Diffuser and Nozzle Design Optimization by Entropy Generation Minimization

Self Cite

Assuming that CFD solutions will be more and more used to characterize losses in terms of drag for external flows and head loss for internal flows, we suggest to replace single-valued data, like the drag force or a pressure drop, by field information about the losses. These information are gained when the entropy generation in the flow field is analyzed, an approach that often is called second law analysis (SLA), referring to the second law of thermodynamics. We show that this SLA approach is straight-forward, systematic and helpful when it comes to the physical interpretation of the losses in a flow field. Various examples are given, including external and internal flows, two phase flow, compressible flow and unsteady flow. Finally, we show that an energy transfer within a certain process can be put into a broader perspective by introducing the entropic potential of an energy.

Section: Advantage Of the Alternative Approachmentioning

confidence: 99%

How to Determine Losses in a Flow Field: A Paradigm Shift towards the Second Law Analysis

Herwig

Schmandt

2014

Self Cite

“…This allows quantification of the overall entropy generation of a system and to examine how irreversibilities are distributed locally throughout the system [24]. Based on the concept of minimal entropy generation and CFD, causes of irreversibilities have been analyzed for a wide range of thermo-fluid processes including laminar and turbulent heat transfer in wall-bounded flows [25][26][27][28][29][30][31][32][33], flows under supercritical thermodynamic conditions [34][35][36][37], reacting flows [38][39][40][41] and also in heat transfer in impinging flows [9,42,43]. Contributions of the theory and application of entropy generation analysis using CFD for different types of engineering systems are reviewed in [24,44,45].…”

Section: Introductionmentioning

confidence: 99%

“…All these DNS studies are restricted to heat and fluid flow problems at low-to-moderate Reynolds numbers and simple geometries due to the high computational cost of DNS. In contrast, entropy generation analysis based on the solution of the Reynolds-averaged Navier-Stokes equations (RANS) have been carried out in many numerical studies (e.g., [26,27,[29][30][31]33,36,37,39,42]) because of the relatively low computational cost of the RANS approach. However, it is well known that the prediction of complex heat and fluid flows based on RANS is not always accurate for many cases, especially for turbulent flows with large scale, unsteady characteristics.…”

Section: Introductionmentioning

confidence: 99%

Entropy Generation Analysis and Thermodynamic Optimization of Jet Impingement Cooling Using Large Eddy Simulation

Ries

Nishad

et al. 2019

In this work, entropy generation analysis is applied to characterize and optimize a turbulent impinging jet on a heated solid surface. In particular, the influence of plate inclinations and Reynolds numbers on the turbulent heat and fluid flow properties and its impact on the thermodynamic performance of such flow arrangements are numerically investigated. For this purpose, novel model equations are derived in the frame of Large Eddy Simulation (LES) that allows calculation of local entropy generation rates in a post-processing phase including the effect of unresolved subgrid-scale irreversibilities. From this LES-based study, distinctive features of heat and flow dynamics of the impinging fluid are detected and optimal operating designs for jet impingement cooling are identified. It turned out that (1) the location of the stagnation point and that of the maximal Nusselt number differ in the case of plate inclination; (2) predominantly the impinged wall acts as a strong source of irreversibility; and (3) a flow arrangement with a jet impinging normally on the heated surface allows the most efficient use of energy which is associated with lowest exergy lost. Furthermore, it is found that increasing the Reynolds number intensifies the heat transfer and upgrades the second law efficiency of such thermal systems. Thereby, the thermal efficiency enhancement can overwhelm the frictional exergy loss.

“…To overcome this problem, Reynolds Averaged Navier Stokes equation (RANS) approaches have been often used to study entropy generation dynamics at high Reynolds numbers. These investigations are reported in few studies [ 22 , 23 , 24 , 25 , 26 ]. However, it is well known that prediction capabilities of RANS models are limited when dealing with turbulent flows with large scale and unsteady characteristics.…”

Section: Introductionmentioning

confidence: 99%

Entropy Generation Assessment for Wall-Bounded Turbulent Shear Flows Based on Reynolds Analogy Assumptions

Ziefuss

Karimi

Ries

et al. 2019

Heat transfer modeling plays a major role in design and optimization of modern and efficient thermal-fluid systems. Further, turbulent flows are thermodynamic processes, and thus, the second law of thermodynamics can be used for critical evaluations of such heat transfer models. However, currently available heat transfer models suffer from a fundamental shortcoming: their development is based on the general notion that accurate prediction of the flow field will guarantee an appropriate prediction of the thermal field, known as the . In this work, an assessment of the capability of the in predicting turbulent heat transfer when applied to shear flows of fluids of different Prandtl numbers will be given. Towards this, a detailed analysis of the predictive capabilities of the concerning entropy generation is presented for steady and unsteady state simulations. It turns out that the provides acceptable results only for mean entropy generation, while fails to predict entropy generation at small/sub-grid scales.