The Experts below are selected from a list of 3763791 Experts worldwide ranked by ideXlab platform
Chang-da Wen - One of the best experts on this subject based on the ideXlab platform.
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Study of Steel Emissivity Characteristics and Application of Multispectral Radiation Thermometry (MRT)
Journal of Materials Engineering and Performance, 2011Co-Authors: Chang-da WenAbstract:Experiments were first conducted to measure the Emissivity values of a variety of steel samples at 700, 800, and 900 K. The effects of wavelength, temperature, alloy composition, and heating time on Emissivity were investigated. Multispectral radiation thermometry (MRT) with linear Emissivity models (LEMs) and log-linear Emissivity models (LLEs) were then applied to predict surface temperature. Parametric influences of the number of wavelengths and order of Emissivity models were examined. Results show that the spectral Emissivity decreases with increasing wavelength and increases with increasing temperature. Steel with higher chromium content has lower Emissivity value because of the chromium oxide protection layer. The spectral Emissivity reaches steady state after the third hour heating due to the surface oxidation becoming fully developed. Increasing the order of polynomial and increasing the number of wavelengths cannot improve temperature measurement accuracy. Overall, the first-order LEM and the first-order LLE showed the best accuracy for different alloys, the number of wavelengths, and temperatures.
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Experimental investigation of Emissivity of aluminum alloys and application of multispectral radiation thermometry
Applied Thermal Engineering, 2011Co-Authors: Chang-da Wen, Tzung Yuan ChaiAbstract:Experiments were first conducted to measure the Emissivity values of a variety of aluminum alloys at 600, 700, and 800 K. The effects of wavelength, temperature, alloy composition, and heating time on Emissivity were investigated. Multispectral radiation thermometry (MRT) with linear Emissivity models (LEM) and log-linear Emissivity models (LLE) were then applied to predict surface temperature. Parametric influences of wavelength number, heating time and order of Emissivity models were examined. Results show that the spectral Emissivity decreases with increasing wavelength and increases with increasing temperature. A stronger alloy effect is evident at higher temperature. The spectral Emissivity reaches steady state after the first hour heating due to the surface oxidation becoming fully-developed. Half of the temperature predictions by MRT Emissivity models provide the absolute temperature error under 10% and quarter of the results are under 5%. Increasing the order of Emissivity model and increasing the number of wavelengths cannot improve temperature measurement accuracy. Overall, LLE models show higher accuracy than LEM models. The first-order and second-order LLE models and the first-order LEM model give good results most frequently and provide the best compensation for different alloys, the number of wavelengths, and temperatures. © 2011 Elsevier Ltd. All rights reserved.
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Investigation of steel Emissivity behaviors: Examination of Multispectral Radiation Thermometry (MRT) Emissivity models
International Journal of Heat and Mass Transfer, 2010Co-Authors: Chang-da WenAbstract:Steel Emissivity behaviors were investigated in this study. Experiments were conducted to measure Emissivity. Six Emissivity models were then applied to examine Multispectral Radiation Thermometry (MRT) on inferring surface temperature. The data show that Emissivity decreases with increasing wavelength. For steel containing high chromium, Emissivity is usually lower than others because of the chromium oxide protection layer. Two Emissivity models provide the best overall compensation for different alloys, number of wavelengths, and temperatures. The results reveal that if the Emissivity model can well represent the real Emissivity behaviors, the more accurate inferred temperature can be achieved. ?? 2009 Elsevier Ltd. All rights reserved.
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Modeling the effects of surface roughness on the Emissivity of aluminum alloys
International Journal of Heat and Mass Transfer, 2006Co-Authors: Chang-da Wen, Issam MudawarAbstract:This study explores the relationship between the Emissivity of aluminum alloy surfaces and surface roughness. Two methods are discussed which yield good overall predictions of the Emissivity of rough surfaces. One method consists of using a mathematical multispectral radiation thermometry (MRT) model for the Emissivity and determining both the surface temperature and the empirical constants in the Emissivity model from radiance measurements. This method requires new Emissivity constants to be determined for each surface topography. This study also presents an alternative method for determining the Emissivity of rough surfaces. This method relies on determining the Emissivity characteristics of a single reference surface and inferring the Emissivity of any other rough surface of the same mat rial by relating a surface roughness function (determined by surface topography instrumentation) of the rough surface to that of the reference surface. Using data for AL 7075 with various degrees of surface roughness, this method is shown to yield better accuracy than the first method.
Issam Mudawar - One of the best experts on this subject based on the ideXlab platform.
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modeling the effects of surface roughness on the Emissivity of aluminum alloys
International Journal of Heat and Mass Transfer, 2006Co-Authors: Issam MudawarAbstract:This study explores the relationship between the Emissivity of aluminum alloy surfaces and surface roughness. Two methods are discussed which yield good overall predictions of the Emissivity of rough surfaces. One method consists of using a mathematical multispectral radiation thermometry (MRT) model for the Emissivity and determining both the surface temperature and the empirical constants in the Emissivity model from radiance measurements. This method requires new Emissivity constants to be determined for each surface topography. This study also presents an alternative method for determining the Emissivity of rough surfaces. This method relies on determining the Emissivity characteristics of a single reference surface and inferring the Emissivity of any other rough surface of the same material by relating a surface roughness function (determined by surface topography instrumentation) of the rough surface to that of the reference surface. Using data for AL 7075 with various degrees of surface roughness, this method is shown to yield better accuracy than the first method.
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Modeling the effects of surface roughness on the Emissivity of aluminum alloys
International Journal of Heat and Mass Transfer, 2006Co-Authors: Chang-da Wen, Issam MudawarAbstract:This study explores the relationship between the Emissivity of aluminum alloy surfaces and surface roughness. Two methods are discussed which yield good overall predictions of the Emissivity of rough surfaces. One method consists of using a mathematical multispectral radiation thermometry (MRT) model for the Emissivity and determining both the surface temperature and the empirical constants in the Emissivity model from radiance measurements. This method requires new Emissivity constants to be determined for each surface topography. This study also presents an alternative method for determining the Emissivity of rough surfaces. This method relies on determining the Emissivity characteristics of a single reference surface and inferring the Emissivity of any other rough surface of the same mat rial by relating a surface roughness function (determined by surface topography instrumentation) of the rough surface to that of the reference surface. Using data for AL 7075 with various degrees of surface roughness, this method is shown to yield better accuracy than the first method.
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Experimental investigation of Emissivity of aluminum alloys and temperature determination using multispectral radiation thermometry (MRT) algorithms
Journal of Materials Engineering and Performance, 2002Co-Authors: Issam MudawarAbstract:Experiments were performed to measure the Emissivity spectra for aluminum (Al) surfaces that are subject to variations in alloy, temperature, heating time, and surface finish. The linear Emissivity model (LEM) and log-linear Emissivity (LLE) model were tested against thermocouple measurements to explore the accuracy of these models at inferring surface temperature. The data show Emissivity decreases with increasing wavelength for λ
S.j. Hook - One of the best experts on this subject based on the ideXlab platform.
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Separating temperature and Emissivity in thermal infrared multispectral scanner data: implications for recovering land surface temperatures
IEEE Transactions on Geoscience and Remote Sensing, 1993Co-Authors: P.s. Kealy, S.j. HookAbstract:The accuracy of three techniques for recovering surface kinetic temperature from multispectral thermal infrared data acquired over land is evaluated. The three techniques are the reference channel method, the Emissivity normalization method, and the alpha Emissivity method. The methods used to recover the temperature of artificial radiance derived from a wide variety of materials. The results indicate that the Emissivity normalization and alpha Emissivity techniques are the most accurate, and recover the temperature of the majority of the artificial radiance spectra to within 1.5 K; the reference channel method produces less accurate results. The primary advantage of the alpha Emissivity method over the Emissivity normalization method is that it works well in terrains of widely varying emissivities, e.g.,those dominated by vegetation and igneous rocks. By contrast, the Emissivity normalization method works well only if the Emissivity used for normalization is close to the maximum Emissivity of the spectra in the scene.
Shunlin Liang - One of the best experts on this subject based on the ideXlab platform.
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Effects of Thermal-Infrared Emissivity Directionality on Surface Broadband Emissivity and Longwave Net Radiation Estimation
IEEE Geoscience and Remote Sensing Letters, 2014Co-Authors: Jie Cheng, Shunlin LiangAbstract:Directionality is ignored in the satellite retrieval of surface thermal-infrared Emissivity, which will unavoidably affect the estimates of surface broadband Emissivity and surface longwave net radiation. The purpose of this work is to quantify the effects of Emissivity directionality. First, three types of Emissivity data are used to calculate hemispherical Emissivity and the difference between directional broadband Emissivity and hemispherical broadband Emissivity. The Emissivity directionality is highly significant, and the directional Emissivity decreases with increasing view angles. A view angle within 45° -60° can be found whose directional Emissivity is highly close to the hemispherical Emissivity, and the difference between the calculated directional and hemispherical broadband Emissivity is zero. The difference between the atmospheric downward radiation and blackbody radiation at surface temperature is then determined by extensive simulations. Finally, the error ranges of surface longwave net radiation are presented. If the sensor scan angle is within ±55°, the error can reach as high as 17.48 and 14.05 W/m2 for water and bare ice, respectively; the error is less than 2.74 W/m2 for snow with different radii; the error can reach 4.11 W/m2 for sun crust; the error is less than 5.14 W/m2 for minerals, sand, slime and gravel; and clay has the smallest error at 1.02 W/m2.
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An optimization algorithm for separating land surface temperature and Emissivity from multispectral thermal infrared imagery
IEEE Transactions on Geoscience and Remote Sensing, 2001Co-Authors: Shunlin LiangAbstract:Land surface temperature (LST) and Emissivity are important\ncomponents of land surface modeling and applications. The only practical\nmeans of obtaining LST at spatial and temporal resolutions appropriate\nfor most modeling applications is through remote sensing. While the\npopular split-window method has been widely used to estimate LST, it\nrequires known Emissivity values. Multispectral thermal infrared imagery\nprovides us with an excellent opportunity to estimate both LST and\nEmissivity simultaneously, but the difficulty is that a single\nmultispectral thermal measurement with N bands presents N equations in\nN+1 unknowns (N spectral emissivities and LST). In this study, we\ndeveloped a general algorithm that can separate land surface Emissivity\nand LST from any multispectral thermal imagery, such as\nmoderate-resolution imaging spectroradiometer (MODIS) and advanced\nspaceborne thermal emission and reflection radiometer (ASTER) data. The\ncentral idea was to establish empirical constraints, and regularization\nmethods were used to estimate both Emissivity and LST through an\noptimization algorithm. It allows us to incorporate any prior knowledge\nin a formal way, The numerical experiments showed that this algorithm is\nvery effective (more than 43.4% inversion results differed from the\nactual LST within 0.5°, 70.2% within 1° and 84% within\n1.5°), although improvements are still needed
P.s. Kealy - One of the best experts on this subject based on the ideXlab platform.
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Separating temperature and Emissivity in thermal infrared multispectral scanner data: implications for recovering land surface temperatures
IEEE Transactions on Geoscience and Remote Sensing, 1993Co-Authors: P.s. Kealy, S.j. HookAbstract:The accuracy of three techniques for recovering surface kinetic temperature from multispectral thermal infrared data acquired over land is evaluated. The three techniques are the reference channel method, the Emissivity normalization method, and the alpha Emissivity method. The methods used to recover the temperature of artificial radiance derived from a wide variety of materials. The results indicate that the Emissivity normalization and alpha Emissivity techniques are the most accurate, and recover the temperature of the majority of the artificial radiance spectra to within 1.5 K; the reference channel method produces less accurate results. The primary advantage of the alpha Emissivity method over the Emissivity normalization method is that it works well in terrains of widely varying emissivities, e.g.,those dominated by vegetation and igneous rocks. By contrast, the Emissivity normalization method works well only if the Emissivity used for normalization is close to the maximum Emissivity of the spectra in the scene.
