Structure-Aware Reliability Analysis of Large-Scale Linear Sensor Systems

A linear sensor system is a system in which the sensor measurements have a linear relationship to the source variables that cannot be measured directly. Linear sensor systems are widely deployed in advanced manufacturing processes, wireless transportation systems, electrical grid systems, and oil and gas pipeline systems to monitor and control various physical phenomena critical to the smooth function of such systems. The source variables capture these complex physical phenomena which are then estimated based on the sensor measurements. Two of the critical parameters to be considered while modeling any linear sensor system are the degree of redundancy and reliability. The degree of redundancy is the minimum number of sensor failures that a system can withstand without compromising the identifiability of any source variables. The reliability of a sensor system is a probabilistic evaluation of the ability of a system to tolerate sensor failures. Unfortunately, the existing approaches to compute the degree of redundancy and estimate the reliability are limited in scope due to their inability to solve problems in large-scale. In this research, we establish a new knowledge base for computing the degree of redundancy and estimating the reliability of large-scale linear sensor systems. We first introduce a heuristic convex optimization algorithm that uses techniques from compressed sensing to find highly reliable approximate values for the degree of redundancy. Due to the distributed nature of linear sensor systems often deployed in practical applications, many of these systems embed certain structures. In our second approach, we study these structural properties in detail utilizing matroid theory concepts of connectivity and duality and propose decomposition theorems to disintegrate the redundancy degree problem into subproblems over smaller subsystems. We solve these subproblems using mixed integer programming to obtain the degree of redundancy of the overall system. We further extend these decomposition theorems to help with dividing the reliability evaluation problem into smaller subproblems. Finally, we estimate the reliability of the linear sensor system by solving these subproblems employing mixed integer programming embedded within a recursive variance reduction framework, a technique commonly used in network reliability literature. We implement and test developed algorithms using a wide range of standard test instances that simulate real-life applications of linear sensor systems. Our computational studies prove that the proposed algorithms are significantly faster than the existing ones. Moreover, the variance of our reliability estimate is significantly lower than the previous estimates

Methodology to quantify leaks in aerosol sampling system components

Filter holders and continuous air monitors (CAMs) are used extensively in the nuclear industry. It is important to minimize leakage in these devices and in recognition of this consideration, a limit on leakage for sampling systems is specified in ANSI/HPS N13.1-1999; however the protocol given in the standard is really germane to measurement of significant leakage, e.g., several percent of the sampling flow rate. In the present study, a technique for quantifying leakage was developed and that approach was used to measure the sealing integrity of a CAM and two kinds of filter holders. The methodology involves use of sulfur hexafluoride as a tracer gas with the device being tested operated under dynamic flow conditions. The leak rates in these devices were determined in the pressure range from 2.49 kPa (10 In. H2O) vacuum to 2.49 kPa (10 In. H2O) pressure at a typical flow rate of 56.6 L/min (2 cfm). For the two filter holders, the leak rates were less than 0.007% of the nominal flow rate. The leak rate in the CAM was less than 0.2% of the nominal flow rate. These values are well within the limit prescribed in the ANSI standard, which is 5% of the nominal flow rate. Therefore the limit listed in the ANSI standard should be reconsidered as lower values can be achieved, and the methodology presented herein can be used to quantify lower leakage values in sample collectors and analyzers. A theoretical analysis was also done to determine the nature of flow through the leaks and the amount of flow contribution by the different possible mechanisms of flow through leaks

Numerical modeling of species transport in turbulent flow and experimental study on aerosol sampling

Numerical simulations were performed to study the turbulent mixing of a scalar species in straight tube, single and double elbow flow configurations. Different Reynolds Averaged Navier Stokes (RANS) and Large Eddy Simulation (LES) models were used to model the turbulence in the flow. Conventional and dynamic Smagorinsky sub-grid scale models were used for the LES simulations. Wall functions were used to resolve the near wall boundary layer. These simulations were run with both two-dimensional and three-dimensional geometries. The velocity and tracer gas concentration Coefficient of Variations were compared with experimental results. The results from the LES simulations compared better with experimental results than the results from the RANS simulations. The level of mixing downstream of a S-shaped double elbow was higher than either the single elbow or the U-shaped double elbow due to the presence of counter rotating vortices. Penetration of neutralized and non-neutralized aerosol particles through three different types of tubing was studied. The tubing used included standard PVC pipes, aluminum conduit and flexible vacuum hose. Penetration through the aluminum conduit was unaffected by the presence or absence of charge neutralization, whereas particle penetrations through the PVC pipe and the flexible hosing were affected by the amount of particle charge. The electric field in a space enclosed by a solid conductor is zero. Therefore charged particles within the conducting aluminum conduit do not experience any force due to ambient electric fields, whereas the charged particles within the non-conducting PVC pipe and flexible hose experience forces due to the ambient electric fields. This increases the deposition of charged particles compared to neutralized particles within the 1.5â PVC tube and 1.5â flexible hose. Deposition 2001a (McFarland et al. 2001) software was used to predict the penetration through transport lines. The prediction from the software compared well with experiments for all cases except when charged particles were transported through non-conducting materials. A Stairmand cyclone was designed for filtering out large particles at the entrance of the transport section

Structure-Aware Reliability Analysis of Large-Scale Linear Sensor Systems

A linear sensor system is a system in which the sensor measurements have a linear relationship to the source variables that cannot be measured directly. Linear sensor systems are widely deployed in advanced manufacturing processes, wireless transportation systems, electrical grid systems, and oil and gas pipeline systems to monitor and control various physical phenomena critical to the smooth function of such systems. The source variables capture these complex physical phenomena which are then estimated based on the sensor measurements. Two of the critical parameters to be considered while modeling any linear sensor system are the degree of redundancy and reliability. The degree of redundancy is the minimum number of sensor failures that a system can withstand without compromising the identifiability of any source variables. The reliability of a sensor system is a probabilistic evaluation of the ability of a system to tolerate sensor failures. Unfortunately, the existing approaches to compute the degree of redundancy and estimate the reliability are limited in scope due to their inability to solve problems in large-scale. In this research, we establish a new knowledge base for computing the degree of redundancy and estimating the reliability of large-scale linear sensor systems. We first introduce a heuristic convex optimization algorithm that uses techniques from compressed sensing to find highly reliable approximate values for the degree of redundancy. Due to the distributed nature of linear sensor systems often deployed in practical applications, many of these systems embed certain structures. In our second approach, we study these structural properties in detail utilizing matroid theory concepts of connectivity and duality and propose decomposition theorems to disintegrate the redundancy degree problem into subproblems over smaller subsystems. We solve these subproblems using mixed integer programming to obtain the degree of redundancy of the overall system. We further extend these decomposition theorems to help with dividing the reliability evaluation problem into smaller subproblems. Finally, we estimate the reliability of the linear sensor system by solving these subproblems employing mixed integer programming embedded within a recursive variance reduction framework, a technique commonly used in network reliability literature. We implement and test developed algorithms using a wide range of standard test instances that simulate real-life applications of linear sensor systems. Our computational studies prove that the proposed algorithms are significantly faster than the existing ones. Moreover, the variance of our reliability estimate is significantly lower than the previous estimates