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At least 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Validation of the AltRAM Physics Models for use with Compressed Natural Gas

The Alternative Fuels Risk Assessment Models (A1tRAM) toolkit combines Quantitative Risk Assessment (QRA) with simulations of unignited dispersion, ignited turbulent diffusion flames, and indoor accumulation with delayed ignition of fuels. The models of the physical phenomena need to be validated for each of the fuels in the toolkit. This report shows the validation for methane which is being used as a surrogate for natural gas. For the unignited dispersion model, seven previously published experiments from credible sources were used to validate. The validation looked at gas concentrations with respect to the distance from the release point. Four of these were underexpanded jets (i.e. release velocity equal to or greater than local speed of sound) and the other three subsonic releases. The methane plume model in AltRAM matched both varieties well, with higher accuracy for the underexpanded releases. For the jet flame model, we compared the heat flux and thermal radiation data reported from five separate turbulent jet flame experiments to the quantities calculated by A1tRAM. Four of the five datasets were for underexpanded diffusion jets flames. While the results still match well enough to give a good estimate of what is occurring, the error is higher than what was seen with the plume model. For the underexpanded flames A1tRAM provided reasonable approximations, which would lead to conservative risk assessments. Some modeling errors can be attributed to environmental effects (i.e. wind) since most large scale flame experiments are conducted outdoors. A1tRAM has been shown to be a reasonably accurate tool for calculating the concentration or flame properties of natural gas releases. Improvements could still be made for the plume of subsonic releases and radiative heat fluxes to reduce the conservative nature of these predictions. These models can provide valuable information for the risk assessment of natural gas infrastructure.

03 NATURAL GAS↗
DOE OSTI2021

Soil enzymes as indicators of soil function: A step toward greater realism in microbial ecological modeling

Soil carbon (C) and nitrogen (N) cycles and their complex responses to environmental changes have received increasing attention. However, large uncertainties in model predictions remain, partially due to the lack of explicit representation and parameterization of microbial processes. One great challenge is to effectively integrate rich microbial functional traits into ecosystem modeling for better predictions. Here, using soil enzymes as indicators of soil function, we developed a competitive dynamic enzyme allocation scheme and detailed enzyme-mediated soil inorganic N processes in the Microbial-ENzyme Decomposition (MEND) model. We conducted a rigorous calibration and validation of MEND with diverse soil C-N fluxes, microbial C:N ratios, and functional gene abundances from a 12-year CO 2 × N grassland experiment (BioCON) in Minnesota, USA. In addition to accurately simulating soil CO 2 fluxes and multiple N variables, the model correctly predicted microbial C:N ratios and their negative response to enriched N supply. Model validation further showed that, compared to the changes in simulated enzyme concentrations and decomposition rates, the changes in simulated activities of eight C-N-associated enzymes were better explained by the measured gene abundances in responses to elevated atmospheric CO 2 concentration. In conclusion, our results demonstrated that using enzymes as indicators of soil function and validating model predictions with functional gene abundances in ecosystem modeling can provide a basis for testing hypotheses about microbially mediated biogeochemical processes in response to environmental changes. Further development and applications of the modeling framework presented here will enable microbial ecologists to address ecosystem-level questions beyond empirical observations, toward more predictive understanding, an ultimate goal of microbial ecology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Validating Connected, Automated, and Electric Vehicle Models and Simulation - Research Performance Progress Report

The objective of this project is to test connected and automated vehicles with both electrified and internal combustion engine powertrains to support updates and validation of modeling and simulation tools. This includes the development of the components and network architecture to execute and collect empirical data for multiple scenarios and traffic interactions. Specific program objectives include: • Translate Lab algorithms into vehicle and infrastructure controls • Conduct physical testing at realistic scale • Evaluate system performance • Improve models using empirical data • Improve control algorithms from lessons learned • Identify system and algorithm assumptions which need refinement It is important to note that the objective of this project was not to demonstrate the efficacy of the selected algorithms to improve energy efficiency but rather to validate and improve modeling and simulation tools using empirical data. While it is a desirable outcome to concurrently demonstrate improved energy efficiency through use of these algorithms, and in most cases that was the outcome, the success of this project was not predicated on the performance of the algorithm towards improving energy efficiency across all scenarios and test matrices.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Interface-Driven Plasticity in Metal–Ceramic Nanolayered Composites: Direct Validation of Multiscale Deformation Modeling via In Situ Indentation in TEM

Here, we present in situ indentation in a transmission electron microscope on Al-TiN multilayers with individual layer thicknesses of 50 nm and 2.7 nm to explore the effect of length scales on the plastic co-deformability of a metal and a ceramic. At 50 nm, plasticity was confined to the Al layers with brittle fracture in the TiN layers. At 5 nm and below, cracking in TiN was suppressed with co-deformation evident in both layers. The in situ transmission electron microscopy (TEM) straining results demonstrate a profound size effect in enhancing plastic co-deformability in nanoscale metal-ceramic multilayers, as well as direct validation of ex situ and 3-D elastic–plastic deformation models.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multi-Omics Driven Metabolic Network Reconstruction and Analysis of Lignocellulosic Carbon Utilization in Rhodosporidium toruloides

An oleaginous yeast Rhodosporidium toruloides is a promising host for converting lignocellulosic biomass to bioproducts and biofuels. In this work, we performed multi-omics analysis of lignocellulosic carbon utilization in R. toruloides and reconstructed the genome-scale metabolic network of R. toruloides . High-quality metabolic network models for model organisms and orthologous protein mapping were used to build a draft metabolic network reconstruction. The reconstruction was manually curated to build a metabolic model using functional annotation and multi-omics data including transcriptomics, proteomics, metabolomics, and RB-TDNA sequencing. The multi-omics data and metabolic model were used to investigate R. toruloides metabolism including lipid accumulation and lignocellulosic carbon utilization. The developed metabolic model was validated against high-throughput growth phenotyping and gene fitness data, and further refined to resolve the inconsistencies between prediction and data. We believe that this is the most complete and accurate metabolic network model available for R. toruloides to date.

09 BIOMASS FUELS↗
DOE OSTI2021

Validation of theory-based models for the control of plasma currents in W7-X divertor plasmas

A theory-based model for the control of plasma currents for steady-state operation in W7-X is proposed and intended for model-based plasma control. The conceptual outline implies the strength of physics-based models: it offer approaches applicable to future conditions of fusion devices or next-step machines. The application at extrapolated settings is related to the validity range of the theory model. Therefore, the predictive power of theory-based control models could be larger than for data-driven approaches and limitations can be predicted from the validity range for the prediction of bootstrap currents in W7-X. The model predicts the L/R response when density or heating power is changed. The model is based on neoclassical bootstrap current calculations and validated for different discharge conditions. While the model was found to be broadly applicable for conducted electron-cyclotron-heated discharges in W7-X, limits were found for cases when the polarization of the electron cyclotron heating was changed from X2 to O2-heating. The validity assessment attempts to quantify the potential of the derived model for model-based control in the operational space (density, heating power) of W7-X.

neoclassical modelling↗
DOE OSTI2017

Reactive transport in the complex heterogeneous alluvial aquifer of Fortymile Wash, Nevada

Yucca Mountain, Nevada, had been extensively investigated as a potential deep geologic repository for storing high-level nuclear wastes. Previous field investigations of stratified alluvial aquifer downstream of the site revealed that there is a hierarchy of sedimentary facies types. There is a corresponding log conductivity and reactive surface area subpopulations within each facies at each scale of sedimentary architecture. Here in this paper, we use a Lagrangian-based transport model in order to analyze radionuclide dispersion in the saturated alluvium of Fortymile Wash, Nevada. First, we validate the Lagrangian model using high-resolution flow and reactive transport simulations. Then, we used the validated model to investigate how each scale of sedimentary architecture may affect long-term radionuclide transport at Yucca Mountain. Results show that the reactive solute dispersion developed by the Lagrangian model matches the ensemble average of numerical simulations well. The link between the alluvium spatial variability and reactive solute dispersion at different spatiotemporal scales is demonstrated using the Lagrangian model. Finally, the longitudinal dispersivity of the reactive plume can be on the order of hundreds to thousands of meters, and it may not reach its asymptotic value even after 10,000 years of travel time and 2–3 km of travel distance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Modeling, analysis, and code/data validation of DIII-D tokamak divertor experiments on ELM and non-ELM plasma tungsten sputtering erosion

Abstract We analyzed recent DIII-D tokamak tungsten divertor probe experiments using advanced, coupled, sputter erosion/redeposition, plasma, and surface response code packages. Modeling is done for ELMing H-mode, and L-mode plasmas, impinging on various size tungsten deposits on Divertor Material Evaluation System (DiMES) carbon probes. The simulations compute 3D, full kinetic, sub-gyromotion, impurity sputtering and transport, including changes in tungsten surface composition and response due to mixed deuterium and carbon ions irradiation. Per our analysis, ELM (edge localized mode) plasma sputtering in DIII-D mostly involves free-streaming high energy (∼500–1000 eV) D + and C +6 ions, with high near-surface plasma density. L-Mode sputtering is due to impurity sputtering (C, W) only, with lower density. All cases show complete redeposition of tungsten on the divertor, with significant redeposition on the tungsten spots themselves, and low self-sputtering. Comparison of ELM plasma gross tungsten erosion simulation results with in-situ spectroscopic data is good, as are code/data comparisons of net erosion using post-exposure Rutherford backscattering (RBS) data for the L-mode probes. The analysis, extrapolated to a full tungsten divertor, implies low net erosion and negligible plasma contamination from sputtering. These results support the use of high-Z plasma facing surfaces in ITER and beyond.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Utilizing 3D Mechanical Earth Models for Calibration and Validation in a Large-Scale Carbon Capture and Storage Project in North Dakota

Conference paper presented at 17th International Conference on Greenhouse Gas Control Technologies (GHGT-17), Calgary, Alberta, Canada, October 20–24, 2024. Three-dimensional (3D) mechanical earth models (MEMs) are pivotal in assessing geological sites for carbon dioxide (CO 2 ) storage and mitigating potential risks associated with storage and injection. The Energy & Environmental Research Center is exploring innovative carbon storage monitoring techniques at a CO 2 storage site near Beulah, North Dakota. These methods aim to provide lower-impact, faster feedback for commercial carbon capture and storage (CCS) projects. The research includes monitoring CO 2 injection using various approaches: 1) an automated, integrated modular monitoring station; 2) time-lapse electromagnetic (EM) field surveys; 3) drone-based surveillance; 4) time-lapse seismic techniques; and 5) advanced wellbore monitoring.

02 PETROLEUM↗
DOE OSTI2012

Rigorous Kinetic Modeling and Optimization Study of a Modified Claus Unit for an Integrated Gasification Combined Cycle (IGCC) Power Plant with CO{sub 2} Capture

The modified Claus process is one of the most common technologies for sulfur recovery from acid gas streams. Important design criteria for the Claus unit, when part of an Integrated Gasification Combined Cycle (IGCC) power plant, are the ability to destroy ammonia completely and the ability to recover sulfur thoroughly from a relatively low purity acid gas stream without sacrificing flame stability. Because of these criteria, modifications to the conventional process are often required, resulting in a modified Claus process. For the studies discussed here, these modifications include the use of a 95% pure oxygen stream as the oxidant, a split flow configuration, and the preheating of the feeds with the intermediate pressure steam generated in the waste heat boiler (WHB). In the future, for IGCC plants with CO{sub 2} capture, the Claus unit must satisfy emission standards without sacrificing the plant efficiency in the face of typical disturbances of an IGCC plant, such as rapid change in the feed flow rates due to load-following and wide changes in the feed composition because of changes in the coal feed to the gasifier. The Claus unit should be adequately designed and efficiently operated to satisfy these objectives. Even though the Claus process has been commercialized for decades, most papers concerned with the modeling of the Claus process treat the key reactions as equilibrium reactions. Such models are validated by manipulating the temperature approach to equilibrium for a set of steady-state operating data, but they are of limited use for dynamic studies. One of the objectives of this study is to develop a model that can be used for dynamic studies. In a Claus process, especially in the furnace and the WHB, many reactions may take place. In this work, a set of linearly independent reactions has been identified, and kinetic models of the furnace flame and anoxic zones, WHB, and catalytic reactors have been developed. To facilitate the modeling of the Claus furnace, a four-stage method was devised so as to determine which set of linearly independent reactions would best describe the product distributions from available plant data. Various approaches are taken to derive the kinetic rate expressions, which are either missing in the open literature or found to be inconsistent. A set of plant data is used for optimal estimation of the kinetic parameters. The final model agrees well with the published plant data. Using the developed kinetics models of the Claus reaction furnace, WHB, and catalytic stages, two optimization studies are carried out. The first study shows that there exists an optimal steam pressure generated in the WHB that balances hydrogen yield, oxygen demand, and power generation. In the second study, it is shown that an optimal H{sub 2}S/SO{sub 2} ratio exists that balances single-pass conversion, hydrogen yield, oxygen demand, and power generation. In addition, an operability study has been carried out to examine the operating envelope in which both the H{sub 2}S/SO{sub 2} ratio and the adiabatic flame temperature can be controlled in the face of disturbances typical for the operation of an IGCC power plant with CO{sub 2} capture. Impact of CO{sub 2} capture on the Claus process has also been discussed.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

Development of Gamma Background Radiation Digital Twin with Machine Learning Algorithms: Application of Unsupervised Machine Learning to Detection of Anomalies and Nuisances in Gamma Background Radiation Environmental Screening Data

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for development of a digital twin of gamma radiation background, and for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. In one segment of work, we developed a gamma background estimation model using a Longshort term memory (LSTM) network for one-step CPS time series prediction. The LSTM model was validated with two data sets of measurements from two independent NaI detectors positioned on a mobile platform. The data sets contained background radiation only and no orphan isotope sources. The LSTM model was constructed and tested using data from one of the detectors. Performance of the LSTM model was validate through one-step prediction of CPS time series of another NaI detector without re-training. This approach allows to create a digital twin for nuclear background estimation. Using LSTM, it could be possible to detect a source through subtraction of the estimated counts from the measured background. In another segment of work, we investigated detection of gamma emitting sources in the presence of complex background using unsupervised machine learning. Spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Integration of renewable energy generation and storage systems for emissions reduction in an islanded campus microgrid

Microgrid connected building communities are projected to play an integral part in the clean energy transition. These types of systems, when integrated with distributed energy resources (DERs) such as combined heat and power (CHP), district heating and cooling, renewable generation, and energy storage, can provide clean, reliable power to critical facilities and vulnerable communities. The intermittent nature of renewable generation is a challenge when integrating renewables into any grid system, but particularly in islanded microgrids. The University of Texas at Austin (UT) operates an islanded microgrid powered by a CHP plant, while also utilizing district heating and cooling systems and thermal energy storage (TES). High fidelity operating data was used to develop a validated reduced order model of UT’s integrated campus energy systems to serve as a testbed for use in a case study. Hypothetical renewable energy installations on land owned by UT in west Texas were modeled and integrated into the validated campus models along with battery energy storage (BES). Simulations showed that a combination of renewable energy from wind, and optimally controlled 24-hour thermal and battery storage systems could reduce carbon dioxide emissions on campus by 45.4%. The additional retrofit of burner systems to utilize hydrogen natural gas blends resulted in an overall annual emissions reduction of 54.7%. Carbon capture and storage eliminated the majority of the remaining emissions with increased plant energy expenditure. The presented simulations display the practical limitations of a CHP system complemented by renewable generation and short-term storage in eliminating emissions. Furthermore, results highlight the need for further research and development in long duration storage technologies and hydrogen fueled turbines to increase penetration of renewable energy and reduce emissions.

CHP↗
DOE OSTI2014

SERIIUS-MAGEEP Visiting Scholars Program

Recent studies have assessed closed-loop supercritical carbon dioxide (s-CO 2 ) Brayton cycles to be a higher energy-density system in comparison to equivalent superheated steam Rankine systems. At turbine inlet conditions of 700°C and 20 MPa, a cycle thermal efficiency of ~50% can be achieved. Achieving these high efficiencies will help concentrating solar power (CSP) technologies to become a competitive alternative to current power generation methods. To incorporate an s-CO 2 Brayton power cycle in a solar power tower system, the development of a solar receiver capable of providing an outlet temperature of 700°C (at 20 MPa) is necessary. To satisfy the temperature requirements of an s-CO 2 Brayton cycle with recuperation and recompression, the s-CO 2 must undergo a temperature rise of ~200°C as it flows through the solar receiver. The main objective is to develop an optical-thermal-fluid and structural model to validate a tubular receiver that will receive a heat input ~0.33 MWth from the heliostat field at the National Solar Thermal Test Facility (NSTTF), Albuquerque, NM, USA. We also commenced the development of computational models and testing of air receivers being developed by the Indian Institute of Science (IISc) and the Indian Institute of Technology in Bombay (IIT-B). The helical tubular receiver is expected to counteract the effect of thermal expansion while using a cavity to reduce the radiative and convective losses. Initially, this receiver will be tested for a temperature range of 100-300°C under 1 MPa of pressurized air. The helical air receiver will be exposed to 10kWth to achieve a temperature rise of ~200°C. Preliminary tests to validate the modeling will be performed before the design and construction of a larger scale receiver. Lastly, I focused on the development of a new computational tool that would allow us to perform a nodal creep-fatigue analysis on the receivers and heat exchangers being developed. This tool was developed using MATLAB and is capable of processing the results obtained from ANSYS Fluent and Structural combined, which was limited when using commercial software. The main advantage of this code is that it can be modified to run in parallel making it more affordable and faster compared to commercial codes available. The code is in the process of validation and is currently being compared to nCode Design Life.

25 ENERGY STORAGE↗
DOE OSTI2017

Predicting the performance uncertainty of a 1-MW pilot-scale carbon capture system after hierarchical laboratory-scale calibration and validation

A challenging problem in designing pilot-scale carbon capture systems is to predict, with uncertainty, the adsorber performance and capture efficiency under various operating conditions where no direct experimental data exist. Motivated by this challenge, we previously proposed a hierarchical framework in which relevant parameters of physical models were sequentially calibrated from different laboratory-scale carbon capture unit (C2U) experiments. Specifically, three models of increasing complexity were identified based on the fundamental physical and chemical processes of the sorbent-based carbon capture technology. Results from the corresponding laboratory experiments were used to statistically calibrate the physical model parameters while quantifying some of their inherent uncertainty. The parameter distributions obtained from laboratory-scale C2U calibration runs are used in this study to facilitate prediction at a larger scale where no corresponding experimental results are available. In this paper, we first describe the multiphase reactive flow model for a sorbent-based 1-MW carbon capture system then analyze results from an ensemble of simulations with the upscaled model. The simulation results are used to quantify uncertainty regarding the design’s predicted efficiency in carbon capture. In particular, we determine the minimum gas flow rate necessary to achieve 90% capture efficiency with 95% confidence.

bubbling bed↗
DOE OSTI2018

A multiscale microstructural approach to ductile-phase toughened tungsten for plasma-facing materials

Increasing fracture toughness and decreasing the ductile-brittle transition temperature of a tungsten-alloy has been shown to be feasible by ductile-phase toughening of tungsten. In this method, a ductile phase is included in a brittle tungsten matrix to prevent fracture propagation by crack bridging or crack deflection. This paper models the deformation behavior of ductile phase toughened tungsten materials such as tungsten-copper composites using a multiscale modeling approach that involves a microstructural dual-phase (copper-tungsten) region of interest where the constituent phases are finely discretized and are described by a continuum damage mechanics model. Large deformation, damage and fracture are allowed to occur and are modeled in this region that is connected to the adjacent homogenized elastic regions to form a macroscopic structure such as a test specimen. This paper illustrates such a multiscale modeling approach to analyze un-notched and single-edge notched (SENB) tungsten-copper composite specimens subjected to three-point bending. The predicted load-displacement responses and crack propagation patterns are compared to the corresponding experimental results to validate the model.

Nguyen, Ba Nghiep↗
DOE OSTI2016

Effects of Hot Streak and Phantom Cooling on Heat Transfer in a Cooled Turbine Stage Including Particulate Deposition

The objective of this research effort was to develop a validated computational modeling capability for the characterization of the effects of hot streaks and particulate deposition on the heat load of modern gas turbines. This was accomplished with a multi-faceted approach including analytical, experimental, and computational components. A 1-year no cost extension request was approved for this effort, so the total duration was 4 years. The research effort succeeded in its ultimate objective by leveraging extensive experimental deposition studies complemented by computational modeling. Experiments were conducted with hot streaks, vane cooling, and combinations of hot streaks with vane cooling. These studies contributed to a significant body of corporate knowledge of deposition, in combination with particle rebound and deposition studies funded by other agencies, to provide suitable conditions for the development of a new model. The model includes the following physical phenomena: elastic deformation, plastic deformation, adhesion, and shear removal. It also incorporates material property sensitivity to temperature and tangential-normal velocity rebound cross-dependencies observed in experiments. The model is well-suited for incorporation in CFD simulations of complex gas turbine flows due to its algebraic (explicit) formulation. This report contains model predictions compared to coefficient of restitution data available in the open literature as well as deposition results from two different high temperature turbine deposition facilities. While the model comparisons with experiments are in many cases promising, several key aspects of particle deposition remain elusive. The simple phenomenological nature of the model allows for parametric dependencies to be evaluated in a straightforward manner. This effort also included the first-ever full turbine stage deposition model published in the open literature. The simulations included hot streaks and simulated vane cooling. The new deposition model was implemented into the CFD model as a wall boundary condition, with various particle sizes investigated in the simulation. Simulations utilizing a steady mixing plane formulation and an unsteady sliding mesh were conducted and the flow solution of each was validated against experimental data. Results from each of these simulations, including impact and capture distributions and efficiencies, were compared and potential reasons for differences discussed in detail. The inclusion of a large range of particle sizes allowed investigation of trends with particle size, such as increased radial migration and reduced sticking efficiency at the larger particle sizes. The unsteady simulation predicted lower sticking efficiencies on the rotor blades than the mixing plane simulation for the majority of particle sizes. This is postulated to be due to the preservation of the hot streak and cool vane wake through the vane-rotor interface (which are smeared out circumferentially in the mixing-plane simulation). The results reported here represent the successful implementation of a novel deposition model into validated vane-rotor flow solutions that include a non-uniform inlet temperature profile and simulated vane cooling.

42 ENGINEERING↗
DOE OSTI2021

A library of AI-assisted FAIR water cycle and related disturbance datasets to enable model training, parameterization and validation

This whitepaper is responsive to focal area Data acquisition and assimilation enabled by machine learning, AI, and advanced methods. Here we describe how FAIR (Findable, Accessible, Reusable, Interoperable) datasets related to water cycle extremes are essential for successful implementation of ML in Earth System and other models. We also describe how AI can be used to acquire and integrate water cycle data related to extreme events to create a library of FAIR datasets for training and evaluating algorithms.

58 GEOSCIENCES↗
DOE OSTI2023

Automated bidirectional coupling of multiscale models of aerosol dosimetry: Validation with subject-specific deposition data

Assessing the toxicity of airborne particulate matter or the efficacy of inhaled drug depends upon accurate estimates of deposited fraction of inhaled materials. In silico approaches can provide important insights into site- or airway-specific deposition of inhaled aerosols in the respiratory system. In this study, we improved on our recently developed 3D/1D model that simulate aerosol transport and deposition in the whole lung over multiple breath cycles. A subject-specific multiscale lung model of a healthy male subject using computational fluid-particle dynamics (CFPD) in a 3D model of the oral cavity through the large bronchial airways entering each lobe was bidirectionally coupled with a recently improved Multiple Path Particle Dosimetry (MPPD) model to predict aerosol deposition over the entire respiratory tract over multiple breaths for four conditions matching experimental aerosol exposures in the same subject from which the model was developed. These include two particle sizes (1 and 2.9 μm) and two subject-specific breathing rates of ~300 ml/s (slow breathing) and ~750 ml/s (fast breathing) at a target tidal volume of 1 L. In silico predictions of retained fraction were 0.31 and 0.29 for 1 μm and 0.66 and 0.62 for 2.9 μm during slow and fast breathing, respectively, and compared well with experimental data (1 μm: 0.31 ± 0.01 (slow) and 0.27 ± 0.01 (fast), 2.9 μm: 0.63 ± 0.03 (slow) and 0.68 ± 0.02 (fast)). Furthermore, these results provide a great deal of confidence in the validity and reliability of our approach.

60 APPLIED LIFE SCIENCES↗