scholarly journals Development of work based learning (WBL) learning model in heat transfer courses

2020 ◽  
Vol 1481 ◽  
pp. 012113
Author(s):  
Ambiyar ◽  
Ganefri ◽  
Suryadimal ◽  
Nizwardi Jalinus ◽  
Raimon Efendi ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Learning Model ◽  
Transfer Courses

scholarly journals PENERAPAN PENDEKATAN KONTEKSTUAL DALAM MODEL PEMBELAJARAN KOOPERATIF TIPE JIGSAW UNTUK MENCAPAI HASIL BELAJAR MATERI PERPINDAHAN KALOR PADA SISWA KELAS VII SMP NEGERI 2 AMBON

2019 ◽  
Vol 4 (1) ◽  
pp. 208-223
Author(s):  
Nur Hamida Siregar
Keyword(s):  
Heat Transfer ◽  
Cooperative Learning ◽  
Learning Outcomes ◽  
Learning Process ◽  
Learning Model ◽  
Quality Of Education ◽  
Average Score ◽  
Contextual Approach ◽  
Quantitative Descriptive

The quality of education in Indonesia was in the worst position in Asian from 12 countries surveyed in 2010 by PERC, so more efforts by teachers are needed to improve the quality of education. One of the efforts is through the application of good and appropriate learning models to the characteristics of subjects. This study aimed to determine the achievement of learning outcomes of heat transfer subject of students of class VII6 SMP Negeri 2 Ambon through application of a contextual approach in Jigsaw type cooperative learning model. This research was a quantitative descriptive, with 40 students. This study found that the average score of initial achievement was 29.22 (failed qualification). While during the learning process with Jigsaw cooperative learning model, the average cognitive achievement was 83.34 (good qualification), affective was 82.66 (good qualification), and psychomotor was 88.75 (good qualification). While there were formative test results obtained the average achievement score was 74.77 (sufficient qualification), and the average of the final score was 81.71 (good qualification). It can be concluded that the learning process by applying a contextual approach in Jigsaw type cooperative learning model was proven to help students in achieving the learning outcomes of heat transfer subject.

scholarly journals MODEL PROBLEM BASED LEARNING MENGGUNAKAN MEDIA AUDIOVISUAL UNTUK MENINGKATKAN HASIL BELAJAR PESERTA DIDIK PADA SUBTEMA PERPINDAHAN KALOR DISEKITAR KITA DI KELAS V SD NEGERI 132 PALEMBANG

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Imam Adi Nugroho
Keyword(s):  
Heat Transfer ◽  
Student Learning ◽  
Learning Outcomes ◽  
Model Problem ◽  
Problem Based Learning ◽  
Student Learning Outcomes ◽  
Learning Model ◽  
Male Students ◽  
Audiovisual Media ◽  
5Th Grade

This research aims to improve students learning outcomes in the sub-themes of heat transfer around us in 5th-grade students of SD Negeri 132 Palembang. through the application of the Problem Based Learning Model using Audiovisual Media. This research uses the method of classroom action with two cycles of action. Data collection techniques that are used are test and observation. Action class research has four stages namely Planning, implementation, observation, and reflection. The Subject in the research is 5th-grade student of SD Negeri 132 Palembang with a total of 36 students consisting of 20 females students and 16 male students. The results of this research indicate that the completeness of student learning outcomes in the first cycle amounted to 61,11% with an average 64,30%, in cycle II the completeness of student learning outcomes increased to 83.33% with an average of 81.52. From the result of observation, in the first cycle,the percentage of student activity reached 71.45 within the active category, in the second cycle there was an increase of 81.09% within the very active category. So it can be concluded that the application of the Problem Based Learning model using Audiovisual can improve the learning outcomes of students in the heat transfer sub-theme around us in 5th-grade students of SD Negeri 132 Palembang. Keywords: Model Problem-Based Learning, Audiovisual Media, Students learning outcomes

scholarly journals Derivation and Uncertainty Quantification of a Data-Driven Subcooled Boiling Model

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5987
Author(s):  
Jerol Soibam ◽  
Achref Rabhi ◽  
Ioanna Aslanidou ◽  
Konstantinos Kyprianidis ◽  
Rebei Bel Fdhila
Keyword(s):  
Heat Transfer ◽  
Deep Learning ◽  
Void Fraction ◽  
Wall Temperature ◽  
Flow Boiling ◽  
Learning Model ◽  
Data Driven ◽  
Subcooled Boiling ◽  
Deep Learning Model ◽  
Quantities Of Interest

Subcooled flow boiling occurs in many industrial applications where enormous heat transfer is needed. Boiling is a complex physical process that involves phase change, two-phase flow, and interactions between heated surfaces and fluids. In general, boiling heat transfer is usually predicted by empirical or semiempirical models, which are horizontal to uncertainty. In this work, a data-driven method based on artificial neural networks has been implemented to study the heat transfer behavior of a subcooled boiling model. The proposed method considers the near local flow behavior to predict wall temperature and void fraction of a subcooled minichannel. The input of the network consists of pressure gradients, momentum convection, energy convection, turbulent viscosity, liquid and gas velocities, and surface information. The outputs of the models are based on the quantities of interest in a boiling system wall temperature and void fraction. To train the network, high-fidelity simulations based on the Eulerian two-fluid approach are carried out for varying heat flux and inlet velocity in the minichannel. Two classes of the deep learning model have been investigated for this work. The first one focuses on predicting the deterministic value of the quantities of interest. The second one focuses on predicting the uncertainty present in the deep learning model while estimating the quantities of interest. Deep ensemble and Monte Carlo Dropout methods are close representatives of maximum likelihood and Bayesian inference approach respectively, and they are used to derive the uncertainty present in the model. The results of this study prove that the models used here are capable of predicting the quantities of interest accurately and are capable of estimating the uncertainty present. The models are capable of accurately reproducing the physics on unseen data and show the degree of uncertainty when there is a shift of physics in the boiling regime.

scholarly journals INNOVATION IN GREEN PROCESS ENGINEERING UNDERGRADUATE LABORATORY COURSE - INTEGRATED LABORATORIES FOR PARTICULATE OPERATIONS, HEAT TRANSFER AND MASS TRANSFER COURSES

2013 ◽  
Author(s):  
Chunbao (Charles) Xu ◽  
L. Sang ◽  
D. Bao ◽  
H. Siddiqui ◽  
K. Abbott ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Mass Transfer ◽  
Process Engineering ◽  
Team Work ◽  
Green Process ◽  
Liquid Phase Mass Transfer ◽  
Laboratory Course ◽  
Stirred Reactor ◽  
Undergraduate Laboratory ◽  
Transfer Courses

In the past two years since 2011, the course instructor (Dr. Xu), along with the students in the Green Process Engineering (GPE) class and TAs, has developed an innovative undergraduate laboratory course that integrates laboratories for particulate operations, heat and mass transfer courses. The integrated lab course runs as research projects that apply and integrate the concepts reviewed in the above courses. One of the key objectives of this course is to train team work and leadership. To this end, the students are grouped into 4 groups, and each group carries out one of the following 4 projects for 6h/week and approx.6 weeks, rotates the projects and completes all by the end of this full-year course: (1) Particulate operations - heterogeneous catalyst particles (Au/MgAl2O4) formation, handling and characterization; (2) Convective heat transfer enhancement in a stirred tank reactor; (3) Liquid phase mass transfer in a gas-liquid stirred reactor system; (4) A green process for the production of acetic acid via aqueous phase oxidation of ethanol with air using Au/MgAl2O4 catalyst: effects of mass transfer and reaction kinetics. As the course learning objectives, students should be able to propose experimental methodologies and design their own experimental procedure, secure and prepare their own experimental materials and equipment and facilities, perform the experiments and collect data, interpret the experimental results using the principles and knowledge from the relevant courses, and present their results effectively.

Solution of Similarity Transformation Equations for Boundary Layers Using Spreadsheets

2004 ◽  
Author(s):  
Mohammad H. N. Naraghi
Keyword(s):  
Heat Transfer ◽  
Boundary Layer ◽  
Fluid Mechanics ◽  
Flat Plate ◽  
Thermal Boundary Layer ◽  
Similarity Transformation ◽  
Present Approach ◽  
Published Data ◽  
Boundary Layer Equations ◽  
Transfer Courses

A spreadsheet based solution of the similarity transformation equations of laminar boundary layer equations is presented. In this approach the nonlinear third order differential equations, for both the hydrodynamic and the thermal boundary layer equations, are discretesized using a simple finite difference approach which is suitable for programming spreadsheet cells. This approach was implemented to solve the similarity transform equations for a flat plate (Blasius equations). The thermal boundary layer result was used to obtain the heat transfer correlation for laminar flow over a flat plate in the form of Nu = Nu(Pr,Re). The relative difference between results of the present approach and those of published data are less than 1%. This approach can be easily covered in the undergraduate. Fluid Mechanics and Heat Transfer courses. Also, it can be incorporated in graduate Viscous Fluid Mechanics and Convection Heat Transfer courses. Application of the present approach is not limited to the flat plat boundary layer analysis. It can be used for the solution of a number of similarity transformation equations, including wedge flow problem and natural convection problems that are covered in graduate level courses.

scholarly journals CFD and Machine Learning based Simulation of Flow and Heat Transfer Characteristics of Micro Lattice Structures

2021 ◽  
Vol 850 (1) ◽  
pp. 012034
Author(s):  
Disha Deb ◽  
Harish Rajan ◽  
Rajiv Kundu ◽  
R Mohan
Keyword(s):  
Heat Transfer ◽  
Machine Learning ◽  
Pressure Drop ◽  
Simulated Data ◽  
Learning Model ◽  
Fluid Temperature ◽  
Lattice Structures ◽  
Ansys Fluent ◽  
Machine Learning Model ◽  
Input Parameters

Abstract In this paper, systematic CFD analysis using ANSYS Fluent was carried out to generate the dataset for developing the Machine Learning model, which predicts the average final temperature of water and the pressure drop from the set of input parameters considered for applications. There are six micro lattice structures, kagome, tetrahedral, pyramidal, hexagonal, windward bent and hexagonal-windward bent, modelled for this study using FUSION 360 by Autodesk. The study of heat transfer between liquid water and the micro lattice structures realized with the independent variables, initial fluid flow velocity, lattice temperature, and fluid temperature as well as lattice materials and its different structures. About 2146 output data of average final fluid temperature and the pressure drop were collected from the CFD simulations by varying input parameters. To predict the output parameter against the set of input parameters, Machine Learning model with regression based classification algorithm was adopted while training the ML model. The quality metric of the ML model was calculated using residual sum of squares method. The final average temperature of the fluid and pressure drop as predicted by the ML model is closer to simulated data.

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