A neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine

In order to properly utilize the available fuel and oxidizer of a liquid propellant rocket engine, the mixture ratio is closed loop controlled during main stage (65 percent - 109 percent power) operation. However, because of the lack of flight-capable instrumentation for measuring mixture ratio, the value of mixture ratio in the control loop is estimated using available sensor measurements such as the combustion chamber pressure and the volumetric flow, and the temperature and pressure at the exit duct on the low pressure fuel pump. This estimation scheme has two limitations. First, the estimation formula is based on an empirical curve fitting which is accurate only within a narrow operating range. Second, the mixture ratio estimate relies on a few sensor measurements and loss of any of these measurements will make the estimate invalid. In this paper, we propose a neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine. The estimator is an extension of a previously developed neural network based sensor failure detection and recovery algorithm (sensor validation). This neural network uses an auto associative structure which utilizes the redundant information of dissimilar sensors to detect inconsistent measurements. Two approaches have been identified for synthesizing mixture ratio from measurement data using a neural network. The first approach uses an auto associative neural network for sensor validation which is modified to include the mixture ratio as an additional output. The second uses a new network for the mixture ratio estimation in addition to the sensor validation network. Although mixture ratio is not directly measured in flight, it is generally available in simulation and in test bed firing data from facility measurements of fuel and oxidizer volumetric flows. The pros and cons of these two approaches will be discussed in terms of robustness to sensor failures and accuracy of the estimate during typical transients using simulation data

Integrated health monitoring and controls for rocket engines

Current research in intelligent control systems at the Lewis Research Center is described in the context of a functional framework. The framework is applicable to a variety of reusable space propulsion systems for existing and future launch vehicles. It provides a 'road map' technology development to enable enhanced engine performance with increased reliability, durability, and maintainability. The framework hierarchy consists of a mission coordination level, a propulsion system coordination level, and an engine control level. Each level is described in the context of the Space Shuttle Main Engine. The concept of integrating diagnostics with control is discussed within the context of the functional framework. A distributed real time simulation testbed is used to realize and evaluate the functionalities in closed loop

Implementation of a model based fault detection and diagnosis for actuation faults of the Space Shuttle main engine

In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the space shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the space shuttle main engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults

Identifying the challenges and facilitators of implementing a COPD care bundle.

BACKGROUND: Care bundles have been shown to improve outcomes, reduce hospital readmissions and reduce length of hospital stay; therefore increasing the speed of uptake and delivery of care bundles should be a priority in order to deliver more timely improvements and consistent high-quality care. Previous studies have detailed the difficulties of obtaining full compliance to bundle elements but few have described the underlying reasons for this. In order to improve future implementation this paper investigates the challenges encountered by clinical teams implementing a chronic obstructive pulmonary disease (COPD) care bundle and describes actions taken to overcome these challenges. METHODS: An initial retrospective documentary analysis of data from seven clinical implementation teams was undertaken to review the challenges faced by the clinical teams. Three focus groups with healthcare professionals and managers explored solutions to these challenges developed during the project. RESULTS: Documentary analysis identified 28 challenges which directly impacted implementation of the COPD care bundle within five themes; staffing, infrastructure, process, use of improvement methodology and patient and public involvement. Focus groups revealed that the five most significant challenges for all groups were: staff too busy, staff shortages, lack of staff engagement, added workload of the bundle and patient coding issues. The participants shared facilitating factors used to overcome issues including: shifting perceptions to improve engagement, further education sessions to increase staff participation and gaining buy-in from managers through payment frameworks. CONCLUSIONS: Maximising the impact of a care bundle relies on its successful and timely implementation. Teams implementing the COPD care bundle encountered challenges that were common to all teams and sites. Understanding and learning from the challenges faced by previous endeavours and identifying the facilitators to overcoming these barriers provides an opportunity to mitigate issues that waste time and resources, and ensures that training can be tailored to the anticipated challenges

ABA and Oxygen Crosstalk During Seed Development

No abstract availabl

Elastic moduli approximation of higher symmetry for the acoustical properties of an anisotropic material

The issue of how to define and determine an optimal acoustical fit to a set of anisotropic elastic constants is addressed. The optimal moduli are defined as those which minimize the mean squared difference in the acoustical tensors between the given moduli and all possible moduli of a chosen higher material symmetry. The solution is shown to be identical to minimizing a Euclidean distance function, or equivalently, projecting the tensor of elastic stiffness onto the appropriate symmetry. This has implications for how to best select anisotropic constants to acoustically model complex materials.Comment: 20 page

Junctions and thin shells in general relativity using computer algebra I: The Darmois-Israel Formalism

We present the GRjunction package which allows boundary surfaces and thin-shells in general relativity to be studied with a computer algebra system. Implementing the Darmois-Israel thin shell formalism requires a careful selection of definitions and algorithms to ensure that results are generated in a straight-forward way. We have used the package to correctly reproduce a wide variety of examples from the literature. We present several of these verifications as a means of demonstrating the packages capabilities. We then use GRjunction to perform a new calculation - joining two Kerr solutions with differing masses and angular momenta along a thin shell in the slow rotation limit.Comment: Minor LaTeX error corrected. GRjunction for GRTensorII is available from http://astro.queensu.ca/~grtensor/GRjunction.htm