1 February 2012
STORM-WATER HARVESTING SYSTEM INPUT SUBSYSTEM
Luis Hernan
RAIN-WATER HARVESTING SYSTEM —INPUT SUBSYSTEM
Context!
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Precedents !
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Water level sensing!
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Ultrasonic sensors!
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Pressure and force sensitive!
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Testing!
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6
Output voltage!
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Smooth function!
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Integration!
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Further improvement!
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References !
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RAIN-WATER HARVESTING SYSTEM —INPUT SUBSYSTEM
Context On a previous stage of this work, a grasshopper definition was prepared to ‘actuate’ a deployable structure using a series of slider. The concept of that script embedded configuration variables in this sliders. Notwithstanding, performance concept for this stage comprised a more physical approach to the idea of an actuated rain harvesting system. Rain harvesting structures are designed as runoff and collection surfaces. Following the principle stating that a contaminated runoff surface makes for poor quality collected water, it is proposed for this structures to be actuated in the event of rain. Therefore, if a certain amount of rainwater is detected— heretofore referred to as threshold—an embedded system will send a signal for the structure to be deployed. In this manner, it is assured the system will be collecting water only when the intensity of water is considerable.
Figure 1 eTape sensor PN-6573P-8
Figure 2 Overview of final circuit configuration. Sensor is connected to a servo in order to test for interferance
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RAIN-WATER HARVESTING SYSTEM —INPUT SUBSYSTEM
Two models stand for rain precipitation recording. The tipping bucket is based on a moving lever mounted on a sewsaw-like support. When enough water is accumulated, the lever tips discharging accumulated water (Mitchell, 2007). In terms of electronic instruments, this model is common as tipping is used to trigger a signal. However, a number of factor gives TB a considerable amount of uncertainty. Water is condensed on the lever by using a funnel. Thus, a delay may be induced as the lever can remain to tip long after rain has stopped (Groisman and Legates, 1994). Furthermore, TP are known for undercatchment systematic error, specially in light rain events or when environmental temperature is high enough as to evaporate water in the funnel (Mitchell, 2007) The Standard Rain Gauge (SRG) remains as the most reliable model to record and report rain intensity. (Groisman and Legates, 1994)(NOAA, 2010) Modern rain gauges were originally designed by George James Symons in 1863, whilst preceding the British meteorological society. The basic design features a five inch collector funnel connected to a measuring tube. As precaution, both elements are enclosed within an overflown container should an excessive precipitation occurs in the observation period. When data is collected manually, a SRG incorporates a measuring stick in order to report precipitation in a length unit—either mm or inches— This research will investigate on the options for an automated measuring stick. The most reliable option will be tested and incorporated in an Arduino+Grasshopper environment to actuate a prototype of the Deployable Rain Water Harvesting system. As a joint project, this research will focus on the input part—the ability for the system to collect and process information on the environment.
Precedents
Water level sensing A number of readily available sensors can be used to gather information on water level. Main characteristics will be reviewed in order to evaluate the best solution.
Figure 3 Connections to micro-controller are made by crocodile terminals.
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RAIN-WATER HARVESTING SYSTEM —INPUT SUBSYSTEM
Ultrasonic sensors Ultrasonic sensors evaluate the position of any object by gauging the time it takes for a sound wave to return from emitting point (O'Sullivan and Igoe, 2004). As water is a reflecting surface for high frequency waves, it is possible to be used in water level sensing applications. Although ultrasonic sensors are common, level measurement transducers require a focalised beam in order to work in containers— enclosing walls induce false readings as offer a reflection surface as well. Solutions generally fall on the industrial applications. As a general guide, they offer a measuring range of 0.3 to 0.8 meters with a beam angle of 10º and a working frequency of 44 Khz. Furthermore, they draw an electric input of 15 volts and weight an average of 1 kilogram. (Siemens, 2009) (Xylem, 2010) Given the above mentioned characteristics, ultrasonic sensors are not suitable for water level sensing in small scales.
Figure 4 Arduino with breadboard and E3 470R + 220R resistors
Pressure and force sensitive Pressure sensor operate on the basis of hydrostatic pressure. Gauge sits inside measure liquid at a fixed position and ‘weight’ the water column above it. Alternatively, a force sensitive sensor can be place in the bottom of the liquid container to record changes in weight from both container and anything within. Both sensors act by measuring change of configuration in a surface when subject to mechanical stress. (O'Sullivan and Igoe, 2004) In turn, a gradient of changing voltage is returned as measurement representation. As it happens with ultrasonic transducers, hydrostatic pressure sensors are more suited for industrial application—measuring large liquid quantities—. By way of alternative, external force sensors may be a good solution in small scales. Small force sensing resistors are capable of measuring a given amount of force in the range of 0.1N to 10N. Nonetheless, their resolution is not very accurate. Thus they are more commonly used as a presence sensor rather than a weighting scale (Interlink, 2010)
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RAIN-WATER HARVESTING SYSTEM —INPUT SUBSYSTEM
An alternative using the same logic of operation is the eTape PN-6573P-8 Continuos level sensor. It provides a resistive output inverse to the measured level. The circuit, comprised by a series of printed electronic elements, is housed inside an insulated chamber and varies resistance according to an external hydrostatic pressure. When submerged in fluids, upper level will the highest point in which electrical current is interrupted. Therefore, the lower the measured liquid level, the higher electrical resistance is yielded by the sensor. Given this analysis, the eTape sensor has been chosen as a sensing aid in the Rain Harvesting deployable structure project.
Testing The eTape Continuos fluid level sensor PN-6573P-8 can be modelled as a variable resistor in a simple voltage divider circuit. Following sample circuits in data-sheet, sensor should be connected to a 5v source power following a fixed value axial lead resistor.
Output voltage Recommended resistance values are 385 mOhms in empty state and 60 mOhms (+/- 10%) when full. When working in this scale, the sensor offers a 0.794 cm resolution with a 16 /cm gradient. However, RM value for is not provided in data-sheet. In order to attain specified resolution, a first dry test was conducted to determine best circuit configuration. An initial circuit was set up to test output values when using different RM values. In order to work in common ground, all resistors where axial leads 1/4w with a 5% tolerance. Every cycle was conducted five times in a circular container. Water was poured and drained to obtain full and empty state values.
Empty sensor output
Full sensor output
E3 2k2 (2.2 kOhm)
164
17
E3 1k0 (1.0 kOhm)
304
39
E3 470R + E3 220R (Combined 690 Ohm)
389
56
E3 470R (470 Ohm)
486
82
E3 220R (220 Ohm)
675
172
RM Resistor Value
Table 1 Output readings for full pouring cycle. Values correspond to different resistor configuration
The best configuration is using a combination of 470R + 220R resistor Circuit was tested for any electronic noise, i.e. random fluctuation in output voltage, when working with servo motors in the same board. As the final stage of this project includes a joint effort to produce an input-output dynamic system, it is predicted the need to include a servomotor in the circuit structure. As chosen servomotor runs on a 5V input source, it is necessary to connect it to the same source as the sensor.
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RAIN-WATER HARVESTING SYSTEM —INPUT SUBSYSTEM
In order to evaluate fluctuation in voltage exerted by sensor, five test were conducted on the circuit. Test consisted of pouring and draining water to evaluate output voltage whilst sending a rotation signal to servo. Results are as follows Table 2 Typical empty and full state sensor readings test
Test Number
Empty sensor output
Full sensor output
One
390
56
Two
390
58
Three
389
59
Four
387
57
Five
388
56
It is concluded that values remain within the 10% tolerance stated in data-sheet. Electrical noise is discarded to have a significant effect on readings.
Smooth function Readings coming from sensor are interpolated in a inversely proportional lineal function. With the purpose of testing, a series of tests were conducted. Sensor was placed in the testing container and performed readings for full pouring cycles—water was poured in the empty test container until full and later drained. Using gOwl plugin for Grasshopper, returned values were recorded into a .xls file. Results are as follows.
400 Figure 5 Mapped values for full pouring cycle test.
300
200
100
0
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RAIN-WATER HARVESTING SYSTEM —INPUT SUBSYSTEM
Due to the analog nature of the chosen sensor, generated voltage values are more prone to spurious readings—erratic values which deviate form normal point distribution. (Mitchell, 2007)
400
400
300
300
200
200
100
100
0
0
Figure 7 & 8 Point distribution for pouring cycle.
Figure 6 Sensor under pouring cycle test
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RAIN-WATER HARVESTING SYSTEM —INPUT SUBSYSTEM
Figure 7 shows point distribution for returned voltages when values are transformed by smooth function. Figure 8 shows returned voltages for the same experiment after values are passed through smooth function. Values read directed from sensor have more discontinuities in their normal distribution, having considerable gaps in the progression of values. Moreover, erratic values may induce error when fed into a logical function. In order to tackle this potential error, an interpolation function is implemented to average readings considering a 14 element sample. After transformation, data is fed to a logical comparison function to test against predefined threshold. Nonetheless, a side-effect from smooth function is observed. For every given reading, values are transformed by means of calculating a ‘smooth’ distribution taking into consideration N number of samples —previous sensor readings. Therefore, true values are ‘delayed’ in distribution, taking a period of approximately 2500 milliseconds to be reflected.
Figure 9 Sensor under test. Signal controls a volume animation feedback and actuation of virtual model
Figure 10 shows reading for the first 4.5 seconds of a pouring cycle. Values in the far right column reflect true readings as produced by the sensor. Between 1500 and 2000 milliseconds, returned voltage is an average of 371 mΩ. However, this value is reflected on the smooth distribution until 4500 milliseconds, showing a 2500 milliseconds delay. Considering general behaviour observed in Figure 7 and 8, delay is expected to be shorter in middle reading bands —in values ranging from 330mΩ to 130mΩ—
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RAIN-WATER HARVESTING SYSTEM —INPUT SUBSYSTEM
Smooth Time voltage reading (milliseconds) mΩ
True voltage reading mΩ
—Smooth function—
—Raw data—
500
387
387
1000
386
379
1500
385
373
2000
384
369
2500
382
363
3000
380
358
3500
378
353
4000
375
348
4500
372
341
Figure 10 Returned voltage values. Data recorded directly from sensor and after being smoothed is compared
According to official specifications, the sensor has an active sensitive length of 218.4 mm. A total gradient of 333 mΩ is remapped to length numeric domain by means of an inverse lineal distribution. Formulas are as follows.
Y − Y1 = m(X − X1 ) Y = −1.4880x + 389
Output values are used throughout the script to test actual liquid height against predetermined decision thresholds. As means of visual feedback, volume is calculated using output values for water column height. Resulting values are used to render an extrude component. Additionally, hard volume value is displayed in viewports on top of feedback animation.
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RAIN-WATER HARVESTING SYSTEM —INPUT SUBSYSTEM
Integration A further integration of the script was made to control both virtual and physical models in real time. Data is recorded in a list-type structure. Values are then evaluated against a predefines threshold—representing a height in liquid volume—Once evaluated, decision is stored as a boolean value. Data structure is continuously sorted and first item is retrieved. In this manner, once the decision function has triggered the “deploy” event, structured will be prevented to retract immediately. This is specially useful if erratic readings are still present. System is therefore capable of being aware of environment and take decisions based on this information.
Further improvement Design in this stage of the process was validated in a simple container to test how effective is the chosen sensor as a measuring stick in a Standard Rain Gauge Model. A further step in this work should include sensor deployment in exterior spots. Thus an enclosure should be designed to emulate further the casing of commercial rain gauges.
References GROISMAN, P. Y. & LEGATES, D. R. 1994. The Accuracy of United States Precipitation Data. Bulletin of the American Meteorological Society, 75, 215-227. INTERLINK 2010. FSR 400 Series Square Force Sensing Resistor. MITCHELL, H. B. 2007. Multi-sensor data fusion: an introduction, Springer Verlag. NOAA, N. W. S. W. F. O. 2010. Standard Rain Gauge [Online]. Available: http:// www.crh.noaa.gov/iwx/program_areas/coop/8inch.php [Accessed December 2nd 2011]. O'SULLIVAN, D. & IGOE, T. 2004. Physical computing: sensing and controlling the physical world with computers, Thomson. SIEMENS 2009. Ultrasonic transducers Echomax XRS-5. In: INSTRUMENTS, S. (ed.). XYLEM 2010. WL400 Water Level Sensor. Water.
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