parametric effects
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Author(s):  
Zhenyu She ◽  
Vijay K. Dhir

Abstract Saturated water at one atmosphere pressure was boiled on horizontal copper discs of diameters 1.0,1.5 and 2.0 cm. respectively. The contact angle was varied from 10 to 80 degrees by controlling thermal oxidation of the discs, while the surrounding vessel size was changed by placing glass tubes of different inner diameters around the discs. Nucleate boiling heat transfer data were obtained up to critical heat flux (CHF), where vapor removal patterns were photographed. Dominant wavelengths at vapor jet interface and vapor jet diameters were measured from the photographs of the well wetted discs. For a well wetted surface, the magnitude of CHF increased when the heater size was reduced from 2.0 to 1.0 cm. Improving the wettability enhanced the CHF substantially, whereas the increased size of the liquid holding vessel had a smaller effect. The highest measured CHF is 233 W/cm2 or 2.11 times Zuber's CHF prediction for infinite horizontal flat plates. It was obtained on a 1.0 cm. disc of contact angle about 10 degrees surrounded by a large vessel. The CHF for this surface was increased from 201 to 233 W/cm2 when the ratio of heater size to surrounding vessel size was reduced from 1 to about 0.


2021 ◽  
Author(s):  
Emma R. McClure ◽  
Van P. Carey

Abstract Exploring parametric effects in pool boiling is challenging because the dependence of the resulting surface heat flux is often non-linear, and the mechanisms can interact in complex ways. Historically, parametric effects in nucleate boiling processes have been deduced by fitting relations obtained from physical models to experimental data and from correlated trends in non-dimensionalized data. Using such approaches, observed trends are often influenced by the framing of the analysis that results from the modeling or the collection of dimensionless variables used. Machine learning strategies can be attractive alternatives because they can be constructed either to minimize biases or to emphasize specific biases that reflect knowledge of the system physics. The investigation summarized here explores the use of machine learning methods as a tool for determining parametric trends in boiling heat transfer data and as a means for developing methods to predict boiling heat transfer. Results are presented that demonstrate how a genetic algorithm and an artificial neural network (ANN) can be used to extract heat flux dependencies of a binary mixture on wall superheat, gravity, Marangoni effects, and pressure. The results provide new insight into how gravity and Marangoni effects interact in boiling processes of this type. The results also demonstrate how machine learning tools can clarify how different mechanisms interact in the boiling process, as well as directly providing the ability to predict heat transfer performance for nucleate boiling. Each technique demonstrated clear advantages depending on whether speed, accuracy, or an explicit mathematical model was prioritized.


2021 ◽  
Author(s):  
Ganesh Arunkumar Samdani ◽  
Sai Sashankh Rao ◽  
Vishwas Paul Gupta

Abstract In PMCD operations, reservoir gas is expected to migrate uphole, and the uncertainty in gas migration rates under downhole conditions leads to challenges in planning logistics and fluid requirements. Estimates of migration velocities based on current methods (e.g. Taylor-bubble correlation) are highly conservative and involves simplifying assumptions. This paper presents a systematic approach to understanding the fundamentals of gas migration in wellbores, relates it to field data, and provides recommendations to improve PMCD design and planning. Our approach includes analysis of PMCD field data, multiphase flow literature and computational flow simulations. The field data on gas migration is used to establish the field-scale parametric effects and observed trends. Multiphase flow literature is used to qualitatively understand some of these parametric effects at downhole conditions. A comparison between multiphase flow literature and field data overwhelmingly demonstrates the gaps in understanding of underlying physics. 3-dimensional multiphase CFD simulations for a representative well geometry and downhole conditions are used to understand gas migration physics at downhole conditions and the reasons for its sensitivity to different conditions. CFD simulations showed a strong impact of pressure on bubble breakup. As a result, the gas migrates as a slow-moving swarm of smaller bubbles. The formation of smaller bubbles from a given gas volume is a rate dependent process and requires a finite time to reach to an equilibrium/steady-state. The field conditions provide both high downhole pressure and sufficient length-scale for formation of smaller slow-moving bubbles. For the same reason, small scale-experiments are limited in their application for field-scale designs due to use of low pressure and/or insufficient length-scales. The CFD results also compare well with field data in showing ~30% holdup of migrating gas at low migration rates and negligible effect of rotation and wellbore geometry i.e. annulus vs openhole. The extent and rate of disintegration of gas volume (bubble) has a negative correlation with well inclination, liquid viscosity, and surface tension. The rheology and liquid viscosity also affect the ability of liquid to sweep the gas back into the reservoir and therefore it is expected to have an optimum range for a given PMCD application. Use of high viscosity fluids for typical downhole well conditions is counterproductive and results in higher gas migration rates and therefore not recommended. The understanding of downhole physics is expected to improve logistics/storage/ planning/fluid choice and lead to lower gas migration rates and reliable operation. The same approach can be applied to other operations and scenarios where gas migration velocities are a key design factor.


2021 ◽  
Author(s):  
Md. Ekramul Islam ◽  
M. Ali Akbar

Abstract The dual-core optical fiber has significant applications in optical electronics for long-wave propagation, especially in telecommunication fibers. The aim of this article is to study the parametric effects on solitary wave propagation and characteristic aspects of long-wave traveling through optical fibers by establishing some standard and wide-spectrum solutions via the improved Bernoulli sub-equation function (IBSEF) method and the new auxiliary equation (NAE) approach. The investigated solitary wave solutions are ascertained as an integration of hyperbolic, exponential, rational and trigonometric functions and can be extensively applicable in optics. The physical significance of the solutions attained is illustrated for the definite values of the included parameters through depicting the 3D profiles. The solitons profile represents different types of waves associated with the free parameters which are related to the wave number and velocity of the solutions. It turns out that the obtained solutions through both the methods are potential and might be used in further works to interpret the various fields in telecommunication fiber which can reduce casualties that ensue in essence.


2021 ◽  
Author(s):  
Milena Quinci ◽  
Alexander Belden ◽  
Valerie Goutama ◽  
Dayang Gong ◽  
Suzanne Hanser ◽  
...  

Listening to pleasurable music is known to engage the brain's reward system, but little is known about how this engagement develops over time. Here we show for the first time that brain network connectivity can change longitudinally as a result of a personalized receptive music-based intervention (MBI) in cognitively unimpaired older adults. Using a combination of whole-brain regression, seed-based connectivity analysis, and representational similarity analysis (RSA), we compared fMRI responses during a simple music listening task in older adults before and after an eight-week personalized music listening program. Participants rated self-selected and researcher-selected musical excerpts on liking and familiarity. Parametric effects of liking, familiarity, and selection showed significant activation of auditory, reward, default mode, and sensorimotor areas both pre- and post-intervention. Seed-based connectivity comparing pre- and post-intervention showed a significant increase in functional connectivity between auditory regions and the medial prefrontal cortex (mPFC), and this auditory-mPFC connectivity was modulated by participant liking and familiarity ratings. RSA showed significant representations of selection and novelty in auditory regions at both time-points, and an increase in striatal representation of musical stimuli following intervention. Taken together, results show a sensitivity of auditory, reward, default, and sensorimotor regions to individual differences in music familiarity and liking, as well as a shift in brain network dynamics following the personalized MBI. Results show how regular music listening can provide an auditory channel towards the mPFC, thus offering a potential neural mechanism supporting healthy brain aging.


2021 ◽  
Vol 125 ◽  
pp. 110383
Author(s):  
A.V.S. Oliveira ◽  
D. Stemmelen ◽  
S. Leclerc ◽  
T. Glantz ◽  
A. Labergue ◽  
...  

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