D
Journal of Earth Science and Engineering 3 (2013) 545-553
DAVID
PUBLISHING
Development of Multilevel Meteorological Data Acquisition System for Radiation Emergency Decision Support D.N. Krishnakumar, A. Bagavath Singh, Anjukumari, R. Baskaran, M.T. Jose and B. Venkatraman Radiological Safety Division, Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamilnadu, India
Received: June 15, 2013/Accepted: July 20, 2013/Published: August 25, 2013. Abstract: This paper describes a development of multilevel meteorological data acquisition system implemented at Kalpakkam coastal site for atmospheric dispersion and model validation studies. Meteorological data are one of the most important inputs into any air dispersion model. As a part of atmospheric dispersion modeling studies and developing a methodology to forecast the site-specific dispersion characteristics, the real time monitoring of meteorological parameters assumes significance. This is achieved by erecting met towers instrumented at multilevel and single level at different locations with sensors for measuring various meteorological parameters. Real-world data logging applications involve not only just acquiring and recording signals, but also combination of offline analysis, display, report generation and data sharing. This paper covers development of low cost compact MMDAS (modular meteorological data acquisition system), its performance evaluation, field deployment test and data comparison analysis with fast response and high accuracy internationally acclaimed sonic anemometer. The system is based on embedded modules from Advantech and is designed to acquire analogue and digital signals from a multilevel instrumented met tower. The collected data are transferred from remote base station to central server for storage and further processing using wireless interface. MMDAS has many advantages like cost effectiveness, less complex signal conditioning electronics and easy maintenance. This system has good application during radiation emergency as well as site specific meteorological data collection and model validation studies. Key words: Radiation emergency, meteorological data acquisition, MMDAS, dispersion modeling, sonic anemometer.
1. Introduction Analysis of the environmental radiological impact under design–based probable accidental releases for any proposed nuclear facility is a regulatory requirement and therefore, as a part of it, a study of atmospheric dispersion of radio nuclides and the consequent dose to the public becomes important [1] The meteorological condition at Kalpakkam, located at east coast of India at 12.501° N and 80.101° E, like any coastal site, is non-stationary and Corresponding author: A. Bagavath Singh, scientific officer, main research fields: meteorological measurements micrometeorology, atmospheric boundary layer studies, application of meteorological data methods in atmospheric dispersion estimates, environmental impact analysis, microclimate studies. E-mail:
[email protected].
non-homogeneous due to thermally driven land-sea breeze circulation. This gives rise to a variety of complex atmospheric dispersion conditions. The measurement of various meteorological parameters along with the radiation level assumes significance for the local scale and micro scale site specific characterization in order to ensure that annual dose commitment to the general public residing at the site boundary (1.6 km distance) is well within the regulatory limits, i.e., 1 m Sv/yr [2]. These parameters have significance for predicting the affected areas in case of radiation emergencies [3]. A thorough understanding is possible only with study of real time and long-term measurements of the various met parameters. Three 50 m lattice type guyed multilevel
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meteorological towers, one 16 m tower, one 9 m single level mast equipped with sensors from NRG Systems, Inc and two sonic anemometers and three AWS (Automatic weather stations) supplement the real time met measurements at Kalpakkam required for local and micro scale dispersion modeling studies [4]. These 50 m towers are located one near the coast and the other two at 6-7 km distance and one at 14 km from the coastline respectively. The 9 m mast is deployed at Latitude 12.36° N, Longitude 80.15° E inside the IGCAR (Indira Gandhi Centre for Atomic Research) campus. These towers are instrumented with analogue wind vane for wind direction and cup anemometer for wind speed, RTD (resistance temperature detector) for temperature, hygrometer for relative humidity, and barometric sensor for pressure and indigenously developed Autonomous Gamma Dose Logger [5] for radiation. These meteorological parameters and radiation are normally measured by mounting the suitable sensors on booms attached to masts or towers at required heights. Apart from these, three sonic anemometers are also used to collect data for experimental purpose. Though 2 m and 10 m are standard heights of measurements, multilevel measurements are also common. The present data acquisition system is based on dedicated hardware like microprocessor based intelligent sensor to computer interface modules (ADAM 4000 series from Advantech [11]) and supporting signal conditioning embedded modules available from different manufactures. The application software is developed in DASY Lab (Data Acquisition System Laboratory) software platform and can be resided in a computer and thus the complete system would be a dedicated unit needing specialized expertise in handling the same [6]. Also, the signal processing and acquisition through serial communication port is done by using different modules and electronic circuits, which made the system complex, though well organized. Apart from radiation emergency decision support systems
meteorological data acquisition systems have wide applications in renewable energy systems especially in PV (photovoltaic) installation where whether data are being collected continuously for evaluation purposes [7]. An autonomous remote WDAS (weather data acquisition system), using PIC (programmable interface controller) microcontroller interface and collecting data to server using RF (radio frequency) transmitter is widely used for this purpose [8]. But this type of system lacks local storage and data loss may occur in case of link failure to the central server. Another useful application of meteorological data acquisition system is to generate climatology for the specific terrain [9]. This requires meteorological data from many stations in a tropical environment over a period of time and hence it necessitates the continuous operation of meteorological data acquisition systems in the field. Commercially available sonic anemometers, meteorological data loggers and AWS are also used at various locations to collect met data from masts. Such commercially available data loggers normally come as a package like data logger and a set of sensors for single level. These types of data loggers usually do not support multilevel data logging. Moreover, these data loggers support sensors of the same make and user would face difficulties for repairing the system in case of failure. Also, these types of commercially available data loggers do not give provision to measure radiation level at the location. Hence, a low cost, compact and scalable MMDAS (modular meteorological data acquisition system) is developed in Radiological Safety Division, IGCAR. The primary aim of the development is to build a meteorological and radiation data acquisition system which is scalable, versatile, more general in nature and supports multilevel data logging from different type of sensors with user friendly and customized user interface. This paper discusses the design and development of compact modular meteorological data acquisition system based on using APAX modules [11]
Development of Multilevel Meteorological Data Acquisition System for Radiation Emergency Decision Support
i.e., APAX (Advantech Programmable Automation Controller) modules uses open development architecture with embedded design which integrates, processing and networking in a modular system design. The system is capable of acquiring analogue and digital signals from different type of sensors through different add-on modules. The system does not need pre-conditioning of the incoming signal from the sensors and hence easy to maintain due to the absence of complex signal conditioning circuits. Also, the system supports dual power supply and can be operated in 24 Volt Direct Current, making it portable and useful in the field for short term measurements. These types of systems have good role in radiation emergencies as it instantly feeds the met data to the decision support systems through wireless interface. This type of data acquisition system attached to the movable mast can be highly useful for acquiring data in different locations during wind validation experiments where data from met tower at different locations are to be collected simultaneously. Though MMDAS is capable of acquiring data simultaneously from all the sensors from a 50 m met tower, the field evaluation is carried out with sensors mounted at single level, i.e., 9 m, as sonic anemometer provides wind parameters and temperature at the same height. The expediency and redundancy of the system is further increased by providing wireless interface to central server in addition to local data storage. The design and development of the system and comparison of the data logged by the system with internationally acclaimed sonic anemometer is presented in this paper.
through surge suppressor boards. These surge suppressor boards consist of gas discharge tubes and voltage suppressor diodes for protection against lightning and voltage surges. The signal inputs are directly given to the respective APAX modules. The block diagram of the MMDAS base station is shown in Fig. 1. Though each input module of the system is capable of acquiring many channels simultaneously, single channel in each add-on module is shown in the figure for simplicity. A cup anemometer is commonly used for wind speed measurements. A four-pole magnet induces a sine wave voltage into a coil producing an output signal (80 mV peak to peak) with frequency proportional to wind speed. The output signal from the anemometer requires some form of preparation (conditioning) before they can be digitized and fed to digital input card, APAX 5080, as the card accepts only digital pulses of ±10 V. Hence, an external indigenously developed signal conditioning circuit is used for all sensors connected to digital input card that make the output suitable for reading through it. The circuit consists of two comparators (LM324) operating in a Schmitt trigger mode with a lower discriminator cut-off of 40 mV to avoid noise interference from the sensor. The circuit operates with almost any type of input waveform, and it gives a pulse-type output. Wind direction sensor provides an output voltage directly proportional to the wind direction when a constant DC excitation voltage (5 V) is applied to the
2. Design and Development of MMDAS The meteorological measurement system consists of meteorological sensors installed on the met tower and a data acquisition hardware and controlling application software. The hardware consists of actual sensors, mounted at various levels on the tower and connected to respective individual signal conditioners
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Fig. 1
Block diagram of MMDAS.
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Development of Multilevel Meteorological Data Acquisition System for Radiation Emergency Decision Support
potentiometer of the sensor. The output is directly fed to APAX 5017 analogue input add-on card of MMDAS. Temperature and relative humidity are measured by using a combined sensor probe. Humidity is measured with the plug-in HygroClip digital probe (HYGROMER C94 capacitive humidity sensor) and temperature is measured by a separate fast response RTD probe (Pt100 1/3 DIN RTD) located next to the humidity sensor. The output of the RTD probe is directly fed to the RTD Module APAX 5013 and output voltage of the relative humidity sensor is fed to the analogue input add-on card APAX 5017. Pressure sensing is provided by barometric sensor, this operates from 500 mbar to 1,100 mbar. The analogue voltage output is directly fed to the APAX 5017 for data acquisition and storing. Precipitation sensing is done with rain gauge which uses a proven tipping bucket mechanism for simple and effective rainfall measurement. The output “Tic” or pulse is fed to the digital input card, ADAM 5080, through an electronic conditioning circuit for removing the noise from the sensor. Environmental radiation is calculated by acquiring the output pulses from the indigenously developed Autonomous Gamma Dose Logger [5] through the digital input card, APAX 5080. The category and number of sensors equipped with MMDAS are summarized in Table 1 with respective calibration factors applied to it. 2.1 Design and Integration of Base Station Hardware The development of the base station system is classified into two, i.e., design and integration of hardware and development of system application software. Hardware of MMDAS is based on Advantech APAX controller and add-on modules connected to the APAX 5571 system. APAX system stands for Advantech Programmable Automation Controllers. These modules used in a modularity design approach for multiple functions with additional channel acquisition capabilities. APAX-5000 series versatility, flexibility and scalability can fully satisfy
complicated automation needs and reduce the effort of the engineers and as a result reducing the development time. It also supports Microsoft dot net frame work so that the data acquisition through the add-on cards would be done in high level language. The main controller has a back plane and the user has options to insert the cards of his own choice depending on the requirements, i.e., the type of signal to be processed (Analogue, digital, RTD etc.). Each input channel in these add-on cards can be individually programmed to log signal from the sensors. This facilitates the system work independent of the make/model of sensors with which it is connected. The calibration factors for each channel are stored in the SD (secure digital) memory card of the system. The main advantage of this system is that no preliminary signal conditioning circuit is used for incoming signals except for digital signals. 2.2 Application Software for MMDAS Base Station The data acquisition from the sensors, applying calibration factors and storing the data in the SD memory card, file archiving and management and instantaneous display of data in the screen (if monitor is connected) are controlled by application software developed in VB.Net platform. Application software also provides sensor selection capabilities, archiving data, visualization and commonly used ASCII text file data storage format. In order to have better understanding and easy extendibility, the full software is organized in different modules and functions. COATS (commercially off the shelf) method is followed in the development. The “DLL” files provided by vendor are modified suitably for acquiring the signals through add-on cards. Fig. 2 depicts the software flow chart for MMDAS system. Each add-on card is programmed to acquire pulses for every 10 s interval through all its channels. The average CPS (counts per second) is calculated and stored in a temporary variable after applying respective calibration factors for connected sensors. This data were updated in the user interface front screen
Development of Multilevel Meteorological Data Acquisition System for Radiation Emergency Decision Support Table 1
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Various categories and numbers of sensors connected to MMDAS system from 50 m meteorological tower.
Parameter sensor type Wind speed, NRG 40#C Wind direction, NRG 200P Temperature, Rotronics MP 100H Relative humidity, Rotronics MP 100H Pressure, RM Young 61302 Precipitation, RM Young 52203 Radiation Autonomous Gamma Dose Logger
No. of sensors 5 5 5 3 1 1 1
Calibration f(Hz) × 0.765 + 0.35 Vout (Volt) × 72 100 × Vout (Volt) 120 × Vout (Volt)+ 500 0.1 × (no. of tic) f (Hz) × 55.63
Unit m/s Deg Deg C % mbar mm nSv/h
for a day is 14 kb and hence SD card of 8 GB would store data for years. The base station is programmed to send the data to the master station in every 10 min. 2.3 Configuration Software for MMDAS
Fig. 2
Software flow chart for MMDAS base station.
in every 10 s. The CPS value estimated for each channel is further averaged for 10 min and corresponding standard deviation is calculated. The “SSSYYMMDD.TXT” where, SSS represents station code, YY is the year of acquisition, MM is the month of acquisition and DD is the date of acquisition average value and corresponding standard deviation for all the channels are appended to the day file with time stamp. The file name is so chosen that it is unique and self-explanatory and is given by. This method has an advantage that it is easy to identify the data file for a particular day from a bunch of data. The size of file
This configuration software facilitates the user to input the calibration factors for different sensors and stores in a text file. Main program is developed such way that it reads appropriate engineering units, offset values, calibration coefficient as input files and convert them accordingly during the data acquisition process. The method has another advantage that it automatically fetches the factors in case of power on-off conditions. The software is so developed that during the real time running of the program, the data acquisition is automatically initiated and continued. This configuration software is a separate program developed for inputting and storing engineering conversion factors for the sensors in a file. The calibration equations have the general form: yi = mi xi+ ci th where, yi is the i sensor output in physical units, xi is the ith sample and mi, ci are calibration constants for each sensor. This configuration software takes “m” and “c” values for different sensors and stores in a text file. Main program read these factors from the input configuration text file and apply correction and conversion accordingly. The method has another advantage that it automatically fetches the factors in case of power on-off conditions. 2.4 Wireless Transmission to Central Server A provision to transmit the data files to central server using RF transceivers has been implemented
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Development of Multilevel Meteorological Data Acquisition System for Radiation Emergency Decision Support
using spectra 2,430 modules. The RF Tx/Rx modules can be configured in point to point configuration or Master to Multipoint configuration. Due care has to be taken to ensure that the line of sight criterion is met when long distance data transmission and reception is involved. As is known, the data communication through RF is subject to the terrain topology, antenna type and gain, height of the antenna installation and partly on the weather conditions. The data acquisition software installed at a particular station sends the data to the master in every 10 min interval with the prefix of the station code. The server decodes the prefix and stores the data in the respective file. The module has been configured for point to point communication. This setup can be easily extended to master to multi point in case of more number of base stations.
3. Server Application Program The server side program is developed in VB.NET to collect data from the remote base stations and store the data in the file located in server computer. The different remote stations are identified from their station codes. The program is so developed with metafile information such as prefix station code with data being send which will help the server indentify the station data and stores the met data in the respective base station file. The data from the file can be read and plotted in real time for further analysis. This data can be inputted to decision support systems in the required format supported by dispersion models. This type of systems would give complete radiological status in case of radiation emergencies so as to implement rapid action plans.
4. Field Deployment and Performance of the System The performance evaluation of the system was carried out by erecting a meteorological tower of 9 m at Latitude 12.36°N, Longitude 80.15° E instrumented with wind vane, wind cup (NRG systems), temperature and relative humidity sensors (Rotronic Inc) in single
level. The compact MMDAS is used to acquire data from diverse sensor inputs and stored data in standalone memory card. The acquired data with the system are compared with the sonic anemometer (RM Young, Model No. 81000) installed at a distance of 2 m from the meteorological tower. The sonic anemometer has three opposing pairs of ultrasonic transducers that are arranged so that measurements are made through a common volume. It measures three dimensional wind velocity and speed of sound based on the transit time of ultrasonic acoustic signals. Sonic temperature is derived from speed of sound which is corrected for crosswind effects. The authenticity of the data collected by MMDAS with instrumented mast is evaluated and field tested by comparing the wind direction, wind speed and temperature given by ultra sonic anemometer which is installed on nearby tripod mast. The system is deployed in the field for comparative studies with ultra sonic anemometer for seven days [10]. The minimum threshold, accuracy and resolution of MMDAS and sonic anemometer are summarized in Table 2. It is evident from Table 2 that the accuracy and resolution of the sonic anemometer is found to be better than that of the MMDAS. Hence, sonic anemometers are more useful where there is a need for measurement of small but non zero, wind, over a period of time. However, the current resolution of MMDAS is adequate for dispersion modelling studies. The wind direction pattern from the ultra sonic anemometer and the mechanical wind vane sensor connected to the MMDAS with respect to time of study is represented in Fig.3a and showed excellent agreement with each other. The correlation between the collected data from both the systems is analyzed by making a linear regression shown in Fig. 3b and estimating the correlation coefficient. The correlation coefficient (r) of the linear regression is found to be 0.9977 which indicates that the data obtained from mechanical wind wane sensor connected to MMDAS are in good agreement with sonic anemometer data.
Development of Multilevel Meteorological Data Acquisition System for Radiation Emergency Decision Support Comparison of starting threshold, accuracy and resolution of MMDAS and sonic anemometer system. Sensors
MMDAS
Wind speed Wind direction Temperature Wind speed Wind direction Sonic temperature
Sonic anemometer
Starting threshold 0.35 m/s -
+0.1 m/s +3.6 deg ±0.2 °C ±0.01 m/s ±2 degree ±2 °C
-
However, a maximum deviation of ±10% is observed for the wind cup sensor data with respect to sonic anemometer. The difference in reading is contributed by the differences in sensor accuracies, sampling time and also difference in measurement technologies used in both the sensors [9]. The wind speed pattern is also compared with the respective sonic anemometer data and the comparison pattern for both the systems with respect to the study period is found to be well correlated as shown in Fig. 4a. The linear regression of data from sonic anemometer and the wind cup sensor connected to MMDAS is shown in Fig.4b. The correlation coefficient (r) obtained from the plot is 0.9902 which depicts that data obtained from wind cup sensor connected to MMDAS are excellent agreement with sonic anemometer data. However, a maximum variation of +12% was observed for wind speeds less than 0.5 m/s. This higher deviation at very low wind speeds is due to the fact that the presence of minimum threshold for mechanical wind speed sensors and no such threshold exists for sonic anemometer. The average variation is found to be less than +5% for wind speeds above 0.5 m/s. This implicates that sonic anemometer is well suited for very low near calm wind speed measurements. Comparison pattern of data obtained from sensor connected to MMDAS and sonic anemometer over the time period of study is found to be identical as shown in Fig.5a. The linear regression plot of the data is shown in Fig. 5b. The correlation coefficient (r) was estimated to be 0.989 and the average deviation of MMDAS data from sonic anemometer is found to be
Resolution of system 0.1m/s 1 deg 0.1 °C 0.01m/s 0.1degree -
Accuracy
0.01 m/s 350
Sonic Data (Deg)
System
300 250 200 150 100 50 0 350
MMDAS Data(Deg)
Table 2
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--
Date
250 200 150 100 50 12/11/20 12/11/21 12/11/21 12/11/22 12/11/23 12/11/23 12/11/24
--
Date
(a)
(b) Fig. 3 (a) Comparison of wind direction data for MMDAS and ultra sonic anemometer; (b) linear regression of wind direction data for MMDAS and ultra sonic anemometer.
less than +2%. This indicates that the temperature measured by the sensor connected to MMDAS is in good agreement with temperature measured by sonic anemometer. In addition to the above mentioned meteorological data, viz., wind direction, wind speed, temperature, the MMDAS has provision for multilevel data acquisition of relative humidity, pressure, radiation,
Development of Multilevel Meteorological Data Acquisition System for Radiation Emergency Decision Support
552 3.5
Sonic Data ( oC)
Sonic Data (m/s)
3.0 2.5 2.0 1.5 1.0 0.5
30
24
0.0
12/11/2012/11/2112/11/2112/11/2212/11/2312/11/2312/11/24
12/11/2012/11/2112/11/2112/11/2212/11/2312/11/2312/11/24 3.0
Date
2.5 2.0 1.5 1.0 0.5 0.0 12/11/2012/11/2112/11/2112/11/2212/11/2312/11/2312/11/24
Date
32
MMDAS DATA ( oC)
MMDAS Data (m/s)
3.5
30 28 26 24 12/11/2012/11/2112/11/2112/11/2212/11/2312/11/2312/11/24
Date
Date
(a)
(a)
(b) Fig. 4 (a) Comparison of the wind speed data for MMDAS and ultra sonic anemometer; (b) linear regression of the wind speed data for MMDAS and ultra sonic anemometer.
(b) Fig. 5 (a) Comparison of the temperature from MMDAS and ultra sonic anemometer; (b) linear regression of the temperature from MMDAS and ultra sonic anemometer.
etc. through different add-on modules provided. Since met data from ultra sonic anemometer are restricted to met parameters like wind speed, wind direction, temperature in single level, i.e., 9 m, comparison studies have been carried out by installing the sensors at the same level and acquiring the data through MMDAS. The other parameters like radiation, pressure and relative humidity of the MMDAS are checked using hand held measuring devices in regular intervals. The deviation in the readings obtained in the system from the standard ultrasonic anemometer is attributed to the
errors in the sensors and not with the conditioning and controlling electronics associated with the MMDAS.
5. Conclusions A compact modular meteorological data acquisition system with customized user interface that supports multilevel data logging from different type of sensors is developed. The system uses Advantech APAX series of modular add-on cards for analogue and digital data acquisition. The data acquisition, file archival and management are controlled by application software developed in VB.NET. The
Development of Multilevel Meteorological Data Acquisition System for Radiation Emergency Decision Support
system derived field data were compared with high resolution sonic anemometer measurements at single level. It is found that data from sensors connected to MMDAS are in good agreement with sonic anemometer. The MMDAS system costs approximately 1/3 of the commercially available multilevel data loggers. The main advantages of the system are cost effectiveness; support multilevel data logging, user friendly software interface and also a direct replacement for the present system which has complex analogue electronics based signal conditioning circuits. The system supports dual power supply and can be operated in 24 V DC, making it portable and also useful in the field for short term field experiments. The expediency and redundancy of the system can be further increased by providing wireless interface to central server in addition to local data storage which would further help in rapid decision making in case of radiation emergencies.
Acknowledgments The authors would like to thank Shri. S.A.V. Satyamurthy, Director, EIRSG (Electronics, Instrumentation and Radiological Safety Group) for his support. The authors would also like to thank to Shri Gopalakrishnan and Surya Prakash for their valuable suggestions and support for carrying out the work.
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