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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Transformational challenge reactor analysis to inform preconceptual core design decisions: Sensitivity study of transient analysis in a hydride-moderated microreactor

The Transformational Challenge Reactor (TCR) program aims to demonstrate a revolutionary design approach enabled by advanced manufacturing and data analytics in the design of nuclear reactors. This article discusses scoping analyses of preconceptual designs to inform TCR design decisions and the evaluation of sensitivities and uncertainties on postulated transient scenarios. The applicability of the systems codes TRACE and RELAP5-3D to TCR transient analysis are examined, and RELAP5-3D models are used to examine the transient response of two candidate core designs at multiple power levels. Then, the uncertainty quantification code RAVEN is used to quantify the effect of several design parameters on reactivity-initiated accident (RIA) progression at hot zero power (HZP) and hot full power (HFP) as well as to assess the impact of uncertainties in transient parameters for the pressurized loss of forced cooling (PLOFC).When results were compared, TRACE and RELAP5-3D showed good agreement in their ability to predict system behavior, but RELAP5-3D calculations were closer to analytical predictions for the RIA. Furthermore, models for a PLOFC accident in two designs (a UO2 and TRISO core) at multiple power levels showed greater temperature margins for the TRISO core at all power levels. Using this information, along with other scoping analyses and constraints, the TCR design team selected a power level of 3 MWth and a TRISO-based core design. For this design RAVEN was applied to vary RIA parameters in RELAP5-3D models at HZP and HFP to understand the effect on figures of merit such as peak power, fuel and coolant temperature, and energy deposition. This sensitivity study found that the inserted reactivity worth was the most important parameter controlling all figures of merit, but for insertion up to 1.5$ no failure of TCR fuel is anticipated. For constant reactivity insertions, the magnitude of the fuel temperature coefficient was found to have the greatest effect on all figures of merit under most circumstances. These results not only demonstrate the anticipated robust safety of the proposed TCR fuel form but also provide a reference for future metal-hydride moderated systems to understand RIA behavior. In the PLOFC, the impact of heat transfer enhancement due to wavy flow channel effects dominated the variance in peak temperatures, and variations in heat exchanger elevation provided the greatest control on natural circulation flow rate. Additionally, no fuel particle failure is anticipated in the PLOFC.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

A novel stress-induced martensitic transformation in a single-phase refractory high-entropy alloy

High-entropy alloys (HEAs) provide a new perspective to design metastable alloys with the stress-induced martensitic transformation (SIMT) for overcoming the strength-ductility trade-off. Here, we report a novel SIMT, orthorhombic to hexagonal close-packed martensite, in a single orthorhombic refractory HEA (Ti 16 Zr 35 Hf 35 Ta 14 RHEA), showing a good yield strength-ductility matching. The analysis of the elastic distortion energy (ΔE els ) of Ti 16 Zr 35 Hf 35 Ta 14 and several other RHEAs reveals that severe lattice distortion is a key factor which causes this SIMT. Combined the “d-electron alloy design” approach with the ΔE els , the phase configuration and SIMT path in RHEAs can be well predicted. Our work brings new insights between the lattice distortion and SIMT of RHEAs, benefiting the metastable alloy development.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multivariate prognosis of battery advanced state of health via transformers

De-risking energy storage investments necessary to meet CO 2 reduction targets requires a deep understanding of the connections between battery health, design, and use. The historical definition of the battery state of health (SOH) as the percentage of current versus initial capacity is inadequate for this purpose, motivating an expanded SOH consisting of an interrelated set of descriptors including capacity, energy, ionic and electronic impedances, open-circuit voltages, and microstructure metrics. In this work, we introduce deep transformer networks for the simultaneous prognosis of 28 battery SOH descriptors using two cycling datasets representing six lithium-ion cathode chemistries, multiple electrolyte/anode compositions, and different charge-discharge scenarios. The accuracy of these predictions for battery life (with an unprecedented mean absolute error of 19 cycles in predicting end of life for a lithium-iron-phosphate fast-charging dataset) illustrates the promise of deep learning toward providing enhanced understanding and control of battery health.

25 ENERGY STORAGE↗
DOE OSTI2025

Application of Lagrangian techniques for calculating the on-axis rotational transform

The Floquet exponents of periodic field lines are studied through the variations of the magnetic action on the magnetic axis, which is assumed to be elliptical. The near-axis formalism developed by Mercier, Solov'ev and Shafranov is combined with a Lagrangian approach. The on-axis Floquet exponent is shown to coincide with the on-axis rotational transform. A discrete solution suitable for numerical implementation is introduced, which gives the Floquet exponents as solutions to an eigenvalue problem. This discrete formalism expresses the exponents as the eigenvalues of a $6$ X $6$ matrix.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Revealing the Structural Evolution and Phase Transformation of O3-Type NaNi 1/3 Fe 1/3 Mn 1/3 O 2 Cathode Material on Sintering and Cycling Processes

O-type layered oxide cathode materials can be easily synthesized for a full sodium stoichiometry with high specific capacity, but they all suffer from a capacity fade on cycling. The sintering process control and optimization are critical to ensure a high quality and consistency of the prepared cathode materials with stable structure. Herein, in situ high-energy X-ray diffraction (HEXRD) was first employed to investigate the phase evolution of the oxides during the sintering process of O3-type NaNi 1/3 Fe 1/3 Mn 1/3 O 2 . The in situ HEXRD and both operando Bragg coherent diffraction (BCXD) and coherent multicrystal diffraction (CMCD) were utilized to investigate the phase transformation of the cathode materials during the sodiation/desodiation process. A composite structure comprised of O-type and P-type oxides was formed after the initial electrochemical activation of the cathode material, resulting in good structural and electrochemical stability. This finding implies that strain engineering can be a new design philosophy for the development of next generation high-performance sodium ion cathodes.

25 ENERGY STORAGE↗
DOE OSTI2022

Characterization of Sequentially Extracted Soil Organic Matter by Electrospray Ionization and Atmospheric Pressure Photoionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

Soil organic matter (SOM) is a complex mixture of small molecules and biopolymers that are active in various biogeochemical processes. However, the chemical diversity of biopolymer-derived SOM remains poorly explored. Identifying this diversity is important because global environmental changes may well alter SOM chemistry, as field experiments are beginning to show. Here, organic solvent-extractable (DcMe-SOM), base-hydrolyzable (KOH Hy -SOM), and CuO-oxidizable (CuO Ox -SOM) SOM fractions from a forest with a long-term nitrogen addition experiment were sequentially extracted and characterized by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) coupled with negative-ion electrospray ionization (ESI) or atmospheric pressure photoionization (APPI). From DcMe-SOM to CuO Ox -SOM, the total number of assigned formulas, average O/C ratio, aromaticity, and unsaturation degree of SOM continuously increased, while the average m/z and H/C ratio decreased. Moreover, the dominant chemical category shifted from lipid-like components to phytochemical- and protein-like components. Complementary to ESI, APPI effectively facilitated detection of additional compounds with low polarity. With long-term nitrogen addition, the average m/z, unsaturation degree, aromaticity, and oxidation state of SOM increased, and more aromatic nitrogen-containing formulas were detected in CuOOx-SOM. Our study demonstrates that chronic nitrogen deposition in forests alters both the small molecules and biopolymers of SOM fundamentally.

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

A Generalized Transformer-Based Pulse Detection Algorithm

Pulse-like signals are ubiquitous in the field of single molecule analysis, e.g., electrical or optical pulses caused by analyte translocations in nanopores. The primary challenge in processing pulse-like signals is to capture the pulses in noisy backgrounds, but current methods are subjectively based on a user-defined threshold for pulse recognition. Here, we propose a generalized machine-learning based method, named pulse detection transformer (PETR), for pulse detection. PETR determines the start and end time points of individual pulses, thereby singling out pulse segments in a time-sequential trace. It is objective without needing to specify any threshold. It provides a generalized interface for downstream algorithms for specific application scenarios. PETR is validated using both simulated and experimental nanopore translocation data. It returns a competitive performance in detecting pulses through assessing them with several standard metrics. Finally, the generalization nature of the PETR output is demonstrated using two representative algorithms for feature extraction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Improving the Transformation Efficiency of Synechococcus sp. PCC 7002 via Methylome-Guided Premethylation of DNA

Cyanobacteria are promising microbial platforms for a diverse set of biotechnology applications, from living materials to photosynthetic chemical production, but are less well characterized than commonly engineered microbes such as Escherichia coli. This study facilitates genetic engineering in Synechococcus sp. PCC 7002, a fast-growing, halotolerant, and naturally competent strain, by identifying ten native methylation motifs and designing shuttle strains that mimic the native methylation state by expressing a subset of heterologous methyltransferases. DNA methylation in E. coli with as few as two active methyltransferases increased transformation efficiency up to 30-fold across four distinct integration sites in PCC 7002. This work provides an experimental framework to bypass native restriction-modification systems for efficient genome editing and metabolic engineering in nonmodel bacteria.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

High Mass Analysis with a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer: From Inorganic Salt Clusters to Antibody Conjugates and Beyond

Analysis of proteins and complexes under native mass spectrometric (MS) and solution conditions was typically performed using time-of-flight (ToF) analyzers, due to their routine high m/z transmission and detection capabilities. However, over recent years, the ability of Orbitrap-based mass spectrometers to transmit and detect a range of high molecular weight species is well documented. Herein, we describe how a 15 Tesla Fourier transform ion cyclotron resonance mass spectrometer (15 T FT-ICR MS) is more than capable of analyzing a wide range of ions in the high m/z scale (>5000), in both positive and negative instrument polarities, ranging from the inorganic cesium iodide salt clusters; a humanized IgG1k monoclonal antibody (mAb; 148.2 kDa); an IgG1-mertansine drug conjugate (148.5 kDa, drug-to-antibody ratio; DAR 2.26); an IgG1-siRNA conjugate (159.1 kDa; ribonucleic acid to antibody ratio; RAR 1); the membrane protein aquaporin-Z (97.2 kDa) liberated from a C8E4 detergent micelle; the empty MSP1D1-nanodisc (142.5 kDa) and the tetradecameric chaperone protein complex GroEL (806.2 kDa; GroEL dimer at 1.6 MDa). We also investigate different regions of the FT-ICR MS that impact ion transmission and desolvation. Finally, we demonstrate how the transmission of these species and resultant spectra are highly consistent with those previously generated on both quadrupole-ToF (Q-ToF) and Orbitrap instrumentation. This article serves as an impactful example of how FT-ICR mass analyzers are competitive to Q-ToFs and Orbitraps for high mass detection at high m/z.

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

Electrochemically induced amorphous-to-rock-salt phase transformation in niobium oxide electrode for Li-ion batteries

Intercalation-type metal oxides are promising negative electrode materials for safe rechargeable lithium-ion batteries due to the reduced risk of Li plating at low voltages. Nevertheless, their lower energy and power density along with cycling instability remain bottlenecks for their implementation, especially for fast-charging applications. Here, we report a nanostructured rock-salt Nb 2 O 5 electrode formed through an amorphous-to-crystalline transformation during repeated electrochemical cycling with Li + . This electrode can reversibly cycle three lithiums per Nb 2 O 5 , corresponding to a capacity of 269 mAh g –1 at 20 mA g –1 , and retains a capacity of 191 mAh g –1 at a high rate of 1 A g –1 . Further, it exhibits superb cycling stability with a capacity of 225 mAh g –1 at 200 mA g –1 for 400 cycles, and a Coulombic efficiency of 99.93%. We attribute the enhanced performance to the cubic rock-salt framework, which promotes low-energy migration paths. Our work suggests that inducing crystallization of amorphous nanomaterials through electrochemical cycling is a promising avenue for creating unconventional high-performance metal oxide electrode materials.

25 ENERGY STORAGE↗
DOE OSTI2024

Sustainable urban transformations based on integrated microgrid designs

The impacts of natural hazards on infrastructure, enhanced by climate change, are increasingly more severe emphasizing the necessity of resilient energy grids. Microgrids, tailored energy systems for specific neighbourhoods and districts, play a pivotal role in sustaining energy supply during main grid outages. These solutions not only mitigate economic losses and well-being disruptions against escalating hazards but also enhance city resilience in alignment with Sustainable Development Goal (SDG) 11. However, disregarding socioeconomic factors in defining microgrid boundaries risks perpetuating inequalities and impeding progress towards other SDG 11 targets, including fair democratic participation. Our approach integrates social and technical indicators to bolster urban microgrid planning. Through a case study in a US county, we illustrate how integrated microgrid planning effectively intertwines urban resilience, well-being and equity while promoting sustainable development. This study underscores the importance of integrated microgrid planning for sustainable and resilient urban transformation amid environmental and societal challenges.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Parallel high-frequency magnetic sensing with an array of flux transformers and multi-channel optically pumped magnetometer for hand MRI application

Here, we investigate an approach for parallel high-frequency magnetic sensing based on a multi-channel radio frequency (RF) optically pumped magnetometer (OPM) coupled to multiple flux transformers (FTs) with a focus on hand magnetic resonance imaging (MRI) application at ultra-low field (ULF). Multiple RF OPM sensing channels are realized by using a single large-area alkali-metal vapor cell and two laser beams for pumping and probing, shared for all the channels. This design leads to significant cost reduction when multi-channel sensing is desirable, as in the case of ULF MRI. The FT, composed of two connected coils, serves as a transmitter of a target magnetic field to the OPM, while decoupling the OPM from untargeted magnetic fields in the sensing area that can limit the OPM performance. For hand MRI application, theoretical and numerical analysis is performed to determine an optimal geometry for the FT array that could improve signal-to-noise ratio (SNR) and sufficiently reduce crosstalk between FTs. We estimate that the optimized multi-channel FT-OPM sensor can achieve a magnetic field sensitivity of the order of 1 fT / Hz 1 / 2 above 100 kHz, which would be sufficient for 1 mm resolution MRI. In general, the multi-channel capability enables simultaneous magnetic measurements, thus reducing the sensing time and improving the SNR, and we anticipate many applications of the multi-channel FT-OPM sensor beyond the targeted here hand MRI: anatomical parallel ULF MRI of the human brain and other parts of the body, airport security screening, magnetic material imaging, and many others.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Direct measurement of storage and loss behavior in AFM force–distance experiments using the modified Fourier transformation

Force–distance curve experiments are commonly performed in atomic force microscopy (AFM) to obtain the viscoelastic characteristics of materials, such as the storage and loss moduli or compliances. The classic methods used to obtain these characteristics consist of fitting a viscoelastic material model to the experimentally obtained AFM data. Here, we demonstrate a new method that utilizes the modified discrete Fourier transform to approximate the storage and loss behavior of a material directly from the data, without the need for a fit. Additionally, one may still fit a model to the resulting storage and loss behavior if a parameterized description of the material is desired. In contrast to fitting the data to a model chosen a priori, departing from a model-free description of the material's frequency behavior guides the selection of the model, such that the user may choose the one that is most appropriate for the particular material under study. To this end, we also include modified Fourier domain descriptions of commonly used viscoelastic models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Neutron-producing gas puff Z-pinch experiments on a fast, low-impedance, 0.5 MA linear transformer driver

A study on the neutron production from single and double gas puff Z-pinches on the CESZAR linear transformer driver with ~0.45 MA current and 170 ns rise time is presented. Total neutron yield measurements made with a LaBr activation detector are compared for three configurations, using a double nozzle setup. When a single, hollow, deuterium gas shell was used, reliable implosions could only be attained at higher load mass than the optimal value to match implosion time with the driver rise time, with neutron yields of ~10 6 per pulse. The use of a double gas puff configuration with a deuterium center jet allowed a reduction in the shell density and operation closer to machine-matched conditions, recording up to (4.1 ± 0.3) × 10 7 neutrons/pulse when either Kr or D 2 was used in the shell. For a comparable mass and implosion time, using a higher atomic-number gas in the outer shell results in more unstable plasma surface and smaller plasma radius at the location of instability bubbles, which, however, do not seem to consistently correlate with a higher neutron yield. Comparing implosion dynamics with models and neutron yields with literature scaling suggests that the machine current is not well coupled to the plasma during the final stages of compression. Optimizing current and energy coupling to the pinched plasma is critical to improving performance, particularly in low-impedance drivers.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Multiphysics Analyses of the Bottom Components of the 3D Printed Transformational Challenge Reactor

This research presents multi-physics analyses on the bottom components of the Transformational Challenge Reactor (TCR) facility. These components include the bottom axial reflector and the steel exit cone. The bottom axial reflector is made of pure silicon carbide elements hosting helium cooling channels These elements are 3D printed and therefore can host any arbitrary shape of the helium cooling channels. The design of the bottom reflector considers the neutronics and thermo-fluid dynamics performances as well as the manufacturing process optimization. More precisely, the best design of the bottom reflector reduces neutron leakage by avoiding straight cylindrical helium channels that facilitate neutron leakage, minimizes the helium flow pressure drop, and reduces the number of 3D printed silicon carbide pieces. The exit cone steel structure collects the hot helium from the bottom fuel assemblies and channels the cold helium to the top of the fuel assemblies. The steel simultaneous contact with hot and cold helium flows sets a large thermal gradient. Different designs of the exit cone are proposed to reduce the steel equivalent stress from the helium thermal load. The multi-physics analyses have been performed using Ansys Fluent, Ansys Mechanical, STAR-CCM+, and Serpent computer programs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Serpent and MCNP Calculations of the Energy Deposition in the Transformational Challenge Reactor

This paper focuses on the calculation of the energy deposition in the Transformational Challenge Reactor by two major Monte Carlo codes: Serpent and MCNP. The first software computation relies on Kinetic Energy Released per unit Mass (KERMA) factors while the second one relies on Q-values. The results from these two independent computation methodologies are in very good agreement; however, Serpent runs much faster than MCNP (for the same computational model) and allows for a detailed energy deposition distribution from a 1-mm-side square mesh with a relative statistical error between 0.5% and 1%. This detailed energy deposition is suitable for multiphysics analyses aimed at design optimizations. In order to calculate the energy deposition, Serpent needs enhanced ACE files (distributed by the software developers). Unlike other Monte Carlo software that uses inputs based on Python or Java languages, the Serpent input syntax is very similar to that of MCNP; a Python script can convert a MCNP input to a Serpent input in seconds. For simulations not requiring the calculation of the energy deposition, Serpent can also read nuclear data from MCNP ACE files, which eventually improves the comparison of the results of the two codes.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

A transient near to far field transformation method and verification benchmarking procedure

The numerical calculation of electromagnetic far fields in the time-domain requires a near to far field transformation (NTFF) method. While time-domain NTFF methods for popular finite-difference time-domain (FDTD) approaches are well established, there is little discourse on NTFF methods for finite-element time-domain (FETD) codes. Here, this work is concerned with the development of an NTFF method for the Empire FETD code, which utilizes curl and divergence conforming elements. This discretization presents a difficulty in obtaining the equivalent electric current for the NTFF. Straightforward finite element interpolation of the fields is shown to give poor accuracy. Alternative interpolation methods are recommended. An expanding magnetic quadrupole pulse benchmark problem, which is fully developed in the appendices, provides the basis for quantitative comparison.

FETD↗
DOE OSTI2026

Predicting Atomistic Transitions with Transformers

Accurate knowledge of the atomistic transition pathways in materials and material surfaces is crucial for many material science problems. However, conventional simulation techniques used to find these transitions are extremely computationally intensive. Even with large-scale, accelerated material simulations, the computational cost constrains the applicable domain in practice. Machine learning models, with the potential to learn the complex emergent behaviors governing atomistic transitions as a fast surrogate model, have great promise to predict transitions with a vastly reduced computational cost. Here, we demonstrate how transformers can be trained to predict atomistic transitions in nano-clusters. We show how we evaluate physical validity of the predictions and how a multitude of additional, different microstates can be generated by slightly varying the data provided to the model.

36 MATERIALS SCIENCE↗