scholarly journals Development of a Simplified Regression Equation for Predicting Underground Temperature Distributions in Korea

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2894
Author(s):  
Sung-Woo Cho ◽  
Pyeongchan Ihm
Keyword(s):  
Temperature Distribution ◽  
Surface Temperature ◽  
Amplitude Ratio ◽  
Ground Surface ◽  
Regression Equation ◽  
Ground Temperature ◽  
Coefficient Of Determination ◽  
Outdoor Temperature ◽  
Geothermal Heat ◽  
Temperature Distributions

The Korea Meteorological Administration (KMA) measures outdoor temperature and ground surface temperature at 95 observation points, but monthly ground temperatures by depth, which are important for using geothermal heat, are only provided for nine points. Since the ground temperature is known in the vicinity of only nine observation points, it is very difficult to predict underground temperature in the field. This study develops a simplified regression equation for predicting underground temperature distributions, compares the prediction results with the experimental data of Korea’s representative areas and the data measured in this study, and examines the validity of the developed regression equation. The regression equation for predicting temperature amplitudes at ground depths of 1.0, 3.0, and 5.0 m was derived using the amplitude ratio of outdoor temperature and surface temperature provided by KMA at nine points in Korea from 2006 to 2015. The coefficient of determination was as high as 0.93 (95% confidence level). In addition, the field-measured ground temperature distribution at a depth of 3 m was in good agreement with the predicted ground temperature distribution in Changwon districts for the whole of 2017.

scholarly journals Thermal conductivity of porous ice in hailstone shells

1996 ◽  
Vol 42 (141) ◽  
pp. 195-200
Author(s):  
Guoguang Zheng ◽  
Roland List
Keyword(s):  
Thermal Conductivity ◽  
Heat Flux ◽  
Temperature Distribution ◽  
Surface Temperature ◽  
Linear Dependence ◽  
Internal Heat ◽  
Density Range ◽  
Surface Temperature Distribution ◽  
Temperature Distributions ◽  
Evolving Surface

AbstractThe thermal conductivity and diffusivity of porous ice accreted on spherical and spheroidal hailstone models were measured over a density range of 620–915 kg m−3. By scanning the evolving surface temperature distributions during cooling in a cold airflow the thermal conductivity was varied in iterative fashion until the internal heat flux produced the correct surface temperature distribution. The results indicate a linear dependence of the thermal conductivity,ki, and diffusivity,αi, on density. For example, lowering the density by 10% lowerskiby 15%. Within the range of cloud conditions, the density variations affect the thermal parameters more than temperature does. The results also indicate a continuous decrease of the thermal conductivity from bulk ice via consolidated porous ice to loosely packed snow.

scholarly journals Relationship between urban heat island and population density of districts in Ho Chi Minh City derived from Sentinel - 3 SLSTR data

2021 ◽  
Vol 62 (5) ◽  
pp. 67-75
Author(s):  
Ha Thu Thi Le ◽  
Trung Van Nguyen ◽  
Khoa Ngoc Nguyen ◽  
Phuong Dang Nguyen ◽  
Tuyet Thi Vo ◽  
...  
Keyword(s):  
Population Density ◽  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface ◽  
Ground Surface ◽  
Ho Chi Minh City ◽  
Coefficient Of Determination ◽  
Heat Island ◽  
Infrared Spectral ◽  
Urban Heat

The urban heat island occurs due to the causes of the urbanization process, of which the main reason is an increase in population density leading to the changes in artificial objects on the ground surface. Recently, using the Split - Window algorithm for two thermal infrared spectral channels with wavelengths of 11 µm and 12 µm to calculate the daily surface temperature with two times of day and night serves to determine the change of land surface temperature. This method is intended to improve the reliability of the results and high technical efficiency. This study uses Sentinel - 3 SLSTR data to determine urban heat island in the districts of Ho Chi Minh City compared to areas bordering the city on May 15th 2020. In addition, population density is calculated according to the results of the census in 2020. The linear relationship between the urban heat island and population density was built with the coefficient of determination R2=0.64.

Analytic Models for Temperature Distribution Near a Nanoantenna

2013 ◽  
Author(s):  
Casey N. Brock ◽  
Greg Walker ◽  
Zack Coppens
Keyword(s):  
Temperature Distribution ◽  
Boundary Conditions ◽  
Surface Temperature ◽  
Temperature Fields ◽  
Analytic Model ◽  
Superposition Model ◽  
Metallic Structures ◽  
Temperature Variations ◽  
Temperature Distributions ◽  
Analytic Models

Thermoplasmonic structures produce highly localized temperature fields. For simplicity, researchers often use a superposition of representative spheres to model the temperature in the non-conductive region (presumably a substrate) near the metallic structures deposited on the substrate. The superposition model provides reasonable solutions, but direct comparison to experiments is difficult because local temperature variations at the nanoscale are not accessible. Moreover, the model requires several approximations. Therefore, we compare this model to other analytic models to determine the efficacy of the superposition approach in capturing temperature distributions close to the surface of the substrate and to capture realistic boundary conditions. Results indicate that a 3D analytic model can relax approximations required for the superposition model and show that the superposition model consistently over-predicts the surface temperature.

Thermal conductivity of porous ice in hailstone shells

1996 ◽  
Vol 42 (141) ◽  
pp. 195-200
Author(s):  
Guoguang Zheng ◽  
Roland List
Keyword(s):  
Thermal Conductivity ◽  
Heat Flux ◽  
Temperature Distribution ◽  
Surface Temperature ◽  
Linear Dependence ◽  
Internal Heat ◽  
Density Range ◽  
Surface Temperature Distribution ◽  
Temperature Distributions ◽  
Evolving Surface

AbstractThe thermal conductivity and diffusivity of porous ice accreted on spherical and spheroidal hailstone models were measured over a density range of 620–915 kg m−3. By scanning the evolving surface temperature distributions during cooling in a cold airflow the thermal conductivity was varied in iterative fashion until the internal heat flux produced the correct surface temperature distribution. The results indicate a linear dependence of the thermal conductivity, ki, and diffusivity, αi, on density. For example, lowering the density by 10% lowers ki by 15%. Within the range of cloud conditions, the density variations affect the thermal parameters more than temperature does. The results also indicate a continuous decrease of the thermal conductivity from bulk ice via consolidated porous ice to loosely packed snow.

scholarly journals Identification of The Geothermal Heat at Mount Iyang-Argopuro Based on a Data Image of Landsat 8 Satellite and a Data Gravity of GGMPlus Satellite

2021 ◽  
Vol 4 (1) ◽  
pp. 44
Author(s):  
Ega Abi Bahtiar ◽  
Agus Suprianto ◽  
S. Supriyadi
Keyword(s):  
Surface Temperature ◽  
Spectral Band ◽  
Satellite Image ◽  
Bouguer Anomaly ◽  
Ground Surface ◽  
Landsat 8 ◽  
Low Density ◽  
Low Gravity ◽  
Geothermal Heat ◽  
Rock Structure

In Indonesia is the Iyang-Argopuro Volcano Complex. The Research uses remote sensing methods and gravity methods conducted to study thermal anomalies and subsurface structures using Data images of Landsat 8 satellite and a data gravity of GGMPlus satellite. Moreover, the study aims to estimate the number of hoisting manifestations of the earth's heat at the compound of the Iyang-Argopuro volcano. Landsat 8 satellite image data is a spectral band (band 1-9) and a thermal band (band 10 and 11). The data was done in radiometric corrections, radiance correction, and reflex corrections, and was thus obtained a value of ground surface temperature (LST). The data was done in radiometric corrections, radiance correction, and reflectance corrections, to get an LST value. Processing data gravity of GGMPlus satellites with a bouguer correction, terrain corrections up to get a Complete Bouguer Anomaly (ABL) value. Furthermore, the ABL value consisting of regional and local anomalies is separated using an upward continuation filter to obtain local anomaly values. The results obtained from this study are five points of geothermal manifestation locations in the Iyang-Argopuro Volcano Complex which are located in the northern, central, southern and eastern parts. Manifestations in the northern, central, and northeastern parts have ground surface temperatures of 24-31 ̊C. While manifestations in the central and eastern parts have a surface temperature of land 21-31 ̊C. The high ground temperature values at the five locations were associated with low gravity values ranging from -20 mGal to -10 mGal. The low gravity value is assumed to have a rock structure with a low-density value. Low-density rock structures have the possibility of an outflow zone causing the soil surface temperature to be relatively high.

PENGARUH SARANA BELAJAR DAN MOTIVASI BELAJAR TERHADAP KEMANDIRIAN BELAJAR MAHASISWA (Studi Empirical Pada Mahasiswa Beasiswa Bidikmisi UPBJJ-UT Ternate)

2018 ◽  
Vol 19 (2) ◽  
pp. 68
Author(s):  
Raden Sudarwo ◽  
Yusuf Yusuf ◽  
Anfas Anfas
Keyword(s):  
Student Learning ◽  
Significant Influence ◽  
Sense Of Self ◽  
Regression Equation ◽  
Learning Motivation ◽  
Coefficient Of Determination ◽  
Learning Tools ◽  
Constant Number ◽  
Self Sufficiency ◽  
Student Independence

This study aims to determine the influence of learning facilities and student learning motivation towards the independence of student learning. The result of the research shows that there is positive and significant influence of learning tool (X1) on learning independence (Y). It is obtained by tvalue (2,159) with p = 0,034 <0,05 and ttable at 5% significant level with df = 78 equal to 1,991. There is a positive and significant influence of learning motivation (X2) on learning independence (Y). It is obtained tvalue (7,858) with p = 0,000 <0,05 and ttable at 5% significant level with df = 78 equal to 1,991. There is a positive and significant influence of learning facilities (X1) and learning motivation (X2) simultaneously to the independence of learning (Y). This shows the coefficient of double correlation RY (1,2) = 0,746 and R² = 0,557 and price Fvalue equal to 48,980 with p = 0,000 <0,05 and Ftable = 3,11 at 5% significant level. Coefficient value X1 = 0,186 and X2 = 0,647, constant number equal to 8,650 so that can be made regression equation Y = 8,650 + 0,186X1 + 0,647X2. The higher the learning means (X1) and the learning motivation (X2), the higher the learning independence (Y). Coefficient of Determination is R² of 0,557. Means 55,7% learning independence is explained by learning tools and learning motivation. Meanwhile, 44,3% is explained by other factors not discussed in this study. The study concludes that partially, learning facilities and student learning motivation has a positive and significant effect on student independence (self-sufficiency) in learning.  In addition, both learning facility and motivation have a positive and significant effect on student learning independence or sense of self-sufficiency. Penelitian ini bertujuan untuk mengetahui pengaruh fasilitas belajar dan motivasi belajar siswa terhadap kemandirian belajar siswa. Hasil penelitian menunjukkan bahwa ada pengaruh yang positif dan signifikan sanara belajar (X1) terhadap kemandirian belajar (Y). Hal ini diperoleh dengan nilai thitung (2,159) dengan p = 0,034 <0,05 dan ttabel pada 5% tingkat signifikan dengan df = 78 sama dengan 1,991. Ada pengaruh positif dan signifikan motivasi belajar (X2) pada kemandirian belajar (Y). Diperoleh nilai thitung (7,858) dengan p = 0,000 <0,05 dan ttabel pada taraf signifikan 5% dengan df = 78 sebesar 1,991. Ada pengaruh yang positif dan signifikan dari fasilitas belajar (X1) dan motivasi belajar (X2) secara bersamaan terhadap kemandirian belajar (Y). Hal ini menunjukkan koefisien korelasi ganda RY (1,2) = 0,746 dan R² = 0,557 dan harga Fhitung sebesar 48,980 dengan p = 0,000 <0,05 dan Ftabel = 3,11 pada taraf signifikan 5%. Nilai koefisien X1 = 0,186 dan X2 = 0,647, bilangan konstan sebesar 8,650 sehingga dapat dibuat persamaan regresi Y = 8,650 + 0,186X1 + 0,647X2. Semakin tinggi nilai sarana belajar (X1) dan motivasi belajar (X2), semakin tinggi kemandirian belajar (Y). Koefisien Determinasi adalah R² 0,557. Berarti 55,7% kemandirian belajar dijelaskan oleh alat belajar dan motivasi belajar. Sementara itu, 44,3% dijelaskan oleh faktor-faktor lain yang tidak dibahas dalam penelitian ini. Penelitian ini menyimpulkan bahwa secara parsial, baik ketersediaan sarana prasaran belajar dan motivasi berpengaruh positif dan signifikan pada kemandirian mahasiswa, dari dari kedua variable tersebut motivasi mempunyai pengaruh lebih besar. Secara simultan ketersediaan sarana prasarana dalam belajar dan pembelajaran, serta motivasi berpengaruh positif terhadap kemandirian belajar.

scholarly journals 3D Thermal Monitoring of Jointed Rock Masses through Infrared Thermography and Photogrammetry

2021 ◽  
Vol 13 (5) ◽  
pp. 957
Author(s):  
Guglielmo Grechi ◽  
Matteo Fiorucci ◽  
Gian Marco Marmoni ◽  
Salvatore Martino
Keyword(s):  
Surface Temperature ◽  
Infrared Thermography ◽  
Point Cloud ◽  
Engineering Geology ◽  
Point Clouds ◽  
Jointed Rock ◽  
Geometric Accuracy ◽  
Rock Masses ◽  
Natural Systems ◽  
Temperature Distributions

The study of strain effects in thermally-forced rock masses has gathered growing interest from engineering geology researchers in the last decade. In this framework, digital photogrammetry and infrared thermography have become two of the most exploited remote surveying techniques in engineering geology applications because they can provide useful information concerning geomechanical and thermal conditions of these complex natural systems where the mechanical role of joints cannot be neglected. In this paper, a methodology is proposed for generating point clouds of rock masses prone to failure, combining the high geometric accuracy of RGB optical images and the thermal information derived by infrared thermography surveys. Multiple 3D thermal point clouds and a high-resolution RGB point cloud were separately generated and co-registered by acquiring thermograms at different times of the day and in different seasons using commercial software for Structure from Motion and point cloud analysis. Temperature attributes of thermal point clouds were merged with the reference high-resolution optical point cloud to obtain a composite 3D model storing accurate geometric information and multitemporal surface temperature distributions. The quality of merged point clouds was evaluated by comparing temperature distributions derived by 2D thermograms and 3D thermal models, with a view to estimating their accuracy in describing surface thermal fields. Moreover, a preliminary attempt was made to test the feasibility of this approach in investigating the thermal behavior of complex natural systems such as jointed rock masses by analyzing the spatial distribution and temporal evolution of surface temperature ranges under different climatic conditions. The obtained results show that despite the low resolution of the IR sensor, the geometric accuracy and the correspondence between 2D and 3D temperature measurements are high enough to consider 3D thermal point clouds suitable to describe surface temperature distributions and adequate for monitoring purposes of jointed rock mass.

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