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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Structure of nonevaporating sprays - Measurements and predictions

Structure measurements were completed within the dilute portion of axisymmetric nonevaporating sprays (SMD of 30 and 87 microns) injected into a still air environment, including: mean and fluctuating gas velocities and Reynolds stress using laser-Doppler anemometry; mean liquid fluxes using isokinetic sampling; drop sizes using slide impaction; and drop sizes and velocities using multiflash photography. The new measurements were used to evaluate three representative models of sprays: (1) a locally homogeneous flow (LHF) model, where slip between the phases was neglected; (2) a deterministic separated flow (DSF) model, where slip was considered but effects of drop interaction with turbulent fluctuations were ignored; and (3) a stochastic separated flow (SSF) model, where effects of both interphase slip and turbulent fluctuations were considered using random sampling for turbulence properties in conjunction with random-walk computations for drop motion. The LHF and DSF models were unsatisfactory for present test conditions-both underestimating flow widths and the rate of spread of drops. In contrast, the SSF model provided reasonably accurate predictions, including effects of enhanced spreading rates of sprays due to drop dispersion by turbulence, with all empirical parameters fixed from earlier work.

Solomon, A. S. P.↗
DOE OSTI2023

Multiscale modeling of solute diffusion in triblock copolymer membranes

We develop a multiscale simulation model for diffusion of solutes through porous triblock copolymer membranes. The approach combines two techniques: self-consistent field theory (SCFT) to predict the structure of the self-assembled, solvated membrane and on-lattice kinetic Monte Carlo (kMC) simulations to model diffusion of solutes. Solvation is simulated in SCFT by constraining the glassy membrane matrix while relaxing the brush-like membrane pore coating against the solvent. The kMC simulations capture the resulting solute spatial distribution and concentration-dependent local diffusivity in the polymer-coated pores; we parameterize the latter using particle-based simulations. We apply our approach to simulate solute diffusion through nonequilibrium morphologies of a model triblock copolymer, and we correlate diffusivity with structural descriptors of the morphologies. We also compare the model’s predictions to alternative approaches based on simple lattice random walks and find our multiscale model to be more robust and systematic to parameterize. Furthermore, our multiscale modeling approach is general and can be readily extended in the future to other chemistries, morphologies, and models for the local solute diffusivity and interactions with the membrane.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Interaction of Strain Refined Precipitates and Recrystallized Grains in Nb-Ti Microalloyed Steel during Continuous Casting, Hot-Core Heavy Reduction Rolling, and Reheating Process

Hot-core heavy reduction rolling (HHR{sup 2}) is an energy-saving process that directly rolls continuously cast slabs at the end of solidification that have a large temperature gradient. Here, we elucidated the interaction of strain refined precipitates and recrystallized grains in Nb-Ti microalloyed steel during continuous casting, hot-core heavy reduction rolling, and reheating process. Furthermore, theoretical models were established to calculate recrystallization driving force and pinning force at temperatures in the range of 850-1300 °C by a two-stage interrupted compression test using a Gleeble-3800 thermomechanical simulator. The study indicated that a random dispersion of strain refined precipitates with a size range of ~ 6-10 nm was obtained in the hot-core heavy reduction rolled slab, which was different from the coarse and uneven distribution of precipitates in a conventional slab. Upon reheating, the austenite grains in the hot-core heavy reduction rolled slab were significantly refined and evenly distributed along the thickness direction. This is attributed to the random dispersion of strain refined precipitates formed in the HHR{sup 2} process.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

Abstract The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

Jin, Ling↗
DOE OSTI2018

An Efficient Algorithm for Automatic Structure Optimization in X-ray Standing-Wave Experiments

We present that x-ray standing-wave photoemission experiments involving multilayered samples are emerging as unique probes of the buried interfaces that are ubiquitous in current device and materials research. Such data require for their analysis a structure optimization process comparing experiment to theory that is not straightforward. In this work, we present a new computer program for optimizing the analysis of standing-wave data, called SWOPT, that automates this trial-and-error optimization process. The program includes an algorithm that has been developed for computationally expensive problems: so-called black-box simulation optimizations. It also includes a more efficient version of the Yang X-ray Optics Program (YXRO) [Yang, S.-H., Gray, A.X., Kaiser, A.M., Mun, B.S., Sell, B.C., Kortright, J.B., Fadley, C.S., J. Appl. Phys. 113, 1 (2013)] which is about an order of magnitude faster than the original version. Human interaction is not required during optimization. We tested our optimization algorithm on real and hypothetical problems and show that it finds better solutions significantly faster than a random search approach. The total optimization time ranges, depending on the sample structure, from minutes to a few hours on a modern laptop computer, and can be up to 100x faster than a corresponding manual optimization. Finally, these speeds make the SWOPT program a valuable tool for realtime analyses of data during synchrotron experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Challenges of conventional iterative all-atom and coarse-grained multiscale molecular dynamics

In this work, we evaluate the biomolecular dynamics behaviors when conventionally iterating between all-atom (AA) and coarse-grained (CG) molecular dynamics (MD) simulations over multiple cycles. We implemented the workflow to iterate between AA and CG in OpenMM, namely the iterative multiscale MD (iMMD) simulation workflow. In particular, we aim to identify practical applications for iterating between AA and CG simulations in a conventional manner without any constraints or model modifications. We evaluate the iMMD workflow on four representative systems, spanning folding of two soluble proteins and protein-protein as well as protein-lipid interactions of two membrane proteins. We observe that iteration between AA and CG representations could help the soluble proteins exit undesirable metastable states to fold, resulting from random protein structural distortions due to cycling. Consequently, the most reliable use of iterative AA and CG simulations appears to be to accelerating complex lipid mixing for membrane-bound protein systems rather than sampling protein conformational space. Our work explores the practical usages and limitations for iterative AA and CG simulations using readily available AA and CG force fields. The evaluated iMMD workflow in OpenMM is made available at https://github.com/lanl/iMMD.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Spatial quantum-interference landscapes of multi-site-controlled quantum dots coupled to extended photonic cavity modes

Abstract A compact platform to integrate emitters in a cavity-like support is to embed quantum dots (QDs) in a photonic crystal (PhC) structure, making them promising candidates for integrated quantum photonic circuits. The emission properties of QDs can be modified by tailored photonic structures, relying on the Purcell effect or strong light-matter interactions. However, the effects of photonic states on spatial features of exciton emissions in these systems are rarely explored. Such effect is difficult to access due to random positions of self-assembled QDs in PhC structures, and the fact that quantum well excitons’ wavefunctions resemble photonic states in a conventional distributed Bragg reflector cavity system. In this work, we instead observe a spatial signature of exciton emission using site-controlled QDs embedded in PhC cavities. In particular, we observe the detuning-dependent spatial repulsion of the QD exciton emissions by polarized imaging of the micro-photoluminescence, dependent on the controlled QD’s position in a spatially extended photonic pattern. The observed effect arises due to the quantum interference between QD decay channel in a spatially-extended cavity mode. Our findings suggest that integration of site-controlled QDs in tailored photonic structures can enable spatially distributed single-photon sources and photon switches.

Physics↗
DOE OSTI2021

EcoPLOT: dynamic analysis of biogeochemical data

Motivation: We have created EcoPLOT (parameterized linkage of omics-driven technologies), a web-app for the dynamic, interactive analysis of biogeochemical datasets that combines state-of-the-art analysis tools to statistically and graphically explore environmental, geochemical and microbiome datasets. Using the iterative random forest, a machine learning algorithm, EcoPLOT allows for the de novo discovery of drivers which exhibit significant impact on plant, microbial or soil dynamics. Availability and implementation: EcoPLOT is built entirely within the R language. It can be accessed through any system where R is installed, including Windows, Mac and most Linux systems. EcoPLOT is free to use and can be accessed at https://github.com/cdsanchez18/EcoPLOT.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Quenched disorder in the triangular lattice antiferromagnet YbZn 2⁢ GaO 5

We investigate the crystal electric field (CEF) excitations of Yb 3+ ions in powder samples of the triangular-lattice rare-earth-based antiferromagnet YbZn 2⁢ GaO 5 using inelastic neutron scattering (INS). Three CEF excitations from the ground-state Kramers doublet were observed, each exhibiting significant broadening beyond instrumental resolution. Combining temperature-dependent INS and neutron powder diffraction, we identify a significant static contribution to this broadening and attribute it to heterogeneous coordination of Yb 3+ ions due to Ga 3+ /Zn 2+ site mixing. Rietveld refinement of neutron powder diffraction indicates that 35% of Ga occupies the Zn site and 60% of Zn occupies the Ga site. We show with a point charge model for the CEF Hamiltonian that heterogeneous coordination of Yb3+ ions leads to broadened CEF peaks. First-principles calculations demonstrate that the random Ga 3+ /Zn 2+ distribution can produce the distortions of the YbO 6 octahedra observed from neutron diffraction. Because the documented heterogeneity will extend to exchange interactions, our results suggest that disorder is a significant factor in the unusual magnetism previously reported in YbZn 2 ⁢GaO 5 , including broad low-energy magnetic excitations and the absence of magnetic ordering down to 0.3 K.

Crystal field excitations↗
NASA NTRS1985

Enhanced growth of whistlers due to bunching of untrapped electrons

Man-made signals propagating in the whistler mode in the magnetoshere have been observed to be amplified and to trigger other VLF emissions in the absence of other detectable magnetospheric signals in ground-based recordings. The absence of other magnetospheric signals implies that the growth rate of the triggered wave is enhanced over background noise. It is shown that, for at least low amplitude triggering waves, the phase bunching of untrapped electrons can account for the observed enhanced growth. The phase bunching is initially produced by perturbations in the electron trajectories due to their interactions with the front of the wave. Due to the inhomogeneity of the geomagnetic field, the phase-bunched electrons are able to gyroresonantly interact with a later portion of the triggering wave. Because the electrons are phase bunched, the electrons can lose energy to the wave faster than phase-random electrons and are thereby able to produce enhanced growth. Plasma conditions for which this phase bunching is effective in producing enhanced growth are derived.

Winglee, R. M.↗
NASA NTRS1990

Energy and spectral dynamics in forced compressible turbulence

Numerical simulations are used to study compressible turbulence with microscale Reynolds numbers up to 40 and rms Mach numbers M up to 0.9. The flows are randomly forced, with energy supplied to either the rotational or compressive components of kinetic energy, which is then transferred to internal energy through the pressure-dilatation interaction and viscous dissipation terms. Coupling between the two components of kinetic energy by the advection term is relatively weak, and most energy introduced to either component by the external force is transferred, without passing through the other component, to internal energy. A statistically quasi-equilibrium of kinetic energy is realized while internal energy increases steadily. The spectral form of the rotational component of velocity, which hardly depends on M, is very close to that for incompressible flow. On the other hand, the compressive component depends strongly on M, especially at large wave numbers.

Kida, Shigeo↗
DOE OSTI2025

Interactions Between Climate Mean and Variability Drive Future Agroecosystem Vulnerability

ABSTRACT Agriculture is crucial for global food supply and dominates the Earth's land surface. It is unknown, however, how slow but relentless changes in climate mean state, versus random extreme conditions arising from changing variability , will affect agroecosystems' carbon fluxes, energy fluxes, and crop production. We used an advanced weather generator to partition changes in mean climate state versus variability for both temperature and precipitation, producing forcing data to drive factorial‐design simulations of US Midwest agricultural regions in the Energy Exascale Earth System Model. We found that an increase in temperature mean lowers stored carbon, plant productivity, and crop yield, and tends to convert agroecosystems from a carbon sink to a source, as expected; it also can cause local to regional cooling in the earth system model through its effects on the Bowen Ratio. The combined effect of mean and variability changes on carbon fluxes and pools was nonlinear, that is, greater than each individual case. For instance, gross primary production reduces by 9%, 1%, and 13% due to change in mean temperature, change in temperature variability, and change in both temperature mean and variability, respectively. Overall, the scenario with change in both temperature and precipitation means leads to the largest reduction in carbon fluxes (−16% gross primary production), carbon pools (−35% vegetation carbon), and crop yields (−33% and −22% median reduction in yield for corn and soybean, respectively). By unambiguously parsing the effects of changing climate mean versus variability and quantifying their nonadditive impacts, this study lays a foundation for more robust understanding and prediction of agroecosystems' vulnerability to 21st‐century climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Phonon gas model for thermal conductivity of dense, strongly interacting liquids

Developing predictive thermal property models for liquids based on microscopic principles has been elusive. The difficulty is that liquids have gas-like and solid-like attributes that are at odds when considering the frameworks of microscopic models: Models for gases are simple due to randomness and low density, whereas models for crystalline solids rely on symmetry and long-range order for easier calculation. The short-range order in liquids does, however, provide structure to neighboring molecules similar to amorphous solids, and there have been recent advances indicating that collective vibrational modes store heat in liquids. Models combining Debye approximations from solid-state physics and Frenkel’s theory of liquids can accurately predict the heat capacity of liquids. Phonon-like dispersions in liquids have also been widely observed in neutron scattering experiments. These developments motivate us to propose a model where high-frequency vibrational modes, which travel at the speed of sound and have a mean free path on the order of the average intermolecular distance, conduct heat in liquids. We use this liquid phonon gas model to calculate the thermal conductivity of liquids with varying intermolecular interaction energies from strongest to weakest—Coulomb, hydrogen-bonding, Keesom, and London dispersion energy. Generally, the model is more accurate as the intermolecular interaction energy and density of liquids increase. The calculated thermal conductivity of Coulombic-bound molten sodium nitrate and hydrogen-bonded water is within 1.46% and 2.98% of the experimentally measured values, respectively, across their entire temperature ranges. Further modal analysis of the velocity and the mean free path of collective vibrations could establish the liquid phonon gas model as an accurate model for weakly interacting liquids as well.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Toward particle-resolved accuracy in Euler–Lagrange simulations of multiphase flow using machine learning and pairwise interaction extended point-particle (PIEP) approximation

This study presents two different machine learning approaches for the modeling of hydrodynamic force on particles in a particle-laden multiphase flow. Results from particle-resolved direct numerical simulations (PR-DNS) of flow over a random array of stationary particles for eight combinations of particle Reynolds number (Re) and volume fraction (φ) are used in the development of the models. The first approach follows a two-step process. In the first flow prediction step, the perturbation flow due to a particle is obtained as an axisymmetric superposable wake using linear regression. In the second force prediction step, the force on a particle is evaluated in terms of the perturbation flow induced by all its neighbors using the generalized Faxén form of the force expression. In the second approach, the force data on all the particles from the PR-DNS simulations are used to develop an artificial neural network (ANN) model for direct prediction of force on a particle. Due to the unavoidable limitation on the number of fully resolved particles in the PR-DNS simulations, direct force prediction with the ANN model tends to over-fit the data and performs poorly in the prediction of test data. In contrast, due to the millions of grid points used in the PR-DNS simulations, accurate flow prediction is possible, which then allows accurate prediction of particle force. This hybridization of multiphase physics and machine learning is particularly important, since it blends the strength of each, and the resulting pairwise interaction extended point-particle model cannot be developed by either physics or machine learning alone.

42 ENGINEERING↗
NASA NTRS2003

Self-Supervised Dynamical Systems

Some progress has been made in a continuing effort to develop mathematical models of the behaviors of multi-agent systems known in biology, economics, and sociology (e.g., systems ranging from single or a few biomolecules to many interacting higher organisms). Living systems can be characterized by nonlinear evolution of probability distributions over different possible choices of the next steps in their motions. One of the main challenges in mathematical modeling of living systems is to distinguish between random walks of purely physical origin (for instance, Brownian motions) and those of biological origin. Following a line of reasoning from prior research, it has been assumed, in the present development, that a biological random walk can be represented by a nonlinear mathematical model that represents coupled mental and motor dynamics incorporating the psychological concept of reflection or self-image. The nonlinear dynamics impart the lifelike ability to behave in ways and to exhibit patterns that depart from thermodynamic equilibrium. Reflection or self-image has traditionally been recognized as a basic element of intelligence. The nonlinear mathematical models of the present development are denoted self-supervised dynamical systems. They include (1) equations of classical dynamics, including random components caused by uncertainties in initial conditions and by Langevin forces, coupled with (2) the corresponding Liouville or Fokker-Planck equations that describe the evolutions of probability densities that represent the uncertainties. The coupling is effected by fictitious information-based forces, denoted supervising forces, composed of probability densities and functionals thereof. The equations of classical mechanics represent motor dynamics that is, dynamics in the traditional sense, signifying Newton s equations of motion. The evolution of the probability densities represents mental dynamics or self-image. Then the interaction between the physical and metal aspects of a monad is implemented by feedback from mental to motor dynamics, as represented by the aforementioned fictitious forces. This feedback is what makes the evolution of probability densities nonlinear. The deviation from linear evolution can be characterized, in a sense, as an expression of free will. It has been demonstrated that probability densities can approach prescribed attractors while exhibiting such patterns as shock waves, solitons, and chaos in probability space. The concept of self-supervised dynamical systems has been considered for application to diverse phenomena, including information-based neural networks, cooperation, competition, deception, games, and control of chaos. In addition, a formal similarity between the mathematical structures of self-supervised dynamical systems and of quantum-mechanical systems has been investigated.

Zak, Michail↗
DOE OSTI2023

Uncertainty quantification for neutrino opacities in core-collapse supernovae and neutron star mergers

We perform an extensive study of the correlations between the neutrino-nucleon inverse mean free paths (IMFPs) and the underlying equation of states (EoSs). Strong interaction uncertainties in the neutrino mean free path are investigated in different density regimes. The nucleon effective mass, the nucleon chemical potentials, and the residual interactions in the medium play an important role in determining neutrino-nucleon interactions in a density-dependent manner. Here we study how the above quantities are constrained by an EoS consistent with (i) nuclear mass measurements, (ii) proton-proton scattering phase shifts, and (iii) neutron star observations. We then study the uncertainties of both the charged current and the neutral current neutrino-nucleon inverse mean free paths due to the variation of these quantities, using the Hartree-Fock+random phase approximation method. Finally, we calculate the Pearson correlation coefficients between (i) the EoS-based quantities and the EoS-based quantities; (ii) the EoS-based quantities and the IMFPs; (iii) the IMFPs and the IMFPs. We find a strong impact of residual interactions on neutrino opacity in the spin and spin-isospin channels, which are not well constrained by current nuclear modelings.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1987

Real-time hierarchically distributed processing network interaction simulation

The Telerobot Testbed is a hierarchically distributed processing system which is linked together through a standard, commercial Ethernet. Standard Ethernet systems are primarily designed to manage non-real-time information transfer. Therefore, collisions on the net (i.e., two or more sources attempting to send data at the same time) are managed by randomly rescheduling one of the sources to retransmit at a later time interval. Although acceptable for transmitting noncritical data such as mail, this particular feature is unacceptable for real-time hierarchical command and control systems such as the Telerobot. Data transfer and scheduling simulations, such as token ring, offer solutions to collision management, but do not appropriately characterize real-time data transfer/interactions for robotic systems. Therefore, models like these do not provide a viable simulation environment for understanding real-time network loading. A real-time network loading model is being developed which allows processor-to-processor interactions to be simulated, collisions (and respective probabilities) to be logged, collision-prone areas to be identified, and network control variable adjustments to be reentered as a means of examining and reducing collision-prone regimes that occur in the process of simulating a complete task sequence.

Zimmerman, W. F.↗