New approach to dynamic modelling of vapour-compression liquid chillers: artificial neural networks

Bechtler, H.; Browne, M.W.; Bansal, Pradeep; Kecman, Vojislav

doi:10.1016/s1359-4311(00)00093-4

Cited by 191 publications

(77 citation statements)

References 5 publications

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“…In addition, statistical values such as the RMSE, cv, and R 2 , which correspond to different numbers of boundary elements in the analysed system, are given in Tables 1 and 2. The error can be estimated using the RMSE, defined as [51]:…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Some statistical methods, such as the root mean square error (RMSE), the coefficient of variation (c v ), the coefficient of multiple determinations (R 2 ), and the relative error (e r ) may be used to compare simulated and analytical (exact) values of the flow's velocity and temperature to validate the model. The error can be estimated using the RMSE, defined as [51]:…”

Section: Testing the Modelmentioning

confidence: 99%

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Numerical Simulation of the Laminar Forced Convective Heat Transfer between Two Concentric Cylinders

Sârbu

Iosif

2017

Computation

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Abstract:The dual reciprocity method (DRM) is a highly efficient numerical method of transforming domain integrals arising from the non-homogeneous term of the Poisson equation into equivalent boundary integrals. In this paper, the velocity and temperature fields of laminar forced heat convection in a concentric annular tube, with constant heat flux boundary conditions, have been studied using numerical simulations. The DRM has been used to solve the governing equation, which is expressed in the form of a Poisson equation. A test problem is employed to verify the DRM solutions with different boundary element discretizations and numbers of internal points. The results of the numerical simulations are discussed and compared with exact analytical solutions. Good agreement between the numerical results and exact solutions is evident, as the maximum relative errors are less than 5% to 6%, and the R 2 -values are greater than 0.999 in all cases. These results confirm the effectiveness and accuracy of the proposed numerical model, which is based on the DRM.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Testing the Modelmentioning

confidence: 99%

Numerical Simulation of the Laminar Forced Convective Heat Transfer between Two Concentric Cylinders

Sârbu

Iosif

2017

Computation

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“…The used simulation model performance was evaluated using three statistical indices: the coefficient of multiple determinations (R 2 ), the root mean square error (RMSE) and the coefficient of variation (CV), as defined below (Bechthler et al, 2001;Dawson et al, 2007). There indices were used to compare simulated and measured (actual) values of the air temperature.…”

Section: Validation Of Simulation Modelmentioning

confidence: 99%

Numerical modelling of high-temperature radiant panel heating system for an industrial hall

Sârbu

Adriana

2018

Int. j. adv. appl. sci.

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This paper shows how the use of the numerical modelling method of the radiant heat flux depending on the absorption and reflection coefficients and the energy transmission degree can optimise a ceramic radiant panel heating system for an industrial hall with geometrical dimensions of 80308 m and heat demand of 277.3 kW using Systema software. The optimal location of the 14 ceramic panels obtained through modelling is selected so that by controlled conducting of the heat flux of 1.03 W/m 2 emitted by the radiant panels towards the working area can ensure adequate comfort. The heating system configuration flexibility enables both changing the location of radiant panels and the heat flux orientation. Thus, the operative temperature to the outdoor walls resulted in the range 19.219.7 °C, and the uniform operative temperature in the working area is equal to 22.6 °C in accordance with the international standards ISO 7730 and ASHRAE 55. Additionally, the expected mark (EM) value of 0.04 calculated as the ratio between the predicted mean vote (PMV) and the predicted percent dissatisfied (PPD) indexes indicates the assurance of adequate thermal comfort conditions, according to the same standards. The estimated energy consumption and operating costs show that liquefied petroleum gas utilisation is clearly the best solution with respect to methane and diesel for fuel supply of the modelled radiant heating system.

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“…For details the reader is referred to [21]. Some applications of RBFs can be found in [25][26][27][28].…”

Section: Mathematical Modelmentioning

confidence: 99%

Evaluation of the Use of Artificial Neural Networks for the Simulation of Hybrid Solar Collectors

Facão

Varga

Oliveira

2004

International Journal of Green Energy

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In the last decade, artificial neural networks (ANNs) have been receiving an increasing attention for simulating engineering systems due to some interesting characteristics such as learning capability, fault tolerance, speed and non-linearity. This paper describes an alternative approach to assess two types of hybrid solar collector/heat pipe systems (plate heat pipe type and tube heat pipe type) using ANNs.Multiple Layer Perceptrons (MLPs) and Radial Basis Networks (RBFs) were considered. The networks were trained using results from mathematical models generated by Monte Carlo simulation. The mathematical models were based on energy balances and resulted in a system of non-linear equations. The solution of the models was very sensitive to initial estimates, and convergence was not obtained under certain conditions. Between the two neural models, MLPs performed slightly better than RBFs. It can be concluded that similar configurations were adequate for both collector systems. It was found that ANNs simulated both collector efficiency and heat output with high accuracy when "unseen" data were presented to the networks. An important advantage of a trained ANN over the mathematical models is that convergence is not an issue and the result is obtained almost instantaneously.

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New approach to dynamic modelling of vapour-compression liquid chillers: artificial neural networks

Cited by 191 publications

References 5 publications

Numerical Simulation of the Laminar Forced Convective Heat Transfer between Two Concentric Cylinders

Numerical Simulation of the Laminar Forced Convective Heat Transfer between Two Concentric Cylinders

Numerical modelling of high-temperature radiant panel heating system for an industrial hall

Evaluation of the Use of Artificial Neural Networks for the Simulation of Hybrid Solar Collectors

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