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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 OSTI2019

The Borg Cube Simulation: Cosmological Hydrodynamics with CRK-SPH

A challenging requirement posed by next-generation observations is a firm theoretical grasp of the impact of baryons on structure formation. Cosmological hydrodynamic simulations modeling gas physics are vital in this regard. A high degree of modeling flexibility exists in this space, making it important to explore a range of methods in order to gauge the accuracy of simulation predictions. We present results from the first cosmological simulation using Conservative Reproducing Kernel Smoothed Particle Hydrodynamics (CRK-SPH). We employ two simulations: one evolved purely under gravity, and the other with nonradiative hydrodynamics. Each contains 2 × 2304 3 cold dark matter plus baryon particles in an 800 h -1 Mpc box. We compare statistics to previous nonradiative simulations including power spectra, mass functions, baryon fractions, and concentration. We find self-similar radial profiles of gas temperature, entropy, and pressure and show that a simple analytic model recovers these results to better than 40% over two orders of magnitude in mass. We quantify the level of nonthermal pressure support in halos and demonstrate that hydrostatic mass estimates are biased low by 24% (10%) for halos of mass 10 15 (10 13 ) h -1 M⊙. Here, we compute angular power spectra for the thermal and kinematic Sunyaev–Zel’dovich effects and find good agreement with the low-ℓ Planck measurements. Finally, artificial scattering between particles of unequal mass is shown to have a large impact on the gravity-only run, and we highlight the importance of better understanding this issue in hydrodynamic applications. This is the first in a simulation campaign using CRK-SPH, with future work including subresolution gas treatments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Adaptive resolution simulation of polarizable supramolecular coarse-grained water models

Multiscale simulations methods, such as adaptive resolution scheme, are becoming increasingly popular due to their significant computational advantages with respect to conventional atomistic simulations. For these kind of simulations, it is essential to develop accurate multiscale water models that can be used to solvate biophysical systems of interest. Recently, a 4-to-1 mapping was used to couple the bundled-simple point charge water with the MARTINI model. Here, we extend the supramolecular mapping to coarse-grained models with explicit charges. In particular, the two tested models are the polarizable water and big multiple water models associated with the MARTINI force field. As corresponding coarse-grained representations consist of several interaction sites, we couple orientational degrees of freedom of the atomistic and coarse-grained representations via a harmonic energy penalty term. This additional energy term aligns the dipole moments of both representations. We test this coupling by studying the system under applied static external electric field. We show that our approach leads to the correct reproduction of the relevant structural and dynamical properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

The Distributed Geothermal Market Demand Model (dGeo): Documentation

The National Renewable Energy Laboratory (NREL) developed the Distributed Geothermal Market Demand Model (dGeo) as a tool to explore the potential role of geothermal distributed energy resources (DERs) in meeting thermal energy demands in the United States. The dGeo model simulates the potential for deployment of geothermal DERs in the residential and commercial sectors of the continental United States for two specific technologies: ground-source heat pumps (GHP) and geothermal direct use (DU) for district heating. To quantify the opportunity space for these technologies, dGeo leverages a highly resolved geospatial database and robust bottom-up, agent-based modeling framework. This design is consistent with others in the family of Distributed Generation Market Demand models (dGen; Sigrin et al. 2016), including the Distributed Solar Market Demand (dSolar) and Distributed Wind Market Demand (dWind) models. dGeo is intended to serve as a long-term scenario-modeling tool. It has the capability to simulate the technical potential, economic potential, market potential, and technology deployment of GHP and DU through the year 2050 under a variety of user-defined input scenarios. Through these capabilities, dGeo can provide substantial analytical value to various stakeholders interested in exploring the effects of various techno-economic, macroeconomic, financial, and policy factors related to the opportunity for GHP and DU in the United States. This report documents the dGeo modeling design, methodology, assumptions, and capabilities.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

A Data-Driven Approach to Nation-Scale Building Energy Modeling

In 2019, 125 million U.S. residential and commercial buildings consumed $412 billion in energy bills. These buildings currently consume 40% of the nation's primary energy, 73% of electricity, 80% of energy during peak electric grid use, and responsible for 39% of greenhouse gas emissions [14]. Urban-scale building energy modeling has grown significantly in the past decade, allowing individual campuses or communities of buildings to be modeled, simulated, and cost-effective solutions for intelligent management to be identified and implemented. While traditionally limited to individual counties and usually less than 2,000 buildings, the Automatic Building Energy Modeling (AutoBEM) soft-ware suite has been developed to process unconventional, nation-scale data sources to generate unique OpenStudio and EnergyPlus models of each building. Through the use of High Performance Computing (HPC) resources, every U.S. building has been simulated. This paper showcases the data layout, node partitioning, algorithmic approaches, and analytic results that were used to create, share, and analyze 124.4 million U.S. building models.

Berres, Andy↗
DOE OSTI2026

Quantifying Membrane Structure and Dynamics during Bioproduct Production in Zymomonas mobilis by Molecular Simulation

The conversion of lignocellulosic biomass into biofuels and bioproducts by microbial biorefineries is central to a sustainable chemical industry. Zymomonas mobilis is one such biorefinery chassis and is resistant to ethanol stress, leading to its use in biomass conversion to biofuels and bioproducts. However, Z. mobilis growth is often inhibited by organic acids, aldehydes, alcohols, ketones, and amides found in biomass hydrolysate. The resulting slow growth inhibits production and as a result drives up the price for the resulting products. One hypothesis is that these molecules interact with or disrupt the bacterial membrane, triggering stress responses and hindering growth. To test this hypothesis at the molecular level, we employ all-atom molecular dynamics (MD) simulations to investigate lignocellulose-derived small molecules and their impact on a biologically relevant Z. mobilis membrane model. Simulations were conducted across a range of inhibitor concentrations from 0 to 2.5 mol %, analyzing key membrane properties such as area per lipid (APL), membrane thickness, lipid-order parameter (−S CH ), lateral diffusion coefficient (D xy ), and permeability coefficient (Pm). From simulation, we observed altered membrane structure and dynamics at these modest small molecule concentrations commonly found in hydrolysates. Generally, the membranes become thinner, with a higher area per lipid and lower-order parameter as the small molecule concentration increases. These trends are stronger for more hydrophobic molecules with greater hydrophobic bulk, as isobutanol, propanol, and propanoic acid showed greater membrane perturbations as the concentration increased compared to other small molecules. Tracking small molecule distributions directly in our equilibrium simulations allows us to determine concentration-dependent free energy profiles for these molecules. While the trends are noisy, generally the barriers to crossing the membrane decrease as the concentration increases, indicating that the membranes become leakier as small molecule concentrations rise. Comparing between native Z. mobilis membranes with hopanoids and membranes sharing the same phospholipid composition but without hopanoids, hopanoids stabilize and order the membrane for smaller molecules to maintain membrane structure but appear insufficient for larger hydrophobic molecules like isobutanol. These findings provide a mechanistic understanding of how small molecules found in biomass degradation streams interact with the Z. mobilis membrane, offering valuable insights for future strain engineering efforts to optimize biofuel and bioproduct synthesis from biomass feedstocks by highlighting limits to small molecule tolerance. This knowledge can guide the modification of membrane composition to develop more robust microbes, thereby improving microbial survival and yields in industrial contexts.

Singh, Nitin Kumar [Michigan State Univ., East Lan↗
DOE OSTI2019

Urban pollution greatly enhances formation of natural aerosols over the Amazon rainforest

One of the least understood aspects in atmospheric chemistry is how urban emissions influence the formation of natural organic aerosols, which affect Earth’s energy budget. The Amazon rainforest, during its wet season, is one of the few remaining places on Earth where atmospheric chemistry transitions between preindustrial and urban pollution-influenced conditions. Here we integrate insights from several laboratory measurements and simulate the formation of secondary organic aerosols (SOA) in the Amazon using a high-resolution chemical transport model. Simulations show that urban emissions of oxides of nitrogen from Manaus, a city of ~2 million people, greatly enhance production of biogenic SOA by 60-200% on average with peak enhancements of 400%, through the increased oxidation of gas-phase organic carbon emitted by the forests. Simulated enhancements agree with aircraft measurements, and are much larger than those reported over other locations. Furthermore, the implication is that increasing anthropogenic emissions in future years might substantially enhance biogenic SOA formation in pristine locations like the Amazon.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

EASY-SHIFT v Alpha

The software is a generic, price- and load-responsive control algorithm integrating heat pumps with thermal energy storage. The algorithm leverages simple models of the system and easily accessible data to schedule operation of heat pumps and thermal energy storage in ways that minimize the cost of operating the heating/cooling system. This tool is specifically designed to be easy to interact with, and something that industry partners are able to adopt. There are two current state of the art approaches. Industry tends to develop very simple algorithms, with predetermined schedules that are not capable of changing operation in response to changes in operating environment. For example, a control designed to avoid high-price electricity from 5-8 PM will not be able to adapt if the high-price period changes to 4-9 PM. Academia commonly develops algorithms called Model predictive control (MPC). MPC requires extensive data and highly trained staff to develop a specific type of simulation model of the building, connect the building to optimization algorithms, and leverage powerful computers. Industry, with limited time/finance budgets for any project, is resistant to adopting MPC due to the associated high complexity and cost.

Grant, Peter [Lawrence Berkeley National Laborator↗
DOE OSTI2022

Collaborative Research: Particle Simulation of Collisionless Magnetic Reconnection Under Finite Guide Field

Magnetic reconnection is believed to be one of the most fundamental physical processes in laboratory and space plasmas. Fundamental physics of collisionless reconnection under general conditions of finite guide magnetic field, realistic electron-to-ion mass ratio, and in 3D geometries is still not adequately understood. The major goal of this project is to study the physics of collisionless magnetic reconnection in a current sheet under a finite guide field and with a realistic ion-to-electron mass ratio mi/me. Under the support of this DoE grant, we have made significant progress in (1) the investigation of linear and nonlinear current sheet instabilities under a wide range of guide field using the 3-D gyro kinetic electron and fully kinetic ion (GeFi) particle simulation model and (2) the investigation of fully nonlinear reconnection process with a strong guide field with the GeFi simulations. Furthermore, (3) we have extended the study to the realistic geometry of the magnetopause using 3D global hybrid simulations. This study, from local to global scales and for 3-D physics at plasma boundaries, has provides advanced theoretical understanding of the fundamental physics of magnetic reconnection in laboratory and space plasma regimes on multi-scales.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Verification Testing of OLI Systems Mixed Solvent Electrolyte Model for the Na-K-Mg-Ca-H-Cl-SO 4 -OH-HCO 3 -CO 3 -CO 2 -H 2 ) System to High Ionic Strength at 25°C

This technical report summarizes model verification results and summary statistics for 41 evaporite mineral solubility cases evaluated by Savannah River National Laboratory using OLI Systems’ aqueous electrolyte thermodynamic modeling software. The 41 verification cases containing a total of 60 solubility curves comprise mineral solubility data from low to high ionic strength at 25°C for the eight-component system Na-K-Mg-Ca-H-Cl-SO 4 -OH-HCO 3 -CO 3 -CO 2 -H 2 O as reported by Harvie et al. (1984). Thermodynamic calculations were executed using OLI Systems’ Stream Analyzer computation module within the OLI Studio software platform (Ver. 11.0, Rev. 11.0.1.9). The Mixed Solvent Electrolyte (MSE) thermodynamic framework was chosen for this investigation because of its superiority in modeling high ionic-strength inorganic salt solutions and actinide redox chemistry and solubility, both of which are relevant to the geological repository conditions at the Waste Isolation Pilot Plant in Carlsbad, New Mexico. Mineral solubility data in various inorganic salt solutions were digitized and extracted from figures generated by Harvie et al. (1984). For each of the 60 solubility curves, a case-specific chemistry model and input file were generated in OLI Studio using OLI Stream Analyzer and the MSE (H 3 O + ion) public databank provided by OLI Systems. Model simulation results were exported to Microsoft Excel to calculate summary statistics and to generate graphs comparing the OLI model predictions to the solubility data. Summary statistics include residuals (model – data) and concordance (accuracy × precision, where precision is indicated by the Pearson correlation coefficient and accuracy accounts for bias and scale differential). Private databanks were not developed, and activity coefficient model regressions were not performed to improve OLI model fits to the data. Of the 41 model verification plots, 83% have a mean of the percent residuals less than or equal to 25%. Similarly, 75% display a concordance greater than or equal to 0.75. Only seven of the 41 verification plots fail to show good agreement between the model and data. Of these seven, three are relevant to the WIPP repository because they involve the Mg-OH-Cl-SO 4 -CO 3 aqueous system. The remaining four address salt solubilities at the pH extremes (strong acid and strong base). It should be noted that in two of the three Mg-OH-Cl-SO 4 -CO 3 system cases, the regressed Harvie et al. (1984) solubility curve also deviated from the data. Lack of agreement between the OLI model-predicted solubility curves and the data is attributable to one or more of the following: specific solid species are not included in the OLI MSE databank; there is significant variation among the different solubility datasets chosen by Harvie et al. (1984); the OLI MSE model’s thermodynamic parameters were determined using different solubility datasets; and the activity coefficient parameters for certain relevant ion-ion and ion-molecule pairs have not been optimized via data regression. Two recommendations for future work are to (1) evaluate solubility data for the Mg-OH-Cl-SO 4 -CO 3 system at high ionic strength and, if necessary, develop a private OLI MSE database that includes missing species and, where necessary, regressed standard state properties and interaction parameters; (2) perform similar verification testing of the OLI model for actinide solubility data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Deploying E3’s RESERVE Tool to Enable Advanced Operation of Clean Grids

Energy and Environmental Economics, Inc. (E3) developed an open-source machine learning model, RESERVE, for deriving ancillary services timeseries in deeply decarbonized electricity grids. E3 used a bespoke PLEXOS production simulation model of the California Independent System Operator’s (CAISO) balancing area to validate RESERVE’s ability to enable production cost, greenhouse gas emissions (GHG), and renewable energy curtailment savings. These savings were modeled by comparing PLEXOS cases with RESERVE’s outputs to PLEXOS cases with CAISO’s incumbent reserve product in the Western Energy Imbalance Market (EIM)’s 15-minute market. E3 also tested cases with solar operating flexibly to provide reserves. E3 found that, in a 2030 modeling year, using RESERVE and flexible solar enabled significant production cost, GHG and curtailment savings versus the incumbent CAISO method in cases with low penetrations of lithium-ion batteries. However, with the full 14 gigawatts (about 30% of peak CAISO demand) of 4-hour lithium-ion batteries that are expected to be installed by 2030, these savings approach zero due to batteries saturating ancillary services markets. E3 also found significant savings under a 2019 benchmarking year.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

SPRUCE Data-constrained Projections of Methane Fluxes in a Northern Minnesota Peatland in Response to Elevated CO2 and Warming: Modeling Archive

This modeling archive dataset contains results from a study developed a data-informed process-based model using methane chamber measurement data from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. The study used existing manual CH4 flux measurements collected from the ambient environment from 2011-2014 and developed a data-informed process-based methane module. The module was incorporated into the Terrestrial ECOsystem (TECO) model before its parameters were constrained with multiple years of methane flux data for forecasting CH4 emission under five warming and two elevated CO2 experimental treatments at SPRUCE. This effort modeled wetland methane (CH4) fluxes with process modeling to better understand uncertainties and the effects of increased temperature and carbon dioxide (CO2) on methane fluxes. Ma et al. (2017) contains results from this modeling effort. This dataset contains 6 files: (1) a user guide in PDF (*.pdf) format, (2) a readme in PDF (*.pdf) format, (3) the posterior distribution of parameters values in comma-separate values (*.csv) format, (4) a compressed (*.zip) folder containing model simulated results of methane emission for different pathways under different environmental conditions, (5) source code of the TECO model used in the study in Fortran 90 (*.f90) format, and (6) a file-level metadata file in comma-separate values (*.csv). In the August 2026 update to this dataset, the associated Fortran 90 code script, TECO_SPRUCE_ME.f90, and a user guide were added to this dataset. Two file names were changed: 2.Posterior_distribution.csv to 2_Posterior_distribution.csv and 3.Simulations_data.zip to 3_Simulations_data.zip and Files_1_3_legends is now provided as a PDF (*.pdf) instead of in Microsoft Word format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Biomass burning aerosol transport and vertical distribution over the South African-Atlantic region

Optically thick smoke aerosol plumes originating from biomass burning (BB) in the southwestern African Savanna during the austral spring are transported westward by the free tropospheric winds to primarily overlie vast stretches of stratocumulus cloud decks in the southeast Atlantic. In this work, we evaluated the simulations of long-range transport of BB aerosol by the Goddard Earth Observing System (GEOS-5) and four other global aerosol models over the complete South African-Atlantic region using Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations to find any distinguishing or common model biases. Models, in general, captured the vertical distribution of aerosol over land but exhibited some common features after long-range transport of smoke plumes that were distinct from that of CALIOP. Most importantly, the model-simulated BB aerosol plumes quickly descend to lower levels just off the western coast of the continent, while CALIOP data suggest that smoke plumes continue their horizontal transport at elevated levels above the marine boundary layer. This is crucial because the sign of simulated aerosol semidirect effect can change depending on whether the bulk of the absorbing aerosols is present within or above the cloud levels in a model. The levels to which the aerosol plumes get subsided and the steepness of their descent vary amongst the models and amongst the different subregions of the domain. Investigations into possible causes of differences between GEOS-5 and CALIOP aerosol transport over the ocean revealed a minimal role of aerosol removal process representation in the model as opposed to model dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Modeling the impact of liana infestation on the demography and carbon cycle of tropical forests

There is mounting empirical evidence that lianas affect the carbon cycle of tropical forests. However, no single vegetation model takes into account this growth form, although such efforts could greatly improve the predictions of carbon dynamics in tropical forests. In this study, we incorporated a novel mechanistic representation of lianas in a dynamic global vegetation model (the Ecosystem Demography Model). We developed a liana-specific plant functional type and mechanisms representing liana-tree interactions (such as light competition, liana-specific allometries, and attachment to host trees) and parameterized them according to a comprehensive literature meta-analysis. We tested the model for an old-growth forest (Paracou, French Guiana) and a secondary forest (Gigante Peninsula, Panama). The resulting model simulations captured many features of the two forests characterized by different levels of liana infestation as revealed by a systematic comparison of the model outputs with empirical data, including local census data from forest inventories, eddy flux tower data, and terrestrial laser scanner-derived forest vertical structure. The inclusion of lianas in the simulations reduced the secondary forest net productivity by up to 0.46 t C ha -1 year -1 , which corresponds to a limited relative reduction of 2.6% in comparison with a reference simulation without lianas. However, this resulted in significantly reduced accumulated above-ground biomass after 70 years of regrowth by up to 20 t C /ha (19% of the reference simulation). Ultimately, the simulated negative impact of lianas on the total biomass was almost completely cancelled out when the forest reached an old-growth successional stage. Our findings suggest that lianas negatively influence the forest potential carbon sink strength, especially for young, disturbed, liana-rich sites. In light of the critical role that lianas play in the profound changes currently experienced by tropical forests, this new model provides a robust numerical tool to forecast the impact of lianas on tropical forest carbon sinks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Online Machine Learning for Accelerating Molecular Dynamics Modeling of Cells

We developed a biomechanics-informed online learning framework to learn the dynamics with ground truth generated with multiscale modeling simulation. It was built on Summit-like supercomputers, which were also used to benchmark and validate our framework on one physiologically significant modeling of deformable biological cells. We generalized the century-old equation of Jeffery orbits to a new equation of motion with additional parameters to account for the flow conditions and the cell deformability. Using simulation data at particle-based resolutions for flowing cells and the learned parameters from our framework, we validated the new equation by the motions, mostly rotations, of a human platelet in shear blood flow at various shear stresses and platelet deformability. Our online framework, which surrogates redundant computations in the conventional multiscale modeling by solutions of our learned equation, accelerates the conventional modeling by three orders of magnitude without visible loss of accuracy.

multiscale modeling↗
DOE OSTI2019

Effect of Subshelf Melt Variability on Sea Level Rise Contribution From Thwaites Glacier, Antarctica

Modeling and observations suggest that Thwaites Glacier, West Antarctica, has begun unstable retreat. Concurrently, oceanographic observations have revealed substantial multiyear variability in the temperature of the ocean water driving retreat through melting of the ice shelf that restrains inland glacier flow. Using an ensemble of 72 ice–sheet model simulations that include an idealized representation of ocean temperature variability, we find that variable ice–shelf melting causes delays in grounding line retreat, mass loss, and sea level contribution relative to steady forcing. Modeled delays are up to 43 years after 500 years of simulation, corresponding to a 10% reduction in glacier mass loss. Delays are primarily caused by asymmetric melt forcing in the presence of variability. For the “warm cavity” conditions beneath Thwaites Ice Shelf, increases in access of warm, deeper water are unable to raise water temperatures in the cavity by much, whereas increases in access of significantly colder, shallow water reduce cavity water temperatures substantially. This leads to lowered mean melt rates under variable ocean temperature forcing. Additionally, about one quarter of the mass loss delay is caused by a nonlinear ice dynamic response to varying ice–shelf thinning rate, which is amplified during the initial phases of unstable, bed–topography–driven retreat. Mass loss rates under variability differ by up to 50% from ensemble mean values at any given time. Our results underscore the need for taking climate variability into account when modeling ice sheet evolution and for continued efforts toward the coupling of ice sheet models to ocean and climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Advanced Reactors-Intermediate Heat Exchanger (IHX) Coupling: Theoretical Modeling and Experimental Validation

The overall goal of the research project was to model the behavior of the advanced reactorintermediate heat exchange system and to develop advanced control techniques for off-normal conditions. The specific objectives defined for the project were: 1. To develop the steady-state thermal hydraulic design of the intermediate heat exchanger (IHX); 2. To develop mathematical models to describe the advanced nuclear reactor-IHX-chemical process/power generation coupling during normal and off-normal operations, and to simulate models using multiphysics software; 3. To develop control strategies using genetic algorithm or neural network techniques and couple these techniques with the multiphysics software; 4. To validate the models experimentally The project objectives were accomplished by defining and executing four different tasks corresponding to these specific objectives. The first task involved selection of IHX candidates and developing steady state designs for those. The second task involved modeling of the transient and offnormal operation of the reactor-IHX system. The subsequent task dealt with the development of control strategies and involved algorithm development and simulation. The last task involved experimental validation of the thermal hydraulic performances of the two prototype heat exchangers designed and fabricated for the project at steady state and transient conditions to simulate the coupling of the reactor- IHX-process plant system. The experimental work utilized the two test facilities at The Ohio State University (OSU) including one existing High-Temperature Helium Test Facility (HTHF) and the newly developed high-temperature molten salt facility.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

On the Role of Airborne Ice Nucleating Particles in Primary and Secondary Ice Formation Processes in Convective Midlatitude Clouds

Abstract Formation of ice in mixed-phase clouds may proceed via primary nucleation and secondary ice production (SIP). Primary nucleation involves the presence of ice nucleating particles (INPs), while SIP follows primary nucleation, resulting in ice crystal number concentrations N ice exceeding the number of INPs. The ice formation pathways in young congestus clouds are not well understood nor are the environmental conditions favorable for the onset of SIP. Coincident airborne measurements of INPs and N ice collected during the Secondary Production of Ice in Cumulus Experiment (SPICULE) campaign over the U.S. central Great Plains are reported for young congestus clouds. Number concentrations of INPs and N ice at temperatures between −10° and −20°C were used to categorize whether SIP was active in each analyzed cloud pass. Observational data suggested that fragmentation of freezing droplets (FFDs) was occurring and the most likely SIP mechanism for ice enhancement during the early stages of the sampled clouds, where higher cloud-base temperatures and stronger updrafts produced conditions favorable for the onset of FFD. Simple model simulations of a congestus cloud were used to compare predictions of N ice from newly implemented SIP mechanisms (including FFD) alongside those from the existing Hallett–Mossop (HM) mechanism. Although predicted N ice , when all SIP mechanisms were active, was in good agreement with observations, the dominant SIP mechanism in the model was predicted to be HM. The rate of heterogeneous freezing in raindrops was likely inhibiting realistic FFD rates in the simulated cloud. These discrepancies between the observations and simulations underscore the need for extended laboratory studies to formulate improved model representations of SIP in convective clouds.

Patnaude, Ryan J. [Department of Atmospheric Scien↗

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