<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1390-860X</journal-id>
<journal-title><![CDATA[Ingenius. Revista de Ciencia y Tecnología ]]></journal-title>
<abbrev-journal-title><![CDATA[Ingenius]]></abbrev-journal-title>
<issn>1390-860X</issn>
<publisher>
<publisher-name><![CDATA[Universidad Politécnica Salesiana]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1390-860X2018000200021</article-id>
<article-id pub-id-type="doi">10.17163/ings.n20.2018.02</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[DISEÑO DE UNA RED NEURONAL PARA LA PREDICCIÓN DEL COEFICIENTE DE PÉRDIDAS PRIMARIAS EN RÉGIMEN DE FLUJO TURBULENTO]]></article-title>
<article-title xml:lang="en"><![CDATA[DESIGN OF A NEURAL NETWORK FOR THE PREDICTION OF THE COEFFICIENT OF PRIMARY LOSSES IN UTRBULENT FLOW REGIME]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Castillo-Calderón]]></surname>
<given-names><![CDATA[Jairo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Solórzano-Castillo]]></surname>
<given-names><![CDATA[Byron]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Moreno-Moreno]]></surname>
<given-names><![CDATA[José]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional de Loja  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional de Loja  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Nacional de Loja  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2018</year>
</pub-date>
<numero>20</numero>
<fpage>21</fpage>
<lpage>27</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_arttext&amp;pid=S1390-860X2018000200021&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_abstract&amp;pid=S1390-860X2018000200021&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_pdf&amp;pid=S1390-860X2018000200021&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  La presente investigación está orientada al diseño de una red neuronal para la predicción del factor de fricción en régimen de flujo turbulento, siendo este indispensable para el cálculo de pérdidas primarias en conductos cerrados o tuberías. Se utiliza Neural Networks Toolbox de MATLAB® para diseñar la red neuronal artificial (RNA), con retropropagación, cuya base de datos comprende 724 puntos obtenidos del diagrama de Moody. Las variables de entrada de la RNA son el número de Reynolds y la rugosidad relativa de la tubería; la variable de salida es el coeficiente de fricción. Utilizando el algoritmo de entrenamiento de Levenberg-Marquardt se entrena la RNA con distintas topologías, variando el número de capas ocultas y el número de neuronas ocultas en cada capa. Con una estructura 2-30-30-1 de la RNA se obtuvo el mejor resultado, exhibiendo un error cuadrático medio (ECM) de 1,75E-8 y un coeficiente de correlación de Pearson R de 0,99999 entre la salida de la red neuronal y la salida deseada. Además, mediante un análisis descriptivo de variable en el software SPSS®, se obtiene que el error relativo medio es de 0,162 %, indicando que el modelo diseñado es capaz de generalizar con alta precisión.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  This investigation is focused on the design of a neural network for the prediction of the friction factor in turbulent flow regime, being this factor indispensable for the calculation of primary losses in closed ducts or pipes. MATLAB® Neural Networks Toolbox is used to design the artificial neural network (ANN), with backpropagation. The database includes 724 points obtained from the Moody diagram. The Reynolds number and the relative roughness of the pipe are the input variables of the ANN, the output variable is the coefficient of friction. The Levenberg-Marquardt algorithm is used for training the ANN by using different topologies, varying the number of hidden layers and the number of neurons that are hidden in each layer. The best result was obtained with a 2-30-30-1 topology, exhibiting a mean squared error (MSE) of 1.75E-8 and a Pearson correlation coefficient R of 0.99999 between the neural network output and the desired output. Furthermore, a descriptive analysis of the variable was performed in the SPSS® software, where the mean relative error obtained was 0.162 %, indicating that the designed model is able to generalize with high accuracy.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[diagrama de Moody]]></kwd>
<kwd lng="es"><![CDATA[factor de fricción]]></kwd>
<kwd lng="es"><![CDATA[pérdida de carga]]></kwd>
<kwd lng="es"><![CDATA[red neuronal artificial]]></kwd>
<kwd lng="es"><![CDATA[retropropagación]]></kwd>
<kwd lng="es"><![CDATA[flujo turbulento]]></kwd>
<kwd lng="en"><![CDATA[Moody diagram]]></kwd>
<kwd lng="en"><![CDATA[friction factor]]></kwd>
<kwd lng="en"><![CDATA[head loss]]></kwd>
<kwd lng="en"><![CDATA[artificial neural network]]></kwd>
<kwd lng="en"><![CDATA[backpropagation]]></kwd>
<kwd lng="en"><![CDATA[turbulent flow]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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