A Sensitivity Study of the Porcine Head Subjected to Bump Impact

2016 ◽  
Author(s):  
Kimberly A. Thompson ◽  
Adam C. Sokolow ◽  
Juliana Ivancik ◽  
Timothy G. Zhang ◽  
William H. Mermagen ◽  
...  
Keyword(s):  
Human Brain ◽  
3D Model ◽  
Porcine Model ◽  
Load Transfer ◽  
Numerical Study ◽  
Sensitivity Study ◽  
Complex Problem ◽  
Model Parameters ◽  
Skin Thickness ◽  
The Impact

Understanding load transfer to the human brain is a complex problem that has been a key subject of recent investigations [4–6]. Because the porcine is a gyrencephalic species, having greater structural and functional similarities to the human brain than other lower species outlined in the literature, it is commonly chosen as a surrogate for human brain studies [7]. Consequently, we have chosen to use a porcine model in this work. To understand stress wave transfer to and through the brain, it is important to fully characterize the nature of the impact (i.e. source, location, and speed) as well as the response of the constituent tissues under such impact. We suspect the material and topology of these tissues play an important role in their response. In this paper, we report on a numerical study assessing the sensitivity of model parameters for a 6-month old Gottingen mini-pig model, under bump loading. In this study, 2D models are used for computational simplicity. While a 3D model is more realistic in nature, a 2D representation is still valuable in that it can provide trends on parameter sensitivity that can help steer the development of the 3D model. In this work, we investigate the variation of skull and skin thickness, evaluate material variability of the skull, and consider the effects of nasal cavities on load transfer. Eighty simulations are computed in LS-DYNA and analyzed in MATLAB. The results of this study will provide useful knowledge on the necessary components and parameters of the porcine model and therefore provide more confidence in the analysis. This is an essential first step as we look toward bridging the gap between correlates of injury in animal and human models.

scholarly journals Modeling Bacterial Regrowth and Trihalomethane Formation in Water Distribution Systems

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 463
Author(s):  
Gopinathan R. Abhijith ◽  
Leonid Kadinski ◽  
Avi Ostfeld
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Mechanistic Model ◽  
Water Distribution Systems ◽  
Sensitivity Study ◽  
Operating Conditions ◽  
Growth Rate Constant ◽  
Model Parameters ◽  
Bacterial Regrowth ◽  
The Impact

The formation of bacterial regrowth and disinfection by-products is ubiquitous in chlorinated water distribution systems (WDSs) operated with organic loads. A generic, easy-to-use mechanistic model describing the fundamental processes governing the interrelationship between chlorine, total organic carbon (TOC), and bacteria to analyze the spatiotemporal water quality variations in WDSs was developed using EPANET-MSX. The representation of multispecies reactions was simplified to minimize the interdependent model parameters. The physicochemical/biological processes that cannot be experimentally determined were neglected. The effects of source water characteristics and water residence time on controlling bacterial regrowth and Trihalomethane (THM) formation in two well-tested systems under chlorinated and non-chlorinated conditions were analyzed by applying the model. The results established that a 100% increase in the free chlorine concentration and a 50% reduction in the TOC at the source effectuated a 5.87 log scale decrement in the bacteriological activity at the expense of a 60% increase in THM formation. The sensitivity study showed the impact of the operating conditions and the network characteristics in determining parameter sensitivities to model outputs. The maximum specific growth rate constant for bulk phase bacteria was found to be the most sensitive parameter to the predicted bacterial regrowth.

scholarly journals Finite Element Analysis Of Airfield Flexible Pavement

2014 ◽  
Vol 60 (3) ◽  
pp. 323-334 ◽  
Author(s):  
G. Leonardi
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
3D Model ◽  
Numerical Study ◽  
Permanent Deformation ◽  
Flexible Pavement ◽  
Finite Element Code ◽  
Element Analysis ◽  
Pavement Response ◽  
The Impact

Abstract The paper presents a numerical study of an aircraft wheel impacting on a flexible landing surface. The proposed 3D model simulates the behaviour of flexible runway pavement during the landing phase. This model was implemented in a finite element code in order to investigate the impact of repeated cycles of loads on pavement response. In the model, a multi-layer pavement structure was considered. In addition, the asphalt layer (HMA) was assumed to follow a viscoelastoplastic behaviour. The results demonstrate the capability of the model in predicting the permanent deformation distribution in the asphalt layer.

Numerical study of the liquid-solid interface properties for binary alloys using phase-field crystal approach

2013 ◽  
Vol 1535 ◽  
Author(s):  
Muhammad Ajmal Choudhary ◽  
Julia Kundin ◽  
Heike Emmerich
Keyword(s):  
Phase Field ◽  
Solid Phase ◽  
Numerical Study ◽  
Liquid Droplet ◽  
Alloy System ◽  
Model Parameters ◽  
Systematic Analysis ◽  
Solid Interface ◽  
The Impact ◽  
Phase Field Crystal

ABSTRACTThe phase-field crystal (PFC) method has emerged as a promising technique to simulate the evolution of crystalline patterns with atomistic resolution on mesoscopic time scales. We use a 2D PFC model based on Elder et al. [Phy. Rev. B 75, 064107 (2007)] to perform a systematic analysis of a liquid-solid interface for a binary alloy system. We propose the method of determining interfacial energies for a curved liquid-solid interface by stabilizing the circular solid seed in the surrounding liquid phase and the liquid droplet in the solid phase for various seed sizes in a finite system. We also investigate the impact of model parameters on the resulting interface energies. The interface energies are computed with various system sizes in order to study the system size effects. In particular, we compare the simulation results in the form of the interface energy as a function of radius with the existing theories.

scholarly journals Constraints and biases in a tropospheric two-box model of OH

2018 ◽  
Author(s):  
Stijn Naus ◽  
Stephen A. Montzka ◽  
Sudhanshu Pandey ◽  
Sourish Basu ◽  
Ed J. Dlugokencky ◽  
...  
Keyword(s):  
Hydroxyl Radical ◽  
3D Model ◽  
Model Simulation ◽  
Box Model ◽  
Surface Measurement ◽  
Model Parameters ◽  
Ch4 Emissions ◽  
Wide Range ◽  
The Impact ◽  
Bias Corrections

Abstract. The hydroxyl radical (OH) is the main atmospheric oxidant and the primary sink of the greenhouse gas CH4. In two recent studies, constraints on the hydroxyl radical (OH) were derived using a tropospheric two-box model of methyl chloroform (MCF) and CH4. When OH variations as derived in this set-up were propagated to the CH4 budget, the constraints on OH from MCF still allowed for a wide range of CH4 emission scenarios. This is important, because global CH4 emissions are generally considered best constrained by the global lifetime of CH4, which is determined mainly by OH. Here, we investigate how the use of a tropospheric two-box model in these studies can have affected derived constraints on OH, due to the simplifying assumptions inherent to a two-box model. First, instead of prescribing fixed model parameters for interhemispheric transport, chemical loss rates and loss to the stratosphere, we derive species- and time-dependent quantities from a full 3D transport model simulation. We find significant deviations between the magnitude and time-dependence of the parameters we derive, and the assumptions commonly reported and adopted in literature. Moreover, using output from the 3D model simulations, we investigated differences between the burden seen by the surface measurement network of the National Oceanic and Atmospheric Administration and the true tropospheric burden. Next, we accounted for these biases in a two-box model inversion of MCF and CH4, to investigate the impact of the biases on OH constraints. We find that the sensitivity of interannual OH anomalies to the biases is modest (1–2 %), relative to the significant uncertainties on derived OH (5–8 %). However, in an inversion where we implemented all four bias corrections simultaneously, we did find a shift to a positive OH trend over the 1994–2015 period. Moreover, the magnitude of derived global mean OH and by extent that of global CH4 emissions are affected much more strongly by the bias corrections than their anomalies (∼ 10 %). In this way, we identified and quantified direct limitations in the two-box model approach that can possibly be corrected for when a full 3D simulation is used to inform the two-box model. This derivation is, however, an extensive and species-dependent exercise. Therefore, a good alternative would be to move the inversion problem of OH to a 3D model completely. It is crucial to account for the limitations of two-box models in future attempts to constrain the atmospheric oxidative capacity, especially because though MCF and CH4 behave similarly in large parts of our analysis, it is not obvious that this should be the case for alternative tracers that potentially constrain OH, other than MCF.

Estimating OLED Display Device Lifetime from pixel and screen brightness and its application

2019 ◽  
Vol 2019 (1) ◽  
pp. 331-338 ◽  
Author(s):  
Jérémie Gerhardt ◽  
Michael E. Miller ◽  
Hyunjin Yoo ◽  
Tara Akhavan
Keyword(s):  
Image Processing ◽  
Power Consumption ◽  
Model Parameters ◽  
Display Device ◽  
Image Processing Algorithm ◽  
Pixel Value ◽  
Device Lifetime ◽  
The Impact ◽  
Oled Display

In this paper we discuss a model to estimate the power consumption and lifetime (LT) of an OLED display based on its pixel value and the brightness setting of the screen (scbr). This model is used to illustrate the effect of OLED aging on display color characteristics. Model parameters are based on power consumption measurement of a given display for a number of pixel and scbr combinations. OLED LT is often given for the most stressful display operating situation, i.e. white image at maximum scbr, but having the ability to predict the LT for other configurations can be meaningful to estimate the impact and quality of new image processing algorithms. After explaining our model we present a use case to illustrate how we use it to evaluate the impact of an image processing algorithm for brightness adaptation.

A numerical study of the effect of the number of turns of coil on the heat produced in the induction heating process in the 3d model

2018 ◽  
Vol 18 (3) ◽  
pp. 408-419
Author(s):  
A J shokri ◽  
M H Tavakoli ◽  
A Sabouri Dodaran ◽  
M S Akhondi Khezrabad ◽  
◽  
...  
Keyword(s):  
Induction Heating ◽  
3D Model ◽  
Numerical Study ◽  
Heating Process

scholarly journals Imaging with Coherent X-rays: From the Early Synchrotron Tests to SYNAPSE

2021 ◽  
Vol 7 (8) ◽  
pp. 132
Author(s):  
Giorgio Margaritondo ◽  
Yeukuang Hwu
Keyword(s):  
Human Brain ◽  
International Collaboration ◽  
Phase Contrast ◽  
Asia Pacific ◽  
X Rays ◽  
New Techniques ◽  
Simple Description ◽  
Contrast Radiography ◽  
The Impact ◽  
Extract Information

The high longitudinal and lateral coherence of synchrotron X-rays sources radically transformed radiography. Before them, the image contrast was almost only based on absorption. Coherent synchrotron sources transformed radiography into a multi-faceted tool that can extract information also from “phase” effects. Here, we report a very simple description of the new techniques, presenting them to potential new users without requiring a sophisticated background in advanced physics. We then illustrate the impact of such techniques with a number of examples. Finally, we present the international collaboration SYNAPSE (Synchrotrons for Neuroscience—an Asia-Pacific Strategic Enterprise), which targets the use of phase-contrast radiography to map one full human brain in a few years.

scholarly journals Model of thermal buoyancy in cavity-ventilated roof constructions

2021 ◽  
pp. 174425912098418
Author(s):  
Toivo Säwén ◽  
Martina Stockhaus ◽  
Carl-Eric Hagentoft ◽  
Nora Schjøth Bunkholt ◽  
Paula Wahlgren
Keyword(s):  
Analytical Model ◽  
Flow Rate ◽  
Numerical Study ◽  
Air Flow ◽  
Wind Pressure ◽  
Air Flow Rate ◽  
Test Set ◽  
Air Cavity ◽  
Thermal Buoyancy ◽  
The Impact

Timber roof constructions are commonly ventilated through an air cavity beneath the roof sheathing in order to remove heat and moisture from the construction. The driving forces for this ventilation are wind pressure and thermal buoyancy. The wind driven ventilation has been studied extensively, while models for predicting buoyant flow are less developed. In the present study, a novel analytical model is presented to predict the air flow caused by thermal buoyancy in a ventilated roof construction. The model provides means to calculate the cavity Rayleigh number for the roof construction, which is then correlated with the air flow rate. The model predictions are compared to the results of an experimental and a numerical study examining the effect of different cavity designs and inclinations on the air flow rate in a ventilated roof subjected to varying heat loads. Over 80 different test set-ups, the analytical model was found to replicate both experimental and numerical results within an acceptable margin. The effect of an increased total roof height, air cavity height and solar heat load for a given construction is an increased air flow rate through the air cavity. On average, the analytical model predicts a 3% higher air flow rate than found in the numerical study, and a 20% lower air flow rate than found in the experimental study, for comparable test set-ups. The model provided can be used to predict the air flow rate in cavities of varying design, and to quantify the impact of suggested roof design changes. The result can be used as a basis for estimating the moisture safety of a roof construction.

scholarly journals Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Clustering Algorithm ◽  
Health Interventions ◽  
Transmission Model ◽  
Model Parameters ◽  
Public Health Interventions ◽  
Forecasting Accuracy ◽  
Time Lagged ◽  
The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

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