Axial Distribution of Bulk Temperature and Void Fraction in a Heated Channel With Inlet Subcooling

1970 ◽  
Vol 92 (4) ◽  
pp. 595-609 ◽  
Author(s):  
S. Y. Ahmad
Keyword(s):  
Bulk Temperature ◽  
Temperature Profiles ◽  
Axial Distribution ◽  
Subcooled Liquid ◽  
Axial Temperature ◽  
Proposed Model ◽  
Wide Range ◽  
Heated Channel ◽  
Different Sources ◽  
Heated Length

A theoretical model is developed to determine the axial temperature distribution of subcooled liquid. It is a simple function of a heat transfer and a condensation parameter. The proposed model satisfactorily correlates the measured bulk temperature profiles. The corresponding mid fraction is computed by using a new empirical slip correlation, valid in both subcooled and bulk boiling regions. The resulting axial void profile has been compared (over the entire heated length) with steam and water data from six different sources, covering a wide range of pressure, mass flux, surface heat flux, inlet subcooling and channel geometry. The method gives satisfactory agreement with experimental data.

scholarly journals Thermal-Hydraulic Investigations of a Horizontal Dry Cask Simulator

2020 ◽  
Author(s):  
Ramon J. M. Pulido ◽  
Eric R. Lindgren ◽  
Samuel G. Durbin ◽  
Alex Salazar
Keyword(s):  
Best Practices ◽  
Thermal Loading ◽  
Spent Nuclear Fuel ◽  
Data Sets ◽  
Temperature Profiles ◽  
Benchmark Data ◽  
Axial Temperature ◽  
Wide Range ◽  
Cooling Air Flow ◽  
Cooling Air

Abstract Recent advances in horizontal cask designs for commercial spent nuclear fuel have significantly increased maximum thermal loading. This is due in part to greater efficiency in internal conduction pathways. Carefully measured data sets generated from testing of full-sized casks or smaller cask analogs are widely recognized as vital for validating thermal-hydraulic models of these storage cask designs. While several testing programs have been previously conducted, these earlier validation studies did not integrate all the physics or components important in a modern, horizontal dry cask system. The purpose of this investigation is to produce data sets that can be used to benchmark the codes and best practices presently used to calculate cladding temperatures and induced cooling air flows in modern, horizontal dry storage systems. The horizontal dry cask simulator (HDCS) has been designed to generate this benchmark data and complement the existing knowledge base. Transverse and axial temperature profiles along with induced-cooling air flow are measured using various backfills of gases for a wide range of decay powers and canister pressures. The data from the HDCS tests will be used to host a blind model validation effort.

scholarly journals Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 930
Author(s):  
Fahimeh Hadavimoghaddam ◽  
Mehdi Ostadhassan ◽  
Ehsan Heidaryan ◽  
Mohammad Ali Sadri ◽  
Inna Chapanova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Viscosity Data ◽  
Oil Viscosity ◽  
Pvt Data ◽  
Proposed Model ◽  
Wide Range ◽  
Engineering Problems ◽  
Functional Forms ◽  
Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

scholarly journals Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

2019 ◽  
Vol 11 (6) ◽  
pp. 608 ◽  
Author(s):  
Yun-Jia Sun ◽  
Ting-Zhu Huang ◽  
Tian-Hui Ma ◽  
Yong Chen
Keyword(s):  
Remote Sensing ◽  
Real Data ◽  
Total Variation Regularization ◽  
Joint Sparsity ◽  
Alternating Direction ◽  
Proposed Model ◽  
Comparison Results ◽  
Wide Range ◽  
Image Edges ◽  
Full Consideration

Remote sensing images have been applied to a wide range of fields, but they are often degraded by various types of stripes, which affect the image visual quality and limit the subsequent processing tasks. Most existing destriping methods fail to exploit the stripe properties adequately, leading to suboptimal performance. Based on a full consideration of the stripe properties, we propose a new destriping model to achieve stripe detection and stripe removal simultaneously. In this model, we adopt the unidirectional total variation regularization to depict the directional property of stripes and the weighted ℓ 2 , 1 -norm regularization to depict the joint sparsity of stripes. Then, we combine the alternating direction method of multipliers and iterative support detection to solve the proposed model effectively. Comparison results on simulated and real data suggest that the proposed method can remove and detect stripes effectively while preserving image edges and details.

scholarly journals A Model for Analysing the Collective Dynamic Behaviour and Characterising the Exploitation of Population-Based Algorithms

2014 ◽  
Vol 22 (1) ◽  
pp. 159-188 ◽  
Author(s):  
Mikdam Turkey ◽  
Riccardo Poli
Keyword(s):  
Population Dynamics ◽  
Dynamic Behaviour ◽  
Fitness Landscape ◽  
Population Based ◽  
Levels Of Abstraction ◽  
Collective Dynamic ◽  
Model Studies ◽  
Proposed Model ◽  
Wide Range ◽  
Multiple Levels

Several previous studies have focused on modelling and analysing the collective dynamic behaviour of population-based algorithms. However, an empirical approach for identifying and characterising such a behaviour is surprisingly lacking. In this paper, we present a new model to capture this collective behaviour, and to extract and quantify features associated with it. The proposed model studies the topological distribution of an algorithm's activity from both a genotypic and a phenotypic perspective, and represents population dynamics using multiple levels of abstraction. The model can have different instantiations. Here it has been implemented using a modified version of self-organising maps. These are used to represent and track the population motion in the fitness landscape as the algorithm operates on solving a problem. Based on this model, we developed a set of features that characterise the population's collective dynamic behaviour. By analysing them and revealing their dependency on fitness distributions, we were then able to define an indicator of the exploitation behaviour of an algorithm. This is an entropy-based measure that assesses the dependency on fitness distributions of different features of population dynamics. To test the proposed measures, evolutionary algorithms with different crossover operators, selection pressure levels and population handling techniques have been examined, which lead populations to exhibit a wide range of exploitation-exploration behaviours.

Prediction of High Viscosity Liquid/Gas Two-Phase Slug Length in Horizontal and Slightly Inclined Pipelines

2021 ◽  
Author(s):  
Omar Shaaban ◽  
Eissa Al-Safran
Keyword(s):  
Numerical Optimization ◽  
Flow Behavior ◽  
Mechanistic Model ◽  
High Viscosity ◽  
Oil Well ◽  
Liquid Slug ◽  
Two Phase ◽  
Proposed Model ◽  
Wide Range ◽  
Flow Closure

Abstract The production and transportation of high viscosity liquid/gas two-phase along petroleum production system is a challenging operation due to the lack of understanding the flow behavior and characteristics. In particular, accurate prediction of two-phase slug length in pipes is crucial to efficiently operate and safely design oil well and separation facilities. The objective of this study is to develop a mechanistic model to predict high viscosity liquid slug length in pipelines and to optimize the proper set of closure relationships required to ensure high accuracy prediction. A large high viscosity liquid slug length database is collected and presented in this study, against which the proposed model is validated and compared with other models. A mechanistic slug length model is derived based on the first principles of mass and momentum balances over a two-phase slug unit, which requires a set of closure relationships of other slug characteristics. To select the proper set of closure relationships, a numerical optimization is carried out using a large slug length dataset to minimize the prediction error. Thousands of combinations of various slug flow closure relationships were evaluated to identify the most appropriate relationships for the proposed slug length model under high viscosity slug length condition. Results show that the proposed slug length mechanistic model is applicable for a wide range of liquid viscosities and is sensitive to the selected closure relationships. Results revealed that the optimum closure relationships combination is Archibong-Eso et al. (2018) for slug frequency, Malnes (1983) for slug liquid holdup, Jeyachandra et al. (2012) for drift velocity, and Nicklin et al. (1962) for the distribution coefficient. Using the above set of closure relationships, model validation yields 37.8% absolute average percent error, outperforming all existing slug length models.

Treatment of Arrhythmias by Radiofrequency Ablation

2013 ◽  
Author(s):  
M. Erol Ulucakli ◽  
Evan P. Sheehan
Keyword(s):  
Radiofrequency Ablation ◽  
Cardiac Tissue ◽  
Clinical Applications ◽  
Bioheat Transfer ◽  
Temperature Profiles ◽  
Cellular Injury ◽  
Rf Ablation ◽  
Transcatheter Therapy ◽  
Wide Range ◽  
Primary Modality

Radiofrequency ablation may be described as a thermal strategy to destroy tissue by increasing its temperature and causing irreversible cellular injury. Radiofrequency ablation is a relatively new modality which has found use in a wide range of medical applications and gained acceptance. RF ablation has been used to destroy tumors in the liver, prostate, breasts, lungs, kidneys, bones, and eyes. One of the early clinical applications was its use in treating supraventricular arrhythmias by selectively destroying cardiac tissue. Radiofrequency ablation has become established as the primary modality of transcatheter therapy for the treatment of symptomatic arrhythmias. Radiofrequency catheter ablation of cardiac arrhythmias was investigated using a finite-element based solution of the bioheat transfer equation. Spatial and temporal temperature profiles in the cardiac tissue were visualized.

Estimation of CO2 Diffusivity in Brine by Use of the Genetic Algorithm and Mixed Kernels-Based Support Vector Machine Model

2018 ◽  
Vol 141 (4) ◽  
Author(s):  
Qihong Feng ◽  
Ronghao Cui ◽  
Sen Wang ◽  
Jin Zhang ◽  
Zhe Jiang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Diffusion Coefficient ◽  
Co2 Storage ◽  
Co2 Injection ◽  
Support Vector ◽  
Hybrid Technique ◽  
Machine Model ◽  
Proposed Model ◽  
Wide Range

Diffusion coefficient of carbon dioxide (CO2), a significant parameter describing the mass transfer process, exerts a profound influence on the safety of CO2 storage in depleted reservoirs, saline aquifers, and marine ecosystems. However, experimental determination of diffusion coefficient in CO2-brine system is time-consuming and complex because the procedure requires sophisticated laboratory equipment and reasonable interpretation methods. To facilitate the acquisition of more accurate values, an intelligent model, termed MKSVM-GA, is developed using a hybrid technique of support vector machine (SVM), mixed kernels (MK), and genetic algorithm (GA). Confirmed by the statistical evaluation indicators, our proposed model exhibits excellent performance with high accuracy and strong robustness in a wide range of temperatures (273–473.15 K), pressures (0.1–49.3 MPa), and viscosities (0.139–1.950 mPa·s). Our results show that the proposed model is more applicable than the artificial neural network (ANN) model at this sample size, which is superior to four commonly used traditional empirical correlations. The technique presented in this study can provide a fast and precise prediction of CO2 diffusivity in brine at reservoir conditions for the engineering design and the technical risk assessment during the process of CO2 injection.

scholarly journals Numerical Model for the Analysis of Thermal Transients in District Heating Networks

2020 ◽  
Vol 197 ◽  
pp. 01004
Author(s):  
Martina Capone ◽  
Elisa Guelpa ◽  
Vittorio Verda
Keyword(s):  
Carbon Dioxide Emissions ◽  
Renewable Energy Sources ◽  
Management Strategies ◽  
District Heating ◽  
Equivalent Model ◽  
Computational Time ◽  
Physical Parameters ◽  
Thermal Transients ◽  
Proposed Model ◽  
Wide Range

As District Heating (DH) networks are experiencing an evolution towards the so-called 4th generation, there is a need to update the currently used models to take into account the ever-increasing complexity of this technology. Indeed, to further improve the reduction in energy consumption and carbon-dioxide emissions, a wide range of technologies and management strategies are being introduced within district heating, such as a large exploitation of Renewable Energy Sources (RES). As a consequence, thermal transients assume a major importance, posing the need to redefine the relevant physical parameters and to develop a model which accurately describes their behaviour. In this framework, this paper proposes a quantitative analysis of the influence of the pipe heat-capacity on the model. Moreover, an equivalent-model, which is able to take into account the two heat capacities of steel and water in just one equation, is proposed and compared with two commonly used approaches. One of the features of the proposed model is the suitability for application to large networks. To prove its capabilities, an application to the Turin district heating network, which is among the largest systems in Europe, is proposed. Results show significant improvements in terms of accuracy over computational time ratio.

Introductory remarks

1977 ◽  
Vol 280 (972) ◽  
pp. 105-105
Keyword(s):  
Climatic Change ◽  
Food Supply ◽  
Time Scale ◽  
Food Production ◽  
Rapid Change ◽  
Climatic Fluctuations ◽  
The Past ◽  
Wide Range ◽  
The Subject ◽  
Different Sources

In the last year or two there has been a remarkable increase in the interest, both popular and scientific, in the subject of climatic change. This stems from a recognition that even a highly technological society is vulnerable to the effects of climatic fluctuations and indeed may become more so, as margins of surplus food production are reduced, and nations become more interdependent for their food supply. In this respect our concern is with quite small changes - a degree (Celsius) or less in temperature and 10 % or so in rainfall. Probably we may discount some of the more alarmist suggestions of an imminent and rapid change towards near glacial conditions as these are based on very sketchy evidence. However, whatever the time-scale of climatic fluctuations with which we are concerned, we may hope to learn a great deal which is relevant to the factors which will control our future climate from the study of its more extreme vagaries in the past. Information relevant to the weather in such extreme periods is coming forward in increasing detail and volume from a wide range of disciplines. The variety of the evidence, its lack of precision as a strict measure of climate, and the number of different sources all make it difficult for an individual to build up a clear picture of past climates. However such a picture is needed, if explanations and interpretation are to be possible. Ideally one would need a synchronous picture of the climate of the whole world at selected epochs in the past. Various international programmes are directed to forming such pictures.

Trajectory and Characteristics of Buoyancy and Momentum Dominated Horizontal Jet Flames From Circular and Elliptic Burners

2005 ◽  
Vol 128 (4) ◽  
pp. 300-310 ◽  
Author(s):  
Tracy Smith ◽  
Chendhil Periasamy ◽  
Benjamin Baird ◽  
S. R. Gollahalli
Keyword(s):  
Nitric Oxide ◽  
Reynolds Number ◽  
Flame Temperature ◽  
Major Axis ◽  
Equivalent Diameter ◽  
Flame Stability ◽  
Temperature Profiles ◽  
Burner Exit ◽  
Wide Range ◽  
Nitric Oxide Emissions

Relative effects of buoyancy and momentum on the characteristics of horizontally oriented circular (Circ) and elliptic (E) burner flames in a quiescent environment over a wide range of jet exit velocities are presented. The major axis of the elliptic burner was oriented horizontally and vertically (referred to as Emaj and Emin flames, respectively). Propane was used as fuel and a small amount of hydrogen was piloted to attach flames to the burner. Global flame characteristics such as flame dimensions, centerline trajectory, emission indices (EI) and radiative fraction, and in-flame transverse concentration and temperature profiles were measured. At a jet exit Reynolds number (Rej) of 2000, based on the area-equivalent diameter of the burner, the flame characteristics were affected by the burner geometry and its orientation. Also, the vertical dimension of the burner exit dictated buoyancy effects. At Rej=12,500, the influence of burner geometry or its orientation was negligible. Elliptic burner flames exhibited lower liftoff and blowout velocities than circular burner flames. Furthermore, the flame stability and nitric oxide emissions were not much affected by the orientation of elliptic burner. Although the elliptic burners produced higher EINO at lower jet exit velocities, the variation in EINO among three burners (Circ, Emaj, and Emin) was insignificant at higher velocities. Some effects of buoyancy on EICO were observed at lower jet exit velocities and the EICO was the lowest for the burners with largest buoyancy flux. Elliptic burner flames produced greater peak flame temperature than the corresponding circular burner flames under most conditions.

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