= F + m m + ext in out momentum d~ P ~ ˙ V~ − ~r × m ˙ V~ ~0 LO,sys P P Angular dt = MO + ~r × m + ext in out momentum dEsys Q˙ in,net + W˙ in,net
equations are converted to a set of algebraic equations using a weighted integral statement (e.g., weak-form Galerkin and least-squares formulation). For example, a weak-form Galerkin formulation of the governing equations, Eq. 1–2, can be stated as: find the solution {u, p} ∈ S h such that for all {w, q} ∈ V h the following equation is satisfied: Z ∂u w· ρ + ρu · ∇u − ρg − (∇ · w)p dΩ Ω ∂t Z − ∇q · u dΩ + (suitable stability terms) ZΩ = {w · (−pI + τ ) · n
cognitive learning gains be-tween the simulation and paper format groups. The simulation and paper cohorts each consistedof 18 students, when the data is combined across the Spring and Fall semesters.Figure 6: Learner preferences. The statements that learners are prompted to rank in the learnerpreference survey are displayed above each histogram. Learners from both the simulation andpaper cohorts strongly favored the simulation learning format over the paper learning format.5.1 Learner PreferencesThe results for the learner preferences survey are shown in Fig. 6. To examine if there were anypotential bias due to the order in which students completed the formats, we first conducted nor-mality checks using the Shapiro-Wilk test (αSW = 0.05) and Q-Q
balance, 1st law of thermodynamics andsecond law of thermodynamics for control volumes. Please use these equations to answerquestions 15 through 20. dmcv = m i − m e (A) dt i edEcv V2 V2 = Q cv − W cv + m i hi + i + gzi − m e he + e + gze (B) dt i 2 e 2 •dS cv Qj • • • = + mi si − me se + cv (C) dt j Tj i e15. (5 points)Consider the schematic drawing of a general control volume shown bellow. Place mcv, Ecv, Scv
disciplinary cultures from the student perspective,” in 2018 ASEE Annual Conference & Exposition, 2018. [5] B. Batson, “’Other’reasons to invert a class,” in 2016 ASEE Annual Conference & Exposition, 2016. [6] ——, “Introducing metacognition to sophomores and juniors and its effect on academic performance,” in 2018 ASEE Annual Conference & Exposition, 2018. [7] M. A. McVey, C. W. Luchies, and A. J. Villicana, “Impact of high-performing teams on student learning,” in 2017 ASEE Annual Conference & Exposition, 2017. [8] R. D. Manteufel, “Electronic technology used in engineering thermodynamics,” in 2006 GSW, 2006. [9] Q. Dunsworth and Y. Wu, “Effective review of prerequisites: Using videos to flip the reviewing process in a
using long-short term memory and function-based hierarchicalframework,” Annals of Nuclear Energy, vol. 119, p. 287-299, 2018.[5] X. Li, X.-M., Fu, F.-R. Xiong, and X.-M. Bai, “Deep learning-based unsupervisedrepresentation clustering methodology for automatic nuclear reactor operating transientidentification,” Knowledge-Based Systems, vol. 204, p. 106178, 2020.[6] C. Jorge, A. Mol, C. Pereira, M. Aghina, and D. Nomiya, “Human-system interface based onspeech recognition: application to a virtual nuclear power plant control desk.” Progress inNuclear Energy, vol. 52, p. 379–386, 2010.[7] W. Li, M. Peng, and Q. Wang, “Fault detectability analysis in PCA method during conditionmonitoring of sensors in a nuclear power plant,” Annals of Nuclear Energy
, “Quality in Interpretive Engineering Education Research: Reflections on an Example Study,” J of Engineering Edu, vol. 102, no. 4, pp. 626–659, Oct. 2013, doi: 10.1002/jee.20029.[33] S. Dawadi, S. Shrestha, and R. A. Giri, “Mixed-Methods Research: A Discussion on its Types, Challenges, and Criticisms,” JPSE, vol. 2, no. 2, pp. 25–36, Feb. 2021, doi: 10.46809/jpse.v2i2.20.[34] M. Q. Patton, Qualitative Research & Evaluation Methods: Integrating Theory and Practice. Thousand Oaks, California: SAGE Publications, Inc., 2014.[35] J. Aronson, “A Pragmatic View of Thematic Analysis,” TQR, Apr. 1995, doi: 10.46743/2160-3715/1995.2069.[36] V. Braun and V. Clarke, “Using thematic analysis in psychology,” Qualitative Research in
,” Sustain Sci 6, 203–218 (2011).[2] Matthias T., Tomašević, I., Stevenson, M., Ting Q., Huisingh, D. “A systematic review of theliterature on integrating sustainability into engineering curricula,” J of Cleaner Production. (2018), Vol.181, pp. 608-617[3] Leicht, A., Heiss, J.“Global Citizenship Education: Topics and learning objectives,” UNESCO (2015).[4] Guerra, A., "Integration of sustainability in engineering education: Why is PBL an answer?",International Journal of Sustainability in Higher Education, (2017) Vol. 18 No. 3, pp. 436-454.[5] Wiek, A., Xiong, A., et al. "Integrating problem- and project-based learning into sustainabilityprograms: A case study on the School of Sustainability at Arizona State University," J. Educ. Sustain.Dev., (2014
, “Criteria for Accrediting Engineering Programs, 2022 - 2023,” ABET. Accessed: Jan. 18, 2024. [Online]. Available: https://www.abet.org/accreditation/accreditation-criteria/criteria-for-accrediting- engineering-programs-2022-2023/[13] X. Zhang, Y. Yang, M. Shi, A. Ming, and P. Wang, “Novel energy identification method for shallow cracked rotor system,” Mechanical Systems and Signal Processing, vol. 186, p. 109886, 2023.[14] Z. Jia, Y. Yang, Q. Zheng, and W. Deng, “Dynamic analysis of Jeffcott rotor under uncertainty based on Chebyshev convex method,” Mechanical Systems and Signal Processing, vol. 167, p. 108603, 2022.[15] H. Yao, Y. Wang, Y. Cao, and B. Wen, “Multi-stable nonlinear energy sink for rotor system
://www.nsf.gov/funding/pgm_summ.jsp?pims_id=133748a[17] M. Q. Patton, Qualitative research & evaluation methods: Integrating theory and practice. Sage publications, 2014.[18] M. B. Miles, M. A. Huberman, and J. Saldaña, Qualitative Data Analysis. A Methods Sourcebook. 2014. doi: 10.1136/ebnurs.2011.100352.[19] M. Borrego, J. E. Froyd, and T. S. Hall, “Diffusion of engineering education innovations: A survey of awareness and adoption rates in U.S. engineering departments,” Journal of Engineering Education, vol. 99, no. 3, pp. 185–207, 2010, doi: 10.1002/j.2168- 9830.2010.tb01056.x.Appendix A – Rough Draft of Learning InnovationReal World Engineering – Cooling Rat BrainsIntroduction – It is common knowledge that
algorithms from a given team will look very similar, but theindividual submission provides accountability that each student is at least writing up a plan andstarting to think deeply about each problem.Before the second class in the learning cycle, students are told to work with their teams to comeup with 25 questions for each problem by the start of the next class (a reduced version of a 100question Q-storm session [11, pp. 154–155]). They must also submit one of those questions foreach homework problem a day prior to the class. The instructor develops a response to eachquestion and during the second class, discusses the students’ submitted questions. Sometimes theinstructor might provide a direct answer; sometimes the instructor will ask for
response corresponds to a rating of 1 or 2 (strongly disagree or disagree), a neutralresponse corresponds to 3, and a positive response corresponds to 4 or 5 (agree or stronglyagree). Note: the number of responses is 20-21, however the total response may not add to 100%due to rounding. Construct Q# Statement Negative Neutral Positive Response: Response Response: Disagree Agree Growth Q1 I prefer to work on my own 19% 19% 61.9% Mindset through the design process. Q2 I believe the design review