A Wind Flow Perspective in Western Ghats, Coimbatore, Tamil Nadu
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ISSN (Print) : 0974-6846 Indian Journal of Science and Technology, Vol 8(28), DOI: 10.17485/ijst/2015/v8i28/87123, October 2015 ISSN (Online) : 0974-5645 Fog Harvesting – A Wind Flow Perspective in Western Ghats, Coimbatore, Tamil Nadu M. Abhiram1*, N. Dhivya Priya1 and P. Geetha2 1Department of Civil Engineering, Amrita Vishwa Vidyapeetham, Coimbatore - 641112, Tamil Nadu, India 2Department of Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore - 641112, Tamil Nadu, India Abstract During the past few decades the world has witnessed a phenomenal rise in the growth of population, which has in turn resulted in an increase in the demand for fresh water. Thus, creating an exigency to identify an alternative sustainable freshwater resource to subdue the rising demand for fresh water. Fog, an often-overlooked aspect of the hydrological process, can be one such resource. Several experimental studies were conducted around the globe to gauge the potential of fog as an alternative freshwater resource in high altitude regions. Studies have indicated, that the sites, which were chosen to assess the fog potential of a region, have mostly been random, which might have undermined the fog harvesting impediment in the experimentation process. This paper is an endeavor to address these issues. In this paper, a hybrid potential of the region. The inaccessibility and the harsh environment of the high altitude terrain have also been significant faopgp hroaarvches btientgw seietens torfa da irteiognioanl .f oAg c aqsuea nsttuifdicya htiaosn bs eaennd d monaeth uesminagt itchails mapopdreoliancgh, tuos iannga lpyhzyes tihcael flyo gb haaservde ismtinpga cptiootnen mtioald oefl Waideestde rbny Gchoamtsp ilnex E tetirmraaidna ai nreaglyiosins ahnads 1b0e0e5n4 ’u4s7e.d4 ”tNo7 q6u05an3t’0if0y. 6th”Ee process of fog collection and identification of potential and has been observed to produce an average yield of 7.67 ± 0.7Lday . has− b1 een identified as an ideal location for fog harvesting Keywords: Complex Terrain Analysis, Fog Harvesting, Impaction Model 1. Introduction provide water for about 35 people (minimum drinking water usage of 7.5 Lcapita–1day-1)6. During the past few decades the world has experienced Such countless experiences with fog harvesting, and phenomenal rise in the population growth, thus cre- throughout the globe have proved that fog has poten- ating an exigent need to find an alternate source of fresh tial to be an alternate fresh water supply in high altitude water. Fog, which is an integral part of the hydrology of regions. In this paper an hybrid approach between tra- the high altitude and coastal regions, can be one such ditional fog quantifications and mathematical modelling resource. Historical records indicate that fog has been to quantify the fog harvesting potential of a region, using harvested to provide potable water to communities liv- physically-based impaction model7 aided by complex ter- ing in the regions with high fog frequency like Palestine, rain analysis to quantify fog collection. Using complex Atacama Desert1 and Canary Islands2. Since the past two terrain analysis, which is used to model the wind over the decades, extensive studies have been done across the terrain, ideal locations for fog harvesting have been iden- world to assess the potential of fog as a fresh water source. tified. This approach has been adopted to assess the fog Few notable examples include South Africa3, Nepal4 harvesting potential of the mountain ranges in Ettimadai, and Chile5. It has been observed that, on an average, a Tamilnadu, India and also identify the ideal site for 40m2 fog harvester, in these regions, has the potential to harvesting in these mountain ranges. *Author for correspondence Fog Harvesting – A Wind Flow Perspective in Western Ghats, Coimbatore, Tamil Nadu 2. Fog Harvesting and fog water droplets with the mesh due to inertial forces 7 Characteristics and is the primary contributor to fog water deposition . Fog water droplets that pass close by the mesh material, Meteorologically speaking, fog can be defined as ground typically within one droplet radius, but do not directly touching cloud with a horizontal visibility less than impact it are captured via direct interception. Brownian 1000m8. This widely adopted perception of fog considers diffusion generally has very little impact on fog collec- the spatial coverage of fog in the horizontal and vertical tion, as it is only a significant factor in droplets with directions. But, this definition of fog neglects, most of the diameters less than 0.1 × 10–6m. For most practical pur- essential elements of fog like fog event duration; liquid poses, the contributions from Brownian diffusion and water content; droplet size. This definition also is lim- direction interception can be ignored, and impaction ited based on the observation position. In this paper fog is the primary mechanism of fog water collection7. Fog has been considered as the presence of suspended water water collected by a mesh screen can be predicted using droplets that are either a condensate of water vapour, or the following equation7,10. the remnants of a large sea spray droplets or evaporation qw= AnV (1) raindrops. Fog droplets diameters usually range from1 to 50μm7. Most of the water in the fog is present in the form Where q is the rate of water collected (Lh–1), w is the liq- of gaseous phase rather than liquid phase, which infers uid water content in the air (gm–3), A is the cross-sectional that the amount of water carried by the fog is very limited, area of the mesh, n is the efficiency of the fog collector that to depending upon the density and has a value of 0.2g based on stokes equation (unit less) and V is the velocity to 0.5g of liquid water per m3. Though, there is no stan- of the wind (ms–1). dard classification of fog, it has been observed that many Efficiency of the fog collection, can be qualified using authors classify fog based on its formation. stokes number. Stokes number, is a dimensionless quantity, Fog harvesting is a simple phenomenon. A standard which can be defined as the ratio of the characteristic time of fog harvester consists of mesh, which is supported by a particle to the characteristic dimension of the obstacle12. a metal structure. A gutter is fixed in the bottom of the mesh, to collect the water. This collected water is trans- tu00 stk = (2) ported to a storage unit. For experimental purposes, a l0 tipping bucket can be attached before the storage unit. However, predicting fog collection rates is difficult, in part Where t0 is the relaxation time of the particle, u0 is the fluid due to the lack of standards in measuring and reporting velocity and l0 is the characteristic dimension of the par- data9. Studies sometimes only consider annual averages ticle. When the particle in motion has a very low Reynolds or maximum collection values, and may lack quality number, the drag coefficient is inversely proportional control procedures or the reporting of uncertainties. to the Reynolds number and the relaxation time of the 13 Standardization of reporting data and the inclusion of particle can be computed from the following equation uncertainties will enable future studies to draw a clearer 2 = pddd relationship between environmental conditions and fog t0 (3) collection rates. Currently, two models, an impaction 18ug model and an efficiency model, have been developed to 3 Where pd is the particle density (gm ), dd is the particle describe the processes occurring during fog harvesting diameter (m) and u is the gas dynamic viscosity ((N.s.m–1). 7,10,11 g with a mesh collector . In this study, Impaction model Once Stokes number has been identified, efficiency of the has been used to gauge the fog harvesting potential of harvester can be computed using the following relation, Western Ghats mountain range in Ettimadai region. when stk 0.087 stk2 3. Fog Water Deposition Model nimp = (4) (.stk + 06)2 Fog water is collected on the mesh through the mechanisms of impaction, direct interception, and Where stk is the Stokes number of the fog (unit less). Brownian diffusion. Impaction is the physical collision of The Stokes number is the ratio of the stopping distance 2 Vol 8 (28) | October 2015 | www.indjst.org Indian Journal of Science and Technology M. Abhiram, N. Dhivya Priya and P. Geetha of a fog particle in air relative to the characteristic for the past 5 years, in the location under consideration dimension of the fog collector mesh. The Stokes num- (Table 1), Average LWC has been found to be 0.04 gm–1. It ber for fog increases with both droplet diameter and must be noted that, the value of LWC from the visibility wind speed, but typically ranges from 0.6 to 60.7 for the data is an approximation. majority of droplets (5–50 10–6m in diameter)when the wind speed is 1ms–1 7. The Stokes number can be opti- 4.2 Area of Harvester mized by selecting mesh with the proper characteristic Though the area of the fog harvesters currently used are dimension for the locations wind speeds and droplet in the range of 1m2 to 50m2. A Standard Fog Collector16, diameters. which is employed to assess the fog water deposition rate, The target fog particles size usually varies in the size is a fog collector, with a mesh area of 1m2. A similar fog range of 10 µm to 50 µm in size, with its density 1000 harvester has been employed to measure the fog water kgm-3 and the harvesting unit with the mesh size of deposition rate. 1.5mm. Efficiency of the harvesting unit depends upon wind velocity and particle diameter. It can be inferred 4.3 Efficiency of the Harvester from the above equations, that the value of efficiency is directly proportional to the droplet diameter and wind An ideal property of the fog harvesting mesh should be velocity.