Displaying all 8 results
- Conference Session
- Instrumentation Division Technical Session 2
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- 2017 ASEE Annual Conference & Exposition
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- Vivek Pillarisetty P.E., Purdue University Northwest; Akram Hossain, Purdue University Northwest
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) + ๐พ๐ ๐ ๐๐ (๐ ) = ๐ธ๐ (๐ ) (4) ๐พ๐ก Page 4 of 15Now we must find Tm(s) in terms of ำจm (s) if we are to separate the input and output variables andobtain the transfer function ำจm (s) / Ea(s). Following Figure shows typical equivalent mechanicalloading on a motor. Jm is the equivalent inertia at the armature and includes both the armatureinertia and, as we will see later, the load inertia reflected to the armature. Dm is the equivalentviscous damping at the armature and includes armature viscous damping and, as we will see later,the load viscous damping reflected to the armature
- Conference Session
- Instrumentation Division Technical Session 2
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- 2020 ASEE Virtual Annual Conference Content Access
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- Daniel Dannelley, Embry-Riddle Aeronautical University, Prescott; Elliott Bryner, Embry-Riddle Aeronautical University
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thelimitations of simplifying assumptions can affect the prediction (i.e. course calculations vs. realworld measurements).9 - Sonic Flow MeasurementThis exercise uses an engineered piping system to create a contained shock wave. Two pipes areseparated by a flange containing a plastic diaphragm. The first pipe is slowly pressurized untilthe diaphragm is burst and a shock wave is transmitted down the second pipe, then reflected backthrough the system. The second pipe is instrumented with two pressure sensors along the line oftravel, which allows for determination of the pressure rise as well as velocity of the shock wavecorresponding to the initial pass as well as the following reflections. This exercise focuses oncomparing the experimental results to the
- Conference Session
- Instrumentation Division Technical Session 1
- Collection
- 2020 ASEE Virtual Annual Conference Content Access
- Authors
- Bradley Lane Kicklighter P.E., University of Southern Indiana
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Proximity SensorThe AutomationDirect CK2-CP-1H capacitive proximity sensor [8] has an 8 mm sensingdistance and consumes 530 mW (see Figure 5).Figure 5: Capacitive Proximity Sensor (AutomationDirect)A capacitive proximity sensor senses metals and non-metals [7]. This sensor has a normallyclosed output, so the output is off when it senses metal. Thus, the sensor output will be on whenthe hole in the disk is in line with the sensor.5.2.3 Photoelectric Retroreflective SensorThe AutomationDirect FBP-DP-0E retroreflective sensor [9] has a 2.5 m sensing distance andconsumes 480 mW (see Figure 6).Figure 6: Retroreflective Sensor and Retroreflector (AutomationDirect)A retroreflective sensor emits light that is reflected back to it by a retroreflector [7
- Conference Session
- Instrumentation Division Technical Session 3
- Collection
- 2015 ASEE Annual Conference & Exposition
- Authors
- Xavier Shastri Domnique Henry, University of Maryland, Eastern Shore; Lei Zhang, University of Maryland, Eastern Shore; Abhijit Nagchaudhuri, University of Maryland, Eastern Shore; Madhumi Mitra Ph.D., University of Maryland, Eastern Shore; Christopher E Hartman, University of Maryland, Eastern Shore; Christopher Alan Toney II, University of Maryland Eastern Shore ; Ayokunle Adeyemi Akangbe, University of Maryland Eastern Shore
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]. Page 26.720.4 Figure 2. Kolbโs cycle of experiential learning[16].According to the Kolbโs cycle, the four experiential learning stages are Concrete experience,Reflective observation, Abstract conceptualization and Active experimentation. In this project, theKolbโs Experiential Learning Theory is applied during the EMDLS design and implementation toimprove student learning and professional development.2. Project OutlineThis project focuses on the design and deployment of a microprocessor based EMDLS to collectenvironmental data from the 3,000 L raceway tank which forms the basis of the microalgae andshrimp aquaculture system. The data provided by the system will be used in the daily managementof the operation as well as
- Conference Session
- Instrumentation Division Technical Session 4
- Collection
- 2015 ASEE Annual Conference & Exposition
- Authors
- Aidan F. Browne, University of North Carolina, Charlotte; David George Vutetakis Jr, University of North Carolina, Charlotte
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trainersthemselves are often different . In such a case, students are learning to do different tasks withdifferent equipment. However, by combining the trainers and controllers to perform the sametasks, this puts emphasis on the differences between technologies by solving the same problemson different equipment. This also adds familiarity to equipment that can translate into real worldpractical uses.ConclusionsThe cost of training systems often carries a high price tag when purchased as an off-the-shelfproduct. This cost is reflected not necessarily by the raw value of the parts, but by the integrationof the plant and the controllers along with any necessary specialized software. This effect iscompounded when multiple training setups are needed for
- Conference Session
- Instrumentation Division Technical Session 2
- Collection
- 2017 ASEE Annual Conference & Exposition
- Authors
- Asad Yousuf, Savannah State University; Mohamad A. Mustafa, Savannah State University; Keenya Mosley, Savannah State University; Mir M. Hayder, Savannah State University; Christopher S. Stronen, Savannah Technical College
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2008, Henderson et al [4] developed the four categories of change strategiesmodel to classify strategies that have been used to conceptualize or to create change inundergraduate STEM education. The similarity of these categories to those developed through anindependent review of an overlapping literature base [5] suggests that the four categories arerobust and meaningful. Following are the four categories: โ Disseminating Curriculum and Pedagogy: This category is based on the phenomena of tell/teach individuals about new teaching conceptions and/or practices and encourage their use. โ Developing Reflective Teachers: Encourage/Support individuals to develop new teaching conceptions and/or practices โ Enacting Policy: Enact new
- Conference Session
- Instrumentation Division Technical Session 2
- Collection
- 2017 ASEE Annual Conference & Exposition
- Authors
- Tae-Hoon Kim, Purdue University Northwest; Lash B. Mapa, Purdue University Northwest; Deepak Ramamurthy; Feroja Goni
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- Diversity
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temperatures. In addition, the antenna setup should be carefullyselected for temperature. For example, at room temperature, if the RFID antenna is required 2ftof antenna to antenna distance, the best distance between tag and the antenna is 3ft for the bestdetection rate. At the higher temperature, T = 130ยฐF, if the tag is located 4ft away from thereading antenna, 4ft of antenna distance or DCA = 2ft is the best for the maximum reading rate.There are couple of limitations. First, the environmental temperature is simulated using smallbox and only the tag is inside of the box while the antennas are outside. This implies that onlythe tag is affected by the temperature, while the reflected signal from the passive tag is partiallyaffected. In other word
- Conference Session
- Instrumentation Division Technical Session 1
- Collection
- 2016 ASEE Annual Conference & Exposition
- Authors
- Priom Chakraborty, Purdue University - Calumet; Akram Hossain, Purdue University - Calumet; Vivek Pillarisetty P.E., Purdue University - Calumet
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- Instrumentation
in this field will reflect in the results much better than to present days. Page 14 of 14References[1] Bolton, W., & NetLibrary, Inc. (2006). Programmable logic controllers. Amsterdam: Elsevier/Newnes.[2] Kamel, K., & Kamel, E. (2013). Programmable logic controllers: Industrial control.[3] Rohner, P. (1996). PLC: automation with programmable logic controllers: A textbook for engineers and technicians. Sydney: UNSW Press.[4] Shahin, T. M. M., EDC 2002, & Engineering Design Conference. (2002). Computer-based design: Engineering Design Conference 2002 [held at King's College London, UK on the 9-11 July 2002]. Bury St