Journal articles: 'Heat source identification'

1

Kriegsmann, G. A., and W. E. Olmstead. "Source identification for the heat equation." Applied Mathematics Letters 1, no. 3 (1988): 241–45. http://dx.doi.org/10.1016/0893-9659(88)90084-5.

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Momose, Kazunari, Tetsuya Murai, Toshihiko Asami, and Yoshinobu Hosokawa. "Identification of Heat-Source Using Neural Network." Transactions of the Japan Society of Mechanical Engineers Series C 59, no. 567 (1993): 3431–36. http://dx.doi.org/10.1299/kikaic.59.3431.

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Erdem, A., D. Lesnic, and A. Hasanov. "Identification of a spacewise dependent heat source." Applied Mathematical Modelling 37, no. 24 (December 2013): 10231–44. http://dx.doi.org/10.1016/j.apm.2013.06.006.

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Xie, Yanmei, Caihong Ma, Yindi Zhao, Dongmei Yan, Bo Cheng, Xiaolin Hou, Hongyu Chen, Bihong Fu, and Guangtong Wan. "The Potential of Using SDGSAT-1 TIS Data to Identify Industrial Heat Sources in the Beijing–Tianjin–Hebei Region." Remote Sensing 16, no. 5 (February 22, 2024): 768. http://dx.doi.org/10.3390/rs16050768.

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It is crucial to detect and classify industrial heat sources for sustainable industrial development. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data were first introduced for detecting industrial heat source production areas to address the difficulty in identifying factories with low combustion temperatures and small scales. In this study, a new industrial heat source identification and classification model using SDGSAT-1 TIS and Landsat 8/9 Operational Land Imager (OLI) data was proposed to improve the accuracy and granularity of industrial heat source recognition. First, multiple features (thermal and optical features) were extracted using SDGSAT-1 TIS and Landsat 8/9 OLI data. Second, an industrial heat source identification model based on a support vector machine (SVM) and multiple features was constructed. Then, industrial heat sources were generated and verified based on the topological correlation between the identification results of the production areas and Google Earth images. Finally, the industrial heat sources were classified into six categories based on point-of-interest (POI) data. The new model was applied to the Beijing–Tianjin–Hebei (BTH) region of China. The results showed the following: (1) Multiple features enhance the differentiation and identification accuracy between industrial heat source production areas and the background. (2) Compared to active-fire-point (ACF) data (375 m) and Landsat 8/9 thermal infrared sensor (TIRS) data (100 m), nighttime SDGSAT-1 TIS data (30 m) facilitate the more accurate detection of industrial heat source production areas. (3) Greater than 2~6 times more industrial heat sources were detected in the BTH region using our model than were reported by Ma and Liu. Some industrial heat sources with low heat emissions and small areas (53 thermal power plants) were detected for the first time using TIS data. (4) The production areas of cement plants exhibited the highest brightness temperatures, reaching 301.78 K, while thermal power plants exhibited the lowest brightness temperatures, averaging 277.31 K. The production areas and operational statuses of factories could be more accurately identified and monitored with the proposed approach than with previous methods. A new way to estimate the thermal and air pollution emissions of industrial enterprises is presented.

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Tsai, Richard, Stanley Osher, and Yingying Li. "Heat source identification based on $l_1$ constrained minimization." Inverse Problems and Imaging 8, no. 1 (March 2014): 199–221. http://dx.doi.org/10.3934/ipi.2014.8.199.

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Eremin, A. V., E. V. Stefanyuk, and L. S. Abisheva. "HEAT SOURCE IDENTIFICATION BASED ON ANALYTICAL SOLUTIONS OF THE HEAT-CONDUCTION PROBLEM." Izvestiya Visshikh Uchebnykh Zavedenii. Chernaya Metallurgiya = Izvestiya. Ferrous Metallurgy 59, no. 5 (January 1, 2016): 339–46. http://dx.doi.org/10.17073/0368-0797-2016-5-339-346.

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KUBO, Shiro, Kohzaburo OHNAKA, and Kiyotsugu OHJI. "Identification of heat-source and force using boundary integrals." Transactions of the Japan Society of Mechanical Engineers Series A 54, no. 503 (1988): 1329–34. http://dx.doi.org/10.1299/kikaia.54.1329.

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Hettlich, F., and W. Rundell. "Identification of a discontinuous source in the heat equation." Inverse Problems 17, no. 5 (September 7, 2001): 1465–82. http://dx.doi.org/10.1088/0266-5611/17/5/315.

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Ling, Leevan, Masahiro Yamamoto, Y. C. Hon, and Tomoya Takeuchi. "Identification of source locations in two-dimensional heat equations." Inverse Problems 22, no. 4 (June 26, 2006): 1289–305. http://dx.doi.org/10.1088/0266-5611/22/4/011.

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Lu, Zhong-Rong, Tiancheng Pan, and Li Wang. "A sparse regularization approach to inverse heat source identification." International Journal of Heat and Mass Transfer 142 (October 2019): 118430. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2019.07.080.

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BniLam, Noori, and Rafid Al-Khoury. "Parameter identification algorithm for ground source heat pump systems." Applied Energy 264 (April 2020): 114712. http://dx.doi.org/10.1016/j.apenergy.2020.114712.

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Jeffs, James, Andrew McGordon, Alessandro Picarelli, Simon Robinson, Yashraj Tripathy, and Widanalage Widanage. "Complex Heat Pump Operational Mode Identification and Comparison for Use in Electric Vehicles." Energies 11, no. 8 (August 1, 2018): 2000. http://dx.doi.org/10.3390/en11082000.

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Previous research has focused on the use of heat pumps in electric vehicles, with the focus on recuperating heat from, normally, ambient and one thermal source on the vehicle. Here 5 potential thermal sources on a vehicle have been identified and thorough testing on the benefit of each source has been performed. The results presented suggest the motor, a thermal storage device, and cabin exhaust extraction should be used >80% of the time according to the scenarios tested, while battery heating and transmission heat extraction should be used subject to conditions on the ambient temperature and drive cycle.

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13

Yang, Xiao-Mei, and Zhi-Liang Deng. "A Point Source Identification Problem for a Time Fractional Diffusion Equation." Advances in Mathematical Physics 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/485273.

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An inverse source identification problem for a time fractional diffusion equation is discussed. The unknown heat source is supposed to be space dependent only. Based on the use of Green’s function, an effective numerical algorithm is developed to recover both the intensities and locations of unknown point sources from final measurements. Numerical results indicate that the proposed method is efficient and accurate.

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Ma, Caihong, Xin Sui, Yi Zeng, Jin Yang, Yanmei Xie, Tianzhu Li, and Pengyu Zhang. "Classification of Industrial Heat Source Objects Based on Active Fire Point Density Segmentation and Spatial Topological Correlation Analysis in the Beijing–Tianjin–Hebei Region." Sustainability 14, no. 18 (September 7, 2022): 11228. http://dx.doi.org/10.3390/su141811228.

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The development of industrial infrastructure in the Beijing–Tianjin–Hebei(BTH) region has been accompanied by a disorderly expansion of industrial zones and other inappropriate development. Accurate industrial heat source classification data become important to evaluate the policies of industrial restructuring and air quality improvement. In this study, a new classification of industrial heat source objects model based on active fire point density segmentation and spatial topological correlation analysis in the BTH Region was proposed. First, industrial heat source objects were detected with an active fire point density segmentation method using NPP-VIIRS active fire/hotspot data. Then, industrial heat source objects were classified into five categories based on a spatial topological correlation analysis method using POI data. Then, identification and classification results were manually validated based on Google Earth imagery. Finally, we evaluated the factors influencing the number of industrial heat sources based on an OLS regression model. A total of 493 industrial heat source objects were identified in this study with an identification accuracy of 96.14%(474/493). Compared with results for nighttime fires, the number of industrial heat source objects that were identified was higher, and the spatial coverage was greater; the minimum size of the detected objects was also smaller. Based on the function of the identified industrial heat source objects, the objects in the BTH region were then divided into five categories: cement plants (21.73%), steel plants (53.80%), coal and chemical industry (12.66%), oil and gas developments (7.81%), and other (4.01%). An analysis of their operations showed that the number of industrial heat source objects in operation in the BTH region tended to first rise and then decline during the 2012–2021 period, with the peak being reached in 2013. The results of this study will aid the rationalization of industrial infrastructure in the BTH region and, by extension, in China as a whole.

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Cheng, Wei, Ling-Ling Zhao, and Chu-Li Fu. "Source term identification for an axisymmetric inverse heat conduction problem." Computers & Mathematics with Applications 59, no. 1 (January 2010): 142–48. http://dx.doi.org/10.1016/j.camwa.2009.08.038.

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Stock, Jan, André Xhonneux, and Dirk Müller. "Framework for the Automated Identification of Possible District Heating Separations to Utilise Present Heat Sources Based on Existing Network Topology." Energies 15, no. 21 (November 6, 2022): 8290. http://dx.doi.org/10.3390/en15218290.

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The ambitious climate targets of the European Union emphasise the necessity to reduce carbon dioxide emissions in the building sector. Therefore, various sustainable heat sources should be used in existing district heating systems to cover the heat demands of buildings. However, integrating on-site heat sources into large existing district heating networks could be challenging due to temperature or capacity limitations since such large district heating systems are often supplied by large fossil-based heating plants. Most sustainable heat sources that should be utilised in district heating systems differ in their geographical locations or have limited heat capacities and, therefore, cannot easily replace conventional heating plants. The resulting difficulty of integrating limited heat sources into large district heating networks could be tackled by separating the existing network structure into two independent heat distribution networks. In this study, we present a developed framework that automatically recommends which network parts of an existing district heating system could be hydraulically separated in order to utilise a present heat source that is not yet in use. In this way, a second, standalone district heating system, supplied by the utilised heat source, could be established. The framework applies a community detection algorithm to the existing district heating network to first identify communities in the structure. Neighbouring communities are aggregated to larger network areas, taking into account that these areas could be supplied with the available amount of heat. These network areas are classified as possible areas for separation if the shortest connection path to the utilised heat source is within a certain distance. Subsequently, the found possibilities for network separation are simulated to test a feasible district heating operation and to evaluate the environmental and economic impacts. The presented framework is tested with a meshed and a spanning-tree network structure. Overall, the developed framework presents an approach to utilise present heat sources in separated network structures by automatically identifying, testing and evaluating possible network separations.

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Hartel, Udo, Alexander Ilin, Steffen Sonntag, and Vesselin Michailov. "Nonlinear Optimization Methods for the Determination of Heat Source Model Parameters." Materials Science Forum 879 (November 2016): 2008–13. http://dx.doi.org/10.4028/www.scientific.net/msf.879.2008.

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In this paper the technique of parameter identification is investigated to reconstruct the 3D transient temperature field for the simulation of laser beam welding. The reconstruction bases on volume heat source models and makes use of experimental data. The parameter identification leads to an inverse heat conduction problem which cannot be solved exactly but in terms of an optimal alignment of the simulation and experimental data. To solve the inverse problem, methods of nonlinear optimization are applied to minimize a problem dependent objective function.In particular the objective function is generated based on the Response Surface Model (RSM) technique. Sampling points on the RSM are determined by means of Finite-Element-Analysis (FEA). The scope of this research paper is the evaluation and comparison of gradient based and stochastic optimization algorithms. The proposed parameter identification makes it possible to determine the heat source model parameters in an automated way. The methodology is applied on welds of dissimilar material joints.

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Ignaszak, Zenon, and Paweł Popielarski. "Problems of Heat Source Modeling in Iso–Exothermic Materials Used as Riser Sleeves in Foundry." Materials Science Forum 514-516 (May 2006): 1438–42. http://dx.doi.org/10.4028/www.scientific.net/msf.514-516.1438.

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The modeling of heat transfer in materials containing exothermic components must take into consideration the presence of heat sources in the Fourier–Kirchhoff equation. The aim of this investigation was the identification of real and effective thermophysical parameters of the insulating–exothermic materials used as riser sleeves containing these exothermic heat sources. The experiments of steel pouring into the mould, containing different insulating and exothermic sleeves were carried out, using thermocouples measurement systems (thermal analysis of casting–mould system). Then the thermophysical coefficients of these materials were calculated using inverse problem solution. The worked time–dependent formula of exothermic reaction heat (heating yield in W/m3) was called heat source function. The paper presents the basis and the practical expression of heat source by different functions, its justification and the results of simulations using these functions. The numerical system Calcosoft and its Inverse Solution procedure were applied.

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19

Li, Zhenjun, Zechen Lu, Chunyu Zhao, Fangchen Liu, and Ye Chen. "Heat Source Forecast of Ball Screw Drive System Under Actual Working Conditions Based on On-Line Measurement of Temperature Sensors." Sensors 19, no. 21 (October 29, 2019): 4694. http://dx.doi.org/10.3390/s19214694.

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In view of the time-varying complexity of the heat source for the ball screw feed system, this paper proposes an adaptive inverse problem-solving method to estimate the time-varying heat source and temperature field of the feed system under working conditions. The feed system includes multiple heat sources, and the rapid change of the moving heat source increases the difficulty of its identification. This paper attempts to develop a numerical calculation method for identifying the heat source by combining the experiment with the optimization algorithm. Firstly, based on the theory of heat transfer, a new dynamic thermal network model was proposed. The temperature data signal and the position signal of the moving nut captured by the sensors are used as input to optimize the solution of the time-varying heat source. Then, based on the data obtained from the experiment, finite element software parametric programming was used to optimize the estimate of the heat source, and the results of the two heat source prediction methods are compared and verified. The other measured temperature points obtained by the experiment were used to compare and verify the inverse method of this numerical calculation, which illustrates the reliability and advantages of the dynamic thermal network combined with the genetic algorithm for the inverse method. The method based on the on-line monitoring of temperature sensors proposed in this paper has a strong application value for heat source and temperature field estimation of complex mechanical structures.

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20

Mayeli, Peyman, and Mehdi Nikfar. "Temperature identification of a heat source in conjugate heat transfer problems via an inverse analysis." International Journal of Numerical Methods for Heat & Fluid Flow 29, no. 10 (October 7, 2019): 3994–4010. http://dx.doi.org/10.1108/hff-05-2018-0193.

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Purpose The present study aims to perform inverse analysis of a conjugate heat transfer problem including conduction and forced convection via the quasi-Newton method. The inverse analysis is defined for a heat source that is surrounded by a solid medium which is exposed to a free stream in external flow. Design/methodology/approach The objective of the inverse design problem is finding temperature distribution of the heat source as thermal boundary condition to establish a prescribed temperature along the interface of solid body and fluid. This problem is a simplified version of thermal-based ice protection systems in which the formation of ice is avoided by maintaining the interface of fluid and solid at a specified temperature. Findings The effects of the different pertinent parameters such as Reynolds number, interface temperature and thermal conductivity ratio of fluid and solid mediums are analyzed. Originality/value This paper fulfils the analysis to study how thermal based anti-icing system can be used with different heat source shapes.

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Ribeiro, S. S., G. C. Oliveira, and G. Guimarães. "AN INVERSE PROBLEM USING GREEN’S FUNCTIONS AND TFBGF METHOD TO INDENTIFICATE A MOVING HEAT SOURCE IN 3D HEAT CONDUCTION." Revista de Engenharia Térmica 17, no. 2 (December 28, 2018): 87. http://dx.doi.org/10.5380/reterm.v17i2.64135.

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Moving heat source are present in numerous practical problems in engineering. For example, machining process as the Gas tungsten arc welding (GTAW), laser welding, friction stirwleing process or milding problem. Moving heat source are also present in biological heating as the metabolism or in heat thermal treatment. All these cases, the heat input identification is a complex task and represents an important factor in the process optimization. The aim of this work is to investigate both the temperature field as the heat flux delivered to a piece during a process with moving heat source.

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Han, F., C. W. Liang, G. L. Shi, L. Wang, and K. Y. Li. "Clinical applications of internal heat source analysis for breast cancer identification." Genetics and Molecular Research 14, no. 1 (2015): 1450–60. http://dx.doi.org/10.4238/2015.february.13.24.

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Ait Ben Hassi, El Mustapha, Salah‐Eddine Chorfi, and Lahcen Maniar. "Identification of source terms in heat equation with dynamic boundary conditions." Mathematical Methods in the Applied Sciences 45, no. 4 (November 3, 2021): 2364–79. http://dx.doi.org/10.1002/mma.7933.

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Cannon, J. R., and Paul DuChateau. "Structural identification of an unknown source term in a heat equation." Inverse Problems 14, no. 3 (June 1, 1998): 535–51. http://dx.doi.org/10.1088/0266-5611/14/3/010.

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Diligenskaya, Anna, and Andrey Mandra. "Determination of Space and Time Dependent Function of Internal Heat Source in Heat Conductivity Equation." Applied Mechanics and Materials 698 (December 2014): 668–73. http://dx.doi.org/10.4028/www.scientific.net/amm.698.668.

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In the present work we propose the method for the solution inverse heat conduction problem that consists of the identification of unknown internal heat source function depending simultaneously on space and time variables. The source function can be represented as a separable variable. We consider an inverse problem of determining the heat source function based on temperature measurements.Inverse problem is formulated as an optimization problem, a variational formulation for solving the optimization problem is given. The estimation of internal heat source is investigated with the analytical method combined with the method for approximate modal definition of temperature state and source function. The temperature state and desired control input are represented in the form of finite expansion in terms of orthogonal system of eigenfunctions. The required eigencoefficients of temperature state can be determined using temperature measurements. Then eigencoefficients of heat source function can be defined sequentially.Some numerical examples are provided to show the efficiency of the proposed method.

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Pasaribu, After Helfert, Angga Bakti Pratama, and Dedy Pramudityo. "Heat Source Identification by Reprocessing Geoscience Data to Update Conceptual Model of Pentadio Geothermal Prospect Area." IOP Conference Series: Earth and Environmental Science 1159, no. 1 (March 1, 2023): 012009. http://dx.doi.org/10.1088/1755-1315/1159/1/012009.

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Abstract The Pentadio Geothermal Prospect Area, Gorontalo, has been studied for geothermal development in the exploration stage. The previous conceptual model that has been produced shows that magma is the heat source of the geothermal system only based on geochemistry of manifestations that had high SO4 2- concentrations. However, the presence of tertiary intrusion based on regional geological data and high SO4 2- concentration cannot be strong evidence to prove the heat source is associated with magmatic system. The purposes of this research are updating the conceptual model, defining the heat source, and producing a different perspective from the previous study for the geothermal system in Pentadio Geothermal Prospect Area by providing a further analysis based on geoscience data reprocessing that had been acquired before. The methods consist of geological structure analysis that involves defining the permeability and geochemistry analysis for interpreting the type of reservoir fluid. Gravity data were reprocessing produced the regional and residual maps, and 3D modelling analysis for determining subsurface condition and heat source possibility in Pentadio Geothermal Prospect Area. The Pentadio Geothermal System is correlated with Gorontalo Graben which controls the permeability in the system. The fluid from geothermal manifestations near Limboto Lake has alkaline pH even though it has high SO4 2- content and low magnesium content. Gravity data shows the regional fault (NW-SE) has a depth of more than 4 km and seems to have the possibility of producing heat influx to the near surface. It is suspected as a heat source of the geothermal system in this area. There is no evidence for magma intrusion to be a heat source in the subsurface by geology and geophysics model. The high concentration of SO4 − in manifestations was influenced by sediment contamination in this area.

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Mendioroz, Arantza, Alazne Castelo, Ricardo Celorrio, and Agustín Salazar. "Vertical Cracks Excited in Lock-in Vibrothermography Experiments: Identification of Open and Inhomogeneous Heat Fluxes." Sensors 22, no. 6 (March 17, 2022): 2336. http://dx.doi.org/10.3390/s22062336.

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Lock-in vibrothermography has proven to be very useful to characterizing kissing cracks producing ideal, homogeneous, and compact heat sources. Here, we approach real situations by addressing the characterization of non-compact (strip-shaped) heat sources produced by open cracks and inhomogeneous fluxes. We propose combining lock-in vibrothermography data at several modulation frequencies in order to gather penetration and precision data. The approach consists in inverting surface temperature amplitude and phase data by means of a least-squares minimization algorithm without previous knowledge of the geometry of the heat source, only assuming knowledge of the vertical plane where it is confined. We propose a methodology to solve this ill-posed inverse problem by including in the objective function penalty terms based on the expected properties of the solution. These terms are described in a comprehensive and intuitive manner. Inversions of synthetic data show that the geometry of non-compact heat sources is identified correctly and that the contours are rounded due to the penalization. Inhomogeneous smoothly varying fluxes are also qualitatively retrieved, but steep variations of the flux are hard to recover. These findings are confirmed by inversions of experimental data taken on calibrated samples. The proposed methodology is capable of identifying heat sources generated in lock-in vibrothermography experiments.

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Huntul, M. J. "Identification of the unknown heat source terms in a 2D parabolic equation." Journal of King Saud University - Science 33, no. 6 (September 2021): 101524. http://dx.doi.org/10.1016/j.jksus.2021.101524.

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Min, Tao, Shunquan Zang, and Shengnan Chen. "Source strength identification problem for the three-dimensional inverse heat conduction equations." Inverse Problems in Science and Engineering 28, no. 6 (September 16, 2019): 827–38. http://dx.doi.org/10.1080/17415977.2019.1665663.

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Amin, Mohammed Elmustafa, and Xiangtuan Xiong. "Source identification problems for radially symmetric and axis-symmetric heat conduction equations." Applied Numerical Mathematics 138 (April 2019): 1–18. http://dx.doi.org/10.1016/j.apnum.2018.12.013.

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Singh, R. N., and Ajay Manglik. "Identification of radiogenic heat source distribution in the crust: A variational approach." Sadhana 25, no. 2 (April 2000): 111–18. http://dx.doi.org/10.1007/bf02703753.

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Hussein, M. S., D. Lesnic, B. T. Johansson, and A. Hazanee. "Identification of a multi-dimensional space-dependent heat source from boundary data." Applied Mathematical Modelling 54 (February 2018): 202–20. http://dx.doi.org/10.1016/j.apm.2017.09.029.

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Ribeiro, Sidney, Ana Paula Fernandes, Daniel Fernandes da Cunha, Marcio Bacci da Silva, Jerry Shan, and Gilmar Guimaraes. "Estimation of a Moving Heat Source due to a Micromilling Process Using the Modified TFBGF Technique." Mathematical Problems in Engineering 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/9105940.

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Moving heat sources are present in numerous engineering problems as welding and machining processes, heat treatment, or biological heating. In all these cases, the heat input identification represents an important factor in the optimization of the process. The aim of this study is to investigate the heat flux delivered to a workpiece during a micromilling process. The temperature measurements were obtained using a thermocouple at an accessible region of the workpiece surface while micromilling a small channel. The analytical solution is calculated from a 3D transient heat conduction model with a moving heat source, called direct problem. The estimation of the moving heat source uses the Transfer Function Based on Green’s Function Method. This method is based on Green’s function and the equivalence between thermal and dynamic systems. The technique is simple without iterative processes and extremely fast. From the temperature on accessible regions it is possible to estimate the heat flux by an inverse procedure of the Fast Fourier Transform. A test of micromilling of 6365 aluminium alloy was made and the heat delivered to the workpiece was estimated. The estimation of the heat without use of optimization technique is the great advantage of the technique proposed.

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Dou, Fangfang. "Wavelet-Galerkin Method for Identifying an Unknown Source Term in a Heat Equation." Mathematical Problems in Engineering 2012 (2012): 1–22. http://dx.doi.org/10.1155/2012/904183.

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We consider the problem of identification of the unknown source in a heat equation. The problem is ill posed in the sense that the solution (if it exists) does not depend continuously on the data. Meyer wavelets have the property that their Fourier transform has compact support. Therefore, by expanding the data and the solution in the basis of the Meyer wavelets, high-frequency components can be filtered away. Under the additional assumptions concerning the smoothness of the solution, we discuss the stability and convergence of a wavelet-Galerkin method for the source identification problem. Numerical examples are presented to verify the efficiency and accuracy of the method.

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Fatullayev, Afet Golayoğlu. "Numerical method of identification of an unknown source term in a heat equation." Mathematical Problems in Engineering 8, no. 2 (2002): 161–68. http://dx.doi.org/10.1080/10241230212907.

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A numerical procedure for an inverse problem of identification of an unknown source in a heat equation is presented. Approach of proposed method is to approximate unknown function by polygons linear pieces which are determined consecutively from the solution of minimization problem based on the overspecified data. Numerical examples are presented.

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Silva, Paulo M. P., Helcio R. B. Orlande, Marcelo J. Colaço, Panayiotis S. Shiakolas, and George S. Dulikravich. "Identification and design of source term in a two-region heat conduction problem." Inverse Problems in Science and Engineering 15, no. 7 (October 2007): 661–77. http://dx.doi.org/10.1080/17415970701198159.

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Asserin, Olivier, Danièle Ayrault, Philippe Gilles, Evelyne Guyot, and Jeanne Schroeder. "Identification of a heat source model for multipass narrow groove GMA welding process." Welding in the World 58, no. 2 (November 8, 2013): 161–69. http://dx.doi.org/10.1007/s40194-013-0109-4.

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Rundell, William, and Zhidong Zhang. "On the Identification of Source Term in the Heat Equation from Sparse Data." SIAM Journal on Mathematical Analysis 52, no. 2 (January 2020): 1526–48. http://dx.doi.org/10.1137/19m1279915.

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MATSUMOTO, Toshiro, Keisuke KAMIYA, Takashi ISHIUCHI, and Takahiro NAITO. "533 Identification of heat source distribution in 3D structures using dual reciprocity BEM." Proceedings of Conference of Tokai Branch 2006.55 (2006): 257–58. http://dx.doi.org/10.1299/jsmetokai.2006.55.257.

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Beddiaf, Sara, Laurent Autrique, Laetitia Perez, and Jean-Claude Jolly. "Heating source localization in a reduced time." International Journal of Applied Mathematics and Computer Science 26, no. 3 (September 1, 2016): 623–40. http://dx.doi.org/10.1515/amcs-2016-0043.

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Abstract Inverse three-dimensional heat conduction problems devoted to heating source localization are ill posed. Identification can be performed using an iterative regularization method based on the conjugate gradient algorithm. Such a method is usually implemented off-line, taking into account observations (temperature measurements, for example). However, in a practical context, if the source has to be located as fast as possible (e.g., for diagnosis), the observation horizon has to be reduced. To this end, several configurations are detailed and effects of noisy observations are investigated.

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Ponzini, G., G. Crosta, and M. Giudici. "Identification of thermal conductivities by temperature gradient profiles: One‐dimensional steady flow." GEOPHYSICS 54, no. 5 (May 1989): 643–53. http://dx.doi.org/10.1190/1.1442691.

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The comparison model method (CMM) is applied to the identification of spatially varying thermal conductivity in a one‐dimensional domain. This method deals with the discretized steady‐state heat equation written at the nodes of a lattice, a lattice which models a stack of plane parallel layers. The required data are temperature gradient and heat source (or sink) values. The unknowns of this inverse problem are not nodal values but internode thermal conductivities, which appear in the node heat balance equation. The conductivities, e.g., the solutions to the inverse problem obtained by the CMM, are a one‐parameter family. In order to achieve uniqueness (which coincides with identifiability in this case), a suitable value of this parameter must be found. To this end we consider (1) parameterization, i.e., introducing equality constraints between the unknown coefficients, (2) the use of two data sets at least in a subdomain, and (3) self‐identifiability. Each of these items formally translates the available a priori information about the system, e.g., geophysical properties of the layers. Under the assumption that an admissible solution exists, we evaluate the effects on the solution of two types of data noise: additive noise affecting temperature and a kind of multiplicative noise affecting heat‐source terms. In the numerical examples we provide, parameterization is combined with the CMM in order to obtain the unique solution to a test inverse problem, the geophysical data of which come from a well drilled across Tertiary layers in central Italy. Finally, we consider data perturbed by pseudorandom noise. More precisely, we add noise to temperature gradients and obtain stability estimates which correctly predict the numerical results. In particular, if the layers where the parameterization constraint applies contain a strong source term (e.g., due to flowing water), the solution is relatively insensitive to noise. Also, for multiplicative noise affecting heat‐source terms, theoretical stability predictions are confirmed numerically. The main advantage of the CMM is its simple algebraic formulation. Its implementation in the field by means of a pocket calculator allows both a consistency check on the data being collected and estimates of the unknown values of conductivity.

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Gaume, Benjamin, Yassine Rouizi, Frédéric Joly, and Olivier Quéméner. "Identification of radiant source in an enclosure by reduced model." Journal of Physics: Conference Series 2116, no. 1 (November 1, 2021): 012112. http://dx.doi.org/10.1088/1742-6596/2116/1/012112.

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Abstract We propose an original method to recover from a few measurement points the integrity of the temperature field of a furnace heated by a radiant thermal source. The radiant thermal source is first identified via a low order reduced model based on based on AROMM (Amalgam Reduced Order Modal Model) method which preserves the integrity of the geometry. The minimization is performed via a trust-region reflective least squares algorithm implemented in MATLAB “lsqcurvefit” function. From that identified heat flux, the integrity of the thermal field is then recovered by direct simulation thanks to a reduced model of higher rank to have a better precision. The treated application is a complex titanium piece heated by two radiant panels placed in a furnace. With four measuring points, the temperature of the whole thermal scene is retrieved at all times with an average error around 1 K on the studied object.

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Dai, Yuwei, Minzhang Hou, Haidong Wang, and Wanli Tu. "Source Location Identification in an Ideal Urban Street Canyon with Time-Varying Wind Conditions under a Coupled Indoor and Outdoor Environment." Buildings 13, no. 12 (December 15, 2023): 3121. http://dx.doi.org/10.3390/buildings13123121.

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Source location identification methods are typically applied to steady-state conditions under pure indoor or outdoor environments, but under time-varying wind conditions and coupled indoor and outdoor environments, the applicability is not clear. In this study, we proposed an improved adjoint probability method to identify the pollutant source location with time-varying inflows in street canyons and used scaled outdoor experiment data to verify the accuracy. The change in inflow velocity will affect the airflow structure inside the street canyons. Outdoor wind with a lower temperature will exchange heat with the air with a higher temperature inside the street canyon, taking away part of the heat and reducing the heat of the air inside the street canyons. Moreover, the room opening will produce some air disturbance, which is conducive to the heat exchange between the air near the opening and the outdoor wind. Furthermore, the fluctuations of the upper wind will influence the diffusion of the tracer gas. We conducted three cases to verify the accuracy of the source identification method. The results showed that the conditioned adjoint location probability (CALP) of each case was 0.06, 0.32, and 0.28. It implies that with limited pollutant information, the improved adjoint probability method can successfully identify the source location in the dynamic wind environments under coupled indoor and outdoor conditions.

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Shen, Yu, and Xiangtuan Xiong. "Identifying the Heat Source in Radially Symmetry and Axis-Symmetry Problems." Symmetry 16, no. 2 (January 23, 2024): 134. http://dx.doi.org/10.3390/sym16020134.

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This paper solves the inverse source problem of heat conduction in which the source term only varies with time. The application of the discrete regularization method, a kind of effective radial symmetry and axisymmetric heat conduction problem source identification that does not involve the grid integral numerical method, is put forward. Taking the fundamental solution as the fundamental function, the classical Tikhonov regularization method combined with the L-curve criterion is used to select the appropriate regularization parameters, so the problem is transformed into a class of ill-conditioned linear algebraic equations to solve with an optimal solution. Several numerical examples of inverse source problems are given. Simultaneously, a few numerical examples of inverse source problems are given, and the effectiveness and superiority of the method is shown by the results.

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Wang, Dacheng, Yanmei Xie, Caihong Ma, Yindi Zhao, Dongmei Yan, Hongyu Chen, Bihong Fu, Guangtong Wan, and Xiaolin Hou. "Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager." Applied Sciences 14, no. 6 (March 14, 2024): 2450. http://dx.doi.org/10.3390/app14062450.

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Industrial heat sources (IHSs) are key contributors to anthropogenic heat, air pollution, and carbon emissions. Accurately and automatically detecting their production areas (IHSPAs) on a large scale is vital for environmental monitoring and decision making, yet this is challenged by the lack of high-resolution thermal data. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data with the highest resolution (30 m) in the civilian field and a three-band advantage were first introduced to detect IHSPAs. In this study, an IHSPA identification model using multi-features extracted from SDGSAT-1 TIS and Landsat OLI data and support vector machine (SVM) was proposed. First, three brightness temperatures and four thermal radiation indices using SDGSAT-1 TIS and Landsat OLI data were designed to enlarge the temperature difference between IHSPAs and the background. Then, 10 features combined with three indices from Landsat OLI images with the same spatial resolution (30 m) and stable data were extracted. Second, an IHSPA identification model based on SVM and multi-feature extraction was constructed to identify IHSPAs. Finally, the IHS objects were manually delineated and verified using the identified IHSPAs and Google Earth images. Some conclusions were obtained from different comparisons in Wuhai, China: (1) IHSPA identification based on SVM using thermal and optical features can detect IHSPAs and obtain the best results compared with different features and identification models. (2) The importance of using thermal features from the SDGSAT-1 TIS to detect IHSPAs was demonstrated by different importance analysis methods. (3) Our proposed method can detect more IHSs, with greater spatial coverage and smaller areas, compared with the methods of Ma and Liu. This new way to detect IHSPAs can obtain higher-spatial-resolution emissions of IHSs on a large scale and help decision makers target environmental monitoring, management, and decision making in industrial plant processing.

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Pandolfi, Luciano. "Riesz systems, spectral controllability and a source identification problem for heat equations with memory." Discrete & Continuous Dynamical Systems - S 4, no. 3 (2011): 745–59. http://dx.doi.org/10.3934/dcdss.2011.4.745.

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Lee, Dong-Si, and Kee-Keun Lee. "Heat Source Identification Technique of Aircraft and Flare using 2-color Detectable Infrared Sensors." Transactions of The Korean Institute of Electrical Engineers 64, no. 7 (July 1, 2015): 1031–39. http://dx.doi.org/10.5370/kiee.2015.64.7.1031.

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Videcoq, Etienne, Olivier Quemener, Myriam Lazard, and Alain Neveu. "Heat source identification and on-line temperature control by a Branch Eigenmodes Reduced Model." International Journal of Heat and Mass Transfer 51, no. 19-20 (September 2008): 4743–52. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2008.02.029.

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Murio, Diego A. "Numerical identification of interface source functions in heat transfer problems under nonlinear boundary conditions." Computers & Mathematics with Applications 24, no. 4 (August 1992): 65–76. http://dx.doi.org/10.1016/0898-1221(92)90008-6.

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Borukhov, V. T., and G. M. Zayats. "Identification of a time-dependent source term in nonlinear hyperbolic or parabolic heat equation." International Journal of Heat and Mass Transfer 91 (December 2015): 1106–13. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2015.07.066.

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Journal articles on the topic 'Heat source identification'

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