Determining Parametric Effects for Droplet Evaporation on Nanoporous Superhydrophillic Surfaces Using Machine Learning Techniques

2021 ◽  
Author(s):  
Emma R. McClure ◽  
Van P. Carey
Keyword(s):  
Machine Learning ◽  
Cooling System ◽  
Spray Cooling ◽  
Droplet Evaporation ◽  
Contact Angles ◽  
Machine Learning Techniques ◽  
Metallic Surfaces ◽  
Droplet Spreading ◽  
Droplet Vaporization ◽  
Speed Accuracy

Abstract Experimental results demonstrate that droplet vaporization on metal surfaces can be significantly enhanced with the application of a nanoporous, superhydrophilic surface coating. A thin layer of ZnO nanopillars can be easily seeded and grown on most metallic surfaces to achieve nanoscale pores between pillars, and ultra-low apparent contact angles. These surface coatings have the potential to improve spray cooling processes, and can be easily scaled up to larger and more complex heat exchangers. In order to characterize the potential improvement to a spray cooling system it is important to understand the dependence on system parameters, and to have a clear model of droplet vaporization on such surfaces. There are a number of surface and impact parameters that will affect the droplet spreading and subsequent vaporization on the surface. The surface contact angle, wicking speed and impact velocity all interact to affect the maximum spread of the droplet and the speed at which the droplet reaches this state. Along with variations in droplet volume and wall superheat, the model for droplet vaporization becomes more complex and nonlinear. Instead of exploring a single parameter at a time, machine learning tools can be utilized to determine the dependence of droplet evaporation time on these parameters simultaneously. In this study a genetic algorithm and a neural network were used to develop a droplet evaporation model for these superhydrophilic surfaces. Each algorithm demonstrated clear advantages depending on whether speed, accuracy, or an explicit mathematical model was prioritized.

Experimental Investigation and Numerical Simulation of Liquid Droplet Vaporization in a Compression System With Wet Compression

2004 ◽  
Author(s):  
Kunpeng Zhang ◽  
Song Zou ◽  
Weiming Pan ◽  
Fei Xue
Keyword(s):  
Cooling System ◽  
Liquid Droplet ◽  
Spray Cooling ◽  
Liquid Droplets ◽  
Compression Process ◽  
Compression System ◽  
Droplet Vaporization ◽  
Spray System ◽  
Good Accord ◽  
Wet Compression

Experimental study of spray cooling system with wet compression has been carried out on a small hot wind tunnel. A mathematical model is established for liquid droplets evaporization and a 3D calculation bound on the model have been carried out. The results are in good accord with the experimental data. This paper will be good reference for spray system design for a wet compression system. And the detail information of the experiment and numerical computation take us further understanding of the real wet compression process.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

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