Work-in-Progress: High-Frequency Environmental Monitoring Using a Raspberry Pi-Based System

Basu, Debarati; Purviance, John Stanton Goldstein; Maczka, Darren K.; Brogan, Daniel S.; Lohani, Vinod K.

doi:10.18260/p.25103

Cited by 6 publications

(9 citation statements)

References 8 publications

(3 reference statements)

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“…Our preliminary results from the numerical solutions of the isotropic 3dMHD equations (i.e., no mean magnetic fields) yield multiscaling similar to those obtained from the 3dMHD Eqs. driven by large-scale forces [29]. Although no such numerical results exist for the case with a mean magnetic field, it will presumably be true for such a situation also.…”

Section: Energy Spectra For Incompressible Fluidsmentioning

confidence: 96%

Universal properties of three-dimensional magnetohydrodynamic turbulence: do Alfvén waves matter?

Basu¹,

Bhattacharjee²

2005

J. Stat. Mech.

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We analyse the effects of the propagating Alfvén waves, arising due to non-zero mean magnetic fields, on the nonequilibrium steady states of three-dimensional (3d) homogeneous Magnetohydrodynamic (MHD) turbulence. In particular, the effects of Alfvén waves on the universal properties of 3dMHD turbulence are studied in a one-loop self-consistent mode-coupling approach. We calculate the kinetic-and magnetic energy-spectra. We find that even in the presence of a mean magnetic field the energy spectra are Kolmogorov-like, i.e., scale as k −5/3 in the inertial range where k is a Fourier wavevector belonging to the inertial range. We also elucidate the multiscaling of the structure functions in a log-normal model by evaluating the relevant intermittency exponents, and our results suggest that the multiscaling deviations from the simple Kolmogorov scaling of the structure functions decrease with increasing strength of the mean magnetic field. Our results compare favourably with many existing numerical and observational results.

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Section: Energy Spectra For Incompressible Fluidsmentioning

confidence: 96%

Universal properties of three-dimensional magnetohydrodynamic turbulence: do Alfvén waves matter?

Basu¹,

Bhattacharjee²

2005

J. Stat. Mech.

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“…The LEWAS field site draws power from both the grid and a pair of photovoltaic panels and connects to the campus wireless network through a high-gain antenna in order to transmit measured data from instruments. At present, the LEWAS has the following four stages 12,13 ( Figure 2): 1) Instruments that are used for environmental monitoring including an acoustic Doppler current profiler, a water quality sonde and a weather station taking measurements every 1-3 min., 2) data processing occurring locally on a Raspberry Pi, 3) data storage on a remote server and 4) end user interfaces (local and remote), which enable users to visualize LEWAS data, e.g., the Online Watershed Learning System (OWLS) (www.lewas.centers.vt.edu/dataviewer) for research and education. The LEWAS has been designed as a flexible and expandable environmental monitoring system that can easily be adapted and deployed in a wide variety of settings.…”

Section: Background Of the Lewas Labmentioning

confidence: 99%

“…The Raspberry Pi also consumed less power than the cRIO did and had flexibility in being compatible with several programming languages 12 . At this time, the lab also transitioned from using LabVIEW to python as the language for processing the data, since python is an open source language.…”

Section: ) Python On a Raspberry Pimentioning

confidence: 99%

“…At this time, the lab also transitioned from using LabVIEW to python as the language for processing the data, since python is an open source language. In the summer of 2014, REU fellow J. Purviance was able to develop code in python to acquire data from the three main instruments and store it in a local database on the Raspberry Pi 12 . This system was further improved by lab members into the current four stages.…”

Section: ) Python On a Raspberry Pimentioning

confidence: 99%

“…The project of the mentee of 2014 was related to the development of the processing and storage stage of the LEWAS lab using python programming on a Raspberry Pi. The work has resulted into one conference publication 12 .…”

Section: First Author's Experience: Planning and Setting Timelines Fomentioning

confidence: 99%

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Combined Contribution of 12 REU Students to the Development of the LEWAS Lab

Basu

McDonald

Brogan

et al.

2016 ASEE Annual Conference &Amp; Exposition Proceedings

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Ms. Basu, is a PhD student in Engineering Education, advised by Dr. Lohani in the LEWAS Lab. She holds BS and MS in Computer Science and Engineering. For her dissertation, she is interested in analyzing user tracking data to examine students' learning of environmental monitoring concepts facilitated by a cyberlearning system. As a graduate teaching assistant, she has experience in teaching engineering problem solving and design process to freshman students in a project based environment at VT. She has experience in organizing and administering NSF/REU site for Interdisciplinary Water Sciences and Engineering. She has taken initiative in modifying the assessment instrument for this REU site and is gathering experience in the field of assessment and evaluation. She has experience in developing LEWAS-based modules and working with first-year curriculum. She also mentors undergraduates from CS, engaged in the expansion of LEWAS. Mr. Walter McDonald, Virginia TechWalter McDonald is a Ph.D. Candidate, jointly advised by Drs. Dymond and Lohani, in the CEE program at Virginia Tech with a focus in water-resources engineering. He received a B.S. in civil engineering from Texas Tech University and a M.S. in civil engineering from Texas A&M University. He has had extensive training in hydrology and currently works in the LEWAS lab, where he conducts urban hydrology research. He has developed and implemented curricula for introducing the LEWAS into multiple courses at Virginia Western Community College and Virginia Tech. He also has international collaboration experience in first-year course development, engineering education research, and real-time watershed monitoring. Mr. Daniel S Brogan, Virginia TechDaniel S. Brogan is a doctoral candidate in the Department of Engineering Education at Virginia Tech. Working under Dr. Vinod K. Lohani, he has had a leading role in the development and implementation work of the Learning Enhanced Watershed Assessment System (LEWAS) Lab for more than three years. His dissertation research involves the development and classroom integration of the Online Watershed Learning System (OWLS), a guided, open-ended learning environment that is driven by HTML5 (http://www.lewas.centers.vt.edu/dataviewer/) and serves as a user interface to the LEWAS Lab. Prior to his time at Virginia Tech, he has a background in remote sensing, data analysis and signal processing from his time at the University of New Hampshire, where he received his bachelor's and master's degrees in electrical engineering. Darren K Maczka, Virginia Tech Department of Engineering EducationDarren Maczka is a Ph.D. student in Engineering Education at Virginia Tech. His background is in control systems engineering and information systems design and he received his B.S. in Computer Systems Engineering from The University of Massachusetts at Amherst. He has several years of experience teaching and developing curricula in the department of Electrical and Computer Engineering at Virginia Tech. The Learning Enhanced Watershed Assessment Syste...

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