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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Detection of special nuclear materials with the associate particle technique

In the frame of the French trans-governmental R and D program against chemical, biological, radiological, nuclear and explosives (CBRN-E) threats, CEA is studying the detection of Special Nuclear Materials (SNM) by neutron interrogation with fast neutrons produced by an associated particle sealed tube neutron generator. The deuterium-tritium fusion reaction produces an alpha particle and a 14 MeV neutron almost back to back, allowing tagging neutron emission both in time and direction with an alpha particle position-sensitive sensor embedded in the generator. Fission prompt neutrons and gamma rays induced by tagged neutrons which are tagged by an alpha particle are detected in coincidence with plastic scintillators. This paper presents numerical simulations performed with the MCNP-PoliMi Monte Carlo computer code and with post processing software developed with the ROOT data analysis package. False coincidences due to neutron and photon scattering between adjacent detectors (cross talk) are filtered out to increase the selectivity between nuclear and benign materials. Accidental coincidences, which are not correlated to an alpha particle, are also taken into account in the numerical model, as well as counting statistics, and the time-energy resolution of the data acquisition system. Such realistic calculations show that relevant quantities of SNM (few kg) can be distinguished from cargo and shielding materials in 10 min acquisitions. First laboratory tests of the system under development in CEA laboratories are also presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRSDate not supplied

Time-History Statistics of Soot Formation in A Model Gas Turbine Combustor

Soot formation is a complex dynamic and intermittent process determined by properties of the fuel, combustor design, and combustor operation. Although the major steps in soot formation (i.e., formation of precursors, inception, growth and evolution) are similar for a variety of carbonaceous fuels, applications, and operating conditions, it remains unclear when the temporal transition between these steps occurs. An engineering prediction tool coupled with computational fluid physics (CFD), therefore needs to accurately model all these complex steps. To develop such a model, we propose the time-history concept for understanding the time dependency of soot formation as a function of local properties (i.e., temperature, velocity, local fuel air ratio, etc.). We continue our previous work with modeling the DLR aero-combustor [1] with our updated in-house CFD code, Open National Combustion Code (OpenNCC), that now includes a Multiple Time-Scale Flamelet Progress Variable approach and a the semi-empirical two-equation soot model. We injected massless tracer particles upstream of the injector region of the combustor to collect time-history statistics of the solution variables. The correlations between the collected statistics with respect to the experimental soot volume fraction data showed that time-history effect of certain flow variables, including turbulent kinetic energy (TKE), and multiple species is indeed important for soot formation. We then conducted a time-history based correlation analysis to determine the key species and the concentration ranges critical for soot formation (C6H5-based nucleation, acetylene-based surface growth, and oxidation with OH and O2). Based on the time-history correlation coefficient (THCC) analysis, we propose possible modifications to improve the current two-equation model.

LES↗
NASA NTRS1994

Energy density of ionospheric and solar wind origin ions in the near-Earth magnetotail during substorms

Comprehensive energy density studies provide an important measure of the participation of various sources in energization processes and have been relatively rare in the literature. We present a statistical study of the energy density of the near-Earth magnetotail major ions (H(+), O(+), He(++), He(+)) during substorm expansion phase and discuss its implications for the solar wind/magnetosphere/ionosphere coupling. Our aim is to examine the relation between auroral activity and the particle energization during substorms through the correlation between the AE indices and the energy density of the major magnetospheric ions. The data we used here were collected by the charge-energy-mass (CHEM) spectrometer on board the Active Magnetospheric Particle Trace Explorer (AMPTE)/Charge Composition Explorer (CCE) satellite in the near-equatorial nightside magnetosphere, at geocentric distances approximately 7 to 9 R(sub E). CHEM provided the opportunity to conduct the first statistical study of energy density in the near-Earth magnetotail with multispecies particle data extending into the higher energy range (greater than or equal to 20 keV/E). the use of 1-min AE indices in this study should be emphasized, as the use (in previous statistical studies) of the (3-hour) Kp index or of long-time averages of AE indices essentially smoothed out all the information on substorms. Most distinct feature of our study is the excellent correlation of O(+) energy density with the AE index, in contrast with the remarkably poor He(++) energy density - AE index correlation. Furthermore, we examined the relation of the ion energy density to the electrojet activity during substorm growth phase. The O(+) energy density is strongly correlated with the pre-onset AU index, that is the eastward electrojet intensity, which represents the growth phase current system. Our investigation shows that the near-Earth magnetotail is increasingly fed with energetic ionospheric ions during periods of enhanced dissipation of auroral currents. The participation of the ionosphere in the substorm energization processes seems to be closely, although not solely, associated with the solar wind/magnetosphere coupling. That is, the ionosphere influences actively the substorm energization processes by responding to the increased solar wind/magnetosphere coupling as well as to the unloading dissipation of stored energy, with the increased feeding of new material into the magnetosphere.

Daglis, Loannis A.↗
DOE OSTI2026

Beam-dump ceiling and its experimental implications: The case of a portable experiment

We generalize the nature of the so-called beam-dump “ceiling” beyond which the improvement on the sensitivity reach in the search for fast-decaying mediators dramatically slows down, and we point out its experimental implications that motivate tabletop-sized beam-dump experiments for the search. Light (bosonic) mediators are well-motivated new-physics particles, as they can appear in dark-sector portal scenarios and models to explain various laboratory-based anomalies. Due to their low mass and feebly interacting nature, beam-dump-type experiments, utilizing high-intensity particle beams, can play a crucial role in probing the parameter space of such visibly decaying mediators—in particular, the “prompt decay” region, where the mediators feature relatively large coupling and mass. We present a general and semianalytic proof that the ceiling effectively arises in the prompt-decay region of an experiment and show its insensitivity to data statistics, background estimates, and systematic uncertainties, considering a concrete example, the search for axion-like particles interacting with ordinary photons at three benchmark beam facilities: PIP-II at FNAL, and SPS and LHC-dump at CERN. We then identify optimal criteria to perform a cost-effective and short-term experiment to reach the ceiling, demonstrating that very short-baseline compact experiments enable access to the parameter space unreachable thus far.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Bidisperse and polydisperse suspension rheology at large solid fraction

At the same solid volume fraction, bidisperse and polydisperse suspensions display lower viscosities, and weaker normal stress response, compared to monodisperse suspensions. The reduction of viscosity associated with size distribution can be explained by an increase of the maximum flowable, or jamming, solid fraction. In this work, concentrated or "dense" suspensions are simulated under strong shearing, where thermal motion and repulsive forces are negligible, but we allow for particle contact with a mild frictional interaction with interparticle friction coefficient of 0.2. Aspects of bidisperse suspension rheology are first revisited to establish that the approach reproduces established trends; the study of bidisperse suspensions at size ratios of large to small particle radii (2 to 4) shows that a minimum in the viscosity occurs for zeta slightly above 0.5, where zeta=phi_{large}/phi is the fraction of the total solid volume occupied by the large particles. The simple shear flows of polydisperse suspensions with truncated normal and log normal size distributions, and bidisperse suspensions which are statistically equivalent with these polydisperse cases up to third moment of the size distribution, are simulated and the rheologies are extracted. Prior work shows that such distributions with equivalent low-order moments have similar phi_{m}, and the rheological behaviors of normal, log normal and bidisperse cases are shown to be in close agreement for a wide range of standard deviation in particle size, with standard correlations which are functionally dependent on phi/phi_{m} providing excellent agreement with the rheology found in simulation. The close agreement of both viscosity and normal stress response between bi- and polydisperse suspensions demonstrates the controlling in influence of the maximum packing fraction in noncolloidal suspensions. Microstructural investigations and the stress distribution according to particle size are also presented.

Pednekar, Sidhant↗
DOE OSTI2017

Kinetic theory-based numerical modeling and analysis of bi-disperse segregated mixture fluidized bed

We discuss a series of continuum Euler-Euler simulations of an initially mixed bi-disperse fluidized bed which segregates under certain operating conditions. The simulations use the multi-phase kinetic theory-based description of the momentum and energy exchanges between the phases by Simonin’s Group [see e.g. Gourdel, Simonin and Brunier (1999). Proceedings of 6th International Conference on Circulating Fluidized Beds, Germany, pp. 205-210]. The discussion and analysis of the results focus on the fluid-particle momentum exchange (i.e. drag). Simulations using mono- and poly-disperse fluid-particle drag correlations are analyzed for the Geldart D-type size bi-disperse gas-solid experiments performed by Goldschmidt et al. [Powder Tech., pp. 135-159 (2003)]. The poly-disperse gas-particle drag correlations account for the local particle size distribution by using an effective mixture diameter when calculating the Reynolds number and then correcting the resulting force coefficient. Simulation results show very good predictions of the segregation index for bidisperse beds with the mono-disperse drag correlations contrary to the poly-disperse drag correlations for which the segregation rate is systematically under-predicted. The statistical analysis of the results shows a clear separation in the distribution of the gas-particle mean relaxation times of the small and large particles with simulations using the mono-disperse drag. In contrast, the poly-disperse drag simulations have a significant overlap and also a smaller difference in the mean particle relaxation times. This results in the small and large particles in the bed to respond to the gas similarly without enough relative time lag. The results suggest that the difference in the particle response time induce flow dynamics favorable to a force imbalance which results in the segregation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Evaluation of Machine Learning Approaches to Estimate Aerosol Mixing State Metrics in Atmospheric Models

Aerosol mixing state describes how aerosol compositions are distributed among atmospheric aerosol particles in a population. Oversimplified assumptions of aerosol mixing state in atmospheric modeling can introduce errors in estimations of weather and climate-relevant aerosol microphysical properties. A more comprehensive representation of the aerosol mixing state can be achieved in principle with a Particle-resolved Monte Carlo (PartMC) model but at added computational cost that may be prohibitive for direct invocation in operational numerical weather prediction or multi-year climate simulations. The aim of our research is to explore the machine learning (ML) methodologies for estimating aerosol mixing state metrics, which we define here in three different ways: with respect to (a) hygroscopicity; (b) optical properties; and (c) chemical species abundance. We adopted a data-driven approach, leveraging deep learning and statistical learning techniques, to take advantage of massive PartMC model simulations. First, we performed particle-resolved simulations by PartMC to create a series of scenarios considering a range of global environmental conditions. Each scenario consists of aerosol populations and corresponding mixing state metrics. The gas concentration, aerosol mass concentration, environmental variables and mixing state metrics of each population constitute the datasets for machine learning implementations. We have adopted and evaluated various configurations of machine learning methodologies in this investigation, embracing deep learning, Extreme Gradient Boosting (XGBoost) algorithm, and the ensemble approaches. After a rigorous model selection process, we identified an appropriate model to derive estimates of aerosol mixing state metrics. We used the computational and data resources of the Oak Ridge Leadership Computing Facility (OLCF) and the ORNL Compute and Data Environment for Science (CADES). The NVIDIA DGX-1 hardware was used for the prototyping of the ML models. Our approach has allowed us to gain a new understanding of how machine learning methodologies can be applied to improve the representation of aerosol mixing state in atmospheric models and benefit the atmospheric research community. Next, we plan to extend our research and methodology to quantify some of the aerosol-related uncertainties in the E3SM Atmospheric Model (EAM) Version 1.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Wormholes from heavy operator statistics in AdS/CFT

We construct higher dimensional Euclidean AdS wormhole solutions that reproduce the statistical description of the correlation functions of an ensemble of heavy CFT operators. We consider an operator which effectively backreacts on the geometry in the form of a thin shell of dust particles. Assuming dynamical chaos in the form of the ETH ansatz, we demonstrate that the semiclassical path integral provides an effective statistical description of the microscopic features of the thin shell operator in the CFT. The Euclidean wormhole solutions provide microcanonical saddlepoint contributions to the cumulants of the correlation functions over the ensemble of operators. We finally elaborate on the role of these wormholes in the context of non-perturbative violations of bulk global symmetries in AdS/CFT.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1989

Statistical fluctuations as the origin of nontopological solitons

Nontopological solitons can be formed during a phase transition in the early universe as long as some net charge can be trapped in regions of false vacuum. It has been previously suggested that a particle-antiparticle asymmetry would provide a source for such trapped charge. It is pointed out that, for the model and parameters considered, statistical fluctuations provide a much larger concentration of charge, and are therefore, the dominant source of charge fluctuations in solitogenesis.

Griest, Kim↗
DOE OSTI2022

Landau discriminants

Scattering amplitudes in quantum field theories have intricate analytic properties as functions of the energies and momenta of the scattered particles. In perturbation theory, their singularities are governed by a set of nonlinear polynomial equations, known as Landau equations, for each individual Feynman diagram. The singularity locus of the associated Feynman integral is made precise with the notion of the Landau discriminant, which characterizes when the Landau equations admit a solution. In order to compute this discriminant, we present approaches from classical elimination theory, as well as a numerical algorithm based on homotopy continuation. These methods allow us to compute Landau discriminants of various Feynman diagrams up to 3 loops, which were previously out of reach. For instance, the Landau discriminant of the envelope diagram is a reducible surface of degree 45 in the three-dimensional space of kinematic invariants. We investigate geometric properties of the Landau discriminant, such as irreducibility, dimension and degree. In particular, we find simple examples in which the Landau discriminant has codimension greater than one. Furthermore, we describe a numerical procedure for determining which parts of the Landau discriminant lie in the physical regions. In order to study degenerate limits of Landau equations and bounds on the degree of the Landau discriminant, we introduce Landau polytopes and study their facet structure. Finally, we provide an efficient numerical algorithm for the computation of the number of master integrals based on the connection to algebraic statistics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2012

Space Radiation Cancer Risks and Uncertainities for Different Mission Time Periods

Space radiation consists of solar particle events (SPEs), comprised largely of medium energy protons (less than several hundred MeV); and galactic cosmic ray (GCR), which includes high energy protons and high charge and energy (HZE) nuclei. For long duration missions, space radiation presents significant health risks including cancer mortality. Probabilistic risk assessment (PRA) is essential for radiation protection of crews on long term space missions outside of the protection of the Earth s magnetic field and for optimization of mission planning and costs. For the assessment of organ dosimetric quantities and cancer risks, the particle spectra at each critical body organs must be characterized. In implementing a PRA approach, a statistical model of SPE fluence was developed, because the individual SPE occurrences themselves are random in nature while the frequency distribution of SPEs depends strongly upon the phase within the solar activity cycle. Spectral variability of SPEs was also examined, because the detailed energy spectra of protons are important especially at high energy levels for assessing the cancer risk associated with energetic particles for large events. An overall cumulative probability of a GCR environment for a specified mission period was estimated for the temporal characterization of the GCR environment represented by the deceleration potential (theta). Finally, this probabilistic approach to space radiation cancer risk was coupled with a model of the radiobiological factors and uncertainties in projecting cancer risks. Probabilities of fatal cancer risk and 95% confidence intervals will be reported for various periods of space missions.

Kim,Myung-Hee Y.↗
DOE OSTI2014

Constraining the atmospheric composition of the day-night terminators of HD 189733b: Atmospheric retrieval with aerosols

A number of observations have shown that Rayleigh scattering by aerosols dominates the transmission spectrum of HD 189733b at wavelengths shortward of 1 μm. In this study, we retrieve a range of aerosol distributions consistent with transmission spectroscopy between 0.3-24 μm that were recently re-analyzed by Pont et al. To constrain the particle size and the optical depth of the aerosol layer, we investigate the degeneracies between aerosol composition, temperature, planetary radius, and molecular abundances that prevent unique solutions for transit spectroscopy. Assuming that the aerosol is composed of MgSiO{sub 3}, we suggest that a vertically uniform aerosol layer over all pressures with a monodisperse particle size smaller than about 0.1 μm and an optical depth in the range 0.002-0.02 at 1 μm provides statistically meaningful solutions for the day/night terminator regions of HD 189733b. Generally, we find that a uniform aerosol layer provide adequate fits to the data if the optical depth is less than 0.1 and the particle size is smaller than 0.1 μm, irrespective of the atmospheric temperature, planetary radius, aerosol composition, and gaseous molecules. Strong constraints on the aerosol properties are provided by spectra at wavelengths shortward of 1 μm as well as longward of 8 μm, if the aerosol material has absorption features in this region. We show that these are the optimal wavelengths for quantifying the effects of aerosols, which may guide the design of future space observations. The present investigation indicates that the current data offer sufficient information to constrain some of the aerosol properties of HD189733b, but the chemistry in the terminator regions remains uncertain.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Atomic-scale determination of active facets on the MoVTeNb oxide M1 phase and their intrinsic catalytic activity for ethane oxidative dehydrogenation

Atomic resolution aberration-corrected HAADF-STEM imaging has been used to directly image the basal (001) plane of the catalytically relevant M1 phase in MoVTeNb complex oxides. Facets {010}, {120} and {210} are identified as the most stable lateral terminations of the crystals. For each of these surface facets the average concentration of active sites is determined directly from the images. The most stable {010} facet does not expose active sites and is therefore regarded to be inactive. As a consequence, large flattened M1 particles are concluded to have a lower activity than other morphologies. This hypothesis is validated by a statistical analysis of two samples with rod vs. plate morphology. The concentration of sites and the catalytic activity in ethane oxidative dehydrogenation correlate perfectly, demonstrating quantitatively that the differences in reactivity between MoVTeNbOx catalysts of equal crystallinity and chemical composition depend primarily on the terminating facets that constitute the elongated catalyst particles.

MoVTeNbO, Oxidative Dehydrogenation, M1 phase, STE↗
DOE OSTI2023

Advanced data analysis in inertial confinement fusion and high energy density physics

Bayesian analysis enables flexible and rigorous definition of statistical model assumptions with well-characterized propagation of uncertainties and resulting inferences for single-shot, repeated, or even cross-platform data. This approach has a strong history of application to a variety of problems in physical sciences ranging from inference of particle mass from multi-source high-energy particle data to analysis of black-hole characteristics from gravitational wave observations. The recent adoption of Bayesian statistics for analysis and design of high-energy density physics (HEDP) and inertial confinement fusion (ICF) experiments has provided invaluable gains in expert understanding and experiment performance. In this Review, we discuss the basic theory and practical application of the Bayesian statistics framework. We highlight a variety of studies from the HEDP and ICF literature, demonstrating the power of this technique. Due to the computational complexity of multi-physics models needed to analyze HEDP and ICF experiments, Bayesian inference is often not computationally tractable. Two sections are devoted to a review of statistical approximations, efficient inference algorithms, and data-driven methods, such as deep-learning and dimensionality reduction, which play a significant role in enabling use of the Bayesian framework. We provide additional discussion of various applications of Bayesian and machine learning methods that appear to be sparse in the HEDP and ICF literature constituting possible next steps for the community. We conclude by highlighting community needs, the resolution of which will improve trust in data-driven methods that have proven critical for accelerating the design and discovery cycle in many application areas.

47 OTHER INSTRUMENTATION↗
NASA NTRS1994

Planetary Rings

Just over two decades ago, Jim Pollack made a critical contribution to our understanding of planetary ring particle properties, and resolved a major apparent paradox between radar reflection and radio emission observations. At the time, particle properties were about all there were to study about planetary rings, and the fundamental questions were, why is Saturn the only planet with rings, how big are the particles, and what are they made of? Since then, we have received an avalanche of observations of planetary ring systems, both from spacecraft and from Earth. Meanwhile, we have seen steady progress in our understanding of the myriad ways in which gravity, fluid and statistical mechanics, and electromagnetism can combine to shape the distribution of the submicron-to-several-meter size particles which comprise ring systems into the complex webs of structure that we now know them to display. Insights gained from studies of these giant dynamical analogs have carried over into improved understanding of the formation of the planets themselves from particle disks, a subject very close to Jim's heart. The now-complete reconnaissance of the gas giant planets by spacecraft has revealed that ring systems are invariably found in association with families of regular satellites, and there is ark emerging perspective that they are not only physically but causally linked. There is also mounting evidence that many features or aspects of all planetary ring systems, if not the ring systems themselves, are considerably younger than the solar system

Cuzzi, Jeffrey N.↗
DOE OSTI2013

Geometrical splitting technique to improve the computational efficiency in Monte Carlo calculations for proton therapy

Purpose: To present the implementation and validation of a geometrical based variance reduction technique for the calculation of phase space data for proton therapy dose calculation. Methods: The treatment heads at the Francis H Burr Proton Therapy Center were modeled with a new Monte Carlo tool (TOPAS based on Geant4). For variance reduction purposes, two particle-splitting planes were implemented. First, the particles were split upstream of the second scatterer or at the second ionization chamber. Then, particles reaching another plane immediately upstream of the field specific aperture were split again. In each case, particles were split by a factor of 8. At the second ionization chamber and at the latter plane, the cylindrical symmetry of the proton beam was exploited to position the split particles at randomly spaced locations rotated around the beam axis. Phase space data in IAEA format were recorded at the treatment head exit and the computational efficiency was calculated. Depth-dose curves and beam profiles were analyzed. Dose distributions were compared for a voxelized water phantom for different treatment fields for both the reference and optimized simulations. In addition, dose in two patients was simulated with and without particle splitting to compare the efficiency and accuracy of the technique. Results: A normalized computational efficiency gain of a factor of 10-20.3 was reached for phase space calculations for the different treatment head options simulated. Depth-dose curves and beam profiles were in reasonable agreement with the simulation done without splitting: within 1% for depth-dose with an average difference of (0.2 {+-} 0.4)%, 1 standard deviation, and a 0.3% statistical uncertainty of the simulations in the high dose region; 1.6% for planar fluence with an average difference of (0.4 {+-} 0.5)% and a statistical uncertainty of 0.3% in the high fluence region. The percentage differences between dose distributions in water for simulations done with and without particle splitting were within the accepted clinical tolerance of 2%, with a 0.4% statistical uncertainty. For the two patient geometries considered, head and prostate, the efficiency gain was 20.9 and 14.7, respectively, with the percentages of voxels with gamma indices lower than unity 98.9% and 99.7%, respectively, using 2% and 2 mm criteria. Conclusions: The authors have implemented an efficient variance reduction technique with significant speed improvements for proton Monte Carlo simulations. The method can be transferred to other codes and other treatment heads.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2024

Aerosol Size Distribution Properties Associated With Cold-Air Outbreaks in the Norwegian Arctic

The aerosol particles serving as cloud condensation and ice nuclei contribute to key cloud processes associated with cold-air outbreak (CAO) events but are poorly constrained in climate models due to sparse observations. Here we retrieve aerosol number size distribution modes from measurements at Andenes, Norway, during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) and at Zeppelin Observatory, approximately 1000 km upwind from Andenes at Svalbard. During CAO events at Andenes, the sea-spray-mode number concentration is correlated with strong over-ocean winds with a mean of 8±4 cm−3 that is 71 % higher than during non-CAO conditions. Additionally, during CAO events at Andenes, the mean Hoppel minimum diameter is 6 nm smaller than during non-CAO conditions, though the estimated supersaturation is lower, and the mean number concentration of particles that likely activated in-cloud is 109±61 cm−3 with no statistically significant difference from the non-CAO mean of 99±66 cm−3. For CAO trajectories between Zeppelin Observatory and Andenes, the upwind-to-downwind change in number concentration is the largest for the accumulation mode with a mean decrease of 93±95 cm−3, likely attributable primarily to precipitation scavenging. These characteristic properties of aerosol number size distributions during CAO events provide guidance for evaluating CAO aerosol–cloud interaction processes in models.

accumulation mode particle scavenging↗
DOE OSTI2024

Aerosol size distribution properties associated with cold-air outbreaks in the Norwegian Arctic

The aerosol particles serving as cloud condensation and ice nuclei contribute to key cloud processes associated with cold-air outbreak (CAO) events but are poorly constrained in climate models due to sparse observations. Here we retrieve aerosol number size distribution modes from measurements at Andenes, Norway, during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) and at Zeppelin Observatory, approximately 1000 km upwind from Andenes at Svalbard. During CAO events at Andenes, the sea-spray-mode number concentration is correlated with strong over-ocean winds with a mean of 8±4 cm −3 that is 71 % higher than during non-CAO conditions. Additionally, during CAO events at Andenes, the mean Hoppel minimum diameter is 6 nm smaller than during non-CAO conditions, though the estimated supersaturation is lower, and the mean number concentration of particles that likely activated in-cloud is 109±61 cm −3 with no statistically significant difference from the non-CAO mean of 99±66 cm −3 . For CAO trajectories between Zeppelin Observatory and Andenes, the upwind-to-downwind change in number concentration is the largest for the accumulation mode with a mean decrease of 93±95 cm −3 , likely attributable primarily to precipitation scavenging. These characteristic properties of aerosol number size distributions during CAO events provide guidance for evaluating CAO aerosol–cloud interaction processes in models.

54 ENVIRONMENTAL SCIENCES↗