Results for “breakthrough curve” — Page 4

DOE OSTI2025

Driven probe particle dynamics in a bubble and pattern forming system

We numerically examine the dynamics of a probe particle driven at a constant force through an assembly of particles with competing long-range repulsion and short-range attraction that forms a bubble or stripe state. In the bubble regime, we identify several distinct types of motion, including an elastic or pinned regime where the probe particle remains inside a bubble and drags all other bubbles with it. There is also a plastic bubble phase where the bubble in which the probe particle is trapped is able to move past the adjacent bubbles. At larger drives, there is a breakthrough regime where the probe particle jumps from bubble to bubble and, in some cases, can induce correlated rotations or plastic rearrangements of the particles within the bubbles. At the highest drives, the probe particle moves sufficiently rapidly that the background particles undergo only small distortions. The distinctive dynamic flow states and the transitions between them are accompanied by signatures in the effective drag on the driven particle, jumps in the velocity–force curves, and changes in the time-dependent velocity fluctuations. We map the dynamic phase diagram for this system for varied interaction lengths, bubble sizes, and densities.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Coupling dynamic in situ X-ray micro-imaging and indentation: A novel approach to evaluate micromechanics applied to oil shale

Oil and gas shales are a system where understanding the mechanical properties at the microscale is of paramount importance, e.g. to better understand the behavior of proppant-shale contacts and their role in the evolution of propped fractures in unconventional reservoirs. Here we show for the first time an experiment coupling indentation testing with in situ X-ray imaging in a Green River shale sample. A full compliance curve has been measured with the sample in water, allowing to visualize the indentation of the sample in function of axial load, in a purpose-built system for combined in situ indentation and X-ray imaging. A series of 3D datasets were used for a digital volume correlation study to obtain local strain fields. This analysis has been complemented with the analysis of cracks. Finally, geomechanical modeling has been carried out to replicate and generalize the observed behavior in the shale. This study validated this experimental approach, providing a breakthrough in understanding micro-mechanics in shales, and demonstrates how this class of experiments can be important for studies involving the prediction of the evolution of propped fractures in shale reservoirs, with possible applications in a much larger number of application fields (geothermal, materials science, etc.)

04 OIL SHALES AND TAR SANDS↗

DOE OSTI2023

Enhanced Geothermal Shot Analysis for the Geothermal Technologies Office

In 2021, the U.S. Department of Energy (DOE) began the Energy Earthshots initiatives to accelerate breakthroughs of reliable clean energy solutions within the next 10 years. In 2022, the National Renewable Energy Laboratory (NREL) was asked by the DOE Geothermal Technologies Office (GTO) to provide analysis for developing Energy Earthshot targets for Enhanced Geothermal Systems (EGS), human-made underground reservoirs that extract thermal energy from the earth for electricity generation and/or heating applications. The Enhanced Geothermal Shot analysis is based on the technology assumptions in the 2019 GTO report GeoVision: Harnessing the Heat Beneath Our Feet. For Earthshot, we updated some of the technology cost and performance assumptions based on recent technology advances and updated the EGS resource potential to include more detailed analysis. We used the updated EGS supply cost curves to forecast the amount of geothermal electricity generation that could be deployed in the US by 2050 using a capacity expansion model. The results were used to develop a cost target for EGS. On September 8th, 2022, the Enhanced Geothermal Shot was announced. Its target - reduce the cost of EGS by 90%, to $45 per megawatt hour by 2035. This paper summarizes the cost and resource assumptions used in the Enhanced Geothermal Shot. It describes the assumptions used in the Regional Energy Deployment System (ReEDS) capacity expansion model to forecast geothermal deployment and discusses the results.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Enhanced Geothermal Shot Analysis for the Geothermal Technologies Office: Preprint

In 2021, the U.S. Department of Energy (DOE) began the Energy Earthshots initiatives to accelerate breakthroughs of reliable clean energy solutions within the next 10 years. In 2022, the National Renewable Energy Laboratory (NREL) was asked by the DOE Geothermal Technologies Office (GTO) to provide analysis for developing Energy Earthshot targets for Enhanced Geothermal Systems (EGS), human-made underground reservoirs that extract thermal energy from the earth for electricity generation and/or heating applications. The Enhanced Geothermal Shot analysis is based on the technology assumptions in the 2019 GTO report GeoVision: Harnessing the Heat Beneath Our Feet. For Earthshot, we updated some of the technology cost and performance assumptions based on recent technology advances and updated the EGS resource potential to include more detailed analysis. We used the updated EGS supply cost curves to forecast the amount of geothermal electricity generation that could be deployed in the US by 2050 using a capacity expansion model. The results were used to develop a cost target for EGS. On September 8th, 2022, the Enhanced Geothermal Shot was announced. Its target - reduce the cost of EGS by 90%, to $45 per megawatt hour by 2035. This paper summarizes the cost and resource assumptions used in the Enhanced Geothermal Shot. It describes the assumptions used in the Regional Energy Deployment System (ReEDS) capacity expansion model to forecast geothermal deployment and discusses the results.

analysis↗

DOE OSTI2020

Linking the Dynamic Response of the Carbon Dioxide-Concentrating Mechanism to Carbon Assimilation Behavior in Fremyella diplosiphon

ABSTRACT Cyanobacteria use a carbon dioxide (CO 2 )-concentrating mechanism (CCM) that enhances their carbon fixation efficiency and is regulated by many environmental factors that impact photosynthesis, including carbon availability, light levels, and nutrient access. Efforts to connect the regulation of the CCM by these factors to functional effects on carbon assimilation rates have been complicated by the aqueous nature of cyanobacteria. Here, we describe the use of cyanobacteria in a semiwet state on glass fiber filtration discs—cyanobacterial discs—to establish dynamic carbon assimilation behavior using gas exchange analysis. In combination with quantitative PCR (qPCR) and transmission electron microscopy (TEM) analyses, we linked the regulation of CCM components to corresponding carbon assimilation behavior in the freshwater, filamentous cyanobacterium Fremyella diplosiphon . Inorganic carbon (C i ) levels, light quantity, and light quality have all been shown to influence carbon assimilation behavior in F. diplosiphon . Our results suggest a biphasic model of cyanobacterial carbon fixation. While behavior at low levels of CO 2 is driven mainly by the C i uptake ability of the cyanobacterium, at higher CO 2 levels, carbon assimilation behavior is multifaceted and depends on C i availability, carboxysome morphology, linear electron flow, and cell shape. Carbon response curves (CRCs) generated via gas exchange analysis enable rapid examination of CO 2 assimilation behavior in cyanobacteria and can be used for cells grown under distinct conditions to provide insight into how CO 2 assimilation correlates with the regulation of critical cellular functions, such as the environmental control of the CCM and downstream photosynthetic capacity. IMPORTANCE Environmental regulation of photosynthesis in cyanobacteria enhances organismal fitness, light capture, and associated carbon fixation under dynamic conditions. Concentration of carbon dioxide (CO 2 ) near the carbon-fixing enzyme RubisCO occurs via the CO 2 -concentrating mechanism (CCM). The CCM is also tuned in response to carbon availability, light quality or levels, or nutrient access—cues that also impact photosynthesis. We adapted dynamic gas exchange methods generally used with plants to investigate environmental regulation of the CCM and carbon fixation capacity using glass fiber-filtered cells of the cyanobacterium Fremyella diplosiphon . We describe a breakthrough in measuring real-time carbon uptake and associated assimilation capacity for cells grown in distinct conditions (i.e., light quality, light quantity, or carbon status). These measurements demonstrate that the CCM modulates carbon uptake and assimilation under low-C i conditions and that light-dependent regulation of pigmentation, cell shape, and downstream stages of carbon fixation are critical for tuning carbon uptake and assimilation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Predicting non-linear stress–strain response of mesostructured cellular materials using supervised autoencoder

Recent breakthroughs in advanced manufacturing capabilities have made it possible to design and print sophisticated topologies of cellular structures using diverse engineering materials such as metals, polymers, and ceramics. In these architectured materials, it is often desirable to tailor the mechanical properties by altering the unit cell topology. This necessitates an in-depth understanding of how the topology of the unit cell structure affects the macroscopic behavior of the material in both the linear and the non-linear regimes encountered under large compression. Here, we have developed a machine learning (ML) approach capable of accelerating the prediction of the stress–strain response of a polymer-based cellular structure under uniaxial confined compression. As part of generating the training data for ML, 60,000 mesostructures were generated using a relatively novel approach based on cellular automata, and their corresponding stress–strain responses were obtained from the finite element simulations. Principal component analysis (PCA) was used to reduce the dimensionality of the stress–strain curves. With only 20 principal components, PCA captured 99.89% of the variance in the stress–strain curves while reducing the dimensionality by 5X. ML using supervised autoencoder was able to successfully speed up the prediction of the non-linear stress–strain response of a unit cell by up to 4600X. The proposed method can serve as an efficient data generation tool and a rapid means for predicting the structure–property relationship through accelerated forward modeling of cellular materials under compaction, in cases where the macroscopic stress–strain response is governed by the unit-cell topology.

36 MATERIALS SCIENCE↗