By Zhi-Xin Li, Zeng-Yuan Guo (auth.), Liqiu Wang (eds.)
The time period transport phenomena is used to explain strategies within which mass, momentum, strength and entropy movement approximately in subject. Advances in shipping Phenomena provide state of the art expositions of significant advances by means of theoretical, numerical and experimental reviews from a molecular, microscopic, mesoscopic, macroscopic or megascopic viewpoint around the spectrum of shipping phenomena, from medical enquiries to useful purposes. the yearly overview sequence intends to fill the data hole among on a regular basis released journals and university-level textbooks through offering in-depth evaluation articles over a broader scope than in journals. The authoritative articles, contributed by way of internationally-leading scientists and practitioners, identify the state-of-the-art, disseminate the most recent learn discoveries, function a crucial resource of reference for basics and functions of delivery phenomena, and supply capability textbooks to senior undergraduate and graduate scholars.
This assessment booklet offers state of the art expositions of significant advances through theoretical, numerical and experimental reports from a molecular, microscopic, mesoscopic, macroscopic or megascopic standpoint around the spectrum of delivery phenomena, from clinical enquiries to sensible functions. This new quantity of the yearly evaluation "Advances in shipping Phenomena" sequence presents in-depth evaluation articles protecting the fields of mass move, fluid mechanics, warmth move and thermodynamics.
This overview booklet offers cutting-edge expositions of significant advances via theoretical, numerical and experimental experiences from a molecular, microscopic, mesoscopic, macroscopic or megascopic viewpoint around the spectrum of delivery phenomena, from medical enquiries to functional functions. This new quantity of the once a year overview "Advances in delivery Phenomena" sequence presents in-depth overview articles masking the fields of mass move, fluid mechanics, warmth move and thermodynamics.
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Additional info for Advances in Transport Phenomena 2010
X. -Y. Guo Ai Ao i B o B mid-section Fig. 1 Experiment on Heat Transfer Performance of AEA Tube[17,47] Experimental set-up. The schematic diagram of the experimental set-up is shown in Fig. 36. It is composed of the cold medium loop, hot medium loop, test section and the measure and control system. The effective length of the test tube with an annular structure is 2 m. The fluid outside the AEA tube is deionized water, and the fluid inside the AEA tube is deionized water or 22# lubricating oil.
18 The optimal thermal conductivity distribution MEG principle 4. 7 ˙ 7 80 9. 71 56 71 9. 80 Optimization Principles for Heat Convection 77 4. 77 77 1. 3K 48 1. 7W MEG principle ˙ Qt 2500 W Fig. 19 The optimal temperature field The average temperatures in the domain after optimization based on the MEG principle and EED principle are listed in table 2. It is seen that the difference of average temperature is very small. 21 respectively. 21 that the results are much different from those of the symmetry case.
11 shows a typical volume-to-point heat conduction problem. In the square domain there is a uniform heat source, Φ 100W/m3, the local material conductivity in the domain may vary continuously but the volume-averaged conductivity is kept at 1W/(m·K). -X. -Y. Guo uniform temperature of 300 K are located on the boundary with other boundaries adiabatic. The problem is to find the optimal thermal conductivity distribution which leads to a lowest average temperature in the domain. (32) for the prescribed heat flow rate at the boundary (note: hear the heat flow rate is equal to the total heat source in the domain, the outlet temperature is fixed, we want to find the minimum average temperature of the domain, so as to obtain the minimum temperature difference for heat conduction), we have, 1 2 TdV = Qtδ Tm = δ δ ∫∫∫ Φ ∫∫∫ k ( ∇T ) dV = 0 V V 2 (36) with the constraint, ∫∫∫ kdV = Const (37) V For the above optimization problem, the following functional can be constructed, ⎡1 ⎤ J = ∫∫∫ ⎢ k (∇T ) 2 + λ1k ⎥ dV 2 ⎦ V ⎣ (38) where λ1 is a Lagrange multiplier, which is a constant.
Advances in Transport Phenomena 2010 by Zhi-Xin Li, Zeng-Yuan Guo (auth.), Liqiu Wang (eds.)