人工环境固体壁面对流热量的逆向建模
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摘要
针对逆向确定人工环境固体壁面对流热量所面临的难题,实现固体壁面对流热量的准确量化。基于温度贡献因子方法,采用优化法与正则化方法相结合的策略,降低优化过程所需的计算量及计算时间,建立根据人工环境内温度测量信息逆向确定固体壁面对流热量的数学模型。将逆模型应用于人工环境中某建筑的一间办公室内,逆模型计算值与实测值之间的均方根误差为0. 73 W。结果表明该逆模型可以准确有效地确定人工环境固体壁面对流热量。
In terms of the difficulties of inversely determining the solid wall boundary convective heat fluxes in artificial environments,accurately quantifying the wall boundary convective heat fluxes,an inverse method was studied based on the contribution ratio of indoor climate method to determine the solid wall boundary convective heat fluxes according to temperature measurement information in artificial environments. The Tikhonov regularization strategy coupled with the least-squares optimization was proposed in order to reduce the computational cost and computing time. The convective heat fluxes on solid walls in an office in a building were inversely solved using inverse model. The root mean square( RMS) errors of the model were 0. 73 W. The results show that the developed inverse method can accurately and efficiently determine the solid wall convective heat fluxes in artificial environments.
In terms of the difficulties of inversely determining the solid wall boundary convective heat fluxes in artificial environments,accurately quantifying the wall boundary convective heat fluxes,an inverse method was studied based on the contribution ratio of indoor climate method to determine the solid wall boundary convective heat fluxes according to temperature measurement information in artificial environments. The Tikhonov regularization strategy coupled with the least-squares optimization was proposed in order to reduce the computational cost and computing time. The convective heat fluxes on solid walls in an office in a building were inversely solved using inverse model. The root mean square( RMS) errors of the model were 0. 73 W. The results show that the developed inverse method can accurately and efficiently determine the solid wall convective heat fluxes in artificial environments.
引文
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11 Sousa P F,Borges V L,Pereira I C,et al.Estimation of heat flux and temperature field during drilling process using dynamic observers based on Green's function[J].Applied Thermal Engineering,2012,48(3):144-154
12 Xue Y,Zhai Z Q,Chen Q Y.Inverse prediction and optimization of flow control conditions for confined spaces using a CFD-based genetic algorithm[J].Building and Environment,2013,64(21):77-84
13 Zhai Z Q,Xue Y,Chen Q Y.Inverse design methods for indoor ventilation systems using CFD-based multi-objective genetic algorithm[J].Building Simulation,2014,7(11):661-669
14 Liu W,Chen Q Y.Optimal air distribution design in enclosed spaces using an adjoint method[J].Inverse Problem Science Environment,2015,23(6):760-779
15 Zhang T H,You X Y.A simulation-based inverse design of preset aircraft cabin environment[J].Building and Environment,2014,82(5):20-26
2洪勇吉.低矮建筑非定常绕流大涡模拟影响因素[J].科学技术与工程,2017,17(14):243-250Hong Yongji.Large eddy simulation influence factors of the unsteady flow around a low-rise building[J].Science Technology and Engineering,2017,17(14):243-250
3 Luchesi V M,Coelho R T.An inverse method to estimate the moving heat source in machining process[J].Applied Thermal Engineering,2012,45(2):64-78
4 Ma Y T,Fu C S,Zhang Y.Identification of an unknown source depending on both time and space variables by a variational method[J].Applied Mathematical Modelling,2012,36(11):5080-5090
5 Liu D,Zhao F Y,Wang H,et al.Inverse determination of building heating profiles from the knowledge of measurements within the turbulent slot-vented enclosure[J].International Journal of Heat and Mass Transfer,2012,55(3):4597-4612
6 Reza P,Hassan D,Seyed H T.Solving an inverse heat conduction problem using genetic algorithm:sequential and multi-core parallelization approach[J].Applied Mathematical Modelling,2014,38(15):1948-1958
7 Balázs C,Keith A W,Gyula G.Simultaneous estimation of temperature-dependent volumetric heat capacity and thermal conductivity functions via neural networks[J].International Journal of Heat and Mass Transfer,2014,68(9):1-13
8 Yang F,Fu C X.The modified regularization method for identifying the unknown source on Poisson equation[J].Applied Mathematical Modelling,2012,36(11):756-763
9 Hon Y C,Li M,Melnikov Y A.Inverse source identification by Green's function[J].Engineering Analysis with Boundary,2010,34(4):352-358
10 Boulet M,Marcos B,Moresoli C,et al.Sequential inverse method implemented into CFD software for the estimation of a radiation boundary[J].International Journal Thermal Science,2012,51(9):7-15
11 Sousa P F,Borges V L,Pereira I C,et al.Estimation of heat flux and temperature field during drilling process using dynamic observers based on Green's function[J].Applied Thermal Engineering,2012,48(3):144-154
12 Xue Y,Zhai Z Q,Chen Q Y.Inverse prediction and optimization of flow control conditions for confined spaces using a CFD-based genetic algorithm[J].Building and Environment,2013,64(21):77-84
13 Zhai Z Q,Xue Y,Chen Q Y.Inverse design methods for indoor ventilation systems using CFD-based multi-objective genetic algorithm[J].Building Simulation,2014,7(11):661-669
14 Liu W,Chen Q Y.Optimal air distribution design in enclosed spaces using an adjoint method[J].Inverse Problem Science Environment,2015,23(6):760-779
15 Zhang T H,You X Y.A simulation-based inverse design of preset aircraft cabin environment[J].Building and Environment,2014,82(5):20-26