scholarly journals Equilibrium absorptive partitioning theory between multiple aerosol particle modes

2016 ◽  
Vol 9 (10) ◽  
pp. 3617-3637
Author(s):  
Matthew Crooks ◽  
Paul Connolly ◽  
David Topping ◽  
Gordon McFiggans
Keyword(s):  
Large Scale ◽  
Correction Term ◽  
Total Concentration ◽  
Significant Loss ◽  
Chemical Compositions ◽  
Volatile Material ◽  
Linear System Of Equations ◽  
Partitioning Coefficient ◽  
Scale Models ◽  
Non Linear System

Abstract. An existing equilibrium absorptive partitioning model for calculating the equilibrium gas and particle concentrations of multiple semi-volatile organics within a bulk aerosol is extended to allow for multiple involatile aerosol modes of different sizes and chemical compositions. In the bulk aerosol problem, the partitioning coefficient determines the fraction of the total concentration of semi-volatile material that is in the condensed phase of the aerosol. This work modifies this definition for multiple polydisperse aerosol modes to account for multiple condensed concentrations, one for each semi-volatile on each involatile aerosol mode. The pivotal assumption in this work is that each aerosol mode contains an involatile constituent, thus overcoming the potential problem of smaller particles evaporating completely and then condensing on the larger particles to create a monodisperse aerosol at equilibrium. A parameterisation is proposed in which the coupled non-linear system of equations is approximated by a simpler set of equations obtained by setting the organic mole fraction in the partitioning coefficient to be the same across all modes. By perturbing the condensed masses about this approximate solution a correction term is derived that accounts for many of the removed complexities. This method offers a greatly increased efficiency in calculating the solution without significant loss in accuracy, thus making it suitable for inclusion in large-scale models.

scholarly journals The co-condensation of semi-volatile organics into multiple aerosol particle modes

2016 ◽  
Author(s):  
Matthew Crooks ◽  
Paul Connolly ◽  
David Topping ◽  
Gordon McFiggans
Keyword(s):  
Large Scale ◽  
Correction Term ◽  
Total Concentration ◽  
Significant Loss ◽  
Chemical Compositions ◽  
Volatile Material ◽  
Volatile Organics ◽  
Partitioning Coefficient ◽  
Scale Models ◽  
Non Linear System

Abstract. An existing equilibrium partitioning model for calculating the equilibrium gas/particle concentrations of multiple semi-volatile organics within a bulk aerosol is extended to allow for multiple core aerosol modes of different sizes and chemical compositions. In the bulk aerosol problem the partitioning coefficient determines the fraction of the total concentration of semi-volatile material that is in the condensed phase on the aerosol. This work modifies this definition for multiple polydisperse aerosol modes to account for multiple condensed concentrations; one for each semivolatile on each core aerosol mode. The pivotal assumption in this work is that each aerosol mode contains a core constituent which is involatile thus overcoming the potential problem of smaller particles evaporating completely and then condensing on the larger particles to create a monodisperse aerosol at equilibrium. The resulting coupled non-linear system is approximated by a simpler set of equations in which the organic mole fraction in the partitioning coefficient is set to be the same across all modes. By perturbing the condensed masses about this approximate solution a correction term is derived which accounts for much of the removed complexities. This method offers a greatly increased efficiency in calculating the solution without significant loss in accuracy, thus allowing its inclusion into large scale models feasable.

Cyclic Breathing Simulations in Large Scale Models of the Lung Airway From the Oronasal Opening to the Terminal Bronchioles

2012 ◽  
Author(s):  
D. Keith Walters ◽  
Greg W. Burgreen ◽  
Robert L. Hester ◽  
David S. Thompson ◽  
David M. Lavallee ◽  
...  
Keyword(s):  
Steady State ◽  
Boundary Conditions ◽  
Large Scale ◽  
Whole Body ◽  
Patient Specific ◽  
Cfd Simulations ◽  
Reduced Order ◽  
Scale Models ◽  
Steady State Simulation ◽  
Conducting Zone

Computational fluid dynamics (CFD) simulations were performed for unsteady periodic breathing conditions, using large-scale models of the human lung airway. The computational domain included fully coupled representations of the orotracheal region and large conducting zone up to generation four (G4) obtained from patient-specific CT data, and the small conducting zone (to G16) obtained from a stochastically generated airway tree with statistically realistic geometrical characteristics. A reduced-order geometry was used, in which several airway branches in each generation were truncated, and only select flow paths were retained to G16. The inlet and outlet flow boundaries corresponded to the oronasal opening (superior), the inlet/outlet planes in terminal bronchioles (distal), and the unresolved airway boundaries arising from the truncation procedure (intermediate). The cyclic flow was specified according to the predicted ventilation patterns for a healthy adult male at three different activity levels, supplied by the whole-body modeling software HumMod. The CFD simulations were performed using Ansys FLUENT. The mass flow distribution at the distal boundaries was prescribed using a previously documented methodology, in which the percentage of the total flow for each boundary was first determined from a steady-state simulation with an applied flow rate equal to the average during the inhalation phase of the breathing cycle. The distal pressure boundary conditions for the steady-state simulation were set using a stochastic coupling procedure to ensure physiologically realistic flow conditions. The results show that: 1) physiologically realistic flow is obtained in the model, in terms of cyclic mass conservation and approximately uniform pressure distribution in the distal airways; 2) the predicted alveolar pressure is in good agreement with previously documented values; and 3) the use of reduced-order geometry modeling allows accurate and efficient simulation of large-scale breathing lung flow, provided care is taken to use a physiologically realistic geometry and to properly address the unsteady boundary conditions.

scholarly journals A Toolbox for Analysis and Design of Model Based Diagnosis Systems for Large Scale Models

2017 ◽  
Vol 50 (1) ◽  
pp. 3287-3293 ◽  
Author(s):  
Erik Frisk ◽  
Mattias Krysander ◽  
Daniel Jung
Keyword(s):  
Large Scale ◽  
Analysis And Design ◽  
Model Based ◽  
Scale Models

Quantifying parametric uncertainty in the Rothermel model

2008 ◽  
Vol 17 (5) ◽  
pp. 638 ◽  
Author(s):  
Edwin Jimenez ◽  
M. Yousuff Hussaini ◽  
Scott Goodrick
Keyword(s):  
Variance Reduction ◽  
Input Parameter ◽  
Parametric Uncertainty ◽  
Fire Spread ◽  
The United States ◽  
Linear System Of Equations ◽  
Spread Model ◽  
Non Linear System ◽  
The Impact ◽  
Sensitivity Derivative

The purpose of the present work is to quantify parametric uncertainty in the Rothermel wildland fire spread model (implemented in software such as BehavePlus3 and FARSITE), which is undoubtedly among the most widely used fire spread models in the United States. This model consists of a non-linear system of equations that relates environmental variables (input parameter groups) such as fuel type, fuel moisture, terrain, and wind to describe the fire environment. This model predicts important fire quantities (output parameters) such as the head rate of spread, spread direction, effective wind speed, and fireline intensity. The proposed method, which we call sensitivity derivative enhanced sampling, exploits sensitivity derivative information to accelerate the convergence of the classical Monte Carlo method. Coupled with traditional variance reduction procedures, it offers up to two orders of magnitude acceleration in convergence, which implies that two orders of magnitude fewer samples are required for a given level of accuracy. Thus, it provides an efficient method to quantify the impact of input uncertainties on the output parameters.

scholarly journals The information complexity of learning tasks, their structure and their distance

2021 ◽  
Author(s):  
Alessandro Achille ◽  
Giovanni Paolini ◽  
Glen Mbeng ◽  
Stefano Soatto
Keyword(s):  
Kolmogorov Complexity ◽  
Large Scale ◽  
Parametric Model ◽  
Training Dataset ◽  
Optimization Scheme ◽  
Learning Tasks ◽  
Asymmetric Distance ◽  
Special Cases ◽  
Scale Models ◽  
Real World Datasets

Abstract We introduce an asymmetric distance in the space of learning tasks and a framework to compute their complexity. These concepts are foundational for the practice of transfer learning, whereby a parametric model is pre-trained for a task, and then fine tuned for another. The framework we develop is non-asymptotic, captures the finite nature of the training dataset and allows distinguishing learning from memorization. It encompasses, as special cases, classical notions from Kolmogorov complexity and Shannon and Fisher information. However, unlike some of those frameworks, it can be applied to large-scale models and real-world datasets. Our framework is the first to measure complexity in a way that accounts for the effect of the optimization scheme, which is critical in deep learning.

Sign in / Sign up

Export Citation Format

Share Document