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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Guiding Principles for the Rational Design of Hybrid Materials: Use of DFT Methodology for Evaluating Non‐Covalent Interactions in a Uranyl Tetrahalide Model System

Abstract Together with the synthesis and experimental characterization of 14 hybrid materials containing [UO 2 X 4 ] 2− (X=Cl − and Br − ) and organic cations, we report on novel methods for determining correlation trends in their formation enthalpy (ΔH f ) and observed vibrational signatures. Δ H f values were analyzed through isothermal acid calorimetry and a Density Functional Theory+Thermodynamics (DFT+T) approach with results showing good agreement between theory and experiment. Three factors (packing efficiency, cation protonation enthalpy, and hydrogen bonding energy [ ]) were assessed as descriptors for trends in Δ H f . Results demonstrated a strong correlation between and Δ H f , highlighting the importance of hydrogen bonding networks in determining the relative stability of solid‐state hybrid materials. Lastly, we investigate how hydrogen bonding networks affect the vibrational characteristics of uranyl solid‐state materials using experimental Raman and IR spectroscopy and theoretical bond orders and find that hydrogen bonding can red‐shift U≡O stretching modes. Overall, the tightly integrated experimental and theoretical studies presented here bridge the trends in macroscopic thermodynamic energies and spectroscopic features with molecular‐level details of the geometry and electronic structure. This modeling framework forms a basis for exploring 3D hydrogen bonding as a tunable design feature in the pursuit of supramolecular materials by rational design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Integrated Circular Economy Model System for Direct Lithium Extraction: From Minerals to Batteries Utilizing Aluminum Hydroxide

Aluminum hydroxide, an abundant mineral found in nature, exists in four polymorphs: gibbsite, bayerite, nordstrandite, and doyleite. Among these polymorphs gibbsite, bayerite, and commercially synthesized amorphous aluminum hydroxide have been investigated as sorbent materials for lithium extraction from sulfate solutions. The amorphous form of Al(OH) 3 exhibits a reactivity higher than that of the naturally occurring crystalline polymorphs in terms of extracting Li + ions. This study employed high-temperature oxide melt solution calorimetry to explore the energetics of the sorbent polymorphs. The enthalpic stability order was measured to be gibbsite > bayerite > amorphous Al(OH) 3 . The least stable form, amorphous Al(OH) 3 , undergoes a spontaneous reaction with lithium, resulting in the formation of a stable layered double hydroxide phase. Consequently, amorphous Al(OH) 3 shows promise as a sorbent material for selectively extracting lithium from clay mineral leachate solutions. Further, this research demonstrates the selective direct extraction of Li + ions using amorphous aluminum hydroxide through a liquid–solid lithiation reaction, followed by acid-free delithiation and relithiation processes, achieving an extraction efficiency of 86%, and the maximum capacity was 37.86 mg·g –1 in a single step during lithiation. With high selectivity during lithiation and nearly complete recoverability of the sorbent material during delithiation, this method presents a circular economy model. Furthermore, a life cycle analysis was conducted to illustrate the environmental advantages of replacing the conventional soda ash-based precipitation process with this method, along with a simple operational cost analysis to evaluate reagent and fuel expenses.

25 ENERGY STORAGE↗
DOE OSTI2019

Model systems for screening and investigation of lithium metal electrode chemistry and dendrite formation

The use of lithium metal as an electrode for electrochemical energy storage will provide a significant impact on practical energy storage technology. Unfortunately, the use of lithium metal is plagued with challenging chemical problems. Specifically, the formation of a solid electrolyte interphase layer and the nucleation and growth of lithium dendrites: both must be addressed and controlled in order to achieve a practically useable pure lithium metal electrode. Currently sophisticated experimental techniques and computationally expensive simulations are being used to probe these problems but these methods are arduous and time consuming which delays timely evaluation and insight into the rapidly changing field of advanced energy storage. Here, we report the use of DFT simulations of lithium nanoclusters to investigate and explore lithium metal chemistry with inexpensive computational methods to gain greater insight into electrochemical reductions and the nucleation and growth of dendrites. DME, LiTFSI, and LiFSI reduction energetics and structures with electrode effects from lithium metal are reported providing better physical description of the absolute reduction potential characterization. The electronic structure of the lithium nanoclusters were used to investigate the nucleation and growth of lithium dendrites from an ab initio perspective. The results demonstrate that kinetic processes have more control over non uniform growth than thermodynamic processes. Based on this information, a non ab initio model was created in Matlab that shows the initial stages of dendrite nucleation considering approximately 2000 atoms.

25 ENERGY STORAGE↗
DOE OSTI2022

Using the Criticality Accident Alarm System modeling capabilities in SCALE [Slides]

The following is a summary of advice for CAAS modeling in SCALE. Refer to the SCALE Criticality Safety and Radiation Shielding training slides or to the SCALE manual for exact syntax. Use a mesh for the fission source that is the most adequate for the problem to solve (coarse/fine). Don’t spend unnecessary resources; simplify the model if it does not impact the final results of interest. Be careful to deactivate secondary fissions in MAVRIC or the calculation may never end. Check that k eff and $\overline{\upsilon}$ calculated results are logical. Between KENO and MAVRIC, cross section libraries, materials, geometry, and mesh grid can be the same or different. Iterative calculations are usually complex problems that need variance reduction. It will be hard to find the best solving parameters in the first attempt; expert judgement is needed. Check each step separately. Use Fulcrum to visualize fission source, mesh source, and spatial/energy distributions to find potential errors or impactful imprecisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Electric Vehicle and Infrastructure Systems Modeling in Washington D.C. and Baltimore

This report documents the Argonne-Exelon effort to develop and utilize an agent-based model (ATEAM) of charging demand and infrastructure expansion applicable to the Washington, DC–Baltimore, MD consolidated metropolitan area. This study extends the ATEAM model time horizon to 10 years (from 2020 to 2030), expands agent behavior modeling capabilities, incorporates more granular and extensive empirical data on charging behavior, and analyzes charging needs for a much larger population of PEVs, in keeping with regional goals for significant adoption of ZEVs. With given targets for annual BEV adoption, five scenarios were developed to examine public infrastructure needs and resulting charging load, considering different home charging availabilities, as well as different PEV consumer profiles and public charging infrastructure deployment strategies. Scenario results show that if new chargers (both L2 and DCFC) are spread more widely (as with ubiquitous deployment strategies), there will be less variation in the number of chargers added to each census tract in the study area. More importantly, widespread public charging infrastructure with ubiquitous deployment strategies reduces unmet charging demand and improves charging success, even with heavy reliance on public charging. About 80 percent of BEV drivers can charge on their first attempt in scenarios with ubiquitous deployment strategies. Moreover, widespread public charging infrastructure better meets the demand for more charging, and in return, increases BEV adoption. Low home charging availability produces higher charging loads in public locations, especially during the early morning (around 8:00 a.m.) and late afternoon (around 6:00 p.m.). The evening peak load indicates that drivers are taking advantage of public charging before heading home. Study results also indicate that even with 20 percent home charging availability in 2030, just 20 percent of drivers attempt to charge on a given day. With their relatively high electric range (200+ miles), the BEVs expected to be on the road in 2030 can handle daily commutes without re-charging for a couple of days.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Impacts of cloud superparameterization on projected daily rainfall intensity climate changes in multiple versions of the Community Earth System Model

Abstract Changes in the character of rainfall are assessed using a holistic set of statistics based on rainfall frequency and amount distributions in climate change experiments with three conventional and superparameterized versions of the Community Atmosphere Model (CAM and SPCAM). Previous work has shown that high‐order statistics of present‐day rainfall intensity are significantly improved with superparameterization, especially in regions of tropical convection. Globally, the two modeling approaches project a similar future increase in mean rainfall, especially across the Inter‐Tropical Convergence Zone (ITCZ) and at high latitudes, but over land, SPCAM predicts a smaller mean change than CAM. Changes in high‐order statistics are similar at high latitudes in the two models but diverge at lower latitudes. In the tropics, SPCAM projects a large intensification of moderate and extreme rain rates in regions of organized convection associated with the Madden Julian Oscillation, ITCZ, monsoons, and tropical waves. In contrast, this signal is missing in all versions of CAM, which are found to be prone to predicting increases in the amount but not intensity of moderate rates. Predictions from SPCAM exhibit a scale‐insensitive behavior with little dependence on horizontal resolution for extreme rates, while lower resolution (∼2°) versions of CAM are not able to capture the response simulated with higher resolution (∼1°). Moderate rain rates analyzed by the “amount mode” and “amount median” are found to be especially telling as a diagnostic for evaluating climate model performance and tracing future changes in rainfall statistics to tropical wave modes in SPCAM.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Enabling immersive engagement in energy system models with deep learning

Complex ensembles of energy simulation models have become crucial components of renewable energy research in recent years. Often the increasing computational cost, high-dimensional structure, and other complexities hinder researchers from fully utilizing these data sources for knowledge building. Researchers at National Renewable Energy Laboratory have determined an immersive visualization workflow to dramatically improve user engagement and analysis capability through a combination of low-dimensional structure analysis, deep learning, and custom visualization methods. We present case studies for two energy simulation platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Earth System Grid Federation: Federated and Integrated Climate Data from Multiple Sources. IN: Earth System Modeling, Volume 6

In climate science, the quantity of data in use by 2020 is expected to be in the hundreds of exabytes (EB, where 1 exabyte = 1018 bytes). Current and future heterogeneous climate data will be distributed around the globe and must be harnessed to find solutions to mission-critical problems. Additionally, more requirements and more constraints are needed to expand and integrate new modeling capabilities and tasks, such as climate prediction, uncertainty quantification (UQ) of model performance, test-bed development, and assimilation of more diverse data sets. These data exploration tasks can be complex and time-consuming, and frequently involve numerous resources spread throughout the modeling and observational climate communities. Staff expertise and core competencies must therefore be flexibly applied to multiple projects and programs to accommodate more complex applications and state-of-the-art science analysis, while allowing resources to be adapted to address future areas of interest in climate research.

Climate Project↗
DOE OSTI2015

Functional studies of TcRjl, a novel GTPase of Trypanosoma cruzi, reveals phenotypes related with MAPK activation during parasite differentiation and after heterologous expression in Drosophila model system

The life cycle of the protozoan parasite Trypanosoma cruzi comprises rounds of proliferative cycles and differentiation in distinct host environments. Ras GTPases are molecular switches that play pivotal regulatory functions in cell fate. Rjl is a novel GTPase with unknown function. Herein we show that TcRjl blocks in vivo cell differentiation. The forced expression of TcRjl leads to changes in the overall tyrosine protein phosphorylation profile of parasites. TcRjl expressing parasites sustained DNA synthesis regardless the external stimuli for differentiation. Heterologous expression in the Drosophila melanogaster genetic system strongly suggests a role from TcRjl protein in RTK-dependent pathways and MAPK activation.

60 APPLIED LIFE SCIENCES↗