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NASA NTRS2012

New Therapeutic Window of Regenerative Opportunity in Diabetic Retinopathy by VESGEN Analysis

Vascular pattern may serve as a useful new biomarker principle of complex, multi-scale signaling in pathological, physiological angiogenesis and microvascular remodeling. Each angiogenesis stimulator or inhibitor we have analyzed, including VEGF, bFGF, TGF-beta1, angiostatin and triamcinolone acetonide, has induced a novel "fingerprint" or "signature" biomarker vascular pattern that is spatio-temporally unique. Remodeling vasculature thereby provides an informative read-out of dominant molecular signaling, when analyzed by innovative, fractal-based VESsel GENeration (VESGEN) Analysis software. Using VESGEN to analyze ophthalmic clinical vascular images, we recently introduced a potential paradigm shift to the understanding of early-stage progression that suggests new regenerative opportunities for human diabetic retinopathy (DR), the major blinding disease for working-aged adults. In a pilot study, we discovered that angiogenesis oscillates as a surprising, homeostatic-like regeneration of retinal vessels during early progression of DR (IOVS 51(1):498). Results suggest that the term non-proliferative DR may be a misnomer. In new studies, normalization of the vasculature will be determined from the response of vascular pattern to therapeutic monitoring and treatment. We have mapped and quantified in vivo experimental models of angiogenesis, lymphangiogenesis and intravital blood flow from cellular/molecular to higher systems levels that include a murine model of infant retinopathy of prematurity (ROP); developing and pathological coronary and placental-like vessel models; progressive intestinal inflammation, growing murine tumors, and other pathological, physiological and therapeutically treated tissues of transgenic mice and avian embryos. Vascular Alterations, Visual Impairments (VIIP) & Increased Intracranial Pressure (ICP), Immunosuppression & Bone Loss: NASA-defined risk categories for human space exploration and ISS Utilization

Parsons-Wingert, Patricia A.↗

NASA NTRS2019

A Unified Level of Service Model for NASA Earth Science Data Stewardship

During the past year, the Interagency Implementation and Concepts Team (IMPACT) reviewed existing service models in use at various NASA data centers in an effort to produce a unified, cohesive, and comprehensive Level of Service Model for all of NASA Distributed Active Archive Centers (DAACs). NASA DAACs are responsible for ensuring NASA Earth Science data are accurately and securely ingested, distributed, supported, and preserved. The term “Service" as used here refers to the spectrum of data management activities and outputs provided by DAACs in support of the data cared fo by each data cente. The unified Level-of- Service (LoS) model described in this presentation utilizes both the NASA-defined data product category and the data processing level to easily identify an appropriate level-of-service to be applied to a data product throughout the full data life cycle. This LoS model is to be used by DAACs when appraising incoming data in order to determine the appropriate and required services to provide. The LoS model utilizes a 3-level system in which services build upon previous levels and thereby require greater commitment and effort both on the part of the DAAC personnel and the data producer at the highest level. The LoS model description also contains examples of ways to communicate with data producers and data users what services can be expected, thereby bringing more consistent user experiences across the enterprise. In this presentation, we will outline the features of the LoS model and describe how it relates to the FAIR data practices and the NOAA Maturity Matrix model.

Smith, Deborah↗

NASA NTRS2012

Model-Based Verification and Validation of Spacecraft Avionics

Verification and Validation (V&V) at JPL is traditionally performed on flight or flight-like hardware running flight software. For some time, the complexity of avionics has increased exponentially while the time allocated for system integration and associated V&V testing has remained fixed. There is an increasing need to perform comprehensive system level V&V using modeling and simulation, and to use scarce hardware testing time to validate models; the norm for thermal and structural V&V for some time. Our approach extends model-based V&V to electronics and software through functional and structural models implemented in SysML. We develop component models of electronics and software that are validated by comparison with test results from actual equipment. The models are then simulated enabling a more complete set of test cases than possible on flight hardware. SysML simulations provide access and control of internal nodes that may not be available in physical systems. This is particularly helpful in testing fault protection behaviors when injecting faults is either not possible or potentially damaging to the hardware. We can also model both hardware and software behaviors in SysML, which allows us to simulate hardware and software interactions. With an integrated model and simulation capability we can evaluate the hardware and software interactions and identify problems sooner. The primary missing piece is validating SysML model correctness against hardware; this experiment demonstrated such an approach is possible.

MBV&V↗

NASA NTRSDate not supplied

Developing Deep Learning Models for System Remaining Useful Life Predictions: Application to Aircraft Engines

Prognostics and health management (PHM) is an important part of ensuring reliable operations of complex safety- critical systems. System-level remaining useful life (RUL) estimation is a much more complex problem than making estimations at the component level, and system-level RUL methodologies remain sparse in the literature. Model-based approaches have traditionally worked in the past for components such as capacitors, MOSFETs, batteries, or hard-drives (to name a few examples), but developing high fidelity dynamics models of cyber physical systems that can be used to study the effects of multiple degrading components in the system remains a challenging task. Some initial work on model-based System RUL predictions was demonstrated in Khorasgani, et al [1], but, to generalize the system-level prognostics problem, we have to resort to pure data driven and hybrid approaches. In this work, we propose an end-to-end data- driven framework for developing deep learning models to predict remaining useful life of cyber physical systems operating under unknown faulty conditions. The raw data is organized with a data schema that improves the model development process and down stream data analysis tasks. Due to the unknown faulty conditions, the raw sensor data is transformed into signals that expose the underlying degradation processes, which are then used for model development. Bayesian Optimization is used to tune the model parameters prior to training and validation. We show that this approach results in accurate predictions within 3 cycles to end of life (EOL). We demonstrate the effectiveness of our approach by applying it to the N-CMAPSS turbofan engine dataset recently released by NASA, which includes high fidelity degradation modeling, real world operating conditions, and a large set of fault operating modes.

Prognostics↗

NASA NTRS2017

Characterization of System Level Single Event Upset (SEU) Responses using SEU Data, Classical Reliability Models, and Space Environment Data

We propose a method for the application of single event upset (SEU) data towards the analysis of complex systems using transformed reliability models (from the time domain to the particle fluence domain) and space environment data.

Field Programmable Gate Array (FPGA); Application ↗

NASA NTRSDate not supplied

The Land Ice Representation in the NASA Goddard Seasonal Forecasting System

Models used in seasonal forecasting systems have aimed for a level of sophistication comparable to Earth system models by incorporating complex physical processes, including those relevant to the polar regions. Here, we examine the polar surface climate in version 3 of the NASA Goddard Earth Observing System Sub-seasonal to Seasonal prediction system (GEOS S2S v3). The model is composed of the ⅟₂̊ resolution Jason4.0version of the GEOS AGCM and the MOM5 ocean model at ⅟₄̊ resolution. The GEOSS2S model incorporates interactive aerosols and two-moment cloud microphysics. As compared to version 2, the model incorporates improved radiative transfer and are designed diurnal cycle atmosphere-ocean interface layer. This study aims to understand how the version 3 model compares with version 2, and with contemporary Earth system models.

Richard I Cullather↗

NASA NTRS1993

Correlation of ground tests and analyses of a dynamically scaled Space Station model configuration

Verification of analytical models through correlation with ground test results of a complex space truss structure is demonstrated. A multi-component, dynamically scaled space station model configuration is the focus structure for this work. Previously established test/analysis correlation procedures are used to develop improved component analytical models. Integrated system analytical models, consisting of updated component analytical models, are compared with modal test results to establish the accuracy of system-level dynamic predictions. Design sensitivity model updating methods are shown to be effective for providing improved component analytical models. Also, the effects of component model accuracy and interface modeling fidelity on the accuracy of integrated model predictions is examined.

Javeed, Mehzad↗

NASA NTRS1993

Correlation of ground tests and analyses of a dynamically scaled space station model configuration

Javeed, Mehzad↗

NASA NTRS2006

Marshall Space Flight Center Propulsion Systems Department (PSD) KM Initiative

NASA Marshall Space Flight Center s Propulsion Systems Department (PSD) is four months into a fifteen month Knowledge Management (KM) initiative to support enhanced engineering decision making and analyses, faster resolution of anomalies (near-term) and effective, efficient knowledge infused engineering processes, reduced knowledge attrition, and reduced anomaly occurrences (long-term). The near-term objective of this initiative is developing a KM Pilot project, within the context of a 3-5 year KM strategy, to introduce and evaluate the use of KM within PSD. An internal NASA/MSFC PSD KM team was established early in project formulation to maintain a practitioner, user-centric focus throughout the conceptual development, planning and deployment of KM technologies and capabilities with in the PSD. The PSD internal team is supported by the University of Alabama's Aging Infrastructure Systems Center Of Excellence (AISCE), Intergraph Corporation, and The Knowledge Institute. The principle product of the initial four month effort has been strategic planning of PSD KM implementation by first determining the "as is" state of KM capabilities and developing, planning and documenting the roadmap to achieve the desired "to be" state. Activities undertaken to support the planning phase have included data gathering; cultural surveys, group work-sessions, interviews, documentation review, and independent research. Assessments and analyses have been performed including industry benchmarking, related local and Agency initiatives, specific tools and techniques used and strategies for leveraging existing resources, people and technology to achieve common KM goals. Key findings captured in the PSD KM Strategic Plan include the system vision, purpose, stakeholders, prioritized strategic objectives mapped to the top ten practitioner needs and analysis of current resource usage. Opportunities identified from research, analyses, cultural/KM surveys and practitioner interviews include: executive and senior management sponsorship, KM awareness, promotion and training, cultural change management, process improvement, leveraging existing resources and new innovative technologies to align with other NASA KM initiatives (convergence: the big picture). To enable results based incremental implementation and future growth of the KM initiative, key performance measures have been identified including stakeholder value, system utility, learning and growth (knowledge capture, sharing, reduced anomaly recurrence), cultural change, process improvement and return-on-investment. The next steps for the initial implementation spiral (focused on SSME Turbomachinery) have been identified, largely based on the organization and compilation of summary level engineering process models, data capture matrices, functional models and conceptual-level systems architecture. Key elements include detailed KM requirements definition, KM technology architecture assessment, evaluation and selection, deployable KM Pilot design, development, implementation and evaluation, and justifying full implementation (estimated Return-on-Investment). Features identified for the notional system architecture include the knowledge presentation layer (and its components), knowledge network layer (and its components), knowledge storage layer (and its components), User Interface and capabilities. This paper provides a snapshot of the progress to date, the near term planning for deploying the KM pilot project and a forward look at results based growth of KM capabilities with-in the MSFC PSD.

Caraccioli, Paul↗

NASA NTRSDate not supplied

Framework for National Airspace System (NAS) Level Sustainability Assessment of New Aircraft

This paper introduces a framework for investigating the future sustainability benefits of conceptual vehicles. The framework considers the complex interactions between such vehicles and how it can be flown in the National Airspace System. Aggregate benefits for reduction in fuel use and emissions need to consider fleet wide operations, demand distributions, NAS Infrastructure, and differing business models. To extract true system level benefits multi-objective function optimization for operational energy use, acoustics, emissions and NAS Wide efficiency and interoperability incorporating physic-based system-wide flight simulations in the National Airspace system is required. The framework marries traditional vehicle synthesis processes with a NAS Simulation environment to simulate how a new synthesized vehicle will behave in the NAS.

Sustainability↗

NASA NTRS1992

Development and application of an acceptance testing model

The process of acceptance testing large software systems for NASA has been analyzed, and an empirical planning model of the process constructed. This model gives managers accurate predictions of the staffing needed, the productivity of a test team, and the rate at which the system will pass. Applying the model to a new system shows a high level of agreement between the model and actual performance. The model also gives managers an objective measure of process improvement.

Pendley, Rex D.↗

NASA NTRS2026

Gateway Element and Payload Materials Outgassing Analyses: HALO, HERMES, and ERSA

Gateway was intended to be humanity’s first space station around the Moon, but its development has been paused as the National Aeronautics and Space Administration (NASA) shifts focus to achieving the United States’ National Space Policy goals. Instead of an orbiting lunar outpost, NASA will now pursue the development of a lunar surface base to support a sustained human presence on the Moon. Before the program’s pause, Gateway’s Induced Environments team worked to ensure payloads and elements (i.e., modules) complied with induced environment requirements. Methods developed and insights gained from this work will have applicability to NASA’s Moon Base and the potential repurposing of Gateway elements and payloads, as well as to induced environments modeling for future space stations. The Gateway program’s induced environment included molecular contamination, electric thruster plume sputter and redeposition, and lunar dust transfer from the Human Landing System (HLS). Primary sources of external molecular contamination included materials outgassing, chemical thruster plume contamination, and vacuum venting. The focus of this paper will be on element- and payload-level materials outgassing analyses performed for Gateway Configuration 1, extending the previously-developed framework for Gateway system-level external molecular contamination modeling. Gateway Configuration 1 consisted of the Power and Propulsion Element (PPE) and the Habitation and Logistics Outpost (HALO). It also included payloads like the European Radiation Sensor Array (ERSA) attached to PPE and the Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) attached to HALO. The element- and payload-level analyses to be introduced in this paper for HALO, HERMES, and ERSA enabled high-fidelity descriptions of Gateway’s external molecular contamination environment. Approaches to geometric modeling, meshing, outgassing rate assignment, molecular transport modeling, and analysis methodology will be presented. Element and payload contaminant deposition onto sensitive Gateway receiver surfaces will be summarized and results compared to induced environment requirements. While these results incorporate refinements made over the course of the program, they were not intended to be final. Therefore, modeling assumptions and inputs, potential improvements, and lessons-learned will be documented to inform future work on Moon Base, repurposed elements and payloads, and other space stations.

Gateway↗

NASA NTRS1984

Control algorithms and computer simulation of a stand-alone photovoltaic village power system

At Stand-Alone Photovoltaic (SAPV) power systems increase in size and load diversity, the design and simulation of control subsystems takes on added importance. These SAPV systems represent 'mini utilities' with commensurate controls requirements, albeit with the added complexity of the energy source (sunlight received) being an uncontrollable variable. This paper briefly describes a stand-alone photovoltaic power/load system computerized simulation model. The model was tested against operational data from the Schuchuli stand-alone village photovoltaic system and has achieved acceptable levels of simulation accuracy. The model can be used to simulate system designs although with probable battery modification.

Groumpos, P. P.↗

NASA NTRS2004

Kite: Status of the External Metrology Testbed for SIM

Kite is a system level testbed for the External Metrology system of the Space Interferometry Mission (SIM). The External Metrology System is used to track the fiducial that are located at the centers of the interferometer's siderostats. The relative changes in their positions needs to be tracked to tens of picometers in order to correct for thermal measurements, the Kite testbed was build to test both the metrology gauges and out ability to optically model the system at these levels. The Kite testbed is an over-constraint system where 6 lengths are measured, but only 5 are needed to determine the system. The agreement in the over-constrained length needs to be on the order of 140 pm for the SIM Wide-Angle observing scenario and 8 pm for the Narrow-Angle observing scenario. We demonstrate that we have met the Wide-Angle goal with our current setup. For the Narrow-Angle case, we have only reached the goal for on-axis observations. We describe the testbed improvements that have been made since our initial results, and outline the future Kite changes that will add further effects that SIM faces in order to make the testbed more SIM like.

metrology↗

NASA NTRS1991

A Large-Grain Mapping Approach for Multiprocessor Systems Through Data Flow Model Ph.D. Thesis

A large-grain level mapping method is presented of numerical oriented applications onto multiprocessor systems. The method is based on the large-grain data flow representation of the input application and it assumes a general interconnection topology of the multiprocessor system. The large-grain data flow model was used because such representation best exhibits inherited parallelism in many important applications, e.g., CFD models based on partial differential equations can be presented in large-grain data flow format, very effectively. A generalized interconnection topology of the multiprocessor architecture is considered, including such architectural issues as interprocessor communication cost, with the aim to identify the 'best matching' between the application and the multiprocessor structure. The objective is to minimize the total execution time of the input algorithm running on the target system. The mapping strategy consists of the following: (1) large-grain data flow graph generation from the input application using compilation techniques; (2) data flow graph partitioning into basic computation blocks; and (3) physical mapping onto the target multiprocessor using a priority allocation scheme for the computation blocks.

Kim, Hwa-Soo↗

NASA NTRSDate not supplied

Reduction of Aircraft Noise Uncertainty for a Notional Supersonic Business Jet

NASA supported a study by the International Civil Aviation Organization on the environmental impact of adding supersonic aircraft to the existing global fleet by designing a notional 55-tonne supersonic business jet. The aircraft is referred to as the Supersonic Technology Concept Aeroplane (STCA) and has been used in a multitude of studies over the past few years. One of the many studies on this aircraft was the noise produced by the STCA during landing and take-off (LTO) operations. The LTO noise for the STCA was assessed using contemporary noise prediction tools that have been primarily developed for and utilized by subsonic aircraft, resulting in a high level of uncertainty for the STCA noise predictions. NASA has recently been conducting research and developing tools to reduce the uncertainty for airport noise predictions of supersonic aircraft. The initial focus of the uncertainty reduction was on the jet and inlet-radiated fan noise produced by supersonic engine systems since these sources are dominant during take-off and landing operations for supersonic jets. The results of these efforts are discussed and applied to the STCA model to update the airport noise predictions and associated uncertainty metrics. It is shown that by using new noise source prediction models, the overall system-level cumulative noise uncertainty of the study vehicle is reduced from a standard deviation of 7.8 EPNdB to 2.0 EPNdB.

uncertainty↗

NASA NTRS1972

An analysis of aircraft lateral-directional handling qualities using pilot models.

An analytical study of the combined pilot-aircraft system to develop a procedure for computing pilot ratings for the lateral-directional response. In the analytical procedure the pilot is represented by a linear, second-order pilot model, and the aircraft by linear equations of motion. Two levels of pilot response were described by the pilot model. The first level contains a static gain and a second-order lag function with a time constant of 0.2 second. The second level added a 1-sec lead time constant. The second level is assumed to represent a greater effort on the part of the pilot. If the system response characteristics can be achieved with the first-level pilot model, it is concluded that the aircraft will be given a pilot rating of satisfactory. If the aircraft characteristics are such that the second-level pilot model is required to achieve the prescribed system response, it is concluded that the aircraft will be rated tolerable.

Adams, J. J.↗

NASA NTRS2020

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

This paper investigates using machine learning to rapidly develop empirical models suitable for system-level aircraft noise studies. In particular, machine learning is used to train a neural network to predict the noise spectra produced by a round jet near a surface over a range of surface lengths, surface standoff distances, jet Mach numbers, and observer angles. These spectra include two sources, jet-mixing noise and jet-surface interaction (JSI) noise, with different scale factors as well as surface shielding and reflection effects to create a multi- dimensional problem. A second model is then trained using data from three rectangular nozzles to include nozzle aspect ratio in the spectral prediction. The training and validation data are from an extensive jet-surface interaction noise database acquired at the NASA Glenn Research Center's Aero-Acoustic Propulsion Laboratory. Although the number of training and validation points is small compared a typical machine learning application, the results of this investigation show that this approach is viable if the underlying data are well behaved.

Brown, Cliff↗