CFD-based optimization of a transient heating process in a natural gas fired furnace using neural networks and genetic algorithms

Prieler, René Josef; Mayrhofer, Markus; Gaber, Christian; Gerhardter, Hannes; Schluckner, Christoph; Landfahrer, Martin; Eichhorn-Gruber, Markus; Schwabegger, Günther; Hochenauer, Christoph

doi:10.1016/j.applthermaleng.2018.03.042

Cited by 19 publications

(6 citation statements)

References 42 publications

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“…The essence behind the genetic algorithm is to learn from the natural selection genetic evolution mechanism in nature, and this algorithm is an adaptive probability optimization technology. , After the initial population is generated, the genetic algorithm performs a series of selection, crossover, and mutation operations on it in order to imitate the biological genetic evolution process. Then, the inferior individuals are eliminated, and the excellent individuals are retained to produce a new population until the population has evolved to a state that contains an approximate optimal solution.…”

Section: Numerical Methods and Solution Optimizationmentioning

confidence: 99%

CFD-PBE Simulation of para-Xylene Crystallization Behavior and Process Amplification under Different Operating Conditions

Dong,

Wu,

Liu

et al. 2023

Ind. Eng. Chem. Res.

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The crystallization technology is one of the most potential ways to produce para-xylene (PX) with high purity, but it has some drawbacks such as low yield and difficult to control crystallization due to the insufficient study of the PX nucleation characteristics and growth behavior. To further investigate this issue, this paper couples the Euler−Euler model and population balance equation (PBE) model, correlates the PX crystallization kinetics, and accurately solves the PBE using the Gaussian integral hypothesis and product difference algorithm, which innovatively and directly simulates the PX suspension crystallization process and yields its multiphysical field characteristics. A multiobjective genetic algorithm is then used to study and optimize the crystallization mechanism and variation patterns, and the PX crystallization amplification law is derived. The results can be concluded as follows.There is a complete internal circulation flow in the crystallizer, and the fluid near the stirring paddle, at the entrance, and at the bottom has the strongest molecular kinetic energy and mixing degree. The crystal concentration and size increase exponentially before 750 s of crystallization, after which the growth trend decreases and gradually reaches dynamic equilibrium. The inlet velocity and stirring speed are inversely connected with the average particle size and particle size distribution interval, while supersaturation is positively correlated with both. The supersaturation has the most obvious effect on the crystal size distribution (CSD) curve. In comparison to the original settings, the optimized operating parameters raise the average particle size of PX by 6.68% and decrease the distribution interval by 38.21%. At last, the sedimentation velocity can be employed as an amplification factor to simplify the amplification process. This paper might serve as a theoretical reference for PX crystallization research and process optimization.

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Section: Numerical Methods and Solution Optimizationmentioning

confidence: 99%

CFD-PBE Simulation of para-Xylene Crystallization Behavior and Process Amplification under Different Operating Conditions

Dong,

Wu,

Liu

et al. 2023

Ind. Eng. Chem. Res.

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“…To achieve the pre-defined temperature, the furnace was operated by four natural gas burners at the side walls. For a more homogeneous temperature distribution in the furnace, baffle sheets were placed at each burner to deflect the flame [see (Prieler et al, 2018b)]. The temperature inside the furnace was observed using 12 plate thermocouples.…”

Section: Methodsmentioning

confidence: 99%

Numerical simulation of a fire resistance test and prediction of the flue gas leakage using CFD/FEM coupling

Prieler

Pletzer

Thumser³

et al. 2023

JSFE

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PurposeIn fire resistance tests (FRTs) of building materials, a crucial criterion to pass the test procedure is to avoid the leakage of the hot flue gases caused by gaps and cracks occurring due to the thermal exposure. The present study's aim is to calculate the deformation of a steel door, which is embedded within a wall made of bricks, and qualitatively determine the flue gas leakage.Design/methodology/approachA computational fluid dynamics/finite element method (CFD/FEM) coupling was introduced representing an intermediate approach between a one-way and a full two-way coupling methodology, leading to a simplified two-way coupling (STWC). In contrast to a full two way-coupling, the heat transfer through the steel door was simulated based on a one-way approach. Subsequently, the predicted temperatures at the door from the one-way simulation were used in the following CFD/FEM simulation, where the fluid flow inside and outside the furnace as well as the deformation of the door were calculated simultaneously.FindingsThe simulation showed large gaps and flue gas leakage above the door lock and at the upper edge of the door, which was in close accordance to the experiment. Furthermore, it was found that STWC predicted similar deformations compared to the one-way coupling.Originality/valueSince two-way coupling approaches for fluid/structure interaction in fire research are computationally demanding, the number of studies is low. Only a few are dealing with the flue gas exit from rooms due to destruction of solid components. Thus, the present study is the first two-way approach dealing with flue gas leakage due to gap formation.

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“…[41] did a CFD modeling and optimization of a latent heat storage unit with the help of GA. Daróczyet, Janiga and Thévenin [42] optimized the shape of the airfoil geometry of an H-rotor with the help of CFD and GA. Prieler et al [43] suggested a CFD-based optimization process for the transient heating process in a natural gas fired furnace using ANN and GA. Oh and Chein [44] demonstrated various optimization problems that may be solved by coupling CFD and GA. Sun and Yoon [45] performed multi-objective optimization of a gas cyclone separator using CFD and GA. Pourfattah et al [46] coupled CFD and GA to optimize a vertical twisted tape arrangement in a channel subjected to MWCNT-water nanofluid.…”

Section: Studies Of Heat Transfer Enhancement In Mini/ Micro-channelsmentioning

confidence: 99%

Calibration of κ-ε turbulence model for thermal–hydraulic analyses in rib-roughened narrow rectangular channels using genetic algorithm

2021

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Nowadays, applications of turbulent fluid flow in removing high heat flux in rib-roughened narrow channels are drawing much interest. In this work, an improved version of the κ-ε turbulence model is proposed for better prediction of thermal–hydraulic characteristics of flow inside rib-roughened (pitch-to-rib height (p/k) ratio = 10 and 20) narrow channels (channel height, H = 1.2 mm and 3.2 mm). For this, the four turbulence model parameters, Cμ, Cε1, Cε2, and σk, are calibrated. These parameters are adjustable empirical constants provided for controlling the accuracy of the turbulence model results when needed. The simulated data are used to develop correlations between the relative errors in predicting the friction factor (f), Nusselt number (Nu), and the model parameters using a multivariate nonlinear regression method. These correlations are used to optimize the errors using genetic algorithm. Results reveal that the calibrated parameters are not the same for all the narrow channel configurations. After calibration, the overall predictive improvements are up to 35.83% and 27.30% for p/k = 10 and p/k = 20 respectively when H = 1.2 mm. Also, up to 15.48% and 18.05% improvements are obtained for p/k = 10 and p/k = 20 respectively when H = 3.2 mm. The role of the two parameters Cε1 and Cε2 are found to be of primary importance. Furthermore, three types of nanofluids i.e. Al2O3-water, CuO-water, and TiO2-water are studied using the calibrated model to check the potentiality of heat transfer enhancement. Among them, CuO-water nanofluid is predicted to have around 1.32 times higher value of Nu than pure water for the same narrow channel configuration. Article Highlights κ-ε turbulence model is calibrated for rib-roughened narrow rectangular channels using genetic algorithm. Cε1 and Cε2 are the most influential parameters on the performance of the model inside rib-roughened narrow channel. Suggested calibration process is more effective for channel height of 1.2 mm than 3.2 mm.

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CFD-based optimization of a transient heating process in a natural gas fired furnace using neural networks and genetic algorithms

Cited by 19 publications

References 42 publications

CFD-PBE Simulation of para-Xylene Crystallization Behavior and Process Amplification under Different Operating Conditions

CFD-PBE Simulation of para-Xylene Crystallization Behavior and Process Amplification under Different Operating Conditions

Numerical simulation of a fire resistance test and prediction of the flue gas leakage using CFD/FEM coupling

Calibration of κ-ε turbulence model for thermal–hydraulic analyses in rib-roughened narrow rectangular channels using genetic algorithm

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