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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, satellite vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed, or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery.

Satellites↗
NASA NTRS2014

Application of Fault Management Theory to the Quantitative Selection of a Launch Vehicle Abort Trigger Suite

The theory of System Health Management (SHM) and of its operational subset Fault Management (FM) states that FM is implemented as a "meta" control loop, known as an FM Control Loop (FMCL). The FMCL detects that all or part of a system is now failed, or in the future will fail (that is, cannot be controlled within acceptable limits to achieve its objectives), and takes a control action (a response) to return the system to a controllable state. In terms of control theory, the effectiveness of each FMCL is estimated based on its ability to correctly estimate the system state, and on the speed of its response to the current or impending failure effects. This paper describes how this theory has been successfully applied on the National Aeronautics and Space Administration's (NASA) Space Launch System (SLS) Program to quantitatively estimate the effectiveness of proposed abort triggers so as to select the most effective suite to protect the astronauts from catastrophic failure of the SLS. The premise behind this process is to be able to quantitatively provide the value versus risk trade‐off for any given abort trigger, allowing decision makers to make more informed decisions. All current and planned crewed launch vehicles have some form of vehicle health management system integrated with an emergency launch abort system to ensure crew safety. While the design can vary, the underlying principle is the same: detect imminent catastrophic vehicle failure, initiate launch abort, and extract the crew to safety. Abort triggers are the detection mechanisms that identify that a catastrophic launch vehicle failure is occurring or is imminent and cause the initiation of a notification to the crew vehicle that the escape system must be activated. While ensuring that the abort triggers provide this function, designers must also ensure that the abort triggers do not signal that a catastrophic failure is imminent when in fact the launch vehicle can successfully achieve orbit. That is, the abort triggers must have low false negative rates to be sure that real crew‐threatening failures are detected, and also low false positive rates to ensure that the crew does not abort from non‐crew‐threatening launch vehicle behaviors. The analysis process described in this paper is a compilation of over six years of lessons learned and refinements from experiences developing abort triggers for NASA's Constellation Program (Ares I Project) and the SLS Program, as well as the simultaneous development of SHM/FM theory. The paper will describe the abort analysis concepts and process, developed in conjunction with SLS Safety and Mission Assurance (S&MA) to define a common set of mission phase, failure scenario, and Loss of Mission Environment (LOME) combinations upon which the SLS Loss of Mission (LOM) Probabilistic Risk Assessment (PRA) models are built. This abort analysis also requires strong coordination with the Multi‐Purpose Crew Vehicle (MPCV) and SLS Structures and Environments (STE) to formulate a series of abortability tables that encapsulate explosion dynamics over the ascent mission phase. The design and assessment of abort conditions and triggers to estimate their Loss of Crew (LOC) Benefits also requires in‐depth integration with other groups, including Avionics, Guidance, Navigation and Control(GN&C), the Crew Office, Mission Operations, and Ground Systems. The outputs of this analysis are a critical input to SLS S&MA's LOC PRA models. The process described here may well be the first full quantitative application of SHM/FM theory to the selection of a sensor suite for any aerospace system.

Lo, Yunnhon↗
NASA NTRS1995

Advanced transportation system studies. Technical area 2: Heavy lift launch vehicle development: Technical Results - Volume 2

Sections 10 to 13 of the Advanced Transportation System Studies final report are included in this volume. Section 10 contains a copy of an executive summary that was prepared by Lockheed Space Operations Company (LSOC) to document their support to the TA-2 contract during the first-year period of performance of the contract, May 1992 through May 1993. LSOC participated on the TA-2 contract as part of the concurrent engineering launch system definition team, and provided outstanding heavy lift launch vehicle (HLLV) ground operations requirements and concept assessments for Lockheed Missiles and Space Company (LMSC) through an intercompany work transfer as well as providing specific HLLV ground operations assessments at the direction of NASA KSC through KSC funding that was routed to the TA-2 contract. Section 11 contains a copy of a vehicle-independent, launch system health management requirements assessment. The purpose of the assessment was to define both health management requirements and the associated interfaces between a generic advanced transportation system launch vehicle and all related elements of the entire transportation system, including the ground segment. Section 12 presents the major TA-2 presentations provided to summarize the significant results and conclusions that were developed over the course of the contract. Finally, Section 13 presents the design and assessment report on the first lunar outpost heavy lift launch vehicle.

Source record↗
NASA NTRS2006

Intelligent Sensors and Components for On-Board ISHM

A viewgraph presentation on the development of intelligent sensors and components for on-board Integrated Systems Health Health Management (ISHM) is shown. The topics include: 1) Motivation; 2) Integrated Systems Health Management (ISHM); 3) Intelligent Components; 4) IEEE 1451; 5)Intelligent Sensors; 6) Application; and 7) Future Directions

Figueroa, Jorge↗
NASA NTRS2024

Intelligent Devices/Equipment/Instruments (IDEI) for Enabling Crew Health and Performance on Mars

The Moon to Mars eXploration Systems and Habitation (M2M X-Hab) 2024 Academic Innovation Challenge features a project titled “Intelligent Devices/Equipment/Instruments (IDEI) for Enabling Crew Health and Performance on Mars”. The project calls for the development of prototype IDEIs “that could be used for implementing integrated system health management for Crew Health and Performance (CHP) required for crew living on Mars for extended periods of time”. Thus, the deliverable for the project is not only a prototype exercise device, but also an ontology that provides insight into the best ways to exercise on Mars. To that end, we on the BLiSS team, supported by advisors from industry and academia, set out to ideate an exercise ontology and demonstrate its effectiveness through a functional prototype. To achieve the stakeholders’ requests, the device must operate semi-autonomously, must be an analog for an extant exercise device on Earth, and must provide quantitative information about the exercise and the device’s own state of health. Here, we define “semi-autonomous” as referring to the fact that while the system should be as autonomous as possible, there are some processes that the system cannot fulfill on its own. These include, but are not limited to, user identification, physical exercise reconfiguration, and wearable sensor placement.

llyana Smith↗
NASA NTRSDate not supplied

Enabling in-time Prognostics with Surrogate Modeling through Physics-enhanced Dynamic Mode Decomposition Method

Computational models provide essential quantitative tools for assessing and predicting the health and performance of physical systems. However, high-fidelity models are rarely used in real-time operations or large optimization loops, due to their time-intensive nature. A common approach to improving computational efficiency of prognosis is to employ surrogate models. Such models can significantly decrease computation time for some accuracy loss. In this context, use of Dynamic Mode Decomposition (DMD) is proposed to generate surrogate models for lithium-ion (Li-ion) battery discharge. DMD has been suggested and used successfully in the area of fluid dynamics for over a decade, but it has not been applied to the PHM domain, where far-ahead prediction of nonlinear behavior is crucial to propagate faults or predict Remaining Useful Life (RUL). For Li-ion battery health management, the standard application of DMD using only the observable quantities of interest was unable to capture the nonlinear discharge of batteries exhibited in lab testing. The Koopman theory, however, provides a mechanism to tradeoff low dimensional nonlinear models with high-dimensional linear ones in a DMD framework, by augmenting nonlinear state variables into the system representation. In this way, DMD allows for configurable simulation accuracy dependent on the dimensionality of the Koopman operator. For battery health management, we augmented the observable variables with the hidden states of a higher-fidelity physics model to build the DMD surrogate. In comparison to a high-fidelity model, the surrogate improved computational efficiency with only a minimal loss of accuracy, and enabled long-term prognostics horizons. A generalized method for this was implemented in the prog models python package.

prognostics and health management↗
NASA NTRS2011

Hardware Specific Integration Strategy for Impedance-Based Structural Health Monitoring of Aerospace Systems

The Integrated Vehicle Health Management (IVHM) Project, sponsored by NASA's Aeronautics Research Mission Directorate, is conducting research to advance the state of highly integrated and complex flight-critical health management technologies and systems. An effective IVHM system requires Structural Health Monitoring (SHM). The impedance method is one such SHM technique for detection and monitoring complex structures for damage. This position paper on the impedance method presents the current state of the art, future directions, applications and possible flight test demonstrations.

Owen, Robert B.↗
NASA NTRSDate not supplied

Improving Computational Efficiency of Prognostics Algorithms in Resource-Constrained Settings

The field of prognostics and health management provides quantitative methods for monitoring and predicting the health of physical systems. Prognostics algorithms are useful in that they can be employed to assess the current state of a system, propagate the system state throughout time, and predict potential anomalies or failures that may occur. However, effective prognosis can be challenging to achieve in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for some prognostics applications. In this work, we implement three new algorithmic approaches to prediction (sampling methods, variable prediction time step, variable prediction sample size) with the goal of improving computational efficiency while minimizing decrease in model accuracy. To quantitatively analyze our results, we examine a use-case of degradation of a Lithium-ion battery. Notably, through this work it was found that none of the sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. However, our results show that prediction accuracy is highly dependent on the time step used, and that an appropriate time step can optimize both model accuracy and simulation efficiency. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment in an application-specific manner may prove useful in some applications. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency of prognosis.

Prognostics↗
NASA NTRS2004

Multi-level Simulation of a Real Time Vibration Monitoring System Component

This paper describes the development of a custom built Digital Signal Processing (DSP) printed circuit board designed to implement the Advanced Real Time Vibration Monitoring Subsystem proposed by MSFC Transportation Directorate in 2000 for the Space Shuttle Main Engine Advanced Health Management System (AHMS). This Real Time Vibration Monitoring System (RTVMS) is being developed for ground use as part of the AHMS Health Management Computer-Integrated Rack Assembly (HMC-IRA). The HMC-IRA RTVMS design contains five DSPs which are highly interconnected through individual communication ports, shared memory, and a unique communication router that allows all the DSPs to receive digitized data from two multi-channel analog boards simultaneously. This paper will briefly cover the overall board design but will focus primarily on the state-of-the-art simulation environment within which this board was developed. This 16-layer board with over 1800 components and an additional mezzanine card has been an extremely challenging design. Utilization of a Mentor Graphics simulation environment provided the unique board and system level simulation capability to ascertain any timing or functional concerns before production. By combining VHDL, Synopsys Software and Hardware Models, and the Mentor Design Capture Environment, multiple simulations were developed to verify the RTVMS design. This multi-level simulation allowed the designers to achieve complete operability without error the first time the RTVMS printed circuit board was powered. The HMCIRA design has completed all engineering unit testing and the deliverable unit is currently under development.

Roberston, Bryan↗
NASA NTRS2005

Multi-level Simulation of a Real Time Vibration Monitoring System Component

This paper describes the development of a custom built Digital Signal Processing (DSP) printed circuit board designed to implement the Advanced Real Time Vibration Monitoring Subsystem proposed by Marshall Space Flight Center (MSFC) Transportation Directorate in 2000 for the Space Shuttle Main Engine Advanced Health Management System (AHMS). This Real Time Vibration Monitoring System (RTVMS) is being developed for ground use as part of the AHMS Health Management Computer-Integrated Rack Assembly (HMC-IRA). The HMC-IRA RTVMS design contains five DSPs which are highly interconnected through individual communication ports, shared memory, and a unique communication router that allows all the DSPs to receive digitized data fiom two multi-channel analog boards simultaneously. This paper will briefly cover the overall board design but will focus primarily on the state-of-the-art simulation environment within which this board was developed. This 16-layer board with over 1800 components and an additional mezzanine card has been an extremely challenging design. Utilization of a Mentor Graphics simulation environment provided the unique board and system level simulation capability to ascertain any timing or functional concerns before production. By combining VHDL, Synopsys Software and Hardware Models, and the Mentor Design Capture Environment, multiple simulations were developed to verify the RTVMS design. This multi-level simulation allowed the designers to achieve complete operability without error the first time the RTVMS printed circuit board was powered. The HMC-IRA design has completed all engineering and deliverable unit testing. P

Robertson, Bryan A.↗
NASA NTRS1994

Predictability in space launch vehicle anomaly detection using intelligent neuro-fuzzy systems

Included in this viewgraph presentation on intelligent neuroprocessors for launch vehicle health management systems (HMS) are the following: where the flight failures have been in launch vehicles; cumulative delay time; breakdown of operations hours; failure of Mars Probe; vehicle health management (VHM) cost optimizing curve; target HMS-STS auxiliary power unit location; APU monitoring and diagnosis; and integration of neural networks and fuzzy logic.

Gulati, Sandeep↗
NASA NTRS2007

NASA Glenn Research in Controls and Diagnostics for Intelligent Aerospace Propulsion Systems

With the increased emphasis on aircraft safety, enhanced performance and affordability, and the need to reduce the environmental impact of aircraft, there are many new challenges being faced by the designers of aircraft propulsion systems. The Controls and Dynamics Branch (CDB) at NASA (National Aeronautics and Space Administration) Glenn Research Center (GRC) in Cleveland, Ohio, is leading and participating in various projects in partnership with other organizations within GRC and across NASA, the U.S. aerospace industry, and academia to develop advanced controls and health management technologies that will help meet these challenges through the concept of Intelligent Propulsion Systems. This presentation describes the current CDB activities in support of the NASA Aeronautics Research Mission, with an emphasis on activities under the Integrated Vehicle Health Management (IVHM) and Integrated Resilient Aircraft Control (IRAC) projects of the Aviation Safety Program. Under IVHM, CDB focus is on developing advanced techniques for monitoring the health of the aircraft engine gas path with a focus on reliable and early detection of sensor, actuator and engine component faults. Under IRAC, CDB focus is on developing adaptive engine control technologies which will increase the probability of survival of aircraft in the presence of damage to flight control surfaces or to one or more engines. The technology development plans are described as well as results from recent research accomplishments.

Garg, Sanjay↗
NASA NTRS2021

An Uncertainty Quantification Framework for Autonomous Flight System Tracking and Health Monitoring

This work proposes a perspective towards establishing a framework for uncertainty quantification of autonomous system tracking and health monitoring. The approach leverages the use of a predictive process structure, which maps uncertainty sources and their interaction according to the quantity of interest and the goal of the predictive estimation. It is systematic and uses basic elements that are system agnostic, and therefore needs to be tailored according to the specificity of the application. This work is motivated by the interest in low-altitude unmanned aerial vehicle operations, where awareness of vehicle and airspace state becomes more relevant as the density of autonomous operations grows rapidly. Predicted scenarios in the area of small vehicle operations and urban air mobility have no precedent, and holistic frameworks to perform prognostics and health management (PHM) at the system- and airspace-level are missing formal approaches to account for uncertainty. At the end of the paper, two case studies demonstrate implementation framework of trajectory tracking and health diagnosis for a small unmanned aerial vehicle. This work has been accepted for publication at the International Journal of Prognostics and Health Management Jan 2021. Minor edits have been incorporated to this original submission to incorporate complete overview and software integration.

Uncertainty Quantification↗
NASA NTRS2007

Programs for Testing an SSME-Monitoring System

A suite of computer programs has been developed for special test equipment (STE) that is used in verification testing of the Health Management Computer Integrated Rack Assembly (HMCIRA), a ground-based system of analog and digital electronic hardware and software for "flight-like" testing for development of components of an advanced health-management system for the space shuttle main engine (SSME). The STE software enables the STE to simulate the analog input and the data flow of an SSME test firing from start to finish.

Lang, Andre↗
NASA NTRS2011

Optimal Tuner Selection for Kalman-Filter-Based Aircraft Engine Performance Estimation

An emerging approach in the field of aircraft engine controls and system health management is the inclusion of real-time, onboard models for the inflight estimation of engine performance variations. This technology, typically based on Kalman-filter concepts, enables the estimation of unmeasured engine performance parameters that can be directly utilized by controls, prognostics, and health-management applications. A challenge that complicates this practice is the fact that an aircraft engine s performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. Through Kalman-filter-based estimation techniques, the level of engine performance degradation can be estimated, given that there are at least as many sensors as health parameters to be estimated. However, in an aircraft engine, the number of sensors available is typically less than the number of health parameters, presenting an under-determined estimation problem. A common approach to address this shortcoming is to estimate a subset of the health parameters, referred to as model tuning parameters. The problem/objective is to optimally select the model tuning parameters to minimize Kalman-filterbased estimation error. A tuner selection technique has been developed that specifically addresses the under-determined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine that seeks to minimize the theoretical mean-squared estimation error of the Kalman filter. This approach can significantly reduce the error in onboard aircraft engine parameter estimation applications such as model-based diagnostic, controls, and life usage calculations. The advantage of the innovation is the significant reduction in estimation errors that it can provide relative to the conventional approach of selecting a subset of health parameters to serve as the model tuning parameter vector. Because this technique needs only to be performed during the system design process, it places no additional computation burden on the onboard Kalman filter implementation. The technique has been developed for aircraft engine onboard estimation applications, as this application typically presents an under-determined estimation problem. However, this generic technique could be applied to other industries using gas turbine engine technology.

Simon, Donald L.↗
NASA NTRS2014

A Physics-Based Modeling Framework for Prognostic Studies

Prognostics and Health Management (PHM) methodologies have emerged as one of the key enablers for achieving efficient system level maintenance as part of a busy operations schedule, and lowering overall life cycle costs. PHM is also emerging as a high-priority issue in critical applications, where the focus is on conducting fundamental research in the field of integrated systems health management. The term diagnostics relates to the ability to detect and isolate faults or failures in a system. Prognostics on the other hand is the process of predicting health condition and remaining useful life based on current state, previous conditions and future operating conditions. PHM methods combine sensing, data collection, interpretation of environmental, operational, and performance related parameters to indicate systems health under its actual application conditions. The development of prognostics methodologies for the electronics field has become more important as more electrical systems are being used to replace traditional systems in several applications in the aeronautics, maritime, and automotive fields. The development of prognostics methods for electronics presents several challenges due to the great variety of components used in a system, a continuous development of new electronics technologies, and a general lack of understanding of how electronics fail. Similarly with electric unmanned aerial vehicles, electrichybrid cars, and commercial passenger aircraft, we are witnessing a drastic increase in the usage of batteries to power vehicles. However, for battery-powered vehicles to operate at maximum efficiency and reliability, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. We develop an electrochemistry-based model of Li-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles.

Li-ion Batteries↗
NASA NTRS2008

Self-Powered Wireless Sensors

NASA's integrated vehicle health management (IVHM) program offers the potential to improve aeronautical safety, reduce cost and improve performance by utilizing networks of wireless sensors. Development of sensor systems for engine hot sections will provide real-time data for prognostics and health management of turbo-engines. Sustainable power to embedded wireless sensors is a key challenge for prolong operation. Harvesting energy from the environment has emerged as a viable technique for power generation. Thermoelectric generators provide a direct conversion of heat energy to electrical energy. Micro-power sources derived from thermoelectric films are desired for applications in harsh thermal environments. Silicon based alloys are being explored for applications in high temperature environments containing oxygen. Chromium based p-type Si/Ge alloys exhibit Seebeck coefficients on the order of 160 micro V/K and low thermal conductance of 2.5 to 5 W/mK. Thermoelectric properties of bulk and thin film silicides will be discussed

Dynys, Fred↗
NASA NTRS2008

2008 Year in Review

In February of 2008; NASA Stennis Space Center (SSC), NASA Kennedy Space Center (KSC), and The Applied Research Laboratory at Penn State University demonstrated a pilot implementation of an Integrated System Health Management (ISHM) capability at the Launch Complex 20 of KSC. The following significant accomplishments are associated with this development: (1) implementation of an architecture for ground operations ISHM, based on networked intelligent elements; (2) Use of standards for management of data, information, and knowledge (DIaK) leading to modular ISHM implementation with interoperable elements communicating according to standards (three standards were used: IEEE 1451 family of standards for smart sensors and actuators, Open Systems Architecture for Condition Based Maintenance (OSA-CBM) standard for communicating DIaK describing the condition of elements of a system, and the OPC standard for communicating data); (3) ISHM implementation using interoperable modules addressing health management of subsystems; and (4) use of a physical intelligent sensor node (smart network element or SNE capable of providing data and health) along with classic sensors originally installed in the facility. An operational demonstration included detection of anomalies (sensor failures, leaks, etc.), determination of causes and effects, communication among health nodes, and user interfaces.

Figueroa, Jorge Fernando↗