LTER site. Later network additions must include such specialist sites that are targeted to represent unique missing environments. While this analysis exhaustively considered principal environmental characteristics related to production on working lands, we did not consider the focal agronomic systems under study, or their socio-economic context.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Maximizing machine learning interatomic potential transferability for the discovery of the novel stellated octadecagon Bi18-Pt24 cage structure

Achieving true transferability remains the central challenge for Machine Learning Interatomic Potentials (ML-IAPs) in modeling complex bimetallic nanoclusters across their vast potential energy surfaces. We systematically investigate data selection strategies to optimize the Chebyshev Interaction Model for Efficient Simulation (ChIMES) potential for the Bi-Pt nanoclusters by comparing three innovative sampling methods: Principal Component Analysis (PCA)/k-means (structural diversity), t-distributedStochasticNeighborEmbedding (t-SNE)/k-means (force-space diversity), and hierarchical clustering. Quantitatively, the PCA/k-means strategy proved most effective for global accuracy, yielding the lowest force errors and achieving energy root mean square errors (RMSE) values competitive with Density Functional Theory (DFT), demonstrating excellent accuracy (19.16meV/atom). Structural validation on 34 unique DFT-optimized isomers further confirmed the potential’s high fidelity, with the best model PCA/k-means reproducing structures with an average root mean square deviation (RMSD) of 0.10 Å. However, the t-SNE methods, by maximizing diversity in the force space, demonstrated superior extrapolative power, leading to the more precise prediction of a novel stellated octadecagon Bi18⁢Pt24 cage structure, demonstrating the potential for exploring previously unseen morphologies. Our results establish a clear methodology for strategic data sampling that successfully maximizes ML-IAP transferability, providing an accurate and computationally efficient tool that accelerates the theoretical discovery of complex bimetallic architectures.

Vangheluwe, Raphaël [Université Paris-Saclay, CNRS↗
DOE OSTI2026

Higher-order interaction effects among operating conditions and feedstocks shape reactor microbiomes and fatty acid production profiles

Arrested anaerobic digestion (AAD) offers a promising route for producing fatty acids (FAs) from organic residues, yet optimal conditions for selectively generating medium-chain fatty acids (MCFAs) remain poorly defined. Here, we systematically evaluated the main and interaction effects of pH (5, 7, 9), feedstock (food waste, manure), temperature (35 and 45 °C), and inoculum source on microbiome composition and FA production. Anaerobic digester sludge and a novel bison rumen inoculum were compared. Significant higher-order interactions among operating parameters governed FA profiles and microbiome structure. Butyric acid production was driven by a three-way interaction among pH, feedstock, and temperature (p < 0.001), with maximum concentrations achieved in food waste reactors at pH 5.0 and 35 °C (1.2 ± 0.1 g L −1 with sludge and 1.1 ± 0.3 g L −1 with rumen). MCFA production exhibited significant four-way interactions (p < 0.1 to p < 0.001). At 45 °C and pH 5.0, inoculum source tuned MCFA selectivity: sludge favored pentanoic acid (0.4 ± 0.1 g L −1 ), whereas rumen favored hexanoic and heptanoic acids (up to 0.4 ± 0.2 g L −1 ). Manure reactors produced < 0.2 g L −1 MCFAs under all conditions. Genera, including Megasphaera, Prevotella, and Lactobacillus, were associated with production of specific MCFAs. PICRUSt2-based pathway predictions were consistent with MCFA production patterns and suggested a potential role for lactic acid–driven chain elongation pathways. This study provides insights into how interacting operating conditions shape AAD microbiomes, their FA profiles, and advances the trajectory of research aimed at engineering robust and controllable microbiomes for waste valorization.

09 BIOMASS FUELS↗