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NASA NTRS2025

An Efficient GPU-Accelerated Multi-Source Global Fit Pipeline for LISA Data Analysis

The large-scale analysis task of deciphering gravitational wave signals in the LISA data stream will be difficult, requiring a large amount of computational resources and extensive development of computational methods. Its high dimensionality, multiple model types, and complicated noise profile require a global fit to all parameters and input models simultaneously. In this work, we detail our global fit algorithm, called “Erebor,” designed to accomplish this challenging task. It is capable of analysing current state-of-the-art datasets and then growing into the future as more pieces of the pipeline are completed and added. We describe our pipeline strategy, the algorithmic setup, and the results from our analysis of the LDC2A Sangria dataset, which contains Massive Black Hole Binaries, compact Galactic Binaries, and a parameterized noise spectrum whose parameters are unknown to the user. The Erebor algorithm includes three unique and very useful contributions: GPU acceleration for enhanced computational efficiency; ensemble MCMC sampling with multiple MCMC walkers per temperature for better mixing and parallelized sample creation; and special online updates to reversible-jump (or trans-dimensional) sampling distributions to ensure sampler mixing and accurate initial estimates for detectable sources in the data. We recover posterior distributions for all 15 (6) of the injected MBHBs in the LDC2A training (hidden) dataset. We catalog ∼12000 Galactic Binaries (∼8000 as high confidence detections) for both the training and hidden datasets. All of the sources and their posterior distributions are provided in publicly available catalogs.

LISA global fit↗

NASA NTRS2023

Broader Impacts and Outreach Videos on Ocean Physics and Biogeochemistry

This is just a preliminary NF1676 in advance of filming for a project....A convergent team of experts in biology, chemistry, physics, engineering, mathematics, and computational modeling examine how dynamic and coupled phytoplankton-pathogen-particle-predator linkages coalesce to explain the observed high spatial variability in the efficiency of the export of particulate organic carbon (POC) to the deep ocean. They elucidate and quantify the linkages between viruses and ballast minerals to increase understanding of carbon cycling in the oceanic biological carbon pump and the impact of viruses within it. By providing knowledge than can be used to improve the parameterization of carbon export in Earth system models, the project will help reduce uncertainty in regional marine biogeochemical projections, potentially improving marine ecosystem and fisheries management on timescales from seasons to decades. The project includes activities that provide teaching resources and hands-on training to educators within a ‘Tools of Science’ program that provides a simple and succinct way to communicate the process of scientific research to students in a way that is useful to teachers. The project couples laboratory-based experiments on model host-virus-grazer systems with extensive field based observational and manipulative studies on natural populations of diatoms and coccolithophores, the two phytoplankton groups that account for most of the estimated particulate organic matter flux to the deep ocean. Experiments and measurements integrate diagnostic biological and chemical controls on infection and particle coagulation theory with microscale physics and grazing to quantify links to each hypothesized export mechanism under field-relevant turbulent conditions. Cutting-edge engineering and analytical tools are used to diagnose and track infection dynamics while characterizing and quantifying particle aggregation and disaggregation, mineral dissolution, sinking dynamics, grazing rates, and fecal pellet production at unprecedented resolution and under well-defined, microscale physical regimes. Field campaigns elucidate the relative efficiency of hypothesized mechanisms in stimulating POC export in natural blooms, while providing bulk and size-resolved estimates of POC flux.

Daniel B Whitt↗

NASA NTRSDate not supplied

A New Concurrent Engineering Tool for the New Mission Design Center at NASA Ames Research Center

The NASA Ames Mission Design Center (MDC) has undergone a significant transformation process in recent years, culminating in becoming a true Concurrent Engineering Center. The underlying goal of this transformation is to better serve the Ames PI community in a cost-effective and rapidly-responsive way, providing quality products pivotal to the decision-making proposal strategy of our center. The four pillars of the change focus on four areas: personnel, physical places, tools, and training. Personnel: the MDC switched from using a dedicated pool of engineers—mostly entry-level career—for a matrixed approach with more experienced Subject Matter Experts, usually involved in spaceflight projects. These SMEs get detailed to the MDC for the duration of the study on a part-time or full-time basis. Physical places: The physical aspect pertains mostly to the layout of the new Engineering building and the dedicated concurrent engineering layout to foster collaboration. The current plan includes a dedicated space for concurrent engineering sessions as well as dedicated project rooms for concept studies. Tool: the MDC team is developing a new in-house concurrent engineering tool to facilitate concurrent engineering sessions. The team leveraged the many lessons learned and insight gained from using the current concurrent engineering tool, in use since 2007. SMEs have been involved throughout its design and development, ensuring accuracy and providing validity to the implementation approach. This new tool is database-driven and allows a team to work concurrently on the same model of a mission concept. “Workspaces” are provided for each of the traditional spacecraft mission design disciplines, including cost accounting and systems engineering. There are multiple tiers of fidelity available within each workspace, which can be completed parametrically or independently from the other disciplines as needed or synchronously with the other disciplines. A “commit” step synchronizes a workspace with the mission database and identifies any potential conflicts, along with the user who entered the conflicting data. A Mission Summary workspace enables a Study Lead to run an effective CE session, displaying key graphics, workspace status information, and technical and monetary budget information. This new CE tool will be undergoing Beta Testing with its primary user base and a limited Parts database in the summer of 2020. The team plans to release the first fully operational version in December 2020. Training: working in early concept development and maturation for a space mission requires a unique mindset and being comfortable with uncertainties in an environment where requirements are not yet fully developed, and where changes and trade-offs happen at a high tempo. The MDC started a weekly training program to ensure a common knowledge base on how do develop and mature early concept studies, as well as how to operate in a concurrent engineering environment. In conclusion, the MDC transformation will provide the overall PI community focused on cost-effective small satellite scientific missions with a powerful tool to address and mature early concepts more effectively and efficiently.

David Mauro↗

NASA NTRS2010

Speech Acquisition and Automatic Speech Recognition for Integrated Spacesuit Audio Systems

A voice-command human-machine interface system has been developed for spacesuit extravehicular activity (EVA) missions. A multichannel acoustic signal processing method has been created for distant speech acquisition in noisy and reverberant environments. This technology reduces noise by exploiting differences in the statistical nature of signal (i.e., speech) and noise that exists in the spatial and temporal domains. As a result, the automatic speech recognition (ASR) accuracy can be improved to the level at which crewmembers would find the speech interface useful. The developed speech human/machine interface will enable both crewmember usability and operational efficiency. It can enjoy a fast rate of data/text entry, small overall size, and can be lightweight. In addition, this design will free the hands and eyes of a suited crewmember. The system components and steps include beam forming/multi-channel noise reduction, single-channel noise reduction, speech feature extraction, feature transformation and normalization, feature compression, model adaption, ASR HMM (Hidden Markov Model) training, and ASR decoding. A state-of-the-art phoneme recognizer can obtain an accuracy rate of 65 percent when the training and testing data are free of noise. When it is used in spacesuits, the rate drops to about 33 percent. With the developed microphone array speech-processing technologies, the performance is improved and the phoneme recognition accuracy rate rises to 44 percent. The recognizer can be further improved by combining the microphone array and HMM model adaptation techniques and using speech samples collected from inside spacesuits. In addition, arithmetic complexity models for the major HMMbased ASR components were developed. They can help real-time ASR system designers select proper tasks when in the face of constraints in computational resources.

Huang, Yiteng↗

NASA NTRSDate not supplied

Combining Data with Physical Knowledge for Uncertainty Quantification in Certification and Reliability Analysis

Unifying empirical data with predictive models can enable engineering cost-savings through certification by analysis and reliability-based design. Both concepts require rigorous uncertainty quantification (UQ) and robust understanding and treatment of relevant physics. Combining sampling-based UQ algorithms with high-fidelity simulations creates a computational bottleneck that is often alleviated through the use of machine learning (ML). ML can be used to create computationally efficient surrogates for simulations of complex or high-dimensional physical interactions (e.g., multi-phase interactions associated with melt pools in laser powder bed fusion or spatially-dependent material properties in functionally graded materials). However, negative side effects of ML may include a lack of interpretability and negative correlation between event rarity and simulation accuracy due to a lack of training data. As such, it is important to infuse ML algorithms with physics-based guardrails to provide confidence in their predictions. This talk will provide a brief review of recent NASA research at this intersection of physics-based simulation, ML, and UQ with a focus on certification and reliability analysis.

uncertainty quantification↗

NASA NTRS1978

Evaluation of reforested areas using LANDSAT imagery

The author has identified the following significant results. Visual and automatic interpretation of LANDSAT imagery was used to classify the general Pinus and Eucalyptus according to their age and species. A methodology was derived, based on training areas, to define the legend and spectral characteristics of the analyzed classes. Imager analysis of the training areas show that Pinus taeda is separable from the other Pinus species based on JM distance measurement. No difference of JM measurements was observed among Eucalyptus species. Two classes of Eucalyptus were separated according to their ages: those under and those over two years of age. Channel 6 and 7 were suitable for the discrimination of the reforested classes. Channel 5 was efficient to separated reforested areas from nonforested targets in the region. The automatic analysis shows the highest classification precision was obtained for Eucalyptus over two years of age (95.12 percent).

Dejesusparada, N.↗

NASA NTRS2000

Genesis Trajectory Design

The Genesis mission will launch in 2001, sending the spacecraft into a halo orbit about the Sun-Earth L1 point to collect and return solar wind samples to the Earth for analysis in 2003. One of the most constraining aspects of the mission design is the requirement to return to the designated landing site (the Utah Test and Training Range, UTTR) during daylight hours. The ongoing mission design has led the development of a family of solutions that characterize a broad range of conditions at Earth entry. Characterizing this family provides insight into the possible existence of additional trajectories while also helping to narrow the search space by indicating where additional solutions are unlikely to exist; this contributes to a more efficient utilization of mission design resources.

Bell, Julia L.↗

NASA NTRS2000

Thermodynamic Cycle Analysis of Magnetohydrodynamic-Bypass Hypersonic Airbreathing Engines

The prospects for realizing a magnetohydrodynamic (MHD) bypass hypersonic airbreathing engine are examined from the standpoint of fundamental thermodynamic feasibility. The MHD-bypass engine, first proposed as part of the Russian AJAX vehicle concept, is based on the idea of redistributing energy between various stages of the propulsion system flow train. The system uses an MHD generator to extract a portion of the aerodynamic heating energy from the inlet and an MHD accelerator to reintroduce this power as kinetic energy in the exhaust stream. In this way, the combustor entrance Mach number can be limited to a specified value even as the flight Mach number increases. Thus, the fuel and air can be efficiently mixed and burned within a practical combustor length, and the flight Mach number operating envelope can be extended. In this paper, we quantitatively assess the performance potential and scientific feasibility of MHD-bypass engines using a simplified thermodynamic analysis. This cycle analysis, based on a thermally and calorically perfect gas, incorporates a coupled MHD generator-accelerator system and accounts for aerodynamic losses and thermodynamic process efficiencies in the various engin components. It is found that the flight Mach number range can be significantly extended; however, overall performance is hampered by non-isentropic losses in the MHD devices.

Litchford, R. J.↗

NASA NTRS2012

NASA/SPoRT's GOES-R Activities in Support of Product Development, Management, and Training

The NASA Short-term Prediction Research and Transition (SPoRT) Center supports many activities within the GOES-R Proving Grounds (PG). These include the development of imagery from existing instrumentation as a proxy to future Advanced Baseline Imager (ABI) capabilities on GOES-R. The Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible/Infrared Imager/Radiometer Suite (VIIRS) instruments are used to provide a glimpse of the multi-spectral capabilities that will become the norm as the number of channels and data rate dramatically increase with GOES-R. The NOAA/NWS has plans to provide operational users with all ABI channels at the highest resolution. Data fusion of individual channels into composite red, green, and blue imagery products will assist the end user with this future wave of information. While increasing the efficiency in the operational use of ABI channels, these composites provide only qualitative information. Within the GOES-R PG, SPoRT and other partners are exploring ways to include quantitative information as part of the composite imagery. However, limitations in local hardware processing and/or data bandwidth for users of the GOES-R data stream are challenges to overcome. This presentation will discuss the creation of these composite images as well as possible solutions to address these processing challenges. In a similar manner the Geostationary Lightning Mapper (GLM) to be launched on GOES-R presents several data management challenges. The GLM is a pioneering instrument to quantify total lightning from a geostationary platform. The expected data frequency from the GLM is to be at a sub-minute interval. Users of such a data set may have little experience in handling such a rapid update of information. To assist users, SPoRT is working with the NWS to develop tools within the user fs decision support system to allow tracking and analysis of total lightning from a storm-based perspective. This presentation will discuss the challenges and progress of this tool development work. With new data and products comes the need for user Training. Within the GOES-R PG SPoRT is supporting the demonstration of these future products by providing various training materials to end users. A summary of training provided to operational users will be discussed.

Fuell, Kevin K.↗

NASA NTRS1994

Virtual reality applications in robotic simulations

Virtual reality (VR) provides a means to practice integrated extravehicular activities (EVA)/remote manipulator system (RMS) operations in the on-orbit configuration with no discomfort or risk to crewmembers. VR afforded the STS-61 crew the luxury of practicing the integrated EVA/RMS operations in an on-orbit configuration prior to the actual flight. The VR simulation was developed by the Automation and Robotics Division's Telepresence/Virtual Reality Lab and Integrated Graphics, Operations, and Analysis Lab (IGOAL) at JSC. The RMS Part Task Trainer (PTT) was developed by the IGOAL for RMS training in 1988 as a fully functional, kinematic simulation of the shuttle RMS and served as the RMS portion of the integrated VR simulation. Because the EVA crewmember could get a realistic view of the shuttle and payload bay in the VR simulation, he/she could explore different positions and views to determine the best method for performing a specific task, thus greatly increasing the efficiency of use of the neutral buoyancy facilities.

Homan, David J.↗

NASA NTRS2004

Developing a Simulation-Based Training Program for Non-Traditional Caregivers

Simulation-based training enables learning in controlled environments which nevertheless mimic real-world scenarios. It has proven effective in the training of medical personnel and affords rapid assimilation and integration of necessary skills. Non-traditional caregivers often operate in austere environments, where resource and personnel limitations preclude more standard provision of care by highly trained and fully equipped health care teams. In these settings, training time for the caregivers may be limited, with long gaps between time of the training and use of the skills, a limited grasp of the underlying physiology, and unfamiliarity with "medical English" which can render it difficult to communicate concepts to more advanced practitioners when such interaction can take place, as for example, when telemedicine can be used to project medical skills further forward . Methods: Simulation-based training can assist in the familiarization of caregivers to the environment, ensure adequate execution of skills at the appropriate time(s), and allow practice of telemedicine communication patterns between the mentor and caregiver. Results: Scenario-based training can and has been used for initial and sustainment training modules, including self-taught modules for use in the field. Strict identification of the critical concepts is vital, as is development and practice of technically simple procedures wherever possible. Medical devices can off-load tasks from caregivers, as well as to minimize the necessary level of caregiver knowledge, while integrated simulations among all members of the mission team can improve communication and efficiency. Discuss ion: Nontraditional caregivers face unique challenges when learning to provide medical care. Scenario-based curricula allow lesson plans to be tailored to each group's individual needs, as well as being suited for the participation of numerous groups, including the caregiver, evacuation/transport staff, decision-makers, and hospital-based physician.

Bacal, Kira↗

NASA NTRS2011

Improvements to the Ionizing Radiation Risk Assessment Program for NASA Astronauts

To perform dosimetry and risk assessment, NASA collects astronaut ionizing radiation exposure data from space flight, medical imaging and therapy, aviation training activities and prior occupational exposure histories. Career risk of exposure induced death (REID) from radiation is limited to 3 percent at a 95 percent confidence level. The Radiation Health Office at Johnson Space Center (JSC) is implementing a program to integrate the gathering, storage, analysis and reporting of astronaut ionizing radiation dose and risk data and records. This work has several motivations, including more efficient analyses and greater flexibility in testing and adopting new methods for evaluating risks. The foundation for these improvements is a set of software tools called the Astronaut Radiation Exposure Analysis System (AREAS). AREAS is a series of MATLAB(Registered TradeMark)-based dose and risk analysis modules that interface with an enterprise level SQL Server database by means of a secure web service. It communicates with other JSC medical and space weather databases to maintain data integrity and consistency across systems. AREAS is part of a larger NASA Space Medicine effort, the Mission Medical Integration Strategy, with the goal of collecting accurate, high-quality and detailed astronaut health data, and then securely, timely and reliably presenting it to medical support personnel. The modular approach to the AREAS design accommodates past, current, and future sources of data from active and passive detectors, space radiation transport algorithms, computational phantoms and cancer risk models. Revisions of the cancer risk model, new radiation detection equipment and improved anthropomorphic computational phantoms can be incorporated. Notable hardware updates include the Radiation Environment Monitor (which uses Medipix technology to report real-time, on-board dosimetry measurements), an updated Tissue-Equivalent Proportional Counter, and the Southwest Research Institute Radiation Assessment Detector. Also, the University of Florida hybrid phantoms, which are flexible in morphometry and positioning, are being explored as alternatives to the current NASA computational phantoms.

Semones, E. J.↗

NASA NTRS2024

Vertiport Management from Simulation to Flight: Continued Human Factors Assessment of Vertiport Operations

The Urban Air Mobility concept envisions a new future for transportation in urban environments, primarily focused around vertiport operations. A vertiport refers to an identifiable ground or elevated area used for the vertical takeoff and landing of an aircraft. One critical role within vertiport operations that has been proposed but is under-researched is the vertiport manager. The vertiport manager is expected to manage the ground-to-air operations at the vertiport, but their precise responsibilities, tasks, and challenges are currently undefined. The High Density Vertiplex Subproject at NASA is responsible for developing and testing technologies, concepts, and architectures that will support the infrastructure needed for terminal environments around vertiports. Our previous work investigated ground control station operators performing simulated and live flight operations within a remote operations environment. In the current study, we extended from our previous work to explore the role of a vertiport manager. Specifically, we investigated five participants serving as a vertiport manager across both simulated and live flight operations. We performed multiple knowledge elicitation techniques, a thematic content analysis of qualitative data, and naturalistic observations to create a list of insights, design and training recommendations, and a simplified cognitive task diagram of the vertiport manager’s primary task. Findings from this study are discussed within the context of the current debate on the degree to which this role should be automated. A third alternative is suggested to allow a human vertiport manager to be successful by teaming with increasingly autonomous systems, designing the role to improve safety and efficiency of Urban Air Mobility operations.

AAM↗

NASA NTRS2024

Vertiport Management from Simulation to Flight: Continued Human Factors Assessment of Vertiport Operations

AAM↗

NASA NTRS2025

Efficient GPU-Accelerated MultiSource Global Fit Pipeline for LISA Data Analysis

The large-scale analysis task of deciphering gravitational-wave signals in the LISA data stream will be difficult, requiring a large amount of computational resources and extensive development of computational methods. Its high dimensionality, multiple model types, and complicated noise profile require a global fit to all parameters and input models simultaneously. In this work, we detail our global fit algorithm, called “Erebor,” designed to accomplish this challenging task. It is capable of analyzing current state-of-the-art datasets and then growing into the future as more pieces of the pipeline are completed and added. We describe our pipeline strategy, the algorithmic setup, and the results from our analysis of the LDC2A Sangria dataset, which contains massive black hole binaries, compact galactic binaries, and a parametrized noise spectrum whose parameters are unknown to the user. The Erebor algorithm includes three unique and very useful contributions: GPU acceleration for enhanced computational efficiency; ensemble Markov Chain Monte Carlo (MCMC) sampling with multiple MCMC walkers per temperature for better mixing and parallelized sample creation; and special online updates to reversible-jump (or transdimensional) sampling distributions to ensure sampler mixing and accurate initial estimates for detectable sources in the data.We recover posterior distributions for all 15 (6) of the injected massive black hole binaries (MBHB) in the LDC2A training (hidden) dataset. We catalog ∼12000 galactic binaries (∼8000 as high confidence detections) for both the training and hidden datasets. All of the sources and their posterior distributions are provided in publicly available catalogs.

LISA↗

NASA NTRS1987

Rotorcraft-based emergency medical services in the Caribbean Basin

There is a pressing need for improved health care in general and emergency health care in particular throughout the Caribbean Basin. The importance of rotorcraft as an integral part of the needed system of emergency medical care in the region was investigated. Many of the larger countries in the region currently have the needed infrastructure to implement a national system of rotorcraft-based emergency medical centers within their borders. By helping to establish a system of rotorcraft based health care centers in strategic locations in the Lesser Antilles, the U.S. can assist the islands of the region by demonstrating the concept and establishing a potential training site for the other larger countries of the region. There is sufficient demand for rotorcraft based emergency health care within the Lesser Antilles to locate one center on the island of Puerto Rico and another one of the southern-most islands. With the use of fixed wing aircraft or long range helicopters, the two rotorcraft based centers could provide the region with rapid and efficient emergency health care. The superior speed and range of the XV-15 Tilt Rotor aircraft make it an attractive possibility for emergency transport and rescue in this region.

Smith, R. W.↗

NASA NTRS1993

Modifications to an interactive model of the human body during exercise: With special emphasis on thermoregulation

Since 1988 an interactive computer model of the human body during exercise has been under development by a number of undergraduate students in the Department of Chemical Engineering at Iowa State University. The program, written under the direction of Dr. Richard C. Seagrave, uses physical characteristics of the user, environmental conditions and activity information to predict the onset of hypothermia, hyperthermia, dehydration, or exhaustion for various levels and durations of a specified exercise. The program however, was severely limited in predicting the onset of dehydration due to the lack of sophistication with which the program predicts sweat rate and its relationship to sensible water loss, degree of acclimatization, and level of physical training. Additionally, it was not known whether sweat rate also depended on age and gender. For these reasons, the goal of this creative component was to modify the program in the above mentioned areas by applying known information and empirical relationships from literature. Furthermore, a secondary goal was to improve the consistency with which the program was written by modifying user input statements and improving the efficiency and logic of the program calculations.

Scherb, Megan Kay↗

NASA NTRS2017

Development of Increasingly Autonomous Traffic Data Manager Using Pilot Relevancy and Ranking Data

NASA's Safe Autonomous Systems Operations (SASO) project goal is to define and safely enable all future airspace operations by justifiable and optimal autonomy for advanced air, ground, and connected capabilities. This work showcases how Increasingly Autonomous Systems (IAS) could create operational transformations beneficial to the enhancement of civil aviation safety and efficiency. One such IAS under development is the Traffic Data Manager (TDM). This concept is a prototype 'intelligent party-line' system that would declutter and parse out non-relevant air traffic, displaying only relevant air traffic to the aircrew in a digital data communications (Data Comm) environment. As an initial step, over 22,000 data points were gathered from 31 Airline Transport Pilots to train the machine learning algorithms designed to mimic human experts and expertise. The test collection used an analog of the Navigation Display. Pilots were asked to rate the relevancy of the displayed traffic using an interactive tablet application. Pilots were also asked to rank the order of importance of the information given, to better weight the variables within the algorithm. They were also asked if the information given was enough data, and more importantly the "right" data to best inform the algorithm. The paper will describe the findings and their impact to the further development of the algorithm for TDM and, in general, address the issue of how can we train supervised machine learning algorithms, critical to increasingly autonomous systems, with the knowledge and expertise of expert human pilots.

Le Vie, Lisa R.↗