Multi-objective optimization of a dimpled channel for heat transfer augmentation

Samad, Abdus; Lee, Ki‐Don; Kim, Kwang‐Yong

doi:10.1007/s00231-008-0420-6

Cited by 32 publications

(17 citation statements)

References 21 publications

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“…The grid independency test and validation of computational results were performed and reported in a previous study by Samad et al [17]. As shown in Table I, the design space is first explored by preliminary calculations under geometric and flow constraints and in light of the prior experimental and numerical studies that are reported in the literature, the design space is exploited through LHS [35].…”

Section: Resultsmentioning

confidence: 99%

“…A review of multi-objective optimization methods for engineering was presented by Marler and Arora [22]. A hybrid multi-objective evolutionary algorithm (hybrid MOEA) using elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) combined with a local search strategy [23] has been applied in various engineering-design optimizations including cooling channels [17,24]. and microchannel heat sinks [25,26].…”

Section: Introductionmentioning

confidence: 99%

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Enhanced multi‐objective optimization of a dimpled channel through evolutionary algorithms and multiple surrogate methods

Husain

Lee

Kim

2011

Numerical Methods in Fluids

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SUMMARYUsing a multi-objective evolutionary algorithm (MOEA) and enhanced surrogate approximations, the present study demonstrates the numerical analysis and optimization of staggered-dimple channels. Two surrogates, the response surface approximation (RSA) model and the Kriging (KRG) model, are applied in light of the surrogate fidelity of the approximate analysis. An enhanced Pareto-optimal front is obtained by performing local resampling of the Pareto-optimal front, which provides relatively more accurate Paretooptimal solutions in the design space for each surrogate model. Three dimensionless design variables are selected, which are related to geometric parameters, namely, the channel height, dimple print diameter, dimple spacing, and dimple depth. Two objective functions are selected that are related to the heat transfer and pressure loss, respectively. The objective-function values are numerically evaluated through Reynoldsaveraged Navier-Stokes analysis at the design points that are selected through the Latin hypercube sampling method. Using these numerical simulations two surrogates, viz, the RSA and Kriging models, are constructed for each objective function and a hybrid MOEA is applied to obtain the Pareto-optimal front. For the particular implementation of surrogate models, it is observed that Pareto-optimal predictions of the RSA model are better than those of the KRG model, whereas the KRG model predicts equally well at the off-Pareto-region (region away from the Pareto-optimal solutions), which is not the case with the RSA model. The local resampling of the Pareto-optimal front increases the fidelity of the approximate solutions near the Pareto-optimal region. The ratios of the channel height to the dimple print diameter and of the dimple print diameter to the dimple pitch are found to be more sensitive along the Pareto-optimal front than the ratio of the dimple depth to the print diameter. The decrease of the ratio of the channel height to the dimple diameter and the increase of the ratio of the dimple print diameter to the pitch lead to greater heat transfer at the expense of the pressure loss, whereas the ratio of the dimple depth to the print diameter is rather insensitive to Pareto-optimal solutions. Pareto-optimal solutions at higher values of the Nusselt number are associated with higher values of the pressure loss due to the increased recirculation, mixing of fluid and vorticity generation.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced multi‐objective optimization of a dimpled channel through evolutionary algorithms and multiple surrogate methods

Husain

Lee

Kim

2011

Numerical Methods in Fluids

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“…In this method, first, approximate Pareto optimal solutions are obtained using real Fig. 4 Multi-objective optimization procedure [19] coded NSGA-II developed by Deb et al [17] for two objective functions based on heat transfer and pressure drop. Here, real coded means that the crossover and mutations are conducted in real space to obtain a response of NSGA-II.…”

Section: Multi-objective Evolutionary Algorithm (Moea)mentioning

confidence: 99%

“…Review of multi-objective optimization methods for engineering was presented by Marler and Arora [18]. NSGA-II applications have been reported by Samad et al [19] and Foli et al [20], who applied multi-objective optimization approach using evolutionary algorithm to dimpled channel and micro heat exchanger optimizations, respectively. And, Samad and Kim [21] performed single-and multi-objective shape optimization for a compressor blade.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Optimization of a Laidback Fan Shaped Film-Cooling Hole Using Evolutionary Algorithm

Lee

Husain

Kim

2010

International Journal of Fluid Machinery and Systems

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Laidback fan shaped film-cooling hole is formulated numerically and optimized with the help of three-dimensional numerical analysis, surrogate methods, and the multi-objective evolutionary algorithm. As Pareto optimal front produces a set of optimal solutions, the trends of objective functions with design variables are predicted by hybrid multi-objective evolutionary algorithm. The problem is defined by four geometric design variables, the injection angle of the hole, the lateral expansion angle of the diffuser, the forward expansion angle of the hole, and the ratio of the length to the diameter of the hole, to maximize the film-cooling effectiveness compromising with the aerodynamic loss. The objective function values are numerically evaluated through Reynolds-averaged Navier-Stokes analysis at the designs that are selected through the Latin hypercube sampling method. Using these numerical simulation results, the Response Surface Approximation model are constructed for each objective function and a hybrid multi-objective evolutionary algorithm is applied to obtain the Pareto optimal front. The clustered points from Pareto optimal front were evaluated by flow analysis. These designs give enhanced objective function values in comparison with the experimental designs.

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“…Fan and Yin [8] numerically studied the influence of geometrical parameters on the heat transfer and pressure drop of dimpled jacket in thin-film evaporator. Samad et al [9] numerically optimized staggered dimple arrays on opposite walls of a channel to enhance heat transfer performance. Results indicated that heat transfer rate increased as the dimple depth increased, and at the same time, pressure drop also increased.…”

Section: Introductionmentioning

confidence: 99%

Numerical Study on the Effect of Tube Rows on the Heat Transfer Characteristic of Dimpled Fin

Feng

Liu

et al. 2014

Advances in Mechanical Engineering

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The dimpled fin has excellent heat transfer performance and has attracted a lot of attention to apply on the fin and tube heat exchanger. A study presents to investigate the effects of number of tube rows on the air-side heat transfer characteristics of dimpled fin for velocity ranging from 1 to 3 m/s. The /Δ and /((Δ × )) are used to evaluate the heat transfer performance of the heat exchanger. The results show that the dimpled arrangement can change the mainstream direction, increase the disturbance, and enhance the heat transfer. With the increase of the number of tube rows, the average Nusselt number decreases and /Δ and /((Δ × )) increase gradually. Compared with the multipipe tube rows, the performance of two-row tube is better.

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Multi-objective optimization of a dimpled channel for heat transfer augmentation

Cited by 32 publications

References 21 publications

Enhanced multi‐objective optimization of a dimpled channel through evolutionary algorithms and multiple surrogate methods

Enhanced multi‐objective optimization of a dimpled channel through evolutionary algorithms and multiple surrogate methods

Multi-objective Optimization of a Laidback Fan Shaped Film-Cooling Hole Using Evolutionary Algorithm

Numerical Study on the Effect of Tube Rows on the Heat Transfer Characteristic of Dimpled Fin

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