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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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PowerRoll Ltd and NREL will work on development of perovskite ink formulations and corresponding coating techniques to best coat PowerRoll's micro-structure substrates with high quality, high-performance perovskite materials.
We establish parameter inference for the Poisson canonical polyadic (PCP) tensor model through a latent-variable formulation. Our approach exploits the observation that any random PCP tensor can be derived by marginalizing an unobservable random tensor of one dimension larger. The loglikelihood of this larger dimensional tensor, referred to as the “complete” loglikelihood, is comprised of multiple rank one PCP loglikelihoods. Using this methodology, we first derive maximum likelihood estimators for the PCP model and demonstrate that several existing algorithms for fitting non-negative matrix and tensor factorizations are Expectation-Maximization algorithms. Next, we derive the observed and expected Fisher information matrices for the PCP model. The Fisher information provides us crucial insights into the well-posedness of the tensor model, such as the role that tensor rank plays in identifiability and indeterminacy. For the special case of rank one PCP models, we demonstrate that these results are greatly simplified.
This report presents a comprehensive analysis of the JCZ3 equation of state (EOS) as implemented in the TIGER code, focusing on its thermodynamic framework, mathematical formulations, and implications for modeling gas phase behavior under varying conditions. Overall, the report affirms the TIGER code's foundational robustness and its potential as a valuable tool for simulating high-pressure, high-temperature gas mixtures, while also highlighting areas for further refinement and validation.
This report introduces recovery simulator and analysis (RSA), a framework aimed at enhancing the resilience of electrical grids post-disruption. The RSA model leverages an optimization problem formulation that focuses on maximizing the load served (or optionally customers served) through a coordinated and cooptimized recovery of non-black start generation, transmission lines, feeders and substations subject to labor budget constraints. By integrating advanced linear programming techniques, the simulator selects efficient reocovery pathways, optimizing both short-term and long-term grid recovery strategies. The mathematical framework guides decision-making through a comprehensive evaluation of potential recovery actions, factoring in the trade-offs between labor constraints and load (or optionally customer) restoration efficacy. This enables grid operators to simulate diverse outage scenarios and delineate optimal recovery pathways, thereby prioritizing critical repair tasks and ensuring resource allocation is both economical and effective. The intended use case of RSA is to allow planners to explore many recovery scenarios quickly and determine assets most critical across a wide range of scenarios, and therefore strong candidates for hardening or additional investment. RSA might also be used in an operational setting, following a single event, for exploring efficient recovery pathways.
Emulating the Magnus Formulation of IMSRG
Abstract. The mass conservation equation in the presence of boundary fluxes and chemical reactions from non-equilibrium thermodynamics is used to derive a modified dynamic energy budget (mDEB) model. Compared to the standard dynamic energy budget (sDEB) model (Kooijman, 2009), this modified formulation does not place the dilution effect in the mobilization kinetics of reserve biomass, and it maintains the partition principle for reserve mobilization dynamics for both linear and non-linear kinetics. Overall, the mDEB model shares most features with the sDEB model. However, for biological growth that requires multiple nutrients, the mDEB model is computationally much more efficient by not requiring numerical iterations for obtaining the specific growth rate. In an example of modeling the growth of Thalassiosira weissflogii in a nitrogen-limiting chemostat, the mDEB model was found to have almost the same accuracy as the sDEB model while requiring almost half of the computing time of the sDEB model. Since the sDEB model has been successfully applied in numerous studies, we believe that the mDEB model can help improve the modeling of biological growth and the associated ecosystem processes in various contexts.
Sodium metal batteries have shown considerable potential when operated at ambient temperatures. However, their performance in cold environments is constrained by increased electrolyte resistance with decreasing temperature and dendritic sodium plating associated with unstable solid electrolyte interphase (SEI), which are primarily influenced by the electrolyte composition. In this study, we present an electrolyte formulation that remains thermally stable down to −150 °C, which not only facilitates low internal resistance but also contributes to the formation of a protective SEI under cryogenic conditions. When cycled at −40 °C at 1 mA cm −2 , the sodium metal electrode exhibits a low overpotential of only 16 mV over 750 h; even at an ultra-low temperature of −80 °C, the electrode demonstrates remarkable long-term stability with a low overpotential of 54 mV sustained over 1500 h at 0.5 mA cm −2 . Furthermore, full cell evaluations when paring with Na 3 V 2 (PO 4 ) 3 cathode reveal a high average Coulombic efficiency exceeding 99.1% and a capacity retention over 83% after 100 cycles at both −40 °C and −80 °C.
This presentation summarizes the development of the project called Theoretical Four Pressure Model Development : A Real Characteristic Formulation for RELAP5-3D, A “Well Posed” Equation System in 2-D. This has produced a well-posed system of equations for modeling nuclear power plants (and many other systems).
High-power discharge requirements are critical for lithium-ion batteries (LIBs) used in electric Vertical Takeoff and Landing (eVTOL) vehicles that are increasingly considered in urban mobility. This investigation places a particular emphasis on understanding the impact of electrolytes on discharge processes and rate capability. We aim to compare the discharge behavior of LiBs using a conventional electrolyte (Gen 2: 1.2 M LiPF 6 in EC:EMC) and the dual salt LiTFSI-LiBOB-based electrolyte. Here we carefully examine the profiles of charging and discharging, the behavior during extended cycles, impedance spectroscopy results, and the characteristics of the electrode surface. Our research findings demonstrate the complex relationship between the composition of electrolytes and the specific high-power discharge requirements of electric vertical takeoff and landing (eVTOL) systems. This research highlights the importance of customizing electrolyte compositions to optimize energy storage density while simultaneously enabling higher power extraction to enhance performance in short-range electric aviation.
We propose a novel reformulation of the Vlasov–Ampère equations for plasmas that reveals discrete symmetries that enables simultaneous conservation of mass, momentum and energy; preservation of Gauss’s law; positivity of the distribution function; and consistency with quasi-neutral asymptotics. The approach employs variable and coordinate transformations to yield a coupled system comprising a modified Vlasov equation and associated moment–field equations. The modified Vlasov equation advances a conditional distribution function that excludes mass, momentum and energy densities, which are instead evolved through moment equations enforcing the relevant symmetries, conservation laws and involution constraints. This reformulation aligns naturally with a recent slow-manifold reduction technique, which separates fast electron time scales and simplifies the treatment of the quasi-neutral limit within the reduced moment–field subsystem. Using this framework, we develop a numerical method for the reduced 1D1V subsystem that, for the first time in the literature, satisfies all key physical constraints while maintaining a quasi-neutral asymptotic behaviour. The advantages of the method are demonstrated on canonical electrostatic test problems, including the multiscale ion acoustic shock wave.
Chloroplasts are critical organelles in plants and algae responsible for accumulating biomass through photosynthetic carbon fixation and cellular maintenance through metabolism in the cell. Chloroplasts are increasingly appreciated for their role in biomanufacturing, as they can produce many useful molecules, and a deeper understanding of chloroplast regulation and function would provide more insight for the biotechnological applications of these organelles. However, traditional genetic approaches to manipulate chloroplasts are slow, and generation of transgenic organisms to study their function can take weeks to months, significantly delaying the pace of research. To develop chloroplasts themselves as a quicker and more defined platform, we isolated chloroplasts from the green algae, Chlamydomonas reinhardtii, and examined their photosynthetic function after extraction. Combined with a metabolic modeling approach using flux-balance analysis, we identified key metabolic reactions essential to chloroplast function and leveraged this information into reagents that can be used in a “chloroplast media” capable of maintaining chloroplast photosynthetic function over time ex vivo compared to buffer alone. We envision this could serve as a model platform to enable more rapid design-build-test-learn cycles to study and improve chloroplast function in combination with genetic modifications and potentially as a starting point for the bottom-up design of a synthetic organelle-containing cell.
Triaminotrinitrobenzene-based high explosives such as LX-17 offer high energy density and exceptional safety, yet their long-term aging behavior at low temperatures remains poorly understood. In this study, several legacy and new production lots of LX-17 were subjected to accelerated aging experiments below 100°C, during which the formation rate of the initial degradation product, monofurazan (F1), was monitored. Kinetic analysis was performed using a sample-age-aware computational approach, yielding activation energy estimates of 82–91 kJ mol −1 for the low-temperature initiation step—markedly lower than the ∼200 kJ mol −1 associated with high-temperature thermal decomposition. Extrapolation from established cookoff models supports the conclusion that the dominant degradation mechanism at low temperatures differs from that at high temperatures. In conclusion, our results provide a unified kinetic framework that bridges the gap between in-service conditions and high-temperature damage.
The effect of displacement uncertainty is examined on 2-dimensional strain, calculated using linear surfaces fitted to the displacement field. A classical engineering error propagation method is used to calculate uncertainty in Green-Lagrangian strain calculations. The derived uncertainty is compared to a Monte Carlo simulation and discrepancies under 2% are seen between these two methods. The effect of virtual strain gauge size, displacement uncertainty, and boundaries on the region of interest on the strain uncertainty are considered. Here, an exponential decay relationship is observed between strain uncertainty and virtual strain gauge size, while a linear relationship is seen between strain and displacement uncertainty. For boundaries in the region of interest, strain uncertainty is affected by the reduced number of points available to perform the regression.
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Siegert states (SSs) serve as a useful basis for studying quantum scattering from finite-range potentials. Since they form a discrete instead of continuous set of eigen-solutions, SSs are convenient for performing electronic structure calculations in atoms, molecules, and plasmas. Numerical instabilities may arise, however, in the computation of SSs if the potential vanishes for some extended region, a situation commonly occurring in plasma calculations. Here, in this paper, we identify the cause of these instabilities as the use of non-localized radial basis functions. We thus advocate the use of localized radial basis functions, in particular B-splines, for more robust computations of SSs.
Titanium dioxide (TiO 2 ) particulates are known to exhibit different visible and infrared optical properties compared to the bulk material, showing strong dependence on particle size, crystal structure, and morphology. In this study, the optical properties, sizes, and morphologies of TiO 2 particles from two different sources (nano and fine powders) having a) nominally different particle sizes and b) various crystal polymorph mixture fractions are compared using a combination of single particle mass spectrometry, optical spectroscopies, and aerosol characterization methods. The nano sample was found to be largely particles of the anatase polymorph (88% by mass), while the fine sample was found to consist largely of rutile particles (95% by mass). Two distinct particle morphologies (fractal and compact) were found in each powder sample and could be identified and separated in-situ based on particle aerodynamic properties. The attenuation of near-infrared, visible and ultraviolet light by TiO 2 particles shows strong dependence on particle morphology. Furthermore, while the fine particles were found to have larger near-infrared (675–800 nm) extinction coefficients by mass than the nanoparticles, the reverse was true in the ultraviolet and visible regions (370–675 nm). However, for polydisperse particles with different sizes and shapes, the optical behaviors are not straightforward to directly correlate to a combination of physical parameters.
We present an automated platform for studying electrode corrosion as a function of electrochemical history and electrolyte composition using Pt ORR as a proof-of-concept relevant to next-generation phosphoric acid fuel cells.