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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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The Feasibility of Direct CO2 Conversion Technologies on Impacting Mid-Century Climate Goals
Recent IPCC modeling suggests that to limit global warming below 2 degrees C, "unprecedented" reductions in CO2 emissions will be required during the 2030-2050 time frame. The direct conversion of CO2 to fuels and chemicals using renewable electricity has garnered interest as one route to draw down emissions with the ability to synthesize products at a significantly lower carbon footprint than conventional methods. However, most direct CO2 conversion pathways are only in the early stages of development and face technical and market hurdles prior to scale-up, bringing into question what role they may play in influencing global CO2 emissions within this critical two-decade window. In this perspective, we highlight low-temperature electrolysis as a promising technology for direct CO2 conversion, including discussion on the products that could most impact global emission levels, and importantly, offer insight into the timeline, required advancements, and specific technical targets needed to successfully scale and deploy these technologies at the commercial level.
MaterialsMap: A CALPHAD-based tool to design composition pathways through feasibility map for desired dissimilar materials, demonstrated with resistance spot welding joining of Ag-Al-Cu
Assembly of dissimilar metals can be achieved by different methods, for example, casting, welding, and additive manufacturing (AM). However, undesired phases formed in liquid-phase assembling processes due to solute segregation during solidification diminish mechanical and other properties of the processed parts. In the present work, an open-source software named MaterialsMap, has been developed based on the CALculation of Phase Diagrams (CALPHAD) approach. The primary objective of MaterialsMap is to facilitate the design of an optimal composition pathway for assembling dissimilar alloys with liquid-phases based on the formation of desired and undesired phases along the pathway. In MaterialsMap, equilibrium thermodynamic calculations are used to predict equilibrium phases formed at slow cooling rate, while Scheil-Gulliver simulations are employed to predict non-equilibrium phases formed during rapid cooling. Additionally, by combining these two simulations, MaterialsMap offers a thorough guide for understanding phase formation in various manufacturing processes, assisting users in making informed decisions during material selection and production. As a demonstration of this approach, a compositional pathway was designed from pure Al to pure Cu through Ag using MaterialsMap. The design was experimentally verified using resistance spot welding (RSW).
Feasibility of using crystal channeling for the beam loss mitigation in slow extraction at 8GeV
In the accelerator applications of slow extraction for High Energy Physics (HEP), one of the prime challenges is the mitigation of beam losses, which is becoming increasingly critical with the continuous rise in beam power. A significant breakthrough was achieved earlier at CERN through the successful deployment of proton beam channeling in a crystal at 400GeV, effectively diverting the beam away from extraction septa and offering new avenues for improving slow extraction efficiency. However, a crucial question remains whether this approach is still effective at lower and medium proton beam energies. In this paper, we present the promising results of the computer simulation studies of the septum shadowing at 8GeV for the Mu2e project slow extraction at Fermilab. In conclusion, across the wide range of beam parameters, the beam loss reduction is shown to range between 40% and factor of three.
Diagnosing nuclear power plant pipe wall thinning due to flow accelerated corrosion using a passive, thermal non-destructive evaluation method: Feasibility assessment via numerical experiments
Flow accelerated corrosion (FAC) in nuclear power plant pipes is one of the leading causes of accidents, fatalities, damage and outages. Current FAC identification methods employ expensive sensing technology and are “active” methods, where the response of the piping system to an externally-generated thermal, mechanical or optical excitation must be measured. As a result, these techniques require a disruptive and time-consuming setup. Here we propose a method that utilizes pipe surface temperature measurements to passively monitor for FAC-induced pipe wall thinning without the need for expensive equipment or post-installation setup time. This diagnostic method utilizes a simulation data-driven diagnostic model to estimate the amount of thickness reduction in a pipe based on changes in measured steady-state pipe temperatures. In order to reduce the computational burden of generating large, simulation-based datasets, the behavior of the insulation of the pipe was modeled using a suitably calibrated heat transfer boundary parameter. Additionally, global sensitivity analysis was performed to determine system parameter(s), such as the temperature of water flowing inside the pipe, which significantly affect the steady state pipe wall temperature and could cause errors in diagnosis. Two diagnostic models, one using only the change in steady-state temperature as an indicator for FAC-induced pipe wall thinning and the other using water temperature as an additional diagnostic model input were evaluated for their ability to estimate thickness reductions in a pipe using simulated pipe wall temperature data. For the numerical experiments conducted in this work, both models estimated wall thickness with errors within 0.5 mm, indicating that the proposed technique can potentially be used as a low-cost, first-pass method for FAC monitoring.
A framework for feasibility-level validation of high-resolution wave hindcast models
The value of long-term wave hindcasts for investigating wave climates, wave energy resources, and extreme wave conditions has motivated research developing, calibrating and validating wave hindcast models. Past hindcast model validation studies examined the accuracy in modeling bulk wave parameters of overall sea states without considering the dependency of the model's skill within different sea states. In the present study, a framework for wave hindcast model validation is developed by examining the model accuracy for the most frequently occurring sea states, sea states contributing the most energy to total wave power, sea states associated with hurricane events, and those with the largest model error. Here, validations using bulk wave parameters and frequency-directional spectra at these key sea states and extreme wave conditions based on univariate and bivariate-contour methods provide insights to improve model accuracy, identifying the model's strong and weak points, and pathways for improvement, e.g., modeling wave-current interactions and adjusting wind data. This study adds to a growing body of research demonstrating that a carefully calibrated and verified spectral wave hindcast model can be used to estimate key wave energy parameters over a wide range of wave energy climates, as well as their spatial, temporal, frequency, directional, and probabilistic distributions.
Exploring the physical limits of hydraulic fracture caging to forecast its feasibility for geothermal power generation
Not Available
U.S. lithium resources from geothermal and extraction feasibility
As the demand for electric vehicles (EVs) increases, demand for lithium (Li) is expected to grow over the coming decade. To satisfy the demand for rechargeable batteries, it is imperative to devise solutions for sufficient Li production. Additionally, even though commercial Li currently comes from conventional saline salar brine and spodumene ore extractions, efforts to retrieve Li from geothermal brines are increasing in popularity. In this paper, we evaluate the United States’ potential to supply Li from geothermal brines using Systems Dynamics modeling. The main goals are to assess (1) the viability of US Li supply from geothermal brines and (2) the potential supply chain impact of extracting Li from this source. Results show that Li supply from geothermal brines could (1) reach around 4% to 8% of total US Li supply and (2) be economically viable.
Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms
Understanding the governing principles behind organisms’ metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. Furthermore, this review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.
Process Feasibility Analysis of Waste Biomass Valorization to Biochar and Bio-Oil via Slow and Fast Pyrolysis
The United States has abundant biomass and waste feedstock to support the nation's energy addition and affordability targets. Pyrolysis, a thermochemical conversion process, decomposes lignocellulosic feedstocks into liquid, solid, and gaseous fuels that can contribute to the domestic production of biofuels, biopower, and bioproducts. Growing private sector interest in this technology is a key motivation for this comprehensive techno-economic process modeling analysis of a respective biorefinery that includes feedstock preprocessing, slow and fast pyrolysis, and product separation to bio-oil, biochar, and syngas hydrocarbons. Results show that biochar from slow pyrolysis could achieve minimum selling prices (MSPs) of $\$$188-$\$$260/t, competitive with reported market values, while bio-oil from fast pyrolysis is estimated to yield MSPs of $\$$6.49-$\$$9.68/GGE, approximately twice conventional fuel benchmarks. Sensitivity analysis identifies feedstock cost, product yield, and scale as primary cost drivers, while scenarios involving biochar carbon credits and high value applications may substantially improve economics. Overall, these results suggest that continued innovation in feedstock logistics, process integration, and market development will be critical to achieving economically viable and scalable bioproducts.
Feasibility of Methane Oxidation on SSZ-13 Bridged Pd 2 O x Sites: A Theoretical Study
Not provided.
Feasibility of Metal Oxide Glasses and Polymer Membranes as Sorbents for Gaseous Oxidized Mercury
Mass spectrometry methods are currently under development by the atmospheric mercury (Hg) research community to elucidate the identity of atmospheric oxidized mercury (Hg II ) compounds. Due to high instrument detection limits, materials that can quantitatively preconcentrate atmospheric Hg II without facilitating compound-altering chemical reactions are needed to support these methods. Cation exchange membranes (CEM) and nylon membranes are currently used to preconcentrate ambient Hg II for concentration measurements and Hg II compound estimation, respectively. However, CEM and nylon membranes are poor candidates for observations by mass spectrometry methods due to release of interfering compounds upon heating; glasses do not have this problem. Here, three metal oxide glasses were explored as potential alternatives for Hg II preconcentration for future use with mass spectrometry methods: calcium phosphate (CaP), iron phosphate (FeP), and calcium aluminate (CaAl). The glasses demonstrated quantitative selective capture of HgBr 2 without capture of Hg 0 . Under ambient conditions, the CaP, FeP, and CaAl sorbed 36.4 ± 12.6% of the total HgII as the CEM. However, when Hg concentrations were normalized to surface area, CaP, FeP, and CaAl sorbed more HgBr 2 in the laboratory and ambient HgII compared to CEM. The CEM and CaP retained similar concentrations of HgBr 2 when preloaded samples were deployed in the field. Additionally, a permeation tube-based calibrator was used to load sorbents with HgBr 2 for investigation of HgII retention on CEM and thermal desorption profile changes on nylon membranes during active sampling. Nylon membranes were purchased from three vendors and used to compare HgBr 2 retention; a different HgBr 2 thermal desorption profile was achieved for each vendor’s nylon membrane.
Ethanol as a Liquid Organic Hydrogen Carrier for Seasonal Microgrid Application: Catalysis, Theory, and Engineering Feasibility
Here, we describe the benefits and challenges of a green approach to seasonal energy storage using ethanol as a liquid organic hydrogen carrier (LOHC). We evaluate the cycling efficiency of hydrogen release from ethanol (EtOH) to form ethyl acetate (EtOAc) as the spent LOHC and the subsequent regeneration of EtOH from EtOAc catalyzed by a single molecular catalyst, Ru-MACHO, at a moderate pressure of H 2 , mild reaction temperature, and high selectivity. From experimental and computational studies, we were able to minimize catalyst deactivation, regenerate the active catalyst post reactions, and establish the energy profile of the deactivation pathway relative to the on-cycle pathway catalyzed by Ru-MACHO. Based upon these findings, we performed a reactor design analysis to determine the footprint of an EtOH-based storage system to provide 85 MWh of energy by storing 5 metric tons (MT) of H 2 . We conclude that the heating and cooling required to maintain H 2 partial pressure present a significant engineering challenge for widespread deployment of this system.
Automated exploitation of the big configuration space of large adsorbates on transition metals reveals chemistry feasibility
Mechanistic understanding of large molecule conversion and the discovery of suitable heterogeneous catalysts have been lagging due to the combinatorial inventory of intermediates and the inability of humans to enumerate all structures. Here, we introduce an automated framework to predict stable configurations on transition metal surfaces and demonstrate its validity for adsorbates with up to 6 carbon and oxygen atoms on 11 metals, enabling the exploration of ~10 8 potential configurations. It combines a graph enumeration platform, force field, multi-fidelity DFT calculations, and first-principles trained machine learning. Clusters in the data reveal groups of catalysts stabilizing different structures and expose selective catalysts for showcase transformations, such as the ethylene epoxidation on Ag and Cu and the lack of C-C scission chemistry on Au. Deviations from the commonly assumed atom valency rule of small adsorbates are also manifested. This library can be leveraged to identify catalysts for converting large molecules computationally.