We propose a computational technique to reconstruct internal physiological flows described by sparse point-wise MRI velocity measurements. Assuming that the viscous forces in the flow are negligible, the incompressible flow field can be obtained from a velocity potential that satisfies Laplace's equation. A set of basis functions each satisfying Laplace's equation with appropriately defined boundary data is constructed using the finite-element method. An inverse problem is formulated where higher resolution boundary and internal velocity data are extracted from the point-wise MRI velocity measurements using a least-squares method. From the results we obtained with approximately 100 internal measurement points, the proposed reconstruction method is shown to be effective in filtering out the experimental noise at levels as high as 30%, while matching the reference solution within 2%. This allows the reconstruction of a high-resolution velocity field with limited MRI encoding.
Physics-based approach to the cryogenic flow health management is presented. It is based on fast and time-accurate physics models of the cryogenic flow in the transfer line. We discuss main features of one of these models – the homogeneous moving front model and presents results of its validation. The main steps of the approach including fault detection, identification, and evaluation are discussed. A few examples of faults are presented. It is shown that dynamic features of the faults naturally form a number of ambiguity groups. A D-matrix approach to optimized identification of these faults is briefly outlined. An example of discerning and evaluating faults within one ambiguity group using optimization algorithm is considered in more details. An application of this approach to the Integrated Health Management of cryogenic loading is discussed.
The study is motivated by NASA plans to develop technology for an autonomous cryogenic loading operation including online fault diagnostics as a part of Integrated Health Management system. For years, the diagnostic modeling effort is performed in many paradigms. None of these paradigms independently can provide a complete set of efficiency metrics: better diagnostics, lower run-time, etc. D-matrix, a causal 0-1 relationship between faults and tests, is proposed as a single representation between different model-based diagnostic methods for comparison and communication. This framework is suitable to create a common platform for communication via D-matrix for systems engineering process. The knowledge transfer between modeling techniques is done via D-matrix. In addition, D-matrix provides a common paradigm to compare the embedded knowledge and performance of heterogeneous diagnostic systems. D-matrix is generated from physics models to be used with faster run-time performance D-matrix based diagnostic algorithms. Additionally, we will also investigate if the derived D- matrix and thereby the physics model is sufficient and accurate for efficient diagnostics via iDME tool.
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