Fast Augmentation Algorithms for Maximising the Flow in Repairable Flow Networks After a Component Failure

2011 ◽  
Author(s):  
Michael T. Todinov
Keyword(s):  
Flow Networks ◽  
Component Failure

Case Study—Failure Analysis of a Multiplexer

2016 ◽  
Author(s):  
Michael Woo ◽  
Marcos Campos ◽  
Luigi Aranda
Keyword(s):  
Failure Analysis ◽  
High Reliability ◽  
Component Failure ◽  
Root Cause ◽  
Knowing That ◽  
Cause Of Failure ◽  
The Cost

Abstract A component failure has the potential to significantly impact the cost, manufacturing schedule, and/or the perceived reliability of a system, especially if the root cause of the failure is not known. A failure analysis is often key to mitigating the effects of a componentlevel failure to a customer or a system; minimizing schedule slips, minimizing related accrued costs to the customer, and allowing for the completion of the system with confidence that the reliability of the product had not been compromised. This case study will show how a detailed and systemic failure analysis was able to determine the exact cause of failure of a multiplexer in a high-reliability system, which allowed the manufacturer to confidently proceed with production knowing that the failure was not a systemic issue, but rather that it was a random “one time” event.

scholarly journals Effects of thermo-erosion gullying on hydrologic flow networks, discharge and soil loss

2014 ◽  
Vol 9 (10) ◽  
pp. 105010 ◽  
Author(s):  
Etienne Godin ◽  
Daniel Fortier ◽  
Stéphanie Coulombe
Keyword(s):  
Soil Loss ◽  
Flow Networks

scholarly journals Maximum Entropy Analysis of Flow Networks: Theoretical Foundation and Applications

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 776 ◽  
Author(s):  
Robert K. Niven ◽  
Markus Abel ◽  
Michael Schlegel ◽  
Steven H. Waldrip
Keyword(s):  
Maximum Entropy ◽  
Joint Probability ◽  
Financial Economic ◽  
Flow Networks ◽  
Flow Network ◽  
Physical Constraints ◽  
Generalized Maximum Entropy ◽  
Deterministic Solution ◽  
Network Properties ◽  
The Cost

The concept of a “flow network”—a set of nodes and links which carries one or more flows—unites many different disciplines, including pipe flow, fluid flow, electrical, chemical reaction, ecological, epidemiological, neurological, communications, transportation, financial, economic and human social networks. This Feature Paper presents a generalized maximum entropy framework to infer the state of a flow network, including its flow rates and other properties, in probabilistic form. In this method, the network uncertainty is represented by a joint probability function over its unknowns, subject to all that is known. This gives a relative entropy function which is maximized, subject to the constraints, to determine the most probable or most representative state of the network. The constraints can include “observable” constraints on various parameters, “physical” constraints such as conservation laws and frictional properties, and “graphical” constraints arising from uncertainty in the network structure itself. Since the method is probabilistic, it enables the prediction of network properties when there is insufficient information to obtain a deterministic solution. The derived framework can incorporate nonlinear constraints or nonlinear interdependencies between variables, at the cost of requiring numerical solution. The theoretical foundations of the method are first presented, followed by its application to a variety of flow networks.

scholarly journals Information Flow Networks of Chinese Stock Market Sectors

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 13066-13077 ◽  
Author(s):  
Peng Yue ◽  
Qing Cai ◽  
Wanfeng Yan ◽  
Wei-Xing Zhou
Keyword(s):  
Stock Market ◽  
Information Flow ◽  
Chinese Stock Market ◽  
Flow Networks

Finite element modeling of structural steel component failure at elevated temperatures

Structures ◽  
2016 ◽  
Vol 6 ◽  
pp. 134-145 ◽  
Author(s):  
Mina Seif ◽  
Joseph Main ◽  
Jonathan Weigand ◽  
Therese P. McAllister ◽  
William Luecke
Keyword(s):  
Finite Element ◽  
Finite Element Modeling ◽  
Structural Steel ◽  
Elevated Temperatures ◽  
Steel Component ◽  
Component Failure ◽  
Element Modeling

scholarly journals Asymmetric flow networks

2014 ◽  
Vol 237 (2) ◽  
pp. 566-579 ◽  
Author(s):  
Norma Olaizola ◽  
Federico Valenciano
Keyword(s):  
Asymmetric Flow ◽  
Flow Networks

Automatic Electronic Component Failure-Rate Prediction with MIL-HDBK-217B

1977 ◽  
Vol R-26 (3) ◽  
pp. 214-219 ◽  
Author(s):  
W.W. Gaertner ◽  
D.S. Elders ◽  
D.B. Ellingham ◽  
J.A. Kastning ◽  
W.M. Schreyer
Keyword(s):  
Failure Rate ◽  
Electronic Component ◽  
Component Failure ◽  
Rate Prediction
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