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NASA NTRSDate not supplied

Clinical Decision Support: Path to Functional Requirements

Long-duration, deep-space exploration missions present significant challenges to crew health and performance. These challenges include the individual and combined effects of microgravity, radiation exposure, isolation, limited resources (mass, volume, power, data and crew time), limited options for evacuation and those associated with delayed or constrained communications, all of which demand greater crew autonomy. Specifically, as the communication delays intensify the further we explore space, the unqualified need for Earth-independent medical operations focused on autonomous diagnosis, treatment and prevention will be key to mission continuation and success. To augment the requisite knowledge, skills and abilities (KSAs) of a time-constrained crew operating under stressful conditions, combatting fatigue, and facing a potential medical crisis, a robust clinical decision support system (CDSS) is a probable solution that would facilitate, guide and inform Earth-independent medical operations, while assisting crewmembers through various clinical presentations. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) is expanding the boundaries of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit. ExMC is actively identifying and testing next-generation medical care and crew health maintenance technologies. The Clinical Decision Support (CDS) project addresses gap Medical-701 within the Inflight Medical Conditions risk: “Enhance medical capabilities within an exploration medical system.” Though mass, volume, and power will face increasing constraints, the projected computational capabilities of spacecraft systems will increase exponentially as information technology continues to advance this decade and beyond. Hence, data, software and computational resources will play an essential and synergistic role in maintaining crew health, wellness and performance in deep space missions. The focus of the CDS project is to develop recommended requirements for an in-vehicle CDSS that acts as a ‘virtual assistant’ for delivering optimal health, performance and medical care during exploration missions. The CDSS is envisioned as an integrated, software-based tool deployed on a laptop computer or handheld device. The CDSS will assist the crew and ground support when interacting with knowledge/databases (e.g. records, pharmacy, schedule), instrumentation (e.g. imaging, physiological monitoring devices), and habitat (e.g. wellness system, task performance system) and vehicle systems (e.g. environmental system, communication system). In addition, the human interface will employ a context-based approach that accounts for the crew’s situation. Thus, extraneous and clinically/operationally non-relevant information are reduced to avoid an increase in cognitive load. The framework of an ideal spaceflight CDSS is to include core and advanced analytical features that incorporate work from collaborators yet maintain a flexible platform for integrating new technology in the future. In fiscal year 2021 (FY21), the CDS project identified requirements through two primary mechanisms: (i) the development of software implementation prototypes and (ii) the application of systems engineering processes. The CDS project developed and tested a series of increasingly complex system prototypes that were based on use cases derived from the CDSS concept of operations (ConOps). These software implementations yielded insights on CDSS functionality as well as lessons learned that provided the initial requirements for CDSS capability. By applying a systems engineering (SE) approach, medical scenarios provided in the ConOps and the use cases for software implementation underwent functional decomposition to identify CDSS functionality. Also, systems-based modeling language (SysML) tools such as activity diagrams were developed from the same ConOps and use cases to identify CDSS functionality. The lessons learned from software implementation defined both specific requirements and broad areas of requirements. Within these defined broad requirement areas, further analysis of the SE products identified specific capability that resulted in the final functional requirements. In summary, the software prototypes, functional decomposition of the ConOps and use cases, and SysML diagrams provided the basis for the CDSS requirements developed in FY21. In the upcoming year, these requirements will be refined for their final ExMC baseline review in latter FY22.

Clinical decision support↗
NASA NTRSDate not supplied

Clinical Decision Support Project

As NASA plans for exploration missions into deep space, significant challenges are realized due to the distance from Earth. Beside the effects of microgravity and radiation exposure, the astronauts face the additional constraints of isolation, lack of resupply, increasingly difficult evacuation, and delayed and disrupted communication with ground-based medical care providers. These constraints require a paradigm shift from current medical care where crews rely on the real-time communications with ground-based medical care providers toward Earth-independent medical operations for astronaut medical care. Medical expertise and decision-making are ground-based for current International Space Station and planned Lunar missions. However, a deep space exploration crew will need to autonomously perform the detection, diagnosis, treatment, and prevention of medical conditions. One approach to provide Earth-independent medical operations is to augment the requisite knowledge, skills, and abilities (KSAs) of a time-constrained crew—operating under stressful conditions, combatting fatigue, and facing a potential medical crisis—with a robust clinical decision support system (CDSS). The CDSS is envisioned as an integrated, software-based tool deployed on a laptop computer or handheld device. The CDSS will assist the crew and ground support when interacting with knowledge/data bases (e.g. records, pharmacy, schedule), instrumentation (e.g. imaging, physiological monitoring devices), and habitat (e.g. wellness system, task performance system) and vehicle systems (e.g. environmental system, communication system). In addition, the human interface will employ a context-based approach that accounts for the crew’s situation. Thus, extraneous and clinically/operationally non-relevant information are reduced to avoid an increase in cognitive load. The framework of an ideal spaceflight CDSS is to include core and advanced analytical features that maintain a flexible platform for integrating new technology in the future. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) is expanding the boundaries of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit by actively identifying and testing next-generation medical care and crew health maintenance technologies. The Clinical Decision Support (CDS) project addressed ap Medical-701 within the Inflight Medical Conditions risk: “We need to increase inflight medical capabilities and identify new capabilities that (a) maximize benefit and/or (b) reduce “costs” on human system/mission/vehicle resources.” Though mass, volume, and power will face increasing constraints, the projected computational capabilities of spacecraft systems will increase exponentially as information technology advances in this decade and beyond. Hence, data, software, and computational resources will play an essential and synergistic role in maintaining crew health, wellness, and performance in deep space missions. The focus of the CDS project was to develop recommended requirements for an in-vehicle CDSS that acts as a ‘virtual assistant’ for delivering optimal health, performance, and medical care during exploration missions. In fiscal year 2022 (FY22), the CDS project was chartered to baseline and/or revise all CDS project related documentation and update the CDS project model to include the revised CDSS Concept of Operations, revised systems-based modeling language (SysML) activity diagrams, and baseline requirements. The focus of this presentation will be an overview of the CDS products and CDS model content.

Decision Support↗
NASA NTRS2012

Water Treatment Systems for Long Spaceflights

Space exploration will require new life support systems to support the crew on journeys lasting from a few days to several weeks, or longer. These systems should also be designed to reduce the mass required to keep humans alive in space. Water accounts for about 80 percent of the daily mass intake required to keep a person alive. As a result, recycling water offers a high return on investment for space life support. Water recycling can also increase mission safety by providing an emergency supply of drinking water, where another supply is exhausted or contaminated. These technologies also increase safety by providing a lightweight backup to stored supplies, and they allow astronauts to meet daily drinking water requirements by recycling the water contained in their own urine. They also convert urine into concentrated brine that is biologically stable and nonthreatening, and can be safely stored onboard. This approach eliminates the need to have a dedicated vent to dump urine overboard. These needs are met by a system that provides a contaminant treatment pouch, referred to as a urine cell or contaminant cell, that converts urine or another liquid containing contaminants into a fortified drink, engineered to meet human hydration, electrolyte, and caloric requirements, using a variant of forward osmosis (FO) to draw water from a urine container into the concentrated fortified drink as part of a recycling stage. An activated carbon pretreatment removes most organic molecules. Salinity of the initial liquid mix (urine plus other) is synergistically used to enhance the precipitation of organic molecules so that activated carbon can remove most of the organics. A functional osmotic bag is then used to remove inorganic contaminants. If a contaminant is processed for which the saline content is different than optimal for precipitating organic molecules, the saline content of the liquid should be adjusted toward the optimal value for that contaminant. A first urine treatment method converts urine into a fortified sports drink, resembling Gatorade, using a first urine cell.

FLynn, Michael T.↗
NASA NTRSDate not supplied

Wildfire Segmentation From Remotely Sensed Data Using Quantum-Compatible Conditional Vector Quantized-Variational Autoencoders

Wildfires represent a critical environmental hazard with multifaceted implications for ecosystems, communities, and public health [1]. The escalating frequency and intensity of wildfires globally have intensified the urgency for robust segmentation methodologies to facilitate effective mitigation, response, and recovery strategies [2]. Accurate wildfire segmentation is pivotal for delineating fire boundaries, assessing progression patterns, and prioritizing resource allocation during emergency scenarios. Furthermore, precise segmentation enables stakeholders, including policymakers, environmental scientists, and emergency responders, to formulate evidence-based strategies, thereby minimizing socio-economic disruptions and ecological degradation. Consequently, advancing wildfire segmentation techniques through innovative technological interventions remains a paramount research imperative. Although foundational in wildfire segmentation, traditional deterministic models exhibit inherent limitations that compromise their efficacy in dynamic and uncertain environments. These models often operate on rigid algorithms prioritizing deterministic classifications, thereby overlooking the inherent complexities and uncertainties associated with wildfire behavior and satellite data variability. Such deterministic frameworks tend to produce oversimplified representations that fail to capture the intricate nuances of evolving fire dynamics, spatial heterogeneity, and environmental interactions [1]. Consequently, the deterministic approach’s propensity for uncertainty collapsing [1, 3] hampers the accuracy, reliability, and applicability of segmentation outcomes in real-world scenarios. Contrastingly, stochastic models offer a more nuanced and adaptable framework for wildfire segmentation. By integrating probabilistic elements into the modeling paradigm, stochastic approaches, particularly probabilistic approaches such as variational auto encoders (VAEs) [4], facilitate comprehensive uncertainty assessment, enabling researchers to quantify and incorporate uncertainties into segmentation outcomes effectively. This probabilistic nature empowers stochastic models to encapsulate variability, account for data inconsistencies, and adapt to evolving environmental conditions, enhancing segmentation accuracy, reliability, and robustness. Embracing stochastic methodologies thus catalyzes advancements in wildfire science by fostering a more holistic, adaptive, and resilient segmentation framework. Despite VAEs demonstrating significant promise in various applications, they come with inherent limitations that have garnered attention within the machine learning community. One of the primary drawbacks lies in their reliance on static priors, which essentially assume a fixed distribution for latent variables, thereby limiting the model’s flexibility to capture complex data structures effectively [5]. This static nature leads to suboptimal representations, especially when dealing with complex and high-dimensional data. Additionally, VAEs often struggle with generating sharp and realistic samples, a phenomenon commonly referred to as mode collapse [5, 7, 6]. Furthermore, the optimization process in VAEs, which involves balancing the reconstruction loss and the regularization term, can sometimes be challenging to fine-tune [7]. In recent efforts to address these shortcomings, alternative approaches like Vector Quantized Variational Auto encoders(VQ-VAEs) [7], address the challenges by incorporating discrete latent variables and leveraging techniques that enhance the quality and diversity of generated samples while maintaining efficient training dynamics. VQ-VAEs propose a dynamic prior distribution generation mechanism that diverges from the static priors commonly associated with traditional VAEs. This dynamic approach allows for more adaptive and context-aware latent variable representations, thereby potentially capturing complex data structures more effectively. Unlike autoregressive prior models such as PixelCNN, which, despite their ability to model dependencies across data dimensions, suffer from significant computational inefficiencies and lack flexibility in handling diverse datasets. In our work, we propose to use a generative quantum-compatible approach to help alleviate the shortcomings of autoregressive prior model in VQ-VAEs. Restricted Boltzmann Machines (RBMs) are a viable alternative prior model that can learn prior distributions in a faster and more flexible manner. In this research endeavor, we meticulously curate a state-of-the-art dataset leveraging satellite MODIS data in conjunction with VIIRS fire masks, derived from Fire Radiative Power (FRP), thereby encapsulating diverse wildfire scenarios and environmental contexts. We developed a conditional VQ-VAE architecture with the RBM prior model that is trained in a supervised manner for segmenting wildfire masks. This innovative approach synergistically harnesses deep learning capabilities, enabling the generation of segmentation maps characterized by heightened precision, granularity, and contextual relevance. Furthermore, replacing the autoregressive prior learning method proposed by the original VQ-VAE with a prior density approximation via quantum-compatible RBM facilitates expedited inference processes, augments flexibility in prior sampling, optimizes computational efficiency and establishes a groundbreaking benchmark in wildfire segmentation methodologies.

quantum machine learning↗
NASA NTRS1996

Trends and Issues in Fuzzy Control and Neuro-Fuzzy Modeling

Everyday experience in building and repairing things around the home have taught us the importance of using the right tool for the right job. Although we tend to think of a 'job' in broad terms, such as 'build a bookcase,' we understand well that the 'right job' associated with each 'right tool' is typically a narrowly bounded subtask, such as 'tighten the screws.' Unfortunately, we often lose sight of this principle when solving engineering problems; we treat a broadly defined problem, such as controlling or modeling a system, as a narrow one that has a single 'right tool' (e.g., linear analysis, fuzzy logic, neural network). We need to recognize that a typical real-world problem contains a number of different sub-problems, and that a truly optimal solution (the best combination of cost, performance and feature) is obtained by applying the right tool to the right sub-problem. Here I share some of my perspectives on what constitutes the 'right job' for fuzzy control and describe recent advances in neuro-fuzzy modeling to illustrate and to motivate the synergistic use of different tools.

Chiu, Stephen↗
NASA NTRS1988

It's time to reinvent the general aviation airplane

Current designs for general aviation airplanes have become obsolete, and avenues for major redesign must be considered. New designs should incorporate recent advances in electronics, aerodynamics, structures, materials, and propulsion. Future airplanes should be optimized to operate satisfactorily in a positive air traffic control environment, to afford safety and comfort for point-to-point transportation, and to take advantage of automated manufacturing techniques and high production rates. These requirements have broad implications for airplane design and flying qualities, leading to a concept for the Modern Equipment General Aviation (MEGA) airplane. Synergistic improvements in design, production, and operation can provide a much needed fresh start for the general aviation industry and the traveling public. In this investigation a small four place airplane is taken as the reference, although the proposed philosophy applies across the entire spectrum of general aviation.

Stengel, Robert F.↗
NASA NTRS1983

Structural optimization - Challenges and opportunities

A review of developments in structural optimization techniques and their interface with growing computer capabilities is presented. Structural design steps comprise functional definition of an object, an evaluation phase wherein external influences are quantified, selection of the design concept, material, object geometry, and the internal layout, and quantification of the physical characteristics. Optimization of a fully stressed design is facilitated by use of nonlinear mathematical programming which permits automated definition of the physics of a problem. Design iterations terminate when convergence is acquired between mathematical and physical criteria. A constrained minimum algorithm has been formulated using an Augmented Lagrangian approach and a generalized reduced gradient to obtain fast convergence. Various approximation techniques are mentioned. The synergistic application of all the methods surveyed requires multidisciplinary teamwork during a design effort.

Sobieszczanski-Sobieski, J.↗
NASA NTRS1984

Structural optimization: Challenges and opportunities

A review of developments in structural optimization techniques and their interface with growing computer capabilities is presented. Structural design steps comprise functional definition of an object, an evaluation phase wherein external influences are quantified, selection of the design concept, material, object geometry, and the internal layout, and quantification of the physical characteristics. Optimization of a fully stressed design is facilitated by use of nonlinear mathematical programming which permits automated definition of the physics of a problem. Design iterations terminate when convergence is acquired between mathematical and physical criteria. A constrained minimum algorithm has been formulated using an Augmented Lagrangian approach and a generalized reduced gradient to obtain fast convergence. Various approximation techniques are mentioned. The synergistic application of all the methods surveyed requires multidisciplinary teamwork during a design effort.

Sobieszczanski-Sobieski, J.↗
NASA NTRS2020

Electric Aircraft Cooling with Bio-Inspired Exergy Management

Future air vehicles will increasingly incorporate electrical powertrains that require very tight system-level integration of power, propulsion, thermal, fault protection, and airframe technologies. This chapter provides an overview of a new category of thermal energy conversion technology and describes the development of the resulting Thermal Recovery Exergy Efficient System, TREES. TREES can be used to enable a fully solid-state integrated thermal and fault management electric aircraft protection system, while synergistically managing and recycling both the low-grade waste heat from electrical components and the high-grade waste heat from engine components. This is achieved with exergy amplification of the powertrain waste heat, a new class of fast flight-weight breakers, a new class of long variable-conductance heat pipes with multiple switchable condensers, a new class of turbofan-integrated heat exchangers, and a gradient-based powertrain system optimizer. This approach is like the human body’s thermal management system in which the heart, arteries, and veins are analogous to the turbofan, acoustic tubes, and heat pipes proposed herein.

Rodger W Dyson↗
NASA NTRS1992

Synergistic action of the benzene metabolite hydroquinone on myelopoietic stimulating activity of granulocyte/macrophage colony-stimulating factor in vitro

The effects of in vitro pretreatment with benzene metabolites on colony-forming response of murine bone marrow cells stimulated with recombinant granulocyte/macrophage colony-stimulating factor (rGM-CSF) were examined. Pretreatment with hydroquinone (HQ) at concentrations ranging from picomolar to micromolar for 30 min resulted in a 1.5- to 4.6-fold enhancement in colonies formed in response to rGM-CSF that was due to an increase in granulocyte/macrophage colonies. The synergism equaled or exceeded that reported for the effects of interleukin 1, interleukin 3, or interleukin 6 with GM-CSF. Optimal enhancement was obtained with 1 microM HQ and was largely independent of the concentration of rGM-CSF. Pretreatment with other authentic benzene metabolites, phenol and catechol, and the putative metabolite trans, trans-muconaldehyde did not enhance growth factor response. Coadministration of phenol and HQ did not enhance the maximal rGM-CSF response obtained with HQ alone but shifted the optimal concentration to 100 pM. Synergism between HQ and rGM-CSF was observed with nonadherent bone marrow cells and lineage-depleted bone marrow cells, suggesting an intrinsic effect on recruitment of myeloid progenitor cells not normally responsive to rGM-CSF. Alterations in differentiation in a myeloid progenitor cell population may be of relevance in the pathogenesis of acute myelogenous leukemia secondary to drug or chemical exposure.

NASA Discipline Environmental Health↗
NASA NTRS1994

The Evolution of Technology in the Deep Space Network: A History of the Advanced Systems Program

The Deep Space Network (DSN) of 1995 might be described as the evolutionary result of 45 years of deep space communication and navigation, together with the synergistic activities of radio science and radar and radio astronomy. But the evolution of the DSN did not just happen - it was carefully planned and created. The evolution of the DSN has been an ongoing engineering activity, and engineering is a process of problem solving under constraints, one of which is technology. In turn, technology is the knowledge base providing the capability and experience for practical application of various areas of science, when needed. The best engineering solutions result from optimization under the fewest constraints, and if technology needs are well anticipated (ready when needed), then the most effective engineering solution is possible. Throughout the history of the DSN it has been the goal and function of DSN advanced technology development (designated the DSN Advanced Systems Program from 1963 through 1994) to supply the technology needs of the DSN when needed, and thus to minimize this constraint on DSN engineering. Technology often takes considerable time to develop, and when that happens, it is important to have anticipated engineering needs; at times, this anticipation has been by as much as 15 years. Also, on a number of occasions, mission malfunctions or emergencies have resulted in unplanned needs for technology that has, in fact, been available from the reservoir of advanced technology provided by the DSN Advanced Systems Program. Sometimes, even DSN engineering personnel fail to realize that the organization of JPL permits an overlap of DSN advanced technology activities with subsequent engineering activities. This can result in the flow of advanced technology into DSN engineering in a natural and sometimes almost unnoticed way. In the following pages, we will explore some of the many contributions of the DSN Advanced Systems Program that were provided to DSN Engineering and Implementation. These contributions are, for the most part, unique capabilities that have met the requirements of flight projects for 45 years. These unique capabilities include not only the world's best deep-space communications system, but also outstanding competency in the fields of radio metric measurement, radar and radio astronomy, and radio science.

Layland, J. W.↗
NASA NTRS1982

Ecological optimality in water-limited natural soil-vegetation systems. I - Theory and hypothesis

The solution space of an approximate statistical-dynamic model of the average annual water balance is explored with respect to the hydrologic parameters of both soil and vegetation. Within the accuracy of this model it is shown that water-limited natural vegetation systems are in stable equilibrium with their climatic and pedologic environments when the canopy density and species act to minimize average water demand stress. Theory shows a climatic limit to this equilibrium above which it is hypothesized that ecological pressure is toward maximization of biomass productivity. It is further hypothesized that natural soil-vegetation systems will develop gradually and synergistically, through vegetation-induced changes in soil structure, toward a set of hydraulic soil properties for which the minimum stress canopy density of a given species is maximum in a given climate. Using these hypotheses, only the soil effective porosity need be known to determine the optimum soil and vegetation parameters in a given climate.

Eagleson, P. S.↗
NASA NTRS2010

Altitude Variation of the CO2(V2)-O Quenching Rate Coefficient in Mesosphere and Lower Thermosphere

Among the processes governing the energy balance in the mesosphere and lower thermosphere (MLT), the quenching of CO2(v2) vibrational levels by collisions with oxygen atoms plays an important role. However, the k(CO2-O) values measured in the lab and retrieved from atmospheric measurements vary from 1.5 x 10(exp -12)cu cm/s through 9.0 x 10(exp -12)cu cm/s that requires further studying. In this work we used synergistic data from a ground based lidar and a satellite infrared radiometer to estimate k(CO2-O). We used the night- and daytime temperatures between 80 and 110 km measured by the Colorado State University narrow-band sodium (Na) lidar located at Fort Collins, Colorado (41 N, 255E) as ground truth of the SABER/TIMED nearly simultaneous ( +/-10 minutes) and common volume (within +/-1 degree in latitude, +/-2 degrees in longitude) observations. For each altitude in 80-110 km interval we estimate an 'optimal" value of k(CO2-O) needed to minimize the discrepancy between the simulated 15 micron CO2 radiance and that measured by the SABER/TIMED instrument. The k(CO2-O) obtained in this way varies in altitude from 3.5 x 10(exp -12)cu cm/s at 80 km to 5.2 x 10(exp -12)cu cm/s for altitudes above 95 km. We discuss this variation of the rate constant and its impact on temperature retrievals from 15 pm radiance measurements and on the energy budget of MLT.

Feofilov, A.↗
NASA NTRS2002

The Role of Formal Experiment Design in Hypersonic Flight System Technology Development

Hypersonic airbreathing engine (scramjet) powered vehicles are being considered to replace conventional rocket-powered launch systems. Effective utilization of scramjet engines requires careful integration with the air vehicle. This integration synergistically combines aerodynamic forces with propulsive cycle functions of the engine. Due to the highly integrated nature of the hypersonic vehicle design problem, the large flight envelope, and the large number of design variables, the use of a statistical design approach in design is effective. Modern Design-of-Experiments (MDOE) has been used throughout the Hyper-X program, for both systems analysis and experimental testing. Application of MDOE fall into four categories: (1) experimental testing; (2) studies of unit phenomena; (3) refining engine design; and (4) full vehicle system optimization. The MDOE process also provides analytical models, which are also used to document lessons learned, supplement low-level design tools, and accelerate future studies. This paper will discuss the design considerations for scramjet-powered vehicles, specifics of MDOE utilized for Hyper-X, and present highlights from the use of these MDOE methods within the Hyper-X Program.

McClinton, Charles R.↗
NASA NTRS2010

Altitude Variation of the CO2 (V2)-O Quenching Rate Coefficient in Mesosphere and Lower Thermosphere

Among the processes governing the energy balance in the mesosphere and lower thermosphere (mlt), the quenching of CO2(N2) vibrational levels by collisions with oxygen atoms plays an important role. However, the k(CO2-O) values measured in the lab and retrieved from atmospheric measurements vary from 1.5 x 10(exp -12) cubic centimeters per second through 9.0 x 10(exp -12) cubic centimeters per second that requires further studying. In this work we used synergistic data from a ground based lidar and a satellite infrared radiometer to estimate K(CO2-O). We used the night- and daytime temperatures between 80 and 110 km measured by the colorado state university narrow-band sodium (Na) lidar located at fort collins, colorado (41N, 255E) as ground truth of the saber/timed nearly simultaneous (plus or minus 10 minutes) and common volume (within plus or minus 1 degree in latitude, plus or minus 2 degrees in longitude) observations. For each altitude in 80-110 km interval we estimate an "optimal" value of K(CO2-O) needed to minimize the discrepancy between the simulated 15 mm CO2 radiance and that measured by the saber/timed instrument. The K(CO2-O) obtained in this way varies in altitude from 3.5 x 10(exp -12) cubic centimeters per second at 80 km to 5.2 x 10(exp -12) cubic centimeters pers second for altitudes above 95 km. We discuss this variation of the rate constant and its impact on temperature retrievals from 15 mm radiance measurements and on the energy budget of mlt.

Feofilovi, Artem↗
NASA NTRS1993

Extended ecosystem signatures with application to Eos synergism requirements

The primary objective is to define the advantages of synergistically combining optical and microwave remote sensing measurements for the determination of biophysical properties important in ecosystem modeling. This objective was approached in a stepwise fashion starting with ground-based observations of controlled agricultural and orchard canopies and progressing to airborne observations of more natural forest ecosystems. This observational program is complemented by a parallel effort to model the visible reflectance and microwave scattering properties of composite vegetation canopies. The goals of the modeling studies are to verify our basic understanding of the sensor-scene interaction physics and to provide the basis for development of inverse models optimized for retrieval of key biophysical properties. These retrieval algorithms can then be used to simulate the expected performance of various aspects of Eos including the need for simultaneous SAR and HIRIS observations or justification for other (non-synchronous) relative timing constraints and the frequency, polarization, and angle of incidence requirements for accurate biophysical parameter extractions. This program completed a very successful series of truck-mounted experiments, made remarkable progress in development and validation of optical reflectance and microwave scattering models for vegetation, extended the scattering models to accommodate discontinuous and periodic canopies, developed inversion approaches for surface and canopy properties, and disseminated these results widely through symposia and journal publications. In addition, the third generation of the computer code for the microwave scattering models was provided to a number of other US, Canadian, Australian, and European investigators who are currently presenting and publishing results using the MIMICS research code.

Ulaby, Fawwaz T.↗
NASA NTRS2005

CAPS Simulation Environment Development

The final design for an effective Comet/Asteroid Protection System (CAPS) will likely come after a number of competing designs have been simulated and evaluated. Because of the large number of design parameters involved in a system capable of detecting an object, accurately determining its orbit, and diverting the impact threat, a comprehensive simulation environment will be an extremely valuable tool for the CAPS designers. A successful simulation/design tool will aid the user in identifying the critical parameters in the system and eventually allow for automatic optimization of the design once the relationships of the key parameters are understood. A CAPS configuration will consist of space-based detectors whose purpose is to scan the celestial sphere in search of objects likely to make a close approach to Earth and to determine with the greatest possible accuracy the orbits of those objects. Other components of a CAPS configuration may include systems for modifying the orbits of approaching objects, either for the purpose of preventing a collision or for positioning the object into an orbit where it can be studied or used as a mineral resource. The Synergistic Engineering Environment (SEE) is a space-systems design, evaluation, and visualization software tool being leveraged to simulate these aspects of the CAPS study. The long-term goal of the SEE is to provide capabilities to allow the user to build and compare various CAPS designs by running end-to-end simulations that encompass the scanning phase, the orbit determination phase, and the orbit modification phase of a given scenario. Herein, a brief description of the expected simulation phases is provided, the current status and available features of the SEE software system is reported, and examples are shown of how the system is used to build and evaluate a CAPS detection design. Conclusions and the roadmap for future development of the SEE are also presented.

Murphy, Douglas G.↗
NASA NTRS2024

Mission and Vehicle-Level Updates for the Parallel Electric-Gas Architecture with Synergistic Utilization Scheme (PEGASUS) Concept Aircraft

NASA created the PEGASUS concept with the goal of lowering mission energy (a surrogate for operating cost) compared to other regional aircraft by leveraging electrified aircraft propulsion (EAP). Since its inception, researchers have explored multiple facets of PEGASUS in varying fidelity but have not completed a rigorous, integrated design. The goal of this memorandum is to provide an updated design using recent studies and improved methods. This memorandum explores the initial vehicle concept and concept of operations, while considering ways to improve both the mission concept of operations and the integrated vehicle-level performance. Additionally, the design and analysis methodologies for EAP-enabled aircraft concepts are improved in several areas. This research incorporates new propulsion-airframe integration and wing weight surrogates to model the impacts of wingtip propulsors on the configuration. Detailed weight and balance calculations enable calculating dynamic stability and flight qualities within the conceptual design environment. Ultimately, the vehicle is optimized to reduce "well-to-wake" equivalent CO2, CO2e, rather than minimizing either fuel (or total energy) consumption or maximum takeoff weight. Using fuel/energy or takeoff weight leads to conflicting optima for hybrid-electric aircraft. Two aircraft are developed to provide points of comparison for PEGASUS: an advanced conventional turboprop vehicle and a hybrid-electric variant. The results show that the PEGASUS concept can reduce CO2e relative to the advanced turboprop or a hybrid-electric propulsion architecture, albeit with an increase in maximum takeoff weight. PEGASUS's maximum takeoff weight is 55% heavier than the advanced conventional turboprop but releases 18% less CO2e for a 400 nmi mission. Over the same mission, PEGASUS's maximum takeoff weight is 47% heavier than the comparator hybrid-electric vehicle but releases 12% less CO2e. This study shows that the PEGASUS configuration reduces CO2e through its use of wingtip propulsors and that its benefit is not solely a result of switching to a hybrid-electric propulsion architecture. PEGASUS achieves this reduction in CO2e while maintaining satisfactory Level 1 or 2 flight qualities for all of its longitudinal- and lateral-directional modes.

Hybrid-electric↗