2017 ASABE Annual Meeting Template

2017 Annual Meeting Paper Template for ASABE Authors New Process—Upload your completed paper to etouches by June 9, 2017 to be in the online Technical Library when the annual meeting starts. https://www.eiseverywhere.com/eSites/asabe2017/Homepage Uploads begin April 1, 2017. Check www.asabe.org/ManuscriptTemplates. ITSC Paper Awards Deadline - May 1 This Microsoft Word filename should match your paper number – example: “1700987.docx”. You must enter your paper number in THREE locations below. Have questions? Click on the FAQ link www.asabe.org/AIMpaperFAQ. • Please follow these instructions. Processing time is delayed when these instructions are not followed. • The tables below are necessary for online indexing of author names. They will not be printed with your paper. • The title, abstract, keywords, etc. of your paper must have certain Word styles for online searches. • New (2017) instructions below about fonts, styles, and references. Use website links for help. Use Times New Roman font for text except inside figures. Inside figures, use a sans serif font, such as Arial, for clarity, however, the caption under the figure should be Times New Roman. For citations in the text, use the name, date system. For the references section at the end of the paper, use APA 6th style. These changes make this paper like our journal format. You may want to publish this paper in our journals. See www.asabe.org/SubmitJournalManuscript for more. Revised 12/13/2016.

See the ASABE website for more information for authors. Author 1 (one author only) First Name (or initial) Jorge

Middle Name (or initial)

Surname Flores-Velazquez

Role (ASABE member, etc.)

Email jorge_flores@tl aloc.imta.mx

Contact author? yes or no yes

Affiliation for Author 1 Organization www.imta.gob.mx

Address

Country Mexico

Phone for contact author 7773293600

Author 2 (one author only) First Name (or initial) Waldo


Surname Ojeda-Bustamante


Email [email protected] mta.mx


Affiliation for Author 2 Organization www.imta.gob.mx

Address

Country Mexico


Author 3—repeat the Author and Affiliation tables for each additional author First Name (or initial) Cruz

Middle Name (or initial) Ernesto

Surname Aguilar-Rodriguez


Email ernesto.8702@h otmail.com


Affiliation Organization Instituto Tecnológico Superior de Los Reyes

Address Carretera Los Reyes-Jacona km 3 Col. Libertad

Country Mexico

Phone for contact author +52 3541013901

Author 4—repeat the Author and Affiliation tables for each additional author First Name (or initial) Abraham

Middle Name (or initial) Jesus


Surname Arzeta-Rios

Email ab_arzeta@hot mail.com


Affiliation Organization www.imta.gob.mx

Address

Country Mexico


Author 5—repeat the Author and Affiliation tables for each additional author First Name (or initial) Federico



Surname Villarreal-Guerrero

Email fvillareal@uach. mx


Affiliation Organization Universidad Autónoma de Chihuahua

Address C. Escorza 900, Col. Centro 31000. Chihuahua, Chih. México

Country Mexico

Phone for contact author 614 434-03-04

Paper number and page range Paper number on the line below 1700902 Pages 1-_9____

An ASABE Meeting Presentation DOI: 10.13031/aim.201700902 Paper Number: 1700902

COMPUTATIONAL FLUID DYNAMICS (CFD) TO ZONING HORTICULTURAL GREENHOUSES IN MEXICO Jorge Flores-Velázquez1, W. Ojeda-Bustamante1, Cruz Aguilar-Rodríguez2, Abraham Arzeta-Rios1, Federico Villareal-Guerreo3 1

2

Instituto Mexicano de Tecnología del Agua. Paseo Cuauhnahuac 8532, Col. Progreso. 62550. Jiutepec, Morelos. Mexico. (+52) 777 329 36 58. [email protected]

Instituto Tecnológico Superior de Los Reyes. Los Reyes, Michoacán. México. Carretera Los Reyes-Jaconá. Km 3. Col. Libertad. (+52) 354 101 39 01. [email protected]@hotmail.com 3

Universidad Autónoma de Chihuahua. C. Escorza 900, Col. Centro, Chihuahua, Chih. México. +52 (614) 439 1500. [email protected]

Written for presentation at the 2017 ASABE Annual International Meeting Sponsored by ASABE Spokane, Washington July 16-19, 2017

ASABE 2017 Annual International Meeting

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ABSTRACT. Horticultural production in Mexico is consolidated with the development of technologies. Among the main adversities facing the agricultural sector is the great variety of climates in the country. Having a strategy to know the consumption of resources in the production process can be a tool for decision-making. It was developed a numerical model using Computational Fluid Dynamics and once validated, it was used to generate the profiles that describe the movement of air and distribution of temperature gradients that allowed the characterization of the microclimate in a typical Mexican greenhouse under local climatic conditions and whose analysis allowed alternative management improvement in the performance and operability thereof. Numerical simulations showed that CFD is a suitable tool for the study and improvement of climate control in greenhouses and also that a validated model can generate alternatives and assumptions that manage climate inside the greenhouse by controlling environmental factors in order to increase crop production. A model will allow to evaluate a priori the production costs depending on the climatic region where it is. Keywords. Zenithal greenhouse, ventilation, computational fluid dynamics, Navier-Stokes equations, numerical model, microclimate.

INTRODUCTION Mexico presents several regions with natural conditions suitable for production in greenhouses, which is why protected agriculture (PA) has developed rapidly. The production in greenhouse allows to have different conditions to those that are cultivated in open field, which generates advantage to obtain the agricultural production in advance. Due to this trend, various elements, tools, materials and structures have been used to protect crops and thus obtain better quality products (Juárez-López et al., 2011). The growth rate of protected agriculture in Mexico is 1200 hectares per year, with screenhouses and low-technology greenhouses predominating, with an annual average growth of protected agriculture of 12%. The main crops grown in protected agriculture are tomato (70%), pepper (16%) and cucumber (10%). In recent years, the diversification of crops such as papaya, strawberry, habanero pepper, flowers and aromatic plants has intensified (AMHPAC, 2015). PA is a system characterized by the use of various structures for production, in order to protect crops by minimizing restrictions and effects caused by climatic phenomena. Since risk is a factor associated with agriculture, this system has as basic characteristic the protection against the risks presented by this activity. The main risks are climatological, economic or limitations of productive resources. In addition, the modification of food production with the PA has generated benefits for producers (Moreno-Reséndez, Aguilar-Durón, & Luévano-González, 2011). According to Juárez-López et al. (2011) the greenhouses are agricultural constructions equipped with a translucent cover that confers them some independence of the external environment, in order to reproduce climatic conditions suitable for the growth and development of the culture established in its interior. Greenhouses allow to modify and control the main environmental factors more efficiently than the other structures used to protect crops In the Atlas of water vulnerability in Mexico to climate change (2015), it is considered that it is essential to promote national technical capacities in the management of information for the generation of scenarios; Its correct interpretation and application is important to know both current and future vulnerability, generating sustained strategic plans. Up-to-date information on the projections of the possible future climate is essential to identify and analyze the vulnerability of the population, the ecosystem and the infrastructure, and with it, to adapt to the current climate and its change, undertaking actions aimed at reducing this vulnerability. A numerical tool for the simulation of greenhouse performance scenarios is known as Computational Fluid Dynamics, also known as CFD. To be able to carry out the simulation and climatic discretization will be use a Computation Fluid Mechanics software, which consists of the solution of mathematical models based on balances and energy. CFD techniques treat the values of the dependent variables as primary unknowns in a finite number of places, then sets The authors are solely responsible for the content of this meeting presentation. The presentation does not necessarily reflect the official position of the American Society of Agricultural and Biological Engineers (ASABE), and its printing and distribution does not constitute an endorsement of views which may be expressed. Meeting presentations are not subject to the formal peer review process by ASABE editorial committees; therefore, they are not to be presented as refereed publications. Publish your paper in our journal after successfully completing the peer review process. See www.asabe.org/JournalSubmission for details. Citation of this work should state that it is from an ASABE meeting paper. EXAMPLE: Author’s Last Name, Initials. 2017. Title of presentation. ASABE Paper No. ---. St. Joseph, MI.: ASABE. For information about securing permission to reprint or reproduce a meeting presentation, please contact ASABE at www.asabe.org/permissions (2950 Niles Road, St. Joseph, MI 49085-9659 USA).1 ASABE 2017 Annual International Meeting

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of algebraic equations are derived from the fundamental equations applied to the domain and are solved by pre-established algorithms. Although commercial computational fluid dynamics programs have been designed with a user-friendly environment, it is a good idea to use benchmark problems to minimize the possibility of erroneously specifying the configuration of the numerical model (Al-helal, 1998; Rico-García, López-Cruz, Herrera-Ruiz, Soto-Zarazúa, & CastañedaMiranda, 2008; P. Romero-Gomez, Lopez-Cruz, & Choi, 2008; P. M. Romero-Gomez, 2005). CFD simulation also serves to detect design deficiencies (J. Flores-Velázquez, Mejía, Montero, & Rojano, 2011); for example, the air that enters the first window leaves the second without getting mixed with the air in the area that would occupy the plants. The use of CFD for the study of greenhouses in Mexico can be considered relatively new, thanks to these studies some relationships have been found in terms of design and production; Flores-Velazquez (2010) found that without the natural aerial ventilation, it is possible to find a linear relationship between the increase in temperature and the length of the greenhouse; Campen, Kempkes, and Bot (2009) showed that climate change through a ventilation system is more homogenous, allowing a more efficient control than with the conventional method of steam extraction. Dehumidifiers and air conditioners reduce the overall moisture difference between the center and the lower areas of a greenhouse, as demonstrated by (Kim et al., 2008), who, using a 3D model, was able to identify the heterogeneous distribution of relative humidity in a greenhouse. According to (Majdoubi, Boulard, Fatnassi, & Bouirden, 2009), an increase in air temperature precedes a more moderate increase in specific humidity. Studies like those of (Jorge Flores-Velázquez, Rojano, Rojas-Rishor, & Bustamante, 2015); Villagrán (2016); Villagrán, Gil, Acuña, and Bojacá (2012), show negative effects due to poor management of greenhouses owing to poor design or changes in temperatures that may exist. This paper aims to analyze the current conditions, design and effects of climate change on the activities of protected agriculture in Mexico, studying the needs of protected agriculture under conditions of climatic variability and limited availability of resources, which will allow to infer the problems in horticultural production, which would be presented in the future by the effects of climate change.

MATERIALS AND METHODS The research was carried out in Michoacán, Jalisco, San Luis Potosí, Sinaloa, and the State of Mexico; for which the average annual temperature of each of the study areas was obtained. Figure 1 shows the prototype of the greenhouse, which is a saw-tooth greenhouse that was simulated considering the changes in the flow generated by the crop. A three-dimensional CFD model was applied to analyze the influence of the average annual temperature inside a chapel greenhouse including the crop. The ANSYS Workbench 17.0 computational package was used to carry out the simulations following the flowchart shown in Figure 2. In this paper, we present the results of a model that combines natural ventilation and forced ventilation. a

b

Figure 1. Construction of the computational model: a) greenhouse dimensions and b) domain dimensions and mesh


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Figure 2. Flowchart of processing performed

Table 1 shows the properties of the materials considered in the CFD model, such as soil, plastic cover and crop biomass. The area of the vents, which is covered with anti-insect mesh, was treated as a porous medium and its characteristics are shown in Table 2. Validation was performed by comparing the experimental results of (T. Boulard, Haxaire, Lamrani, Roy, & Jaffrin, 1999) who worked with a greenhouse to analyze its air thermal gradients. Table 1. Materials used in the model. Material

Density

Cp

Thermal conductivity

Plastic Biomass Soil Air

925.5 1000 1400

1600 4180 1738

0.33 0.6 1.5

Table 2. Insect proof screen characteristics (porous jump condition) Face permeability

Porous medium thickness

Pressure-Jump coefficient

2.86e-9

0.000372

11131.45

Numerical Model In this model are considered several scenarios according to the regions of study. In determining the influence of the outside temperature inside the greenhouse, were considered the average temperatures of each state and simulated under the same conditions of wind speed. The wind flow in the model was assessed assuming an incompressible viscous fluid, represented by the Navier-Stokes equations (Eq.1) in their generalized form:

   . u  .   S t

 

(1)

where the four terms that compound this equation are: transient, convection, diffusion the variable



  and a source S terms,

is the dependent variable that describes the flow characteristics at an specific time point location,



is the

 

density, u the speed, in a 3D space, = (x, y, z, t). The insect proof screen (Porous jump condition) The use of anti-insect mesh in the greenhouse vents was modeled in terms of permeability and porosity (Miguel, Van de Braak, & Bot, 1997). The equation of the fluid motion through a porous mesh can be derived from Forchheimer equation (Eq. 2).

C  p   u F u  x K K

( 2)

u

where  = fluid dynamic viscosity (kg. m-1 s-1); K = intrinsic permeability of the medium (m2); (Y), also called coefficient of nonlinear load loss, ASABE 2017 Annual International Meeting



= air density [kg m-3];

u

CF

= inertial factor

= air speed [m s-1], and

 x , y = the Page 4

thickness (es) of the porous material [m]. Taking the experimental data from Kamaruddin (1999); Miguel et al. (1997); Teitel (2001); Valera, Álvarez, and Molina (2006) using the technique of regression equations derived to calculate the permeability (Y) (eq. 3) and loss coefficient inertia (K) (Eq. 4) according to the porosity of the mesh (). The characteristics of the mesh are shown in Table 1.

(3)

Y  0.0342  2.5917

(4)

K  2 e 7  3.5331

The crop simulation (Porous Zone Condition) The simulation of the crop is using the approach of a porous medium, where the pressure drop due to the inertia effect can be expressed by Forchheimer equation (eq. 2); This is included in the source term of the time Navier Stokes equation (Eq. 1), the interest variable (  ) represents the momentum caused by the effect of growing friction, this friction force can be expressed per unit volume of coverage using the formula shown as (Eq. 5):

S   L CD v 2

(5)

Where L refers to the density of crop leaf area (m2 m-3), and C D is the drag coefficient and v is the air velocity (m·s-1), Grid arrangement and boundary conditions The whole greenhouse was meshed in ANSYS Meshing software (Figure X) with dimensions 32x34x9.45 large x width x high inserted into a domain (Figure 1c) of 192x170x23 large x width x high. The mesh consisted of tetrahedral, hexahedral and pyramidal elements and was exported to workbench, where the 3-D simulations were made, with external wind speed of 2.41 m·s-1. The turbulence model was the SKE (Standard k-epsilon) with standard wall functions and viscous heating activated. Dimensions are shown in Table 2. The greenhouse was placed 64 m from the entrance and 68 m from the right side of the domain. The area of the vents was 28.8 m x 2 m. The cultivation was established with a height of 0.7 m, a width of row of 0.7 m, distance between furrows of 0.9 m, and a length of 30 m. The area of the fans was 1 m2. The scenario evaluated was that the flow-oriented vent was only provided with the anti-insect screen, while the vent on the opposite side remained closed, being considered as a wall covered by plastic. The fans were considered as extractors and the vents of the upper part of the greenhouse were considered closed. Table 2. Computational model screenhouse sizes and Initial condition of simulations. Dimensions (Lxwxh)

Cells Number

Soil Heat transfer

Wind Vel.

Fan Pressure Jump

32x34x9.45

467624

200 W m-2

2.41 m s-1

10 Pa

RESULTS AND DISCUSSION Figure 3 shows the monthly temperature in each of the study areas. Of the states studied, the one with the highest annual average temperature is Sinaloa and the lowest is Estado de Mexico. The highest temperatures occur during the months of May to October for most of the states studied, while the lowest temperatures occur between the months of December to February. This information helps to determine when the establishment of a crop is more suitable and costs associated with production out of time.


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Figure 3. Temperature in the studied states.

The temperature distribution inside the greenhouse in the different scenarios was characterized by a reduction of the temperature in the part near the open air, although it was not the same for the area near the fans. The highest temperature was found between the soil and the crop, in agreement with the results of Boulard, et al., (1999), being the greenhouse of the State of Mexico (Figure 4) the one that presented lower temperatures in the interior and the state of Sinaloa that presented higher temperatures. This distribution of temperatures will benefit from the heat provided by the soil and the crop in the lower part of the greenhouse. The reduction of the temperature in the area close to the vents is due not only to the entry of fresh air but also to the effect generated by the extraction of air at the opposite side. a

b

Figure 4. Distribution of temperatures inside the greenhouse in a) the state of Mexico and b) the state of Sinaloa.

The greenhouses of the states of Jalisco, Michoacán and San Luis Potosí (Figure 5) present the same pattern of temperature distribution as those of the aforementioned states, however, the state of Michoacán is observed to be cooler than the other two states.


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a

b

c

350

350

340

340

330

330

Temperature (K)

Temperature (K)

Figure 5. Distribution of temperatures inside the greenhouse in a) the state of Michoacán, b) Jalisco and c) San Luis Potosí.

320 310

320 310

300

300

290

290

280

280 60

70

80

90

100

60

70

80

Z (m)

90

100

Z(m)

Figure 6. Data of a) distribution of the temperature in the central plane of the greenhouse of the State of Mexico and b) the adjustment of the data to a curve.

The data part was chosen in the range of 65 to 95, to decrease the influence of the endpoints. Subsequently, the data were adjusted to a curve. Once the adjustment was obtained, it was plotted with all the data (Figure 6). It can be observed that the data have a good fit to the main part of the data, so that the obtained curve can be used to make the comparisons of temperatures of the greenhouses in the studied states. 300 299

Temperature (K)

298 297 296 295 294 293 292 291 290 65

70

75

80

85

90

95

Z(m) Estado de México

Jalisco

Michoacán

San Luis Potosí

Sinaloa

Figure 7. Comparing the temperature inside the greenhouses in the studied states. ASABE 2017 Annual International Meeting

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It can be seen that the temperature tends to increase as it moves away from the area of the open vent. Likewise, since the greenhouses of the states of Jalisco, Michoacán and San Luis Potosí have similar temperatures (Figure X), similar operating conditions could be used to maintain the appropriate temperature for the crop.

CONCLUSION In this work, it was possible to analyze the effect of the annual average temperature in the States of Mexico, Jalisco, Michoacán, San Luis Potosí and Sinaloa on the internal distribution of temperatures in a chapel type greenhouse, considering tomato cultivation and the use of fans. Air movement and temperature distribution were described by the development of a CFD model, which allowed the characterization of the micro climate in a typical Mexican greenhouse under local climatic conditions. With CFD modeling it is possible to evaluate production costs depending on the climatic regions in which the greenhouses are found, as well as to infer the problems that may arise during agricultural production due to the effects of climate change.

REFERENCES Al-helal, I. (1998). A computational fluid dynamics study of natural ventilation in arid region greenhouses. PhD Thesis. (PhD Thesis), Ohio State University. AMHPAC. (2015). Agricultura Protegida en México. http://www.amhpac.org/es/index.php/homepage/agricultura-protegida-en-mexico

Retrieved

Arreguín, F. I., López, M., Rodríguez, O., & Montero, M. J. (2015). Atlas de vulnerabilidad hídrica en México ante el cambio climático. In IMTA (Ed.), (pp. 148). Jiutepec, Mor. Boulard, T. (2012). Recent trends in protected cultivations-microclimate studies: a review. Paper presented at the IV International Symposium on Models for Plant Growth, Environmental Control and Farm Management in Protected Cultivation- 957. Boulard, T., Haxaire, R., Lamrani, M. A., Roy, J. C., & Jaffrin, A. (1999). Characterization and Modelling of the Air Fluxes induced by Natural Ventilation in a Greenhouse. Journal of Agricultural Engineering Research, 74(2), 135-144. doi:http://dx.doi.org/10.1006/jaer.1999.0442 Campen, J. B., Kempkes, F. L. K., & Bot, G. P. A. (2009). Mechanically controlled moisture removal from greenhouse. Biosystems engineering, 102(4), 424-432. Flores-Velazquez, J. (2010). Climate analysis in the main models of greenhouse in Mexico (mesh shade and Baticenital multitunnel) using CFD. PhD Tesis. Almeria University, Spain. Flores-Velázquez, J., Mejía, E., Montero, J. I., & Rojano, A. (2011). Numerical analysis of the inner climate in a mechanically-ventilated greenhouse with three spans. Agrociencia, 45, 545-560. Flores-Velázquez, J., Rojano, A., Rojas-Rishor, A., & Bustamante, W. O. (2015). Computational Fluid Dynamics Achievements Applied to Optimal Crop Production in a Greenhouse. In C. Liu (Ed.), New Perspectives in Fluid Dynamics (pp. Ch. 04). Rijeka: InTech. Juárez-López, P., Bugarín-Montoya, R., Castro-Brindis, R., Sánchez-Monteón, A. L., Cruz-Crespo, E., & Balois-Morales, R. (2011). Estructuras utilizadas en la agricultura protegida. Revista Fuente Año, 3(8). Kamaruddin, R. (1999). A naturally ventilated crop protection structure for tropical conditions. Cranfield University. UK. Kim, K., Yoona, J. Y., Kwonb, H. J., Hana, J. H., Sonc, J. E., Namd, S. W., . . . Lee, I. B. (2008). 3-d cfd analysis of relative humidity distribution in greenhouse with a fog cooling system and refrigerative dehumidifiers. Biosystems engineering, 100, 245-255. Majdoubi, H., Boulard, T., Fatnassi, H., & Bouirden, L. (2009). Airflow and microclimate patterns in a one-hectare Canary type greenhouse: an experimental and CFD assisted study. Agricultural and Forest Meteorology, 149(6), 10501062. Miguel, A., Van de Braak, N., & Bot, G. (1997). Analysis of the airflow characteristics of greenhouse screening materials. Journal of Agricultural Engineering Research, 67(2), 105-112. Moreno-Reséndez, A., Aguilar-Durón, J., & Luévano-González, A. (2011). CARACTERÍSTICAS DE LA AGRICULTURA PROTEGIDA Y SU ENTORNO EN MÉXICO. Revista Mexicana de Agronegocios, 15(29). Rico-García, E., López-Cruz, I. L., Herrera-Ruiz, G., Soto-Zarazúa, G. M., & Castañeda-Miranda, R. (2008). Effect of ASABE 2017 Annual International Meeting

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temperature on greenhouse natural ventilation under hot conditions: CFD simulations. Journal of Applied Sciences, 8(24), 4543-4551. Romero-Gomez, P., Lopez-Cruz, I. L., & Choi, C. Y. (2008). Analysis of greenhouse natural ventilation under the environmental conditions of central Mexico. Transactions of the ASABE, 51(5), 1753-1761. Romero-Gomez, P. M. (2005). Modeling natural ventilation for Mexican greenhouses. Thesis MsC. In Agricultural and Biosystems Engineering. University of Arizona. Teitel, M. (2001). The effect of insect-proof screens in roof openings on greenhouse microclimate. Agricultural and Forest Meteorology, 110(1), 13-25. doi:http://doi.org/10.1016/S0168-1923(01)00280-5 Valera, D., Álvarez, A., & Molina, F. (2006). Aerodynamic analysis of several insect-proof screens used in greenhouses. Spanish Journal of Agricultural Research, 4(4), 273-279. Villagrán, E. A. (2016). Diseño y evaluación climática de un invernadero para condiciones de clima intertropical de montaña. Tesis Maestría., Universidad Nacional de Colombia. Villagrán, E. A., Gil, R., Acuña, J. F., & Bojacá, C. R. (2012). Optimization of ventilation and its effect on the microclimate of a colombian multispan greenhouse. Agronomia Colombiana, 30(2).


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