Buoyancy-Induced Flows and Transport

Gebhart, B.; Jaluria, Yogesh; Mahajan, Roop L.; Sammakia, Bahgat; Yovanovich, M. M.

doi:10.1115/1.3226555

Cited by 512 publications

(404 citation statements)

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“…In most of the works on convective heat transfer, (for instance Bejan 2004, Gebhart et al 1988, Incropera and De Witt 1985, Kakaç and Yener 1995, Kaviany 1994, Padet 2010, Taine and Petit 1989, the mixed convection is shortly treated relative to the forced, natural, phase change convection modes, or sometimes simply omitted. This reality is related to the fact that mixed convection is a complex subject for which many elements are missing for a general and coherent approach.…”

Section: Fundamentals Of Mixed Convectionmentioning

confidence: 99%

Mixed thermal convection: fundamental issues and analysis of the planar case

Padet

Cotta

Mladin

et al. 2015

An. Acad. Bras. Ciênc.

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This paper aims to renew interest on mixed thermal convection research and to emphasize three issues that arise from the present analysis: (i) a clear definition of the reference temperature in the Boussinesq approximation; (ii) a practical delimitation of the three convective modes, which are the forced convection (FC), mixed convection (MC) and natural (or free) convection (NC); (iii) and, finally, a uniform description of the set FC/MC/NC in the similarity framework. The planar case, for which analytical solutions are available, allows a detailed illustration of the answers here advanced to the above issues.

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Section: Fundamentals Of Mixed Convectionmentioning

confidence: 99%

Mixed thermal convection: fundamental issues and analysis of the planar case

Padet

Cotta

Mladin

et al. 2015

An. Acad. Bras. Ciênc.

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“…Numerical models with constant properties (Eversteyn et al, 1970;Gebhart et al, 1988;Spall, 1996) and Boussinesq approximations (Gray and Giorgini, 1976;Gebhart et al, 1988) have been utilized to simplify the complex simulations. Wang et al.…”

Section: Experiments or Simulations Of The Cvd Processesmentioning

confidence: 99%

Systematic Strategy for Modeling and Optimization of Thermal Systems With Design Uncertainties

Jaluria¹,

Gea²,

Lin³

2010

Frontiers in Heat and Mass Transfer

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Thermal systems play significant roles in the engineering practice and our lives. To improve those thermal systems, it is necessary to model and optimize the design and the operating conditions. More importantly, the design uncertainties should be considered because the failures of the thermal systems may be very dangerous and produce large loss. This review paper focuses on a systematic strategy of modeling and optimizing of the thermal systems with the considerations of the design uncertainties. To demonstrate the proposed strategy, one of the complicated thermal systems, Chemical Vapor Deposition (CVD), is simulated, parametrically modeled, and optimized. The operating conditions, inlet velocities and susceptor temperatures, are the most significant factors in the CVD and are chosen as the design variables. Several responses -including the percentage of the working area, the mean of the deposition rate, the root mean square of the deposition, and the surface kurtosis -are chosen based on the physical needs and statistical foundations, and are utilized to represent the productivity and the quality of the thin-film deposition. One of the Response Surface Method (RSM), the Radial Basis Function (RBF), is employed to formulate the objective and constraint functions for the optimization. However, it is not until the design uncertainties are considered that the thermal system designs have high risk of the violations of the performance constraints. The Reliability-Based Design Optimization (RBDO) algorithms are used to solve the optimization problems with the design uncertainties. The most famous RBDO methods are the Reliability Index Approach (RIA) and the Performance Measure Approach (PMA). In RBDO, probabilistic constraints are established with respect to either normally or non-normally distributed random variables. The optimal solutions are found subjected to the allowable level of the failure probabilities. The Monte Carlo Simulation (MCS) results can be used to evaluate the failure probabilities. As a result, the proposed systematic strategy of parametrically modeling and optimizing with design uncertainties can be applied to either experiments or simulations of other thermal systems to quantitatively represent the performances, improve their productivity, maintain the quality control, and reduce the probability of the system failure.

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“…Our model focuses on the convective heat transfer between the thermal mass and the air using a bulk model of the thermal mass (cf [4]). Convective heat transfer between the thermal mass and the interior air is typically described by a heat transfer coefficient which depends on a variety of factors, including surface orientation and air properties [1]. Here we consider an idealised problem, and assume the heat transfer coefficient has constant value f which is representative of the overall heat transfer rate, and typically around 2.5 W m À2 K À1 (cf [4]).…”

Section: Theoretical Model Of Heat and Mass Transfermentioning

confidence: 99%