7 resultados para Thermal modeling
em Cambridge University Engineering Department Publications Database
Resumo:
The cost of large-eddy simulation (LES) modeling in various zones of gas turbine aeroengines is outlined. This high cost clearly demonstrates the need to perform hybrid Reynolds-averaged Navier-Stokes-LES (RANS-LES) over the majority of engine zones because the Reynolds number is too high for pure LES. The RANS layer is used to cover over the fine streaks found in the inner part of the boundary layer. The hybrid strategy is applied to various engine zones, which is shown to typically give much greater predictive accuracy than pure RANS simulations. However, the cost estimates show that the RANS layer should be disposed within the low-pressure turbine zone. Also, the nature of the flow physics in this zone makes LES most sensible. © 2012 by Begell House, Inc.
Resumo:
Hybrid methods based on the Reynolds Averaged Navier Stokes (RANS) equations and the Large Eddy Simulation (LES) formulation are investigated to try and improve the accuracy of heat transfer and surface temperature predictions for electronics systems and components. Two relatively low Reynolds number flows are studied using hybrid RANS-LES, RANS-Implicit-LES (RANS-ILES) and non-linear LES models. Predictions using these methods are in good agreement with each other, even using different grid resolutions. © 2008 IEEE.
Resumo:
This paper presents stochastic implicit coupling method intended for use in Monte-Carlo (MC) based reactor analysis systems that include burnup and thermal hydraulic (TH) feedbacks. Both feedbacks are essential for accurate modeling of advanced reactor designs and analyses of associated fuel cycles. In particular, we investigate the effect of different burnup-TH coupling schemes on the numerical stability and accuracy of coupled MC calculations. First, we present the beginning of time step method which is the most commonly used. The accuracy of this method depends on the time step length and it is only conditionally stable. This work demonstrates that even for relatively short time steps, this method can be numerically unstable. Namely, the spatial distribution of neutronic and thermal hydraulic parameters, such as nuclide densities and temperatures, exhibit oscillatory behavior. To address the numerical stability issue, new implicit stochastic methods are proposed. The methods solve the depletion and TH problems simultaneously and use under-relaxation to speed up convergence. These methods are numerically stable and accurate even for relatively large time steps and require less computation time than the existing methods. © 2013 Elsevier Ltd. All rights reserved.
Resumo:
Brushless doubly fed induction generator (BDFIG) has substantial benefits, which make it an attractive alternative as a wind turbine generator. However, it suffers from lower efficiency and larger dimensions in comparison to DFIG. Hence, optimizing the BDFIG structure is necessary for enhancing its situation commercially. In previous studies, a simple model has been used in BDFIG design procedure that is insufficiently accurate. Furthermore, magnetic saturation and iron loss are not considered because of difficulties in determination of flux density distributions. The aim of this paper is to establish an accurate yet computationally fast model suitable for BDFIG design studies. The proposed approach combines three equivalent circuits including electric, magnetic and thermal models. Utilizing electric equivalent circuit makes it possible to apply static form of magnetic equivalent circuit, because the elapsed time to reach steady-state results in the dynamic form is too long for using in population-based design studies. The operating characteristics, which are necessary for evaluating the objective function and constraints values of the optimization problem, can be calculated using the presented approach considering iron loss, saturation, and geometrical details. The simulation results of a D-180 prototype BDFIG are compared with measured data in order to validate the developed model. © 1986-2012 IEEE.