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At least 991 records · Page 55
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NASA NTRSDate not supplied

A Parallelized Oxidation-Driven Surface Recession Framework in DSMC Code, SPARTA

Spacecrafts rely on ablative thermal protection systems (TPS) made of composites consisting of a carbon-based reinforcement and a polymeric matrix. These materials are designed to withstand high-temperature oxidation and surface recession during re-entry into the Earth's atmosphere. However, ablation occurs due to a complex interplay of thermal, mechanical, and chemical factors, making it challenging to determine the individual impact of each on the TPS's overall degradation. In this study, we have developed an ablation model that can leverage a finite rate carbon oxidation model to predict material recession and surface states more accurately. Stochastic PArallel Rarified-gas Time-accurate Analyzer (SPARTA), a direct-simulation Monte Carlo (DSMC) code, is modified to allow oxidation-driven ablation of implicitly defined carbon surfaces. In SPARTA, implicit surfaces are generated from the grid corner point values via a marching cubes algorithm, therefore creating a new set of surface elements every time ablation is performed. The finite-rate oxidation model developed by Gopalan et. al can perform both gas-surface and pure-surface reactions and is now adapted to tally surface data on a per grid cell basis. The ablation functionality was also adjusted so once the reactions have occurred, the number of reactions leading to CO formation can be converted to corner point reduction values; therefore, carbon removal is directly proportional to surface recession. We also briefly discuss some unique challenges associated with parallelizing this dynamic surface state and geometry. Finally, we analyze the performance of this parallelized implicit chemistry model with simple 2D and 3D benchmark cases by producing surface state statistics, area changes over time, and visualization across a range of surface temperatures and processors with and without load-balancing.

DSMC↗
DOE OSTI2023

Statistical Analysis of the Limiting Dynamics of Two dimensional Boussinesq Turbulent Systems

In the current study, we investigate rotational and stratified turbulent systems within the framework of the Boussinesq equations. The effects of rotation and stratification give rise to a variety of dynamical regimes in atmospheric and oceanic turbulence. To develop a better understanding of the fundamental flow properties through a more straightforward mathematical framework, we commence our research with a simplified two-dimensional Boussinesq model that incorporates rotation and stratification effects. We explore two distinct limiting dynamics, considering pure rotation and pure stratification separately. We examine these intriguing dynamics through numerical investigations, applying the exact solution theory of Boussinesq equations and examining small-scale perturbations to the exact solution under rotation and stratification. We conduct an analysis of statistical quantities associated with the turbulent state variables. The results indicate that, under weak rotation at the equilibrium statistical state, the flow field exhibits vortex flow. However, strong rotation transforms the flow into vertical shear flow. We observe both downscale and upscale energy transfers with a decay rate of k −3 in the absence of external forcing and dissipation. Furthermore, we delve into the study of internal gravity wave mode interactions in the simplified two-dimensional Boussinesq system, which is a crucial aspect of geophysical turbulence due to gravity. Our findings reveal that the time series of the mode coefficients exhibit wave-like interactions between wave modes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Stochastic nonlinear analysis of unidirectional fiber composites using image-based microstructural uncertainty quantification

Here, we present a data-driven nonlinear uncertainty quantification and propagation framework to study the microstructure-induced stochastic performance of unidirectional (UD) carbon fiber reinforced polymer (CFRP) composites. The proposed approach integrates (1) microscopic image characterization, (2) stochastic microstructure reconstruction, and (3) efficient multiscale finite element simulations enabled by self-consistent clustering (SCA) analysis. To model the complex microstructural variability, the proposed UQ methods take the non-Gaussian uncertainty sources into account through a distribution-free sampling approach leveraging nonparametric and asymptotic statistical tools. A hierarchical conditional sampling strategy enables the simultaneous sampling of multiple sources of uncertainties. Our approach provides insights into the impact of microstructural variabilities, which are shown to have an increasing impact on the nonlinear responses of UD CFRP parts under progressive compression loading and ultimately on the failure rate over time. We discover that before CFRP parts start to fail, a characteristic time period emerges with distinctive uncertainty distributions specific to the microstructure variability and the probability of failure. Identifying the failure time period is crucial to the reliability prediction, which is an essential component of CFRP design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Description of the multinucleon transfer mechanism for Ca 48 + Pu 244 and Kr 86 + Pt 198 reactions in a quantal transport approach

Multinucleon transfer (MNT) reactions involving heavy projectile and target combinations stand as a promising method for synthesizing new neutron-rich exotic nuclei, which may not be possible using hot or cold fusion reactions or fragmentation. Exploring the mechanisms behind MNT reactions is essential and it requires a comprehensive theoretical framework that can explain the physical observables in these reactions. This work aims to show that the quantal diffusion approach based on the stochastic mean-field (SMF) theory is capable of explaining the reaction dynamics observed in MNT reactions. Primary product mass distributions in 48 Ca + 244 Pu reaction at E c.m. = 203.2 MeV and 86 Kr + 198 Pt reaction at E c.m. = 324.2 MeV are calculated and compared with the available experimental data. In this work, we utilize the time-dependent Hartree-Fock (TDHF) calculations to analyze the mean-field reaction dynamics computationally in the reactions 48 Ca + 244 Pu and 86 Kr + 198 Pt for a broad range of initial angular momenta. Quantal transport description based on the SMF approach is used to calculate quantal diffusion coefficients and mass variances in 48 Ca + 244 Pu and 86 Kr + 198 Pt systems. The primary products arising from quasifission reactions are described by joint probability distribution in the SMF approach and those arising from fusion-fission are estimated by using the statistical deexcitation code gemini + +. Mean values of charge and mass numbers, scattering angles of the primary reaction products, and the total kinetic energies after the collision are calculated within the TDHF framework for a broad range of initial angular momenta. Throughout all the collisions, drift toward the mass symmetry and large mass dispersion associated with this drift are observed. Here, the calculated primary fragment and mass distributions using the SMF approach successfully explain experimental observations for the 48 Ca + 244 Pu and 86 Kr + 198 Pt systems. The primary mass distributions, mean values of binary products, and mass dispersions are determined and results are compared with the available experimental data. The observed agreement between the experimental data and SMF results highlights the effectiveness of the quantal diffusion mechanism based on the SMF approach, which does not include any adjustable parameters other than standard parameters of Skyrme energy density functional.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Multi-fidelity information fusion with concatenated neural networks

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.

42 ENGINEERING↗
NASA NTRSDate not supplied

Exoplanet Yield Sensitivity for the Hybrid Lyot Coronagraph from end-to-end modeling for LUVOIR-A

NASA's future direct imaging flagship mission will require telescope mirror segmentation to survey a statistically significant population of nearby stellar systems by increasing aperture size beyond that achieved by traditional space-borne monolithic apertures thus far. Segmentation is a path for the size expansion, but it entails a challenge due to the introduction of temporal pupil discontinuities in terms of both phase and amplitude. Modeling of the integrated performance of the coronagraph with the expected dynamical profile of the telescope is an important step toward realistic estimates of the mission's science yields. We present results of a study using the hybrid Lyot coronagraph with a 15-m on-axis, segmented optical telescope assembly (OTA), akin to that of the Large UV / Optical /Infrared (LUVOIR) mission. We have developed a high-fidelity, end-to-end model as part of the Segmented Coronagraph Design and Analysis (SCDA) study that includes diffractive optical propagations at the 1e-10 contrast level across the optical train including the OTA and intermediate re-imaging optics inside the coronagraph instrument. We perform a sensitivity analysis to assess the contrast sensitivity to wavefront perturbations for a set of key telescope terms including segment-level low-order and mid-spatial frequency aberrations and evaluate expected exoplanet yields obtained in the presence of these sets of aberrations. Industry-provided segment-level wavefront aberration datasets involving the following mechanisms are input to the end-to-end model: (1) thermal gradients and accelerations, and (2) telescope dynamics including line-of-sight and segment jitter. Dynamically averaged PSFs in the presence of aberrations are fed into AYO (Altruistic Yield Optimization), a mission design reference simulator, that then assesses the scientific yield sensitivities.

high-contrast imaging↗
DOE OSTI2025

Stochastic 3D reconstruction of cracked polycrystalline NMC particles using 2D SEM data

Li-ion battery performance is strongly influenced by the 3D microstructure of its cathode particles. Cracks within these particles develop during calendaring and cycling, reducing connectivity but increasing reactive surface, making their impact on battery performance complex. Understanding these contradictory effects requires a quantitative link between particle morphology and battery performance. However, informative 3D imaging techniques are time-consuming, costly and rarely available, such that analyses often have to rely on 2D image data. This paper presents a novel stereological approach for generating virtual 3D cathode particles exhibiting crack networks that are statistically equivalent to those observed in 2D sections of experimentally measured particles. Consequently, 2D image data suffices for deriving a full 3D characterization of cracked cathodes particles. Such virtually generated 3D particles could serve as geometry input for spatially resolved electro-chemo-mechanical simulations to enhance our understanding of structure-property relationships of cathodes in Li-ion batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Molecular dynamics simulation of hydrodynamic transport coefficients in plasmas

Molecular dynamics simulations are used to calculate transport coefficients in a two-component plasma interacting through a repulsive Coulomb potential. The thermal conductivity, electrical conductivity, electrothermal coefficient, thermoelectric coefficient, and shear viscosity are computed using the Green–Kubo formalism over a broad range of Coulomb coupling strength, 0.01 ≤ Γ ≤ 140. Emphasis is placed on testing standard results of the Chapman–Enskog solution in the weakly coupled regime (Γ ≪ 1) using these first-principles simulations. As expected, the results show good agreement for Γ ≲ 0.1. However, this agreement is only possible if careful attention is paid to the definitions of linear constitutive relations in each of the theoretical models, a point that is often overlooked. For example, the standard Green–Kubo expression for thermal conductivity is a linear combination of thermal conductivity, electrothermal, and thermoelectric coefficients computed in the Chapman–Enskog formalism. Meaningful results for electrical conductivity are obtained over the full range of coupling strengths explored, but it is shown that potential and virial components of the other transport coefficients diverge in the strongly coupled regime (Γ ≫ 1). In this regime, only the kinetic components of the transport coefficients are meaningful for a classical plasma.

Electrical conductivity↗
NASA NTRS1994

Progress in modeling atmospheric propagation of sonic booms

The improved simulation of sonic boom propagation through the real atmosphere requires greater understanding of how the transient acoustic pulses popularly termed sonic booms are affected by humidity and turbulence. A realistic atmosphere is invariably somewhat turbulent, and may be characterized by an ambient fluid velocity v and sound speed c that vary from point to point. The absolute humidity will also vary from point to point, although possibly not as irregularly. What is ideally desired is a relatively simple scheme for predicting the probable spreads in key sonic boom signature parameters. Such parameters could be peak amplitudes, rise times, or gross quantities obtainable by signal processing that correlate well with annoyance or damage potential. The practical desire for the prediction scheme is that it require a relatively small amount of knowledge, possibly of a statistical nature, concerning the atmosphere along, the propagation path from the aircraft to the ground. The impact of such a scheme, if developed, implemented, and verified, would be that it would give the persons who make planning decisions a tool for assessing the magnitude of environmental problems that might result from any given overflight or sequence of overflights. The technical approach that has been followed by the author and some of his colleagues is to formulate a hierarchy of simple approximate models based on fundamental physical principles and then to test these models against existing data. For propagation of sonic booms and of other types of acoustic pulses in nonturbulent model atmospheres, there exists a basic overall theoretical model that has evolved as an outgrowth of geometrical acoustics. This theoretical model depicts the sound as propagating within ray tubes in a manner analogous to sound in a waveguide of slowly varying cross-section. Propagation along the ray tube is quasi-one-dimensional, and a wave equation for unidirectional wave propagation is used. A nonlinear term is added to this equation to account for nonlinear steepening, and the formulation has been carried through to allow for spatially varying sound speed, ambient density, and ambient wind velocities. The model intrinsically neglects diffraction, so it cannot take into account what has previously been mentioned in the literature as possibly important mechanisms for turbulence-related distortion. The model as originally developed could predict an idealized N-waveform which often agrees with data in terms of peak amplitude and overall positive phase duration. It is possible, moreover, to develop simple methods based on the physics of relaxation processes for incorporating molecular relaxation into the quasi-one-dimensional model of nonlinear propagation along ray tubes.

Pierce, Allan D.↗
NASA NTRS2007

Comparison of the US and Russian Cycle Ergometers

The purpose of this study was to compare the U.S. and Russian cycle ergometers focusing on the mechanical differences of the devices and the physiological differences observed while using the devices. Methods: First, the mechanical loads provided by the U.S. Cycle Ergometer with Vibration Isolation System (CEVIS) and the Russian Veloergometer were measured using a calibration dynamometer. Results were compared and conversion equations were modeled to determine the actual load provided by each device. Second, ten male subjects (32.9 +/- 6.5 yrs, 180.6 +/- 4.4 cm; 81.9 +/- 6.9 kg) experienced with both cycling and exercise testing completed a standardized submaximal exercise test protocol on CEVIS and Veloergometer. The exercise protocol involved 8 sub-maximal workloads each lasting 3 minutes for a total of 24 minutes per session, or until the end of the stage when the subject reached 85% of peak oxygen consumption or age-predicted maximum heart rate (220 - age). The workload started at 50 Watts (W), increased to 100 W, and then increased 25 W every 3 minutes until reaching a peak workload of 250 W. Physiological variables were then compared at each workload by repeated measures ANOVA or paired t-tests (p<0.05). Results: While both CEVIS and Veloergometer produced significantly lower workloads than the displayed workload, CEVIS produced even lower loads than Veloergometer (p<0.05) at each indicated workload. Despite this fact, the only physiological variables that showed a significant difference between the ergometers were VE (125 - 250W), VO2 (175 and 250 W), and VCO2 (175 W). All other physiological data were not statistically different between CEVIS and Veloergometer. Conclusion: Although workloads were different between ergometers, relatively few physiological differences were observed. Therefore, CEVIS workloads of 87.5 - 262.5 W can be rounded to the nearest 25 W increment and performed on the Veloergometer.

Norcross, Jason↗
DOE OSTI2021

Detection of photon statistics and multimode field correlations by Raman processes

Glauber’s g (2) -function provides a common measure of quantum field statistics through two-photon coincidence counting in Hanbury Brown–Twiss measurements. Here, we propose to use nonlinear optical signals as a tool for the characterization of quantum light. In particular, we show that Raman measurements provide an alternative direct probe for a different component of the four-point correlation function underlying the g (2) -function. We illustrate this capacity for a specific quantum state obtained from a frequency conversion process. Our work points out how the analysis of controlled optical nonlinear processes can provide an alternative window toward the analysis of quantum light sources.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2021

Estimation of Snow Mass Information via Assimilation of C-Band Synthetic Aperture Radar Backscatter Observations Into an Advanced and Surface Model

This study assimilated Sentinel-1 C-band backscatter observations over snow-covered terrain into the Noah-Multiparameterization land surface model using support vector machine (SVM) regression and an ensemble Kalman filter to improve the modeled terrestrial snow mass estimates. The data assimilation (DA) experiment was conducted across Western Colorado from September 2016 to August 2017. As part of the DA experiments, the impact of a rule-based update was evaluated by comparing snow water equivalent (SWE) estimates via DA (with [ DAv1 ] and without [ DAv2 ] the rule-based update) against SNOTEL SWE measurements. Results confirmed that rule-based update helped minimize SVM controllability issues, and in turn, improved the accuracy of SWE estimates relative to both open loop (OL) and DAv2 . Comparison of SWE estimates from Sentinel-1 DAv1 against SNOTEL SWE revealed that 75% of stations showed improvements in bias and correlation coefficient relative to the OL. Assimilated SWE estimates also showed statistical improvements during both the snow accumulation and snow ablation periods. However, unbiased root mean square error showed a slight increase during the snow ablation period due to the large variability in the electromagnetic response of C-band backscatter over deep and/or wet snow. Improvement of the SWE estimates also resulted in improving river discharge estimates compared to in situ measurements. River discharge using Sentinel-1 DAv1 improved the Nash–Sutcliffe efficiency at all available stations. These results suggest that physically constrained SVM can serve as an efficient observation operator for snow mass DA through explicit consideration of the first-order C-band scattering mechanisms over different terrestrial snow conditions.

Jongmin Park↗
DOE OSTI2025

Accelerating phase field simulations through a hybrid adaptive Fourier neural operator with U-net backbone

Prolonged contact between a corrosive liquid and metal alloys can cause progressive dealloying. For one such process as liquid-metal dealloying (LMD), phase field models have been developed to understand the mechanisms leading to complex morphologies. However, the LMD governing equations in these models often involve coupled non-linear partial differential equations (PDE), which are challenging to solve numerically. In particular, numerical stiffness in the PDEs requires an extremely refined time step size (on the order of 10 -12 s or smaller). This computational bottleneck is especially problematic when running LMD simulation until a late time horizon is required. This motivates the development of surrogate models capable of leaping forward in time, by skipping several consecutive time steps at-once. In this paper, we propose a U-shaped adaptive Fourier neural operator (U-AFNO), a machine learning (ML) based model inspired by recent advances in neural operator learning. U-AFNO employs U-Nets for extracting and reconstructing local features within the physical fields, and passes the latent space through a vision transformer (ViT) implemented in the Fourier space (AFNO). We use U-AFNOs to learn the dynamics of mapping the field at a current time step into a later time step. We also identify global quantities of interest (QoI) describing the corrosion process (e.g., the deformation of the liquid-metal interface, lost metal, etc.) and show that our proposed U-AFNO model is able to accurately predict the field dynamics, in spite of the chaotic nature of LMD. Most notably, our model reproduces the key microstructure statistics and QoIs with a level of accuracy on par with the high-fidelity numerical solver, while achieving a significant 11, 200 × speed-up on a high-resolution grid when comparing the computational expense per time step. Finally, we also investigate the opportunity of using hybrid simulations, in which we alternate forward leaps in time using the U-AFNO with high-fidelity time stepping. We demonstrate that while advantageous for some surrogate model design choices, our proposed U-AFNO model in fully auto-regressive settings consistently outperforms hybrid schemes.

36 MATERIALS SCIENCE↗
NASA NTRS1996

Modelling the Soft X-Ray and EUV Emission in Classical Novae: EUVE and ROSAT Observations of V1974 Cygni

We have conducted an extensive analysis of the observability of Classical Novae with the EUVE Lex/B and Al/Ti/C detectors. Predicted count rates have been computed using optically thin, isothermal plasma models for solar and metal-rich compositions, and hot ONeMg white dwarf model atmospheres. We find EUVE to be quite sensitive to both the EUV and soft X-ray emission emitted by the underlying hot white dwarf during novae outbursts, except for the coolest temperatures with very high intervening hydrogen column density. These results are used to interpret the emission detected during the EUVE all-sky survey of Nova Cygni 1992 (approx. = V1974 Cyg), 279-290 days after visual maximum. We find the best fit to the observed emission from V1974 Cyg arises from a hot ONeMg white dwarf with surface temperature approx. 4 x l0(exp 5) K and a mas of approx. 1.2 solar mass, and derive an interstellar hydrogen column density of approx. 3 x 10(exp 21)/sq cm. Virtually all this emission arises from supersoft X-rays rather than the EUV. We also report the detection of V1974 Cyg with the EUVE Deep Survey detector at 549 days after visual maximum. This observation is compatible with the above properties, indicating that the mechanism responsible for the soft X-ray emission, connected with the underlying white dwarf, had not yet entirely turned off. We also present analysis of a ROSAT PSPC observation which is contemporaneous with the EUVE survey observations; this independently confirms the high column density we derived from the EUVE survey observation. Light curves for the EUVE and ROSAT observations are presented. Statistical tests for variability show that all of these observations are indeed highly variable over various time scales. The EUVE survey data shows one day variations, the EUVE DS data show approx. 30 minute fluctuations, while the ROSAT data vary rapidly on time scales of seconds. The EUVE data shows no periodic variability on any time scale. The implications of the rapid variability are briefly discussed.

Stringfellow, Guy S.↗
DOE OSTI2024

Probabilistic inference of the structure and orbit of Milky Way satellites with semi-analytic modelling

Semi-analytic modelling furnishes an efficient avenue for characterizing dark matter haloes associated with satellites of Milky Way-like systems, as it easily accounts for uncertainties arising from halo-to-halo variance, the orbital disruption of satellites, baryonic feedback, and the stellar-to-halo mass (SMHM) relation. We use the SatGen semi-analytic satellite generator, which incorporates both empirical models of the galaxy–halo connection as well as analytic prescriptions for the orbital evolution of these satellites after accretion onto a host to create large samples of Milky Way-like systems and their satellites. By selecting satellites in the sample that match observed properties of a particular dwarf galaxy, we can infer arbitrary properties of the satellite galaxy within the cold dark matter paradigm. For the Milky Way’s classical dwarfs, we provide inferred values (with associated uncertainties) for the maximum circular velocity v max and the radius r max at which it occurs, varying over two choices of baryonic feedback model and two prescriptions for the SMHM relation. While simple empirical scaling relations can recover the median inferred value for v max and r max , this approach provides realistic correlated uncertainties and aids interpretability. We also demonstrate how the internal properties of a satellite’s dark matter profile correlate with its orbit, and we show that it is difficult to reproduce observations of the Fornax dwarf without strong baryonic feedback. Furthermore, the technique developed in this work is flexible in its application of observational data and can leverage arbitrary information about the satellite galaxies to make inferences about their dark matter haloes and population statistics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Turbulent burning velocity of lean premixed hydrogen/air flames at engine conditions: Effects of turbulence intensity and length scale

For turbulent lean premixed hydrogen flames with strong thermodiffusively instabilities, most previous studies have focused on the influence of turbulence intensity, whereas the role of turbulence length scale is less well understood. Here, this study addresses this gap by conducting direct numerical simulations (DNS) of statistically planar turbulent premixed flames for a lean (ϕ=0.35) hydrogen/air mixture under independently varied turbulence intensity (u') and length scale (l T ) at engine-relevant thermodynamics conditions. Results show that as u' increases, the flame front becomes increasingly wrinkled, forming smaller cellular structures. In contrast, l T variations do not significantly alter the size of these structures. For the turbulent burning velocity (S T ), the normalized S T (i.e., S T /S L , where S L is the laminar flame speed) increases linearly with u', driven by both enhanced flame surface wrinkling (i.e., increased A T /A L ) and enhanced local burning rate (i.e., increased I 0 ). However, increasing l T reduces I 0 , despite a continued increase in A T /A L , resulting in only a marginal increase in S T /S L . To reveal the underlying mechanisms, especially the decreasing trend of I 0 with l T , local flame dynamics analyses are performed. It is found that as l T increases, the interaction between thermodiffusive effects and turbulence weakens due to the reduced tangential strain rate, while the flame curvature remains largely unchanged. This suppresses local reactivity enhancement and thus decreases I 0 , In contrast, an increase in u' enhances the interaction by amplifying both curvature fluctuation and tangential strain rate, leading to increased local reactivity (increased I 0 ). Finally, based on the DNS data, several new scaling models are proposed for the three global properties, S T /S L , A T /A L , and I 0 , and showed improvements compared to existing models. These findings provide new insights into the flame-turbulence interactions in thermodiffusively unstable hydrogen flames. The DNS dataset is also useful for the development of turbulent combustion models applicable to practical engine simulations.

Engine-relevant condition↗
NASA NTRS2001

Interactions Between the Thermohaline Circulation and Tropical Atlantic SST in a Coupled General Circulation Model

Tropical Atlantic SST shows a (statistically well-defined) decadal time scale in a 104-year simulation of unforced variability by a coupled general circulation model (CGCM). The SST anomalies superficially resemble observed Tropical Atlantic variability (TAV), and are associated with changes in the atmospheric circulation. Brazilian rainfall is modulated with a decadal time scale, along with the strength of the Atlantic trade winds, which are associated with variations in evaporation and the net surface heat flux. However, in contrast to observed tropical Atlantic variability, the trade winds damp the associated anomalies in ocean temperature, indicating a negative feedback. Tropical SST anomalies in the CGCM, though opposed by the surface heat flux, are advected in from the Southern Hemisphere mid-latitudes. These variations modulate the strength of the thermohaline circulation (THC): warm, salty anomalies at the equator sink drawing cold, fresh mid-latitude water. Upon reaching the equator, the latter inhibit vertical overturning and advection from higher latitudes, which allows warm, salty anomalies to reform, returning the cycle to its original state. Thus, the cycle results from advection of density anomalies and the effect of these anomalies upon the rate of vertical overturning and surface advection. This decadal modulation of Tropical Atlantic SST and the thermohaline circulation is correlated with ocean heat transport to the Northern Hemisphere high latitudes and Norwegian Sea SST. Because of the central role of equatorial convection, we question whether this mechanism is present in the current climate, although we speculate that it may have operated in palaeo times, depending upon the stability of the tropical water column.

Miller, Ron↗
DOE OSTI2023

Dynamics and transport in the boundary-driven dissipative Klein-Gordon chain

Motivated by experiments on chains of superconducting qubits, we consider the dynamics of a classical Klein-Gordon chain coupled to coherent driving and subject to dissipation solely at its boundaries. As the strength of the boundary driving is increased, this minimal classical model recovers the main features of the "dissipative phase transition" seen experimentally. Between the transmitting and non-transmitting regimes on either side of this transition (which support ballistic and diffusive energy transport respectively), we observe additional dynamical regimes of interest. These include a regime of superdiffusive energy transport at weaker driving strengths, together with a "dissipative breather crystal" regime at stronger driving strengths, which is characterized by emergent translation symmetry, ballistic energy transport, and coherent oscillations of a nonlinear normal mode. Here, we propose a non-local Lyapunov exponent as an experimentally measurable diagnostic of many-body chaos in this system, and more generally in open systems that are only coupled to an environment at their boundaries.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗