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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Characterization of ultrafast-laser ablation of micro-structures in Li-ion battery anode and cathode materials: Morphology, rate, and efficiency

Characterization of the rate and quality of ultrafast-laser ablation of Li-ion battery (LIB) electrode materials is presented for a collection of common and next-generation electrodes. Laser ablated micro-structures on the surface of LIB electrodes have been shown to provide dramatic enhancement of high-rate capability and electrode wetting. However, industrial adoption is hampered by a lack of data enabling informed choice of laser parameters and predicting process throughput. Here this work bridges this gap by providing characterization of the ablation process at more laser parameters (laser fluence and number of pulses used) than are currently available in the literature. Further, we expand on previous graphite and lithium iron phosphate (LFP) ablation work by extending ablation characterization to new LIB materials, providing high data resolution and adopting new characterization metrics which are relevant for industrial application of this technology. Ablated pores are characterized by their ablated depth, volume, and how the depth and volume ablation rate changes as a function of pore depth. Finally, we provide a detailed characterization of the morphology of laser ablated micro-structures which informs how material and laser parameters affect the quality of laser-processed electrodes.

25 ENERGY STORAGE↗
DOE OSTI2023

Radiative cooling textiles using industry-standard particle-free nonporous micro-structured fibers

Abstract Thermal radiation is a major dissipative pathway for heat generated by the human body and offers a significant thermoregulation mechanism over a wide range of conditions. We could use this in garment design to enhance personal cooling, which can improve the wearing comfort of garments or even result in energy savings in buildings. At present, however, radiative cooling has received insufficient attention in commercial design and production of textiles for wearable garments. Textiles that efficiently transmit the radiative heat were recently demonstrated, but either do not utilize standard weaving and knitting processes for wearable garments or require substantial process modifications. Here, we demonstrate the design and implementation of large-scale radiative cooling textiles for localized cooling management and enhanced thermal comfort using industry-standard particle-free nonporous micro-structured fibers that are fully compatible with standard textile materials and production methods. The micro-structured fibers, yarns and fabrics are part of a hierarchical photonic structure design that renders the textiles highly infrared transparent (up to > 0.8) while assuring visual opacity (up to 0.99). We design radiative cooling textiles with first-principles electromagnetic methods and fabricate them using commercial textile materials and formation facilities. Our “fabless” approach is confirmed by very good quantitative agreement between design and measurements. The resulting fabrics exhibit wearability properties expected of wearable textiles, and lower skin temperature by ≥ 3 °C compared to conventional textiles, which offers the potential for > 30 % energy savings in buildings and increases wearing comfort by significantly reducing the reliance on latent heat dissipation for thermoregulation.

Catrysse, Peter B.↗
DOE OSTI2025

Micro-structural features and material properties impact on adhesive metal joints via computational modeling and machine learning

The quality of structural bonding in practical applications depends on various factors arising from materials, pre-processing conditions, and manufacturing. Understanding how these factors influence bonding performance and determining their relative importance are of significant interest. Thus, this study evaluates the effects of microstructural features and material properties on the structural strength of adhesively-bonded metal joints at the submillimeter scale, utilizing a combination of Finite Element Modeling (FEM) and Machine Learning (ML) with Gradient Boosting Regression (GBR). The microstructural features include adhesive thickness, internal voids within the adhesive, adherend-adhesive interfacial voids, void size and volume fraction, and surface roughness. The material properties include the constitutive behavior of the adhesive, as well as the adherend-adhesive interfacial strength and fracture energy. The changes in structural strength and morphologies of the bonded metal structures with respect to different microstructural features and material properties were clarified by FEM. By further leveraging ML-GBR, the sequence of importance of these factors affecting bonding performance across various scenarios was summarized. This work provides valuable insights into the development of improved structural bonding for adhesive joints in industries such as automotive , aerospace, and beyond.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Demonstration of new fracture criteria based on micro-structure and compare to empirical model and measurements

The US nuclear industry is currently exploring extending the peak rod average burnup limits above the current regulatory limit of 62 GWd/tU. A potential concern for fuel exceeding the burnup limit is the fragmentation during a temperature transient such as a loss-of-coolant accident (LOCA). In the event of cladding failure, the fragmented fuel could relocate and disperse within the reactor. The potential impact of fuel fragmentation, relocation and dispersal (FFRD) on licensing assumptions for burnup extension has focused a great amount of efforts and research on this topic.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Physics-informed machine-learning model of temperature evolution under solid phase processes

We model temperature dynamics during Shear Assisted Proccess Extrusion (ShAPE), a solid phase process that plasticizes feedstock with a rotating tool and subsequently extrudes it into a consolidated tube, rod, or wire. Control of temperature is critical during ShAPE processing to avoid liquefaction, ensure smooth extrusion, and develop desired material properties in the extruded products. Accurate modeling of the complicated thermo-mechanical feedbacks between process inputs, material temperature, and heat generation presents a significant barrier to predictive modeling and process design. In particular, connecting micro-structural scale mechanisms of heat generation to macro-scale predictions of temperature can become computationally intractable. In this work we use a neural network (NN) model of heat generation to bridge this gap, by combining it with a simplified model of the temperature dynamics due to conduction and convection to capture the macro scale evolution of temperature. We inform the construction of the NN heat generation model using crystal plasticity simulations at the micro-structural scale to model the effects of process inputs on generation of heat. We achieved close fits of the temperature dynamics model to a diverse experimental data-set. Further, the relationships learned by the NN model between process inputs and heat generation showed qualitative agreement with those predicted by crystal plasticity simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A versatile pressure-cell design for studying ultrafast molecular-dynamics in supercritical fluids using coherent multi-pulse x-ray scattering

Supercritical fluids (SCFs) can be found in a variety of environmental and industrial processes. They exhibit an anomalous thermodynamic behavior, which originates from their fluctuating heterogeneous micro-structure. Characterizing the dynamics of these fluids at high temperature and high pressure with nanometer spatial and picosecond temporal resolution has been very challenging. The advent of hard x-ray free electron lasers has enabled the development of novel multi-pulse ultrafast x-ray scattering techniques, such as x-ray photon correlation spectroscopy (XPCS) and x-ray pump x-ray probe (XPXP). These techniques offer new opportunities for resolving the ultrafast microscopic behavior in SCFs at unprecedented spatiotemporal resolution, unraveling the dynamics of their micro-structure. However, harnessing these capabilities requires a bespoke high-pressure and high-temperature sample system that is optimized to maximize signal intensity and address instrument-specific challenges, such as drift in beamline components, x-ray scattering background, and multi-x-ray-beam overlap. We present a pressure cell compatible with a wide range of SCFs with built-in optical access for XPCS and XPXP and discuss critical aspects of the pressure cell design, with a particular focus on the design optimization for XPCS.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Modeling ablator defects as a source of mix in high-performance implosions at the National Ignition Facility

Recent indirect drive inertial confinement fusion implosions on the National Ignition Facility (NIF) [Spaeth et al., Fusion Sci. Technol. 69, 25 (2016)] have crossed the threshold of ignition. However, performance has been variable due to several factors. One of the leading sources of variability is the quality of the high-density carbon (HDC) shells used as ablators in these experiments. In particular, these shells can have a number of defects that have been found to correlate with the appearance of ablator mix into the hot spot and a degradation in nuclear yield. These defects include pits on the ablator surface, voids in the ablator bulk, high-Z debris from the Hohlraum wall that adheres to the capsule surface, and finally the inherent granular micro-structure of the crystalline HDC itself. This paper summarizes high-resolution modeling of each of these mix sources in two recent high-performance NIF implosion experiments. The simulated impact from a range of individual capsule defects is found to be broadly consistent with the trends seen in experiment, lending credence to the modeling results and the details of the mixing process that they reveal. Interestingly, modeling of the micro-structure inherent to HDC shows that this perturbation source results in considerable mixing of the deuterium–tritium fuel with ablator material during the implosion. The reduction in fuel compression from this mix results in an approximately factor of two reduction in neutron yield in current implosions and emphasizes the importance of mitigating this significant performance degradation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Revealing the complex chemistry of grain boundaries in K-doped BaFe 2 As 2 with atom probe tomography

Iron-based superconductors have attractive properties for high-field applications, but there is a lack of understanding of the effect of grain boundary chemistry on the in-field performance. The near atomic-scale resolution, ppm sensitivity and 3D analysis offered by atom probe tomography make it a powerful tool to investigate the nanoscale structure and chemistry of these defects in fine-grained K-doped BaFe 2 As 2 samples. A computational method to systematically extract and compare the Gibbsian interfacial excess of chemical species across grain boundaries has been explored in this work. The robustness of the method has been tested by evaluating the effects of selected variables on simulated APT datasets. The accuracy and precision of the calculated Gibbsian interfacial excess were found to be stable over a range of analysis conditions: varying grain boundary widths and detection efficiencies, spatial precisions below 1.5 nm, and bin widths between 1.2 and 1.6 nm. For the K-doped BaFe 2 As 2 samples studied, segregation of As, Ba, K and impurities of O, Na, and Sb were found at grain boundaries. The Gibbsian excess values were found to vary widely between different boundaries, showing the complexity of the grain boundary chemistry in this material. Possible links between the observed critical current density (Jc) of these samples and their nano- and micro-structure have also been investigated and discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Intermittent cluster dynamics and temporal fractional diffusion in a bulk metallic glass

Glassy solids evolve towards lower-energy structural states by physical aging. This can be characterized by structural relaxation times, the assessment of which is essential for understanding the glass’ time-dependent property changes. Conducted over short times, a continuous increase of relaxation times with time is seen, suggesting a time-dependent dissipative transport mechanism. By focusing on micro-structural rearrangements at the atomic-scale, we demonstrate the emergence of sub-diffusive anomalous transport and therefore temporal fractional diffusion in a metallic glass, which we track via coherent x-ray scattering conducted over more than 300,000 s. At the longest probed decorrelation times, a transition from classical stretched exponential to a power-law behavior occurs, which in concert with atomistic simulations reveals collective and intermittent atomic motion. Our observations give a physical basis for classical stretched exponential relaxation behavior, uncover a new power-law governed collective transport regime for metallic glasses at long and practically relevant time-scales, and demonstrate a rich and highly non-monotonous aging response in a glassy solid, thereby challenging the common framework of homogeneous aging and atomic scale diffusion.

42 ENGINEERING↗
DOE OSTI2023

An efficient instance segmentation approach for studying fission gas bubbles in irradiated metallic nuclear fuel

Abstract Gaseous fission products from nuclear fission reactions tend to form fission gas bubbles of various shapes and sizes inside nuclear fuel. The behavior of fission gas bubbles dictates nuclear fuel performances, such as fission gas release, grain growth, swelling, and fuel cladding mechanical interaction. Although mechanical understanding of the overall evolution behavior of fission gas bubbles is well known, lacking the quantitative data and high-level correlation between burnup/temperature and microstructure evolution blocks the development of predictive models and reduces the possibility of accelerating the qualification for new fuel forms. Historical characterization of fission gas bubbles in irradiated nuclear fuel relied on a simple threshold method working on low-resolution optical microscopy images. Advanced characterization of fission gas bubbles using scanning electron microscopic images reveals unprecedented details and extensive morphological data, which strains the effectiveness of conventional methods. This paper proposes a hybrid framework, based on digital image processing and deep learning models, to efficiently detect and classify fission gas bubbles from scanning electron microscopic images. The developed bubble annotation tool used a multitask deep learning network that integrates U-Net and ResNet to accomplish instance-level bubble segmentation. With limited annotated data, the model achieves a recall ratio of more than 90%, a leap forward compared to the threshold method. The model has the capability to identify fission gas bubbles with and without lanthanides to better understand the movement of lanthanide fission products and fuel cladding chemical interaction. Lastly, the deep learning model is versatile and applicable to the micro-structure segmentation of similar materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The reactive flow evolution of the polymer-bonded explosive PBX 9502: Experiments and model validation in extreme pressure regimes

The shock-to-detonation transition properties of the triaminotrinitrobenzene based PBX 9502 high explosive (HE) are experimentally and computationally explored in extremely high input pressure conditions. These include both slightly sub-Chapman–Jouguet and overdriven input pressure conditions, namely, ∼25 and ∼31 GPa, respectively. Our experiments capture the transient buildup of a shock-induced reaction via measurement of HE and polymethyl methacrylate window interface particle velocity profiles for a variety of sample thicknesses for this insensitive HE. These observations necessitate extremely thin explosive samples, and the high rates of reaction provide a considerable challenge to optical diagnostics. Samples at these thicknesses also provide an opportunity for evaluation of potential micro-structure effects on the resulting shock-to-detonation-transition measurements. To address this, the thin samples are also characterized via x-ray micro-computed tomography. Finally, a pair of previously established continuum-level detonation performance modeling approaches for PBX 9502 were used to analyze the experiments. The employed model variants crucially differ in their definition of each model’s empirical reaction rate functional form, utilization of shock state quantities, and local flow variable dependencies. As a result, the present experiments provide a novel platform to evaluate the quantitative and qualitative consequences stemming from these modeling choices in a challenging initiation scenario, largely beyond the chosen calibration range of either model. This new experimental information will provide a platform for both improved physics and model parameterizations for this well-studied explosive.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Regularizing INR with Diffusion Prior for Self-Supervised 3D Reconstruction OF Neutron Computed Tomography Data

Recently, generative diffusion priors have made huge strides as inverse problem solvers, including the ability to be adapted for inference on out-of-distribution data. Concurrently, implicit neural representations (INRs) have emerged as fast and lightweight inverse imaging solvers that are amenable to hybrid approaches that combine learned priors with traditional inverse problem formulations. In this paper, we present a diffusive computed tomography (CT) inversion framework for regularizing INRs called Diffusive INR (DINR), designed to enable high-quality reconstruction from sparse-view neutron CT. Pretrained purely on synthetic data, DINR is evaluated on simulated and experimentally obtained observations of concrete microstructures, where traditional reconstruction methods suffer substantial degradation when the number of views is reduced. Our approach delivers superior performance, reduces reconstruction artifacts, and achieves gains in PSNR and SSIM, enabling accurate micro-structural characterization even under extreme data limitations compared to state-of-the-art sparse-view reconstruction techniques.

Hossain, Maliha [ORNL]↗
DOE OSTI2023

A Physics-Based Data-Driven Approach for Modeling of Environmental Degradation in Elastomers

Abstract Elastomers are now commonly used in a number of industries, including aerospace, structure, transportation, shipbuilding, and automotive, due to their excellent workability, formability, and flexibility. During their activity, elastomers are subjected to harsh environmental conditions, which decreases their resilience. False predictions made early in their lives can have major financial and environmental implications. Elastomers’ performance and properties, such as strength, durability, and density, are influenced by chemical changes in these materials, known as degradation, which occurs over time. This process can alter the morphology of a polymer matrix as well as cause chain scission and cross-linking, resulting in different behaviors than that of the unaged material. To demonstrate the effect of thermaloxidative aging on the mechanical behavior of elastomers, several experimental and theoretical models have been proposed. In view of the large volume of experimental data available on micro-structural evolution in the course of aging, we propose a physics-based data-driven approach to overcome the shortcomings of both phenomenological and micro-mechanical models. This work presents a novel thermodynamically consistent, multiagent machine-learned model for predicting the constitutive behavior of cross-linked elastomers during environmental aging, such as thermo-oxidative and hydrolytic aging for various states of deformation. Single mechanism degradation changes the polymer matrix over time where it is causing chain scission, reduction of cross-links, and morphology change. To capture the idealized Mullins effect and permanent set due to the effect of single aging mechanisms on nonlinear mechanical responses of elastomers, we propose a data-driven model for simulating inelastic elements in a polymer matrix. By using a sequential order reduction, we were able to reduce the 3D stress-strain tensor mapping problem to a small number of super-constrained 1D mapping problems. To systematically classify such mapping problems into a few categories, an assembly of multiple replicated conditional neural network learning agents (L-agents) is used based on our recent work. Each category is represented by a different type of agent. The effect of deformation history, aging time, and aging temperature is captured by this model. The model is validated using a broad collection of data, ranging from our experimental results to data from the literature. In addition, thermodynamic consistency and frame independence are investigated. The most significant achievements of this model are its precision, simplicity, and prediction of inelasticity under various states of deformation. The model’s accuracy and simplicity make it a good option for commercial and industrial applications. Conveniently, due to the model modular nature, it can be expanded in the future to include viscoelasticity and non-isotropic formation for better precision.

Ghaderi, Aref↗
DOE OSTI2023

Constitutive Modeling of the Cross-Linked Polymers During the Trio-Aging Mechanism: Effects of Water, Oxygen, UV

Abstract The synergized effects of diverse environmental conditions such as humidity, temperature, UV, and oxygen, are considered through a generic model by combining the concepts of network alteration, hygrothermal, and Photo-thermo oxidation Models [1–4]. Hygrothermal and Photo-oxidation are considered dual aging mechanisms which are due to the synergized effects of moisture-oxygen, and UV-oxygen, respectively at elevated temperatures. Here Trio-aging is considered as the synergized effects of Hygrothermal, and Photo-oxidation which the main environmental elements involved in polymer degradation are oxygen, water, and UV radiation where each of which has a different micro-structural impact on the polymer matrix. While oxygen imposes chain scission and cross-link formation, water, and UV radiation mainly cause chain scission. Similar to our recent models, the dual network hypothesis of Topolosky is utilized and as a result, the strain energy of the polymer matrix is written in terms of two states namely unaged and infinity. The infinity state network, itself, is decomposed into three networks, namely brittle, flexible, and dead networks. While the brittle network models the cross-link formation as sub-structural phenomena, the flexible and dead networks are utilized to represent the cross-link reduction and chain scission. The contribution of these networks in the infinity state is based on the amount of relevant environmental elements. The model is developed for a domain with plenty of oxygen and holds true for a gradual aging process that happens in super-thin samples.

Bahrololoumi, Amir↗
DOE OSTI2023

Validation of MFUEL Metal Fuel Performance Models of SAS4A/SASSYS-1

The fuel characterization models of SAS4A/SASSYS-1 (SAS) have recently been extended to include a new U-Pu-Zr metal fuel model, MFUEL. MFUEL is equipped with mechanistic physics based models to predict the pre-transient characterization and transient response of metal fuel, with emphasis on fuel melting, cladding failure, and the metal fuel’s impact on core reactivity. The MFUEL model will be available in the full version of SAS4A/SASSYS-1 5.7, which is scheduled to be released in June 2023. Fast reactor fuel pins that operated in EBR-II and FFTF with low smear density U-Zr and U-Pu-Zr metal fuels and irradiation resistant ferritic-martensitic cladding showed significant advantages in achieving high burnups and assuring inherent safety characteristics during anticipated transients, design basis events, and beyond design basis events. For safety analysis, a fuel performance model must be able to predict (1) Fuel pin mechanics and compositional and dimensional changes, (2) Clad failure, and (3) Fuel pin thermal resistance. Achieving these high level goals accurately is strongly related to the model performance of individual physical processes taking place within a fuel pin during its lifetime. Metal fuels typically operate above the mid-point temperature of melting during normal, as well as off-normal, conditions. At these elevated temperatures, the availability of thermal activation provides a driving force for various diffusional processes leading to complex phase transformations, micro-structure evolution, significant amounts of fuel swelling, interconnected porosity formation, excessive amounts of fission gas release, and fuel clad chemical interactions. Clad failure in fast reactors primarily occurs as a result of creep rupture augmented by clad wastage formation. The reaction is driven by thermal creep induced dislocation motion, grain boundary cavity nucleation, growth and breakup of grain boundaries. The high level complexity and limited available data requires introducing physics-based modeling approaches to gain extrapolation ability and sensitivity with respect to various conditions. The objective of this report is to perform validation of MFUEL using the experimental data for (1) Normal operation EBR-II fuel behavior, (2) Normal operation PHENIX fuel behavior, (3) HT9 Pressure tube ramp-and-hold creep rupture tests, (4) Whole Pin Furnace (WPF) creep strain, creep rupture and eutectic tests, (5) Fuel Behavior Test Apparatus (FBTA) eutectic tests, and (6) TREAT M5-7 OverPower tests up to clad failure. Section-2 includes a brief description of the MFUEL models. A detailed description of the MFUEL physics-based, semi-empirical models will be presented in the SAS V 5.7 theory manual. Section-3, 4, and 5 describes the validation effort for the pre-transient irradiation, furnace transients, and TREAT M-Series transients, respectively.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

TopTemp: Parsing Precipitate Structure from Temper Topology

Technological advances are in part enabled by the development of novel manufacturing processes that give rise to new materials or material property improvements. Development and evaluation of new manufacturing methodologies is labor-, time-, and resource-intensive expensive due to complex, poorly defined relationships between advanced manufacturing process parameters and the resulting microstructures. In this work, we present a topological representation of temper (heat-treatment) dependent material micro-structure, as captured by scanning electron microscopy, called TopTemp. We show that this topological representation is able to support temper classification of microstructures in a data limited setting, generalizes well to previously unseen samples, is robust to image perturbations, and captures domain interpretable features. The presented work outperforms conventional deep learning baselines and is a first step towards improving understanding of process parameters and resulting material properties.

Kassab, Lara↗