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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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Video about CALiPER Report Series 21 on LED Linear Lamps and Troffer Lighting, featuring interviews with Tracy Beeson and Naomi Miller of Pacific Northwest National Laboratory.
The Klynac concept integrates an electron gun, a radio frequency (RF) power source, and a coupled-cavity linear accelerator into a single resonant system. The klystron is essentially a conventional klystron structure with an input cavity, some number of intermediate cavities and an output cavity. The accelerator structure is, likewise, a conventional on-axis coupled structure. The uniqueness is the means of coupling the klystron output cavity to the accelerator. The coupler is a resonant coupler rather than an ordinary transmission line. The geometry of such a system need not be coaxial. However, if the klystron and accelerator are coaxial we can eliminate the need for a separate cathode for the accelerator by injecting some of the klystron beam into the accelerator. Such a device can be made cylindrical which is ideal for some applications.
Particle accelerator are scientific instruments that allow scientists to collide particles together at incredible energies to study the secrets of the universe. However, there are many manners in which particle accelerators can be constructed. In this video, Fermilab’s Dr. Don Lincoln explains the pros and cons of circular and linear accelerators.
The ability to assess and optimize economics of biomass resource utilization for the production of fuels, chemicals and power is essential for the ultimate success of a bioenergy industry. The team of authors, consisting of members from the National Renewable Energy Laboratory (NREL) and the Idaho National Laboratory (INL), has developed simple biorefinery linear programming (LP) models to enable the optimization of theoretical or existing biorefineries. The goal of this analysis is to demonstrate how such models can benefit the developing biorefining industry. It focuses on a theoretical multi-pathway, thermochemical biorefinery configuration and demonstrates how the biorefinery can use LP models for operations planning and optimization in comparable ways to the petroleum refining industry. Using LP modeling tools developed under U.S. Department of Energy's Bioenergy Technologies Office (DOE-BETO) funded efforts, the authors investigate optimization challenges for the theoretical biorefineries such as (1) optimal feedstock slate based on available biomass and prices, (2) breakeven price analysis for available feedstocks, (3) impact analysis for changes in feedstock costs and product prices, (4) optimal biorefinery operations during unit shutdowns / turnarounds, and (5) incentives for increased processing capacity. These biorefinery examples are comparable to crude oil purchasing and operational optimization studies that petroleum refiners perform routinely using LPs and other optimization models. It is important to note that the analyses presented in this article are strictly theoretical and they are not based on current energy market prices. The pricing structure assigned for this demonstrative analysis is consistent with $4 per gallon gasoline, which clearly assumes an economic environment that would favor the construction and operation of biorefineries. The analysis approach and examples provide valuable insights into the usefulness of analysis tools for maximizing the potential benefits of biomass utilization for production of fuels, chemicals and power.
This poster provides an overview on a linear approximation line pack model for integrated electricity and natural gas systems OPF.
Flexible loads have great potential to improve the power grid's flexibility and stability. To effectively control large ensembles of heterogeneous loads, reliable models thereof are required. The paper presents a data-driven modeling and control approach to manage flexible loads for providing grid services. We leverage a linear parameter-varying autoregressive moving average (LPV-ARMA) model to describe the aggregate load response, where the parameters in the model are used to capture external environment impacts (e.g., weather). A gain-scheduling feedback controller is then developed to adapt to environmental variations. This data-driven approach can be easily applied to different types of loads in various environmental conditions. In addition to the ensemble controller, distributed load controllers are designed to deliver grid services, while maintaining the quality of service of inherent load tasks. We demonstrate the work on the IEEE 37-node distribution system for real-time power regulation services through control of thermostatically controlled loads.
Presentation on the "Effects of Fluid Axial Conduction on Liquid Metal Natural Circulation and Linear Stability" 35 slides
The Alfvén-cyclotron instability is driven by an ion temperature anisotropy such that T ⊥ /T || >1 where ⊥ and || denote directions perpendicular and parallel to a uniform background magnetic field B o , respectively. The computations presented here consider a model of a magnetized, homogeneous, collisionless plasma. Two representations of the proton velocity distribution are considered: a single bi-Maxwellian and a magnetospheric-like configuration of two components, a more dense, relatively cool, isotropic component and a less dense, relatively hot, bi-Maxwellian component which drives the instability. Only wave propagation parallel to B o is considered. Here, using numerical solutions of the full kinetic linear dispersion equation, concise analytic expressions for the scaling of the dimensionless maximum instability growth rate and the corresponding dimensionless real frequency are derived as functions of three dimensionless variables: the hot proton temperature anisotropy, the relative hot proton density, and the hot proton β || . Furthermore, using one-dimensional hybrid particle-in-cell simulations of this same instability, a third relation for the scaling of the maximum amplitude of the dimensionless fluctuating magnetic field energy density is derived.
Machine learning (ML) is relied on for materials spectroscopy. It is challenging to make ML models fail because statistical correlations can mimic the physics without causality. Here, using a benchmark band-excitation piezoresponse force microscopy polarization spectroscopy (BEPS) dataset the pitfalls of the so-called “better”, “faster”, and “less-biased” ML of electromechanical switching are demonstrated and overcome. Using a toy and real experimental dataset, it is demonstrated how linear nontemporal ML methods result in physically reasonable embedding (eigenvalues) while producing nonsensical eigenvectors and generated spectra, promoting misleading interpretations. A new method of unsupervised multimodal hyperspectral analysis of BEPS is demonstrated using long-short-term memory (LSTM) β-variational autoencoders (β-VAEs) . By including LSTM neurons, the ordinal nature of ferroelectric switching is considered. Further, to improve the interpretability of the latent space, a variational Kullback–Leibler-divergency regularization is imposed . Finally, regularization scheduling of β as a disentanglement metric is leveraged to reduce user bias. Combining these experiment-inspired modifications enables the automated detection of ferroelectric switching mechanisms, including a complex two-step, three-state one. Ultimately, this work provides a robust ML method for the rapid discovery of electromechanical switching mechanisms in ferroelectrics and is applicable to other multimodal hyperspectral materials spectroscopies.
Understanding the emergent electronic structure in twisted atomically thin layers has led to the exciting field of twistronics. However, practical applications of such systems are challenging since the specific angular correlations between the layers must be precisely controlled and the layers have to be single crystalline with uniform atomic ordering. Here, in this work, an alternative, simple, and scalable approach is suggested, where nanocrystalline two-dimensional (2D) film on 3D substrates yields twisted-interface-dependent properties. Ultrawide-bandgap hexagonal boron nitride (h-BN) thin films are directly grown on high in-plane lattice mismatched wide-bandgap silicon carbide (4H-SiC) substrates to explore the twist-dependent structure-property correlations. Concurrently, nanocrystalline h-BN thin film shows strong non-linear second-harmonic generation and ultra-low cross-plane thermal conductivity at room temperature, which are attributed to the twisted domain edges between van der Waals stacked nanocrystals with random in-plane orientations. First-principles calculations based on time-dependent density functional theory manifest strong even-order optical nonlinearity in twisted h-BN layers. This work unveils that directly deposited 2D nanocrystalline thin film on 3D substrates could provide easily accessible twist-interfaces, therefore enabling a simple and scalable approach to utilize the 2D-twistronics integrated in 3D material devices for next-generation nanotechnology.
Aminopolymer-based adsorbents are a prominent class of materials being used for direct air capture of CO 2 at the industrial scale. However, improving their working lifetime, specifically by increasing their resilience to oxidative degradation, remains an ongoing challenge. Toward this end, functionalization of aminopolymers with non-amine functionalities such as hydroxyls has emerged in recent years as a promising strategy toward improving adsorbent lifetime. Although there is a growing body of work demonstrating the effectiveness of this approach and investigating the origin of this improved stability, studies to date have primarily focused on branched aminopolymer systems such as branched poly(ethylenimine). In this work, hydroxyl-functionalized linear poly(ethylenimine) is used to continue to probe the underlying protective mechanism of this strategy. A combination of thermogravimetric analysis, NMR relaxometry, differential scanning calorimetry, and computational simulations is used to better understand the relationship between the extent of chemical functionalization, physical properties, and adsorbent performance.
Abstract Hydrofluoroethers (HFEs) have been adopted widely as electrolyte cosolvents for battery systems because of their unique low solvating behavior. The electrolyte is currently utilized in lithium‐ion, lithium–sulfur, lithium–air, and sodium‐ion batteries. By evaluating the relative solvating power of different HFEs with distinct structural features, and considering the shuttle factor displayed by electrolytes that employ HFE cosolvents, we have established the quantitative structure–activity relationship between the organic structure and the electrochemical performance of the HFEs. Moreover, we have established the linear free‐energy relationship between the structural properties of the electrolyte cosolvents and the polysulfide shuttle effect in lithium–sulfur batteries. These findings provide valuable mechanistic insight into the polysulfide shuttle effect in lithium–sulfur batteries, and are instructive when it comes to selecting the most suitable HFE electrolyte cosolvent for different battery systems.
Original synthetic and structure determination methods were used to make a protein molecule with an unprecedented linear-loop polypeptide chain topology, and to characterize its X-ray structure.
Linear scaling relationships (SRs), which relate binding energies of adsorbates across a space of catalyst surfaces, have been extensively explored for metal and oxide surfaces, but little is known about their properties at interfaces between metal nanoparticles and oxide supports, which are ubiquitous in heterogeneous catalysis. Using periodic DFT calculations, scaling principles are extended to bifunctional Au/oxide interfaces. Adopting a Au nanorod on doped MgO (100) as a model, SRs for species participating in water gas shift, methanol synthesis, and oxidation reactions are reported. SR slopes are not constrained by the bond order conservation rule postulated for metals, oxides, and zeolites, potentially permitting greater flexibility in catalyst design strategies. Finally, the deviation from bond counting, along with the physical origin of scaling behavior at interfaces, are explored using a conceptual framework involving electrostatic interactions at the Au/oxide interface.
Abstract Hydrofluoroethers (HFEs) have been adopted widely as electrolyte cosolvents for battery systems because of their unique low solvating behavior. The electrolyte is currently utilized in lithium‐ion, lithium–sulfur, lithium–air, and sodium‐ion batteries. By evaluating the relative solvating power of different HFEs with distinct structural features, and considering the shuttle factor displayed by electrolytes that employ HFE cosolvents, we have established the quantitative structure–activity relationship between the organic structure and the electrochemical performance of the HFEs. Moreover, we have established the linear free‐energy relationship between the structural properties of the electrolyte cosolvents and the polysulfide shuttle effect in lithium–sulfur batteries. These findings provide valuable mechanistic insight into the polysulfide shuttle effect in lithium–sulfur batteries, and are instructive when it comes to selecting the most suitable HFE electrolyte cosolvent for different battery systems.
In this study, we formed core–shell-like polyelectrolyte complexes (PECs) from an anionic bottlebrush polymer with poly (acrylic acid) side chains with a cationic linear poly (allylamine hydrochloride). By varying the pH, the number of side chains of the polyanionic BB polymers (Nbb), the charge density of the polyelectrolytes, and the salt concentration, the phase separation behavior and salt resistance of the complexes could be tuned by the conformation of the BBs. By combining the linear/bottlebrush polyelectrolyte complexation with all-liquid 3D printing, flow-through tubular constructs were produced that showed selective transport across the PEC membrane comprising the walls of the tubules. These tubular constructs afford a new platform for flow-through delivery systems.
Bimetallic catalysts offer enhanced catalytic performance through synergistic interactions between the two metals, allowing them to break the linear scaling relations and reach high electrocatalytic activity. This study presents bimetallic aerogel-based catalyst synthesized as a covalent, three-dimensional framework containing neighboring iron and manganese sites. The aerogel structure provides a high surface area and porosity, facilitating an ultra-high active site density and efficient mass transport. The MnFe porphyrrole's unique structure is obtained by alternately linking Mn-porphyrin and Fe-corrole complexes. It exhibited outstanding performance with an onset potential of 0.99 V RHE . Comparative studies with a free-base Fe porphyrrole catalyst (E onset 0.97 V RHE ) revealed that while Mn incorporation led to only a slight improvement in half-cell performance, it resulted in significantly enhanced performance in anion exchange membrane fuel cell. The MnFe catalyst achieved an OCV of 0.97 V and a peak power density of 0.27 W cm −2 , outperforming the free-base Fe counterpart. Using density functional theory calculations, we show that the higher ORR activity of MnFe-porphyrrole is due to charge transfer between Mn and Fe atoms, which is absent in the reference free-base Fe-porphyrrole. These findings underscore the advantages of bimetallic catalysts in improving ORR activity and fuel cell efficiency by leveraging synergistic effects.
Abstract Linear inverse models (LIMs) are used to explore predictability and information content of the Madden–Julian Oscillation (MJO). Hindcast skill for outgoing longwave radiation (OLR) related to the MJO on intraseasonal timescales in the tropics has been examined for a variety of LIMs using OLR and optionally 200 and 850 hPa zonal wind information channels. The dependence of OLR hindcast skill on wind channels was evaluated by randomizing in time, averaging in space, or omitting data entirely. Results show positive prediction skill (relative to climatology) up to 3 weeks and wind information, mostly at the largest scales, adds 1–2 days of skill.