Evaluating Reliability of Stochastic Flow Networks

1989 ◽  
Vol 3 (4) ◽  
pp. 493-509 ◽  
Author(s):  
George S. Fishman ◽  
Tien-Yi Danny Shaw
Keyword(s):  
Distribution Systems ◽  
Monte Carlo Sampling ◽  
Sampling Plan ◽  
Stochastic Flow ◽  
Statistical Efficiency ◽  
Worst Case ◽  
Flow Network ◽  
Oil Distribution ◽  
Coefficients Of Variation ◽  
Specialized Region

This paper describes a highly efficient Monte Carlo sampling plan for evaluating the probability that the flow value in a stochastic flow network is greater than or equal to a prespecified level d. A stochastic flow network can characterize communication, transportation, and water or oil distribution systems. The paper first derives lower and upper bounds on the probability of interest and then describes how one can concentrate sampling in a specialized region of the arc capacity state space to increase the statistical efficiency of the resulting estimate. The paper also gives expressions for worst-case sample sizes needed to meet specified bounds on variances and coefficients of variation and illustrates the proposed sampling plan with an example.

A Comparative Study of Different Approaches for Finding the Upper Boundary Points in Stochastic-Flow Networks

2014 ◽  
Vol 10 (3) ◽  
pp. 13-23 ◽  
Author(s):  
Seyed Mehdi Mansourzadeh ◽  
Seyed Hadi Nasseri ◽  
Majid Forghani-elahabad ◽  
Ali Ebrahimnejad
Keyword(s):  
Information System ◽  
Comparative Study ◽  
Test Problems ◽  
Stochastic Flow ◽  
Flow Networks ◽  
Flow Network ◽  
Random Test ◽  
Boundary Points ◽  
Exclusion Method ◽  
Upper Boundary

An information system network (ISN) can be modeled as a stochastic-flow network (SFN). There are several algorithms to evaluate reliability of an SFN in terms of Minimal Cuts (MCs). The existing algorithms commonly first find all the upper boundary points (called d-MCs) in an SFN, and then determine the reliability of the network using some approaches such as inclusion-exclusion method, sum of disjoint products, etc. However, most of the algorithms have been compared via complexity results or through one or two benchmark networks. Thus, comparing those algorithms through random test problems can be desired. Here, the authors first state a simple improved algorithm. Then, by generating a number of random test problems and implementing the algorithms in MATLAB, the proposed algorithm is demonstrated to be more efficient than some existing ones in medium-sized networks. The performance profile introduced by Dolan and More is used for analyzing the output of programs.

A Robust Pressure Controller for a Variable Speed AC Motor Pump: Application to Aircraft Hydraulic Power Packages

2018 ◽  
Author(s):  
Nils Trochelmann ◽  
Phillip Bischof Stump ◽  
Frank Thielecke ◽  
Dirk Metzler ◽  
Stefan Bassett
Keyword(s):  
Distribution Systems ◽  
Controller Design ◽  
Dynamic Performance ◽  
Operating Conditions ◽  
Gear Pump ◽  
Variable Speed ◽  
Hydraulic Power ◽  
Worst Case ◽  
Wide Range ◽  
Pressure Controller

Highly integrated electro-hydraulic power packages with electric motor-driven pumps (EMP) are a key technology for future aircraft with electric distribution systems. State of the art aircraft EMPs are robust but lack efficiency, availability, and have high noise emissions. Variable speed fixed displacement (VSFD-) EMPs, combining a permanent magnet synchronous motor and an internal gear pump, show promising properties regarding noise reduction and energy efficiency. Though, meeting the strict dynamic requirements is tough with this EMP-concept. Speed limitations and inertia impose strong restrictions on the achievable dynamic performance. Moreover, the requirements must be met under a wide range of operating conditions. For a prototype aircraft VSFD-EMP a robust pressure controller design is proposed in this paper. In a first step the operating conditions of the EMP are defined, analyzing environmental conditions and impacts of the interfacing aircraft systems. Nonlinear and linear control design models are developed and validated by measurements at an EMP test rig built for this project. A conventional cascade pressure control concept is selected. This is motivated by the demand for simple, reliable, and proven solutions in aerospace applications. A controller is designed by applying classical loop shaping techniques. Robust stability and performance of the system are investigated through a subsequent μ-analysis. Finally, the controller is tested under nominal and worst case conditions in nonlinear simulations.

Modeling and Applications on Timed Stochastic-Flow Networks

2015 ◽  
Vol 22 (05) ◽  
pp. 1550023
Author(s):  
Shin-Guang Chen
Keyword(s):  
Network Reliability ◽  
Daily Life ◽  
Transportation Network ◽  
Stochastic Flow ◽  
Production Network ◽  
Flow Networks ◽  
Stochastic Behavior ◽  
Flow Network ◽  
Numerical Examples ◽  
Time Consumption

A stochastic-flow network (SFN) is a network whose flow has stochastic behavior or probabilistic multi-states. A timed stochastic-flow network (TSFN) is a SFN whose flow spends time to go through the network. Traditionally, the evaluation of network reliability does not consider time consumption for the flow to get through the network. However, there are lots of daily-life networks which can be regarded as TSFNs, such as the transportation network, the production network, etc. Their flow spends time to get through the network, and they are not yet explored in the literature. This paper proposes approaches to evaluate the reliability of such networks. Some numerical examples are discussed to illustrate the proposed method.

scholarly journals A Greedy Sampling Design Algorithm for the Modal Calibration of Nodal Demand in Water Distribution Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Yu Shao ◽  
Shipeng Chu ◽  
Tuqiao Zhang ◽  
Y. Jeffrey Yang ◽  
Tingchao Yu
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Sampling Design ◽  
Monte Carlo Sampling ◽  
Design Algorithm ◽  
Demand Distribution ◽  
Simulation Step ◽  
Optimization Simulation ◽  
Calibration Accuracy ◽  
The Cost

This paper presents a greedy optimization algorithm for sampling design to calibrate WDS hydraulic model. The proposed approach starts from the existing sensors and sequentially adds one new sensor at each optimization simulation step. In each step, the algorithm tries to minimize the calibration prediction uncertainty. The new sensor is installed in the location where the uncertainty is greatest but also sensitive to other nodes. The robustness of the proposed approach is tested under different spatial and temporal demand distribution. We found that both the number of sensors and the perturbation ratio affect the calibration accuracy as defined by the average nodal pressure deviation itself and its variability. The plot of the calibration accuracy versus the number of sensors can reasonably guide the trade-off between model calibration accuracy and number of sensors placed or the cost. This proposed approach is superior in calibration accuracy and modeling efficiency when compared to the standard genetic algorithm (SGA) and Monte Carlo Sampling algorithm (MCS).

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