• Assessment of the Contribution of Traffic Emissions to the Mobile Vehicle Measured PM2.5 Concentration by Means of WRF-CMAQ Simulations

      Molders, Nicole; Tran, Huy N.Q. (Alaska University Transportation Center, Fairbanks Northstar Borough, 2012)
    • Assessment of tight gas sands in Cook Inlet Basin

      Patel, Kanhaiyalal U.; Ogbe, David O.; Zhu, Tao; Patil, Shirish L. (2005-05)
      The Cook Inlet Basin is the source for all of the natural gas used in south-central Alaska. The estimated ultimate recovery from existing Cook Inlet gas fields is approximately 8.5 trillion cubic feet (tcf) and the proven reserves remaining on January 1, 2004 were 1.8 tcf. It will be difficult to meet the peak demand for gas in south-central Alaska after 2009. Cook Inlet Basin contains vast quantities of unconventional gas resources in tight sands. Resources-in-place and producible gas reserves from the tight sands are unknown. It is likely that these tight sands will be developed as additional gas reserves and will be produced along with the high permeability conventional gas reserves in order to meet both local and export demands. The objectives of this study are to quantify the distribution of tight gas sands; to estimate the resources in place and producible gas reserves in the Cook Inlet Basin; and to predict the post-stimulation gas production. Rate transient analysis, well log analysis and reservoir stimulation analysis were therefore conducted on selected key tight sand wells. Results indicate that the tight gas can play an important role in meeting south-central Alaska's gas demand beyond 2009.
    • An Atmospheric carbon monoxide transport model for Fairbanks, Alaska

      Carlson, Robert F.; Fox, John (University of Alaska, Institute of Water Resources, 1976-06)
      A comprehensive computer model of atmospheric carbon monoxide transport has been developed for Fairbanks, Alaska. The model, based on a finite element method computational scheme, accents input from specified vehicle traffic parameters inc1uding miles per day, number of cold starts, and total idle time. The carbon monoxide concentrations are calculated for specified time intervals at numerous points throughout the urban area. A test of the model against the data of January 22, 1975, indicates a good correspondence. Extremely high carbon monoxide concentration were calculated at an unmeasured point down wind of the business district. The model should prove useful for a number of community needs including parking management, planning and zoning, episode strategy planning, and carbon monoxide forecasting.
    • Attenuation and Effectiveness of Triclopyr and 2, 4-D Along Alaska Highway Rights-of-Way in a Continental and a Coastal Subarctic Environment

      Barnes, David; Seefeldt, Steve (Alaska University Transportation Center, 2009-12)
      After more than 20 years of only mechanical brush cutting, ADOT&PF evaluated the use of herbicides to manage vegetation that interferes with line-of-sight and maintenance of the roadway. While researchers have investigated herbicide effectiveness and attenuation in more-temperate climates, little study has focused on cold regions. The purpose of this project was to measure the effectiveness and attenuation of two different selective auxin-type herbicides, 2, 4 dichlorophenoxyacetic acid (2,4-D), and 3,5,6-trichloro-2-pyridinyl acetic acid (triclopyr) in two subarctic climates; an extremely cold continental climate and a maritime climate. Conclusions from this study will aid the ADOT&PF in developing a plan for controlling vegetation along highway rights-of-way in Alaska.
    • Attenuation and Effectiveness of Triclopyr and 2,4-D Along Alaska Highway Rights-of-Way in a Continental and a Coastal Subarctic Environment

      Barnes, David L.; Seefeldt, Steve (Alaska University Transportation Center, Alaska Department of Transportation and Public Facilities, 2009)
    • Attitude determination for small satellites using gps signal-to-noise ratio

      Peters, Daniel; Raskovic, Dejan; Hawkins, Joseph; Thorsen, Denise (2014-05)
      An embedded system for GPS-based attitude determination (AD) using signal-to-noise (SNR) measurements was developed for CubeSat applications. The design serves as an evaluation testbed for conducting ground based experiments using various computational methods and antenna types to determine the optimum AD accuracy. Raw GPS data is also stored to non-volatile memory for downloading and post analysis. Two low-power microcontrollers are used for processing and to display information on a graphic screen for real-time performance evaluations. A new parallel inter-processor communication protocol was developed that is faster and uses less power than existing standard protocols. A shorted annular patch (SAP) antenna was fabricated for the initial ground-based AD experiments with the testbed. Static AD estimations with RMS errors in the range of 2.5° to 4.8° were achieved over a range of off-zenith attitudes.
    • AUTC Newsletter v1 n1

      Alaska University Transportation Center (Alaska University Transportation Center, University of Alaska Fairbanks, 2007-04)
    • AUTC Newsletter v2 n1

      Alaska University Transportation Center (Alaska University Transportation Center, University of Alaska Fairbanks, 2008-06)
    • AUTC Newsletter v2 n2

      Alaska University Transportation Center (Alaska University Transportation Center, University of Alaska Fairbanks, 2009-03)
    • AUTC Newsletter v3 n1

      Alaska University Transportation Center (Alaska University Transportation Center, University of Alaska Fairbanks, 2009-05)
    • AUTC Newsletter v3 n2

      Alaska University Transportation Center (Alaska University Transportation Center, University of Alaska Fairbanks, 2010-02)
    • AUTC newsletter v4 n1

      Alaska University Transportation Center (Alaska University Transportation Center, University of Alaska Fairbanks, 2010-05)
    • AUTC Newsletter v4 n2

      Alaska University Transportation Center (Alaska University Transportation Center, University of Alaska Fairbanks, 2010-12)
    • AUTC Newsletter v5 n1

      Alaska University Transportation Center (Alaska University Transportation Center, University of Alaska Fairbanks, 2011-05)
    • AUTC Newsletter v5 n2

      Alaska University Transportation Center (Alaska University Transportation Center, University of Alaska Fairbanks, 2012-04)
    • AUTC Newsletter v6 n1

      Alaska University Transportation Center (Alaska University Transportation Center, University of Alaska Fairbanks, 2012-06)
    • AUTC Newsletter v6 n2

      Alaska University Transportation Center (Alaska University Transportation Center, University of Alaska Fairbanks, 2013-01)
    • AUTC Newsletter v7 n1

      Alaska University Transportation Center (Alaska University Transportation Center, University of Alaska Fairbanks, 2013-07)
    • Automated processing system for tidal analysis of MF radar winds

      Vemula, Sreenivas (2005-12)
      The medium frequency (MF) radar at Platteville, Colorado (40.18° N, 104.7° W) is used to estimate the zonal and meridional wind motions in the middle atmosphere. This radar has been in operation since January 2000. We currently have four years of wind estimates sampled every five minutes. An automated processing system has been developed in IDL to process these estimates and obtain the monthly mean winds and tidal parameters. The automated processing currently processes the wind estimates in time domain analysis using a least square fitting technique. The criteria for determining when the estimated tidal parameters are valid have been studied along with the error analysis of the data and processing. The diurnal and semidiurnal parameters are obtained using this least square fitting method and these tidal parameters are assumed to be valid only when the condition number is less than 10. In the spectral domain, the fast Fourier transform and Lomb-Scargle periodogram methods have been studied. A test signal is generated and its performance using both FFT and Lomb-Scargle methods are discussed for three different cases which are equivalent to our actual data. The results of the wind estimates from 2000-2003 collected using the MF radar have been processed using the automated processing system. This automated processing system can be used to generate the wind parameters on a 24 hour, 7 day a week basis for an elaborate study. Our data are compared with MF radar data from Saskatoon, Canada and Urbana, lllinois. Most of the time our data are similar to the behavior of GSWM-02 model.
    • Automatic detection of sensor calibration errors in mining industry

      Pothina, Rambabu; Ganguli, Rajive; Ghosh, Tathagata; Lawlor, Orion; Barry, Ronald (2017-12)
      Sensor errors cost the mining industry millions of dollars in losses each year. Unlike gross errors, "calibration errors" are subtle, develop over time, and are difficult to identify. Economic losses start accumulating even when errors are small. Therefore, the aim of this research was to develop methods to identify calibration errors well before they become obvious. The goal in this research was to detect errors at a bias as low as 2% in magnitude. The innovative strategy developed relied on relationships between a variety of sensors to detect when a given sensor started to stray. Sensors in a carbon stripping circuit at a gold processing facility (Pogo Mine) in Alaska were chosen for the study. The results from the initial application of classical statistical methods like correlation, aggregation and principal component analysis (PCA), and the signal processing methods (FFT), to find bias (±10%) in "feed" sensor data from a semi-autogenous (SAG) grinding mill operation (Fort Knox mine, Alaska) were not promising due to the non-linear and non-stationary nature of the process characteristics. Therefore, those techniques were replaced with some innovative data mining techniques when the focus shifted to Pogo Mine, where the task was to detect calibration errors in strip vessel temperature sensors in the carbon stripping circuit. The new techniques used data from two strip vessel temperature sensors (S1 and S2), four heat exchanger related temperature sensors (H1 through H4), barren flow sensor (BARNFL) and a glycol flow sensor (GLYFL). These eight sensors were deemed to be part of the same process. To detect when the calibration of one of the strip vessel temperature sensors, S1, started to stray, tests were designed to detect changes in relationship between the eight temperature sensors. Data was filtered ("threshold") based on process characteristics prior to being used in tests. The tests combined basic concepts such as moving windows of time, ratios (ratio of one sensor data to data from a set of sensors), tracking of maximum values, etc. Error was triggered when certain rules were violated. A 2% error was randomly introduced into one of the two strip vessel temperature data streams to simulate calibration errors. Some tests were less effective than others at detecting the simulated errors. The tests that used GLYFL and BARNFL were not very effective. On the other hand, the tests that used total "Heat" of all the heat exchanger sensors were very effective. When the tests were administered together ("Combined test"), they have a high success rate (95%) in terms of True alarms, i.e., tests detecting bias after it is introduced. In those True alarms, for 75% of the cases, the introduction of the error was detected within 39.5 days. A -2% random error was detected with a similar success rate.