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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 217 records · Page 12

Search for a Cloud Phase Feedback in the Arctic Climate System

This project was motivated by a hypothesis involving the transition in lower troposphere temperatures across the freezing point of water. Specifically, at temperatures just below to about ten degrees below freezing, Arctic clouds should be in a mixed-phase, with strong influences from secondary ice production (e.g., the Hallett-Mossop process). At temperatures just above freezing, clouds should become entirely deglaciated. We hypothesized that, with multiple years of ARM data, a statistically significant change could be detected in cloud radiative properties and surface radiative fluxes that could be directly attributed to this phase change. Furthermore, in a gradually warming climate, these phase change-related responses in surface radiation would represent a cloud phase feedback as part of Arctic amplification. We designed this research project to coincide with a new ARM Arctic cloud radar product developed by Ed Luke and collaborators at BNL (Luke et al., 2021: PNAS, doi:10.1073/pnas.2021387118) that explicitly contains SIP cloud properties retrieved from radar data. This research project has successfully concluded after analysis of a much larger North Slope of Alaska (NSA) data sample than originally anticipated, and the results are quite different than originally hypothesized.

58 GEOSCIENCES↗

Southeast Regional CO 2 Utilization and Storage Acceleration Partnership (SECARB-USA): Needs Assessment Framework for Storage Complexes Contributed to EDX (Work Product 2.1.c)

On September 30, 2021, a Needs Assessment Framework for Storage Complexes was completed for the SECARB-USA region by the Southern States Energy Board (SSEB) and The University of Texas at Austin Bureau of Economic Geology (UT-BEG). The Needs Assessment Framework for Storage Complexes documented a region-wide assessment to identify data needed to advance storage projects at sites of relevance to industrial, academic, and government stakeholders. The region-wide assessment addresses the needs of CO2 source, storage, and utilization stakeholders, finance and insurance institutions, state and local government agencies, local stakeholders at prospective storage complexes, environmental non-governmental organizations (NGOs), and others as identified by the Partners. This assessment was tested against various potential storage sites and modified to include site-specific issues, such as surface or pore space rights. The framework was provided to stakeholders for review and then used in other tasks, such as the ML initiative (Subtask 3.4) and storage complex readiness evaluation (Subtask 4.2). In accordance with the SOPO, this assessment satisfied completion of Work Product 2.1.b. The OSTI ID is 3015803, and the DOI link is https://doi.org/10.2172/3015803. On February 28, 2022, SSEB uploaded Work Product 2.1.b to NETL’s Energy Data eXchange (EDX). As such, Work Product 2.1.c is completed as documented below.

42 ENGINEERING↗

Develop and verify soil/structure interaction for pile/foundation interaction

Phase II of the Offshore Code Comparison Collaboration, Continued, with Correlation and unCertainty (OC6) project was used to verify the implementation of a new soil-structure interaction (SSI) model for use within offshore wind turbine modeling software. The REDWIN Macro-element model implemented and verified in this study enables a computationally efficient way to model the linear and nonlinear SSI problem, including hysteretic damping, of a monopile structure. The modeling approach was integrated into several modeling tools and a series of increasingly complex simulations was conducted using the IEA 10MW reference turbine mounted on a monopile support structure to verify the coupling between the tools and the REDWIN Macro-element SSI model. This campaign includes only numerical verification between various software and modeling approaches so no experimental measurements are available. The load cases (LC) considered include: LC1 – static response of the tower and substructure LC2 – frequency and mode-shape analysis of the tower and substructure LC3 – response of the tower and substructure due to wind-only loading LC4 – response of the tower and substructure due to wave-only loading LC5 – response of the tower and substructure due to wind and wave loading. Detailed properties of the modeled system are found in the following reference, “Bergua, Roger, Amy Robertson, Jason Jonkman, and Andy Platt. 2021. "Specification Document for OC6 Phase II: Verification of an Advanced Soil-Structure Interaction Model for Offshore Wind Turbines.” Golden, CO: National Renewable Energy Laboratory. NREL/TP-5000-79938. https://www.nlr.gov/docs/fy21osti/79938.pdf. Details on the results from the OC6 Phase II project can be found in the following reference, “Bergua R, Robertson A, Jonkman J, et al. OC6 Phase II: Integration and verification of a new soil–structure interaction model for offshore wind design.” Wind Energy. 2022;25(5):793-810. doi:10.1002/we.2698

17 WIND ENERGY↗

Synthetic Atmospheric River Ensembles Generated by Deep-AR

This dataset contains 35,850 synthetic landfalling atmospheric river (AR) realizations generated by the Deep-AR two-stage deep-learning framework over the Northeast Pacific and U.S. West Coast. The archive contains 25 stochastic ensemble members for each of 1,434 held-out observed seed events. Each synthetic realization is initialized from conditions 48 hours before the corresponding observed AR landfall and is generated autoregressively at 6-hour intervals over a 144-hour period. Deep-AR combines a deterministic residual network (ResNet) that advances the large-scale atmospheric state with a Wasserstein generative adversarial network (WGAN) that produces stochastic, high-resolution fields. Each HDF5 file contains 0.25° gridded synthetic integrated vapor transport components (qu, qv), 10 m wind components (u10, v10), and 6-hour accumulated precipitation on a common 200 × 480 grid. The files also include coordinate and datetime arrays. This dataset supports AR hazard analysis, ensemble-based uncertainty characterization, precipitation-extremes research, and regional stress testing. Synthetic files follow the naming convention deepar.model.YYYYMMDD.HHMMSS.vNN.h5. YYYYMMDD.HHMMSS identifies the UTC initial-condition timestamp, which occurs 48 hours before the diagnosed observed landfall, and vNN identifies the zero-padded ensemble member, ranging from v01 through v25. Each synthetic file can be paired with its corresponding observed file by matching the initial-condition timestamp. The paired observed file follows the naming convention deepar.obs.YYYYMMDD.HHMMSS.h5 and is available in the separately registered oracle/deepar.obs dataset at https://wdh.energy.gov/ds/oracle/deepar.obs (DOI: https://doi.org/10.21947/3377671).

17 WIND ENERGY↗

Polarized positrons at Ce\(^+\)BAF

A baseline concept for a continuous wave (CW) polarized positron injector was developed for the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab [1]. This concept is based on positron beam generation by a high current polarized electron beam (1 mA, 120 MeV, >85% polarization) irradiating a 4 mm thick rotating water-cooled tungsten target or liquid metal target. The update on the development of the Ce+BAF injector concept including the polarized electron source, the development of high power targets, simulations of positron capture, the design of the transport line from the positron injector to the CEBAF North Linac and the planned experiment to measure the upper limits of transverse and longitudinal emittances accepted by the 12 GeV CEBAF optics are presented. [1] J. Grames et al., "Positron beams at Ce+BAF", in Proc. 14th Int. Particle Accelerator Conf. (IPAC’23), Venice, Italy, MOPL152, pp. 896–899 (2023). doi:10.18429/JACoW-IPAC2023-MOPL152

Ushakov, Andriy↗

2023 Billion-Ton Report

The U.S. Department of Energy Bioenergy Technology Office's (BETO's) 2023 Billion-Ton Report (BT23) is an assessment of renewable carbon resources potentially available in the United States. BT23 explores these resources in terms of quantity, price, geographical density and distribution, and market maturity. Resource quantities in this report are limited by specified economic and environmental sustainability constraints. Good practices are needed to ensure biomass production has positive environmental outcomes. BT23 supports BETO's mission, particularly the 2023 Multi-Year Program Plan. To access 2023 Billion-Ton Report PDFs, appendices, and high-level messages, navigate to the 2023 Billion-Ton Report landing page at https://energy.gov/eere/bioenergy/2023-billion-ton-report-assessment-us-renewable-carbon-resources on the U.S. Department of Energy Bioenergy Technologies Office website. To access information about the quality assumptions used in this report, please see the Biomass Feedstock Library at https://bioenergylibrary.inl.gov/Home/Home.aspx Please cite the 2023 Billion-Ton Report as: U.S. Department of Energy. 2024. 2023 Billion‐Ton Report: An Assessment of U.S. Renewable Carbon Resources. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. ORNL/SPR-2024/3103. doi: 10.23720/BT2023/2316165. Contact Us

BT23↗

Chapter 1: Background and Introduction

Langholtz, M. H. 2024. “Chapter 1: Background and Introduction.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316166.

BT23↗

Chapter 2 Biomass Currently Used for Energy and Coproducts

Jacobson, R., and S. Curran. 2024. “Chapter 2: Biomass Currently Used for Energy and Coproducts.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316167.

BT23↗

Chapter 3: Waste Resources and Byproducts

Milbrandt, A., and A. Badgett. 2024. “Chapter 3: Waste Resources and Byproducts.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316168.

Milbrandt, Anelia↗

Chapter 4: Biomass from the Forested Land Base

Davis, M., L. Lambert, R. Jacobson, D. Rossi, C. Brandeis, J. Fried, B. English, et al. 2024. “Chapter 4: Biomass from the Forested Land Base.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316170.

BT23↗

Chapter 5: Biomass from Agriculture

Hellwinckel, C., D. de la Torre Ugarte, J. L. Field, and M. Langholtz. 2024. “Chapter 5: Biomass from Agriculture.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316171.

Hellwinckel, Chad↗

Chapter 6: Sustainability and Good Practices

Efroymson, R. A., M. H. Langholtz, K. L. Kline, D. de la Torre Ugarte, C. Hellwinckel, T. R. Hawkins, E. S. Parish, et al. 2024. “Chapter 6: Sustainability and Good Practices.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316172.

Efroymson, Rebecca A↗

Chapter 7.1: Microalgae

Microalgae is a unique biomass resource that does not need to compete for land and water with other biomass feedstocks because it can be cultivated on low-quality unencumbered land using noncompetitive water types including saline and wastewater. It is included as a complementary resource alongside other biomass feedstocks reported in this study, albeit at higher biomass production costs reflective of more capital-intensive farming operations than typical for terrestrial biomass. Higher biomass costs can be offset by the potential to produce value-added coproducts unique to compositional constituents of microalgae. Relative to BT16, this chapter reflects the latest analysis from the 2022 Algae Harmonization Update, which uses the latest parameterized and high-performing saline algal strain, second-generation carbon capture of point-source waste CO2 and high-pressure pipeline transport resolved to specific point-source types, saline water sourcing up to 40,000 mg/L total dissolved solids for source and makeup water salinity, blowdown water treatment and recycle, and brine disposal handling. National-scale algal biomass availability potential was calculated at 152 million tons/yr ash-free dry weight (AFDW) (191 million tons/yr dry weight) at an average biomass productivity of 26.2 g/m2 -day AFDW (about 50 tons/acre/yr dry weight).1 The algal biomass was cultivated on 3.9 million acres of multi-criteria screened and potentially available land for CONUS and fixed 268 million tons of waste CO2 based on biomass uptake. The algae biomass can be produced at an average MBSP of $674/ton AFDW ($536/ton dry weight) in 2020 dollars,2 corresponding to a total energy potential of 3.3 quads/yr at an average MBSP of $31.2/MMBtu (higher-heating-value [HHV] basis).

algae↗

Chapter 7.2: Macroalgae

Chapter 7.2 — Coleman, A., K. Davis, J. DeAngelo, T. Saltiel, B. Saenz, L. Miller, K. Champion, E. Harrison, and A. Otwell. 2024. “Chapter 7.2: Macroalgae.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316176.

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Chapter 7.3: CO2 Emissions from Stationary Sources

Chapter 7.3 — Badgett, A., G. Cooney, J. Hoffmann, and A. Milbrandt. 2024. “Chapter 7.3: CO2 Emissions from Stationary Sources.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316177.

Badgett, Andrew↗

Chapter 8: Looking Forward and Next Steps

U.S. Department of Energy. 2024. “Chapter 8: Looking Forward and Next Steps.” In 2023 Billion‐Ton Report. M. H. Langholtz (Lead). Oak Ridge, TN: Oak Ridge National Laboratory. doi: 10.23720/BT2023/2316179.

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Chlamydomonas reinhardtii responses to Fe-excess, Fe-deficiency, and Fe-limitation in either photoautotrophic or mixotrophic growth

A systems level analysis of Chlamydomonas reinhardtii grown photoautotrophically or mixotrophically with a reduced carbon source, acetate, under four different defined Fe stages of Fe-replete, Fe-deficient, Fe-limited, or Fe-excess. Samples were digested with trypsin, labeled with TMT 10-Plex, then analyzed by LC-MS/MS. Data was searched with MS-GF+ using PNNL's DMS Processing pipeline. [doi:10.25345/C5707X12X] [dataset license: CC0 1.0 Universal (CC0 1.0)]

59 BASIC BIOLOGICAL SCIENCES↗