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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

ISG-2: properties of the second International Simple Glass

Given the importance of glass materials to society, their durability when exposed to aqueous solutions is a critical area for research, particularly for vitrified radioactive wastes. This spurred an international team to fabricate a standardized composition based on waste immobilization glass called the International Simple Glass (ISG), which has been the subject of numerous experimental and computational studies focused on aqueous corrosion resistance. With the original batch of ISG nearly depleted, the international team designed and fabricated a standard glass material, ISG-2, where half the Ca in the original composition was replaced with Mg by mole. This paper presents information on both the ISG-2 composition and a new batch with the same nominal composition as the original ISG, designated ISG-1, including their homogeneity, their physical and thermal properties. The results of static alteration experiments are presented as well to provide a baseline for future aqueous corrosion performance investigations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Validation of Numerical Tools for Calculating Reactivity Feedback in Sodium Fast Reactors Using SEFOR Experimental Data

The Southwest Experimental Fast Oxide Reactor (SEFOR) was an experimental sodium-cooled fast breeder reactor operated from 1969 to 1972 with experiments designed to measure Doppler reactivity feedback in a wide temperature range from around 350 °F to temperatures approaching the melting point of mixed oxide fuel of around 5000 °F, providing valuable data for code validations. Co-supported by the Department of Energy (DOE) Fast Reactor Program (FRP) and the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the SEFOR benchmark project focused on using the experimental data to validate numerical tools that are used in industry and academia to design and license sodium-cooled fast reactors (SFRs). By the end of FY-25, substantial progress was achieved in the SEFOR benchmark study. A variety of numerical tools commonly used for modeling SFRs were applied to develop models for SEFOR core configurations I-D, I-E, I-I, and I-J. These included Monte Carlo codes such as MCNP, Serpent, and Shift; deterministic codes such as the legacy Argonne Reactor Computation (ARC) suite and the high-fidelity NEAMS code Griffin; and the system analysis code SAS4A/SASSYS-1 (SAS). Using these models, both SEFOR zero-power experiments and power-ascending tests were successfully simulated. Comparisons were performed against experimental measurements of core criticalities, reflector worth, kinetics parameters (Λ/βeff), isothermal reactivity feedback (from 350 °F to 760 °F at zero power), and power-ascending reactivity feedback (as power increased from 0.4 MW to 17 MW). In general, these comparisons demonstrated very good agreement between numerical results and experimental data. In Fiscal Year 26 (FY-26), the SEFOR benchmark project will continue to address the modeling issues identified in FY-25. Effort will focus on the simulation of reactivity insertion transients in SEFOR core II using the ARC/SAS model. Future work will also focus on incorporating BISON into the SEFOR core modeling process to enable the first Multiphysics simulations of the isothermal tests based on the MOOSE framework.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Super-swelling behavior of stacked lipid bilayer systems

Bilayer systems comprising lipid mixtures are the most well-studied model of biological membranes. While the plasma membrane of the cell is a single bilayer, many intra- and extra-cellular biomembranes comprise stacks of bilayers. Most bilayer stacks in nature are periodic, maintaining a precise water layer separation between bilayers. That equilibrium water separation is governed by multiple inter-bilayer forces and is highly responsive. Biomembranes re-configure inter-bilayer spacing in response to temperature, composition, or mass transport cues. In synthetic bilayer systems for applications in cosmetics or topical treatments, control of the hydration level is a critical design handle. Herein we investigate a binary lipid system that leverages key inter-bilayer forces leading to unprecedented levels of aqueous swelling while maintaining a coherent multilamellar form. We found that combining cationic lipids with bicontinuous cubic phase-forming lipids (lipids with positive Gaussian modulus), results in the stabilization of multilamellar phases against repulsive steric forces that typically lead to bilayer delamination at high degrees of swelling. Using ultra-small-angle X-ray scattering alongside confocal laser scanning microscopy, we characterized various super-swelled states of 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP) and glycerol monooleate (GMO) lipids, as well as other analogous systems, at varied concentration and molar ratios. Through these experiments we established swelling profiles of various binary lipid systems that were near-linear with decreasing lipid volume fraction, showing maximum swelling with periodicity well above 200 nanometers.

42 ENGINEERING↗
DOE OSTI2021

A Multi-modal Approach to Understanding Degradation of Organic Photovoltaic Materials

State-of-the-art organic photovoltaic (OPV) materials are composed of complex, chemically diverse polymeric and molecular structures that form highly intricate solid-state interactions, collectively yielding exceptional tunability in performance and aesthetics. These properties are especially attractive for semitransparent power-generating windows or shades in living environments, greenhouses, or other architectural integrations. However, before such a future is realized, a broader and deeper understanding of property stability must be acquired. Stability during operating and environmental conditions is critical, namely, material color steadfastness, optoelectronic performance retention, morphological rigidity, and chemical robustness. To date, no single investigation encompasses all four distinct, yet interconnected, metrics. Here, we present a multimodal strategy that captures a dynamic and interconnected evolution of each property during the course of an accelerated photobleaching experiment. We demonstrate this approach across relevant length scales (from molecular to visual macroscale) using X-ray photoelectron spectroscopy, grazing-incidence X-ray scattering, microwave conductivity, and time-dependent photobleaching spectroscopies for two high-performance semitransparent OPV blends—PDPP4T:PC 60 BM and PDPP4T:IEICO-4F, with comparisons to the stabilities of the individual components. We present direct evidence that specific molecular acceptor (fullerene vs nonfullerene) designs and the resulting donor–acceptor interactions lead to distinctly different mechanistic routes that ultimately arrive at what is termed “OPV degradation.” We directly observe a chemical oxidation of the cyano endcaps of the IEICO-4F that coincides with a morphological change and large loss in photoconductivity while the fullerene acceptor-containing blend demonstrates a significantly greater fraction of oxygen uptake but retains 55% of the photoconductivity. Finally, this experimental roadmap provides meaningful guidance for future high-throughput, multimodal studies, benchmarking the sensitivity of the different analytical techniques for assessing stability in printable active layers, independent of complete device architectures.

14 SOLAR ENERGY↗
DOE OSTI2022

Chlorine Worth Study in Support of PF-4 Operations [Slides]

The primary concern of this study is if experiments can be designed/conducted to provide technical justification to NCS in order to increase mass limits. This goal is in direct support of 30 pits per year mission, one of the top priorities for NNSA. The accelerated timeline is as follows: Initial tasking, June 2020; preliminary design completion (CED-1 equivalent), November 2020; final design completion (CED-2 equivalent), March 2021; part receipt and inspection completion (CED-3a equivalent), October 2021; experiment execution (CED-3b equivalent), December 2021; experiment documentation write-up completion (start CED-4a equivalent), January 2022; and ICSBEP target (CED-4a equivalent), Fall 2022.

2019 ARCHIMEDES LDRD project↗
DOE OSTI2025

Structurally Driven Selective Adsorption of Hydrocarbons by Metal Substitution in Isostructural Rare-Earth Metal–Organic Frameworks

The design and realization of highly selective nanoporous materials are necessary to target critical separations across industries. By leveraging pore size, pore shape, and linker functionalization, the design of nanoporous solid adsorbents will enable the rapid production of energy efficient separation materials for high-value gas mixtures. This study uses a combination of modeling, synthesis, and gas adsorption testing to investigate a new class of small-pore isostructural rare-earth (RE) 2,5-dihydroxyterephthalic acid (DOBDC) metal–organic frameworks (MOFs) (RE: Pr-, Gd-, Er-, Yb; DOBDC = 2,5-dihydroxyterephthalic acid) and their adsorption selectivity for acetylene/ethylene mixtures. Density functional theory simulations identified that selective binding of acetylene over ethylene in the Gd-, Er-, and Yb-DOBDC MOFs was due to hydrogen-bonding between acetylene and the linker hydroxyl. Adsorption experiments validated the computational results by identifying mechanisms that control the acetylene/ethylene adsorption selectivity and high acetylene adsorption. Furthermore, dynamic column breakthrough experiments with the Gd-DOBDC MOF validated the simulations and indicated that ethylene can be separated from acetylene in a mixture containing 1 vol % acetylene and 39 vol % ethylene (balance argon). In conclusion, the results highlight the complexity of gas binding in functional porous materials and how combining modeling and experiment enables a fundamental understanding of gas–framework interactions that can be leveraged for the design of future separation materials.

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

Autonomous and dynamic precursor selection for solid-state materials synthesis

Abstract Solid-state synthesis plays an important role in the development of new materials and technologies. While in situ characterization and ab-initio computations have advanced our understanding of materials synthesis, experiments targeting new compounds often still require many different precursors and conditions to be tested. Here we introduce an algorithm (ARROWS 3 ) designed to automate the selection of optimal precursors for solid-state materials synthesis. This algorithm actively learns from experimental outcomes to determine which precursors lead to unfavorable reactions that form highly stable intermediates, preventing the target material’s formation. Based on this information, ARROWS 3 proposes new experiments using precursors it predicts to avoid such intermediates, thereby retaining a larger thermodynamic driving force to form the target. We validate this approach on three experimental datasets, containing results from over 200 synthesis procedures. In comparison to black-box optimization, ARROWS 3 identifies effective precursor sets for each target while requiring substantially fewer experimental iterations. These findings highlight the importance of domain knowledge in optimization algorithms for materials synthesis, which are critical for the development of fully autonomous research platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Design and characterization of a single-channel microwave interferometer for the Helicon Physics Prototype eXperiment

A heterodyne interferometer for measuring the plasma electron density in Helicon Physics Prototype eXperiment (HPPX) has been developed and characterized in the laboratory. The heterodyne-type interferometer uses a probing beam of 85 GHz and a reference beam of 85.7 GHz by using two Gunn oscillators for facilitating frequency modulation. The cutoff density for this frequency is 8.96 x 10 19 m –3 that is much higher than the nominal electron density of the HPPX plasmas. The maximum deflection angle due to the refraction effect is estimated to be 0.63° assuming a parabolic electron density profile, and the impact on the measurement is negligible for the plasmas with a size of 100 mm. Two identical HDPE lenses act as the vacuum windows, and the lenses working together with the transmitter and receiver horns, form a beam with the beam waist of about 20 mm located at the plasma center. The characterization results of the lens and the critical microwave components show good performance. The plasma electron density measurement results are in line with the expectation. Furthermore, the Signal to Noise Ratio (SNR) of the measurement is defined as SNR = 20lg($\bar{n}_{e}$/S), where $\bar{n}_{e}$ and S are respectively the average electron density and the standard deviation of the signal, and the SNR is 80 dB. Finally, this demonstrates the system's capability of providing a clear and detailed density resolution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

On fast simulation of dynamical system with neural vector enhanced numerical solver

The large-scale simulation of dynamical systems is critical in numerous scientific and engineering disciplines. However, traditional numerical solvers are limited by the choice of step sizes when estimating integration, resulting in a trade-off between accuracy and computational efficiency. To address this challenge, we introduce a deep learning-based corrector called Neural Vector (NeurVec), which can compensate for integration errors and enable larger time step sizes in simulations. Our extensive experiments on a variety of complex dynamical system benchmarks demonstrate that NeurVec exhibits remarkable generalization capability on a continuous phase space, even when trained using limited and discrete data. NeurVec significantly accelerates traditional solvers, achieving speeds tens to hundreds of times faster while maintaining high levels of accuracy and stability. Moreover, NeurVec’s simple-yet-effective design, combined with its ease of implementation, has the potential to establish a new paradigm for fast-solving differential equations based on deep learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Beam Hardening Artifact Reduction in X-Ray CT Reconstruction of 3D Printed Metal Parts Leveraging Deep Learning and CAD Models

Nondestructive evaluation (NDE) of additively manufactured (AM) parts is important for understanding the impacts of various process parameters and qualifying the built part. X-ray computed tomography (XCT) has played a critical role in rapid NDE and characterization of AM parts. However, XCT of metal AM parts can be challenging because of artifacts produced by standard reconstruction algorithms as a result of a confounding effect called “beam hardening.” Beam hardening artifacts complicate the analysis of XCT images and adversely impact the process of detecting defects, such as pores and cracks, which is key to ensuring the quality of the parts being printed. In this work, we propose a novel framework based on using available computer-aided design (CAD) models for parts to be manufactured, accurate XCT simulations, and a deep-neural network to produce high-quality XCT reconstructions from data that are affected by noise and beam hardening. Using extensive experiments with simulated data sets, we demonstrate that our method can significantly improve the reconstruction quality, thereby enabling better detection of defects compared with the state of the art. We also present promising preliminary results of applying the deep networks trained using CAD models to experimental data obtained from XCT of an AM jet-engine turbine blade.

Ziabari, Amir↗
DOE OSTI2025

Microreactor Assembly Transportation Cask Model Description for Criticality Safety Validation Basis Assessment

Criticality safety analyses are completed on a transportation cask used for microreactor assembly shipment to provide an example of model and analysis to industry for reproducing this type of study on their microreactor fuel shipment. The fuel assembly considered is based on a gas-cooled microreactor (GC-MR), which utilizes HALEU fuel in the form of TRISO particles and utilizes various design options considered in industry designs. Various versions of this GC-MR assembly were studied, with and without YH2 moderator, providing similar conclusions. The shipment cask design is revised based on an existing design ES-3100, developed by Y-12 for the transport of highly enriched uranium (HEU), but is enlarged to hold the GC-MR fuel assembly. Criticality safety analysis for the cask/GC-MR fuel assembly package was performed using the CSAS6 sequence of SCALE6.3.2, utilizing the ENDF/B-VII.1 based continuous energy neutron library, and the analysis strictly follows the guideline from NRC reference reports. Different scenarios, e.g. normal operation, undamaged cask with water flooded, damaged cask with optimal water moderation, have been analyzed and it could be concluded the package would always have a large margin of subcriticality even packed in an infinite array. Sensitivity and similarity analyses are also performed using the TSUNAMI sequence of SCALE6.3.2, and the similarity analysis uses all the experiments from the ICSBEP Handbook with Intermediate and Mixed Enriched Uranium (IEU) and Low Enriched Uranium (LEU) systems together with additional ones that are sponsored by the DNCSH program. These similarity analyses indicate that dry cases have no similar benchmark experiments (ck values greater than 0.8), which may become problematic if more assemblies are shipped together (or a fully loaded core is shipped) and margin to criticality is reduced. However, the damaged cask models with flooded assemblies exhibited similarities to many experiments with ck values greater than 0.8.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

A Physical Metric for Inertial Confinement Fusion Capsules

The performance of fusion capsules on the National Ignition Facility (NIF) is strongly affected by the physical properties of the hot deuterium–tritium (DT) fuel, such as the mass, areal density, and pressure of the hot spot at the stagnation time. All of these critical quantities depend on one measured quantity, which is the ratio of the specific peak implosion energy to the specific internal energy of the hot spot. This unique physical quantity not only can measure the incremental progress of the inertial confinement fusion capsules towards ignition but also measures the conversion of the peak implosion kinetic energy of the pusher shell into the internal energy of the hot fuel in a capsule. Analysis of existing NIF shots to date are performed. The ratio metric is compared quantitatively with the ignition criterion. Results provide new perspectives on the NIF experiments by which the performance of the burning plasma can be determined and controlled through the fine tune of the implosion parameters, which improves future designs and predictions of the ignition capsules.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

West Africa Battery Energy Storage Systems (BESS) Capacity Building - Discussion of BESS Tariffs: Payment Structures and Case Studies [Slides]

Battery Energy Storage Systems (BESS) are emerging as critical assets for enhancing grid reliability, integrating renewable energy, and enabling system flexibility. Yet, the regulatory and financial frameworks that determine how BESS projects are compensated vary widely across jurisdictions. This presentation explores international case studies - from Honduras, Costa Rica, Chile, South Africa, Mexico, and Brazil - to illustrate how tariff design and payment structures are evolving to support large-scale BESS deployment. The cases highlight a range of ownership and revenue models, including cost-of-service mechanisms, energy and capacity payments, and market-based arbitrage, as well as hybrid approaches under development. The discussion will examine key challenges such as defining remuneration for ancillary services, addressing double charging, and accounting for efficiency losses and degradation over time. By comparing experiences across markets, the presentation identifies emerging best practices for valuing BESS and designing tariffs that align technical performance with economic incentives, providing insights for regulators, utilities, and policymakers pursuing storage integration.

25 ENERGY STORAGE↗
DOE OSTI2024

Probabilistic Analysis of Uncertainty in ATRC Flux Profiles

ATRC is a replica of the larger ATR design and is used to conduct research and obtain data such as flux measurements, excess reactivity, and loading requirements before being loaded into ATR. One method for determining the impact an experiment will have at ATR is by looking at the axial flux profile along the fuel rod in the corresponding ATRC experiment; however, flux wand measurements includes large amounts of variation which makes drawing conclusions from the data difficult. This poster describes a definitive method to propagate the uncertainty from ATRC measurements using Python code.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Gas-Liquid Flow Modeling for Renewable Fuels Production

Aerobic/anaerobic and gas fermentation pathways have emerged as promising new technologies for the generation of renewable fuels/chemicals from biomass derived sugars, and mixtures of greenhouse/energy rich gas streams (CO2/CH4/H2/CO) via microbial action. Example pathways include sugars-to-ethanol conversion, biomethanation (CO2/H2 to CH4), biogas upgrading, CO fermentation and wet-waste conversion. Gas and liquid phase transport, mass-transfer, and mixing physics at large length scales can significantly affect microbial conversion rates, particularly when the microbial reaction requires a narrow set of conditions. These phenomena are difficult to study in small-scale bench-top reactors that are typically well-mixed. Predictive computational fluid dynamics (CFD) based simulations can therefore aid in the scale-up, design and optimization of these reactors. This work presents multiphase Euler-Euler CFD simulations of at-scale (~500 m3) bioreactors. Our mathematical model treats the gas and liquid as interpenetrating phases. This approach reduces the computational complexity of tracking individual gas bubbles that are several orders of magnitude smaller than reactor dimensions. We solve the Reynolds averaged Navier-Stokes (RANS) multiphase equations that account for phase and chemical species transport, interphase mass and momentum transfer and uses a phenomenological model for gas uptake by microbes. We use a customized solver derived from open-source CFD toolbox, OpenFOAM [1], to perform these simulations, which has been validated against small-scale reactors in our previous work [2]. There is currently a knowledge-gap regarding bubble-size distributions when using gas mixtures with vastly different properties, which can have a significant impact overall mass-transfer. For example, hydrogen bubbles are more buoyant compared to other relatively heavier gases (CO2/CH4/CO), resulting in a large distribution of residence times and bubble sizes. This work therefore develops a deeper understanding of bubble dynamics and interphase mass transfer in such heterogenous gas mixtures through well-resolved computational models. We use a population balance model (PBM) for bubble-size-distribution modeling that is validated against small-scale experiments in our solver with an uncertainty quantification study for bubble coalescence and break-up model parameters. Results pertaining to multiple simulations of gas-fermentation reactors are presented where gas mixtures with varying compositions of CO2/CH4/CO/H2 are imposed at the sparger boundaries. The spatio-temporal variations in bubble-size distribution and mass transfer coefficient are analyzed for varying superficial velocities and gas-compositions for varying sizes of bubble-column and airlift reactors. This work will also examine the performance of different reactor designs, viz. bubble column reactor, airlift reactor with an internal draft tube, and a stirred-tank reactor with Rushton impellers. Reactor mass-transfer coefficient, gas hold-up, and dissolved gas distribution are critically analyzed among reactors, and sensitivity studies pertaining to gas flow rates and reactor geometry will be presented. [1] Weller, H., Tabor, G., Jasak, H. and Fureby, C., A tensorial approach to computational continuum mechanics using object-oriented techniques, Computers in physics, 12, 6, 620--631, 1998. [2] Rahimi, M., Sitaraman, H., Humbird, D. and Stickel, J., Computational fluid dynamics study of full-scale aerobic bioreactors: Evaluation of gas-liquid mass transfer, oxygen uptake, and dynamic oxygen distribution, Chemical Engineering Research and Design, 139: 283-295.

BIOMASS FUELS↗
DOE OSTI2022

On the Fidelity of Computational Models for the Flow of Milled Loblolly Pine: A Benchmark Study on Continuum-Mechanics Models and Discrete-Particle Models

The upstream of bioenergy industry has suffered from unreliable operations of granular biomass feedstocks in handling equipment. Computational modeling, including continuum-mechanics models and discrete-particle models, offers insightful understandings and predictive capabilities on the flow of milled biomass and can assist equipment design and optimization. This paper presents a benchmark study on the fidelity of the continuum and discrete modeling approaches for predicting granular biomass flow. We first introduce the constitutive law of the continuum-mechanics model and the contact law of the coarse-grained discrete-particle model, with model parameters calibrated against laboratory characterization tests of the milled loblolly pine. Three classical granular material flow systems (i.e., a lab-scale rotating drum, a pilot-scale hopper, and a full-scale inclined plane) are then simulated using the two models with the same initial and boundary conditions as the physical experiments. The close agreement of the numerical predictions with the experimental measurements on the hopper mass flow rate, the hopper critical outlet width, the material stopping thickness on the inclined plane, and the dynamic angle of repose, clearly indicates that the two methods can capture the critical flow behavior of granular biomass. The qualitative comparison shows that the continuum-mechanics model outperforms in parameterization of materials and wall friction, and large-scale systems, while the discrete-particle model is more preferred for discontinuous flow systems at smaller scales. Industry stakeholders can use these findings as guidance for choosing appropriate numerical tools to model biomass material flow in part of the optimization of material handling equipment in biorefineries.

Jin, Wencheng↗
DOE OSTI2024

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗