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Results for “Transition Model”
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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Critical points of the random cluster model with Newman–Ziff sampling
Here, we present a method for computing transition points of the random cluster model using a generalization of the Newman–Ziff algorithm, a celebrated technique in numerical percolation, to the random cluster model. The new method is straightforward to implement and works for real cluster weight q > 0. Furthermore, results for an arbitrary number of values of q can be found at once within a single simulation. Because the algorithm used to sweep through bond configurations is identical to that of Newman and Ziff, which was conceived for percolation, the method loses accuracy for large lattices when q > 1. However, by sampling the critical polynomial, accurate estimates of critical points in two dimensions can be found using relatively small lattice sizes, which we demonstrate here by computing critical points for non-integer values of q on the square lattice, to compare with the exact solution, and on the unsolved non-planar square matching lattice. The latter results would be much more difficult to obtain using other techniques.
Relativistic Hybrid Stars with Sequential First-order Phase Transitions in Light of Multimessenger Constraints
In this work, we consider the properties of compact stars in which quark matter has low- and high-density phases that are separated by a first-order phase transition. Thus, unlike the commonly considered case of a single phase transition from hadronic to quark matter, our models of hybrid stars contain sequential phase transitions from hadronic matter to low- and then to high-density quark matter phases. We extend our previous study of the parameter space of hybrid stars with a single phase transition to those with sequential phase transitions, taking into account the constraints on the mass and radius of neutron stars from the NICER experiment, the experimental inferences of the neutron skin thickness of the lead nucleus by the PREX-II experiment, and constraints on the tidal deformability from the gravitational-wave event GW170817. We determine the range of the masses for which both twin and triplet configurations, i.e., identical-mass stars with two and three different values of radii, arise.
Assessment of Precipitating Marine Stratocumulus Clouds in the E3SMv1 Atmosphere Model: A Case Study from the ARM MAGIC Field Campaign
This paper presents a process-oriented evaluation of precipitating stratocumulus and its transition to cumulus in version 1 of the Energy Exascale Earth System Model (E3SMv1) using comprehensive case-study observations from a field campaign of the Atmospheric Radiation Measurement program (ARM). The E3SMv1 single-column model (SCM) of the marine boundary layer and its low clouds and precipitation are compared to observations including subcloud drizzle retrievals from a combination of Doppler radar and lidar backscatter measurements. Additionally, the SCM is also compared to a large-eddy simulation (LES) of the same case. The combination of advanced remote sensing observations and LES is a powerful framework to evaluate the physical parameterizations of large-scale models. Given the observed large-scale environment, the E3SMv1 SCM realistically represents the evolution of clouds and boundary layer structure during the stratocumulus-to-cumulus transition. The model well simulates the liquid water path and its diurnal cycle in the stratocumulus period as well as the two-layer vertical thermodynamic structure and lower cloud fraction in the transition period. E3SMv1’s success in simulating the cloud in the stratocumulus period permitted examination of its precipitation processes. Here problems were identified with E3SMv1 producing an unrealistically small subcloud precipitation fraction, an unrealistic double peak in the vertical profiles of precipitation mass, and drizzle that evaporates too close to the surface. Further model diagnostics determined that these unrealistic characteristics resulted from an overly long microphysics time step and an unrealistic parameterization of the precipitation fraction. These results imply that careful consideration of these issues is needed in order to better simulate precipitation processes in marine stratocumulus.
Probing Transit Timing Variation and Its Possible Origin with 12 New Transits of TrES-3b
We present 12 new transit light curves of the hot-Jupiter TrES-3b observed during 2012−2018 to probe the transit timing variation (TTV). By combining the midtransit times determined from these 12 transit data with those reestimated through uniform procedure from 71 transit data available in the literature, we derive new linear ephemeris and obtain the timing residuals that suggest the possibility of TTV in the TrES-3 system. However, the frequency analysis shows that the possible TTV is unlikely to be periodic, indicating the absence of an additional body in this system. To explore the other possible origins of TTV, the orbital decay and apsidal precession ephemeris models are fitted to the transit time data. We find the decay rate of TrES-3b to be P-dot {sub q}=−4.1±3.1 ms yr{sup −1}, and the corresponding estimated modified stellar tidal quality factor of Q{sub ∗}{sup ′}∼1.11×10{sup 5} is consistent with the theoretically predicted values for the stars hosting the hot-Jupiters. The shift in the transit arrival time of TrES-3b after 11 years is expected to be T {sub shift} ∼ 69.55 s, which is consistent with the rms of the timing residuals. Besides, we find that the apsidal precession ephemeris model is statistically less probable than the other considered ephemeris models. It is also discussed that despite the fact that the linear ephemeris model appears to be the most plausible model to represent the transit time data, the possibility of the orbital decay cannot be completely ruled out in the TrES-3 system. To confirm this, further high-precision and high-cadence follow-up observation of transits of TrES-3b would be important.
Modeling scale-up of particle coating by atomic layer deposition
Atomic layer deposition (ALD) is a promising technique to functionalize particle surfaces for energy applications including energy storage, catalysis, and decarbonization. In this work, we present a set of models of ALD particle coating to explore the transition from lab scale to manufacturing. Our models encompass the main particle coating manufacturing approaches including rotary bed, fluidized bed, and continuously vibrating reactors. These models provide key metrics, such as throughput and precursor utilization, required to evaluate the scalability of ALD manufacturing approaches and their feasibility in the context of energy applications. Our results show that designs that force the precursor to flow through fluidized particles transition faster to a transport-limited regime where throughput is maximized. They also exhibit higher precursor utilization. In the context of continuous processes, our models indicate that it is possible to achieve self-extinguishing processes with almost 100% precursor utilization. A comparison with past experimental results of ALD in fluidized bed reactors shows excellent qualitative and quantitative agreement.
Toward precision cosmochronology
The continuous cooling of a white dwarf is punctuated by events that affect its cooling rate. The most significant of these events is the crystallization of its core, a phase transition that occurs once the C/O interior has cooled down below a critical temperature. This transition releases latent heat, as well as gravitational energy due to the redistribution of the C and O ions during solidification, thereby slowing down the evolution of the white dwarf. The unambiguous observational signature of core crystallization–a pile-up of objects in the cooling sequence–was recently reported. However, existing evolution models struggle to quantitatively reproduce this signature, casting doubt on their accuracy when used to measure the ages of stellar populations. The timing and amount of the energy released during crystallization depend on the exact form of the C/O phase diagram. Using the advanced Gibbs–Duhem integration method and state-of-the-art Monte Carlo simulations of the solid and liquid phases, we obtained a very accurate version of this phase diagram that allows a precise modeling of the phase transition. Despite this improvement, the magnitude of the crystallization pile-up remains underestimated by current evolution models. We conclude that latent heat release and O sedimentation alone are not sufficient to explain the observations, and that other unaccounted physical mechanisms, possibly 22 Ne phase separation, play an important role.
Remarks on nuclear matter: How an ω 0 condensate can spike the speed of sound, and a model of Z ( 3 ) baryons
Not Available
Presenting a Model to Predict Changing Snow Albedo for Improving Photovoltaic Performance Simulation
As photovoltaic (PV) deployment increases worldwide, PV systems are being installed more frequently in locations that experience snow cover. The higher albedo of snow, relative to the ground, increases the performance of PV systems in northern and high-altitude locations by reflecting more light onto the PV modules. Accurate modeling of the snow’s albedo can improve estimates of PV system production. Typical modeling of snow albedo uses a simple two-value model that sets the albedo high when snow is present, and low when snow is not present. However, snow albedo changes over time as snow settles and melts and a binary model does not account for transitional changes, which can be significant. Here, we present and validate a model for estimating snow albedo as it changes over time. The model is simple enough to only require daily snow depth and hourly average temperature data, but can be improved through the addition of site-specific factors, when available. We validate this model to quantify its ability to more accurately predict snow albedo and compare the model’s performance against satellite imagery-based methods for obtaining historical albedo data. In addition, we perform modeling using the System Advisor Model (SAM) to show the impact of changes in albedo on energy modeling for PV systems. Overall, our albedo model has a significantly improved ability to predict the solar insolation on PV modules in real time, especially on bifacial PV modules where reflected irradiance plays a larger role in energy production.
Composite fermion nonlinear sigma models
We study particle-hole symmetry at the integer quantum Hall plateau transition using composite fermion mean-field theory. Because this theory implicitly includes some electron-electron interactions, it also has applications to certain fractional quantum Hall plateau transitions. Previous work [P. Kumar et al., Phys. Rev. B 100, 235124 (2019)] using this approach showed that the diffusive quantum criticality of this transition is described by a nonlinear sigma model with topological θ = π term. This result, which holds for both the Dirac and Halperin, Lee, and Read composite fermion theories, signifies an emergent particle-hole (reflection) symmetry of the integer (fractional) quantum Hall transition. Here we consider the stability of this result to various particle-hole symmetry-violating perturbations. In the presence of quenched disorder that preserves particle-hole symmetry, we find that finite longitudinal conductivity at this transition requires the vanishing of a symmetry-violating composite fermion effective mass, which if present would generally lead to θ ≠ π and a corresponding violation of particle-hole symmetric electrical transport σ xy ≠ $\frac1{2}$$\frac{e^2}{h}$ . When the disorder does not preserve particle-hole symmetry, we find that θ can vary continuously within the diffusive regime. Our results call for further study of the universality of the quantum Hall plateau transition.
Catalytic properties of model supported nanoparticles
Nanoparticles of late transition metals are used as catalysts and electrocatalysts for industrial chemical reactions that produce fuels, convert chemical energy to electricity and clean up pollution associated with the generation and use of these fuels. For such applications, metals are usually bonded onto the surfaces of oxide or carbon support materials. To provide the energy needed for sustained economic development, we must develop new and improved solid catalysts and electrocatalysts for a variety of reactions that take better advantage of traditional and alternate energy sources (solar, wind, biomass, etc.) and avoid serious environmental problems. Additionally, since these metals are often expensive and in limited supply (e.g., Pt), we also need to develop catalysts that do not require precious metals. As a consequence, recent years have seen a dramatic increase in research aimed at providing the basic understanding needed to develop new and improved catalysts and electrocatalysts for a variety of reactions that involve nanoparticles of late transition metals supported on oxide or carbon materials. It is therefore timely to present a special issue that focusses on Catalytic Properties of Model Supported Nanoparticles. The collection highlights some of the most exciting new directions for research in this area, and provides in depth examples of some of the most cutting-edge methods, both experimental and theoretical, for studying this class of catalysts, which provide insights into structure, mechanisms, kinetics and energetics with unprecedented accuracy and detail. Finally, as Guest Editors, we present below a few of the many excellent papers published in the collection.
An exploration of machine learning models for the determination of reaction coordinates associated with conformational transitions
Determining collective variables (CVs) for conformational transitions is crucial to understanding their dynamics and targeting them in enhanced sampling simulations. Often, CVs are proposed based on intuition or prior knowledge of a system. However, the problem of systematically determining a proper reaction coordinate (RC) for a specific process in terms of a set of putative CVs can be achieved using committor analysis (CA). Identifying essential degrees of freedom that govern such transitions using CA remains elusive because of the high dimensionality of the conformational space. Various schemes exist to leverage the power of machine learning (ML) to extract an RC from CA. Here, we extend these studies and compare the ability of 17 different ML schemes to identify accurate RCs associated with conformational transitions. We tested these methods on an alanine dipeptide in vacuum and on a sarcosine dipeptoid in an implicit solvent. Our comparison revealed that the light gradient boosting machine method outperforms other methods. In order to extract key features from the models, we employed Shapley Additive exPlanations analysis and compared its interpretation with the “feature importance” approach. For the alanine dipeptide, our methodology identifies ϕ and θ dihedrals as essential degrees of freedom in the C7ax to C7eq transition. For the sarcosine dipeptoid system, the dihedrals ψ and ω are the most important for the cisαD to transαD transition. We further argue that analysis of the full dynamical pathway, and not just endpoint states, is essential for identifying key degrees of freedom governing transitions.
Dataset for Blueprinting Electrified Transit System Implementation
This dataset contains the figures and tabulated results generated from a system-level optimization study of transit fleet electrification planning. The dataset does not include executable modeling code required to reproduce the optimization. The dataset includes results for optimized charging infrastructure deployment by location and power level and service block assignments by fuel type, battery capacity selections, and distributed energy resource sizing. It also contains aggregated financial results, capital expenditures, operating cost summaries, net present cost comparisons across scenarios, and quantified air quality impacts. Results are structured to reflect multiple planning scenarios, including heuristic electrification plans, system-optimized configurations, and sensitivity cases with alternative objective weightings. The modeling was developed using publicly available General Transit Feed Specification data from Omnitrans and standardized modeling assumptions.
Dataset for Blueprinting Electrified Transit System Implementation
This dataset contains the figures and tabulated results generated from a system-level optimization study of transit fleet electrification planning. The dataset does not include executable modeling code required to reproduce the optimization. The dataset includes results for optimized charging infrastructure deployment by location and power level and service block assignments by fuel type, battery capacity selections, and distributed energy resource sizing. It also contains aggregated financial results, capital expenditures, operating cost summaries, net present cost comparisons across scenarios, and quantified air quality impacts. Results are structured to reflect multiple planning scenarios, including heuristic electrification plans, system-optimized configurations, and sensitivity cases with alternative objective weightings. The modeling was developed using publicly available General Transit Feed Specification data from Omnitrans and standardized modeling assumptions.
Quantum-Accurate Multiscale Modeling of Shock Hugoniots, Ramp Compression Paths, Structural and Magnetic Phase Transitions, and Transport Properties in Highly Compressed Metals
Fully characterizing high energy density (HED) phenomena using pulsed power facilities (Z machine) and coherent light sources is possible only with complementary numerical modeling for design, diagnostic development, and data interpretation. The exercise of creating numerical tests, that match experimental conditions, builds critical insight that is crucial for the development of a strong fundamental understanding of the physics behind HED phenomena and for the design of next generation pulsed power facilities. The persistence of electron correlation in HED materials arising from Coulomb interactions and the Pauli exclusion principle is one of the greatest challenges for accurate numerical modeling and has hitherto impeded our ability to model HED phenomena across multiple length and time scales at sufficient accuracy. An exemplar is a ferromagnetic material like iron, while familiar and widely used, we lack a simulation capability to characterize the interplay of structure and magnetic effects that govern material strength, kinetics of phase transitions and other transport properties. Herein we construct and demonstrate the Molecular-Spin Dynamics (MSD) simulation capability for iron from ambient to earth core conditions, all software advances are open source and presently available for broad usage. These methods are multi-scale in nature, direct comparisons between high fidelity density functional theory (DFT) and linear-scaling MSD simulations is done throughout this work, with advancements made to MSD allowing for electronic structure changes being reflected in classical dynamics. Main takeaways for the project include insight into the role of magnetic spins on mechanical properties and thermal conductivity, development of accurate interatomic potentials paired with spin Hamiltonians, and characterization of the high pressure melt boundary that is of critical importance to planetary modeling efforts.
Disorder Dynamics in Battery Nanoparticles During Phase Transitions Revealed by Operando Single-Particle Diffraction
Structural and ion-ordering phase transitions limit the viability of sodium-ion intercalation materials in grid scale battery storage by reducing their lifetime. However, the combination of phenomena in nanoparticulate electrodes creates complex behavior that is difficult to investigate, especially on the single-nanoparticle scale under operating conditions. In this work, operando single-particle X-ray diffraction (oSP-XRD) is used to observe single-particle rotation, interlayer spacing, and layer misorientation in a functional sodium-ion battery. oSP-XRD is applied to Na 2/3 [Ni 1/3 Mn 2/3 ]O 2 , an archetypal P2-type sodium-ion-positive electrode material with the notorious P2-O2 phase transition induced by sodium (de)intercalation. Here, it is found that during sodium extraction, the misorientation of crystalline layers inside individual particles increases before the layers suddenly align just prior to the P2-O2 transition. The increase in the long-range order coincides with an additional voltage plateau signifying a phase transition prior to the P2-O2 transition. To explain the layer alignment, a model for the phase evolution is proposed that includes a transition from localized to correlated Jahn-Teller distortions. The model is anticipated to guide further characterization and engineering of sodium-ion intercalation materials with P2-O2 type transitions. oSP-XRD, therefore, opens a powerful avenue for revealing complex phase behavior in heterogeneous nanoparticulate systems.
Magic-angle semimetals
Abstract Breakthroughs in two-dimensional van der Waals heterostructures have revealed that twisting creates a moiré pattern that quenches the kinetic energy of electrons, allowing for exotic many-body states. We show that cold atomic, trapped ion, and metamaterial systems can emulate the effects of a twist in many models from one to three dimensions. Further, we demonstrate at larger angles (and argue at smaller angles) that by considering incommensurate effects, the magic-angle effect becomes a single-particle quantum phase transition (including in a model for twisted bilayer graphene in the chiral limit). We call these models “magic-angle semimetals”. Each contains nodes in the band structure and an incommensurate modulation. At magic-angle criticality, we report a nonanalytic density of states, flat bands, multifractal wave functions that Anderson delocalize in momentum space, and an essentially divergent effective interaction scale. As a particular example, we discuss how to observe this effect in an ultracold Fermi gas.
Model associated with: "Thermodynamic control on the decomposition of organic matter across different electron acceptors"
This model data package is associated with the publication “Thermodynamic control on the decomposition of organic matter across different electron acceptors” submitted to Soil Biology and Biochemistry (Zheng et al., 2023; https://doi.org/10.1016/j.soilbio.2024.109364).In this research, a thermodynamic modeling framework is built to flexibly incorporate both organic matter (OM) molecules and electron acceptors for estimating potential free energy release from various redox reactions and to further predict reaction rates based on Microbial Transition State Theory. The model package includes scripts for thermodynamic modeling and postprocessing. Input Fourier-transform ion cyclotron resonance (FTICR) data are from a previous experimental study (Boye et al., 2018), and model outputs are free energy predictions and stoichiometric coefficients associated with all possible redox reactions.This data package is associated with the project GitHub repository found at MM_bioenergetic_modeling.This data package contains four folders (Input_FTICR, Model, Output, and Output_processing), a file-level metadata (FLMD) csv, and a data dictionary (dd) csv. Please see Zheng_bioenergetic_modeling_flmd.csv for a list of all files contained in this data package and descriptions for each. The Zheng_bioenergetic_modeling_dd.csv file describes the csv column headers. The “Model” folder contains scripts to run energy balance calculations for each electron acceptor. The “Output” folder contains csv files with stoichiometric information from model simulations. And the "Output_processing" folder contains scripts for reaction rate calculations and to generate plots.