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NASA NTRS2001

Robust Strategy for Rocket Engine Health Monitoring

Monitoring the health of rocket engine systems is essentially a two-phase process. The acquisition phase involves sensing physical conditions at selected locations, converting physical inputs to electrical signals, conditioning the signals as appropriate to establish scale or filter interference, and recording results in a form that is easy to interpret. The inference phase involves analysis of results from the acquisition phase, comparison of analysis results to established health measures, and assessment of health indications. A variety of analytical tools may be employed in the inference phase of health monitoring. These tools can be separated into three broad categories: statistical, rule based, and model based. Statistical methods can provide excellent comparative measures of engine operating health. They require well-characterized data from an ensemble of "typical" engines, or "golden" data from a specific test assumed to define the operating norm in order to establish reliable comparative measures. Statistical methods are generally suitable for real-time health monitoring because they do not deal with the physical complexities of engine operation. The utility of statistical methods in rocket engine health monitoring is hindered by practical limits on the quantity and quality of available data. This is due to the difficulty and high cost of data acquisition, the limited number of available test engines, and the problem of simulating flight conditions in ground test facilities. In addition, statistical methods incur a penalty for disregarding flow complexity and are therefore limited in their ability to define performance shift causality. Rule based methods infer the health state of the engine system based on comparison of individual measurements or combinations of measurements with defined health norms or rules. This does not mean that rule based methods are necessarily simple. Although binary yes-no health assessment can sometimes be established by relatively simple rules, the causality assignment needed for refined health monitoring often requires an exceptionally complex rule base involving complicated logical maps. Structuring the rule system to be clear and unambiguous can be difficult, and the expert input required to maintain a large logic network and associated rule base can be prohibitive.

Santi, L. Michael↗

NASA NTRS1995

The Nature of Procedural Interrupts in Ground Operations in Aerospace Systems

This study proposes a methodology for characterizing the causal context and consequences of procedural interrupts in ground operations tasks. Behavioral science researchers observed 26 tasks at NASA's Kennedy Space Center. From these tasks, 81 procedural interrupts were identified and coded for the following information: source of the interrupt, circumstances surrounding the interrupt, and consequences of the interrupt. We found that more interrupts were equipment or personnel-related than procedure-related. More interrupts were due to unavailable resources or resources being incorrect than resources being inaccessible, and most interrupts resulted in some action being taken. Variation in duration of procedural interrupts across causal categories was also examined. The characterization process helps to identify those interrupts that are disruptive, those that cannot be avoided, and those that may have been prevented through better planning or scheduling of resources. Implications for the use of the methodology and suggestions for further research are discussed.

Irwin, Cheryl M.↗

NASA NTRS2002

Information Theoretic Approaches to Rapid Discovery of Relationships in Large Climate Data Sets

Mutual information as the asymptotic Bayesian measure of independence is an excellent starting point for investigating the existence of possible relationships among climate-relevant variables in large data sets, As mutual information is a nonlinear function of of its arguments, it is not beholden to the assumption of a linear relationship between the variables in question and can reveal features missed in linear correlation analyses. However, as mutual information is symmetric in its arguments, it only has the ability to reveal the probability that two variables are related. it provides no information as to how they are related; specifically, causal interactions or a relation based on a common cause cannot be detected. For this reason we also investigate the utility of a related quantity called the transfer entropy. The transfer entropy can be written as a difference between mutual informations and has the capability to reveal whether and how the variables are causally related. The application of these information theoretic measures is rested on some familiar examples using data from the International Satellite Cloud Climatology Project (ISCCP) to identify relation between global cloud cover and other variables, including equatorial pacific sea surface temperature (SST), over seasonal and El Nino Southern Oscillation (ENSO) cycles.

Knuth, Kevin H.↗

NASA NTRS2002

Incorporating Biological Knowledge into Evaluation of Casual Regulatory Hypothesis

Biological data can be scarce and costly to obtain. The small number of samples available typically limits statistical power and makes reliable inference of causal relations extremely difficult. However, we argue that statistical power can be increased substantially by incorporating prior knowledge and data from diverse sources. We present a Bayesian framework that combines information from different sources and we show empirically that this lets one make correct causal inferences with small sample sizes that otherwise would be impossible.

Chrisman, Lonnie↗

NASA NTRS2003

Using Bond Graphs for Articulated, Flexible Multi-bodies, Sensors, Actuators, and Controllers with Application to the International Space Station

Conceptually, modeling of flexible, multi-body systems involves a formulation as a set of time-dependent partial differential equations. However, for practical, engineering purposes, this modeling is usually done using the method of Finite Elements, which approximates the set of partial differential equations, thus generalizing the approach to all continuous media. This research investigates the links between the Bond Graph method and the classical methods used to develop system models and advocates the Bond Graph Methodology and current bond graph tools as alternate approaches that will lead to a quick and precise understanding of a flexible multi-body system under automatic control. For long endurance, complex spacecraft, because of articulation and mission evolution the model of the physical system may change frequently. So a method of automatic generation and regeneration of system models that does not lead to implicit equations, as does the Lagrange equation approach, is desirable. The bond graph method has been shown to be amenable to automatic generation of equations with appropriate consideration of causality. Indeed human-interactive software now exists that automatically generates both symbolic and numeric system models and evaluates causality as the user develops the model, e.g. the CAMP-G software package. In this paper the CAMP-G package is used to generate a bond graph model of the International Space Station (ISS) at an early stage in its assembly, Zvezda. The ISS is an ideal example because it is a collection of bodies that are articulated, many of which are highly flexible. Also many reaction jets are used to control translation and attitude, and many electric motors are used to articulate appendages, which consist of photovoltaic arrays and composite assemblies. The Zvezda bond graph model is compared to an existing model, which was generated by the NASA Johnson Space Center during the Verification and Analysis Cycle of Zvezda.

Montgomery, Raymond C.↗

NASA NTRS2004

Modeling of Turbulence Generated Noise in Jets

A numerically calculated Green's function is used to predict jet noise spectrum and its far-field directivity. A linearized form of Lilley's equation governs the non-causal Green s function of interest, with the non-linear terms on the right hand side identified as the source. In this paper, contributions from the so-called self- and shear-noise source terms will be discussed. A Reynolds-averaged Navier-Stokes solution yields the required mean flow as well as time- and length scales of a noise-generating turbulent eddy. A non-compact source, with exponential temporal and spatial functions, is used to describe the turbulence velocity correlation tensors. It is shown that while an exact non-causal Green's function accurately predicts the observed shift in the location of the spectrum peak with angle as well as the angularity of sound at moderate Mach numbers, at high subsonic and supersonic acoustic Mach numbers the polar directivity of radiated sound is not entirely captured by this Green's function. Results presented for Mach 0.5 and 0.9 isothermal jets, as well as a Mach 0.8 hot jet conclude that near the peak radiation angle a different source/Green's function convolution integral may be required in order to capture the peak observed directivity of jet noise.

Khavaran, Abbas↗

NASA NTRS1989

Impaired driving performance in shiftworkers: the role of the circadian system in a multifactorial model

A substantial and growing percentage of the U.S. work force now works on a rotating shift schedule. The repeated changes in sleep-wake, meal and work times inherent in such schedules conflict with the dictates of the internal biological clock and have adverse consequences for the health of the shiftworker population. An important consequence of this conflict is impaired performance, both on and off the job, as indexed by the increased incidence of motor vehicle accidents in shift workers. In this paper we report the results of a survey administered to rotating shift and straight day workers at a manufacturing plant in the eastern U.S. This survey documents an increased incidence of motor vehicle accidents or "near misses" in which sleepiness was implicated as a cause by the respondent. Complaints of poor sleep and increased sleepiness were also significantly more common in shiftworkers than day workers. Last, shiftworkers reported higher caffeine and alcohol consumption, and were more likely to use alcohol as a sleep aid. Although causal links cannot be established using these associative observations alone, previously reported experience with alteration of shift schedules, improvement of levels of alertness, and reduction in adverse performance outcomes corroborate the possibility of a causal link and suggest potential interventions.

NASA Discipline Regulatory Physiology↗

NASA NTRS2004

Trend and Variability of China Precipitation in Spring and Summer: Linkage to Sea Surface Temperatures

Observational records in the past 50 years show an upward trend of boreal-summer precipitation over central eastern China and a downward trend over northern China. During boreal spring, the trend is upward over southeastern China and downward over central eastern China. This study explores the forcing mechanism of these trends in association with the global sea-surface temperature (SST) variations on the interannual and inter-decadal timescales. Results based on Singular Value Decomposition analyses (SVD) show that the interannual variability of China precipitation in boreal spring and summer can be well defined by two centers of actions for each season, which are co-varying with two interannual modes of SSTs. The first SVD modes of precipitation in spring and summer, which are centered in southeastern China and northern China, respectively, are linked to an ENSO-like mode of SSTs. The second SVD modes of precipitation in both seasons are confined to central eastern China, and are primarily linked to SST variations over the warm pool and Indian Ocean. Features of the anomalous 850-hPa winds and 700-Wa geopotential height corresponding to these modes support a physical mechanism that explains the causal links between the modal variations of precipitation and SSTs. On the decadal and longer timescale, similar causal links are found between the same modes of precipitation and SSTs, except for the case of springtime precipitation over central eastern China. For this case, while the interannual mode of precipitation is positively correlated with the interannual variations of SSTs over the warm pool and Indian Ocean; the inter-decadal mode is negatively correlated with a different SST mode, the North Pacific mode. The later is responsible for the observed downward trend of springtime precipitation over central eastern China. For all other cases, both the interannual and inter-decadal variations of precipitation can be explained by the same mode of SSTs. The upward trend of springtime precipitation over southeastern China and downward trend of summertime precipitation over northern China are attributable to the warming trend of the ENSO-like mode. The recent frequent summertime floods over central eastern China are linked to the warming trend of SSTs over the warm pool and Indian Ocean.

Yang, Fanglin↗

NASA NTRS2004

InvestigationOrganizer: The Development and Testing of a Web-based Tool to Support Mishap Investigations

InvestigationOrganizer (IO) is a collaborative web-based system designed to support the conduct of mishap investigations. IO provides a common repository for a wide range of mishap related information, and allows investigators to make explicit, shared, and meaningful links between evidence, causal models, findings and recommendations. It integrates the functionality of a database, a common document repository, a semantic knowledge network, a rule-based inference engine, and causal modeling and visualization. Thus far, IO has been used to support four mishap investigations within NASA, ranging from a small property damage case to the loss of the Space Shuttle Columbia. This paper describes how the functionality of IO supports mishap investigations and the lessons learned from the experience of supporting two of the NASA mishap investigations: the Columbia Accident Investigation and the CONTOUR Loss Investigation.

Carvalho, Robert F.↗

NASA NTRS2005

Flight Test Comparison Between Enhanced Vision (FLIR) and Synthetic Vision Systems

Limited visibility and reduced situational awareness have been cited as predominant causal factors for both Controlled Flight Into Terrain (CFIT) and runway incursion accidents. NASA s Synthetic Vision Systems (SVS) project is developing practical application technologies with the goal of eliminating low visibility conditions as a causal factor to civil aircraft accidents while replicating the operational benefits of clear day flight operations, regardless of the actual outside visibility condition. A major thrust of the SVS project involves the development/demonstration of affordable, certifiable display configurations that provide intuitive out-the-window terrain and obstacle information with advanced pathway guidance. A flight test evaluation was conducted in the summer of 2004 by NASA Langley Research Center under NASA s Aviation Safety and Security, Synthetic Vision System - Commercial and Business program. A Gulfstream G-V aircraft, modified and operated under NASA contract by the Gulfstream Aerospace Corporation, was flown over a 3-week period at the Reno/Tahoe International Airport and an additional 3-week period at the NASA Wallops Flight Facility to evaluate integrated Synthetic Vision System concepts. Flight testing was conducted to evaluate the performance, usability, and acceptance of an integrated synthetic vision concept which included advanced Synthetic Vision display concepts for a transport aircraft flight deck, a Runway Incursion Prevention System, an Enhanced Vision Systems (EVS), and real-time Database Integrity Monitoring Equipment. This paper focuses on comparing qualitative and subjective results between EVS and SVS display concepts.

Arthur, Jarvis J., III↗

NASA NTRS2005

PlanWorks: A Debugging Environment for Constraint Based Planning Systems

Numerous planning and scheduling systems employ underlying constraint reasoning systems. Debugging such systems involves the search for errors in model rules, constraint reasoning algorithms, search heuristics, and the problem instance (initial state and goals). In order to effectively find such problems, users must see why each state or action is in a plan by tracking causal chains back to part of the initial problem instance. They must be able to visualize complex relationships among many different entities and distinguish between those entities easily. For example, a variable can be in the scope of several constraints, as well as part of a state or activity in a plan; the activity can arise as a consequence of another activity and a model rule. Finally, they must be able to track each logical inference made during planning. We have developed PlanWorks, a comprehensive system for debugging constraint-based planning and scheduling systems. PlanWorks assumes a strong transaction model of the entire planning process, including adding and removing parts of the constraint network, variable assignment, and constraint propagation. A planner logs all transactions to a relational database that is tailored to support queries for of specialized views to display different forms of data (e.g. constraints, activities, resources, and causal links). PlanWorks was specifically developed for the Extensible Universal Remote Operations Planning Architecture (EUROPA(sub 2)) developed at NASA, but the underlying principles behind PlanWorks make it useful for many constraint-based planning systems. The paper is organized as follows. We first describe some fundamentals of EUROPA(sub 2). We then describe PlanWorks' principal components. We then discuss each component in detail, and then describe inter-component navigation features. We close with a discussion of how PlanWorks is used to find model flaws.

Daley, Patrick↗

NASA NTRS2005

Prediction of Turbulence-Generated Noise in Unheated Jets: JeNo Technical Manual (Version 1.0) - Part 1

The model-based approach, used by the JeNo code to predict jet noise spectral directivity, is described. A linearized form of Lilley's equation governs the non-causal Green s function of interest, with the non-linear terms on the right hand side identified as the source. A Reynolds-averaged Navier-Stokes (RANS) solution yields the required mean flow for the solution of the propagation Green s function in a locally parallel flow. The RANS solution also produces time- and length-scales needed to model the non-compact source, the turbulent velocity correlation tensor, with exponential temporal and spatial functions. It is shown that while an exact non-causal Green s function accurately predicts the observed shift in the location of the spectrum peak with angle as well as the angularity of sound at low to moderate Mach numbers, the polar directivity of radiated sound is not entirely captured by this Green s function at high subsonic and supersonic acoustic Mach numbers. Results presented for unheated jets in the Mach number range of 0.51 to 1.8 suggest that near the peak radiation angle of high-speed jets, a different source/Green s function convolution integral may be required in order to capture the peak observed directivity of jet noise. A sample Mach 0.90 heated jet is also discussed that highlights the requirements for a comprehensive jet noise prediction model.

Khavaran, Abbas↗

NASA NTRS2005

Revealing Relationships among Relevant Climate Variables with Information Theory

The primary objective of the NASA Earth-Sun Exploration Technology Office is to understand the observed Earth climate variability, thus enabling the determination and prediction of the climate's response to both natural and human-induced forcing. We are currently developing a suite of computational tools that will allow researchers to calculate, from data, a variety of information-theoretic quantities such as mutual information, which can be used to identify relationships among climate variables, and transfer entropy, which indicates the possibility of causal interactions. Our tools estimate these quantities along with their associated error bars, the latter of which is critical for describing the degree of uncertainty in the estimates. This work is based upon optimal binning techniques that we have developed for piecewise-constant, histogram-style models of the underlying density functions. Two useful side benefits have already been discovered. The first allows a researcher to determine whether there exist sufficient data to estimate the underlying probability density. The second permits one to determine an acceptable degree of round-off when compressing data for efficient transfer and storage. We also demonstrate how mutual information and transfer entropy can be applied so as to allow researchers not only to identify relations among climate variables, but also to characterize and quantify their possible causal interactions.

Knuth, Kevin H.↗

NASA NTRS2006

Synthetic Vision Enhanced Surface Operations and Flight Procedures Rehearsal Tool

Limited visibility has been cited as predominant causal factor for both Controlled-Flight-Into-Terrain (CFIT) and runway incursion accidents. NASA is conducting research and development of Synthetic Vision Systems (SVS) technologies which may potentially mitigate low visibility conditions as a causal factor to these accidents while replicating the operational benefits of clear day flight operations, regardless of the actual outside visibility condition. Two experimental evaluation studies were performed to determine the efficacy of two concepts: 1) head-worn display application of SVS technology to enhance transport aircraft surface operations, and 2) three-dimensional SVS electronic flight bag display concept for flight plan preview, mission rehearsal and controller-pilot data link communications interface of flight procedures. In the surface operation study, pilots evaluated two display devices and four display modes during taxi under unlimited and CAT II visibility conditions. In the mission rehearsal study, pilots flew approaches and departures in an operationally-challenged airport environment, including CFIT scenarios. Performance using the SVS concepts was compared to traditional baseline displays with paper charts only or EFB information. In general, the studies evince the significant situation awareness and enhanced operational capabilities afforded from these advanced SVS display concepts. The experimental results and conclusions from these studies are discussed along with future directions.

Arthur, Jarvis J., III↗

NASA NTRS2008

Aspects of Synthetic Vision Display Systems and the Best Practices of the NASA's SVS Project

NASA s Synthetic Vision Systems (SVS) Project conducted research aimed at eliminating visibility-induced errors and low visibility conditions as causal factors in civil aircraft accidents while enabling the operational benefits of clear day flight operations regardless of actual outside visibility. SVS takes advantage of many enabling technologies to achieve this capability including, for example, the Global Positioning System (GPS), data links, radar, imaging sensors, geospatial databases, advanced display media and three dimensional video graphics processors. Integration of these technologies to achieve the SVS concept provides pilots with high-integrity information that improves situational awareness with respect to terrain, obstacles, traffic, and flight path. This paper attempts to emphasize the system aspects of SVS - true systems, rather than just terrain on a flight display - and to document from an historical viewpoint many of the best practices that evolved during the SVS Project from the perspective of some of the NASA researchers most heavily involved in its execution. The Integrated SVS Concepts are envisagements of what production-grade Synthetic Vision systems might, or perhaps should, be in order to provide the desired functional capabilities that eliminate low visibility as a causal factor to accidents and enable clear-day operational benefits regardless of visibility conditions.

Bailey, Randall E.↗

NASA NTRS2010

Soil Moisture Controls on Rainfall and Temperature Variability: A Modeler Searches Through Observational Data

The degree to which atmospheric processes respond to variations in soil moisture - a potentially important but largely untapped element of subseasonal to seasonal prediction - can be determined easily and directly for an atmospheric model but cannot be determined directly for nature through an analysis of observations. In atmospheric models) directions of causality can be artificially manipulated; we can avoid difficulties associated with the fact that atmospheric variations have a much larger impact on land state variations than vice-versa. In nature) on the other hand) the dominant direction of causality (the atmosphere forcing the ground) cannot be artificially "turned off") and the statistics associated with this dominant direction overwhelm those of the feedback signal. Observational data) however) do allow a number of indirect measures of landatmosphere feedback. This seminar reports on a series of joint analyses of observational and model data designed to illuminate the degree of land-atmosphere feedback present in the real world. The indirect measures do in fact suggest that feedback in nature, though small) is significant - enough to warrant the development of realistic land initialization strategies for subseasonal and seasonal forecasts.

Koster, Randal↗

NASA NTRS2009

Diagnosis and Reconfiguration using Bayesian Networks: An Electrical Power System Case Study

Automated diagnosis and reconfiguration are important computational techniques that aim to minimize human intervention in autonomous systems. In this paper, we develop novel techniques and models in the context of diagnosis and reconfiguration reasoning using causal Bayesian networks (BNs). We take as starting point a successful diagnostic approach, using a static BN developed for a real-world electrical power system. We discuss in this paper the extension of this diagnostic approach along two dimensions, namely: (i) from a static BN to a dynamic BN; and (ii) from a diagnostic task to a reconfiguration task. More specifically, we discuss the auto-generation of a dynamic Bayesian network from a static Bayesian network. In addition, we discuss subtle, but important, differences between Bayesian networks when used for diagnosis versus reconfiguration. We discuss a novel reconfiguration agent, which models a system causally, including effects of actions through time, using a dynamic Bayesian network. Though the techniques we discuss are general, we demonstrate them in the context of electrical power systems (EPSs) for aircraft and spacecraft. EPSs are vital subsystems on-board aircraft and spacecraft, and many incidents and accidents of these vehicles have been attributed to EPS failures. We discuss a case study that provides initial but promising results for our approach in the setting of electrical power systems.

Knox, W. Bradley↗

NASA NTRS2010

Aircraft Loss-of-Control Accident Analysis

Loss of control remains one of the largest contributors to fatal aircraft accidents worldwide. Aircraft loss-of-control accidents are complex in that they can result from numerous causal and contributing factors acting alone or (more often) in combination. Hence, there is no single intervention strategy to prevent these accidents. To gain a better understanding into aircraft loss-of-control events and possible intervention strategies, this paper presents a detailed analysis of loss-of-control accident data (predominantly from Part 121), including worst case combinations of causal and contributing factors and their sequencing. Future potential risks are also considered.

Belcastro, Christine M.↗