brine wind permeate brackish feedwater 2 Nomenclature A = swept area [ 2m ] memA = membrane surface area [ 2m ] BR = blockage ratio c = blade’s chord length [m] fc = feed flow concentration [ppm] Mc = molar concentration [mol/l] PC = power coefficient ,maxPC = maximum power coefficient d = pipe diameter [m] h = blade’s span [m] Hoffi = Van't Hoff dimensionless constant vJ = flux of the water through the membrane [m/s] fk = overall feed -side mass transfer coefficient [m/s] pL = water permeability constant of the membrane [m3/N·s] ,effpL = effective water permeability constant of the membrane [m3/N·s] N = number of blades p = water pressure at the pump’s exit [Pa] brinep = brine’s pressure [Pa] minp = minimum pressure required [Pa] p = pressure rise across the pump [Pa] memp = pressure difference across the membrane [Pa] valvep = pressure difference across the valve [Pa] brineQ = brine flow rate [m3/s] . feedQ . = feed flow rate [m3/s] perQ = permeate flow rate [m3/s] r = recovery rate; per feed/QQ R = turbine’s radius [m] gasR = ideal g as constant [J/K·mol] Re = Reynolds number cRe = chord -based average Reynolds number; / Uc SEC = specific energy consumption 3[kWh/m ] elecSEC = specific electrical energy consumption 3[kWh/m ] mechSEC = specific mechanical energy consumption 3[kWh/m ] windSEC = specific wind energy consumption 3[kWh/m ] T = turbine torque [N·m] maxT = turbine’s max torque [N·m] TP = transmembrane pressure [Pa] TDS = total dissolved solids [ppm] TDS = time-averaged total dissolv ed solids [ppm] U = effective wind speed [ m/s ] U = average effective wind speed [ m/s ] ,nomU = nominal wind speed [ m/s ] bV = blade velocity [m/s] brineW = brine hydraulic power [W] elecW = desalination electrical power [W] hydW = net hydraulic power [W] shaftW = shaft net mechanical power [W] 3 shaft,maxW = shaft max mechanical power [W] shaft,reqW = shaft required mechanical power [W]  = chord to radius ratio; /cR pump = pump relative efficiency sys = overall system efficiency (wind to hydraulic) sys,avg = average system efficiency sys,max = maximum system efficiency w = feed water temperature [ oC ]  = tip-speed ratio; /RU opt = tip speed ratio at ,maxPC  = air kinematic v iscosity [ 2m /s ]  = osmotic pressure difference across the membrane [bar] co = corrected osmotic pressure difference [bar] a = air density [kg/m3] w = water density [kg/m3]  = solidity; /Nc R TDS = TDS standard deviation  = rotational speed standard deviation  = rotational speed [RPM]  = time-averaged rotational speed [RPM] AM = American multi -bladed BW = brackish water D-WPD = direct wind -powered desalination EC = electrical conductivity HAWT = horizontal -axis wind turbine HP = high pressure HDPE = high density polyethylene MSF = multi -stage flash NLF = natural laminar flow ppm = particles per million PV = photo -voltaic RO = reverse osmosis Re = Reynolds number SW = sea water VAWP = vertical axis wind pump VAWT = vertical axis wind turbine WHO = world health organization 4 1 Introduction The threat of global warming to the earth ’s ecosystem [1] has created a need for zero carbon emission systems, particularly for water supply and managemen t [2,3] . This need is particu larly acute for desalination , which has become a substantial share of the drinking and irrigating water in many countries [4,5] . Annually, it is est imated that the desalination process alone accounts for more than 400 million tons of carbon equivalent emissions [6]. Thus desalination must be rendered more sustainable, both ecologically and with regard to carbon emissions [7]. The two most widespread desalination technologies are thermally -based multi -stage flash (MSF) and membrane -based reverse osmosis (RO), with 17.5% and 69% of worldwide desalination capacity, respectiv ely [8]. RO util izes high -pressure pumps to overcome osmotic pressure in partially permeable membrane s [9–11], but the energetic cost is substantial [7,12] . Significant energy must be invested to desalinate sea and brackish water from total dissolved solids (TDS) levels of 1 ,500-40,000 ppm to palatable water , determined by the world health organization to be less than 600 ppm [13]. The w ind is a prominent energy source that can be used for low carbon -footprint desalination processes [14,15] . However, it is estimated that large -scale conventional wind - powered desalination pla nts, where the electricity and the desalination are two independent processes, can lose up to 40% of the useable wind power in generators, transmission, and pump motors [16–18]. These losses are even greater for small -scale systems. Although various systems and models were studied for direct electrical wind -RO desalination, i.e., without batteries or grid connection [19–25], the proposed systems have several critical disadvantages. For example, for small -scale systems, mechanical/electrical and electrical/mechanical conversion losses are excessive, resulting in significant losses of the useable wind power. Additionally, they demand high capital costs to construct , whereas expensive electrical systems and electronics elevate the system price, making them uneconomical at small scales or for p rivate use [26–29]. The energetic losses described above motivate strongly in favor of the direct mechanical conver sion of wind energy to pumping energy [30,31] . Since the American multi -bladed (AM) wind -pump is a widespread and well-understood system, able to produce relatively high pressures with its piston pump at ground level , it was naturally coupled mechanically to a desalination system. However, AM wind pu mps have inherent limitations resulting in wind -to- hydraulic efficiencies between 4% and 8% [32]. Thus, it is no surprise that AM-driven reverse osmosis desalination systems attain low pumping and desalination efficacies [33–35]. Conversely, a study by Liu et al. [35] demonstrated a direct AM-driven reverse osmosis 5 desalination system with feedback control that can achieve efficienc ies of up to 13%. However, high efficiency is onl y obtained for a negligible wind range. Thus , on average, system efficiency could not surpass 7% and was lower when the TDS feed flow level was altered . In contrast to the decrease in the relevance of high-solidity horizontal axis wind turbines (HAWTs ), that drive the AM pumps, interest in high -solidity vertical axis wind turbines (VAWTs) has surged due to advantages such as wind -direction insensitivity [36], low rotational speed and low noise emissions [37]. Those advantages are amplified for uncharacteristically large blade chord -to-radius ratios [38], where exceptionally low cut -in wind speeds of up to 1.5 m/s and exceptionally low rotational speeds can be achieved that further reduce noise emissions and the probability of bird strikes [39]. The mechanical integration of a VAW T with a pump produces what we refer to as a vertical axis wind pump (VAWP). Lift-based VAWPs [40] are favored over drag-based VAWPs [41] due to their higher aerodynamic efficien cy [42]. Additionall y, low- solidity VAW Ts [43] suffer from low aerodynamic efficiency at low wind speeds and small scales operating at relativ ely low Reynolds numbers . Thus, for small -scale direct wind -powered desalination , high -solidity VAWPs are preferable. The objective of this research is to experimentally evaluate the utility of a high -solidity, high-pressure vertical axis wind pump for bra ckish water desalination. To this end, a small -scale VAWT was constructed and mounted in a wind tunnel. The turbine shaft was interfaced directly with a positive displacement pump that was used to drive the brackish water through a n RO filter. The paper is structured as follows : section 2 describes the experimental configurations ; section 3 presents the result and discussion for the VAWT, the VAWP , and the D-WPD system ; and section 4 summarizes the conclusions and outlook for direct wind -powered desalination system s. 6 2 Experimental Setup Experiments were conducted in a blow -down wind tunnel with a 1 m  1.9 m test section and a length of 3.65 m. The wind tunnel has a maximum speed of 16m/s U= , a relative standard deviation distortion of 1.4% , and a turbulence level of 0.5%. All experiments were performed at steady nominal wind speeds in the range: ,nom 2.8m/s 5.7m/s U . A high -solidity, two -bladed, H-rotor vertical axis wind turbine was constructed and mounted in the center of the test section (see Figure 1). Figure 1: Schematic of the H-rotor vertical axis wind turbine showing its geometry and relevant dimensions. 2.1 Vertical Axis Wind Turbine The turbine configuration employed in this study is based on a two -bladed H -rotor configuration and operates under the principle of dynamic stall (see Figure 1). Due to its low blade speeds, it has low noise emission s [37], a low risk of bird strikes [38], high torque and good self-starting capabilities [44]. It is comprised of a 50.8 mm shaft supported by two self -aligning ball bearings and two 21% thick natural laminar flow (NLF) blades [45] ( 2N= , chord length 300c= mm) that were connected to the shaft by struts (see Figure 1). The blades were h = 970 mmΩwind wind self aligning bearingd = 50.8 mmNLF bladeshaft 7 constructed with a span of 970h= mm, ameliorating the gener ation of strong tip vortices, thus increasing its virtual aspect ratio. It was mounted in the center of the test section, located immediately downstream of the contraction. The turbine radius was 400 mmR= resulting in a swept area of 20.8m ,A a chord -to-radius ratio of / 0.75cR= , solidity of / 1.5Nc R= and a blockage ratio of 41% BR . Due to the relatively high blockage ratio, wind tunnel corrections were implemente d based on Kinsey and Dumas [46], leading to effective wind speeds in the range 4m/s 8.5m/s U . 2.2 Turbine -Pump Interface On the basis of performance estimat ion [38,40] , a Hydra -Cell G03 -E of a membrane -based positive displacement pump, with a nominal flow rate of 4.7 milliliters per r evolution and a maximum pressure of 83 bars , was selected . The turbine shaft was connected to a Kistler 4502A1RAU (1 0.0015 Nm) dynamometer , which, in turn, was connected to the pump directly without any gearing . All mechanical connections were made using flexible couplings to accommodate misalignments . The feed water was pumped from a dedicated feed -water reservoir, placed at the pump level, and since no purification system was employed, the water w as drained back in a closed loop. An HDPE piping, reinforc ed with aluminum, with a diameter of 16 mmd= was employed for the system. The s ystem performance was evaluated prior to interfacing with the desalination module , using a 27 bar (GEMU 543 -S0) flow regulator valve to regulate the system’ s pressure (see Figure 2). The flow regulator was operated by a TTI CPX400DP programable power supply controller and via a dedicated LabVIEW® program. No correlation was observed between the turbine ’s performance and the angle pha se between the turbine and the pump azimuthal angles . 2.3 Desalination System Following the characterization of the wind -pump, the system was coupled directly to a n RO desalination system (see Figure 2). The feed water used w as synthe tic brackish water comprised of desalted water mixed with 2.5, 5.0, and 10 g/L NaCl , which was drawn through a 5-micron Culligan CW5 -BBS sediment filter. Although the brackish water in the Israeli desert is usually at the lower range of these TDS values [47], a relatively wide range was chosen to test the system robustness for various TDSs. The water was maint ained at a constant temperature of 25 1w=   , and electrical conductivity (EC) temperature corrections were made for every measurement. 8 The water was then pressurized through a BW-2521 Max-Flow 2.521, anti -fouling brackish water RO desalination element , and the programable flow regu lator was set at its brine exit. Since the permeate pressure at exit is atmospheric and the pressure drop at the permeate side was negligible due to the low feed flow/flux, the module feed in pressure and transmembrane pressure were considered identical in this study , p TP . The permeate and the brine exiting the module were collected in two separate reservoirs to study the average response of the system . The reservoirs were placed on top of the feed -water reservoir to facilitate water reuse in subsequent experiments. Since the feed water was directly prepared under controlled conditions , there was no need for sand, granular activated carbon, activated carbon blocker, and other pre - and post -treatment filters . Figure 2: Schematic of the experimental wind -powered desalination system. Arrows indicate the flow direction . PS – pressure transducer; FM – flowmeter; EC - electrical conductivity meter; TS – temperature sensor ; PH – PH meter; PT – pitot tube; TM – torqu e meter; RS – rotational speed sensor. Figure 3 shows the feed-side pressure in the module as a function of 2 brineQ , where brineQ being the brine flow rate for different ex periments with four valve opening ratios. The result shows that an assumption of turbulent -mode pressure losses in the valve for all brine flow rates is valid and that the loss coefficients do not depend on the feed water ’s pressure or salinity . Thus, the pressure modeling can be formulated as two parallel restrictor systems, a valve , and a semipermeable membrane. PSFMFM EC FM EC brine windPT PT TS RSTIwind tunnel wallBW-2552 -350 GEMU 543-S0 Hydra -Cell G03-ECW5-BBS permeate feed water EC TI PHHydra -Cell C46B 9 Figure 3: RO module inlet pressure, p , as a function of brine flowrate squared , 2 brineQ 2.4 Data Acquisition Pressure was measured at the pump exit by a 40 0.1 bar BD OEM 26.600, membrane - based pressure transducer. The flow rate was evaluated at the pump exit by a 0.5 -16.5 liter/min, COMAC -Cal, flow -32, magnetic f lowmeter. In addition, the flow rate was measured at the membrane ’s brine and permeate exit s by two Badger Vision, 1005 2F66, 0.1 -2.5 liter/min, flowmeters. HM Digital 716160 EC&TDS meter s were used to determine the EC and ppm of the feed water, brine and permeate. The wind tunnel speed was monitored at 2 meters upstream of the turbine shaft for all experiments, employing two pitot -static probes connected to two low - pressure , 50 0.3 Pascal Siemens QBM3020 -1U, transducers. All the data were acquired and processed via a dedicated real -time LabVIEW® program. The complete dynamic response of the system during the experiment was obtained. For each experiment, a single control parameter was varied : wind speed or load , followed by a settling ti me. All system data were acquired for 60 seconds following steady -state conditions, defined as stabilization of the permeate TDS and  . Steady -state conditions are defined as TDS/ TDS 1%  and / 0.2% for 60 seconds , where TDS and  are the TDS and rotational speed standard deviation , correspondently, and TDS and  are the time -averaged TDS and rotational speed , correspondently . 051015202530 0 0.5 1 1.5 2 2.5pressure [bar] Brine flow rate Q2 brine[m6/s2] x 1010valve 40% open valve 30% open valve 20% open valve 10% open 10 3 Results and Discussion 3.1 Turbine and Wind -Pump Performance The initial objective was to study the hy draulic load of the desalination system to the loads attainable by the turbine pump. W ind turbine efficiency is characterized by the power coefficient : shaft 312p aWCAU (1) where shaftWT= is the shaft power, T is the shaft torque,  is the turbine ’s rotational speed and a is the air densi ty. The ’pump’s mechanical efficiency is defined as: hyd pump shaftW W (2) where hydW is the net hydraulic p ower : hyd feedW pQ= (3) where p is the pressure rise across the pump and feedQ is feed flowrate through the pump. Thus, the overall system efficiency is obtained by simple multiplication, namely: feed sys pump 312p apQCAU= (4) Note that a low hydraulic load cause s the system to operate at low efficiency, while an excessive load arrest s the turbine rotation, as demonstrated in [40]. Wind turbine s are characterized by their performance map, namely PC versus tip speed ratio, defined as: R U  (5) The performance map is e generated by varying the load imposed on the turbine shaft as described in [38], and the turbine employed here operate s in the range of 0.8 1.8 . For each experiment, the turbine was unloaded (regulator valve fully open) to maximum rotati onal speed and then gradually loaded ( new valve setting ) until the total arrestation. Data were acquired under steady -state conditions at each valve setting, as described in section 2.4. Figure 4 and Figure 5 show selected examples of the turbine performance ( PC versus  ) and the pump hydraulic efficiency ( pump versus  ), respectively . The maximum power coefficients , in the range of ,max 0.21 0.24PC , are s eemingly low, especially when compared to 11 commercially -available large -scale VAWTs [48]. It should be noted, however, that these results are, in fact , highly competitive with small -scale turbines since the operational Reynolds number s are very low. The chord -based average Reynolds number for a VAWT is commonly define d as: b cVc UcRe == (6) where  is the kinematic viscosity of the air and bV is the blade velocity . Note that the variation in our data from 50.9 10cRe= ( 4.2 m/s U= ) to 51.5 10cRe= ( 7.2 m/s U= ) at , maxPC produce d a 17% increase in PC. This increase was not unexpected because significant increases in aerodynamic efficiency occur over this Reynolds number range [49,50] . Furthermore, b ased on unique dynamically scaled experiments of Miller et al. [51,52] , more than 50% increases are attainable when 610cRe . Figure 4: Turbine power coefficient as a function of tip -speed ratio for different wind tunnel speeds. As shown in [40], the efficacy of the turbine in conve rting wind energy to mechanical power is essentially independent of the load applied, electric, mechanical , or hydraulic (pump). However, a hydraulic load, or other quasi -linear load s devoid of a rotational speed controller, are able to operate only in sta ble turbine -load  s. As long as the max TT , this operation regime exists for all tip speed ratios higher than the maximum efficiency TSR, opt , where opt is 00.050.10.150.20.25 0.8 1 1.2 1.4 1.6 1.8 2power coefficient / system efficiency tip speed ratio4.2 m/s 4.9 m/s 5.6 m/s 6.4 m/s 7.2 m/s 4.2 m/s 4.9 m/s 5.6 m/s 6.4 m/s 7.2 m/s sysη PC U 12 the t ip speed ratio at ,maxPC , as can be seen in Figure 4, where higher  values yield lower efficiencies. Additionally, subject to the load applied, several stable operational  s may appear at the range of opt . However, we generally avoid operation in this tip speed ratio regime, opt , as a non -stable operation zone for a VAWP. Figure 5: Pump efficiency as a function tip speed ratio for different wind tunnel speeds. As higher wind speed results in more considerable PC, an increase in the system efficiency of the VAWP is also noticed. However, the increase in sys is higher than the increase of the PC due to an additional increase in the pumping efficiency (see Figure 5) of up to 18%, yielding a mechanical to hydraulic efficiency of up to pump 71%= . This increase is due to the lower relative residual torque of the pump . For similar  , higher wind speed s yield higher rotational speeds and torque , increas ing feedQ and p . Due to the increase in torque, the system experiences lower relative residual torque, yielding higher hydraulic efficiencies at higher wind speeds. In addition, since the pump operates at relatively lower rotational speeds and loads than those set in its design, increasing the pump rotational speed and pressure may increase its efficienc y. In this section, we considered the hydraulic efficiency of the D -WPD system as the main parameter to study the turbine ’s efficacy. However, a desalination sy stem ’s efficacy is not rated by its hydraulic efficiency but, generally, by the ratio of permeate flow rate to electrical power 00.20.40.60.81 0.8 1 1.2 1.4 1.6 1.8 2pump efficiency tip speed ratio4.2 m/s 4.9 m/s 5.6 m/s 6.4 m/s 7.2 m/s pumpη U 13 input 3 per elec/ [kWh/m ]QW , also referred to as specific energy consumption (SEC) . Conversely, multiplying the hydraulic efficiency, sys , by the recovery rate, per feed/ r Q Q , yields the net energy per permeate volume . Thus, to increase our permeate production, we need to optimize both the hydraulic efficiency and the recovery rate for a given RO module. Therefore, although sys it is not the main parameter to optimize, it is crucial for optimizing the entire desalination system performance . 3.2 Desalination System Performance Following the wind -pump experiments, the sy stem was connected to a desalination system, as described in section 2.3. The system performance was analy zed for three different feed NaCl concentrations, namely fc= 2,500 ppm, 5,000 ppm and 10,000 ppm. Two types of experiments were performed for each concentration: (i) the wind speed was maintained constant, and the load was varied by changing the feed pressure valve position ; and (ii) the valve position was maintained constant while the wind speed was varied. Accordingly, the influence of the wind speed, valve load, or NaCl concentration on the system ’s performance and various parameters were analyzed . 3.2.1 Feed Flow Response Figure 6 shows the feed flow rate, feedQ as a function of the rotational wind speed . The data showed that the pump displacement is feed 3.8 ml/rev Q   for a system operating at 3 barp , whereas for lower pressures , the displacement i s higher, up to 5.1 ml/rev . The data points within the shaded area are for feed water concentrations of 10,000 ppm and should be ignored due to activation of the pressure relief valve (set at ~27 bars) in these conditions . Constant displacement per revolut ion correspond s to the pump working as an ideal positive displacement pump . Regardless of the turbine rotation speed, the feed flow would simply be a linear function of the turbine rotation speed . If, further, a constant tip speed ratio can be maintained f or different wind speeds, the feed flow would also become a linear function of U . 14 Figure 6: feed water flow rate, feedQ as a function of rotational speed,  for various feed TDS. For a system operating at pressures of 3 barp , the attained pump displacement was feed 3.8 ml/rev Q   . 3.2.2 Pressure Response Figure 7 (a-c) shows the pressure at the RO module inl et as a function of  for the different salinities. Each panel displays the pressures measured in eight separate experiments. Four experiments were performed at constant wind speeds while increasing valve load, resulting in a pressure increase accompanied by a reduction in  , thus reducing the relative feed flow rate (marked as circles) . The additional four experiments are at a constant valve position while increasing the wind speed, resulting in an increa sing feed flow rate and the system ’s pressure (marked as triangles) . All plots indicate that the system response is independent of the varied parameter. This is a welcome result because it implies that both the pressure and flow rate show negligible hyste resis, an adverse feature of mechanical systems . An essential and valuable conclusion that can be drawn from Figure 7 is that the pressure versus rotational speed relationship is linea r, stand ing in contrast to the typical hydraulic system characteristics , where 2pQ [53]. The linearity of the pressure with the rotational speed resembles the assumptions made in [40] for a VAWP -desalination system. 00.30.60.91.21.5 0 50 100 150 200 250 300 350flow rate [l/min] rotational speed [RPM]2500 PPM 5000 PPM 10000 PPM Q=3.8 ml/revQ=5.1 ml/rev pressure relife valve activated 15 Figure 7: (a-c) RO module inlet pressure, P as a function of the rotational speed,  for feed TDS of (a) 2 ,500 ppm (b) 5 ,000 ppm (c) 10 ,000 ppm for various valve settings and wind speeds; and (d) constant valve setting, 20%, for various salinities and wind speeds. 3.2.3 Influence of Salinity on Performance In contrast to the wind -pump system, where water composition has a negligible impact on performance, both the pressure and the pressure gradient /P depend on salinity (see Figure 7 (d)). The flux of the water through an ideal salt -rejecting membrane, vJ , is usually [54] defined as: ( )mem vpJ L p  =  − (7) where pL is the membrane ’s water permeability constant , memp is the pressure difference across the membrane and  is the osmotic pressure difference across the me mbrane. Due to the increase in the water salinity, the osmotic pressure increases, as defined by Van ’t Hoff: Hoff gas Mwi c R= (8) where Hoffi is the dimensionless Van ’t Hoff constant (for NaCl, Hoff 2 i= ), Mc is the molar concentration and gasR is the ideal gas constant. From eqn. (8) we can calculate the osmotic 0510152025 0 50 100 150 200 250 300 350pressure [bar] rotational speed [RPM]2,500 PPM U=4.2 m/s U=5.2 m/s U=6.1 m/s U=7.0 m/s valve 40% open valve 30% open valve 20% open valve 10% openvarying wind speedvarying load(a) 0510152025 0 50 100 150 200 250 300 350pressure [bar] rotational speed [RPM]5,000 PPM U=4.2 m/s U=5.2 m/s U=6.1 m/s U=7.0 m/s valve 40% open valve 30% open valve 20% open valve 10% openvarying wind speedvarying load(b) 0510152025 0 50 100 150 200 250 300 350pressure [bar] rotational speed [RPM]10,000 PPM U=4.2 m/s U=5.2 m/s U=6.1 m/s U=7.0 m/s valve 40% open valve 30% open valve 20% open valve 10% openvarying wind speedvarying load(c) 0510152025 0 50 100 150 200 250 300 350pressure [bar] rotational speed [RPM]valve 20% open 2500 PPM 5000 PPM 10000 PPM U=4.9 m/s U=5.8 m/s U=7.2 m/s U=8.0 m/s(d) varying wind speedvarying salinity 16 pressures of the feed water, namely  2.1 bar, 4.2 bar and 8.4 bar for salinities of 2 ,500 ppm, 5,000 ppm and 10 ,000 ppm, respect ively. As shown by Lee and Kim [55], for NaCl concentration less than 1.5 mol/L, the deviation from Van ’t Hoff osmotic pressure is negligible , thus , those approximations are justifiable. However, this is a simplified definition, as will be further elucidated, eqn . (8) describe s the main physical reasoning for pressure differences corresponding to different salinities. To explain the observed pressure -rise at a constant RPM with increasing salinity, consider a hypothetical case where the turbine operates at ,max PPCC , corresponding to max . Since we use a positive displacement pump, we can assume (see Figure 6), that: feedQ (9) Based on Figure 3, we can assume turbulent losses, thus: 2 valve brine p p Q   (10) where valvep is the pressure -loss across the valve and equel to brinep . Since brine feed perQ Q Q=− and per mem v Q J A= , where memA is the membrane surface area, integration of equations (7) to (9) into eqn. (10), result s in: ( ) ( )2 feed mem mem Hoff gas p M w p Q A L p i c R    −  −  (11) Since valve pp  , we obtain a nonlinear equation, where an increase in the feed salinity, Mc , produces a pressure rise. Additionally, the increase in salinity reduces the permeate production and increases the required shaft power , as denote d by eqn. (7) and (3), respectively. Assuming that the increase in the required shaft power is lower th an the maximum available turbine shaft power : 312 feed shaft,req shaft,max ,max aP pQ W W AU C   =  = (12) an increase in the salinity will increase the power coefficient, thus increasing the system ’s efficiency. This can be observed in Figure 8, where an increase in fc at 6.1 m/s U increases both PC and sys . However, as demonstrated later, a t lower wind speeds, an increase in salinity can arrest the turbine because the turbine cannot match the additional load produced by the desalination system , shaft,req shaft,maxWW . 17 Figure 8: Turbine power coefficient (circles) and hydraulic efficiency (axes) as a function of  for various wind speeds for the various feed water TDS. 3.2.4 Permeate Production Figure 9 (left) presents the permeate flow and recovery rates as a functio n of inlet pressure. Using linear least -squares approximation (see eqn. (8)), we obtain that the osmotic pressures of the feed water are  2.1 bar, 4.2 bar and 8.4 bar for salinities of 2 ,500 ppm, 5 ,000 ppm , and 10,000 ppm, respectively. As estimated, the osmotic pressure obtained from the data matches the calculated osmotic pressure, with up to a 5% error ( Figure 9 (left) ). The non -linearity observed in instances of p→ , are due to the relatively large permeation of the salt as opposed to the relatively slow permeation of the water, making the permeate more saline, at p→ . As obtained from Figure 9 (right), the slope of perQ versus p− decreases with the increase in fc , deviating from eqn. (7), with a constant linear slope, pL . There are two main reasons for the change in the slope: (i) membrane swelling; and (ii) increased osmotic pressure due to concentration polarization . In the first instance, increased salinity is known to cause an osmotic deswelling, reducing the membran e permeability [56]. Additionally, for a given feed flow rate, permeate flux increasing with feed pressure exacerbates concentration polarization, 00.050.10.150.20.25 0.8 1 1.2 1.4 1.6 1.8 2CP & ηsys wind speed [m/s]2,500 PPM 4.2 m/s 5.2 m/s 6.1 m/s 7.0 m/s 4.2 m/s 5.2 m/s 6.1 m/s 7.0 m/s sysη PC U(a) 00.050.10.150.20.25 0.8 1 1.2 1.4 1.6 1.8 2CP & ηsys wind speed [m/s]5,000 PPM 4.2 m/s 5.2 m/s 6.1 m/s 7.0 m/s 4.2 m/s 5.2 m/s 6.1 m/s 7.0 m/s sysη PC U(b) 00.050.10.150.20.25 0.8 1 1.2 1.4 1.6 1.8 2CP & ηsys wind speed [m/s]10,000 PPM 4.2 m/s 5.2 m/s 6.1 m/s 7.0 m/s 4.2 m/s 5.2 m/s 6.1 m/s 7.0 m/s sysη PC U(c) 18 progressively raising the osmotic pressure at the membrane surface above that of the feed  . This results in a corrected osmotic pressure difference co that should be used in eqn. (7), which varies with the permeate flow as () co exp /vfJk   , where fk the overall feed -side mass transfer coefficient [57,58] . When /vfJk is not excessively large , the exponent may be expanded as () co1/ ,vfJk  +   yielding the corrected approximation for the flux of the water through the membrane: ()v v p co p fJJ L p L pk   =  − =  − −  (13) and hence the effective permeability constant : ,eff 1p p p fLL Lk=+ (14) The correction expressed in eqn. (14) shows that , as fc and thus  increase , the slope of perQ versus p decreases , as observed. Figure 9 (left) also shows that, for a co nstant salinity and pressure, higher recovery rates are obtained for slightly lower perQ values. Here , increasing the feed flow rate by increasing wind speed and reducing the restriction of the valve result s in a lower recovery rate at comparable pressures . Figure 9: Permeate flow rate as a function of (left) inlet pressure, p , and recovery rate; and (right) p− , for all experiments conducted. Dashed -doted lines represent linear interpolation of the data for every fc . 0.00.20.40.60.8 0 5 10 15 20 25 30permeate flowrate [l/min] ΔP-Δπ[bar]2500 PPM 5000 PPM 10000 PPM 19 3.2.5 Salt rejection Figure 10 presents the steady -state permeate TDS and corresponding salt rejection attained by the system as a function of the membrane pressure for all experiments conducted. For 5,000fc ppm, all the permeate TDS are below the maximum TDS for potable water recommended by the W orld Health Organization (WHO) [13]. For fc= 10,000 ppm, the allowed TDS values were obtained for pressures 12 barp , approximately 1.5 times the osmotic pres sure difference. From Figure 10 (right), we see that the salt rejection stabilizes for 2.P However, the membrane exhibits a reduction in the rejection rate ( 5,000fc ) when the pressure is higher , yielding higher recovery rates and a reduction of the brine flow . This decrease is due to enhanced concentration polarization, where the salt mass transfer cannot keep up with the salt convection towards the membrane with the transmembrane flow of water. This effectively increas es the salt concentration at the upstream surface of the membrane , in the same manner as co , exponentially enhancing the salt permeation and increasing permeate salinity . It must be stressed that, f ollowing initiation , at cut -in speeds (cut-in), the pump pressure increases to 1.2 p    , and the system experiences a long transient process where permeate TDS values are initially high and slowly drop to the values presented in Figure 10 (left). The obtained permeate TDS values shown in Figure 10 (left) are then the steady -state TDS values obtained after the transient decrease of the TDS. The reason for the transient is that as the press ure builds up to exceed the osmotic pressure, the diffusion of salt through the membrane, which is largely independent of the water flow, is at first significant compared with the flow of water, resulting in a higher salt concentration in the permeate. Con sequently, a field system operating average wind speeds yielding pressures of 1.2p    , would produce saltier permeate than presented in Figure 10. Since the transient process can have a significantly adverse ef fect on permeate salinity, it would be beneficial to integrate a minimal pressure valve upstream of the filter with min 1.2 p   . 20 Figure 10: (Left) Permeate TDS; and (right) Salt rejection, as a function of inl et pressure, p , for all experiments conducted 3.3 Stand -Alone System Study The influence of wind speed and permeate salinity was studied for a system at a constant valve setting (10% open) to determine the system ’s performance under t ypical field conditions . Measurements were made for commonly -encountered wind speeds, namely 4.2 m/s 8.3 m/s U and for the three different feed NaCl concentrations, namely fc= 2,500 ppm, 5,000 ppm , and 10,000 ppm . 3.3.1 Influenc e of Wind Speed on Performance Figure 11 shows the influence of wind speed and permeate salinity on four system parameters, namely: (i) daily permeate flow, (ii) recovery rate, (iii) permeate TDS, and (iv) system efficiency . The results show that at an average wind speed of 7.7 U m/s, the system produces a daily yield of 3 per0.5 m /day Q . Note, how ever, that this is a small laboratory -scale system with a swept area of 0.8 2m and that the yield increases linearly with the swept area. Moreover, an additional increase in permeate production can be expected due to an increase in system efficiency , as mentioned in section 3.1. From dimensional analysi s, at the average wind speeds, increasing the swept area by a factor of 15, to 12 2m , will result in a daily yield in the range 33 per 7.5 m /day 13.25 m /day Q , depending upon the salinity of the feed water. For moderately low average wind speed of 5.0 m/s U , we can expect a yield in the range 332.1 m /day 3.7 m /daydQ . Furthermore, additional optimization can be made by varying system average operational pressures and feed flow for each salinity. 040080012001600 0 5 10 15 20 25 30permeate TDS [PPM] RO pressure [bar]2500 PPM 5000 PPM 10000 PPM max allowed TDS 80%85%90%95%100% 0 5 10 15 20 25 30rejection rate [%] RO pressure [bar]2500 PPM 5000 PPM 10000 PPM 21 Figure 11: (a) Daily permeate flow, (b) recovery rate, (c) permeate TDS, and (b) system efficiency as a function of U for valve at a constant opening rate of 10% The acquired data also indicate a linear correlation between the permeate production and the wind speed . We can derive this relationship based on previous data, namely: perQp (see Figure 9) and p (see Figure 7), yielding perQ . Since U , under the approximation that const , we can deduce that: perQU (15) as confirmed in Figure 11 (a). This linear relationship between perQ and U is valid for all salinities. Under these assumptions, we also note that: pU (16) Additionally, from eqns. (9) and (11), we can deduce that the slope of () pf= increases with the increase in salinity, as shown in Figure 7 (d). Again, assuming that const , from eqns. (1) we gain that: 3 mechWU (17) and from eqns. (15) and (16), we can show that: 03006009001200 3 4 5 6 7 8 9permate flow rate [liter/day] wind speed [m/s]2500 PPM 5000 PPM 10000 PPM(a) 0%20%40%60%80% 3 4 5 6 7 8 9recovery rate wind speed [m/s]2500 PPM 5000 PPM 10000 PPM(b) 02505007501000 3 4 5 6 7 8 9permeate TDS [PPM] wind speed [m/s]2500 PPM 5000 PPM 10000 PPM(c) 0%5%10%15%20% 3 4 5 6 7 8 9System eff [%] wind speed [m/s]2500 PPM 5000 PPM 10000 PPM(d) 22 2 hydra pQ W U  =  (18) Finally, assuming a constant pump efficiency, we can develop the following system efficiency correlation: sys /PCU (19) show ing that the system’s efficiency decrease s with increasing wind speed . This result matches the obtained data from Liu et al. study [35]. In contrast, Figure 11 (d) denotes that increasing wind speed for a system with a constant salinity and valve settings first increase s sys follow ed by a continuous plateau of the system efficiency, and this runs counter to our expectations shown in eqn. (19). There are three main reasons f or the almost constant efficiency of the system . First, significant increases in aerodynamic efficiency occur over this Reynolds number range , as discussed in section 3.1. This increase in wind speed bring s about an increase in PC , which violates the assumption expressed in eqn. (17). The ass umpti ons expressed in eqns. (17) to (19) are only vali d for a lar ge-scale system where Reynolds number effects are small and the pump efficiency is assumed to be constant. Second , the pump efficiency increases markedly with increasing wind speed, as discussed in section 3.1, and therefore sets in contrast to the assumption in (18). This is because the relative residual torque is lower at increased rotational speeds , and the pump operates closer to its design conditions . Third, the approximation p (based on Figure 7), is simply a local approximation of the pressure response, where the correct function is in all probability yp where y is a constant 12y , yielding a corrected correlation of the desalination efficiency: 1 sysy PCU−  (20) On the other hand , the inverse relationship between the wind speed and sys can also arrest the turbine at low wind speeds since ()312 hydra sys, max a W AU  . Thus, a trade -off exists between increased wind -speed range and greater efficiencies at higher wind speeds – as anticipated by the model developed in [40]. In summary, the net result is an almost constant system efficiency of sys 12.5% 15% for a wide range of wind speeds and salinities. Notwithstanding the small scale of the system, these are remark ably high efficiencies. However, it is clear that far greater system efficiencies can be achieved on up -scaled systems where the characteristic blade Reynolds numbers are 610 and pump efficiencies are optimized. 23 3.3.2 Energy Recovery Although the system does not contain an energy recovery device (ERD ), the estimated power available from the brine flow was calculated and presented in Figure 12. This power can be employed to produce electricity using a mi cro-Pelton wheel or a regeneration pump for irrigation or further pumping. The available power in the brine, brineW , is defined as: ()brine brine brine feed 1 W p Q p r Q  − (21) Since P and Q increase approximately linearly with the wind speed, from eqns. (15) and (16), we obtain that the available power in the brine is ()2 brine 1 W U r− . Since turbine power increases with 3U , with constant PC a reduction in the relative available energy from wind energy is observed ( Figure 12 (left) ). We obtain from eqn. (21) that at higher salinities, where the system experience lower recovery rates, brineW will be higher, as shown in Figure 12. Thus, a system that pr oduces lower permeate produc tion due to low recovery rates can increase its efficacy with the use of ERD s. Figure 12: Available Power for energy recovery as a function of wind speed (left) relative available energy from wind energy; and (right) power in watts. 3.3.3 Specific Energy Consumption Generally, desalination systems are not measured by their efficiency, but rather by their energetic efficacy , also known as specific energy consumption (SEC), defined as the energy required in kWh to produce one cubic me ter of permeate . SEC is commonly defined by the electric energy , elecW , supplied to the RO pumps and supporting systems . However, in our case, no electricity is supplied by the system . Therefore , the definition of SEC was adjusted to 0%3%6%9%12% 3 4 5 6 7 8 9recovery availability from total wind energy wind speed [m/s]2500 PPM 5000 PPM 10000 PPM 05101520 3 4 5 6 7 8 9brine energy [W] wind speed [m/s]2500 PPM 5000 PPM 10000 PPM 24 overcome this difference. The compatible SEC of a direct desalination system is defined by the mechanical energy supplied to the pump, mechSEC , and defined as: 7 shaft mech 3 perkWhSEC 2.78 10 mW Q−=   (22) In addition, since the system is driven directly by wind, we define the specific wind energy consumption, 312 7 wind 3 perkWhSEC 2.78 10maAU Q−  =   (23) where 312aAU is the gross available wind power . Figure 13 shows windSEC (left) and mechSEC (right) as a function of wind speed and fc , for a system with the parameters described in section 3.3.1 . Wind energy per cubic meter of 3.5 to 7 kWh of permeate is required for fc of 2,500 ppm and 5 ,000 ppm , respectively for wind speeds 8 m/s U . For 10 ,000 ppm, higher energy input is required of up to 13.5 kWh of wind power for wind speeds of 5.4 m/s 8 m/s U . In order to compare the system energy demand , per cubic meter of permeate , to conventional small to medium -scale BW desalination systems, we can calculate the wind and electrical energy required if the turbine was to produce electricity feeding these desalination plants. First, we calculate the maximum power production of the wind turbine by extracting the maximal power coefficient at each wind speed from Figure 4. Next, we assume that specific electrical energy consumption , elec SEC , of 0.8 to 2.5 kWh/m3 for the BW desalination process with a salinity of 2 g/L up to 10 g/L, with no ERD [59–62] , indic ated by the shaded area in Figure 13, right . Finally, we assume a total electrical/mechanical transmission efficiency of 0.8. Accordingly, we can estimate the wind energy required for a conventional BW desalination plant powered by a small -scale VAWT, indicated by the shaded area in Figure 13, left. The data points fall mainly within the shaded area and indicate that our mechanically simple system is competitive even with optimized conventional wind -powered desalination systems . Note that our conservative assumptions can be improved by increas ing the permeate flow through system optimization or implementing an ERD. The competitiveness of the D -WPD system is further demonstrat ed by a detailed comparison to past wind power desalination systems in the next section. 25 Figure 13: Energy required to produce one cubic meter as a function of wind speed : left – wind energy ; right – mechanical energy . Data p oints – present system; shaded area – conventional BW desalination plant. 3.4 Comparison to Existing Systems This section discusses and compares the D-WPD system with published wind -powered desalination systems . A summary is shown in Table 1. As previously men tioned, wind -powered desalination systems are divided into three main categories: (i) desalination plants using wind - turbine -generated electricity that is coupled to the grid [26–29]; (ii) stand -alone desalination plants powered by electricity generated directly from wind turbines [20,22,24,25,63 –65]; and (iii) stand -alone desalination plants with direct mechanical integration to a wind turbine [33– 35,66] . Grid-coupled desalination plants typically operate at medium (~30kW) to large ( up to ~2.5 MW) scales and beyond, providing wa ter for regional use [26–29]. On low -wind days, the plant may reduce its permeate production rate , but is typically able to use other energy sources , as storage or grid power to run the desalination process and stabilize the permeate production . These systems operate similarly to conventional grid power -based desalination plants , with high -end controllers and sensors, ERD [62], and optimized operation of the desalination process . This usually yields high overall efficiencies, thus producing low SEC. In contrast, stand -alone systems experience large variability in the available power due to the fluctuating nature of the wind. Thus, the system design and feed water TDS determine the permeate production, rejection, recovery and other system parameters for each wind speed. In this manner, stand -alone d irect electrically -based systems possess a considerable advantage over d irect mechanically -based systems due to their ability to vary pump power and system load. T hus, control the system pressure/flow rate, with only a moderate influence from the wind variability. This advantage multiplies when an electrical storage system is coupled to 048121620 3 4 5 6 7 8 9Wind energy required to produce permeate [kWh/m3] wind speed [m/s]2500 PPM 5000 PPM 10000 PPM 0.8 kWh/m^3 system 2.5 kWh/m^3 system 012345 3 4 5 6 7 8 9Mechanical/electrical energy required to produce permeate [kWh/m3] wind speed [m/s]2500 PPM 5000 PPM 10000 PPM 0.8 kWh/m^3 system 2.5 kWh/m^3 system 26 the system, enabling an almost constant production of permeate flow rate and quality. However , a study by Carte et al. [67] demonstrated, using simulations, that a large direct electrical -based coupling is not feasible at highly fluctuating wind conditions. Additionally, these systems suffer from efficiency losses in the generators, inverters, transformers and motors [16–18,22,64] , where as smaller systems typically suffer from higher relative losses. In addition, a direct electrical coupling between a generator and a pump motor can create electrical harmonics when a system experiences rapid load or wind speed changes [25]. This may requir e energy storage or flywheels to smoothen the electrical surges , thereby increasing the system ’s cost . On the other hand, electricity generation can be used to directly remove ions from the water by coupling the generator directly to an electrod ialysis (ED) system. This can achieve high recovery rates and relatively smooth operation at fluctuating wind conditions [64]. In cont rast, direct mechanical integration between a turbine and a desalination system eliminates the need for power electronics. This simplification results in a more reliable and cost - effective system. In addition, eliminating power electronics reduces the over all carbon footprint of the system while supporting a more efficient energy transfer process to produce fresh water. However, direct mechanical systems are exceptionally susceptible to wind fluctuations, as the wind speed impacts both the pump speed and av ailable torque. Therefore, without a system controller, both the permeate flow rate and rejection rate are sensitive to these fluctuations, as demonstrated in the previous section. Consequently, these systems often necessitate water storage. As previously mentioned, larger systems tend to have lower relative losses, increasing the overall system efficiency, SEC and relative permeate cost. However , since these systems were only studied at small scales for off -grid applications 2( 30 m )A , future studies should investigate the economic al and mechanical feasib ility of larger systems in addition to the influence of long -term wind fluctuation on the desalination module and its reliability . The system was compared to several studies investigating stand -alone wind -powered desalination plants, either by electricity generated directly from wind turbines [20,22,24,25,63 – 65], or by a direct mechanical integration to a wind turbine [33–35,66] . Different system parameters (see Table 1) were compared at wind speeds of 4 -8 m/s, representing both the typical wind speed range at low heights, corresponding to small -scale systems, and the average studied wind speeds. For each system, based on the available parameters presented in their study, we compare the daily and relative desalination capacity, system size, operation pressures, system maximum and average hydrauli c efficiencies sys,max and sys,avg (defined in eqn. (4)), rejection, recovery and specific energy consumption ( mechSEC and windSEC , defined in (22) and (23)(. To 27 calculate windSEC for studies that presented only mechSEC , or vice versa, we assumed an average power coefficient of 30% 35%PC . This is a relatively high average power coefficient for small to medium size wind turbines operating a t wind speeds of 4-8 m/s , thus producing a conservative comparison to our system. All systems studied, except [33], have an electronic control system to regulate the pump speed , module pressure , or electricity production and storage. Though , part of the controllers used not only did not improve the system performance, but instead, seemed to reduce the system efficiency and permeate production [33–35]. Despite not having a system controller and its small size, the D -WPD outperform s the direct mechanical [34,35] and electrical [64] wind power BW desalination systems in specific energy consumption, relative efficiency , and relative desalination capacity. However, similar system efficiency and relative desalination capacity were achieved by Park et al. [63], although with higher SEC by utilizing electricity generation and storage. Conversely , from the obtained data [63], at low wind speeds , the turbine power coefficient appears to surpass the Betz limit, implying that wind turbine blockage correction s were not considered. Thus we can assume that the D -WPD system further outperforms [63]. Similarly, higher turbine power output than the stated rated power was obtained from data presented in [65]. The two main reasons the D -WPD maintain s low SEC are: (1) relatively high system efficiency; and (2) relative ly high recovery rate. Compared to the published wind -powered desalination systems, the system ’s relatively high recovery rate is a combination of the utilization of a module with high performance at high pressures and a relatively low feed flow rate to module surface area. The high system efficiency is achieved by correctly matching the required desalination power and available wind power, in addition to using a relatively high -efficiency wind turbine 15% 25%PC , an efficient membrane -based pump leading to relatively high pumping efficiencies in the range pump hyd shaft 50% / 70% WW   . Additionally, by adjusting the system to match a specific salinity range, even lower specific energy consumption is achievable. Compared to the other proposed systems, the main disadvantage of the D -WPD system is the lack of permeate flow rat e control. Thus, an elevated storage tank can be integrat ed to regulate the permeate flow rate. A storage tank can als o maintain the module decontamination by backwashing it when wind speeds are below the turbine cut -in speed. A commercial D -WPD system wil l also require a system break at high wind speeds. This can be achieved using an overpressure safety valve to increase the load on the system. 28 In addition to wind energy, the energy for a desalination process can be compared to other means of renewable ene rgy such as hydro, solar , and wave. However, h ydro energy -based desalination systems are more similar in their operation to conventional desalination systems producing electricity to drive the desalination process. Similarly, wave power -based desalination systems are generally focused on SW desalination , generally considered high cost and unsuitable as small -scale off -grid system s [68,69] . Nevertheless, s olar energy -based desalination system s can compete with small -scale, wind -powered systems [70]. The specific electrical power consumption, elec SEC , of an efficient small -scale PV -RO system to desalinate brackish water, 2,000 PPM 5,500 PPMfc , is 2 kWh/m3, and can reach as low as elec SEC = 1.1 kWh/m3 [61,71,72] . Additionally, PV electrical power production is cheaper than wind -based. In contrast, the D -WPD system require s a smaller land use , and does not require any electrical components which has the protentional to yield a more economical permeate production than small -scale PV systems. Accordingly, an economic comparison should be performed in regions with high solar and wind density. With low speci fic energy consumption, low system complexity compared to previous studies , and a high relative desalination capacity, the D -WPD offers a low -cost solution for off - grid wind -powered small -scale desalination. Due to the turbine’s low noise emission and the reduced probability of bird strikes, the D -WPD is ideal for operating near small communities. 29 Table 1: Summary of D-WPD system to previous wind -powered desalination studies Paper Year Study Type TDS [mg/l] Wind - Desalination System Type Controller Desalination Capacity at 8 m/s [m3/day] Wind Turbine Size [m2] Relative desalination capacity at 8 m/s [m3/m2day] Operation Pressure Evaluated Wind Speeds [m/s] Max System Efficiency Average System Efficiency Salt Rejection Average Recovery at 4-8 m/s SEC elec/mech at 4-8 m/s [kWh/m3] SEC wind at 4-8 m/s [kWh/m3] D-WPD 2023 experiment 2500 direct - MC no 0.75 0.8 0.94 3-25 4-8 17.50% 12.40% 97.5% 65% 0.7-1.2 3.4-6 5000 0.62 0.78 13.40% 96.5% 55% 1.2-1.6 5.1-7.1 10000 0.4 0.50 14.20% 95% 45% 2.2-3.3 9.1-13.5 D-WPD (medium size) 2023 simulation 2500 direct - MC no >20 16 >1.2 3-40 2-10 ≈26 ≈20% 90%- 97% >0.6 < 1 <4 5000 >15 >1 >0.6 ≈1 <4 10000 >12 >0.75 >0.5 < 2 <8 [34] 1992 experiment 2000 direct - MC Yes 0.52 12.6 0.04 6-11 2-8 ≈9.5% 6% 82.40% 9.7% ≈6-72 ≈30-180 4000 0.45 0.04 85.90% 9.1% ≈38-205 6000 0.38 0.03 81% 9.3% ≈45-240 [35] 2002 experiment 2700 direct - MC Yes ≈3 14.5 0.21 5.2-7.2 3.5-9 ≈13% ≈7% 96.8% - 97.8% 23% ≈2.1-8 ≈7-28 3500 ≈4 0.28 ≈9% 19% [63] 2011 simulations 2750 direct - EC Yes ≈2.5 2.5 0.98 ~10 3.7-8.7 ≈16% ≈10% 83%- 92% 15%-40% 1.6-2.8 ≈5-10 5500 1.8 0.71 65%- 91% 3%-25% 2.8-17 ≈9-50 [64] 2016 experiment 5000 direct - EC Yes ≈0.72 ≈1.55 0.48 - 2-10 ≈9% ≈4.5% 92% 85%-94% 3.2-4.4 ≈10-15 [33] 2009 experiment 35000* direct - EC No N/A 19.6 N/A N/A 5-12 ≈<1% ≈<1% N/A ≈15% N/A N/A [66] 2003 calculations 35000* direct - EC Yes 2410 ≈3000 0.80 N/A 8 N/A ≈40% N/A N/A 4-8 ≈10-20 [65] 2004 partial experiment 35000 direct - EC Yes 0.7776 7.1 0.11 34-55 7-9 N/A N/A 97-99 2.5% - 12.5% N/A N/A [20] 2002 simulation 40000 direct - EC Yes 8.5 ≈15 0.57 40-60 5-15 N/A N/A 99% N/A 3.2-4.5 ≈10-15 [29] 2005 experiment 35500 Not-direct Yes 19 ≈40 0.48 55.00 4.5-13 N/A N/A 99% 37% 7 ≈18-30 [25] 2004 experiment 36000 direct - EC, storage and ERD Yes N/A N/A N/A 65 N/A N/A N/A 98.60% 30% 7.5 ≈21 [22] 1997 simulations N/A direct - EC, storage Yes 5 19.6 0.25 40-60 4-8 N/A N/A N/A N/A 25-33 80-100 [24] 2018 simulation 33000 - 38000 direct - EC, w/wo storage Yes 3-20 78.5 0.25 33-53 N/A N/A N/A 98.3% - 99.5% 13%-14% 9-16 ≈30-45 30 4 Conclusions In this paper , a novel direct wind -powered desalination (D -WPD) syst em was experimentally studied for brackish water desalination . A small -scale vertical axis wind turbine , mounted in a wind tunnel , was mechanically interfaced to a positive displacement , high-pressure pump and operated without electricity production or a system controller . The pump was coupled to an RO-based desalination system and used to drive brackish water with TDS levels of 2,500 ppm to 10,000 ppm through the RO module. An extensive parametric study of the system was performed. Accordingly, the influe nce of the feed water salinity, module loading, and wind speed on the system ’s performance and additional system parameters was studied. The empirical and theoretical relationship between the various parameters were developed and explained. Furthermore , the parametric study demonstrated the capability to achieve high efficiency under a variety of design configurations . To determine the system’s performance under typical field conditions, measurements were made for commonly -encountered wind speeds, namel y 4 m/s to 8.5m/s , under a constant valve setting. The study showed that, despite its small scale, 20.8 mA= , the D -WPD system can produce a daily permeate yield of up to 3 per0.6 m /day Q at 6 m/s U= . Moreover, the stand - alone system demonstrated operation at a virtually constant average efficiency of sys13.5% , for a wide range of wind speeds and salinities. This results in a practical and ine xpensive wind - based desalination system, operating with low specific wind energy consumption of 3 windSEC = 3.4-8 kWh/m for feed water at TDS levels up to 5 ,000 ppm, corresponding to 3 elecSEC = 0.7-1.7 kWh/m , and 3 windSEC = 10.7 kWh/m for 10 ,000 ppm. Moreover, based on previous studies , a considerable increase in efficiency is projected (up to 60%) with system upscaling. Subsequently, t he D -WPD was compared to published wind -powered desalination investigations. Despite its size and the lack of a system controller, the D -WPD surpasses prev ious studies in specific energy consumption, relative efficiency , and relative desalination capacity. Therefore, t he D -WPD potentially provides a low -cost solution for off -grid wind -powered small - scale desalination due to its low SEC, high relative desalina tion capacity , and low system complexity compared to previous wind -based desalination studies. Additionally, the turbine ’s low noise emission and the reduced probability of bird strikes make it an ideal choice for off - grid operation. In the quest to obtain a simple and low -cost solution for wind -powered 31 desalination system s, future studies should investigate the economic al and mechanical feasibility of larger direct systems in addition to the influence of long -term wind fluctuation on the desalination system and its reliability. 32