Bootstrap data methodology for sequential hybrid model building

A method for modeling engine operation comprising the steps of: 1. collecting a first plurality of sensory data, 2. partitioning a flight envelope into a plurality of sub-regions, 3. assigning the first plurality of sensory data into the plurality of sub-regions, 4. generating an empirical model of at least one of the plurality of sub-regions, 5. generating a statistical summary model for at least one of the plurality of sub-regions, 6. collecting an additional plurality of sensory data, 7. partitioning the second plurality of sensory data into the plurality of sub-regions, 8. generating a plurality of pseudo-data using the empirical model, and 9. concatenating the plurality of pseudo-data and the additional plurality of sensory data to generate an updated empirical model and an updated statistical summary model for at least one of the plurality of sub-regions

Propulsion Health Management System Development for Affordable and Reliable Operation of Space Exploration Systems

The constraints of future Exploration Missions will require unique integrated system health management capabilities throughout the mission. An ambitious launch schedule, human-rating requirements, long quiescent periods, limited human access for repair or replacement, and long communication delays, all require an integrated approach to health management that can span distinct, yet interdependent vehicle subsystems, anticipate failure states, provide autonomous remediation and support the Exploration Mission from beginning to end. Propulsion is a critical part of any space exploration mission, and monitoring the health of the propulsion system is an integral part of assuring mission safety and success. Health management is a somewhat ubiquitous technology that encompasses a large spectrum of physical components and logical processes. For this reason, it is essential to develop a systematic plan for propulsion health management system development. This paper provides a high-level perspective of propulsion health management systems, and describes a logical approach for the future planning and early development that are crucial to planned space exploration programs. It also presents an overall approach, or roadmap, for propulsion health management system development and a discussion of the associated roadblocks and challenges

Immobilization, stabilization and patterning techniques for enzyme based sensor systems.

Sandia National Laboratories has recently opened the Chemical and Radiation Detection Laboratory (CRDL) in Livermore CA to address the detection needs of a variety of government agencies (e.g., Department of Energy, Environmental Protection Agency, Department of Agriculture) as well as provide a fertile environment for the cooperative development of new industrial technologies. This laboratory consolidates a variety of existing chemical and radiation detection efforts and enables Sandia to expand into the novel area of biochemically based sensors. One aspect of this biosensor effort is further development and optimization of enzyme modified field effect transistors (EnFETs). Recent work has focused upon covalent attachment of enzymes to silicon dioxide and silicon nitride surfaces for EnFET fabrication. They are also investigating methods to pattern immobilized proteins; a critical component for development of array-based sensor systems. Novel enzyme stabilization procedures are key to patterning immobilized enzyme layers while maintaining enzyme activity. Results related to maximized enzyme loading, optimized enzyme activity and fluorescent imaging of patterned surfaces will be presented

Abrupt fault detection and isolation for gas turbine components based on a 1D convolutional neural network using time series data

The FDI step identifies the presence of a fault, its level, type, and possible location. Gas turbine gas-path fault detection and isolation can improve the availability and economy of gas turbine components. Data-driven FDI methods are studied in this paper. Some notable gas turbine FDI challenges include: insensitivity to operating conditions, robust separation of faults, noisy sensor readings and missing data, reliable fault detection in time-varying conditions, and the influence of performance gradual deterioration. For conventional ML methods, the problem with handling time series data is its volume and the associated computational complexity; therefore, the available information must be appropriately compressed via the transformation of high-dimensional data into a low-dimensional feature space with minimal loss of class separability. In order to improve the detection and isolation sensitivity, this paper develops a method for FDI based on CNNs. Work in this paper includes: (1) Defining the problem and assembling a dataset. (2) Preparing data for training, validation and test: data generation, feature engineering, data pre-processing, data formatting. (3) Building up the model. (4) Training and validating the model (evaluation protocol). (5) Optimizing: a. deciding the model size. b. regularizing the model by getting more training data, reducing the capacity of the network, adding weight regularization or adding dropout. c. tuning hyperparameters. (6) Evaluation

Boosting cross-border regions through better cross-border transport services. The European case

Cross-border regions are the laboratories of European integration. Daily interactions across European borders let citizens experience the benefits of the European Union (EU) internal market. Still, many border barriers continue to prevent individuals and organisations from exploiting the full-potential of European border regions and the benefits of a more integrated European territory. Amongst these barriers are the absence or inappropriate supply of cross-border public transport services. In this context, this paper presents potential policy tools to increase border permeability related to cross-border public transport as well as practical results from a few case-studies implemented across Europe.info:eu-repo/semantics/publishedVersio

An Integrated Architecture for Aircraft Engine Performance Monitoring and Fault Diagnostics: Engine Test Results

This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented

An optimal transient growth of small perturbations in thin gaseous discs

A thin gaseous disc with an almost keplerian angular velocity profile, bounded by a free surface and rotating around point-mass gravitating object is nearly spectrally stable. Despite that the substantial transient growth of linear perturbations measured by the evolution of their acoustic energy is possible. This fact is demonstrated for the simple model of a non-viscous polytropic thin disc of a finite radial size where the small adiabatic perturbations are considered as a linear combination of neutral modes with a corotational radius located beyond the outer boundary of the flow.Comment: 15 pages, 5 figures, accepted for publication in Ast

Positronium laser cooling via the 13S1^3S-23P2^3P transition with a broadband laser pulse

We report on laser cooling of a large fraction of positronium (Ps) in free-flight by strongly saturating the $1^3S$-$2^3P$ transition with a broadband, long-pulsed 243 nm alexandrite laser. The ground state Ps cloud is produced in a magnetic and electric field-free environment. We observe two different laser-induced effects. The first effect is an increase in the number of atoms in the ground state after the time Ps has spent in the long-lived $3^3P$ states. The second effect is the one-dimensional Doppler cooling of Ps, reducing the cloud's temperature from 380(20) K to 170(20) K. We demonstrate a 58(9) % increase in the coldest fraction of the Ps ensemble.Comment: 6 pages, 5 figure