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Design influence is accomplished through inclusion of Reliability, Maintainability, and Supportability (RMS) as part of the system’s design characteristics from program/project inception to ensure the system(s) can be supported and maintained throughout the life cycle.
The U.S. Army is currently expanding its fleet of Health Usage Monitoring Systems (HUMS) equipped aircraft at significant rates, to now include over 1,000 rotorcraft. Two different on-board HUMS, the Honeywell Modern Signal Processing Unit (MSPU) and the Goodrich Integrated Vehicle Health Management System (IVHMS), are collecting vibration health data on aircraft that include the Apache, Blackhawk, Chinook, and Kiowa Warrior. The objective of this paper is to recommend the most effective gear condition indicators for fleet use based on both a theoretical foundation and field data. Gear diagnostics with better performance will be recommended based on both a theoretical foundation and results of in-fleet use. In order to evaluate the gear condition indicator performance on rotorcraft fleets, results of more than five years of health monitoring for gear faults in the entire HUMS equipped Army helicopter fleet will be presented. More than ten examples of gear faults indicated by the gear CI have been compiled and each reviewed for accuracy. False alarms indications will also be discussed. Performance data from test rigs and seeded fault tests will also be presented. The results of the fleet analysis will be discussed, and a performance metric assigned to each of the competing algorithms. Gear fault diagnostic algorithms that are compliant with ADS-79A will be recommended for future use and development. The performance of gear algorithms used in the commercial units and the effectiveness of the gear CI as a fault identifier will be assessed using the criteria outlined in the standards in ADS-79A-HDBK, an Army handbook that outlines the conversion from Reliability Centered Maintenance to the On-Condition status of Condition Based Maintenance.
NASA's Vision for Exploration requires a safe, efficient, reliable, and versatile launch vehicle capable ofplacing large payloads into Earth orbit for transfer to the Moon and destinations beyond. The Ares V Cargo Launch Vehicle (CaLV) will provide this heavy lift capability. The Ares V launch concept is shown. When it stands on the launch pad at Kennedy Space Center late in the next decade, the Ares V stack will be almost 360 feet fall. As currently envisioned, it will lift 136 metric tons (300,000 pounds) to a 30-by-160 nautical mile orbit at 28.5-degrees inclination, or 55 metric tons (120,000 pounds) to trans-lunar injection. This paper will cover the latest developments in the Ares V project in 2007 and discuss future activities.
One of the attractive advantages of using linear covariance analysis (LinCov) is that it can accurately produce both navigation errors and trajectory dispersions in a fraction of the time comparable to the standard Monte Carlo analysis approach that is traditionally adopted. It also has the capability of generating delta-v dispersions which is perhaps the most influential performance metric since many aspects of a mission design revolve around the anticipated propellant usage. Accurately capturing this key performance criteria using linear covariance techniques has several subtle caveats raising reasonable doubts and a cautious sense of skepticism. This paper outlines multiple approaches of modeling delta-v usage in LinCov, highlights their limitations and advantages, and ultimately compares their corresponding results to the actual Monte Carlo performance. The theory is also extended to reliably account for an arbitrary thruster configuration and the resultant propellant consumptions due to both translational burns and attitude maneuvers. This provides near instant insight into the impacts of thruster layout design and different vehicle mass properties. These derived techniques are applied and verified using an NRHO rendezvous and docking scenario consistent with upcoming NASA Artemis missions for Orion.
Future missions to the moon or Mars will require the crew to monitor and assess their health and performance more autonomously, with approaches easily useable and interpretable by non-clinicians. If an autonomous oculometric diagnostic-support system proves to be a reliable predictor of impending impairment of visual function in the case of primary ocular pathology on Earth, then it could also be applied in the future to the surveillance of crew to detect the potential progression of sensorimotor and neuro-ocular compromises during extended spaceflight missions, e.g., SANS, and thus guide earlier and more effective countermeasure intervention. Towards that goal, we intend to determine if a set of largely independent eye-movement metrics, called oculometrics, measured using a simple voluntary eye-tracking task that can be completed in just 5 minutes per eye, could provide clinically valid biomarkers of functional visual deficits due to primary ocular pathology. The premise is that oculometrics could be used to monitor slowly evolving ocular pathologies, potentially detecting them even before structural evidence appears in ocular imaging or other current state-of-the-art clinical measures. This study investigates how NASA’s patented oculometric assessment of visual and visuomotor function, shown to correlate with performance in a manual control task, will correlate with standard ophthalmological tests to confirm clinical and thus operational, validity.
Range Process Simulation Tool (RPST) is a computer program that assists managers in rapidly predicting and quantitatively assessing the operational effects of proposed technological additions to, and/or upgrades of, complex facilities and engineering systems such as the Eastern Test Range. Originally designed for application to space transportation systems, RPST is also suitable for assessing effects of proposed changes in industrial facilities and large organizations. RPST follows a model-based approach that includes finite-capacity schedule analysis and discrete-event process simulation. A component-based, scalable, open architecture makes RPST easily and rapidly tailorable for diverse applications. Specific RPST functions include: (1) definition of analysis objectives and performance metrics; (2) selection of process templates from a processtemplate library; (3) configuration of process models for detailed simulation and schedule analysis; (4) design of operations- analysis experiments; (5) schedule and simulation-based process analysis; and (6) optimization of performance by use of genetic algorithms and simulated annealing. The main benefits afforded by RPST are provision of information that can be used to reduce costs of operation and maintenance, and the capability for affordable, accurate, and reliable prediction and exploration of the consequences of many alternative proposed decisions.
The Flexible Array Concentrator Technology (FACT) is a lightweight, high-performance reflective concentrator blanket assembly that can be used on flexible solar array blankets. The FACT concentrator replaces every other row of solar cells on a solar array blanket, significantly reducing the cost of the array. The modular design is highly scalable for the array system designer, and exhibits compact stowage, good off-pointing acceptance, and mass/cost savings. The assembly s relatively low concentration ratio, accompanied by a large radiative area, provides for a low cell operating temperature, and eliminates many of the thermal problems inherent in high-concentration-ratio designs. Unlike other reflector technologies, the FACT concentrator modules function on both z-fold and rolled flexible solar array blankets, as well as rigid array systems. Mega-ROSA (Mega Roll-Out Solar Array) is a new, highly modularized and extremely scalable version of ROSA that provides immense power level range capability from 100 kW to several MW in size. Mega-ROSA will enable extremely high-power spacecraft and SEP-powered missions, including space-tug and largescale planetary science and lunar/asteroid exploration missions. Mega-ROSA's inherent broad power scalability is achieved while retaining ROSA s solar array performance metrics and missionenabling features for lightweight, compact stowage volume and affordability. This innovation will enable future ultra-high-power missions through lowcost (25 to 50% cost savings, depending on PV and blanket technology), lightweight, high specific power (greater than 200 to 400 Watts per kilogram BOL (beginning-of-life) at the wing level depending on PV and blanket technology), compact stowage volume (greater than 50 kilowatts per cubic meter for very large arrays), high reliability, platform simplicity (low failure modes), high deployed strength/stiffness when scaled to huge sizes, and high-voltage operation capability. Mega-ROSA is adaptable to all photovoltaic and concentrator flexible blanket technologies, and can readily accommodate standard multijunction and emerging ultra-lightweight IMM (inverted metamorphic) photovoltaic flexible blanket assemblies, as well as ENTECHs Stretched Lens Array (SLA) and DSSs (Deployable Space Systems) FACT, which allows for cost reduction at the array level.
Geostationary Operational Environmental Satellite (GOES) Image Navigation and Registration (INR) performance is specified at the 3- level, meaning that 99.7% of a collection of individual measurements must comply with specification thresholds. Landmarks are measured by the Replacement Product Monitor (RPM), part of the operational GOES ground system, to assess INR performance and to close the INR loop. The RPM automatically discriminates between valid and invalid measurements enabling it to run without human supervision. In general, this screening is reliable, but a small population of invalid measurements will be falsely identified as valid. Even a small population of invalid measurements can create problems when assessing performance at the 3-sigma level. This paper describes an additional layer of quality control whereby landmarks of the highest quality ("platinum") are identified by their self-consistency. The platinum screening criteria are not simple statistical outlier tests against sigma values in populations of INR errors. In-orbit INR performance metrics for GOES-12 and GOES-13 are presented using the platinum landmark methodology.
Original radar images may be geometrically and radio metrically distorted. This may be a particular problem when multiple angle imagery is analyzed and there is topographic relief in the area of interest. This paper describes a set of techniques designed to combine a multiple angle radar data set with a digital terrain elevation model, to generate a set of new images called secondary image products. These new images are geometrically rectified radar ortho-images radiometrically rectified images, and stereo ortho-images. These secondary images can then reliably be used for thematic interpretation.
A preliminary analysis tool has been created in Microsoft Excel to determine deliverable payload mass, total system mass, and performance of spacecraft systems using various types of propellant feed systems. These mass estimates are conducted by inserting into the user interface the basic mission parameters (e.g., thrust, burn time, specific impulse, mixture ratio, etc.), system architecture (e.g., propulsion system type and characteristics, propellants, pressurization system type, etc.), and design properties (e.g., material properties, safety factors, etc.). Different propellant feed and pressurization systems are available for comparison in the program. This gives the user the ability to compare conventional pressure fed, reciprocating feed system (RFS), autogenous pressurization thrust augmentation (APTA RFS), and turbopump systems with the deliverable payload, inert mass, and total system mass being the primary comparison metrics. Analyses of several types of missions and spacecraft were conducted and it was found that the RFS offers a performance improvement, especially in terms of delivered payload, over conventional pressure fed systems. Furthermore, it is competitive with a turbopump system at low to moderate chamber pressures, up to approximately 1,500 psi. Various example cases estimating the system mass and deliverable payload of several types of spacecraft are presented that illustrate the potential system performance advantages of the RFS. In addition, a reliability assessment of the RFS was conducted, comparing it to simplified conventional pressure fed and turbopump systems, based on MIL-STD 756B; these results showed that the RFS offers higher reliability, and thus substantially longer periods between system refurbishment, than turbopump systems, and is competitive with conventional pressure fed systems. This is primarily the result of the intrinsic RFS fail-operational capability with three run tanks, since the system can operate with just two run tanks.
BACKGROUND Objective Structured Clinical Evaluations (OSCEs) have long been established as a robust methodology for summative assessment of clinical skills and decision-making during medical education. The recent integration of Artificial Intelligence (AI) into clinical decision-making processes has prompted the need for novel evaluation frameworks to assess the efficacy and reliability of AI clinical decision support system (CDSS) tools. This abstract outlines the process of quantitatively evaluating a novel CDSS (“Doc in a Box” Google 2024) trained on curated medical spaceflight data in the psychomotor domain as it interfaces with a human volunteer acting as the crew medical officer (CMO). PURPOSE The AI CDSS under review was developed as part of the Lunar Command and Control Interoperability (LuCCI) project, which is intended to address a gap in how Lunar Surface Systems (LSS) would interoperate across multiple programs, commercial partners, and international partners. The project objective is to define, prototype, integrate, and evaluate an interoperable lunar command, control, data, and software reference architecture to enable autonomy and informatics capability through common standards across LSS. A multi-modal AI-based CDSS compatible with Federated LSS will assist clinicians in diagnosing and managing complex medical conditions by providing evidence-based recommendations through predictive analytics. Given the critical role of decision-support as NASA continues to evolve its Earth-independent medical operations (EIMO), it is imperative to ensure that such AI tools perform reliably and align with clinical standards during progressive lunar and Martian exploration class missions. METHODS The OSCE framework, traditionally used for evaluating human clinicians, was adapted to assess the AI tool's decision-making capabilities in simulated clinical scenarios. In this adapted OSCE, the AI CDSS was tested across a series of structured clinical scenarios designed to mimic real-life spaceflight patient cases. These scenarios included a range of conditions and complexities, allowing for comprehensive assessment of the tool's performance. Key evaluation metrics included accuracy of diagnosis, timeliness of decision-making, and appropriate recommendations for therapies. The OSCE was scored by human physician evaluators who assessed the AI's recommendations in comparison with expert clinicians' medical decision making to ensure alignment with best practices and the standard of care. RESULTS Preliminary results indicate that the AI CDSS demonstrated high accuracy in diagnostic recommendations and decision support across various scenarios. However, certain limitations were noted, such as occasional discrepancies in handling complex or nuanced cases that required a more contextual understanding. Additionally, the tool scored higher on the diagnostic portion of the rubric, with lower scores in the therapeutic recommendations. These findings highlight the importance of continuous refinement and validation of AI tools through rigorous evaluation frameworks like the OSCE. The adaptation of OSCEs for AI tools presents several advantages, including a structured and reproducible approach to evaluation, the ability to test AI systems in diverse clinical scenarios, and the opportunity to benchmark AI performance against established clinical standards to permit charting of future progress as aerospace medicine evolves as a discipline. Remaining challenges include ensuring that these evaluations capture the full spectrum of clinical decision-making scenarios that will be confronted by CMOs during missions and adequately reflecting real-world variability of the austere spaceflight environment. CONCLUSION Employing OSCEs to evaluate AI clinical decision support tools offers a promising approach to validating their clinical utility and efficacy. This methodology not only provides insights into the tool's performance but also fosters ongoing improvement and alignment with standard of care practices. Future research should focus on refining these evaluation processes and addressing limitations to enhance the integration of AI tools in clinical spaceflight settings. REFERENCES Scott S, Hearns V, Barker MA. Testing Clinical Skills: A Look at the OSCE and USMLE Clinical Skills Exams. S D Med. 2019 Oct;72(10):451-453. Majumder MAA, Kumar A, Krishnamurthy K, Ojeh N, Adams OP, Sa B. An evaluative study of objective structured clinical examination (OSCE): students and examiners perspectives. Adv Med Educ Pract. 2019 Jun 5;10:387-397. Karam VY, Park YS, Tekian A, Youssef N. Evaluating the validity evidence of an OSCE: results from a new medical school. BMC Med Educ. 2018 Dec 20;18(1):313.
A diary-like instrument to measure lifestyle regularity (the 'Social Rhythm Metric'-SRM) was given to 96 subjects (48 women, 48 men), 39 of whom repeated the study after at least one year, with additional objective measures of rest/activity. Lifestyle regularity as measured by the SRM related to age, morningness, subjective sleep quality and time-of-day variations in alertness, but not to gender, extroversion or neuroticism. Statistically significant test-retest correlations of about 0.4 emerged for SRM scores over the 12-30 month delay. Diary-based estimates of bedtime and waketime appeared fairly reliable. In a further study of healthy young men, 4 high SRM scorers ('regular') had a deeper nocturnal body temperature trough than 5 low SRM scorers ('irregular'), suggesting a better functioning circadian system in the 'regular' group.
The United States (U.S.) plans to return to the Moon by 2020, with the development of a new human-rated space transportation system to replace the Space Shuttle, which is due for retirement in 2010 after it completes its missions of building the International Space Station and servicing the Hubble Space Telescope. Powering the future of space-based scientific exploration will be the Ares I Crew Launch Vehicle, which will transport the Orion Crew Exploration Vehicle to orbit where it will rendezvous with the Lunar Lander. which will be delivered by the Ares V Cargo Launch Vehicle. This new transportation infrastructure, developed by the National Aeronautics and Space Administration (NASA), will allow astronauts to leave low-Earth orbit for extended lunar exploration and preparation for the first footprint on Mars. All space-based operations begin and are controlled from Earth. NASA's philosophy is to deliver safe, reliable, and cost-effective solutions to sustain a multi-billion-dollar program across several decades. Leveraging 50 years of lessons learned, NASA is partnering with private industry, while building on proven hardware experience. This paper will discuss how the Engineering Directorate at NASA's Marshall Space Flight Center is working with the Ares Projects Office to streamline ground operations concepts and reduce costs. Currently, NASA's budget is around $17 billion, which is less than 1 percent of the U.S. Federal budget. Of this amount, NASA invests approximately $4.5 billion each year in Space Shuttle operations, regardless of whether the spacecraft is flying or not. The affordability requirement is for the Ares I to reduce this expense by 50 percent, in order to allow NASA to invest more in space-based scientific operations. Focusing on this metric, the Engineering Directorate provides several solutions-oriented approaches, including Lean/Six Sigma practices and streamlined hardware testing and integration, such as assembling major hardware elements before shipping to the Kennedy Space Center for launch operations. This paper provides top-level details for several cost saving initiatives, including both process and product improvements that will result in space transportation systems that are designed with operations efficiencies in mind. The Engineering Directorate provides both the intellectual capital embodied in an experienced workforce and unique facilities in which to validate the information technology tools that allow a nationwide team to collaboratively connect across miles that separate them and the engineering disciplines that integrate various piece parts into a whole system. As NASA transforms ground-based operations, it also is transitioning its workforce from an era of intense hands-on labor to a new one of mechanized conveniences and robust hardware with simpler interfaces. Ensuring that space exploration is on sound footing requires that operations efficiencies be designed into the transportation system and implemented in the development stage. Applying experience gained through decades of ground and space op'erations, while using value-added processes and modern business and engineering tools, is the philosophy upon which a new era of exploration will be built to solve some of the most pressing exploration challenges today -- namely, safety, reliability, and affordability.
The purpose of health monitoring systems is to detect failures or defects for increased safety and performance and to provide on-condition maintenance with reduced costs. The problems associated with health monitoring systems include high rates of false alarms and missed failures, which make monitoring an unreliable and costly task. The reason for this is that unaccounted variations invalidate signal modeling assumptions. Our approach was to focus on vibration monitoring of rotating components. We analyzed baseline signals to determine statistical variations, identify and model factors that influence vibrations (pre-production vs. post-production variations), determine hit and false alarm rates with baseline flight data, model and predict effects of defects and variations on vibrations, and develop algorithms and metrics for failure and anomaly detection in the presence of variations.
This slide presentation reviews some of the issues concerning verification and validation testing of autonomous spacecraft routinely culminates in the exploration of anomalous or faulted mission-like scenarios using the work involved during the Dawn mission's tests as examples. Prioritizing which scenarios to develop usually comes down to focusing on the most vulnerable areas and ensuring the best return on investment of test time. Rules-of-thumb strategies often come into play, such as injecting applicable anomalies prior to, during, and after system state changes; or, creating cases that ensure good safety-net algorithm coverage. Although experience and judgment in test selection can lead to high levels of confidence about the majority of a system's autonomy, it's likely that important test cases are overlooked. One method to fill in potential test coverage gaps is to automatically generate and execute test cases using algorithms that ensure desirable properties about the coverage. For example, generate cases for all possible fault monitors, and across all state change boundaries. Of course, the scope of coverage is determined by the test environment capabilities, where a faster-than-real-time, high-fidelity, software-only simulation would allow the broadest coverage. Even real-time systems that can be replicated and run in parallel, and that have reliable set-up and operations features provide an excellent resource for automated testing. Making detailed predictions for the outcome of such tests can be difficult, and when algorithmic means are employed to produce hundreds or even thousands of cases, generating predicts individually is impractical, and generating predicts with tools requires executable models of the design and environment that themselves require a complete test program. Therefore, evaluating the results of large number of mission scenario tests poses special challenges. A good approach to address this problem is to automatically score the results based on a range of metrics. Although the specific means of scoring depends highly on the application, the use of formal scoring - metrics has high value in identifying and prioritizing anomalies, and in presenting an overall picture of the state of the test program. In this paper we present a case study based on automatic generation and assessment of faulted test runs for the Dawn mission, and discuss its role in optimizing the allocation of resources for completing the test program.
Unstructured grid adaptation is a tool to control Computational Fluid Dynamics (CFD) discretization error. However, adaptive grid techniques have made limited impact on production analysis workflows where the control of discretization error is critical to obtaining reliable simulation results. Issues that prevent the use of adaptive grid methods are identified by applying unstructured grid adaptation methods to a series of benchmark cases. Once identified, these challenges to existing adaptive workflows can be addressed. Unstructured grid adaptation is evaluated for test cases described on the Turbulence Modeling Resource (TMR) web site, which documents uniform grid refinement of multiple schemes. The cases are turbulent flow over a Hemisphere Cylinder and an ONERA M6Wing. Adaptive grid force and moment trajectories are shown for three integrated grid adaptation processes with Mach interpolation control and output error based metrics. The integrated grid adaptation process with a finite element (FE) discretization produced results consistent with uniform grid refinement of fixed grids. The integrated grid adaptation processes with finite volume schemes were slower to converge to the reference solution than the FE method. Metric conformity is documented on grid/metric snapshots for five grid adaptation mechanics implementations. These tools produce anisotropic boundary conforming grids requested by the adaptation process.
Digital engineering (DE) and integrated computational materials engineering (ICME) are widely recognized as critical enablers of faster, more affordable, and more reliable aerospace systems. However, many organizations have struggled to realize the promised return on investment (ROI) from digital initiatives. A primary reason is the absence of a shared, decision-focused framework that distinguishes simple digitization of existing workflows from true digital transformation that fundamentally changes how engineering decisions are made. This paper introduces an ICME capability maturity framework that fills this gap. The framework defines six cumulative ICME capability maturity levels (CMLs), explicitly tied to decision authority, engineering integration, optimization, and uncertainty management across material, process, structure, and performance scales. It is designed to complement established readiness metrics such as technology readiness levels (TRLs), manufacturing readiness levels (MRLs), and integration readiness levels (IRLs), by addressing a missing dimension: the conditions required for model-informed decision authority across scales. A unifying figure and capability table illustrate the six-level ICME Capability Maturity Framework, showing how organizations progress from digitization—with limited or negative ROI—to true digital transformation, where ICME-enabled workflows deliver measurable improvements in decision quality, cycle time, risk reduction, and reuse. The framework is intended for both technical practitioners and executive leadership, providing a common language to assess current state, guide roadmaps, align software ecosystem investments, and set realistic expectations for digital transformation outcomes. A regulatory-relevant statement clarifying the relationship between ICME capability and existing certification frameworks is provided.
This dissertation discusses several machine learning techniques to improve routing in delay tolerant networks (DTNs). These are networks in which there may be long one-way trip times, asymmetric links, high error rates, and deterministic as well as non-deterministic loss of contact between network nodes, such as interplanetary satellite networks, mobile ad hoc networks and wireless sensor networks. This work uses historical network statistics to train a multi-label classifier to predict reliable paths through the network. In addition, a clustering technique is used to predict future mobile node locations. Both of these techniques are used to reduce the consumption of resources such as network bandwidth, memory and data storage that is required by replication routing methods often used in opportunistic DTN environments. Thesis contributions include: an emulation tool chain developed to create a DTN test bed for machine learning, the network and software architecture for a machine learning based routing method, the development and implementation of classification and clustering techniques and performance evaluation in terms of machine learning and routing metrics.