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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Progress on Producing an ODS Ferritic Alloy by High Deformation Processing of Reactive Powders

Oxide dispersion-strengthened (ODS) ferritic alloys, such as 14YWT, are considered as benchmarks for fuel cladding and core structural materials in advanced nuclear energy reactor systems that require high temperature strength and creep properties as well as resistance to radiation damage. Unfortunately, the popular processing method of mechanical alloying (MA) is time-consuming, incurs high manufacturing costs, and typically produces anisotropy in the microstructure and mechanical properties of ODS ferritic alloys. These considerations hinder their acceptance in many high-performance, high-temperature materials for advanced nuclear energy reactors. This work package is pursuing an alternative solid-state processing to MA involving high deformation at high temperatures for producing an ODS ferritic alloy for fuel cladding. This report summarizes the concept for this processing method and the progress toward selecting the reactive and ferritic alloy powders, preparing 316L stainless steel cans for filling with blended powder followed by degassing, and choosing the heat treatments and forging parameters for performing the first two forging experiments. The preliminary results of optical microscopy performed on polished samples that were cut in three orthogonal directions revealed remnant porosity in both samples. The analysis of the porosity using ImageJ revealed a lesser amount of ~2.49% in the HiLoMe-1 sample that was processed with the three heat treatments and forging parameters compared to ~5.70% for the Hi-1 sample that was processed with only the first sintering heat treatment and forging parameter. These initial results will provide insights that can be leveraged to improve the densification of the powders in future forging experiments that will be continued into FY24 for determining the optimum heat treatments and forging parameters coupled with detailed microstructure characterization studies for producing the ODS ferritic alloy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Dynamic Mesoscale Materials Science at the Advanced Photon Source [Slides]

We are modernizing our 1980s nuclear deterrent, and moving beyond life extensions (W76, B61, W88) to systems that have more newly (differently) manufactured components (W80, W87). For the first time since the 1980s, we are doing truly new designs (W93) and there will likely be more to respond to emerging deterrence gaps.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Solid State Ionics: Materials Development by Multiscale Modeling and Advanced Manufacturing Techniques

Solid-state ionic materials are an important enabling technology for energy conversion and storage. Solid-state batteries would be a safer and higher energy density alternative to commercially available lithium ion batteries (LIB), however their implementation requires ion conduction in solids at room temperature to occur on the same level as the current generation of liquid electrolytes. Microstructural modifications have been demonstrated to play a major role on ion transport through the control of grain boundary interfaces, which traditionally serve as “blocking” layers. Ultimately, these materials will be fabricated in thin films form as electrolytes in order to minimize ohmic losses in electrochemical devices. This work uses advanced manufacturing techniques in combination with theoretical modeling to implement a science-based approach in the deposition of thin films ion conductors with controlled microstructures used in ceramic energy conversion and storage devices.

25 ENERGY STORAGE↗
DOE OSTI2021

Hydrogen-Bonding Reinforced Flexible Composite Electrodes for Enhanced Energy Storage

The lack of advanced electrode materials is one of the main factors hindering the development of flexible rechargeable aqueous batteries (RABs) for high specific energy density and structural stability. It is also challenging to achieve high-capacity performance for both the positive and negative electrodes simultaneously. In this work, it is demonstrated that, by smartly designing the composite structures of positive and negative electrodes via one-step electrodeposition strategy, the energy storage performance of the RAB is largely enhanced. For positive electrode material synthesis, Co-Cu double hydroxides (Co-Cu-DH) nanosheets are skillfully rooted into electroreduced graphene oxide (eRG) via hydrogen bonding, in which graphene oxide reduction, Co-Cu-DH nucleation/growth, and formation of hydrogen bonding between Co-Cu-DH and eRG simultaneously occur. Moreover, when a RAB based on Co-Cu-DH@eRG//FeOOH@eRG using the same composite design strategy is established, a wide operating voltage window of ≈1.8 V, a high specific energy density of ≈142.8 Wh kg –1 at ≈890 W kg –1 , and long-term cyclic stability (88.5% of capacity retention after 12 000 cycles) are obtained. This study presents a general compositing strategy for the development of advanced electrode materials, and it is expected to stimulate future material synthesis/design in RABs toward the goal of high energy density storage.

25 ENERGY STORAGE↗
DOE OSTI2025

Efficient Measurement of Length Distribution of 1D Nanoparticles in Solution via Optical Polarimetry

The efficient measurement of the length distribution of nanotubes, nanowires, and other one-dimensional (1D) nanoparticles in solution is important to enable their incorporation into materials and devices and to optimize their processing for properties of interest, such as thermal/electrical conductivity or mechanical strength, in suspensions and composites. We report an electric-field (E-field)-assisted optical-polarimetry technique to measure the length distribution of ensembles of high-aspect-ratio particles in dilute suspension. The degree of alignment of polarizable 1D particles suspended in a fluid under Brownian motion explicitly depends on the E-field strength and the particle length. We show that it is possible to extract the length distribution of 1D nanoparticles suspended in an insulating fluid by applying a range of E-fields and using optical polarimetry to measure the corresponding alignment order parameter. Notably, the method is relatively insensitive to the diameter of the 1D particles, which can be poorly known or vary within a sample. The technique is validated with silver nanowires and carbon nanotubes of known lengths, as well as polymer-depletion-length-separated single-wall carbon nanotube samples with length distributions independently measured with analytical ultracentrifugation. Finally, we demonstrate the ability of the optical-polarimetry technique to quantify changes in the length distribution of ultranarrow, sub-nanometer-diameter single-wall carbon nanotubes under different types and durations of ultrasonication. Within its range of applicability (polarizable 1D nanoparticles in the 0.5 to 15 μm length range, constrained by the voltage stability of the media and the suspended particles), the E-field-assisted optical-polarimetry method is a particularly efficient and accurate method to measure the length distribution of nanowires and nanotubes in suspension.

1D nanoparticles↗
DOE OSTI2024

A machine-learning-aided data recovery approach for predicting multi-material thermal behaviors in advanced test reactor capsules

Instrumented experiments conducted at test reactors are essential to the deployment of new advanced reactor systems. Designing new experiments and generating data on specific reactor conditions require significant investments in terms of both time and cost. Finite element analysis software can be used to create high-fidelity models of experiment environments in order to support the actual experiments, but computation time remains a concern in terms of applying outcomes to real-time usage of data (e.g., a digital twin [DT]). Here, the present research proposes a machine-learning (ML) aided approach to making temperature and displacement predictions based on the thickness of the outer gas gap on the experimental capsule used for in-pile demonstration of a novel new thermal conductivity probe in the Advanced Test Reactor (ATR). This capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. Gas gaps existed between the fuel and the rodlet, and between the inner and the outer capsule. The learning data pertained to an experimental capsule's radial distributions of temperature and displacement, as obtained based on Abaqus and the physical features. For the first step of ML sequence, the temperature was predicted using three positional parameters. Next, the displacement was predicted using seven additional parameters. Each physical feature was normalized in order to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement with the simulation results in all cases involving interpolation and extrapolation. Furthermore, data similarity enhancement increased the similarity between the training and the target data, thereby increasing the predictive accuracy of the ML models. In certain extrapolation cases involving limited original ML model accuracy, data similarity enhancement and data recovery was able to somewhat improve this accuracy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Exploration of Neutron-Rich Isotopes around N = 50 via the In-Flight Fission of a 345 MeV/Nucleon 238 U Beam

A search for new neutron-rich isotopes beyond $Z=28$ and $N=50$ was performed using a 345 MeV/nucleon $^{238}$U beam at RI Beam Factory in RIKEN Nishina Center. The BigRIPS separator and state-of-the-art radiation detectors were used to efficiently separate and identify the produced nuclei. For the first time, we have observed and determined the production cross sections for eight neutron-rich isotopes $^{72}$Cr, $^{74,75}$Mn, $^{77}$Fe, $^{79,80}$Co, $^{85}$Cu, and $^{90}$Ga. This study broadens our understanding of nuclear existence and paves the way for investigating the properties of neutron-rich medium-mass nuclei.

Shimizu, Yohei [RIKEN, Saitama (Japan); et al.]↗
DOE OSTI2023

Embracing Anisotropy – Opportunities and Challenges to the Functional Application of Advanced Manufacturing of Energetic Materials

The growing use of additive manufacturing (AM) to manipulate materials, such as metals and plastics, is a result of the desire to realize the potential benefits (i.e., cheaper, faster, and simpler production of complex parts) of the process and the efforts of countless researchers to reduce the method to practice for a broad range of materials. However, in the field of energetic materials (EMs), similar benefits have been broadly claimed, but the transition to practical, industrial applications is still not obvious. The translation of small-scale experimental results to real-world application will have unique challenges, especially due to the nature and requirements of EM functionality. Some of the benefits of AM, such as simpler and faster part creation, are more easily realized. However, attaining the most profound benefits, such as added functionality enabled by spatial gradations of composition or density, will require much more effort from the EMs community to put them into general use.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Artificial Intelligence Designer of Materials and Processes for Advanced Power Generation

In this presentation, ‘deep-freeze’ graphs, ‘convoluted filtering’ networks, ‘mirror-image’ graphs, and adversarial ensemble methods are utilized to support inversion modeling for optimization of the complex compositions and complex processes in design of high-performing alloys, with their properties tailored to the energy application specifications.

Romanov, Vyacheslav↗
DOE OSTI2025

Advances in additive manufacturing, materials, and applications with AI/ML

There is high interest in making digital manufacturing a central facet of the new manufacturing landscape. However, in the materials science world, there is much work and opportunity to realize the full potential of artificial intelligence/machine learning (AI/ML) with regard to the structure–composition–processing–property (SCPP) relationship. For polymers (thermoplastics, thermosets, elastomers) and composites (nanocomposites), the origin of their high performance and even recyclability starts with design and formulation. Processing methods enable more property development based on curing, shape-factor forming, and anisotropic directionality. In subtractive manufacturing, high-performance and engineering polymers can be shaped and milled to very high tolerance and specifications and used as replacements for metals and alloys. In conclusion, this typically relies on digital manufacturing methods but tends to be wasteful in materials.

Lara-Ceniceros, Tania E. [Centro de Investigación ↗
DOE OSTI2020

Advanced Non-Tread Materials for Fuel-Efficient Tires

PPG Industries, Inc. proposes to develop a new silica filler that can increase fuel efficiency by 2% while maximizing key performance properties in non-tread tire components compared to current carbon black/silica filler blends. In this project PPG focused on a sidewall compound. The developed compounds/components will reduce energy losses by approximately 25%, with no more than a 5% loss of resistance to degradative forces (targeting better performance). PPG plans to achieve this goal by characterizing the impact the silica filler can have on rubber properties and then tuning the silica morphology and surface chemistry for this end use. PPG will also provide guidance in optimizing the compound formulation to make the best use of this new filler. A key aspect is to understand and maintain, if not improve, the aging performance as evaluated by crack growth resistance under ozone environment. Two key strategies were used to meet the rolling resistance goal while maintaining other performance properties. The first factor was understanding the surface treatments and chemical makeup of the silica and its effects on compound performance. The second factor was using the best performing silicas and determining the optimum compound modifications to achieve the final sidewall compound performance. Using statistical analysis throughout the optimization process, it was determined that changing the silica morphology played a role in improving the reduction of tear strength while reducing rolling resistance and the improvements observed varied depending on the CB type.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Advanced Manufacturing of Cathode Materials for Lithium-ion Batteries

Original objectives (per the Statement of Project Objectives) centered on developing and translating a continuous pilot process for cathode materials production while targeting (i) a 29% manufacturing cost reduction, (ii) an 82% energy consumption reduction for cathode production, and (iii) a doubling of manufacturing productivity. Planned validation included characterization at appropriate analytical facilities and electrochemical performance evaluation progressing from half-cells to pouch-cell formats, with a pathway to kilogram-scale production of finished cathode materials for representative testing. Scope evolution: During execution, the project team identified that the surfactant-enabled chemistry offered high selectivity for lithium precipitation from brine sources. A formal scope revision shifted the final project year from cathode manufacturing development to direct lithium extraction/lithium recovery, while maintaining the overarching intent of cost reduction and process simplification relevant to the battery supply chain.

25 ENERGY STORAGE↗