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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 OSTI2015

Current developments in soil organic matter modeling and the expansion of model applications. A review

It is important to note that Soil organic matter (SOM) is a great natural resource. It is fundamental to soil and ecosystem functions across a wide range of scales, from site-specific soil fertility and water holding capacity to global biogeochemical cycling. It is also a highly complex material that is sensitive to direct and indirect human impacts. In our SOM research, simulation models play an important role by providing a mathematical framework to integrate, examine, and test the understanding of SOM dynamics. Simulation models of SOM are also increasingly used in more ‘applied’ settings to evaluate human impacts on ecosystem function, and to manage SOM for greenhouse gas mitigation, improved soil health, and sustainable use as a natural resource. Within this context, there is a need to maintain a robust connection between scientific developments in SOM modeling approaches and SOM model applications. This need forms the basis of this review. In this review we first provide an overview of SOM modeling, focusing on SOM theory, data-model integration, and model development as evidenced by a quantitative review of SOM literature. Second, we present the landscape of SOM model applications, focusing on examples in climate change policy. Finally, we conclude by discussing five areas of recent developments in SOM modeling including: (1) microbial roles in SOM stabilization; (2) modeling SOM saturation kinetics; (3) temperature controls on decomposition; (4)SOM dynamics in deep soil layers; and (5)SOM representation in earth system models. Our aim is to comprehensively connect SOM model development to its applications, revealing knowledge gaps in need of focused interdisciplinary attention and exposing pitfalls that, if avoided, can lead to best use of SOM models to support policy initiatives and sustainable land management solutions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Modeling and Simulation of Microstructure Evolution and Deformation in an Irradiated Environment

The ability to predict the behavior of structural components in a nuclear power plant is critical to the nuclear industry. Structural metals in the primary loop of nuclear power plants must endure challenges such as irradiation and mechanical and thermal loading, and these structural metal components must continue to function in potential transient and accident conditions throughout the operational lifetime of the power plant. This extreme operational environment changes the metal microstructure by creating additional defects. The physical interactions of dislocations with these defects govern how the metal will respond to future conditions. Therefore predicting the mechanical response of these metals requires a set of physically based and reliable models of dislocation and defect interactions. These microstructure elements include glide mobile and immobile dislocations, geometrically necessary dislocations, twinning dislocations, irradiation defects, and thermal aging defects. We present here a continuum dislocation dynamics crystal plasticity framework to capture the interaction mechanisms of these dislocations and defects, verified with a combination of benchmark problems and comparisons with experimental data for two different types of structural metals: alpha iron and nickel-based alloys. In our simulations of $\alpha$ iron we highlight the advantages of applying a Monte Carlo stochastic model of cross slip dislocation motion and show the importance of capturing the 3D nature of glide dislocation and self-interstitial atom loop radiation defect interactions. We demonstrate coupling of glide dislocations with geometrically necessary dislocations to capture the influence of lattice bending, including the sensitivity of the geometrically necessary dislocations to changes in the grain boundary angle. We further examine the interaction of glide dislocations with the twin dislocations and thermally aged defects which have been observed in a nickel-based alloy with additional models. Finally we assess the reliability of this crystal plasticity framework by comparing two dislocation glide velocity models across the range of normal operation temperatures. In successfully applying our crystal plasticity framework to multiple metals, we provide further evidence of the reliability of our approach. The results of this mechanism-based continuum dislocation dynamics crystal plasticity framework can be used to inform engineering scale models throughout the nuclear industry.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Electrical Load Modeling and Simulation

Electricity consumer demand response and load control are playing an increasingly important role in the development of a smart grid. Smart grid load management technologies such as Grid FriendlyTM controls and real-time pricing are making their way into the conventional model of grid planning and operations. However, the behavior of load both affects, and is affected by load control strategies that are designed to support electric grid planning and operations. This chapter discussed the natural behavior of electric loads, how it interacts with various load control and demand response strategies, what the consequences are for new grid operation concepts and the computing issues these new technologies raise.

Demand Response↗
DOE OSTI2016

Operational Simulation Tools and Long Term Strategic Planning for High Penetrations of PV in the Southeastern United States

Increasing levels of distributed and utility scale Solar Photovoltaics (PV) will have an impact on many utility functions, including distribution system operations, bulk system performance, business models and scheduling of generation. In this project, EPRI worked with Southern Company Services and its affiliates and the Tennessee Valley Authority to assist these utilities in their strategic planning efforts for integrating PV, based on modeling, simulation and analysis using a set of innovative tools. Advanced production simulation models were used to investigate operating reserve requirements. To leverage existing work and datasets, this last task was carried out on the California system. Overall, the project resulted in providing useful information to both of the utilities involved and through the final reports and interactions during the project. The results from this project can be used to inform the industry about new and improved methodologies for understanding solar PV penetration, and will influence ongoing and future research. This report summarizes each of the topics investigated over the 2.5-year project period.

14 SOLAR ENERGY↗
DOE OSTI2011

Modeling and Simulations of Electron Emission from Diamond-Amplified Cathodes

Emission of electrons from a diamond-amplified cathode was recently demonstrated. This experiment was based on a promising new concept for generation of high-current, high-brightness, and low thermal emittance electron beams. The measurements from transmission and emission experiments have shown the potential to realize the diamond-amplified cathode concept. However, the results indicate that the involved physical properties should be understood in greater detail to build diamond cathodes with optical properties. We have already made progress in understanding the secondary electron generation and charge transport in diamond with the models we implemented in the VORPAL computational framework. We have been implementing models for electron emission from diamond and will present results from 3D VORPAL simulations with the integrated capabilities on generating electrons and holes, initiated by energetic primary electrons, propagation of the charge clouds, and then the emission of electrons into diamond. We will discuss simulation results on the dependence of the electron emission on diamond surface properties.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

The GEWEX LandFlux project: Evaluation of model evaporation using tower-based and globally gridded forcing data

Determining the spatial distribution and temporal development of evaporation at regional and global scales is required to improve our understanding of the coupled water and energy cycles and to better monitor any changes in observed trends and variability of linked hydrological processes. With recent international efforts guiding the development of long-term and globally distributed flux estimates, continued product assessments are required to inform upon the selection of suitable model structures and also to establish the appropriateness of these multi-model simulations for global application. In support of the objectives of the Global Energy and Water Cycle Exchanges (GEWEX) LandFlux project, four commonly used evaporation models are evaluated against data from tower-based eddy-covariance observations, distributed across a range of biomes and climate zones. The selected schemes include the Surface Energy Balance System (SEBS) approach, the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model, the Penman–Monteith-based Mu model (PM-Mu) and the Global Land Evaporation Amsterdam Model (GLEAM). Here we seek to examine the fidelity of global evaporation simulations by examining the multi-model response to varying sources of forcing data. To do this, we perform parallel and collocated model simulations using tower-based data together with a global-scale grid-based forcing product. Through quantifying the multi-model response to high-quality tower data, a better understanding of the subsequent model response to the coarse-scale globally gridded data that underlies the LandFlux product can be obtained, while also providing a relative evaluation and assessment of model performance. Using surface flux observations from 45 globally distributed eddy-covariance stations as independent metrics of performance, the tower-based analysis indicated that PT-JPL provided the highest overall statistical performance (0.72; 61 W m –2 ; 0.65), followed closely by GLEAM (0.68; 64 W m –2 ; 0.62), with values in parentheses representing the R 2 , RMSD and Nash–Sutcliffe efficiency (NSE), respectively. PM-Mu (0.51; 78 W m –2 ; 0.45) tended to underestimate fluxes, while SEBS (0.72; 101 W m –2 ; 0.24) overestimated values relative to observations. A focused analysis across specific biome types and climate zones showed considerable variability in the performance of all models, with no single model consistently able to outperform any other. Results also indicated that the global gridded data tended to reduce the performance for all of the studied models when compared to the tower data, likely a response to scale mismatch and issues related to forcing quality. Rather than relying on any single model simulation, the spatial and temporal variability at both the tower- and grid-scale highlighted the potential benefits of developing an ensemble or blended evaporation product for global-scale LandFlux applications. Hence, challenges related to the robust assessment of the LandFlux product are also discussed.

58 GEOSCIENCES↗
DOE OSTI2023

Multiscale modeling of solute diffusion in triblock copolymer membranes

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

36 MATERIALS SCIENCE↗
DOE OSTI2013

Do Coupled Climate Models Correctly Simulate the Upward Branch of the Deep Ocean Global Conveyor?

The large-scale meridional overturning circulation (MOC) connects the deep ocean, a major reservoir of carbon, to the other components of the climate system and must therefore be accurately represented in Earth System Models. Our project aims to address the specific question of the pathways and mechanisms controlling the upwelling branch of the MOC, a subject of significant disagreement between models and observational syntheses, and among general circulation models. Observations of these pathways are limited, particularly in regions of complex hydrography such as the Southern Ocean. As such, we rely on models to examine theories of the overturning circulation, both physically and biogeochemically. This grant focused on a particular aspect of the meridional overturning circulation (MOC) where there is currently significant disagreement between models and observationally based analyses of the MOC, and amongst general circulation models. In particular, the research focused on addressing the following questions: 1. Where does the deep water that sinks in the polar regions rise to the surface? 2. What processes are responsible for this rise? 3. Do state-of-the-art coupled GCMs capture these processes? Our research had three key components: observational synthesis, model development and model analysis. In this final report we outline the key results from these areas of research for the 2007 to 2012 grant period. The research described here was carried out primarily by graduate student, Daniele Bianchi (now a Postdoc at McGill University, Canada), and Postdoc Stephanie Downes (now a Research Fellow at The Australian national University, Australia). Additional support was provided for programmers Jennifer Simeon as well as Rick Slater.

3He data Synthesis↗
DOE OSTI2019

Development of mathematical model and simulation tool for the high capacity production of carbon fiber

Weight reduction in the transportation is the most effective way for reducing fuel consumption. Carbon fiber composites have been increasingly used by automotive manufacturers in vehicle nonstructural and structural parts. Larger penetration of carbon fiber-based materials requires very large quantities of fiber at a low price, and of consistently high quality. The advancements in carbon fiber technology that are required to achieve this production scale-up and cost reduction, are very challenging. The most expensive production step is the conversion of textile-grade polyacrylonitrile (PAN) precursor to carbon fiber. The conversion takes place in three main stages: stabilization, low and high temperature carbonization. Most of the research to date has focused on the stabilization of the PAN precursors and oxidized PAN fibers. Comparingly little effort has been dedicated to the investigation of processing conditions during low temperature (LT) carbonization. During this stage of fiber conversion, the largest fiber mass loss occurs, and the fundamental carbon fiber structure and material properties are established. The understanding of the conversion processes in the LT furnace, and the effects of spatial and temporal variations of the LT process parameters on the carbon fiber properties are necessary to identify possible process improvements and cost savings. The objective of this research was to develop computational models for gas flow and energy transport in the gas and fiber phases of the LT furnace, and to couple them with the fiber conversion process models. The model is implemented in the three-dimensional framework that enables analysis of process condition variations in all directions. The formulation is based on modified transport equation to simulate steady state conditions and thus significantly reduce computational effort associated with transient methods. The model is implemented in open source computational fluid dynamics (CFD) code OpenFOAM. The model has been demonstrated on a prototype carbon fiber LT furnace and can be extended to other geometries and process conditions. Extensions to other physical effects, such as accounting evolution of multiple gas species, and fiber tensioning are straightforward. The model uses High Performance Computing (HPC) capabilities of OpenFOAM and can be implemented on multi-processor workstations or mid-size computer clusters common in the industrial setting.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Development and Validation of a Polarimetric-MCScene 3D Atmospheric Radiation Model

Polarimetric measurements can substantially enhance the ability of both spectrally resolved and single band imagery to detect the proliferation of weapons of mass destruction, providing data for locating and identifying facilities, materials, and processes of undeclared and proliferant nuclear weapons programs worldwide. Unfortunately, models do not exist that efficiently and accurately predict spectral polarized signatures for the materials of interest embedded in complex 3D environments. Having such a model would enable one to test hypotheses and optimize both the enhancement of scene contrast and the signal processing for spectral signature extraction. The Phase I set the groundwork for development of fully validated polarimetric spectral signature and scene simulation models. This has been accomplished 1. by (a) identifying and downloading state-of-the-art surface and atmospheric polarimetric data sources, (b) implementing tools for generating custom polarimetric data, and (c) identifying and requesting US Government funded field measurement data for use in validation; 2. by formulating an approach for upgrading the radiometric spectral signature model MODTRAN to generate polarimetric intensities through (a) ingestion of the polarimetric data, (b) polarimetric vectorization of existing MODTRAN modules, and (c) integration of a newly developed algorithm for computing polarimetric multiple scattering contributions; 3. by generating an initial polarimetric model that demonstrates calculation of polarimetric solar and lunar single scatter intensities arising from the interaction of incoming irradiances with molecules and aerosols; 4. by developing a design and implementation plan to (a) automate polarimetric scene construction and (b) efficiently sample polarimetric scattering and reflection events, for use in a to be developed polarimetric version of the existing first-principles synthetic scene simulation model, MCScene; and 5. by planning a validation field measurement program in collaboration with the Remote Sensing and Exploitation group at Sandia National Laboratories (SNL) in which data from their ongoing polarimetric field and laboratory measurement program will be shared and, to the extent allowed, tailored for model validation in exchange for model predictions under conditions and for geometries outside of their measurement domain.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2014

Modeling adaptation of carbon use efficiency in microbial communities

In new microbial-biogeochemical models, microbial carbon use efficiency (CUE) is often assumed to decline with increasing temperature. Under this assumption, soil carbon losses under warming are small because microbial biomass declines. Yet there is also empirical evidence that CUE may adapt (i.e., become less sensitive) to warming, thereby mitigating negative effects on microbial biomass. To analyze potential mechanisms of CUE adaptation, I used two theoretical models to implement a tradeoff between microbial uptake rate and CUE. This rate-yield tradeoff is based on thermodynamic principles and suggests that microbes with greater investment in resource acquisition should have lower CUE. Microbial communities or individuals could adapt to warming by reducing investment in enzymes and uptake machinery. Consistent with this idea, a simple analytical model predicted that adaptation can offset 50% of the warming-induced decline in CUE. To assess the ecosystem implications of the rate-yield tradeoff, I quantified CUE adaptation in a spatially-structured simulation model with 100 microbial taxa and 12 soil carbon substrates. This model predicted much lower CUE adaptation, likely due to additional physiological and ecological constraints on microbes. In particular, specific resource acquisition traits are needed to maintain stoichiometric balance, and taxa with high CUE and low enzyme investment rely on low-yield, high-enzyme neighbors to catalyze substrate degradation. In contrast to published microbial models, simulations with greater CUE adaptation also showed greater carbon storage under warming. This pattern occurred because microbial communities with stronger CUE adaptation produced fewer degradative enzymes, despite increases in biomass. Thus, the rate-yield tradeoff prevents CUE adaptation from driving ecosystem carbon loss under climate warming.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

System-of-Systems Approach for Integrated Energy Systems Modeling and Simulation: Preprint

Today’s electricity grid is the most complex system ever built—and the future grid is likely to be even more complex because it will incorporate distributed energy resources (DERs) such as wind, solar, and various other sources of generation and energy storage. The complexity is further augmented by the possible evolution to new retail market structures that provide incentives to owners of DERs to support the grid. To understand and test new retail market structures and technologies such as DERs, demand-response equipment, and energy management systems while providing reliable electricity to all customers, an Integrated Energy System Model (IESM) is being developed at NREL. The IESM is composed of a power flow simulator (GridLAB-D), home energy management systems implemented using GAMS/Pyomo, a market layer, and hardware-in-the-loop simulation (testing appliances such as HVAC, dishwasher, etc.). The IESM is a system-of-systems (SoS) simulator wherein the constituent systems are brought together in a virtual testbed. We will describe an SoS approach for developing a distributed simulation environment. We will elaborate on the methodology and the control mechanisms used in the co-simulation illustrated by a case study.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Clumped isotope constraints on changes in latest Pleistocene hydroclimate in the northwestern Great Basin: Lake Surprise, California

Abstract During the Last Glacial Maximum (LGM) and subsequent deglaciation, the Great Basin in the southwestern United States was covered by numerous extensive closed-basin lakes, in stark contrast with the predominately arid climate observed today. This transition from lakes in the Late Pleistocene to modern aridity implies large changes in the regional water balance. Whether these changes were driven by increased precipitation rates due to changes in atmospheric dynamics, decreased evaporation rates resulting from temperature depression and summer insolation changes, or some combination of the two remains uncertain. The factors contributing to these large-scale changes in hydroclimate are critical to resolve, given that this region is poised to undergo future anthropogenic-forced climate changes with large uncertainties in model simulations for the 21st century. Furthermore, there are ambiguous constraints on the magnitude and even the sign of changes in key hydroclimate variables between the Last Glacial Maximum and the present day in both proxy reconstructions and climate model analyses of the region. Here we report thermodynamically derived estimates of changes in temperature, precipitation, and evaporation rates, as well as the isotopic composition of lake water, using clumped isotope data from an ancient lake in the northwestern Great Basin, Lake Surprise (California). Compared to modern climate, mean annual air temperature at Lake Surprise was 4.7 °C lower during the Last Glacial Maximum, with decreased evaporation rates and similar precipitation rates to modern. During the mid-deglacial period, the growth of Lake Surprise implied that the lake hydrologic budget briefly departed from steady state. Our reconstructions indicate that this growth took place rapidly, while the subsequent lake regression took place over several thousand years. Using models for precipitation and evaporation constrained from clumped isotope results, we determine that the disappearance of Lake Surprise coincided with a moderate increase in lake temperature, along with increasing evaporation rates outpacing increasing precipitation rates. Concomitant analysis of proxy data and climate model simulations for the Last Glacial Maximum are used to provide a robust means to understand past climate change, and by extension, predict how current hydroclimates may respond to expected future climate forcings. We suggest that an expansion of this analysis to more basins across a larger spatial scale could provide valuable insight into proposed climate forcings, and aid in climate model process depiction. Ultimately, our analysis highlights the importance of temperature-driven evaporation as a mechanism for lake growth and retreat in this region.

Geology↗
DOE OSTI2018

BESTEST-GSR (Building Energy Simulation Test - Generation Simulation and Reporting) [SWR 18-23]

Building Energy Simulation Test (BESTEST) is an NREL-developed method to validate the qualitative performance of different whole building simulations engines relative to each other. https://www.nrel.gov/docs/legosti/old/6231.pdf For EnergyPlus® and OpenStudio®, generation of test cases, simulation, and reporting has been a manual or partially automated process that typically took a number of months after a new version of EnergyPlus came out. Building Energy Simulation Test - Generation Simulation and Reporting (BESTEST-GSR) was developed to fully automate this process so it is less error-prone, less time-consuming, and can be quickly run on request for any development branch of EnergyPlus and OpenStudio. The initial use case is to directly support OpenStudio and EnergyPlus development, but the software is configured to support use by external software developers that build tools on top of EnergyPlus and/or OpenStudio. Tools built on top of OpenStudio can leverage the test generation, simulation, and reporting, while tools built on EnergyPlus can leverage the test simulation and reporting. The model generation is accomplished by running OpenStudio measures for envelope, heating, or cooling test cases. At this stage the specified weather file required for a specific test case is assigned to the model. Simulation is enabled using the OpenStudio Command Line Interface (CLI) to run a series of OpenStudio and Reporting measures to a specified seed model. The specified workflow is defined with an OpenStudio OSW file. The first measure generates the test case model, while the second measure is a reporting measure that runs after the simulation to collect necessary simulation results from the EnergyPlus SQL file. The Reporting stage gathers data from individual OpenStudio OSW files and populates a series of BESTEST Microsoft Excel spreadsheets. BESTEST-GSR also gathers zip files from individual simulations for diagnostic purposes.

Goldwasser, David↗
DOE OSTI2015

Stand Alone Battery Thermal Management System

The objective of this project is research, development and demonstration of innovative thermal management concepts that reduce the cell or battery weight, complexity (component count) and/or cost by at least 20%. The project addresses two issues that are common problems with current state of the art lithium ion battery packs used in vehicles; low power at cold temperatures and reduced battery life when exposed to high temperatures. Typically, battery packs are “oversized” to satisfy the two issues mentioned above. The first phase of the project was spent making a battery pack simulation model using AMEsim software. The battery pack used as a benchmark was from the Fiat 500EV. FCA and NREL provided vehicle data and cell data that allowed an accurate model to be created that matched the electrical and thermal characteristics of the actual battery pack. The second phase involved using the battery model from the first phase and evaluate different thermal management concepts. In the end, a gas injection heat pump system was chosen as the dedicated thermal system to both heat and cool the battery pack. Based on the simulation model. The heat pump system could use 50% less energy to heat the battery pack in -20°C ambient conditions, and by keeping the battery cooler at hot climates, the battery pack size could be reduced by 5% and still meet the warranty requirements. During the final phase, the actual battery pack and heat pump system were installed in a test bench at DENSO to validate the simulation results. Also during this phase, the system was moved to NREL where testing was also done to validate the results. In conclusion, the heat pump system can improve “fuel economy” (for electric vehicle) by 12% average in cold climates. Also, the battery pack size, or capacity, could be reduced 5%, or if pack size is kept constant, the pack life could be increased by two years. Finally, the total battery pack and thermal system cost could be reduced 5% only if the system is integrated with the vehicle cabin air conditioning system. The reason why we were not able to achieve the 20% reduction target is because of the natural decay of the battery cell due to the number of cycles. Perhaps newer battery chemistries that are not so sensitive to cycling would have more potential for reducing the battery size due to thermal issues.

25 ENERGY STORAGE↗
DOE OSTI2024

PyOED: An Extensible Suite for Data Assimilation and Model-Constrained Optimal Design of Experiments

This article describes PyOED, a highly extensible scientific package that enables developing and testing model-constrained optimal experimental design (OED) for inverse problems. Specifically, PyOED aims to be a comprehensive Python toolkit for model-constrained OED. The package targets scientists and researchers interested in understanding the details of OED formulations and approaches. It is also meant to enable researchers to experiment with standard and innovative OED technologies with a wide range of test problems (e.g., simulation models). OED, inverse problems (e.g., Bayesian inversion), and data assimilation (DA) are closely related research fields, and their formulations overlap significantly. Thus, PyOED is continuously being expanded with a plethora of Bayesian inversion, DA, and OED methods as well as new scientific simulation models, observation error models, and observation operators. These pieces are added such that they can be permuted to enable testing OED methods in various settings of varying complexities. The PyOED core is completely written in Python and utilizes the inherent object-oriented capabilities; however, the current version of PyOED is meant to be extensible rather than scalable. Specifically, PyOED is developed to “enable rapid development and benchmarking of OED methods with minimal coding effort and to maximize code reutilization.” This article provides a brief description of the PyOED layout and philosophy and provides a set of exemplary test cases and tutorials to demonstrate the potential of the package.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Global Increase in Nearshore Tropical Cyclone Intensification

Abstract Tropical Cyclones (TCs) inflict substantial coastal damages, making it pertinent to understand changing storm characteristics in the important nearshore region. Past work examined several aspects of TCs relevant for impacts in coastal regions. However, few studies explored nearshore storm intensification and its response to climate change at the global scale. Here, we address this using a suite of observations and numerical model simulations. Over the historical period 1979–2020, observations reveal a global mean TC intensification rate increase of about 3 kt per 24‐hr in regions close to the coast. Analysis of the observed large‐scale environment shows that stronger decreases in vertical wind shear and larger increases in relative humidity relative to the open oceans are responsible. Further, high‐resolution climate model simulations suggest that nearshore TC intensification will continue to rise under global warming. Idealized numerical experiments with an intermediate complexity model reveal that decreasing shear near coastlines, driven by amplified warming in the upper troposphere and changes in heating patterns, is the major pathway for these projected increases in nearshore TC intensification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Transport of cosmic-ray protons in intermittent heliospheric turbulence: Model and simulations

The transport of charged energetic particles in the presence of strong intermittent heliospheric turbulence is computationally analyzed based on known properties of the interplanetary magnetic field and solar wind plasma at 1 astronomical unit. The turbulence is assumed to be static, composite, and quasi-three-dimensional with a varying energy distribution between a one-dimensional Alfvénic (slab) and a structured two-dimensional component. The spatial fluctuations of the turbulent magnetic field are modeled either as homogeneous with a Gaussian probability distribution function (PDF), or as intermittent on large and small scales with a q-Gaussian PDF. Simulations showed that energetic particle diffusion coefficients both parallel and perpendicular to the background magnetic field are significantly affected by intermittency in the turbulence. This effect is especially strong for parallel transport where for large-scale intermittency results show an extended phase of subdiffusive parallel transport during which cross-field transport diffusion dominates. The effects of intermittency are found to depend on particle rigidity and the fraction of slab energy in the turbulence, yielding a perpendicular to parallel mean free path ratio close to 1 for large-scale intermittency. Investigation of higher order transport moments (kurtosis) indicates that non-Gaussian statistical properties of the intermittent turbulent magnetic field are present in the parallel transport, especially for low rigidity particles at all times.

79 ASTRONOMY AND ASTROPHYSICS↗