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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Predicting Nanoparticle Suspension Viscoelasticity for Multimaterial 3D Printing of Silica–Titania Glass

A lack of predictive methodology is frequently a major bottleneck in materials development for additive manufacturing. Hence, exploration of new printable materials often relies on the serendipity of trial and error approaches, which is time-consuming, labor-intensive, and costly. In this paper, we present an approach to overcome these issues by quantifying and controlling the viscoelasticity of inks for multimaterial 3D printing of silica–titania glass using direct ink writing (DIW). We formulate simple silica and silica–titania inks from a suspension of fumed silica nanoparticles in an organic solvent with a dissolved molecular titania precursor. We use a small set of experimental rheological data and estimates of interaction potentials from colloidal theory to develop a predictive tool that allows us to design and obtain compatible inks that are matched both in desired rheological properties (viscosity profiles and elastic modulus) as well as solids loading. The model incorporates silica particle volume fraction, particle size, particle size distribution, and titania precursor concentration, and captures the effects of all formulation parameters on the measured viscoelasticity in a single curve. We validate the ink formulations predicted by the model and find that the materials can be very well matched in rheological properties as desired for 3D printing. Using the DIW and heat treatment methods we have reported previously, we use these inks to print and process a fully transparent glass with spatial change in dopant composition and refractive index. Finally, we believe that this approach can be extended to other colloidal systems and allow predictive ink formulation design for desired printability in direct ink write manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A robust dynamic state estimation approach against model errors caused by load changes

Dynamic state estimation (DSE) plays an important role in power system security monitoring and online control. In practice, there are two approaches to implementing DSE. The first approach is distributed DSE, which is based on the assumption that the terminal bus of each generator can be measured by PMUs (phasor measurement units). The assumption cannot be satisfied currently, however, because PMUs usually are installed at important high-voltage buses such as 500-kV buses installed in portions of the grid overseen by the Western Electricity Coordinating Council. Another issue of this approach is that performance of DSE is vulnerable to bad measurement data. The reason for this vulnerability is that DSE is performed separately through measurements at each terminal bus, and measurements at terminal buses are the only measurement upon which DSE can rely. Therefore, important redundant measurements are not included in this approach. The second approach is centralized DSE. This approach does not have the requirement for PMU location, and redundant measurements can be considered fully. However, load changes and grid topology changes impact centralized DSE. In this paper, we propose a new approach for handling the impact of load changes on DSE. We have developed a new algorithm that includes two sequential steps. In the first step, errors caused by load changes are detected by analyzing the difference between prediction results and measured results. In the second step, once model error is detected, a model optimization procedure is run to correct the error so the state estimation error can be mitigated. Simulation results from the IEEE 68 bus system show that the proposed approach can effectively handle model errors caused by load changes.

robust dynamic state estimation, load change, powe↗
DOE OSTI2013

BINARY FREQUENCIES IN A SAMPLE OF GLOBULAR CLUSTERS. I. METHODOLOGY AND INITIAL RESULTS

Binary stars are thought to be a controlling factor in globular cluster evolution, since they can heat the environmental stars by converting their binding energy to kinetic energy during dynamical interactions. Through such interaction, the binaries determine the time until core collapse. To test predictions of this model, we have determined binary fractions for 35 clusters. Here we present our methodology with a representative globular cluster NGC 4590. We use Hubble Space Telescope archival Advanced Camera for Surveys data in the F606W and F814W bands and apply point-spread-function-fitting photometry to obtain high quality color-magnitude diagrams. We formulate the star superposition effect as a Poisson probability distribution function, with parameters optimized through Monte Carlo simulations. A model-independent binary fraction of (6.2 {+-} 0.3)% is obtained by counting stars that extend to the red side of the residual color distribution after accounting for the photometric errors and the star superposition effect. A model-dependent binary fraction is obtained by constructing models with a known binary fraction and an assumed binary mass-ratio distribution function. This leads to a binary fraction range of 6.8%-10.8%, depending on the assumed shape to the binary mass-ratio distribution, with the best fit occurring for a binary distribution that favors low mass ratios (and higher binary fractions). We also represent the method for radial analysis of the binary fraction in the representative case of NGC 6981, which shows a decreasing trend for the binary fraction toward the outside, consistent with theoretical predictions for the dynamical effect on the binary fraction.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Understanding the Role of Extrinsic Impurities in Bulk Nb SRF Cavities

High gradient and high quality factor superconducting radiofrequency (SRF) cavities are essential for efficient particle acceleration in next-generation accelerators. This work demonstrates that the superconducting performance of bulk niobium can be enhanced through a controlled introduction of interstitial oxygen and nitrogen. Thermal and chemical surface treatments modify the concentration and distribution of these impurities, enabling systematic tuning of SRF cavity performance. By combining cavity RF measurements with quantitative materials characterizations, we isolate the impact of impurity concentration on BCS surface resistance and identify the mechanisms by which interstitial impurities alter the superconducting properties of niobium. We find that nitrogen is an order of magnitude more effective than oxygen in reducing BCS resistance at 16 MV/m and that the reduction arises from a combination of mean free path optimization and superconducting gap enhancement. Using these insights, we develop a predictive model that estimates cavity performance directly from room temperature sample material studies, reducing the reliance on full-scale cryogenic tests for surface treatment development. We then apply this model to explore co-doping strategies, demonstrating that combining interstitial oxygen and nitrogen achieves a synergistic reduction in BCS resistance. These results establish co-doping as a viable path for the development of surface-engineered niobium for future high quality factor SRF applications.

Hu, Hannah [Chicago U.]↗
DOE OSTI2020

New vortex‐lift and tangential‐force models for HAWT aerodynamic load prediction

Abstract Horizontal axis wind turbines (HAWTs) experience three‐dimensional rotational and unsteady aerodynamic phenomena at the rotor blades sections. These highly unsteady three‐dimensional effects have a dramatic impact on the aerodynamic load distributions on the blades, in particular, when they occur at high angles of attack due to stall delay and dynamic stall. Unfortunately, there is no complete understanding of the flow physics yet at these unsteady 3D flow conditions, and hence, the existing published theoretical models are often incapable of modelling the impact on the turbine response realistically. The purpose of this paper is to provide an insight on the combined influence of the stall delay and dynamic stall on the blade load history of wind turbines in controlled and uncontrolled conditions. New dynamic stall vortex and nonlinear tangential force coefficient modules, which integrally take into account the three dimensional rotational effect, are also proposed in this paper. This module along with the unsteady influence of turbulent wind speed and tower shadow is implemented in a blade element momentum (BEM) model to estimate the aerodynamic loads on a rotating blade more accurately. This work presents an important step to help modelling the combined influence of the stall delay and dynamic stall on the load history of the rotating wind turbine blades which is vital to have lighter turbine blades and improved wind turbine design systems.

Elgammi, Moutaz↗
DOE OSTI2020

Deep neural network improves the estimation of polygenic risk scores for breast cancer

Polygenic risk scores (PRS) estimate the genetic risk of an individual for a complex disease based on many genetic variants across the whole genome. Here, we compared a series of computational models for estimation of breast cancer PRS. A deep neural network (DNN) was found to outperform alternative machine learning techniques and established statistical algorithms, including BLUP, BayesA, and LDpred. In the test cohort with 50% prevalence, the Area Under the receiver operating characteristic Curve (AUC) were 67.4% for DNN, 64.2% for BLUP, 64.5% for BayesA, and 62.4% for LDpred. BLUP, BayesA, and LPpred all generated PRS that followed a normal distribution in the case population. However, the PRS generated by DNN in the case population followed a bimodal distribution composed of two normal distributions with distinctly different means. This suggests that DNN was able to separate the case population into a high-genetic-risk case subpopulation with an average PRS significantly higher than the control population and a normal-genetic-risk case subpopulation with an average PRS similar to the control population. This allowed DNN to achieve 18.8% recall at 90% precision in the test cohort with 50% prevalence, which can be extrapolated to 65.4% recall at 20% precision in a general population with 12% prevalence. Interpretation of the DNN model identified salient variants that were assigned insignificant p values by association studies, but were important for DNN prediction. These variants may be associated with the phenotype through nonlinear relationships.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Virtual Design Studio (VDS) - Development of an Integrated Computer Simulation Environment for Performance Based Design of Very-Low Energy and High IEQ Buildings

Executive Summary The objective of this study was to develop a “Virtual Design Studio (VDS)”: a software platform for integrated, coordinated and optimized design of green building systems with low energy consumption, high indoor environmental quality (IEQ), and high level of sustainability. This VDS is intended to assist collaborating architects, engineers and project management team members throughout from the early phases to the detailed building design stages. It can be used to plan design tasks and workflow, and evaluate the potential impacts of various green building strategies on the building performance by using the state of the art simulation tools as well as industrial/professional standards and guidelines for green building system design. Engaged in the development of VDS was a multi-disciplinary research team that included architects, engineers, and software developers. Based on the review and analysis of how existing professional practices in building systems design operate, particularly those used in the U.S., Germany and UK, a generic process for performance-based building design, construction and operation was proposed. It distinguishes the whole process into five distinct stages: Assess, Define, Design, Apply, and Monitoring (ADDAM). The current VDS is focused on the first three stages. The VDS considers building design as a multi-dimensional process, involving multiple design teams, design factors, and design stages. The intersection among these three dimensions defines a specific design task in terms of “who”, “what” and “when”. It also considers building design as a multi-objective process that aims to enhance the five aspects of performance for green building systems: site sustainability, materials and resource efficiency, water utilization efficiency, energy efficiency and impacts to the atmospheric environment, and IEQ. The current VDS development has been limited to energy efficiency and IEQ performance, with particular focus on evaluating thermal performance, air quality and lighting environmental quality because of their strong interaction with the energy performance of buildings. The VDS software framework contains four major functions: 1) Design coordination: It enables users to define tasks using the Input-Process-Output flow approach, which specifies the anticipated activities (i.e., the process), required input and output information, and anticipated interactions with other tasks. It also allows task scheduling to define the work flow, and sharing of the design data and information via the internet. 2) Modeling and simulation: It enables users to perform building simulations to predict the energy consumption and IEQ conditions at any of the design stages by using EnergyPlus and a combined heat, air, moisture and pollutant simulation (CHAMPS) model. A method for co-simulation was developed to allow the use of both models at the same time step for the combined energy and indoor air quality analysis. 3) Results visualization: It enables users to display a 3-D geometric design of the building by reading BIM (building information model) file generated by design software such as SketchUp, and the predicted results of heat, air, moisture, pollutant and light distributions in the building. 4) Performance evaluation: It enables the users to compare the performance of a proposed building design against a reference building that is defined for the same type of buildings under the same climate condition, and predicts the percent of improvements over the minimum requirements specified in ASHRAE Standard 55-2010, 62.1-2010 and 90.1-2010. An approach was developed to estimate the potential impact of a design factor on the whole building performance, and hence can assist the user to identify areas that have most pay back for investment. The VDS software was developed by using C++ with the conventional Model, View and Control (MVC) software architecture. The software has been verified by using a simple 3-zone case building. The application of the VDS concepts and framework for building design and performance analysis has been illustrated by using a medium-sized, five story office building that received LEED Platinum Certification from USGBC.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Global particle buildup simulations with gas puff scan: application to WEST discharge

This paper deals with the distribution of sources, transport, and exhaust of particles in a tokamak. Knowledge and understanding of all the physical phenomena involved in the global particle buildup are necessary to study and predict density regimes and subsequently to develop optimized scenarios for tokamak operation in order to control heat and particle exhaust. Neutral particles and their interactions with plasma are central in this perspective. This paper discusses the impact of varying the intensity of particle fueling in 2D transport simulations of a WEST discharge. Simulations are performed with an updated version of SOLEDGE-HDG that allows a more realistic transport of neutrals using a self-consistent diffusive model based on charge exchange and ionization processes. New code capabilities allow the entire WEST poloidal cross section to be simulated in a realistic configuration for both geometry and the range of control parameters. A gas puff scan illustrates the main features of the sheath-limited, high-recycling, and detached regimes, such as the buildup of the temperature gradient and the pressure drop in the scrape-off layer (SOL), the target temperature falling to 1 eV, and the ionization source moving away from the targets, as well as the particle flux rollover. A crude estimate of wall erosion is also provided, showing the respective role of each plasma wall component in each of these regimes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Residual Stress Distribution in a Hydroformed Advanced High Strength Steel Component: Neutron Diffraction Measurements and Finite Element Simulations

Today’s automotive industry is witnessing increasing applications of advanced high strength steels (AHSS) combined with innovative manufacturing techniques to satisfy fuel economy requirements of stringent environmental regulations. The integration of AHSS in novel automotive structure design has introduced huge advantages in mass reduction while maintaining their structural performances, yet several concerns have been raised for this relatively new family of steels. One of those concerns is their potentially high springback after forming, which can lead to geometrical deviation of the final product from its designed geometry and cause difficulties during assembly. From the perspective of accurate prediction, control and compensation of springback, further understanding on the effect of residual stress in AHSS parts is urged. In this work, the residual stress distribution in a 980GEN3 steel part after hydroforming is investigated via experimental and numerical approaches. A non-destructive neutron diffraction technique is adopted to reveal the residual stress profiles across a 94-degree tube bending radius section of the hydroformed part. The finite element analysis (FEA) is also conducted to simulate the hydroforming and the subsequent unloading processes to predict the residual stress distribution within the same region. The correlation between experimental and simulation results is presented and the effectiveness of the FEA model for residual stress prediction is discussed. Findings in this study set the basis for springback analysis with improved accuracy and reliability.

Huang, Lu↗
DOE OSTI2023

Machine learning based simultaneous control of air handling unit discharge air and condenser water temperatures set-point for minimized cooling energy in an office building

In this study, an artificial intelligence based real-time prediction and control model to optimize condenser water temperature and discharge air temperature (DAT) set-points in water-cooled air handling unit (AHU) system has been developed. EnergyPlus-MATLAB co-simulation has been conducted to analyze the developed model's effectiveness. Here, to develop artificial neural networks (ANN) model, embedded neural network objects in MATLAB was utilized. The developed model could decide an optimal temperature set-points based on outdoor air wet-bulb temperature to reflect the Korean climate context. As a result, the developed ANN prediction model showed the predictive performance of Cv(RMSE) of approximately 21%. Compared to the conventional fixed temperature algorithm, which fixes AHU DAT at 14°C and condenser water temperature at 32°C, the ANN based optimized control showed a 22% total cooling energy reduction. These results show that significant energy savings can be achieved by simultaneously controlling condenser water temperature and AHU DAT set-points considering Korean climatic characteristics using AI technologies such as ANN models.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

On the Low Risk of Subsynchronous Resonance (SSR) in Type III Wind Turbines Operating in Grid-Forming Control

We have shown in a previous work that the risk of sub-synchronous resonance (SSR) between a wind power plant with Type III wind turbines and series-compensated transmission lines is low when the wind turbines in the plant are operated in grid-forming mode, instead of the standard grid-following mode. The fundamental mechanism behind the improved damping characteristics is explained in this paper by modeling the positive and negative sequence impedances of Type III wind turbines for GFM operation mode. It is discovered that the GFM control naturally acts against the negative resistance of Type III wind turbines that results from an interaction between the proportional gain of the rotor-side converter current controller and negative slip. The developed sequence impedance models and improved damping behavior are verified using PSCAD simulations of a 2.5-MW Type III GFM wind turbine. The modeling predictions are also supported by experimentally measuring the sequence impedance response of a 2.5-MW Type III wind turbine during operation in GFL and GFM modes.

grid-following turbine↗
DOE OSTI2015

LOW-TEMPERATURE SPECTROSCOPY OF THE {sup 12}C{sub 2}H{sub 2} (υ{sub 1} + υ{sub 3}) BAND IN A HELIUM BUFFER GAS

Buffer gas cooling with a {sup 4}He gas is used to perform laser-absorption spectroscopy of the {sup 12}C{sub 2}H{sub 2} (υ{sub 1} + υ{sub 3}) band at cryogenic temperatures. Doppler thermometry is first carried out to extract translational temperatures from the recorded spectra. Then, rotational temperatures down to 20 K are retrieved by fitting the Boltzmann distribution to the relative intensities of several ro-vibrational lines. The potential of our setup to tune the thermal equilibrium between translational and rotational degrees of freedom is also demonstrated. This can be used to reproduce in a controlled way the regime of non-local thermal equilibrium typically encountered in the interstellar medium. The underlying helium-acetylene collisional physics, relevant for modeling planetary atmospheres, is also addressed. In particular, the diffusion time of {sup 12}C{sub 2}H{sub 2} in the buffer cell is measured against the {sup 4}He flux at two separate translational temperatures; the observed behavior is then compared with that predicted by a Monte Carlo simulation, thus providing an estimate for the respective total elastic cross sections: σ{sub el}(100 K) = (4 ± 1) × 10{sup –20} m{sup 2} and σ{sub el}(25 K) = (7 ± 2) × 10{sup –20} m{sup 2}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

CFD Simulations of a Regenerative Process for Carbon Dioxide Capture in Advanced Gasification Based Power Systems

This project describes the work carried out to prepare a highly reactive and mechanically strong MgO based sorbents and to develop a Population Balance Equations (PBE) approach to describe the evolution of the particle porosity distribution that is linked with Computational Fluid Dynamics (CFD) to perform simulations of the CO2 capture and sorbent regeneration. A large number of MgO-based regenerable sorbents were prepared using low cost and abundant dolomite as the base material. Among various preparation parameters investigated the potassium/magnesium (K/Mg) ratio was identified as the key variable affecting the reactivity and CO2 capacity of the sorbent. The optimum K/Mg ratio is about 0.15. The sorbent formulation HD52-P2 was identified as the “best” sorbent formulation and a large batch (one kg) of the sorbent was prepared for the detailed study. The results of parametric study indicate the optimum carbonation and regeneration temperatures are 360° and 500°C, respectively. The results also indicate that steam has a beneficial effect on the rate of carbonation and regeneration of the sorbent and that the reactivity and capacity of the sorbent decreases in the cycling process (sorbent deactivation). The results indicate that to achieve a high CO2 removal efficiency, the bed of sorbent should be operated at a temperature range of 370-410°C which also favors production of hydrogen through the WGS reaction. To describe the carbonation reaction kinetics of the MgO, the Variable Diffusivity shrinking core Model (VDM) was developed in this project, which was shown to accurately fit the experimental data. An important advantage of this model is that the changes in the sorbent conversion with time can be expressed in an explicit manner, which will significantly reduce the CFD computation time. A Computational Fluid Dynamic/Population Balance Equations (CFD/PBE) model was developed that accounts for the particle (sorbent) porosity distribution and a new version of the method of moments, called Finite size domain Complete set of trial functions Method Of Moments (FCMOM) was used to solve the population balance equations. The PBE model was implemented in a commercial CFD code, Ansys Fluent 13.0. The code was used to test the model in some simple cases and the results were verified against available analytical solution in the literature. Furthermore, the code was used to simulate CO2 capture in a packed-bed and the results were in excellent agreement with the experimental data obtained in the packed bed. The National Energy Laboratory (NETL) Carbon Capture Unit (C2U) design was used in simulate of the hydrodynamics of the cold flow gas/solid system (Clark et al.58). The results indicate that the pressure drop predicted by the model is in good agreement with the experimental data. Furthermore, the model was shown to be able to predict chugging behavior, which was observed during the experiment. The model was used as a base-case for simulations of reactive flow at elevated pressure and temperatures. The results indicate that by controlling the solid circulation rate, up to 70% CO2 removal can be achieved and that the solid hold up in the riser is one of the main factors controlling the extent of CO2 removal. The CFD/PBE simulation model indicates that by using a simulated syngas with a composition of 20% CO2, 20% H2O, 30% CO, and 30% H2, the composition (wet basis) in the reactor outlet corresponded to about 60% CO2 capture with and exit gas containing 65% H2. A preliminary base-case-design was developed for a regenerative MgO-based pre-combustion carbon capture process for a 500 MW IGCC power plant. To minimize the external energy requirement, an extensive heat integration network was developed in Aspen/HYSYS® to produce the steam required in the regenerator and heat integration. In this process, liquid CO2 produced at 50 atm can easily be pumped and sequestered or stored. The preliminary economic analyses indicate that the estimated cost of carbon v capture is in the range of $31-$44/ton, suggesting that a regenerative MgO-Based process can be a viable option for pre-combustion carbon dioxide capture in advanced gasification based power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Probing light quark Yukawa couplings through angularity distributions in Higgs boson decay

We propose to utilize angularity distributions in Higgs boson decay to probe light quark Yukawa couplings at e + e - colliders. Angularities $τ$ a are a class of 2-jet event shapes with variable and tunable sensitivity to the distribution of radiation in hadronic jets in the final state. Using soft-collinear effective theory (SCET), we present a prediction of angularity distributions from Higgs decaying to quark and gluon states at e + e - colliders to NNLL + $\mathcal{O}$(α s ) accuracy. Due to the different color structures in quark and gluon jets, the angularity distributions from H → $q\overline{q}$ and H → gg show different behaviors and can be used to constrain the light quark Yukawa couplings. We show that the upper limit of light quark Yukawa couplings could be probed to the level of ~ 15% of the bottom quark Yukawa coupling in the Standard Model in a conservative analysis window far away from nonperturbative effects and other uncertainties; the limit can be pushed to ≲ 7 – 9% with better control of the nonperturbative effects especially on gluon angularity distributions and/or with multiple angularities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

A New Efficient Quantitative Multi-component Phase Field: Lattice Boltzmann Model for Simulating Ti6Al4V Solidified Dendrite Under Forced Flow

Ti6Al4V is a widely used, multi-component alloy in additive manufacturing, during which the fluid flow in the molten pool significantly affects the solidified dendrites. To predict and further control the microstructure, modeling and simulating the microstructure evolution play a critical role. In this study, a newly developed, efficient, quantitative multi-component phase-field (PF) model is coupled with a lattice Boltzmann (LB) model to simulate Ti6Al4V solidified dendrite evolution under fluid flow. The accuracy and convergence behavior of the model is validated by the Gibbs–Thomson relation at the dendrite tip. Single and multiple two-dimensional (2D) equiaxed dendrite evolution cases under forced flow were simulated. Results show that the dendrite pattern is influenced remarkably by the fluid flow. Underlying mechanisms of the asymmetrical evolution are revealed by discussing the interaction among the flow, composition distribution and dendrite morphology, quantitatively. The dendrite kinetics are also derived, which ascertains the relationship between tip velocity and undercooling and inlet velocity and is the foundation for larger-scale simulation. We believe that the coupled quantitative multi-component PF–LB framework employed in this study helps in investigating the solidified dendrite morphology evolution in a deep and quantitate manner.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Modeling the Size Distribution and Chemical Composition of Secondary Organic Aerosols during the Reactive Uptake of Isoprene-Derived Epoxydiols under Low-Humidity Condition

Reactive uptake of isoprene epoxydiols (IEPOX), which are isoprene oxidation products, onto acidic sulfate aerosols is recognized to be an important mechanism for the formation of isoprene-derived secondary organic aerosol (SOA). While a mechanistic understanding of IEPOX-SOA formation exists, several processes affecting their formation remain uncertain. Evaluating mechanistic IEPOX-SOA models with controlled laboratory experiments under longer atmospherically relevant timescales is critical. Here, we implement our latest understanding of IEPOX-SOA formation within a box model to simulate the measured reactive uptake of IEPOX on polydisperse ammonium bisulfate seed aerosols within an environmental Teflon chamber. The model is evaluated with single-particle measurements of size distribution, volume, density, and composition of aerosols due to IEPOX-SOA formation at timescales of hours. Additionally, we find that the model can simulate the growth of particles due to IEPOX multiphase chemistry, as reflected in increases of the mean particle size and volume concentrations, and a shift of number size distribution to larger sizes. The model also predicts the observed evolution of particle number mean diameter and total volume concentrations at the end of the experiment. We show that in addition to self-limiting effects of IEPOX-SOA coatings, accounting for the molar balance between inorganic and organic sulfate and mass accommodation coefficient of IEPOX are important parameters governing modeling of IEPOX-SOA formation. Thus, models which do not account for the molar sulfate balance and/or diffusion limitations within IEPOX-SOA coatings are likely to predict too high IEPOX-SOA formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Spatial Trends and Drivers of Bedload and Suspended Sediment Fluxes in Global Rivers

Abstract Bedload is notoriously challenging to measure and model; its dynamics, therefore, remains largely unknown in most fluvial systems worldwide. We present results from a global scale bedload flux model as part of the WBMsed modeling framework that well predict the distribution of water discharge, suspended sediment and bedload. The sensitivity of bedload predictions to river slope, particle size, discharge, river width, and suspended sediment were analyzed, showing the model to be most responsive to spatial dynamics in river discharge and slope. The relationship between bedload and total sediment flux is analyzed globally, and for representative longitudinal river profiles (Amazon, Mississippi, and Lena Rivers). The results show that while the proportion of bedload decreases from headwaters to the coasts, there is considerable variability between basins and along river corridors. The topographic and hydrological longitudinal profiles of rivers are shown to be the key drivers of bedload trends, with fluctuations in slope controlling its more local dynamics. Estimates of water and sediment fluxes to global oceans from 2,067 largest river outlets (draining 67% of the global continental area) are provided. Estimated water discharge at 30,579 km 3 /y corresponds well to past estimates; however, sediment flux is higher. Total global particulate load of 17.8 Gt/y is delivered to global oceans, 14.8 Gt/y as washload, 1.1 Gt/y as bedload, and 2.6 Gt/y as suspended bed material. The largest 25 rivers are predicted to transport more than half of the total sediment flux to global oceans.

Cohen, Sagy↗