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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Néel domain walls with bistable chirality in a perpendicularly magnetized ferrimagnetic insulator

Field-free spin-orbit torque-driven domain wall motion in magnetic thin films with perpendicular magnetic anisotropy (PMA) requires the domain walls to have Néel character. Conventionally, Néel domain walls are stabilized by the Dzyaloshinskii-Moriya interaction (DMI) in ultrathin films. Here, in a europium iron garnet thin film with PMA and an additional uniaxial in-plane anisotropy, we demonstrate two bistable Néel domain wall states in the absence of DMI, and the capability to toggle the wall states with an in-plane field pulse and consequently their directions of motion under a current pulse. We present a phase diagram for the bistable Néel domain wall states as a function of in-plane field pulse width and amplitude. By fitting the experimental data to an analytical model of Néel wall reversal through the nucleation and propagation of Bloch lines, we extract the length of the initial reversed domain wall segment and Bloch line nucleation energy barrier. Current-driven motion of in-plane anisotropy stabilized Néel walls is qualitatively different from that of DMI-stabilized ones owing to the different symmetry of the effective fields that stabilize the Néel configuration. Furthermore, we present a proof of principle demonstration for 2-bit random number generation based on the stochastic reversal of domain wall chirality. These results provide critical insight into the topological energy barrier of Bloch lines and identify paths towards domain wall-based memory and computing devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas

One of the biggest challenges to achieve the goal of producing fusion energy in tokamak devices is the necessity of avoiding disruptions of the plasma current due to instabilities. The disruption event characterization and forecasting (DECAF) framework has been developed in this purpose, integrating physics models of many causal events that can lead to a disruption. Two different machine learning approaches are proposed to improve the ideal magnetohydrodynamic (MHD) no-wall limit component of the kinetic stability model included in DECAF. First, a random forest regressor (RFR), was adopted to reproduce the DCON computed change in plasma potential energy without wall effects for a large database of equilibria from the national spherical torus experiment (NSTX). This tree-based method provides an analysis of the importance of each input feature, giving an insight into the underlying physics phenomena. Secondly, a fully-connected neural network has been trained on sets of calculations with the DCON code, to get an improved closed form equation of the no-wall β limit as a function of the relevant plasma parameters indicated by the RFR. The neural network has been guided by physics theory of ideal MHD in its extension outside the domain of the NSTX experimental data. The estimated value has been incorporated into the DECAF kinetic stability model and tested against a set of experimentally stable and unstable discharges. Moreover, the neural network results were used to simulate a real-time stability assessment using only quantities available in real-time. Finally, the portability of the model was investigated, showing encouraging results by testing the NSTX-trained algorithm on the mega ampere spherical tokamak (MAST).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Prevailing Charge Order in Overdoped La 2 – x Sr x CuO 4 beyond the Superconducting Dome

The extremely overdoped cuprates are generally considered to be Fermi liquid metals without exotic orders, whereas the underdoped cuprates harbor intertwined states. Contrary to this conventional wisdom, using Cu L 3 -edge and O K-edge resonant x-ray scattering, we reveal a charge order (CO) correlation in overdoped La 2–x⁢ Sr x ⁢CuO 4 (0.35 ≤ x ≤0.6) beyond the superconducting dome. This CO has a periodicity of ~6 lattice units with correlation lengths of ~20 lattice units. It shows similar in-plane momentum and polarization dependence and dispersive excitations as the CO of underdoped cuprates, but its maximum intensity differs along the c direction and persists up to 300 K. This CO correlation cannot be explained by the Fermi surface instability and its origin remains to be understood. Finally, our results suggest that CO is prevailing in the overdoped metallic regime and requires a reassessment of the picture of overdoped cuprates as weakly correlated Fermi liquids.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Multiple Degradation Mechanisms in Reinforced Concrete Structures, Modeling and Risk Analysis

The overarching goal of this project is to complement ongoing Department of Energy (DOE) Light Water Reactor Sustainability (LWRS)-funded Grizzly concrete modeling development efforts by providing improved multi-physics models for incorporation into the Grizzly and BlackBear codes. Objectives of this work identified at the outset of this project include: 1. Coupling between mechanical damage and transport processes. The mechanical damage will be characterized by nonlinear mechanical constitutive models appropriate for concrete used in nuclear power plants (NPPs). 2. Improved representation of the coupling among the transport process, building on the current moisture and thermal transport models in Grizzly. The parameters for coupling among heat and mass transport in the multi-physics model will be experimentally determined. 3. Coupling among different scale levels of concrete constituents, fine and coarse aggregates, cement paste, hydration products, and pore structure. 4. Probabilistic analysis of the random nature of the heterogeneous concrete structure and the environmental factors (ambient temperature and humidity) and their multiple effects on concrete deterioration. 5. Benchmark verification, validation and uncertainty quantification of the thermo-hygro- chemo-mechanical (THCM) concrete formulation. The end goal of this work was to provide a comprehensive and robust simulation capability that can be applied at the engineering scale for analysis of realistic deterioration scenarios in NPP concrete structures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Efficiently improving the performance of noisy quantum computers

Using near-term quantum computers to achieve a quantum advantage requires efficient strategies to improve the performance of the noisy quantum devices presently available. We develop and experimentally validate two efficient error mitigation protocols named "Noiseless Output Extrapolation" and "Pauli Error Cancellation" that can drastically enhance the performance of quantum circuits composed of noisy cycles of gates. By combining popular mitigation strategies such as probabilistic error cancellation and noise amplification with efficient noise reconstruction methods, our protocols can mitigate a wide range of noise processes that do not satisfy the assumptions underlying existing mitigation protocols, including non-local and gate-dependent processes. We test our protocols on a four-qubit superconducting processor at the Advanced Quantum Testbed. We observe significant improvements in the performance of both structured and random circuits, with up to 86 % improvement in variation distance over the unmitigated outputs. Our experiments demonstrate the effectiveness of our protocols, as well as their practicality for current hardware platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Emulating aerosol optics with randomly generated neural networks

Abstract. Atmospheric aerosols have a substantial impact on climate and remain one of the largest sources of uncertainty in climate prediction. Accurate representation of their direct radiative effects is a crucial component of modern climate models. However, direct computation of the radiative properties of aerosol populations is far too computationally expensive to perform in a climate model, so optical properties are typically approximated using a parameterization. This work develops artificial neural networks (ANNs) capable of replacing the current aerosol optics parameterization used in the Energy Exascale Earth System Model (E3SM). A large training dataset is generated by using Mie code to directly compute the optical properties of a range of atmospheric aerosol populations given a large variety of particle sizes, wavelengths, and refractive indices. Optimal neural architectures for shortwave and longwave bands are identified by evaluating ANNs with randomly generated wirings. Randomly generated deep ANNs are able to outperform conventional multilayer-perceptron-style architectures with comparable parameter counts. Finally, the ANN-based parameterization produces significantly more accurate bulk aerosol optical properties than the current parameterization when compared with direct Mie calculations using mean absolute error. The success of this approach makes possible the future inclusion of much more sophisticated representations of aerosol optics in climate models that cannot be captured by extension of the existing parameterization scheme and also demonstrates the potential of random-wiring-based neural architecture search in future applications in the Earth sciences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Progressive Tree-Based Compression of Large-Scale Particle Data

Scientific simulations and observations using particles have been creating large datasets that require effective and efficient data reduction to store, transfer, and analyze. However, current approaches either compress only small data well while being inefficient for large data, or handle large data but with insufficient compression. Toward effective and scalable compression/decompression of particle positions, we introduce new kinds of particle hierarchies and corresponding traversal orders that quickly reduce reconstruction error while being fast and low in memory footprint. Our solution to compression of large-scale particle data is a flexible block-based hierarchy that supports progressive, random-access, and error-driven decoding, where error estimation heuristics can be supplied by the user. For low-level node encoding, we introduce new schemes that effectively compress both uniform and densely structured particle distributions. Our proposed methods thus target all three phases of a tree-based particle compression pipeline, namely tree construction, tree traversal, and node encoding. In conclusion, the improved efficacy and flexibility of these methods over existing compressors are demonstrated through extensive experimentation, using a wide range of scientific particle datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Ligand Substituent Effects on the Electronic Properties of Lindqvist‐Type Polyoxometalate Multi‐Level‐Switches in the Gas Phase, Solution and on Surfaces

Abstract Although the intrinsic electronic properties of polyoxometalates (POMs) can be greatly influenced by modifying them with organic substituents, their resistive switching behavior on surfaces dependent on the organic substituents remains largely unexplored. In this work, we assessed the importance of electron‐withdrawing and electron‐donating ligand substituents on the material properties of a series of hybrid Lindqvist‐type hexavanadates TBA 2 [V 6 O 13 ((OCH 2 ) 3 CCH 2 OH) 2 ] (TBA 2 V 6 ‐OH), TBA 2 [V 6 O 13 ((OCH 2 ) 3 CMe) 2 ] (TBA 2 V 6 ‐Me), TBA 2 [V 6 O 13 ((OCH 2 ) 3 CNHCOCH 2 Cl) 2 ] (TBA 2 V 6 ‐Cl), and TBA 2 [V 6 O 13 ((OCH 2 ) 3 CNHCOCH 2 ‐OOCC 10 H 15 ) 2 ] (TBA 2 V 6 ‐Ad) as potential resistive random‐access memory (ReRAM) components. Compared to their redox behavior in solution, changing the ligand substituents on surfaces results in no significant effect on the potential and, thus, no effect on the resistance steps in the current‐voltage profiles. However, while the current‐voltage characteristics do not change, the peripheral metal‐free substituents in the trisalkoxide framework of Lindqvist‐type hexavanadate molecules influence the adsorption and switching stability of these POMs on gold. This work highlights the noticeable differences between hexavanadate's redox properties in solution (which follow the trend observed in the gas phase) and hexavanadate's resistive switching properties on conducting surfaces. Importantly, their multi‐state switching behavior is not significantly altered by the different type of substituent at the periphery of the trisalkoxo ligands.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Thermal conductivity of two-dimensional disordered fibrous materials defined by interfiber thermal contact conductance and intrinsic conductivity of fibers

A general framework for the theoretical analysis and numerical calculations of the effective thermal conductivity of two-dimensional homogeneous and isotropic disordered fibrous materials is developed in this work based on the model of random contacts between soft-core spherocylinders. The analysis accounts for the interfiber contact conductance and intrinsic conductivity of fibers and is performed in a wide space of governing parameters that includes the fiber aspect ratio, Biot number calculated for a single thermal contact between fibers, and material density ranging from values corresponding to the percolation threshold up to those characteristic of dense fiber networks. For dense networks, exact theoretical equations for the thermal conductivity of materials composed of spherocylinders with an arbitrary aspect ratio and Biot number are derived. The effect of the intrinsic conductivity of fibers on the thermal transport in fibrous materials is found to depend on the density of contacts and can be significant in sufficiently dense fiber networks even if the Biot number for a single thermal contact is small. Semiempirical corrections to the theoretical equations are derived for small and moderate fiber densities. The power law exponent describing the approximate dependence of the conductivity on the density parameter is found to vary from values close to 1 up to values exceeding 2 when evaluated within different finite ranges of the density parameter. This finding explains the variability of scaling laws for thermal conductivity of fibrous materials suggested in the literature based on numerical simulations performed in different regions of the space of material parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2023

ESR Dipole Power Supply Current Ripple and Noise Specifications

This note presents key findings for the ESR main magnet dipole power supplies (PS), where we find the current ripple specification to be close to or beyond the state-of-the-art. These specifications originate from beam-beam considerations, with the requirement to limit the ripple-induced hadron emittance growth to below 10%/hour. Beam dynamics that drive this PS ripple specification arise from the beam motions at the Interaction Point (IP). The frequency of the motions can be separated into "low", compared to the betatron frequency, and "high", i.e. around the betatron frequency and harmonics. In terms of the driving frequency, "low" implies ƒ<<ƒ 0 v x,y and "fast" means ƒ≈{ƒ 0 v x,y , ƒ 0 (1- v x,y , etc.}, where ƒ 0 =1/ T 0 =78.2 kHz is the revolution frequency, and v x,y are the fractional parts of the betatron tunes. Frequencies higher than ƒ 0 /2 will be folded back due to the particles sampling the field once per turn. To provide flexibility for future lattice adjustments and working point variations, we do not consider the tunes as fixed. Instead, we assume a certain margin and allow them to potentially fall within the range of 0.1< v x,y <0.5. In other words, the high-frequency region spans approximately from 8kHz to 40 kHz. Consequently,, we definte the dipole PS ripple in two distinct frequency ranges: the low-frequency range of [1-8000] Hz and the high-frequency range of [8-40] kHz. For the physics effects we analyzed in this note, there is no distinction between the ripple (which can be approximately reproduced in the frequency domain) and the random noise if both have some power within the frequency bandwidth of interest. Therefore, while we will use the term "ripple" for short, it should always be understood that we are referring to "ripple plus noise." Except for the lower end of the low-frequency range, the impact of the rippling PS current on the beam will be considerably reduced due to the eddy currents induced in the walls of the vacuum chamber. We will account for this effect in the PS ripple specifications to follow. The remaining sections of this note are structure as follows: Section II outlines the beam-beam physics requirements for the positional stability of the beam at the IP. Section III describes the anticipated shielding effect of the eddy currents induced in the vacuum chamber. Section IV derives the ripple requirement for the low-frequency range by propagating the closed orbit ripple resulting from the rippling dipoles to the IP (relevant lattice simulation results are summarized in the Appendix). In Section V, we present the analytical criterion for the ripple in the high-frequency range by considering resonant oscillations of the electron beam around a stable closed orbit near the betatron frequency. Finally, Section VI provides a summary of our findings and discusses related work.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Effect of non-uniform void distributions on the yielding of metals

High-throughput (several thousand) calculations have been carried out to investigate the yield behavior of porous materials with randomly distributed pores, porosity levels over four orders of magnitude and up to a hundred pores per simulation box. To this end, a Galerkin based fast Fourier transform (FFT) formulation was enhanced to deal with high phase contrast materials. In addition, GPU parallelization was employed in solving the governing equation for strain fluctuations using a Krylov iterative solver. Emphasis is laid on the conditions under which percolation of plastically non-deforming zones through the porous network emerge, a regime termed unhomogeneous yielding. By way of contrast, the regime where the plastic strain fluctuations (associated with the heterogeneous void-matrix aggregate) fall below the percolation threshold is defined as homogeneous yielding. Here, we find that nonuniform pore distributions only affect unhomogeneous yielding and have a universal softening effect. The extent of this distribution softening is analyzed as a function of porosity, cell size and number of realizations. Whether the uncovered universal distribution softening has direct implications on failure resistance of porous materials is discussed.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Dynamics of a single polyampholyte chain

Polymers that feature both positive and negative charges along chains, known as polyampholytes, represent a class of materials that hold promise for a new generation of energy storage devices, the design of which will require knowledge of the underlying structure and dynamics. Here, we develop a theory based on the Rouse model for the dynamic structure factor of a single polyampholyte chain in the weak coupling regime (negligible intramolecular electrostatics) or subjected to weak external electric fields (governed by linear response). Neglecting effects of small ions, we find deviations in scaling from the classic Rouse theory and make predictions for scattering experiments performed on polyampholytes. We find that, under weak coupling with arbitrarily strong fields, the dynamics are highly dependent on the charge distribution and consequently look at two representative examples—random charge densities and periodic charge densities—with different scaling properties. Additionally, under weak fields, the dynamics are largely independent of charge distribution. Finally, we investigate the influence of hydrodynamic effects and the implications of including inertial effects in the model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A fast Monte Carlo cell-by-cell simulation for radiobiological effects in targeted radionuclide therapy using pre-calculated single-particle track standard DNA damage data

Introduction: We developed a new method that drastically speeds up radiobiological Monte Carlo radiation-track-structure (MC-RTS) calculations on a cell-by-cell basis. Methods: The technique is based on random sampling and superposition of single-particle track (SPT) standard DNA damage (SDD) files from a “pre-calculated” data library, constructed using the RTS code TOPAS-nBio, with “time stamps” manually added to incorporate dose-rate effects. This time-stamped SDD file can then be input into MEDRAS, a mechanistic kinetic model that calculates various radiation-induced biological endpoints, such as DNA double-strand breaks (DSBs), misrepairs and chromosomal aberrations, and cell death. As a benchmark validation of the approach, we calculated the predicted energy-dependent DSB yield and the ratio of direct-to-total DNA damage, both of which agreed with published in vitro experimental data. We subsequently applied the method to perform a superfast cell-by-cell simulation of an experimental in vitro system consisting of neuroendocrine tumor cells uniformly incubated with 177 Lu. Results and discussion: The results for residual DSBs, both at 24 and 48 h post-irradiation, are in line with the published literature values. Our work serves as a proof-of-concept demonstration of the feasibility of a cost-effective “in silico clonogenic cell survival assay” for the computational design and development of radiopharmaceuticals and novel radiotherapy treatments more generally.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Measuring Young Stars in Space and Time. II. The Pre-main-sequence Stellar Content of N44

The Hubble Space Telescope survey Measuring Young Stars in Space and Time (MYSST) entails some of the deepest photometric observations of extragalactic star formation, capturing even the lowest-mass stars of the active star-forming complex N44 in the Large Magellanic Cloud. We employ the new MYSST stellar catalog to identify and characterize the content of young pre-main-sequence (PMS) stars across N44 and analyze the PMS clustering structure. To distinguish PMS stars from more evolved line of sight contaminants, a non-trivial task due to several effects that alter photometry, we utilize a machine-learning classification approach. This consists of training a support vector machine (SVM) and a random forest (RF) on a carefully selected subset of the MYSST data and categorize all observed stars as PMS or non-PMS. Combining SVM and RF predictions to retrieve the most robust set of PMS sources, we find ∼26,700 candidates with a PMS probability above 95% across N44. Employing a clustering approach based on a nearest neighbor surface density estimate, we identify 16 prominent PMS structures at 1σ significance above the mean density with sub-clusters persisting up to and beyond 3σ significance. The most active star-forming center, located at the western edge of N44's bubble, is a subcluster with an effective radius of ∼5.6 pc entailing more than 1100 PMS candidates. Furthermore, we confirm that almost all identified clusters coincide with known H ii regions and are close to or harbor massive young O stars or YSOs previously discovered by MUSE and Spitzer observations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Cross-national analysis of food security drivers: comparing results based on the Food Insecurity Experience Scale and Global Food Security Index

Abstract The second UN Sustainable Development Goal establishes food security as a priority for governments, multilateral organizations, and NGOs. These institutions track national-level food security performance with an array of metrics and weigh intervention options considering the leverage of many possible drivers. We studied the relationships between several candidate drivers and two response variables based on prominent measures of national food security: the 2019 Global Food Security Index (GFSI) and the Food Insecurity Experience Scale’s (FIES) estimate of the percentage of a nation’s population experiencing food security or mild food insecurity (FI ). We compared the contributions of explanatory variables in regressions predicting both response variables, and we further tested the stability of our results to changes in explanatory variable selection and in the countries included in regression model training and testing. At the cross-national level, the quantity and quality of a nation’s agricultural land were not predictive of either food security metric. We found mixed evidence that per-capita cereal production, per-hectare cereal yield, an aggregate governance metric, logistics performance, and extent of paid employment work were predictive of national food security. Household spending as measured by per-capita final consumption expenditure (HFCE) was consistently the strongest driver among those studied, alone explaining a median of 92% and 70% of variation (based on out-of-sample R 2 ) in GFSI and FI , respectively. The relative strength of HFCE as a predictor was observed for both response variables and was independent of the countries used for model training, the transformations applied to the explanatory variables prior to model training, and the variable selection technique used to specify multivariate regressions. The results of this cross-national analysis reinforce previous research supportive of a causal mechanism where, in the absence of exceptional local factors, an increase in income drives increase in food security. However, the strength of this effect varies depending on the countries included in regression model fitting. We demonstrate that using multiple response metrics, repeated random sampling of input data, and iterative variable selection facilitates a convergence of evidence approach to analyzing food security drivers.

42 ENGINEERING↗
DOE OSTI2020

Effective traplike activated dynamics in a continuous landscape

In this work, we use a simple model to extend network models for activated dynamics to a continuous landscape with a well-defined notion of distance and a direct connection to many-body systems. The model consists of a tracer in a high-dimensional funnel landscape with no disorder. We find a nonequilibrium low-temperature phase with aging dynamics that is effectively equivalent to that of models with built-in disorder, such as the trap model, step model and random energy model. Finally, we compare entropy with energy-driven activation, and we remark that the former is more robust to the choice of the dynamics since it does not depend on whether one uses local or global updates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Long-range and local crystal structures of the S r 1 - x C a x Ru O 3 perovskites

Here, the crystal structures of the Sr 1–x Ca x RuO 3 (0 ≤ x ≤ 1) perovskites are investigated using both long-range and local structural probes. High-resolution synchrotron powder x-ray-diffraction characterization at ambient temperature shows that the materials have orthorhombic long-range crystal structures to high precision, and we support previous work showing that Ca 2+ substitution for Sr 2+ primarily changes the tilting of rigid corner-sharing RuO 6 octahedra at their shared oxygen vertices. X-ray pair-distribution function analysis for SrRuO 3 , CaRuO 3 , and one intermediate composition show them to display monoclinic symmetry at the local level; when averaged over different domain orientations this yields orthorhombic symmetry in the long range, and no long-range or local phase transitions are observed between 80 and 300 K for materials with intermediate compositions. High-resolution transmission electron microscopy shows that the Sr/Ca distribution is random at the nanoscale. Lastly, we plot magnetic characteristics such as the ferromagnetic T c , Curie-Weiss theta, effective moment, and ambient temperature susceptibility vs the octahedral tilt and unit-cell volume.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Dynamic Behavior of Natural Seep Vents: Analysis of Field and Laboratory Observations and Modeling (Final Scientific/Technical Report)

In this project, we have analyzed data collected by the U.S. Department of Energy (DOE), National Energy Technology Laboratory (NETL) in a high pressure water tunnel (HPWT) and data from two research cruises to natural seeps in the Gulf of Mexico to adapt and validate a numerical model to predict the dynamics of natural seeps in the deep oceans. The HPWT data include video observations of the shrinkage rate of individual methane and natural gas bubbles under simulated deep-water conditions. Field data were collected during two cruises by the Gulf Integrated Spill Research (GISR) Consortium led by Texas A&M University and funded by the Gulf of Mexico Research Initiative (GoMRI). These data included in situ observations from a remotely operated vehicle (ROV) of gas bubbles at two natural seep sites in the Gulf and acoustic observations of the natural seep bubble flares in the ocean water column. The acoustic data were from multibeam echosounders, one mounted in a forward-looking orientation on the ROV and another mounted down-looking in the haul of the ship. All of these laboratory and field data were focused on the dynamics of natural gas bubbles at temperatures and pressures favorable for clathrate hydrate formation between the gas and water. Our analyses of this data focused on understanding the mechanisms responsible for gas bubble dissolution within the hydrate stability zone (HSZ) of the oceans. Ice-like hydrate shells may form on the bubble-water interface under these conditions, and it was unknown how this might affect the mass transfer of gas into the ocean. We were able to extract bubble shrinkage rates from the HPWT datasets. Using this data we determined that mass transfer coefficients with and without a hydrate shell match empirical values for bubbles in contaminated systems (so-called dirty bubbles contaminated by naturally occurring surfactants). We also showed that free gas, and not gas hydrate, is the dominant dissolving phase when the hydrate sub-cooling is below 11 degree Celsius (temperature difference between hydrate the hydrate formation temperature and ambient temperator) or the pressure is reducing as bubbles rise through the ocean water column. Using this mass transfer model, our numerical model of bubble dissolution matched the over 200 HPWT experiments with an average error of 10% for predicting the bubble size at the end of an experiment. From field data in the literature, we also observed that gas bubbles dissolve faster when they are initially released, following mass transfer coefficients for so-called clean-bubbles (those not yet contaminated by surfactants). Shortly after release within the HSZ, a hydrate shell forms on the bubble-water interface, and the mass transfer reduces to rates matching those of dirty bubbles. We correlated this transition time from clean to dirty bubble behavior with the initial bubble surface area and the hydrate sub-cooling. With this model for hydrate formation time and using the mass transfer coefficients deduced from the HPWT data, we validated our numerical model for predicting the rise heights of natural seep flares in the oceans. Flare heights are commonly observed in haul-mounted acoustic multibeam data. The numerical model predicts bubbles to rise high in the ocean water column owing to the slower mass transfer rates for dirty bubbles that accompany the majority of their rise time. We found that the numerical model predictions matched the observed flare heights within 5% to 10% accuracy when we compared the rise heights of the largest bubbles released from the seafloor with the bubbles acoustically visible in the multibeam data. Bubbles become acoustically transparent as they shrink to sizes of order 1 mm in diameter for the multibeam frequencies used in the field. The forward-looking multibeam on the ROV also provided data on the lateral spreading of bubbles in natural seep flares. Our analysis of this data showed that spreading follows a diffusion process, with the effective diffusivity correlating with the wobbling length scale of these ellipsoidal bubbles. When we apply this diffusivity in a random displacement model of bubble spreading, our numerical simulations match closely the lateral spread observed by the M3 in the ocean water column. Finally, we compared the seep model predictions for the acoustic properties of these natural seep plumes with that observed by the acoustic instruments in the field. The M3 and EM 302 observations were converted to relative values of target strength using a calibration we obtained in the laboratory for the M3 and using an algorithm from the manufacturer for the EM 302. Comparing the numerical seep model to these data, we obtain good agreement over the whole height of rise of these bubble flares. This further validates the numerical model. Overall, our validated seep model captures the key dynamics of gas bubbles released from natural seeps in the oceans and helps to predict the fate of methane in the water column.

03 NATURAL GAS↗