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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Starting Vortex Identified as Key to Unsteady Ejector Performance

Unsteady ejectors are currently under investigation for use in some pulse-detonation-engine-based propulsion systems. Experimental measurements made in the past, and recently at the NASA Glenn Research Center, have demonstrated that thrust augmentation can be enhanced considerably when the driver is unsteady. In ejector systems, thrust augmentation is defined as = T(sup Total)/T(sup j), where T(sup Total) is the total thrust of the combined ejector and driving jet and T(sup j) is the thrust due to the driving jet alone. There are three images in this figure, one for each of the named thrust sources. The images are color contours of measured instantaneous vorticity. Each image is an ensemble average of at least 150 phase-locked measurements. The flow is from right to left, and the shape and location of each driver is shown on the far right of each image. The emitted vortex is a clearly defined "doughnut" of highly vortical (spinning) flow. In these planar images, the vortex appears as two distorted circles, one above, and one below the axis of symmetry. Because they are spinning in the opposite direction, the two circles have vorticity of opposite sign and thus are different colors. There is also a rectangle shown in each image. Its width represents the ejector diameter that was found experimentally to yield the highest thrust augmentation. It is apparent that the optimal ejector diameter is that which just "captures" the vortex: that is, the diameter bounding the outermost edge of the vortex structure. The exact mechanism behind the enhanced performance is unclear; however, it is believed to be related to the powerful vortex emitted with each pulse of the unsteady driver. As such, particle imaging velocimetry (PIV) measurements were obtained for three unsteady drivers: a pulsejet, a resonance tube, and a speaker-driven jet. All the drivers were tested with ejectors, and all exhibited performance enhancement over similarly sized steady drivers. The characteristic starting vortices of each driver are shown in these images. The images are color contours of measured instantaneous vorticity. Each image is an ensemble average of at least 150 phase-locked measurements. The flow is from right to left. The shape and location of each driver is shown on the far right of each image. The rectangle shown in each image represents the ejector diameter that was found experimentally to yield the highest thrust augmentation. It is apparent that the optimal ejector diameter is that which just "captures" the vortex: that is, the diameter bounding the outermost edge of the vortex structure. Although not shown, it was observed that the emitted vortex spread as it traveled downstream. The spreading rate for the pulsejet is shown as the dashed lines in the top image. A tapered ejector was fabricated that matched this shape. When tested, the ejector demonstrated superior performance to all those previously tested at Glenn (which were essentially of straight, cylindrical form), achieving a remarkable thrust augmentation of 2. The measured thrust augmentation is shown as a function of ejector length. Also shown are the thrust augmentation values achieved with the straight, cylindrical ejectors of varying diameters. Here, thrust augmentation is plotted as a function of ejector length for several families of ejector diameters. It can be seen that large thrust augmentation values are indeed obtained and that they are sensitive to both ejector length and diameter, particularly the latter. Five curves are shown. Four correspond to straight ejector diameters of 2.2, 3.0, 4.0, and 6.0 in. The fifth curve corresponds to the tapered ejector contoured to bound the emitted vortex. For each curve, there are several data points corresponding to different lengths. The largest value of thrust augmentation is 2.0 for the tapered ejector and 1.81 for the straight ejectors. Regardless of their diameters, all the ejectors trend toward peak performance at a particular leng. That the cross-sectional dimensions of optimal ejectors scaled precisely with the vortex dimensions on three separate pulsed thrust sources demonstrates that the action of the vortex is responsible for the enhanced ejector performance. The result also suggests that, in the absence of a complete understanding of the entrainment and augmentation mechanisms, methods of characterizing starting vortices may be useful for correlating and predicting unsteady ejector performance.

Paxson, Daniel E.↗
NASA NTRS2007

The X-43A Six Degree of Freedom Monte Carlo Analysis

This report provides an overview of the Hyper-X research vehicle Monte Carlo analysis conducted with the six-degree-of-freedom simulation. The methodology and model uncertainties used for the Monte Carlo analysis are presented as permitted. In addition, the process used to select hardware validation test cases from the Monte Carlo data is described. The preflight Monte Carlo analysis indicated that the X-43A control system was robust to the preflight uncertainties and provided the Hyper-X project an important indication that the vehicle would likely be successful in accomplishing the mission objectives. The X-43A in-flight performance is compared to the preflight Monte Carlo predictions and shown to exceed the Monte Carlo bounds in several instances. Possible modeling shortfalls are presented that may account for these discrepancies. The flight control laws and guidance algorithms were robust enough as a result of the preflight Monte Carlo analysis that the unexpected in-flight performance did not have undue consequences. Modeling and Monte Carlo analysis lessons learned are presented.

Baumann, Ethan↗
NASA NTRS2008

The X-43A Six Degree of Freedom Monte Carlo Analysis

This report provides an overview of the Hyper-X research vehicle Monte Carlo analysis conducted with the six-degree-of-freedom simulation. The methodology and model uncertainties used for the Monte Carlo analysis are presented as permitted. In addition, the process used to select hardware validation test cases from the Monte Carlo data is described. The preflight Monte Carlo analysis indicated that the X-43A control system was robust to the preflight uncertainties and provided the Hyper-X project an important indication that the vehicle would likely be successful in accomplishing the mission objectives. The X-43A inflight performance is compared to the preflight Monte Carlo predictions and shown to exceed the Monte Carlo bounds in several instances. Possible modeling shortfalls are presented that may account for these discrepancies. The flight control laws and guidance algorithms were robust enough as a result of the preflight Monte Carlo analysis that the unexpected in-flight performance did not have undue consequences. Modeling and Monte Carlo analysis lessons learned are presented.

Baumann, Ethan↗
NASA NTRS2015

A Methodology to Assess the Capability of Engine Designs to Meet Closed-Loop Performance and Operability Requirements

Designing a closed-loop controller for an engine requires balancing trade-offs between performance and operability of the system. One such trade-off is the relationship between the 95 percent response time and minimum high-pressure compressor (HPC) surge margin (SM) attained during acceleration from idle to takeoff power. Assuming a controller has been designed to meet some specification on response time and minimum HPC SM for a mid-life (nominal) engine, there is no guarantee that these limits will not be violated as the engine ages, particularly as it reaches the end of its life. A characterization for the uncertainty in this closed-loop system due to aging is proposed that defines elliptical boundaries to estimate worst-case performance levels for a given control design point. The results of this characterization can be used to identify limiting design points that bound the possible controller designs yielding transient results that do not exceed specified limits in response time or minimum HPC SM. This characterization involves performing Monte Carlo simulation of the closed-loop system with controller constructed for a set of trial design points and developing curve fits to describe the size and orientation of each ellipse; a binary search procedure is then employed that uses these fits to identify the limiting design point. The method is demonstrated through application to a generic turbofan engine model in closed-loop with a simplified controller; it is found that the limit for which each controller was designed was exceeded by less than 4.76 percent. Extension of the characterization to another trade-off, that between the maximum high-pressure turbine (HPT) entrance temperature and minimum HPC SM, showed even better results: the maximum HPT temperature was estimated within 0.76 percent. Because of the accuracy in this estimation, this suggests another limit that may be taken into consideration during design and analysis. It also demonstrates the extension of the characterization to other attributes that contribute to the performance or operability of the engine. Metrics are proposed that, together, provide information on the shape of the trade-off between response time and minimum HPC SM, and how much each varies throughout the life cycle, at the limiting design points. These metrics also facilitate comparison of the expected transient behavior for multiple engine models.

systems analysis↗
NASA NTRS2013

Weight Assessment for Fuselage Shielding on Aircraft With Open-Rotor Engines and Composite Blade Loss

The Federal Aviation Administration (FAA) has been engaged in discussions with airframe and engine manufacturers concerning regulations that would apply to new technology fuel efficient "openrotor" engines. Existing regulations for the engines and airframe did not envision features of these engines that include eliminating the fan blade containment systems and including two rows of counter-rotating blades. Damage to the airframe from a failed blade could potentially be catastrophic. Therefore the feasibility of using aircraft fuselage shielding was investigated. In order to establish the feasibility of this shielding, a study was conducted to provide an estimate for the fuselage shielding weight required to provide protection from an open-rotor blade loss. This estimate was generated using a two-step procedure. First, a trajectory analysis was performed to determine the blade orientation and velocity at the point of impact with the fuselage. The trajectory analysis also showed that a blade dispersion angle of 3deg bounded the probable dispersion pattern and so was used for the weight estimate. Next, a finite element impact analysis was performed to determine the required shielding thickness to prevent fuselage penetration. The impact analysis was conducted using an FAA-provided composite blade geometry. The fuselage geometry was based on a medium-sized passenger composite airframe. In the analysis, both the blade and fuselage were assumed to be constructed from a T700S/PR520 triaxially-braided composite architecture. Sufficient test data on T700S/PR520 is available to enable reliable analysis, and also demonstrate its good impact resistance properties. This system was also used in modeling the surrogate blade. The estimated additional weight required for fuselage shielding for a wing- mounted counterrotating open-rotor blade is 236 lb per aircraft. This estimate is based on the shielding material serving the dual use of shielding and fuselage structure. If the shielding material is not used for dual purpose, and is only used for shielding, then the additional weight per aircraft is estimated to be 428 lb. This weight estimate is based upon a number of assumptions that would need to be revised when applying this concept to an actual airplane design. For example, the weight savings that will result when there is no fan blade containment system, manufacturing limitations which may increase the weight where variable thicknesses was assumed, engine placement on the wing versus aft fuselage, etc.

aircraft safety↗
NASA NTRS2015

A Methodology to Assess the Capability of Engine Designs to Meet Closed-loop Performance and Operability Requirements

Designing a closed-loop controller for an engine requires balancing trade-offs between performance and operability of the system. One such trade-off is the relationship between the 95% response time and minimum high-pressure compressor (HPC) surge margin (SM) attained during acceleration from idle to takeoff power. Assuming a controller has been designed to meet some specification on response time and minimum HPC SM for a mid-life (nominal) engine, there is no guarantee that these limits will not be violated as the engine ages, particularly as it reaches the end of its life. A characterization for the uncertainty in this closed-loop system due to aging is proposed that defines elliptical boundaries to estimate worst-case performance levels for a given control design point. The results of this characterization can be used to identify limiting design points that bound the possible con- troller designs yielding transient results that do not exceed specified limits in response time or minimum HPC SM. This characterization involves performing Monte Carlo simulation of the closed-loop system with controller constructed for a set of trial design points and developing curve fits to describe the size and orientation of each ellipse; a binary search procedure is then employed that uses these fits to identify the limiting design point. The method is demonstrated through application to a generic turbofan engine model in closed- loop with a simplified controller; it is found that the limit for which each controller was designed was exceeded by less than 4.76%. Extension of the characterization to another trade-off, that between the maximum high-pressure turbine (HPT) entrance temperature and minimum HPC SM, showed even better results: the maximum HPT temperature was estimated within 0.76%. Because of the accuracy in this estimation, this suggests another limit that may be taken into consideration during design and analysis. It also demonstrates the extension of the characterization to other attributes that contribute to the performance or operability of the engine. Metrics are proposed that, together, provide information on the shape of the trade-off between response time and minimum HPC SM, and how much each varies throughout the life cycle, at the limiting design points. These metrics also facilitate comparison of the expected transient behavior for multiple engine models.

engine control↗
NASA NTRS1986

Multiprocessing the Sieve of Eratosthenes

The Sieve of Eratosthenes for finding prime numbers in recent years has seen much use as a benchmark algorithm for serial computers while its intrinsically parallel nature has gone largely unnoticed. The implementation of a parallel version of this algorithm for a real parallel computer, the Flex/32, is described and its performance discussed. It is shown that the algorithm is sensitive to several fundamental performance parameters of parallel machines, such as spawning time, signaling time, memory access, and overhead of process switching. Because of the nature of the algorithm, it is impossible to get any speedup beyond 4 or 5 processors unless some form of dynamic load balancing is employed. We describe the performance of our algorithm with and without load balancing and compare it with theoretical lower bounds and simulated results. It is straightforward to understand this algorithm and to check the final results. However, its efficient implementation on a real parallel machine requires thoughtful design, especially if dynamic load balancing is desired. The fundamental operations required by the algorithm are very simple: this means that the slightest overhead appears prominently in performance data. The Sieve thus serves not only as a very severe test of the capabilities of a parallel processor but is also an interesting challenge for the programmer.

Bokhari, S.↗
NASA NTRS1991

Near field interaction of microwave signals with a bounded plasma plume

The objective was to study the effect of the arcjet thruster plume on the performance of an onboard satellite reflector antenna. A project summary is presented along with sections on plasma and electromagnetic modeling. The plasma modeling section includes the following topics: wave propagation; plasma analysis; plume electron density model; and the proposed experimental program. The section on electromagnetic modeling includes new developments in ray modeling and the validation of three dimensional ray results.

Ling, Hao↗
NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗
NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗
NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗
NASA NTRS1974

The failure of notched specimens of boron-fiber reinforced 6061 aluminum alloy

The effect of notches on the strength of boron fiber reinforced 6061 aluminum alloy was determined at room temperature, 300 F, and 600 F. Both unidirectional and cross-plied specimens were tested. The strengths of individual fibers were measured, and upper and lower bounds of composite strength were calculated using bundle theory. Fracture tests were performed on specimens containing center slots of various lengths, and the values of the critical stress intensity factor for initial crack propagation or final failure were calculated. For unidirectional specimens, these parameters depended on thickness; for cross-plied specimens, fairly constant values were obtained at room temperature. The cross-plied material invariably failed at a lower stress as the environmental temperature was raised.

Wright, M. A.↗
NASA NTRS1975

A summary of nonlinear filtering

The formation of nonlinear filtering problems are discussed along with approaches to solving nonlinear filtering problems. An approach to the design and evaluation of the performance of suboptimal filters is presented in terms of upper and lower bounds.

Rhodes, I. B.↗
NASA NTRS1993

Theoretical and simulated performance for a novel frequency estimation technique

A low complexity, open-loop, discrete-time, delay-multiply-average (DMA) technique for estimating the frequency offset for digitally modulated MPSK signals is investigated. A nonlinearity is used to remove the MPSK modulation and generate the carrier component to be extracted. Theoretical and simulated performance results are presented and compared to the Cramer-Rao lower bound (CRLB) for the variance of the frequency estimation error. For all signal-to-noise ratios (SNR's) above threshold, it is shown that the CRLB can essentially be achieved with linear complexity.

Crozier, Stewart N.↗
NASA NTRS2010

Internal Acoustics of a Pintle Valve with Supercritical Helium Flow

Large amplitude flow unsteadiness is a common phenomenon within the high flow rate ducts and valves associated with propulsion systems. Boundary layer noise, shear layers and vortex shedding are a few of the many sources of flow oscillations. The presence of lightly damped acoustic modes can organize and amplify these sources of flow perturbation, causing undesirable loading of internal parts. The present study investigates the self-induced acoustic environment within a pintle valve subject to high Reynolds Number flow of helium gas. Experiments were conducted to measure the internal pressure oscillations of the Ares I Launch Abort System (LAS) Attitude Control Motor (ACM) valve. The AGM consists of a solid propellant gas generator with eight pintle valves attached to the aft end. The pintle valve is designed to deliver variable upstream conditions to an attache( converging diverging nozzle. In order to investigate the full range of operating conditions 28 separate tests were conducted with varying pintle position and upstream pressure. Helium gas was utilized in order to closely mimic the speed of sound of the gas generator exhaust, minimizing required scaling during data analysis. The recordec pressure measurements were interrogated to multiple ends. The development of root mean square (RMS) value! versus Reynolds Number and Pintle position are important to creating bounding unsteady load curves for valve internal parts. Spectral analysis was also performed, helping to identify power spectral densities (PSD) of acoustic natural frequencies and boundary layer noise. An interesting and unexpected result was the identification of an acoustic mode within the valve which does not respond until the valve was over 60% open. Further, the response amplitude around this mode can be as large or larger than those associated with lower frequency modes.

Fishbach, Sean R.↗
NASA NTRS2012

Requirements Flowdown for Prognostics and Health Management

Prognostics and Health Management (PHM) principles have considerable promise to change the game of lifecycle cost of engineering systems at high safety levels by providing a reliable estimate of future system states. This estimate is a key for planning and decision making in an operational setting. While technology solutions have made considerable advances, the tie-in into the systems engineering process is lagging behind, which delays fielding of PHM-enabled systems. The derivation of specifications from high level requirements for algorithm performance to ensure quality predictions is not well developed. From an engineering perspective some key parameters driving the requirements for prognostics performance include: (1) maximum allowable Probability of Failure (PoF) of the prognostic system to bound the risk of losing an asset, (2) tolerable limits on proactive maintenance to minimize missed opportunity of asset usage, (3) lead time to specify the amount of advanced warning needed for actionable decisions, and (4) required confidence to specify when prognosis is sufficiently good to be used. This paper takes a systems engineering view towards the requirements specification process and presents a method for the flowdown process. A case study based on an electric Unmanned Aerial Vehicle (e-UAV) scenario demonstrates how top level requirements for performance, cost, and safety flow down to the health management level and specify quantitative requirements for prognostic algorithm performance.

video acuity↗