Second-Law Analysis: A Powerful Tool for Analyzing Computational Fluid Dynamics (CFD) Results

Abstract

Second-law analysis (SLA) is an important concept in thermodynamics, which basically assesses energy by its value in terms of its convertibility from one form to another. Highly-valued forms of energy in this context are called exergy, defined as those energies from which the maximum theoretical work is obtainable when the energy is interacting with the environment to equilibrium (see Moran et al. [1], for example).The counterpart of exergy, also called available work, is anergy, so that with this concept energy can generally be regarded as the sum of exergy and anergy. As a consequence of the second law of thermodynamics, exergy can be lost but cannot be created. Therefore, in a reversible process, exergy is preserved, whereas irreversibility (like losses in a flow field) leads to a loss of exergy (in favor of the corresponding increasing anergy).Losses in flow and heat transfer fields can, thus, be determined by their impact on the exergy, i.e., by determining the exergy losses. These losses of exergy are thermodynamically linked to the generation of entropy by the so-called Gouy Stodola theorem. It states that the lost exergy corresponds to the product of entropy generation and the environmental temperature (see, again, Moran et al. [1]).From a thermodynamic point of view, the quality of a flow or heat transfer process can be assessed by the entropy generation rate in this process (see Herwig [2]). However, entropy almost never appears in the textbooks of fluid dynamics and heat transfer (see, e.g., Batchelor, et al. [3] and Incropera, et al. [4]). This concept is widely ignored perhaps because the irreversibility effect is often fundamentally important in these two disciplines.After Bejan [5,6] laid the foundation with respect to analyzing and optimizing thermal systems with the SLA approach, the SLA of flow and heat transfer problems received more and more attention. Applying the SLA in computational fluid dynamics (CFD), Kock and Herwig [7] extended this concept to an in-depth analysis of turbulent flows and identified four different mechanisms of entropy generation: dissipation in a mean and fluctuating velocity field and heat flux in a mean and fluctuating temperature field. They also suggested how to calculate the entropy generation rates with the Reynolds Averaged Navier-Stokes (RANS) results. Herwig [2] stated that, with the help of the SLA, one may answer four important questions regarding a momentum and/or heat transfer process:• Which is the ideal process (no entropy generation)? • Where does entropy generation occur in a non-ideal process? • Why does entropy generation occur at a certain location and with certain strength? • How can entropy generation be reduced overall or locally?The purpose of this special issue is to demonstrate how the CFD results can be better interpreted by the SLA. We collected 12 papers in this special issue, covering both engineering applications [8-12] and

Time Evolution Features of Entropy Generation Rate in Turbulent Rayleigh-Bénard Convection with Mixed Insulating and Conducting Boundary Conditions

“…Plotted in Figure 12 , it can be seen that the high values of remain almost the same with with time evolution, which indicates that the thermal entropy generation rate plays a dominated role in the heat transfer irreversibility—the viscous entropy generation can be neglected time evolution. The above phenomenon is well consistent with the importance of heat transfer irreversibility in the previous studies [ 23 , 24 , 25 ]. Wei et al [ 24 ] studied the effect of changing the Prandtl number on the entropy generation rate in two-dimensional RB convection, and argued that the thermal entropy generation rate has a dominant role in the heat transfer irreversibility—the viscous entropy generation can be neglected with the increasing Prandtl number.…”

“…In order to understand the global flow and heat transport loss mechanisms in detail, we will present the detailed statistical characteristic mechanisms of the velocity and temperature gradient fields, in particular the related viscous and thermal local entropy generation rates. The viscous and thermal local entropy generation rates as a criterion are used to provide insight into the local viscous and thermal flow loss in the flow field [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. The viscous and thermal components of the local entropy generation rate are derived in the two-dimensional Cartesian space [ 30 ].…”

Numerical Study on Entropy Generation in Thermal Convection with Differentially Discrete Heat Boundary Conditions

“…reported the influence of different Prandtl numbers on the entropy generation in thermal convection. Jin [ 25 ] demonstrated that entropy is a powerful approach to analyze the computed results by second-law analysis. The local entropy generation rate that contains a large amount of phenomenological information are studied by many researchers, e.g., [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ], Pizzolato et al [ 26 ], Rejane et al [ 27 ], Mahian et al [ 28 ], Sheremet et al [ 29 ], Bhatt et al [ 30 , 31 ], Abbas et al [ 32 ] and Qing et al [ 33 ], etc.…”

Entropy Generation Rates in Two-Dimensional Rayleigh–Taylor Turbulence Mixing

“…Wang et al [ 20 ] discovered that discrete heat boundary conditions can affect entropy generation in natural convection. Jin [ 21 ] reported that entropy is a powerful method by the tool of Computational Fluid Dynamics.…”

“…Wang et al [20] discovered that discrete heat boundary conditions can affect entropy generation in natural convection. Jin [21] reported that entropy is a powerful method by the tool of Computational Fluid Dynamics. Based on the above discussions, the statistics of the viscosity and thermal entropy generation rates in two-dimensional RT turbulence by means of data obtained from the lattice Boltzmann method are investigated in this paper.…”