Analytical Calculations vs. Simulation for Predicting the Performance of Prototype Logging Machines

1981 ◽  
Vol 57 (6) ◽  
pp. 279-283
Author(s):  
C. H. Meng ◽  
J. D. A. Williamson ◽  
P. L. Cottell
Keyword(s):  
Computer Simulation ◽  
Field Data ◽  
Analytical Approach ◽  
Empirical Studies ◽  
Effective Alternative ◽  
Simulation Approach ◽  
Data Simulation

A simple analytical approach to estimating the performance of logging machine prototypes was compared with computer simulation, specifically the program CANLOG. Results from the two methods did not differ appreciably, despite the greater complexity and cost of the simulation approach. However, neither method was particularly reliable, as judged from comparison with available field data. Simulation may be an effective alternative where considerable interaction exists between machines or machine functions in operation, where variable distributions are given substance through empirical studies, and where program documentation is sufficient to place control of the procedure in the hands of the user.

A Computer Simulation Approach for Engineering Air-Jet Spun Yarns

1997 ◽  
Vol 67 (3) ◽  
pp. 223-230 ◽  
Author(s):  
Rangaswamy Rajamanickam ◽  
Steven M. Hansen ◽  
Sundaresan Jayaraman
Keyword(s):  
Computer Simulation ◽  
Computer Simulations ◽  
Fiber Length ◽  
Experimental Investigations ◽  
Simulation Approach ◽  
Air Jet ◽  
Fiber Fineness ◽  
Spun Yarns ◽  
Yarn Count ◽  
Wrap Angle

A computer simulation approach for engineering air-jet spun yarns is proposed, and the advantages of computer simulations over experimental investigations and stand-alone mathematical models are discussed. Interactions of the following factors in air-jet spun yarns are analyzed using computer simulations: yarn count and fiber fineness, fiber tenacity and fiber friction, fiber length and fiber friction, and number of wrapper fibers and wrap angle. Based on the results of these simulations, yarn engineering approaches to optimize strength are suggested.

Gamma ray imaging using coded aperture masks: a computer simulation approach

1991 ◽  
Vol 30 (5) ◽  
pp. 549 ◽  
Author(s):  
J. Jimenez ◽  
Pedro Olmos ◽  
J. L. de Pablos ◽  
J. M. Perez
Keyword(s):  
Computer Simulation ◽  
Gamma Ray ◽  
Coded Aperture ◽  
Simulation Approach ◽  
Gamma Ray Imaging

Analyzing Participant Behaviors in Design Crowdsourcing Contests Using Causal Inference on Field Data

2018 ◽  
Vol 140 (9) ◽  
Author(s):  
Ashish M. Chaudhari ◽  
Zhenghui Sha ◽  
Jitesh H. Panchal
Keyword(s):  
Causal Inference ◽  
Engineering Design ◽  
Field Data ◽  
Task Complexity ◽  
Empirical Studies ◽  
Concept Generation ◽  
Related Factors ◽  
Factors Affecting ◽  
Future Design ◽  
Game Theoretic

Crowdsourcing is the practice of getting ideas and solving problems using a large number of people on the Internet. It is gaining popularity for activities in the engineering design process ranging from concept generation to design evaluation. The outcomes of crowdsourcing contests depend on the decisions and actions of participants, which in turn depend on the nature of the problem and the contest. For effective use of crowdsourcing within engineering design, it is necessary to understand how the outcomes of crowdsourcing contests are affected by sponsor-related, contest-related, problem-related, and individual-related factors. To address this need, we employ existing game-theoretic models, empirical studies, and field data in a synergistic way using the theory of causal inference. The results suggest that participants' decisions to participate are negatively influenced by higher task complexity and lower reputation of sponsors. However, they are positively influenced by the number of prizes and higher allocation to prizes at higher levels. That is, an amount of money on any following prize generates higher participation than the same amount of money on the first prize. The contributions of the paper are: (a) a causal graph that encodes relationships among factors affecting crowdsourcing contests, derived from game-theoretic models and empirical studies, and (b) a quantification of the causal effects of these factors on the outcomes of GrabCAD, Cambridge, MA contests. The implications of these results on the design of future design crowdsourcing contests are discussed.

scholarly journals Hospital beds and personal protective equipment planning for COVID-19 pandemic: A computer simulation approach

2020 ◽  
Author(s):  
Yiruo Lu ◽  
Yongpei Guan ◽  
Jennifer Fishe ◽  
Thanh Hogan ◽  
Xiang Zhong
Keyword(s):  
Computer Simulation ◽  
Personal Protective Equipment ◽  
Census Data ◽  
Discrete Event ◽  
Hospital Setting ◽  
Case Fatality ◽  
Protective Equipment ◽  
Simulation Approach ◽  
Number Of Patients ◽  
Hospital Resources

Abstract Health care systems are at the frontline to fight the COVID-19 pandemic. An emergent question for each hospital is how many general ward and intensive care unit beds are needed and how much personal protective equipment to be purchased. However, hospital pandemic preparedness has been hampered by a lack of sufficiently specific planning guidelines. In this paper, we developed a computer simulation approach to evaluating bed utilizations and the corresponding supply needs based on the operational considerations and constraints in individual hospitals. We built a data-driven SEIR model which is adaptive to control policies and can be utilized for regional forecast targeting a specific hospital’s catchment area. The forecast model was integrated into a discrete-event simulation which modeled the patient flow and the interaction with hospital resources. We tested the simulation model outputs against patient census data from UF Health Jacksonville, Jacksonville, FL. Simulation results were consistent with the observation that the hospital has ample bed resources to accommodate the regional COVID patients. After validation, the model was used to predict future bed utilizations given a spectrum of possible scenarios to advise bed planning and stockpiling decisions. Lastly, how to optimally allocate hospital resources to achieve the goal of reducing the case fatality rate while helping a maximum number of patients to recover was discussed. This decision support tool is tailored to a given hospital setting of interest and is generalizable to other hospitals to tackle the pandemic planning challenge.

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