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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Finite element modeling of biomass hopper flow

Hoppers are widely used biomass handling devices that channel bulk biomass from storage to subsequent handling equipment. Jenike’s longstanding approach, based on the Mohr-Coulomb model, has been successfully used to design hoppers handling cohesionless granular materials such as grains and other agricultural produces. However, designing a hopper to ensure reliable biomass flow is found to be challenging due to cohesion, irregular particle shape, and bulk material elastoplasticity. This study aims to address the biomass handling engineering challenge with alternative constitutive material models concerning the flow behavior of bulk solids. Finite element modeling is an approach that allows for implementation of different material models, whose underlying constitutive theories assist in investigating the origin and manifestation of bulk mechanical behavior of granular materials. This study focuses on the incipient gravity hopper flow of two types of biomass feedstocks, i.e., ground corn stover and Douglas fir wood. Three widely used constitutive material models, i.e., Mohr-Coulomb model, modified Cam-Clay model, and Drucker-Prager/Cap model, are implemented. Using the flow pattern represented by the volume of biomass exhibiting more than 7% of axial strain (Kamath and Puri, 1999), the finite element model predicts that the bulk corn stover particulate material forms an arch, which represents a hampered transition from the static state to the dynamic flow-state out of the hopper, whereas bulk Douglas fir wood particulate material develops a reliable mass flow pattern. A laboratory scale hopper was used to experimentally determine the biomass flow conditions, which were subsequently compared with the predicted onset of flow by a finite element model (FEM). The developed FEM was found to correctly predict the initiation of mass flow for the milled Douglas fir wood, whereas corn stover was predicted to establish a strong core flow suggesting an unreliable handling characteristic. This observation aligns with the reported poor handling of milled corn stover.

09 BIOMASS FUELS↗
DOE OSTI2021

A Study of the Interaction, Morphology, and Structure in Trypsin-Epigallocatechin-3-Gallate Complexes

Understanding the interaction between proteins and polyphenols is of significance to food industries. The aim of this research was to investigate the mode of aggregation for trypsin-EGCG (Epigallocatechin-3-gallate) complexes. For this, the complex was characterized by fluorescence spectroscopy, circular dichroism (CD) spectra, small-angel X-ray scattering (SAXS), and atomic force microscope (AFM) techniques. The results showed that the fluorescence intensity of trypsin-EGCG complexes decreased with increasing the concentration of EGCG, indicating that the interaction between trypsin and EGCG resulted in changes in the microenvironment around fluorescent amino acid residues. The results of CD analysis showed conformational changes in trypsin after binding with EGCG. The results from SAXS analysis showed that the addition of EGCG results in the formation of aggregates of trypsin-EGCG complexes, and increasing the concentration of EGCG resulted in larger aggregates. AFM images showed that the trypsin-EGCG complex formed aggregates of irregular ellipsoidal shapes with the size of about 200 × 400 × 200 nm, with EGCG interconnecting the trypsin particles. Overall, according to these results, it was concluded that the large aggregates of trypsin-EGCG complexes are formed from several small aggregates that are interconnected. The results of this study shed some light on the interaction between digestive enzymes and EGCG.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Are the U.S. Biorefineries Over the Hurdle of 2000 Ton Daily Throughput Yet?

The efficient utilization of lignocellulosic biomass for biofuel and biochemical production is hindered by material handling issues such as clogging and segregation among other challenges. Preprocessing methods such as drying, screening, and milling have improved conversion yield but have not sufficiently enhanced flowability, especially herbaceous biomass. The poor flowability of herbaceous biomass is rooted in some particle attributes that remain less altered by those methods, e.g., irregular particle shape, high roughness, and high compressibility, making it hard to scale up throughput to a key benchmark for a biorefinery – 2000 ton per day. Applying additional preprocessing methods like pelletization and torrefaction to drastically change those particle attributes can improve flow and handling but has not been comprehensively verified through test. The flowability of herbaceous biomass feedstock formats generated by three different preprocessing methods was recently assessed at Idaho National Laboratory’s Biomass Feedstock National User Facility: first, loose particles size reduced from as-received materials; second, pellets produced from an efficient densification process; and third, powders milled from torrefied pellets. Benchmarking tests including static angle of repose, basic flow energy measured in a powder rheometer, and discharge flow in an adjustable hopper, were conducted to evaluate those feedstock formats. Beyond the capacity of existing experimental apparatuses, a digital engineering approach involving flow simulations and AI models were used to identity the material attributes and processing parameters that have dominant influences on flow throughput. Techno-economic analysis focusing on hopper flow as a typical material handling operation was conducted for those feedstock formats. Perspectives will be discussed on whether the 2000-ton daily throughput for a biorefinery is achievable at an acceptable cost by using any of the tested preprocessing methods.

09 - BIOMASS FUELS↗
DOE OSTI2023

Rapid method for computing the mechanical resonances of irregular objects

A solid object's geometry, density, and elastic moduli completely determine its spectrum of normal modes. Solving the inverse problem—determining a material's elastic moduli given a set of resonance frequencies and sample geometry—relies on the ability to compute resonance spectra accurately and efficiently. Established methods for calculating these spectra are either fast but limited to simple geometries, or are applicable to arbitrarily shaped samples at the cost of being prohibitively slow. Here, we describe a method to rapidly compute the normal modes of irregularly shaped objects using entirely open-source software. Our method's accuracy compares favorably with existing methods for simple geometries and shows a significant improvement in speed over existing methods for irregular geometries.

Acoustics↗
DOE OSTI2022

Growth instabilities shape morphology and genetic diversity of microbial colonies

Abstract Cellular populations assume an incredible variety of shapes ranging from circular molds to irregular tumors. While we understand many of the mechanisms responsible for these spatial patterns, little is known about how the shape of a population influences its ecology and evolution. Here, we investigate this relationship in the context of microbial colonies grown on hard agar plates. This a well-studied system that exhibits a transition from smooth circular disks to more irregular and rugged shapes as either the nutrient concentration or cellular motility is decreased. Starting from a mechanistic model of colony growth, we identify two dimensionless quantities that determine how morphology and genetic diversity of the population depend on the model parameters. Our simulations further reveal that population dynamics cannot be accurately described by the commonly-used surface growth models. Instead, one has to explicitly account for the emergent growth instabilities and demographic fluctuations. Overall, our work links together environmental conditions, colony morphology, and evolution. This link is essential for a rational design of concrete, biophysical perturbations to steer evolution in the desired direction.

Golden, Alexander↗
DOE OSTI2022

Seeking regularity from irregularity: unveiling the synthesis–nanomorphology relationships of heterogeneous nanomaterials using unsupervised machine learning

Nanoscale morphology of functional materials determines their chemical and physical properties. However, despite increasing use of transmission electron microscopy (TEM) to directly image nanomorphology, it remains challenging to quantify the information embedded in TEM data sets, and to use nanomorphology to link synthesis and processing conditions to properties. We develop an automated, descriptor-free analysis workflow for TEM data that utilizes convolutional neural networks and unsupervised learning to quantify and classify nanomorphology, and thereby reveal synthesis–nanomorphology relationships in three different systems. While TEM records nanomorphology readily in two-dimensional (2D) images or three-dimensional (3D) tomograms, we advance the analysis of these images by identifying and applying a universal shape fingerprint function to characterize nanomorphology. After dimensionality reduction through principal component analysis, this function then serves as the input for morphology grouping through unsupervised learning. We demonstrate the wide applicability of our workflow to both 2D and 3D TEM data sets, and to both inorganic and organic nanomaterials, including tetrahedral gold nanoparticles mixed with irregularly shaped impurities, hybrid polymer-patched gold nanoprisms, and polyamide membranes with irregular and heterogeneous 3D crumple structures. In each of these systems, unsupervised nanomorphology grouping identifies both the diversity and the similarity of the nanomaterial across different synthesis conditions, revealing how synthetic parameters guide nanomorphology development. Our work opens possibilities for enhancing synthesis of nanomaterials through artificial intelligence and for understanding and controlling complex nanomorphology, both for 2D systems and in the far less explored case of 3D structures, such as those with embedded voids or hidden interfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Large‐Area, High‐Numerical‐Aperture, Freeform Metasurfaces

Abstract Nanophotonic devices are optical platforms capable of unprecedented wavefront control. To push the limits of experimental device performance, scalable design methodologies that combine the simplicity and fabricability of conventional design paradigms with the extended capabilities of freeform optimization are required. A novel gradient‐based design framework for large‐area freeform metasurfaces is introduced in which nonlocal interactions between simply shaped nanostructures, placed on an irregular lattice, are tailored to produce high‐order hybridized modes that support customizable large‐angle scattering profiles. Utilizing this approach, multifunctional super‐dispersive metalenses are designed and experimentally demonstrated. The approach to high‐numerical‐aperture radial metalenses capable of diffraction limited focusing and the generation of donut‐shaped point spread functions is also extended. It is anticipated that these concepts will have utility in super‐resolution microscopy, particle trapping, additive manufacturing, and metrology applications that require ultra‐high numerical apertures.

Optics↗
DOE OSTI2020

Estimating posterior quantity of interest expectations in a multilevel scalable framework

Scalable approaches for uncertainty quantification are necessary for characterizing prediction confidence in large-scale subsurface flow simulations with uncertain permeability. To this end we explore a multilevel Monte Carlo approach for estimating posterior moments of a particular quantity of interest, where we employ an element-agglomerated algebraic multigrid (AMG) technique to generate the hierarchy of coarse spaces with guaranteed approximation properties for both the generation of spatially correlated random fields and the forward simulation of Darcy's law to model subsurface flow. In both these components (sampling and forward solves), we exploit solvers that rely on state-of-the-art scalable AMG. To illustrate the applicability of this approach, numerical tests are performed on two 3D examples-a unit cube and an egg-shaped domain with an irregular boundary-where the scalability of each simulation as well as the scalability of the overall algorithm are demonstrated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Classification of computed thermal tomography images with deep learning convolutional neural network

Thermal tomography (TT) is a computational method for the reconstruction of depth profile of the internal material defects from Pulsed Infrared Thermography (PIT) nondestructive evaluation. Here, the PIT method consists of recording material surface temperature transients with a fast frame infrared camera, following thermal pulse deposition on the material surface with a flashlamp and heat diffusion into material bulk. TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of the subsurface internal defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to the heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify the size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. The performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shapes imported from scanning electron microscopy images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on the TT images of elliptical defects is capable of classifying the shape and orientation of irregular defects.

42 ENGINEERING↗
DOE OSTI2022

Extending the Gutowski model to kidney-bean and elliptically shaped fibers

Efforts by manufacturers to produce more cost-effective carbon fibers have resulted in fibers with irregular cross-section often referred to as kidney-bean shaped fibers. In this research, compaction experiments were performed with a modified laser light section method to evaluate the compaction behavior of kidney-bean shaped carbon fibers. The kidney-bean shaped fibers followed a different compaction behavior compared to the Gutowski model for circular fibers. Additionally, these fibers required an order of magnitude larger force to compact than circular fibers to achieve similar fiber volume fraction, which has implications in infiltration and consolidation efficiency for composites manufacturing. A shape correction factor based on the fiber cross-sectional aspect ratio was proposed to extend the Gutowski model to fibers with irregular cross-sectional shapes. The modified Gutowski model provided an appropriate order of magnitude fit for the kidney-bean fibers. Furthermore, this modification to the Gutowski model recovered the original solution for circular fibers (cross-sectional aspect ratio = 1).

Materials Science↗
DOE OSTI2020

Modifiers versus Channels: Creating Shape-Selective Catalysis of Metal Nanoparticles/Porous Nanomaterials

Shape-selective catalysis is of great research interest for its key role in chemical synthesis. Porous nanomaterials with uniform pore structures, have been proven as ideal supports for metal nanoparticles (MNPs) to generate efficient shape-selective catalysis. However, many widely used commercial irregular porous nanomaterials usually face the challenge to realize satisfactory shape selectivity due to the lack of molecular sieving structures. In this work, we report a concept of creating shape selectivity in MNPs/porous-MNPs using appropriate modifiers. The remained MNPs within the porous nanomaterials can cooperate with the channels to generate selectivity. Such strategy not only applies to regular porous nanomaterials (metal-organic frameworks, zeolites, etc.), but also can be extended to irregular porous nanomaterials (active carbon, P25, etc.). Potentially, the matching among different MNPs, corresponding modifiers and porous nanomaterials makes our strategy promising for wide applications in selective catalytic systems.

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

Micromechanical Fields Associated with Irregular Deformation Twins in Magnesium

Understanding and controlling the development of deformation twins is paramount for engineering strong and stable hexagonal close-packed (HCP) Mg alloys. Actual twins are often irregular in boundary morphology and twin crystallography, deviating from the classical picture commonly used in theory and simulation. In this work, the elastic strains and stresses around irregular twins are examined both experimentally and computationally to gain insight into how twins develop and the microstructural features that influence their development. A nanoprecession electron diffraction (N-PED) technique is used to measure the elastic strains within and around a $\left\{ {10\overline{1}2} \right\}$ tensile twin in AZ31B Mg alloy with nm scale resolution. A full-field elasto-viscoplastic fast Fourier transform (EVP-FFT) crystal plasticity model of the same sub-grain and irregular twin structure is employed to understand and interpret the measured elastic strain fields. The calculations predict spatially resolved elastic strain fields in good agreement with the measurement, as well as all the stress components and the dislocation density fields generated by the twin, which are not easily obtainable from the experiment. The model calculations find that neighboring twins, several twin thicknesses apart, have little influence on the twin-tip micromechanical fields. Furthermore, this work reveals that irregularity in the twin-tip shape has a negligible effect on the development of the elastic strains around and inside the twin. Importantly, the major contributor to these micromechanical fields is the alignment of the twinning shear direction with the twin boundary.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Simulation of radioactive plume transport in the atmosphere including dynamics of particle aggregation and breakup

Accurate prediction of the atmospheric transport of debris particles relies heavily on our knowledge of the size distribution of the particles within a debris cloud. Assuming a fixed particle size during simulations is not always viable since the size distribution of the debris can change during transport. Various microphysical processes, such as aggregation and breakup, influence debris particles and dictate any changes to the size distribution. To track those changes that can occur, a population balance model can be adopted and instituted within a model framework. Nonetheless, many of the models that simulate the transport of radioactive debris following a device-driven fission incident have historically neglected to consider these processes. As such, this work describes our effort to develop a modeling framework capable of simulating the transport and deposition of a radioactive plume generated from a fission incident with a dynamic population balance including particle aggregation and breakup. Herein the impact of aggregation and breakup, individually and collectively, on the particle size distribution is explored using the developed framework. When simulating aggregation, for example, six mechanisms, including Brownian coagulation, the convective enhancement to Brownian coagulation, van der Waals-viscous force correction for Brownian coagulation, gravitational collection, turbulent inertial motion, and turbulent shear, are considered. Brownian coagulation and its corrections have, as one would expect, a large impact on relatively small aggregates. Aggregates with a diameter that is less than or equal to 1.0 μm, for instance, comprise 50.6 vol % of all aggregates in the absence of aggregation and 31.2 vol % when Brownian coagulation and its corrections are considered. Gravitational collection and, to a much lesser extent, turbulent shear and turbulent inertial motion are, conversely, of great importance to relatively large aggregates (i.e., diameter greater than 3.0 μm). Additionally, the individual effects of atmospheric and particle parameters, such as wind speed and particle density, are examined. Of the parameters examined, turbulent energy dissipation and aggregate fractal dimension (i.e., aggregate shape with lower values representing more irregular particles) were of substantial importance since both terms directly impact aggregate stability and, by extension, the breakup rate. Large-scale transport and deposition simulations in a dry atmosphere are also presented and discussed as a proof of concept.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Probing the Mechanical Performance of Micro-architected Porous Structures Through In Situ Characterization and Analysis

Micropores play critical roles in both natural and man-made materials. Such pores take on a variety of shapes and sizes ranging from spherical to irregular sphere-like voids with diameters spanning from the nanometer to millimeter scales. When porous structures are mechanically loaded, the pores direct the stress around their free surfaces, altering the material’s mechanical response relative to fully dense materials. In this study, for the first time, we create micro-architected porous samples using nanolithography and investigate the role of pore morphology by conducting a series of in situ micropillar compression tests in scanning electron microscopy. The findings demonstrate that porosity is the primary factor influencing the mechanical response of these micro-architected materials, as often seen on a macroscopic level. Additionally, we observed that pore geometry had a significant impact on Young’s modulus, yield stress, and strain energy density as a secondary parameter. Then, the extracted Young’s modulus was compared to macroscopic empirical models and determined the analytical models sufficiently described the impact of porosity in the microscopic scale but failed to capture the impact of second-order parameters. Furthermore, these results suggest how porous materials can be tailored to achieve desired mechanical properties based on the engineering applications of interest.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The Effects of Shallow Cumulus Cloud Shape on Interactions Among Clouds and Mixing With Near-Cloud Environments

Cloud lifecycles are influenced by interactions among clouds and mixing with cloud-free environments. Cloud shapes dictate the length of cloud boundaries, which affects these interactions. This study uses a large eddy simulation to examine the relationships between the shallow cumulus cloud shapes, processes at their boundaries, and lifecycles. To characterize the shape of clouds, two metrics are newly defined: one reflects the overall horizontal cloud shape, and the other defines the cloud edge irregularity. Simulated clouds experience increased irregularity, but little changes in aspect ratio. Irregularity-driven cloud perimeter growth strongly indicates cloud splitting, with a more pronounced effect than aspect ratio-driven perimeter growth. Smaller cloud-shell gradients of properties across cloud boundaries are associated with more irregular clouds, suggesting enhanced mixing of clouds with the cloud-free environment. These findings indicate that the shallow cumulus evolution is partly driven by the irregularity of the cloud edge through lateral mixing and cloud splitting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Supermolecular structures of recrystallized starches with amylopectin side chains modified by amylosucrase to different chain lengths

Understanding the supermolecular structures of recrystallized starches is imperative to manufacture novel starches with controllable physicochemical properties enabling novel applications. In this study, native amylopectin (AP) from maize was modified to different chain lengths using amylosucrase from Neisseria polysaccharea. Native AP granules showed well-organized lamellar structures with a periodicity of ca. 9.0 nm whereas modified starches formed heterogeneous structures with the average crystallite size ranging from 19.77 to 29.88 nm. Acidic treatments eroded amorphous domains in both native AP granules and modified starch particles, producing nanocrystals composed of double helices. A-type nanocrystals (~40 nm) from native AP granules showed quadrangular shape with an acute angle of ca. 60°, while irregular B-type nanocrystals were observed for the modified starches. Molecular characterizations suggested that the nanocrystals had relatively uniform chain lengths (DP 16.5–25.2), and the helix length (the thickness of nanocrystals) was positively correlated to the thermal stability of starches.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

In situ study on heavy ion irradiation induced microstructure evolution in single crystal Cu with nanovoids at elevated temperature

Understanding the response of nanovoids to irradiation damage advances our capability to design radiation tolerant materials. Recent in situ studies reveal the shrinkage of hexagon-shaped nanovoids in (110) textured Cu. However, the evolution of voids with irregular geometries under irradiation is less well understood. Here, in situ Kr ion irradiation was performed at 100 °C on single-crystal Cu (112) that possesses nanovoids with high aspect ratio. In situ studies show these elongated voids fragment into smaller voids and shrink gradually with increasing dose. Phase field simulations confirm that fragmentation of the void is governed by the competing kinetics between atoms diffusing toward and away from the elongated nanovoid. In conclusion, cost irradiation analysis reveals the formation of high-density defect clusters.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

Underwater unexploded ordnance discrimination based on intrinsic target polarizabilities – A case study

Seabed unexploded ordnance that resulted partly from the high failure rate among munitions from more than 80 years ago and from decades of military training and testing of weapons systems poses an increasing concern all around the world. Although existing magnetic systems can detect clusters of debris, they are not able to tell whether a munition is still intact requiring special removal (e.g. in situ detonation) or is harmless scrap metal. The marine environment poses unique challenges, and transferring knowledge and approaches from land to a marine environment has not been easy and straightforward. On land, the background soil conductivity is much lower than the conductivity of the unexploded ordnance and the electromagnetic response of a target is essentially the same as that in free space. For those frequencies required for target characterization in the marine environment, the seawater response must be accounted for and removed from the measurements. The system developed for this study uses fields from three orthogonal transmitters to illuminate the target and four three-component receivers to measure the signal arranged in a configuration that inherently cancels the system's response due to the enclosing seawater, the sea–bottom interface and the air–sea interface for shallow deployments. The system was tested as a cued system on land and underwater in San Francisco Bay – it was mounted on a simple platform on top of a support structure that extended 1 m below and allowed the diver to place metal objects to a specific location even in low-visibility conditions. The measurements were stable and repeatable. Furthermore, target responses estimated from marine measurements matched those from land acquisition, confirming that the seawater and air–sea interface responses were removed successfully. Thirty-six channels of normalized induction responses were used for the classification, which was done by estimating the target principal dipole polarizabilities. Our results demonstrated that the system can resolve the intrinsic polarizabilities of the target, with clear distinctions between those of symmetric intact unexploded ordnance and irregular scrap metal. The prototype system was able to classify an object based on its size, shape and metal content and correctly estimate its location and orientation.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗