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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The effect of temperature and burnup on U-10Zr metallic fuel chemical interaction with HT-9: A SEM-EDS study

The fuel cladding chemical interaction (FCCI) between Uranium-Zirconium-based metallic fuel and cladding materials during in-pile service is one of the most constraining phenomena affecting the performance of this fuel system. In this study, we investigated the effect of temperature and burnup on the FCCI development in two U-10 wt.% Zr (U-10Zr) fuel samples with HT-9 cladding irradiated as part of the MFF-3 irradiation test in the Fast Flux Test Facility (FFTF). One sample achieved a burnup of 13.1 at.% and operated with an average inner cladding temperature of 530°C, while the other achieved a burnup of 8.5 at.% and was subjected to an average inner cladding temperature of 615°C. Automated scanning electron microscopy (SEM) back-scattered electron (BSE) imaging of entire fuel cross-sections and SEM energy dispersive x-ray spectroscopy (EDS) analysis on specific fuel-cladding interface regions successfully provided a comprehensive characterization of the depth and type of interaction happening under different irradiation conditions. Further, our analysis shows that FCCI development on both fuel and cladding side is strongly influenced by the inner cladding temperature and, to some extent, the formation and integrity of Zr-rich layers between the fuel and cladding, while the impact of burnup and power is negligible. Measured FCCI thicknesses were compared to BISON simulations using both an empirical model based upon legacy data from the Experimental Breeder Reactor II (EBR II) irradiations and a mechanistic model currently under development for the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, showing satisfactory agreement. Nonetheless, this comparison supports the need for additional microstructural characterization in intermediate ranges of temperature, power, and burnups in prototypic-length pins.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Fracture mechanics approach to TRISO fuel particle failure analysis

Weibull stress-based methods for failure probability assessment have been developed and analyzed to assess the integrity of tristructural isotropic (TRISO) fuel particles during fuel life cycles and accident operating conditions. While simple, these methods entail a number of drawbacks when stress concentrates near crack tips, including finite element mesh size dependency when the Weibull stress is averaged over the finite element domain. Fracture mechanics approaches involving the use of interaction integrals eliminate this lack of mesh convergence and produce consistent fracture predictions. In this work, we use an interaction integral approach to computing stress intensity factors for a crack in the inner pyrolytic carbon layer perpendicular to the silicon carbide layer, which is simplified representation of a failure mode in TRISO particles. Further, the interface between these two TRISO layers has been shown to become porous, which we simulate by considering a transition of mechanical properties over such porous length, i.e. the layers are modeled as a functionally graded material. Aspects such as porosity and thermal and irradiation eigenstrains are considered in computing the stress intensity factor from a fracture mechanics approach and compared with the known Weibull stress failure approach. The methodology introduced in this paper enables a more general fracture probability assessment in TRISO particles and eliminates the need to identify best suited parameters when using local or averaged stress-based failure criterion. Finally, the numerical sensitivity studies show how parameters such as the porous transition zone length, the material stiffness, and creep affect the probability of TRISO fuel particle failure.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Impact of temperature variations on BISON predictions of Ag release in AGR-1 and AGR-2 experiments

Understanding and quantifying the release of fission products like silver (Ag) from TRistructural ISOtropic (TRISO) fuel particles is important to assess the safe operation of advanced high temperature reactors. Although the silicon carbide (SiC) layer of TRISO particles is effective as the main fission product barrier, Ag can be released from intact TRISO particles. A mechanistic model for the effective Ag diffusivity, D eff , was previously developed as a function of temperature and microstructure variables informed by atomistic modeling of Ag diffusivity on the mesoscale. Here in this study, we use this model to explore how experimental temperature uncertainties impact the overall predicted Ag release. This analysis shows that temperature uncertainties have a significant impact on the overall Ag release predictions. Furthermore, we show that the time average volume average temperature (TAVA) temperature is not an appropriate proxy for temperature histories to predict fission product release. We attribute this to the Arrhenius dependence of Ag diffusivity with respect to temperature. The detailed temperature histories, therefore, provide the most accurate results are are of most importance for modeling efforts. Overall, this work shows the importance of considering the experimental uncertainty of the temperature on computational predictions of fission product transport and release and the need for more accurate temperature histories from future experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Wind power production from very large offshore wind farms

In this work, we provide the first quantitative assessment of power production and wake generation from offshore wind energy lease areas along the U.S. east coast. Deploying 15-MW wind turbines, with spacing equal to the European average, yields electricity production of 116 TWh/year or 3% of current national supply. However, power production is reduced by one-third due to wakes caused by upwind wind turbines and wind farms. Under some flow conditions whole wind-farm wakes can extend up to 90 km downwind of the largest lease areas, and the frequency-weighted average area with a 5% velocity deficit is 2.6 times the footprint of the lease areas. Simulations including maritime corridors demonstrate reduction in the wake effects leading to power-efficiency gains and may offer contingent benefits. First-order scaling rules are developed that describe how “wake shadows” from large offshore wind farms scale with prevailing meteorology and wind turbine installed densities.

15 MW↗
DOE OSTI2021

PARIS: Predicting application resilience using machine learning

The traditional method to study application resilience to errors in HPC applications uses fault injection (FI), a time-consuming approach. Furthermore, while analytical models have been built to overcome the inefficiencies of FI, they lack accuracy. In this paper, we present PARIS, a machine-learning method to predict application resilience that avoids the time-consuming process of random FI and provides higher prediction accuracy than analytical models. PARIS captures the implicit relationship between application characteristics and application resilience, which is difficult to capture using most analytical models. We overcome many technical challenges for feature construction, extraction, and selection to use machine learning in our prediction approach. Our evaluation on 16 HPC benchmarks shows that PARIS achieves high prediction accuracy. PARIS is up to 450x faster than random FI (49x on average). Compared to the state-of-the-art analytical model, PARIS is at least 63% better in terms of accuracy and has comparable execution time on average.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Mesoporous Textured Fe-N-C Electrocatalysts as Highly Efficient Cathodes for Proton Exchange Membrane Fuel Cells

A new platinum group metal (PGM)-free proton exchange membrane fuel cell (PEMFC) cathode catalyst materials, synthesized using the VariPore™ method by Pajarito Powder, LLC, are characterized for their structure and activity. The physico-chemical analysis of the iron-nitrogen-carbon (Fe-N-C) electrocatalysts show mesoporous carbon material effectively doped with iron and nitrogen. The materials have an average pore size of 7-8 nm and high specific surface area. The Fe-N-C catalysts exhibit good oxygen reduction reaction (ORR) activity in 0.5 M H2SO4 electrolyte with high half-wave potential and sustainable electrochemical stability over 10,000 repeated potential cycles with insignificant losses in their activities. As cathode catalysts in a PEMFC, the Fe-N-C materials deliver remarkably good fuel cell performance at low overpotential approaching that of the commercial Pt catalyst. The high ORR electrocatalytic activity of these Fe-N-C catalysts is credited to the synergy between nitrogen-moieties, specifically pyrrolic-N, pyridinic-N, and graphitic-N, and iron in addition to the high mesoporosity that facilitate an effective reaction path in boosting the electrocatalytic activity and stability.

DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Mitigation and Diagnosis of Pin-Hole Formation in Polymer Electrolyte Membrane Fuel Cells

In this work, we implement a calendering technique to flatten stray fibers within the gas diffusion media, thereby mitigating pin-hole formation in the hot-pressed MEAs. We have investigated the influence of calendering on the long-term durability for several types of gas diffusion electrodes (GDEs) using a combined chemical and mechanical accelerated stress test (AST). The calendered MEAs demonstrate an average AST lifetime improvement of 77% relative to the as-fabricated MEAs.

AMR↗
DOE OSTI2025

Bayesian optimization of entropy-stabilized metal fluoride conversion cathodes and their synthesis

Fluoride-based conversion cathodes are promising for next-generation Li ion batteries because of their high voltage and energy densities. High entropy earth abundant fluoride cathodes are attractive for sustainable battery because of the potentially higher cycling stability. While Ni and Co containing equimolar alloys stabilized by high entropy were considered previously, Ni and Co free non-equimolar metal alloy compositions might offer new opportunities to increase materials stability and average voltage. This opportunity was pursued using Bayesian optimization of average voltage for the computational prediction of (Fe x1 Cu x2 Zn x3 Mn x4 Mg x5 )F 2 cathode chemistries. Theoretical voltage, room temperature free energy, and electronic structure of potentially high voltage compositions were computed using density functional theory calculations. Here, the experimental realization of non-equimolar quinary Co and Ni free fluorides with stable rutile single phase and improved conversion reaction voltage by up to 28 % compared to the equimolar alloy demonstrated the power of Bayesian optimization methods for the computationally guided experimental synthesis of fluoride cathodes. Finally, non-equimolar cathode chemistries were found with cycling stability superior to known multi-element fluorides.

25 ENERGY STORAGE↗
DOE OSTI2025

200 h of discharge cycling with an all-aqueous copper thermally regenerative ammonia battery

Thermally regenerative ammonia batteries (TRABs) offer an approach to energy storage and electricity generation by harnessing low-grade heat (T < 150 °C). A TRAB discharge resembles that of a flow battery, where electrochemical reactions produce electrical power from energy stored within aqueous electrolytes. While there are many types of TRABs, the all-aqueous copper TRAB (Cu aq -TRAB) has produced the largest power and energy storage densities. Despite many improvements to TRAB performance, most tests have only lasted a few hours, which are not representative of operation times expected of these devices. Herein, we operated a Cu aq -TRAB for 200 h of constant current discharging to assess battery performance and component stability. After 200 h of testing, the average power density increased slightly to 7.2 mW cm -2 , which was within 0.01 % of starting conditions. Likewise, the average energy density for the final cycle was only 0.03 % lower than the initial cycle. The overall insensitivity of the power cell to cycling represents a major milestone in the advancement of TRAB systems. In conclusion, energy dispersive X-ray spectroscopy provides evidence to suggest that these small changes in power and energy density are likely to the membrane acclimating to TRAB electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2026

Optimizing Porous Transport Layer Porosity for Proton Exchange Membrane Water Electrolysis

An empirical model is presented that describes anode-side losses related to porous transport layer (PTL) morphology in proton exchange membrane water electrolysis (PEMWE). The model is based on an advanced voltage breakdown analysis that links various overpotentials to PTL morphology. Custom Ti PTLs, spanning uncommonly low porosities (22 - 31%), were fabricated and analyzed with X-ray CT to obtain pore and particle size distributions. Particle size distributions were consistent across samples with an average particle diameter of 12.0?..mu..m, whereas average pore diameters ranged from 6.0 to 7.0?..mu..m. The PTLs were tested in standard PEMWE cell assemblies with anode catalyst loadings of 0.1 mgIr cm-2 to obtain polarization curves, electrochemical impedance spectra, and augmented Tafel analysis. The PTL-dependent anode side losses were deconvoluted and assigned to excess utilization, concentration, ion transport resistance, and electrical contact resistance overpotentials. The data and model reveal an optimal 20 - 28% PTL porosity region where utilization and contact resistance overpotentials are minimized without triggering concentration and ion transport losses related to water deprivation. The optimal PTL porosity depends on the operating current density and is demonstrated at realistic PEMWE water flow rates to establish PTL design guidance for operation at scale.

08 HYDROGEN↗
DOE OSTI2023

Creating an Advanced Sensor Network to calculate real-time, mass-weighted flue gas composition and air heater leakage of a coal-fired utility boiler under dynamic operating conditions

Utilization of renewable energy sources to minimize the environmental impact of energy production has changed the way utility boilers operate, requiring frequent load cycling between full load and partial loads as low as 30%. Dynamic operation of coal-fired utility boilers significantly reduces boiler efficiency when compared to steady state at full load. Data-driven plant optimization has shown success with coal-fired utility boilers under dynamic operating conditions. The purpose of this work was to create an Advanced Sensor Network (ASN) to provide more extensive real-time data to inform dynamic plant optimization of Net Unit Heat Rate (NUHR). The ASN consists of gas sampling grids in the convective pass of the boiler and downstream of the air heater. These sampling grids allow for quantification of spatial variation of flue gas within the boiler and calculation of mass-weighted composition of flue gas through the combination of composition, velocity, and temperature measurements. The comparison of O 2 between the inlet and outlet of the air heater is used to calculate air leakage in real time. Flue gas composition and air heater leakage are both important factors in boiler efficiency and NUHR. Further, the results of this work support the value of mass-weighted averages for determining flue gas composition accurately. The measurements from the ASN show increased composition stratification during dynamic operation, with an average standard deviation 38% higher than observed during steady-state operation. Air heater leakage was also observed to increase from 2.8% to 5.1% following a load change. Prior to the installation of the ASN, these data would not have been available for dynamic control. These real-time data will be leveraged to calculate and optimize for NUHR during dynamic operation in future work.

42 ENGINEERING↗
DOE OSTI2023

K-shell radiation and bright spot characteristics of high-energy-density Fe-Cr-Ni plasmas influenced by X-pinch load geometry

K-shell radiation is diagnostically advantageous for the study of high-energy-density plasmas. Here, the current work investigates the influence of load geometry on K-shell x-rays in Z-pinch plasmas produced on the 1-MA Zebra generator. Stainless steel (Fe: 69%, Cr: 20%, Ni: 9%) X-pinch wire loads are fielded with an interwire angle of 31° (small-angle) or 62.5° (large-angle), respectively and studied using various x-ray diode, imaging, and spectroscopic diagnostics. The large-angle geometry produced large, individual x-ray-emitting bright spots (> 3 keV) at the wire cross-point averaging areas ≥ 1 mm 2 , with noteworthy soft x-ray (> 0.75 keV) bursts and radiation yields ≤ 14.6 kJ. The small-angle geometry produced multiple x-ray-emitting bright spots extending the pinch axis with smaller average sizes (≤ 0.5 mm 2 ), consistent with stronger current, increased radiation yield (≥ 15.5 kJ), and notable hard x-ray (> 9 keV) bursts, which peak with largest cumulative bright spot size. Spectroscopic analysis is performed with non-LTE collisional-radiative modeling, indicating a cold, nonthermal plasma region radiating from neutral to Ne-like ions, a hot, thermal region radiating from Fe and Cr He- and Li-like ions, and an intermediate region radiating Kα satellites from Li-like to O-like ions. Modeling of the nonthermal and satellite features imply a fractional hot electron abundance ~0.1% and ~0.5% for large- and small-angle geometries, respectively. Relative intensity analysis is performed on Heα and Kα lines, revealing intensity ratios that deviate from the original wire composition. Optical depth estimates allude to optically thick, thermal K-shell plasmas with densities consistent with non-LTE modeling.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Dislocation density in fine grain-size spark-plasma sintered aluminum measured using high brightness synchrotron radiation

In this study, three-dimensional orientation mapping of samples of aluminum prepared by spark plasma sintering (SPS) with average grain sizes of 5 μm and 1 μm has been carried out using high-brightness synchrotron radiation, from which the geometrically necessary dislocation (GND) density has been determined. The low average measured GND density values confirm that the SPS process can be used to produce samples containing grains with dislocation density similar to that of fully recrystallized coarse-grained samples. Values of GND density are also compared to those obtained from electron back-scatter diffraction studies on the same material, highlighting the significantly higher angular resolution of the synchrotron data. For the 5 μm grain-size sample the measured GND density can account for a large fraction of the previously observed positive Hall-Petch deviation of this material. For the 1 μm grain-size sample, however, the GND-based strengthening contribution is much smaller than the reported positive Hall-Petch deviation, such that the additional strength may be reliably associated with dislocation source-limited strengthening.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Ultrasonic additive manufacturing of zirconium: Pilot results

Ultrasonic additive manufacturing (UAM) was successfully applied to the Zirconium material system to create a three-dimensional component prototype. The UAM process resulted in grain size refinement and significant localized deformation. Electron backscatter diffraction analysis revealed activated slip systems only applicable at high deformation temperatures, while texture analysis showed a decrease in measurable texture of the UAM build in comparison to the initial Zr-foil. Average tensile strengths in the X (sonotrode travel), Y (vibration), and Z (build) directions averaged 435 MPa, 458 MPa, and 359 MPa, respectively, demonstrating the viability of UAM for Zr-based materials. Furthermore, the delamination of some Z specimens along foil boundaries during loading suggests an interplay between interfacial Ti impurities introduced during welding and the spatial dependence of weld quality for this material system.

36 MATERIALS SCIENCE↗
DOE OSTI2021

2-Aminoimidazole reduces fouling and improves membrane performance

Biofouling is difficult to control and hinders the performance of membranes in all applications but is of particular concern when natural waters are purified. Fouling, via multiple mechanisms (organic-only, biofouling-only, cell-deposition-only, and organic + biofouling), of a commercially available membrane (control) and a corresponding membrane coated with an anti-biofouling 2-aminoimidazole (2-AI membrane) was monitored and characterized during the purification of a natural water. Results show that the amount of bacterial cell deposition and organic fouling was not significantly different between control and 2-AI membranes; however, biofilm formation, concurrent or not with other fouling mechanisms, was significantly inhibited (95–98%, p < 0.001) by the 2-AI membrane. The limited biofilm that formed on the 2-AI membrane was weaker (as indicated by the polysaccharide to protein ratio) and thus presumably easier to remove. The conductivity rejection by the 2-AI and control membranes was not significantly different throughout the 75-h experiments, but the rejection of dissolved organic carbon by biofouled (biofouling-only, cell-deposition-only, and organic + biofouling) 2-AI membranes was statistically higher (10–12%, p = 0.003–0.07). When biofouled, the water permeance of the 2-AI membranes decreased significantly less (p < 0.05) over 75 hours than that of the control membranes, whether or not other additional types of fouling occurred concurrently. Despite the initially lower water permeances of 2-AI membranes (11% lower on average than controls), the 2-AI membranes outperformed the controls (10–11% higher average water permeance) after biofilm formation occurred. Overall, 2-AI membranes fouled less than controls without detriment to water productivity and solute rejection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Dissolved gas recovery from water using a sidestream hollow-fiber membrane module: First principles model synthesis and steady-state validation

This paper presents a first-principles model for the recovery of dissolved gases from liquids using a sidestream hollow-fiber membrane module. The model avoids the use of new empirical coefficients, thus providing a parametric understanding of the process behavior for future design and optimization of membrane modules. This type of first-principles model could be particularly useful when gas recovery is beneficial to biological or chemical reactions of interest, such as the acetogenesis reactions in two-stage anaerobic digesters. The steady-state behavior of the model was validated against both new experimental data for the recovery of H 2 , CH 4 and H 2 –CH 4 mixtures from pure water, as well as existing published data. The modeled gas recovery predictions agreed with experimental data to an absolute average error of 13%, and an average R value of 0.98. Parametric analysis of mixed-gas recovery suggests possible key transition points in the composition of the recovered gases. For example, at 40 °C, increasing trans-membrane pressure while keeping hydraulic residence time (HRT) under 0.5 s will result in an increase in the ratio of H 2 to CH 4 recovered. Otherwise, increasing trans-membrane pressure will instead decrease the ratio of H 2 to CH 4 recovered. The model has potential to be extended to transient analysis, but has yet to be validated with transient experimental data. Furthermore, this model was successfully implemented in both Python and MATLAB, and provides valuable insights for future net-energy optimization for anaerobic digestion systems with in-situ gas recovery.

Anaerobic Digestion↗
DOE OSTI2024

Generating Buoyancy-Driven Convection in Membrane Distillation

Membrane distillation (MD) is a thermally-driven desalination process that can treat hypersaline brines. Considerable MD literature has focused on mitigating temperature and concentration polarization. This literature largely neglects that temperature and concentration polarization increase the feed density near the membrane. With gravity properly oriented, this increase in density could trigger buoyancy-driven convection and increase permeate production. Convection could also be strengthened by heating the feed channel wall opposite the membrane. To investigate that possibility, we perform a series of experiments using a plate-and-frame direct contact MD system with an active membrane area of 300 cm2 and a feed channel wall heated using a resistive heater. The experiments measure the average transmembrane permeate flux for two gravitational orientations, feed Reynolds numbers between 128 and 1128, and wall heat fluxes up to 12 kW/m2. The results confirm that with gravity properly oriented, wall-heating can trigger buoyancy-driven convection for a wide range of feed Reynolds numbers, and increase permeate production between roughly 20 and 130 %. We estimate, however, that at high Reynolds numbers (Re > 800), more than 70 % of the wall heat is carried out of the MD system by the feed flow, without contributing to permeate production. This suggests the need for longer membranes and heat recovery steps in any future practical implementation.

buoyancy-driven convection↗
DOE OSTI2023

Data-driven acoustic measurement of moisture content in flowing biomass

Measuring the moisture content in flowing biomass is critical to processes such as liquid biofuel conversion, such as biogasoline, biodiesel, bio jet kerosene, etc. However, biomass tends to flow in aggregates, which results in significant inhomogeneities in the amount of biomass flowing in front of a sensor at a given time, and there can be significant overlap in the material properties of dry vs wet biomass, leading to poor signal-to-noise ratio. We present a technique for identifying biomass moisture content using a series of acoustic pitch-catch measurements to quantify the sound speed and acoustic amplitude through the biomass, in conjunction with classical machine learning techniques, including Naive Bayes, Random Forest, and K-Nearest Neighbors classification. We amplify the differences between the acoustic measurements in different moisture levels by collecting a series of pulse-echo measurements, which we sort in order of ascending sound speed. We test the accuracy of the technique on experimentally-prepared batches of corn stover biomass with specified moisture levels and measure the average error in the estimated moisture level as a function of the number of pitch-catch measurements used. We observe average estimation errors as low as 6.7% by increasing the number of measurements and optimizing the hyperparameters. This work presents a novel method determining moisture content in flowing biomass with inhomogeneous flow. Additionally, this technique has application in optimizing biomass conversion processes, as well as other fields including, paper production, natural fiber processing, and mineral extraction.

09 BIOMASS FUELS↗