Thermal Sensor Allocation for Effective and Efficient Heat Transfer Measurements in Transportation Systems
Power plants, electric generators, high-frequency controllers, battery storage, and control units are essential in current transportation and energy distribution networks. To improve the performance and guarantee the endurance of such systems, it is critical to control their operational temperature...
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Published in | Sensors (Basel, Switzerland) Vol. 23; no. 5; p. 2803 |
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Main Authors | , |
Format | Journal Article |
Language | English |
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03.03.2023
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Abstract | Power plants, electric generators, high-frequency controllers, battery storage, and control units are essential in current transportation and energy distribution networks. To improve the performance and guarantee the endurance of such systems, it is critical to control their operational temperature within certain regimes. Under standard working conditions, those elements become heat sources either during their entire operational envelope or during given phases of it. Consequently, in order to maintain a reasonable working temperature, active cooling is required. The refrigeration may consist of the activation of internal cooling systems relying on fluid circulation or air suction and circulation pulled from the environment. However, in both scenarios pulling surrounding air or making use of coolant pumps increases the power demand. The augmented power demand has a direct impact on the power plant or electric generator autonomy, while instigating higher power demand and substandard performance from the power electronics and batteries' compounds. In this manuscript, we present a methodology to efficiently estimate the heat flux load generated by internal heat sources. By accurately and inexpensively computing the heat flux, it is possible to identify the coolant requirements to optimize the use of the available resources. Based on local thermal measurements fed into a Kriging interpolator, we can accurately compute the heat flux minimizing the number of sensors required. Considering the need for effective thermal load description toward efficient cooling scheduling. This manuscript presents a procedure based on temperature distribution reconstruction via a Kriging interpolator to monitor the surface temperature using a minimal number of sensors. The sensors are allocated by means of a global optimization that minimizes the reconstruction error. The surface temperature distribution is then fed into a heat conduction solver that processes the heat flux of the proposed casing, providing an affordable and efficient way of controlling the thermal load. Conjugate URANS simulations are used to simulate the performance of an aluminum casing and demonstrate the effectiveness of the proposed method. |
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AbstractList | Power plants, electric generators, high-frequency controllers, battery storage, and control units are essential in current transportation and energy distribution networks. To improve the performance and guarantee the endurance of such systems, it is critical to control their operational temperature within certain regimes. Under standard working conditions, those elements become heat sources either during their entire operational envelope or during given phases of it. Consequently, in order to maintain a reasonable working temperature, active cooling is required. The refrigeration may consist of the activation of internal cooling systems relying on fluid circulation or air suction and circulation pulled from the environment. However, in both scenarios pulling surrounding air or making use of coolant pumps increases the power demand. The augmented power demand has a direct impact on the power plant or electric generator autonomy, while instigating higher power demand and substandard performance from the power electronics and batteries' compounds. In this manuscript, we present a methodology to efficiently estimate the heat flux load generated by internal heat sources. By accurately and inexpensively computing the heat flux, it is possible to identify the coolant requirements to optimize the use of the available resources. Based on local thermal measurements fed into a Kriging interpolator, we can accurately compute the heat flux minimizing the number of sensors required. Considering the need for effective thermal load description toward efficient cooling scheduling. This manuscript presents a procedure based on temperature distribution reconstruction via a Kriging interpolator to monitor the surface temperature using a minimal number of sensors. The sensors are allocated by means of a global optimization that minimizes the reconstruction error. The surface temperature distribution is then fed into a heat conduction solver that processes the heat flux of the proposed casing, providing an affordable and efficient way of controlling the thermal load. Conjugate URANS simulations are used to simulate the performance of an aluminum casing and demonstrate the effectiveness of the proposed method. |
Audience | Academic |
Author | Saavedra, Jorge Gonzalez Cuadrado, David |
AuthorAffiliation | 2 Gas Turbine Lab, Massachusetts Institute of Technology, 70 Vassar Street, Cambridge, MA 02139, USA 1 European Institute For Aviation Training and Accreditation (EIATA), Universidad Rey Juan Carlos, Fuenlabrada, 28943 Madrid, Spain |
AuthorAffiliation_xml | – name: 1 European Institute For Aviation Training and Accreditation (EIATA), Universidad Rey Juan Carlos, Fuenlabrada, 28943 Madrid, Spain – name: 2 Gas Turbine Lab, Massachusetts Institute of Technology, 70 Vassar Street, Cambridge, MA 02139, USA |
Author_xml | – sequence: 1 givenname: Jorge orcidid: 0000-0002-5914-4397 surname: Saavedra fullname: Saavedra, Jorge organization: European Institute For Aviation Training and Accreditation (EIATA), Universidad Rey Juan Carlos, Fuenlabrada, 28943 Madrid, Spain – sequence: 2 givenname: David surname: Gonzalez Cuadrado fullname: Gonzalez Cuadrado, David organization: Gas Turbine Lab, Massachusetts Institute of Technology, 70 Vassar Street, Cambridge, MA 02139, USA |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36905006$$D View this record in MEDLINE/PubMed |
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Cites_doi | 10.1016/S0142-727X(97)10014-5 10.1016/j.ijheatmasstransfer.2020.119503 10.1115/1.4006313 10.1016/j.applthermaleng.2015.06.003 10.3390/s22218401 10.1115/1.4033267 10.3390/s22103808 10.1016/j.expthermflusci.2021.110398 10.1115/1.4004845 10.1016/j.measurement.2014.09.055 10.1016/0894-1777(88)90043-X 10.1057/palgrave.jors.2602068 10.1115/1.2905435 10.1115/1.4035211 10.1016/j.ijhydene.2015.12.209 10.1115/GT2019-91020 10.1115/1.4046546 10.1115/1.2905437 10.1016/j.ejor.2007.10.013 10.1115/1.4049613 10.1016/j.ijthermalsci.2020.106286 10.1016/j.applthermaleng.2015.02.048 10.1016/S0011-2240(03)00015-4 10.3390/en12193594 10.1007/s13202-015-0175-9 10.1115/1.2960953 10.1109/ITHERM.2019.8757292 10.2514/6.2021-3717 10.6028/NBS.IR.84-3007 10.3390/s22083013 10.4314/ijest.v3i8.1 10.1126/science.220.4598.671 10.1109/APUAVD53804.2021.9615424 10.1016/0040-6031(89)87016-9 10.3390/s22197635 10.1115/1.2752188 10.1115/1.4039026 10.1007/BF02127704 |
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Keywords | uncertainty evaluation thermal sensors heat conduction cooling heat transfer global optimization sensor allocation |
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SubjectTerms | Accuracy Boundary conditions Conduction heating Conductive heat transfer Control equipment Coolant pumps cooling Cooling systems Demand Electric generators Electric power-plants Energy consumption Energy resources Energy storage Global optimization heat conduction Heat conductivity Heat flux Heat transfer Load Performance enhancement Power electronics Power plants Reconstruction Repeaters sensor allocation Sensors Suction Surface temperature System effectiveness Temperature Temperature distribution Thermal analysis thermal sensors Transportation industry Transportation systems |
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Title | Thermal Sensor Allocation for Effective and Efficient Heat Transfer Measurements in Transportation Systems |
URI | https://www.ncbi.nlm.nih.gov/pubmed/36905006 https://www.proquest.com/docview/2785236867/abstract/ https://search.proquest.com/docview/2786104147 https://pubmed.ncbi.nlm.nih.gov/PMC10007535 https://doaj.org/article/750fa72a903e4288963c6367e8ea44d7 |
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