Using artificial neural networks to predict interior velocity coefficients

Krauss, G.; Kindangen, Jefrey I.; Depecker, P.

doi:10.1016/s0360-1323(96)00059-5

Cited by 30 publications

(8 citation statements)

References 2 publications

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“…The transfer function used was a variant of the hyperbolic tangent (Myhara and Sablani, 2001;Hernández-Pérez et al, 2004;Diamantopoulou, 2005), more specifically the hyperbolic tangent sigmoid transfer function (tansig) (Krauss et al, 1997;Demuth et al, 2002) [Eq. 7], which achieves the output much faster and is mathematically equivalent, improving the functioning of the network (Demuth et al, 2002).…”

Section: Methodsmentioning

confidence: 99%

“…For the network a feedforward multilayer percepton was used, trained by means of the backpropagation algorithm, one of the most commonly used algorithms in the references consulted (Krauss et al, 1997;Drake and Packianather, 1998;Myhara and Sablani, 2001;Panchariya et al, 2002;Nordmark, 2002;Avramidis and Iliadis, 2005a;Hernández-Pérez et al, 2004;Diamantopoulou, 2005;Singh et al, 2007;Peng et al, 2007).…”

Section: Methodsmentioning

confidence: 99%

“…f(x) = output value of the neuron x = input value of the neuron As the output values of the transfer function are in the range of (-1, 1) the input and output data were normalized before the network was trained, by means of the equation [8] (Krauss et al, 1997;Demuth et al, 2002;Peng et al, 2007).…”

Section: Methodsmentioning

confidence: 99%

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Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model

Fernández¹,

Esteban²,

Palacios³

et al. 2008

For. syst.

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The physical properties (specific gravity, moisture content, thickness swelling and water absorption) and mechanical properties (internal bond strength, bending strength and modulus of elasticity) were determined on 93 Spanish-manufactured standard particleboards of different thicknesses selected randomly at the end of the production process. The testing methods of the corresponding European standards (EN) were used, except in the case of the thickness swelling and absorption tests, for which the Spanish UNE standard was used. The thickness and the values obtained for the physical properties were entered into an artificial neural network in order to predict the mechanical properties of the board. The fit was compared with the usual multivariate regression models. The use of a neural network made it possible to obtain the values of bending strength, modulus of elasticity and internal bond strength of the boards utilizing the known data, not only of thickness, moisture content and specific gravity, but also of thickness swelling and water absorption. The neural network proposed is much better adapted to the observed values than any of the multivariate regression models obtained.Key words: wood-based panels, physico-mechanical properties, ANN, regression fit, predictive model. ResumenPredicción de propiedades mecánicas del tablero de partículas estándar mediante una red neuronal artificial y comparación con un modelo de regresión multivariante Se han determinado las propiedades físicas (densidad, humedad, hinchazón en espesor y absorción de agua) y mecánicas (tracción perpendicular a las caras, resistencia a flexión y módulo de elasticidad) de 93 tableros de partículas estándar de diferentes espesores, de fabricación española, elegidos aleatoriamente a la salida del proceso de producción, utilizando los métodos de ensayo recogidos en las normas EN correspondientes, excepto en los ensayos de hinchazón y absorción que se ha utilizado norma UNE (española). El espesor y los valores obtenidos de las propiedades físicas han sido introducidos en una red neuronal artificial (RNA) para predecir las propiedades mecánicas del tablero. El ajuste se ha comparado con los habituales modelos de regresión multivariante. La utilización de una red neuronal ha permitido obtener los valores de resistencia a flexión, módulo de elasticidad y resistencia a la tracción perpendicular a las caras de los tableros de partículas a través de los datos conocidos, no sólo de espesor, humedad y densidad sino también de hinchazón en espesor y absorción de agua. La red neuronal propuesta tiene una adecuación a los valores experimentales muy superior a cualquiera de los modelos de regresión multivariante obtenidos.Palabras clave: tableros derivados de la madera, propiedades físico-mecánicas, RNA, ajuste por regresión, modelo predictivo.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model

Fernández¹,

Esteban²,

Palacios³

et al. 2008

For. syst.

View full text Add to dashboard Cite

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“…Artificial neural networks (ANN) have been successfully used to develop such metamodeling relationships between design and objective variables. For example, Krauss et al [13] used the velocity coefficient as the thermal comfort indicator and discussed the influence of CFD-ANN coupling on the interior design; the computation time was reduced considerably. The backpropagation (BP) neural network model was used by Xu et al [14] to replace the CFD model in the simulation of indoor air flow.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Optimization of Indoor Thermal Comfort Parameters with the Adaptive Network‐Based Fuzzy Inference System and Particle Swarm Optimization Algorithm

Yin

Shi

et al. 2017

Mathematical Problems in Engineering

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The goal of this study is to improve thermal comfort and indoor air quality with the adaptive network-based fuzzy inference system (ANFIS) model and improved particle swarm optimization (PSO) algorithm. A method to optimize air conditioning parameters and installation distance is proposed. The methodology is demonstrated through a prototype case, which corresponds to a typical laboratory in colleges and universities. A laboratory model is established, and simulated flow field information is obtained with the CFD software. Subsequently, the ANFIS model is employed instead of the CFD model to predict indoor flow parameters, and the CFD database is utilized to train ANN input-output "metamodels" for the subsequent optimization. With the improved PSO algorithm and the stratified sequence method, the objective functions are optimized. The functions comprise PMV, PPD, and mean age of air. The optimal installation distance is determined with the hemisphere model. Results show that most of the staff obtain a satisfactory degree of thermal comfort and that the proposed method can significantly reduce the cost of building an experimental device. The proposed methodology can be used to determine appropriate air supply parameters and air conditioner installation position for a pleasant and healthy indoor environment.

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“…They behave like human brain. As the brain, ANNs consist of many small, interconnected units [15]. These units called as neuron.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Comparative analysis of various modelling techniques for emission prediction of diesel engine fueled by diesel fuel with nanoparticle additives

Tosun¹,

Özgür²,

Özgür³

et al. 2017

European Mechanical Science

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In this study, emissions of compression ignition engine fueled by diesel fuel with nanoparticle additives was modeled by regression analysis, artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) methods. Cetane number (CN) and engine speed (rpm) were selected as input parameters for estimation of carbon monoxide (CO), oxides of nitrogen (NOx), and carbon dioxide (CO 2 ) emissions. The results of estimation techniques were compared with each other and they showed that regression analysis was not accurate enough for prediction. On the other hand, ANN and ANFIS modelling techniques gave more accurate results with respect to regression analysis; linear and non-linear. Especially ANFIS models can be suggested as estimation method with minimum error compared to experimental results. Keywords:Adaptive neuro fuzzy inference system; Artificial neural network; Diesel engine; Regression analysis INTRODUCTIONIn recent years, depletion of fossil fuels forces researchers to search new alternative fuels. In literature, there are a lot of studies about fuels which have potential to replace fossil fuels used in internal combustion engines. In this respect, various biofuels and alcohols seem as good option [1]. In addition to scarcity of conventional fuels, efforts on performance en-hancement and emission reduction of engines are the other important issues on which engineers and engine manufacturers are working on it. Especially, the stringent emission legislations enforced manufacturers to develop new technologies [2]. Traditional engine research and development studies are both difficult and costly to meet emission limits imposed by legislations. Therefore, these costly studies are replaced by various cost-effective approaches as artificial neural networks (ANN) and computational fluid dynamics (CFD) [3]. ANNs are nonlinear computer algorithms, which can model the behavior of complex nonlinear processes. Recently, this method has been widely applied to various disciplines as automotive engineering [4]. Yusaf et al. studied the effect of using CPO (crude palm oil) -OD (ordinary diesel) blends as fuel on the performance of CI (compression ignition) engine. In addition, engine power output, fuel consumption, and exhaust-gas emission are evaluated and then predicted using ANN technique [5]. Shanmugam et al. used ANN modeling to predict the performance and exhaust emissions of the diesel engine using hybrid fuel and they revealed that the ANN approach could be confidently used to predict the performance and emissions of the diesel engine accurately [6]. Ghazikhani and Mirzaii predicted soot emission of a waste-gated turbo-charged DI diesel engine using ANN. The results showed the ANN approach can be used to accurately predict soot emis-sion of a turbo-charged diesel engine in different opening ranges of waste-gate (ORWG) [7].

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Using artificial neural networks to predict interior velocity coefficients

Cited by 30 publications

References 2 publications

Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model

Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model

Optimization of Indoor Thermal Comfort Parameters with the Adaptive Network‐Based Fuzzy Inference System and Particle Swarm Optimization Algorithm

Comparative analysis of various modelling techniques for emission prediction of diesel engine fueled by diesel fuel with nanoparticle additives

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